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# below makes the pixmap half transparent
# painter = QtGui.QPainter(pixmap)
# painter.setCompositionMode(painter.CompositionMode_DestinationIn)
# painter.fillRect(pixmap.rect(), QtGui.QColor(0, 0, 0, 127))
# painter.end()
drag.setPixmap(pixmap)
drag.setHotSpot(QtCore.QPoint(pixmap.width()/2, pixmap.height()/2))
drag.setPixmap(pixmap)
self.dragActive.emit(True)
result = drag.exec_(QtCore.Qt.MoveAction)
QtGui.QApplication.restoreOverrideCursor()"
19,"def database(self):
""""""
Enters all the metadata into a database
""""""
import sqlite3
try:
os.remove('{}/metadatabase.sqlite'.format(self.reportpath))
except OSError:
pass
# Set the name of the database
db = sqlite3.connect('{}/metadatabase.sqlite'.format(self.reportpath))
# Create a cursor to allow access to the database
cursor = db.cursor()
# Set up the db
cursor.execute('''
CREATE TABLE IF NOT EXISTS Samples (
id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
name TEXT UNIQUE
)
''')
# Create a variable to store the names of the header values for each individual table
# This will store a set of all the headers from all the strains, as there can be some variability present, as
# not all analyses are available for all taxonomic groups
columns = dict()
for sample in self.metadata:
# Create a metadata object to store the new tables
data = MetadataObject()
data.name = sample.name
# Insert each strain name into the Samples table
cursor.execute('''
INSERT OR IGNORE INTO Samples (name)
VALUES ( ? )
''', (sample.name, ))
# Each header in the .json file represents a major category e.g. ARMI, GeneSeekr, commands, etc. and
# will be made into a separate table
for header in sample.datastore.items():
# Allow for certain analyses, such as core genome, not being performed on all strains
try:
# Key and value: data description and data value e.g. targets present: 1012, etc.
for key, value in sorted(header[1].datastore.items()):
# Only the values consisting of dictionaries are of interest
if type(value) == dict:
# Clean the column names so there are no issues entering names into the database
cleanedcolumn = self.columnclean(key)
# Set the table name
tablename = '{}_{}'.format(header[0].replace('.', '_'), cleanedcolumn)
# Create the table (if it doesn't already exist)
cursor.execute('''
CREATE TABLE IF NOT EXISTS {} (
sample_id INTEGER
)
'''.format(tablename))
# Add the attributes with the dictionaries (values) to the metadata object
setattr(data, tablename, GenObject(value))
for gene, result in sorted(value.items()):
# Add the data header to the dictionary
try:
columns[tablename].add(gene)
# Initialise the dictionary the first time a table name is encountered
except KeyError:
columns[tablename] = set()
columns[tablename].add(str(gene))
except (AttributeError, IndexError):
pass
self.tabledata.append(data)
# Iterate through the dictionary containing all the data headers
for table, setofheaders in sorted(columns.items()):
# Each header will be used as a column in the appropriate table
for cleanedcolumn in sorted(setofheaders):
# Alter the table by adding each header as a column
cursor.execute('''
ALTER TABLE {}
ADD COLUMN {} TEXT
'''.format(table, cleanedcolumn))
# Iterate through the samples and pull out the data for each table/column
# for sample in self.metadata:
for sample in self.tabledata:
# Find the id associated with each sample in the Sample table
cursor.execute('''
SELECT id from Samples WHERE name=?
''', (sample.name,))
sampleid = cursor.fetchone()[0]
# Add the sample_id to the table
cursor.execute('''