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import cProfile
import pstats
import time
from pathlib import Path
from typing import List, Tuple
import h5py
import numpy as np
def append_suffix_to_file(file_path: Path, suffix: str = '_INF', ext: str = None):
"""
Adds a suffix to the given file path.
:param file_path: `Path` object to the original file.
:param suffix: `str` suffix to add to the end of the original file name.
:param ext: `str` potential new file extension.
:return: Updated `Path`.
"""
if ext:
file_path = file_path.with_suffix(ext)
new_file_name = file_path.stem + suffix + file_path.suffix
return file_path.with_name(new_file_name)
def is_int_in_list(n: int, l: List[int]) -> int:
if l[0] > n:
return 0
for e in l:
if e == n:
return 1
elif e > n:
return 0
return 0
def array4d_to_h5(array_4ds: Tuple, output_file: Path, group: str = None, datasets: Tuple = 'array_data'):
if len(array_4ds) != len(datasets):
raise ValueError(f'Amount of arrays {len(array_4ds)} must match amount of dataset names {len(datasets)}.')
with h5py.File(output_file, 'a') as h5f:
if group is not None:
grp = h5f.create_group(group)
for i in range(len(array_4ds)):
grp.create_dataset(name=datasets[i], data=array_4ds[i], compression='gzip', compression_opts=9)
else:
for i in range(len(array_4ds)):
h5f.create_dataset(name=datasets[i], data=array_4ds[i], compression='gzip', compression_opts=9)
def h5_to_array4d(input_file: Path) -> np.array:
with h5py.File(input_file, 'r') as h5f:
return np.vstack([np.array(h5f[key]) for key in h5f.keys()])
def combined_test_h5_to_array4d(input_file: Path, pc_size: int = 1024) -> np.array:
with h5py.File(input_file, 'r') as h5f:
data = []
for grp_name in list(h5f.keys()):
grp = h5f[grp_name]
labeled = np.array(grp['labeled'])
unlabeled = np.array(grp['unlabeled'])
data.append(merge_labeled_and_unlabeled_data(labeled, unlabeled, pc_size=pc_size))
return np.vstack(data)
def merge_labeled_and_unlabeled_data(labeled: np.array, unlabeled: np.array, pc_size: int) -> np.array:
missing = pc_size - (labeled.shape[1] + unlabeled.shape[1])
if missing <= 0:
# Returns shape (n_frames, self.pc_size, 14).
return np.concatenate((unlabeled, labeled), axis=1)[:, -pc_size:]
missing_markers = np.random.rand(labeled.shape[0], missing, labeled.shape[-1])
missing_markers[:, :, 0] = 0.
missing_markers[:, :, 1] = 0.
# Returns shape (n_frames, self.pc_size, 14).
return np.concatenate((missing_markers,
unlabeled,
labeled), axis=1)
class Timer:
def __init__(self, txt: str = 'Execution time: ', profiler: bool = False):
self.txt = txt
self.profiler = profiler
def __enter__(self):
self.start_time = time.time()
if self.profiler:
self.p = cProfile.Profile()
self.p.enable()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.end_time = time.time()
dif = self.end_time - self.start_time
print(f"{self.txt}: {dif:.4f} seconds")
if self.profiler:
self.p.disable()
stats = pstats.Stats(self.p).sort_stats('time')
stats.print_stats()
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