sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
be5c0c60935bbda30c43a1d6934f7c8ea9068d42961e18722fbc8f33847af31c | Python | 1,891 | 76 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
import seaborn as sns
def plot_scatter_labels(ax, data_proj, gen_data_proj, proj1, proj2, labels):
ax.scatter(
data_proj[:, proj1],
data_proj[:, proj2],
color="black",
s=50,
label=la... |
1c15ef5af2273a8e5304831fdaa7113457be0887dfbf884237a48d420342d247 | Python | 1,892 | 56 | #!/usr/bin/env python
import pandas as pd
import argparse
import time
import os
import sys
def tidy_promoter_file(baits,out_file,p_id):
cols = ['chr', 'start', 'end', 'frag_id', 'genes']
bait_df = pd.read_csv(baits, sep="\t", names = cols).drop_duplicates()
bait_df['genes'] = bait_df['genes'].str.replac... |
b229eef2d40d737af4381ca1418e485d908cd8f76d09710e048632240199af7f | Python | 1,892 | 64 | import os
import sys
import traceback
from pathlib import Path
from typing import Dict
import numpy as np
import snapatac2 as sa2
import pyprojroot
code_root_dir = str(pyprojroot.here())
pack_dir = f"{code_root_dir}/package/python"
sys.path.insert(0, pack_dir)
import utils #pyright: ignore # noqa: F401, E402
# * log... |
ce7f5092de49168802fb18407df7d491207b26e9c929b525df3fe8542ea9158c | Python | 1,893 | 66 | """A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from PIL import Image
import os
import... |
e9fe59fd108167e80b1ee6b6a95560422bc05d9933371436316add4568fa5272 | Python | 1,893 | 59 | import logging
import os
import tempfile
import unittest
from codecarbon.external.logger import logger, set_logger_format
class TestSetLoggerFormat(unittest.TestCase):
"""Tests for issue #375 — set_logger_format() must not clear user-defined handlers."""
def setUp(self):
self._original_handlers = lo... |
232ef452a2ccd58efad30eadf0a394bf15a113c19186b5e950273a3376a958e3 | Python | 1,894 | 51 | from torch import nn
def get_norm(norm_type: str, num_features=None, spatial_dim: int = None, **kwargs):
if spatial_dim is not None:
assert 1 <= spatial_dim <= 3
if norm_type == 'none':
return nn.Identity(**kwargs)
if norm_type == 'bn':
if spatial_dim is None or spatial_dim == 2:
... |
2cf9630e7df6e258f1cb77e723d419aba764fdc51b4acf44dd1746ba415d25ef | Python | 1,894 | 77 | """
Tests to make sure evaluate module functionality works as expected.
"""
import pytest
import eugene as eu
import numpy as np
import pandas as pd
from pathlib import Path
HERE = Path(__file__).parent
eu.settings.logging_dir = f"{HERE}/_output"
@pytest.fixture
def sdata():
sdata = eu.datasets.random1000()
... |
531298ae0a70c4c6e42b67249cbb69113440d31018ad7af6f42c7da07ed41b71 | Python | 1,894 | 58 | import socketserver
import sys
import rpy2.robjects as robjects
class MyTCPHandler(socketserver.StreamRequestHandler):
def handle(self):
# verbose server
print("%s wrote:" % self.client_address[0])
# self.rfile is a file-like object created by the handler;
# we can now use e.g. re... |
aab2f9376ef35ee113514be9100f5bed9232d3369e2c7d541c6130e172c8489d | Python | 1,894 | 90 | from flask_login import LoginManager, AnonymousUserMixin
from werkzeug.security import check_password_hash
from database import (
UserModel,
DatasetModel,
CategoryModel,
AnnotationModel,
ImageModel
)
login_manager = LoginManager()
class AnonymousUser(AnonymousUserMixin):
@property
def dat... |
c2f700ccdc4c4d190b88f9699d1979a96c8e5d1710722e83fa92a537e8ad6117 | Python | 1,894 | 27 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
c341708282872e67823355ea462c52c7974333bfa8eeeb79f6bb3f2a5ad90c87 | Python | 1,894 | 72 | class FeatureSetting(object):
""" Class to hold parameters for encoded features
Attributes
----------
skip_existing : bool
True if not want to encode already existing features
augment_tiles : bool
True if apply augmentation sequence to tiles
batch_size : int
Size of batc... |
ca072e91d0abed8f980132754eceb13adf5f9fa0cb0483b19946d169f2bbd583 | Python | 1,894 | 61 | """Invariant tests for public grouped option classes."""
from __future__ import annotations
from dataclasses import FrozenInstanceError, fields, is_dataclass
from typing import Any
import pytest
from torchlens.options import (
CaptureOptions,
InterventionOptions,
ReplayOptions,
SaveOptions,
Stre... |
badbc60a807ee31c72ad4a5dbd9ce69dfe6894d3e4f05c1262b8de755b84ff01 | Python | 1,895 | 59 | import sys
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq
from Bio import SeqIO
from utils import get_CDS_loc, getLongestORF
import pandas as pd
import numpy as np
import random
def has_uORF(fasta_file,test_csv):
test_df = pd.read_csv(test_csv,sep='\t').set_index('ID')
is_coding = lambda x : x.st... |
e00998935bfdae6a7de907e94a9a8320f73a183844fad355662a647770c83ead | Python | 1,895 | 61 | import numpy as np
#import pumpprobe as pp
import matplotlib.pyplot as plt
import pyneuronsegmentation as pyns
import struct
import time
folder = "/home/francesco/tmp/"
folder = "/tigress/LEIFER/francesco/bleaching/20190311/pumpprobe_20190311_145010/"
framesN = (np.uint32)(100)
sizex = (np.uint32)(512)
sizey = (np.ui... |
285d7fba947bf1d7296d5b9f937bb6d63aa7520d1a2e1c69470623f93357b2f2 | Python | 1,897 | 50 | """Multiprocessing distributed data parallel support"""
import torch
import torch.distributed as dist
import torch.backends.cudnn as cudnn
import torch.multiprocessing as mp
def get_local_ip_and_match(ip_list):
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
... |
62098ec50857e2d51939b11d189508430dd86d9419b060ff764681160c5e8ef9 | Python | 1,897 | 56 | import os
import argparse
import logging
import autogluon.core as ag
from gluoncv.auto.tasks import ObjectDetection
from gluoncv.auto.estimators import SSDEstimator
from d8.object_detection import Dataset
os.environ['MXNET_ENABLE_GPU_P2P'] = '0'
if __name__ == '__main__':
# user defined arguments
parser = a... |
a01d796e4632741351887e96daf553aaa4e65269598ff479ccee3aa8a0caac12 | Python | 1,897 | 61 | import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class Walker2dEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 125,
}
... |
ba06e7006b8cbfaa44063aa7a5d3ce57c8db0cf2c34152d9bc2bd2ec900120eb | Python | 1,897 | 61 | import os
import sys
import sphinx_rtd_theme
sys.path.insert(0, os.path.abspath(".."))
# -- General configuration ---------------------------------------------------
needs_sphinx = "2.0" # Nicer param docs
suppress_warnings = [
"ref.citation",
"myst.header", # https://github.com/executablebooks/MyST-Parser... |
d59886231bb5a6f40dd90a4bf9fe9155214188951807130790189533c0d6e5af | Python | 1,897 | 58 | import numpy as np
import pandas as pd
from copy import deepcopy
from collections import defaultdict
import auggam.data as data
import pickle as pkl
from spacy.lang.en import English
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
if __name__ == '__main__':
tok_simp = English().tokeniz... |
5260b5d2226fdd28bec35fe0d484a8e074f51ab792212eaf245ec3737c794320 | Python | 1,898 | 77 | import os
import sys
sys.path.append('..')
import numpy as np
import warnings
import argparse
import pandas as pd
from data_load.dataloading import data_preparation_and_train
"""
Examples:
python3 train.py -d data/wav -j data/folds.json
python3 train.py -d data/wav -j data/guitars.json
python3 train.py -d data/wav -... |
2ef22f69745263da364e8d5f3f4076ee9536813b6fc909b5ae2405aac2307de8 | Python | 1,899 | 35 | from irnlm.utils import check_folder
from fmri_encoder.data import fetch_masker
from irnlm.contrasts import plot_brain_fit
if __name__=='__main__':
###
# Replicating Fig6: Supra-lexical vs Lexical
###
saving_folder = 'derivatives/Fig6'
check_folder(saving_folder)
masker = fetch_masker('deriva... |
3d1bbc768d0345b4f934d56af3c2966737a03ac0ed4f4c685eaa1929d80d3b7b | Python | 1,899 | 62 | import logging
import os
import traceback
from logging.handlers import RotatingFileHandler
from click import get_app_dir
from qtpy.QtCore import QObject, Signal
class NoExceptionTracebackFormatter(logging.Formatter):
"""Custom formatter for formatting exceptions without traceback."""
def format(self, record... |
60dd5ca569dba6d73f1f3a147b29439e8e6fba809bb45af145e378f1ab422d9f | Python | 1,900 | 55 | # -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 19:11:53 2021
@author: Adrien Peyrache
This script shows how to use pynapple to compute sleep reactivation, step by step.
See pynacollada_replayExample for a real case example
"""
import pynapple as nap
import pandas as pd
import numpy as np
import seaborn as sns; s... |
aa66ca94baec5bcac47ecaf6ee0f1ffea4cbf2bb4febec0d1d6ba23bbaebbbd8 | Python | 1,900 | 58 | import numpy as np
import deepdish as dd
from readout import load_results
from digest import conds
out_fname = 'nspikes.h5'
def get_nspikes(cfg, ISIs, templ):
'''
Retrieves the average number of spikes per trial, summed across the entire network.
Structure: {STD: {TA: {cond: ndarray(nets*pairs*2)}}}
... |
e09d3ef9f5c143a11d4b115c0d5d5093a81b22d5a46a8cbadd1604149912c06a | Python | 1,900 | 58 | from __future__ import annotations
import random
import numpy as np
import panel as pn
import pytest
from larvaworld.portal.models_architecture.module_inspector_app import (
_ModuleInspectorController,
module_inspector_app,
)
@pytest.fixture(autouse=True)
def _seed() -> None:
random.see... |
4354cb83ab01163ce26647026992326187bfb3347414a1a99b91aacb0cfbc9bf | Python | 1,902 | 44 | """
Predictors based on gammatone spectrograms
Assumes that ``make_gammatone.py`` has been run to create the high resolution
spectrograms.
"""
from pathlib import Path
import eelbrain
# Define paths to data, and destination for predictors
DATA_ROOT = Path("~").expanduser() / 'Data' / 'Alice'
STIMULUS_DIR = DATA_ROO... |
4e9b4bb6fa9ec22285bf8c5004bec5a32d25b60ab1717acd38f98ed8f6ec63c4 | Python | 1,902 | 70 | from __future__ import annotations
from flask import Flask
from flask import Request
from flask import request
from flask.testing import FlaskClient
def test_max_content_length(app: Flask, client: FlaskClient) -> None:
app.config["MAX_CONTENT_LENGTH"] = 50
@app.post("/")
def index():
request.for... |
c76e0865c4b37fa5fc834e66be0f327390edab1ec5ff8ae8037914290e323991 | Python | 1,902 | 44 | import torch
import numpy as np
import torch.nn.functional as F
def transI_fusebn(kernel, bn):
gamma = bn.weight
std = (bn.running_var + bn.eps).sqrt()
return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
def transII_addbranch(kernels, biases):
return sum(kerne... |
cf4bbcb3dbae1617c04562979b10e330ca589b03e492bee9950001c347de439e | Python | 1,902 | 64 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copy warped MRI to the proper BIDS directory.
@author: mikkel
For information about NatMEG-PD please refer to the data descriptor:
Vinding, M. C., Eriksson, A., Comarovschii, I., Waldthaler, J., Manting, C. L.,
Oostenveld, R., Ingvar, M., Svenningsson, P., & Lundqv... |
102a3310d35a7d221a08bd50afe18fcd5963452c6315dc510ea6576c4cef22cc | Python | 1,903 | 53 | from collections.abc import Iterable
from typing import (
TYPE_CHECKING,
)
from galaxy.exceptions import RequestParameterInvalidException
from galaxy.model import DatasetCollectionElement
from . import BaseDatasetCollectionType
from .paired import (
FORWARD_IDENTIFIER,
REVERSE_IDENTIFIER,
)
if TYPE_CHECKI... |
368f25d398ab828d4e9c95dd60064b9bc7923572f1bdcfc4aa1429b12db34894 | Python | 1,903 | 43 | # %%
import os
import numpy as np
import nibabel as nib
# %%
def get_nii_orientation(target):
affine = nib.load(target).header.get_best_affine()
orient = nib.orientations.aff2axcodes(affine)
orient = ''.join(orient)
return orient
# %%
def reorient_vol(vol:np.ndarray, old_orient:(str,tuple), new_orient:(str,... |
b126937afa2b8d61db8859f30450de2085fa1e4a1ee7db59b7417a9eae760557 | Python | 1,903 | 60 | """
This script demonstrates how to plot membrane properties over time.
Passive membrane properties are calculated for every sweep.
Sweeps contain a brief hyperpolarizing square pulse from -70 mV to -80 mV.
See comments in the memtest module for theory and implimentation details.
"""
import os
import sys
PATH_HERE = o... |
1c22c0e4a8088f9f3de2cce980a429bde062bb66dcd679ac2da07a04b725f708 | Python | 1,904 | 66 | import torch
import torchio as tio
from torch.nn.functional import softmax
from fit.fit import Fit
class BaseFit3D(Fit):
def __init__(self, device, amp=False):
super().__init__(amp, device)
def iterate(self, data, model, criterion, is_train=False):
x, y = data['ct'][tio.DATA].to(self.device)... |
25237de12a6abefe5cd4a0f3068d65bb2c3cde627f689ddd7cded07279ac4fa4 | Python | 1,904 | 39 | import unittest
from unittest.mock import Mock
from random import choices
from svim.SVIM_COMBINE import generate_insertion_consensus
from svim.SVSignature import SignatureInsertion, SignatureClusterUniLocal
class TestSVIMConsensus(unittest.TestCase):
def setUp(self):
self.nucleotides = ["A", "C", "G", "T"... |
b4291a7c7da01f9f061f840ebf94460c60606d0cc14b278c7067448df3ae1e90 | Python | 1,904 | 42 | from pl_bolts.datamodules.async_dataloader import AsynchronousLoader
from pl_bolts.datamodules.binary_emnist_datamodule import BinaryEMNISTDataModule
from pl_bolts.datamodules.binary_mnist_datamodule import BinaryMNISTDataModule
from pl_bolts.datamodules.cifar10_datamodule import CIFAR10DataModule, TinyCIFAR10DataModul... |
6d01857eb321243d528ad52dcb70576621266c25db8d72d957ae71577fa7bf15 | Python | 1,905 | 64 | # EUGENe on sdata
def count_kmers_sdata(sdata: SeqData, k: int, frequency: bool = False) -> dict:
"""
Counts k-mers in a given sequence from a SeqData object.
Parameters
----------
sdata : SeqData
SeqData object containing sequences.
k : int
k value for k-mers (e.g. k=3 generate... |
e4661418cea24684385bcf77da7c438b702820143472b5192fdc69638defcc32 | Python | 1,905 | 55 | """Loss functions for Object Detection task."""
from torch import Tensor
from pl_bolts.metrics.object_detection import giou, iou
def iou_loss(preds: Tensor, target: Tensor) -> Tensor:
"""Calculates the intersection over union loss.
Args:
preds: batch of prediction bounding boxes with representation... |
f1424f65620e26e7ab0533bec690ee68a97feab36523287fd044aff0a586cfa1 | Python | 1,905 | 63 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightning as p... |
6f0ffeb94fcf14520f84782ace18ebccfb2bea736d3d62b20d6e4a6d78c05383 | Python | 1,906 | 59 | """Subprocess entry point for isolated viz-identity DOT generation.
Renders every forward-DOT golden in ONE fresh interpreter, constructing both
models BEFORE the first capture so no model ctor ever runs on wrapped torch:
in-process generation inside the pytest session constructed models on
whatever wrap state earlier... |
afa061aed825d73ccdc4528ccbe579d6e530381fa5c578003396bf287b9626f4 | Python | 1,907 | 60 | from pyrosm.data_manager import get_osm_data
from pyrosm.frames import prepare_geodataframe
def get_network_data(
node_coordinates,
way_records,
tags_as_columns,
network_filter,
bounding_box,
slice_to_segments,
filter_type="exclude",
keep_metadata=True,
osm_keys="highway",
step... |
df5d956b2aca57f76d27c50d7836a18a0e0f972c1e9971bc52eccd00da146605 | Python | 1,907 | 87 | import array
import pytest
import rpy2.rinterface as ri
ri.initr()
def test_init_from_bytes_in_seq():
seq = (b'a', b'b', b'c')
v = ri.ByteSexpVector(seq)
assert len(v) == 3
for x, y in zip(seq, v):
assert ord(x) == y
def test_init_from_seq_of_bytes():
seq = (b'a', b'b', b'c')
v = ri... |
17f1436546d9ace3635ef5c70278e459ee16df463219236adc55d42d24a17423 | Python | 1,909 | 43 | # 05/23/2023 Fengyao Yan
# Make semi silico data from the cmp dataset.
import os
import pandas as pd
from multiprocessing import Pool, cpu_count
from decide_organ import _gen_comp
from decide_organ import master
data_raw = '../../data/raw'
cpu_count = 12
if not os.path.exists(f'{data_raw}/tissue_cmp_expression.csv'... |
3d25c4e872624e764d1809a84cfe0ee62d4b911c5a1286c3b5c6e11dfdd10bfd | Python | 1,909 | 58 | import os
import warnings
import h5py
import numpy as np
import scanpy as sc
from scbig import run_scbig, preprocess, setup_seed
warnings.filterwarnings('ignore')
import time
from memory_profiler import profile
@profile
def run(adata):
adata = run_scbig(adata)
from memory_profiler import memory_usage
m... |
ccaaf2d2c765a646161bb344cf1e290123da1217f4b8e13342a05cf88f26d652 | Python | 1,909 | 52 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 12 12:22:02 2022
@author: mikesha
"""
import torch
def find_rigid_alignment(A, B):
"""
See: https://en.wikipedia.org/wiki/Kabsch_algorithm
2-D or 3-D registration with known correspondences.
Registration occurs in the zero centered ... |
e337c850886e53b5b57fd6e295fb5aec191ef6ed5fc1eb5e502041df58901d63 | Python | 1,909 | 60 | from pathlib import Path
import numpy as np
from tensorflow.keras.models import load_model, Model
from barriernn_p.input_generation import create_meta_dataset_predictions
from tqdm.autonotebook import tqdm
#%% ----- Parameters -----
#########
datasets = ['dataset_2208_synth', 'dataset_2208_traj']
base_name_dir = "2208... |
928658d51a0cb867f003ffc61513c6b9c767957be4195c27c9569ae381b6bd87 | Python | 1,910 | 51 | import os, math, operator, sys
import numpy as np
def norm(v):
'''Calculate the norm of vectors stored in an array'''
# Example: nx3 array containing the coordinates of n 3D vectors
# v = np.array([[1, 2, 3]
# [...]
# [4, 5, 6]])
# norm(v) will return array([3.741657... |
c746dc16c847fe5ad1899891efe53de051bb4341452372cfc68b942c2cc17db7 | Python | 1,910 | 54 | import torch
import numpy as np
from os.path import join
from pathlib import Path
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
class DSet(Dataset):
''' LibriSpeech parser which takes raw LibriSpeech structure'''
def __init__(self, path, split):
# Setup
self.... |
e55dec50a8ce5f16ac5461d52998dfe152559e8c285c618f347123ae6881a5de | Python | 1,910 | 61 | from typing import Iterable
import numpy as np
import tensorflow as tf
from angorapy.analysis.util.sindy import compute_z_derivatives, sindy_library_tf, library_size
def build_encoder(layer_sizes: Iterable[int]):
encoder = tf.keras.Sequential([
tf.keras.layers.Dense(size, activation="relu") for size in ... |
1f6214205661f79b21fbbfba1b0b39e5cb7e02b82ca4dc036c6926c69a11d871 | Python | 1,911 | 53 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional a... |
f94c2b926f25816ee98e77441b19008c5e51e0cdcb1c55ce87d39eb734b05c2d | Python | 1,911 | 58 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""Module providing methods for packing and unpakig tags from dictionary: 'unpack_tag_from_dict', 'pack_tag_to_dict'."""
def unpack_tag_from_dict(p_dict):
"""
For given dictionary de... |
fbc01a7d200c45f9969dfdaec33f3b73b3aa042d501ec7e8e3df409113f77016 | Python | 1,911 | 70 | import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateModel
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
def test_train_mn... |
0972e15dd6a281dac843c61b7a36c6e65c8da3aaf48b228941c308c016602798 | Python | 1,912 | 75 | from ..preprocess._helpers import _get_vocab
import numpy as np
import pandas as pd
TINY = np.finfo(float).tiny
def pfm_to_df(
pfm,
vocab: str = "DNA",
):
vocab = _get_vocab(vocab)
pfm_df = pd.DataFrame(pfm, columns=vocab, index=range(pfm.shape[0]))
return pfm_df
def pfms_to_df_dict(
pfms,
... |
4588f46aecf2d6c817254920d477bb251ab5a17fc0ee80a2e5255156ef52bb9d | Python | 1,912 | 55 | import sys
# setting path
sys.path.append('../TM2_segmentation')
import os
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
from dateutil import parser
from datetime import datetime
from scripts.preprocess_utils import find_file_in_path, register_to_template
# load metadata file... |
b6e4c72612a227b3b84454522aa48a65cc9fdecdce6500577ae953e1a1d19ae5 | Python | 1,912 | 71 | """Tests for specparam.utils.select"""
from pytest import raises
from specparam.utils.select import *
###################################################################################################
###################################################################################################
def test_group... |
1900b1bb94cf8f487a25aefcdc6c4561f19bcd5fbcad8e07088ee7bd1bab12de | Python | 1,915 | 64 | from permacache import permacache, stable_hash
import tqdm.auto as tqdm
import torch
import numpy as np
from modular_splicing.data_for_experiments.standardized_sample import (
calibrate_thresholds_on_standardized_sample,
)
from .data import dataset_for_species
def compute_thresholds(model, species):
_, path... |
a0ff97fe4d2f6d20780b0955e06415541010f6e23ff1fac589c75ee728556007 | Python | 1,915 | 52 | # -*- coding: utf-8 -*-
# tests for toy_graphs.py
"""authors:
Thomas Bonald <bonald@enst.fr>
Quentin Lutz <qlutz@enst.fr>"""
import unittest
from sknetwork.data.models import *
class TestModels(unittest.TestCase):
def test_shape(self):
n = 10
for model in [linear_graph, linear_digraph, cyclic_gr... |
4461dbd80d27abdc9abcad4ac9b61207f72731b93092d1dade8235404ab0829b | Python | 1,916 | 69 | import codecs
import os
import sys
from setuptools import find_packages, setup
if sys.version_info < (3, 6):
sys.exit("Sorry, Python < 3.6 is not supported")
def read(rel_path):
here = os.path.abspath(os.path.dirname(__file__))
with codecs.open(os.path.join(here, rel_path), "r") as fp:
return fp... |
6a228d223bd62912625fc8b67c17f1d621501557c5b4d7480a4eddeeb952a44c | Python | 1,916 | 45 | """
Théo Gauvrit
Mars 2022
compute Mean Absolute Error on the evoked response(EPSP) of two groups of recordings.
"""
import numpy as np
import pandas as pd
import pyabf
from sklearn.metrics import mean_absolute_error
from Utils import browse_directory
def mae(group_name, group_path, sampling_freq, stim_timing=1.04, ... |
00f24a6401a30e9dd6a0c7f9bf0ae9d47385dfb27d5f0a0b1300f66575e3b71b | Python | 1,917 | 69 | import subprocess
import os
if __name__ == "__main__":
# Import config
try:
from config import python_path, lib_path
except:
from config_template import python_path, lib_path
if python_path is None:
raise ValueError('"python_path" is not specified')
if lib_path is None:
... |
560e740c7a27a1455c7424fd5b80733205e143ccf61cf9452c965e9ed4374e98 | Python | 1,917 | 62 | #!/usr/bin/env python
import sys
from Bio import SeqIO
def index_genbank_features(gb_record, feature_type, qualifier):
answer = []
for (index, feature) in enumerate(gb_record.features):
if feature.type == feature_type:
start = feature.location.start.position
end = feature.loc... |
29704779beab93fb68257d5161892c27890c447e80d762a72f1f675eb3784db4 | Python | 1,918 | 61 | """
Portal apps and helpers for working with datasets.
"""
from __future__ import annotations
from importlib import import_module
from typing import Any
__all__: list[str] = [
"ImportRequest",
"RawDatasetCandidate",
"WorkspaceDatasetRecord",
"build_workspace_proc_folder",
"discover_raw_datasets",... |
495b3161cb1dcdac08d5f142ac1732b112e155b1d82b62389b0704ef2080166b | Python | 1,918 | 54 | from scipy.stats import ttest_1samp, ttest_ind
from mne.utils import check_random_state
import numpy as np
def _permutation_1samp(X, n_permutations = 10000, alternative = 'two-sided',
seed = None, statfun = None):
'''
computes the permutation distribution of p-values from ttest_1samp
'''
_, p_obs = ... |
a0550363d99bc41db488cd68467e69e0b09e59be14c05492b31002f7ef20a24e | Python | 1,918 | 53 | import pandas as pd
import pysam
import csv
import argparse
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input',
required=True, type=str,
help='File of indel mutations')
parser.add_argument('-b', '--blacklist',
... |
a44a1d42ddc4db2a23805e8d07f47678442d7582f7a0f04eb737b5a0413a2101 | Python | 1,918 | 51 | """Unit tests for LarvaDatasetCollection.get_colors()."""
from __future__ import annotations
from types import SimpleNamespace
import pytest
from larvaworld.lib.process.dataset import LarvaDatasetCollection
from larvaworld.lib import util
def _fake_collection(colors):
datasets = [SimpleNamespace(config=Simple... |
a9a67921eb4698f6e237b8825b93636c0aed61d80841f423a8f099efa4e5585e | Python | 1,918 | 58 | #!/usr/bin/env python
#=========================================================================
#
# Copyright NumFOCUS
#
# 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://... |
bf17c31dd54ef8ab84fc73a2e8cf583c2696a1cd18f47732f3582197cdb7df8c | Python | 1,918 | 60 | from tensorflow.keras.layers import Dropout, MaxPool1D, MaxPool2D, Conv1D, Conv2D, Dense
from tensorflow.keras.models import load_model
from pathlib import Path
def writeShape(shape):
return ", ".join(str(s) for s in shape[1:])
def getName(layer):
if isinstance(layer, Conv2D):
return "Convolution 2D"... |
2fe0c9ff1ce990aef032c6eed68d545903315ecd78f91cfdd05aecde75612d1b | Python | 1,919 | 59 | #!/usr/bin/env python
"""Tests for ClusterPlot."""
import unittest
from stlearn.adds import row_annotations
from tests.utils import path_for_test_data, read_test_data
class TestRowAnnotations(unittest.TestCase):
"""Tests for row annotations."""
@classmethod
def setUpClass(cls):
cls._base_adata... |
5548b7324004ef83fd9724843d785be58da628915a80a76964b91a6d2c390b4d | Python | 1,919 | 67 | import os
from typing import Optional
from omegaconf import DictConfig
from pytorch_lightning.callbacks import ModelCheckpoint, ModelSummary
def create_checkpoint_callback(
save_last: bool = False,
save_top_k: int = 0,
monitor: str = "loss",
dirpath: Optional[str] = None,
) -> ModelCheckpoint:
""... |
c3eed4c35e3fd51cc14eb259a8d7c39905d105e38cc22fccdae9625295942d9a | Python | 1,920 | 58 | '''
Processes datasets for different acceleration factors
author: obanmarcos
'''
from config import *
from concurrent.futures import process
import os, sys
# Where am I asks where you are
sys.path.append(where_am_i())
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utiliti... |
255087b2a12682f8a89710ba4dc51b88f49dd027ca4315b861a8b1a2a7bf45ce | Python | 1,921 | 70 | import argparse
from io import BytesIO
import multiprocessing
from functools import partial
from PIL import Image
import lmdb
from tqdm import tqdm
from mxnet.gluon.data.vision.datasets import ImageFolderDataset
# pylint: disable-all
def resize_and_convert(img, size, quality=100):
img = img.resize(size)
buff... |
38d4f75ab83e2959865830063faa33bbe5ffbb32ee73a0874ce1c02d4037fc30 | Python | 1,921 | 67 | from keras.src import ops
from keras.src.backend.common import standardize_dtype
DTYPE_TO_SIZE = {
**{f"float{i}": i for i in (16, 32, 64)},
**{f"int{i}": i for i in (8, 16, 32, 64)},
**{f"uint{i}": i for i in (8, 16, 32, 64)},
"bfloat16": 16,
"bool": 1,
}
def dtype_size(dtype):
size = DTYPE_... |
db7c9aafe312e85a306b0de1177dedfa194c2bfef58bc8558f0fc1b37fb85c40 | Python | 1,921 | 60 | """Benchmarking module for AlignmentFile functionality"""
import os
import subprocess
import pysam
from TestUtils import BAM_DATADIR
def count_number_lines_with_samtools(fn):
os.system("samtools view {} | wc -l > /dev/null".format(fn))
return 3270
def count_number_lines_with_samtoolspipe(fn):
with sub... |
df0e2efbb50b8bc6e04b245fe175fd0a5f9037278d70c89faedd6f5a0b23778f | Python | 1,921 | 47 | from tensorflow.keras.layers import UpSampling3D
import nibabel as nib
import pandas as pd
import os
def remove_extra_skull(patient):
# get scan file names
print(f"PATIENT {patient}")
nccts = ["/"+file for file in os.listdir("NCCT")]
cta = ["/"+file for file in os.listdir("CTA")]
ncct... |
19d8f05bf3ea9fb579b4de2f3ddff60b9d53a68b660d40d705b80c7ec07d2065 | Python | 1,922 | 50 | from neurogym import _SB3_INSTALLED
from neurogym.envs.native.annubes import AnnubesEnv
from neurogym.wrappers import Monitor
def test_activation_traces(n_trials: int = 10):
"""Tests for neuron activation traces."""
session: dict[str | tuple[str], float] = {"auditory": 0.5, "visual": 0.5}
catch_prob = 0.2... |
76de35be4d43692d47f8bac10426577c84a3d60e4f2c071ac4d4cd33bd2f8f5b | Python | 1,922 | 72 | import numpy as np
import torch
def _2d_morph_conv(seg_oh, selem=None):
assert torch.is_tensor(seg_oh), 'input seg must be a torch tensor'
assert len(seg_oh.shape) == 4, 'seg must be 4d'
nz = seg_oh.shape[1]
if selem is None:
selem = torch.tensor([[0, 1/5, 0], [1/5, 1/5, 1/5], [... |
9edb01fefb2375bbcf2773ba64c21fa411ed7c388997de5c1e200ea9e7e6116a | Python | 1,922 | 63 | import numpy as np
import math
import statistics
import pdb
# ------------------------------------------------------
# 3d distance
def distance3d(a, b):
d = math.sqrt(math.pow(a[0] - b[0], 2) +
math.pow(a[1] - b[1], 2) +
math.pow(a[2] - b[2], 2) * 1.0)
# print("Distance is... |
c2aed18c41704242e1482e41b6740bd0dc6d4d6e447581fad58164ef97e15161 | Python | 1,922 | 53 | import mxnet as mx
from mxnet import nd, autograd, gluon
from mxnet.gluon import nn, Block
from gluoncv.utils.parallel import DataParallelModel, DataParallelCriterion
def test_data_parallel():
# test gluon.contrib.parallel.DataParallelModel
net = nn.HybridSequential()
with net.name_scope():
net.add... |
cf212bc2794285e5d65771941af6061c20eb360cf20dceaae49f9c1ba6211dc2 | Python | 1,922 | 55 | # -*- coding: utf-8 -*-
"""
General framework regrouping the different splitting strategies possible to integrate in the
regression analysis pipeline.
===================================================
A Splitter instanciation requires:
- out_per_fold: the number of run to left out for the test set.
It makes use ... |
f9ab4fbd243c61690c3966090a457d3679cfff1860377e793f2cd9826fb6cc0c | Python | 1,922 | 56 | import os
import numpy as np
import pandas as pd
import nibabel as nib
from os.path import join as pjoin
from magicbox.io.io import CiftiReader, save2cifti
from cxy_visual_dev.lib.predefine import proj_dir, mmp_map_file,\
Hemi2stru, LR_count_32k
anal_dir = pjoin(proj_dir, 'analysis')
work_dir = pjoin(anal_dir, 'AH... |
02b9835760bd30a15ac2be8a094bca19bbb32e82da6000b4628637794cad696e | Python | 1,923 | 57 | import glob
from ase import io as ase_io
from pymatgen.io.ase import AseAtomsAdaptor
from pymatgen.io.qchem.outputs import QCOutput
from instance_mongodb import instance_mongodb_sei
if __name__ == "__main__":
"""Store all data in representative xyz files for easy visualisation."""
db = instance_mongodb_sei... |
857f2d904b873a83196b74b35ad80b7a24661f1fa44a32e5283782e802f7b39d | Python | 1,923 | 55 | import warnings
import pandas as pd
import numpy as np
from numpy.random import default_rng
from scipy.stats import expon, weibull_min
warnings.simplefilter("ignore")
def synthetic_data_random(n_samples=100, as_dataframe=True):
coefficients = [2, 4, 6]
rng = default_rng(seed=0)
x1 = rng.standard_normal((... |
c17b60de5f5045b2d9db47e3cd2abfab2e02630751b8e81934c86041152e60bd | Python | 1,923 | 51 | """W051-TRACK / AUD-CODE 2.15: an ``echo=`` sink-open failure must release the
capture reservation on the error path.
The echo sink opens its path eagerly; before the fix the open ran between the
capture-slot claim and the forward's own guard, so holding the exception (REPL
``sys.last_traceback``, pytest ``excinfo``) ... |
d2dbcc765a7c7bf5a8ff6cc062cc715005bb36d2bc81f694207b131b61a4a915 | Python | 1,923 | 71 | import pandas as pd
import os
from sqlalchemy import create_engine
import codes3d
import snps
import sys
def get_snp_list(test_dir):
'''
rs1794265 # biallelic
rs1689510 # at start of MboI fragment; ensures fragments are 0-based
'''
return pd.read_csv(
os.path.join(test_dir, 'test_snps.txt'... |
f5f9f45d66238b9f1515795957fb9eaaa83387cec5d5511104382fecbad3b8ca | Python | 1,923 | 70 | from modular_splicing.models_for_testing.width_models import (
AM_21,
AM_17,
AM_13,
AM_13_more_layers,
AM_13_secondary,
AM_13_secondary_control,
AM_13_flanks,
AM_13_reprocessed_21,
AM_13_reprocessed_45,
)
normal_spec = dict(
type="H5Dataset",
sl=5000,
datapoint_extractor... |
669616a2bc3062cd46b9e06cd04c79d8a2b5004f978ef2ff20f928d8939106af | Python | 1,924 | 85 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
=================================
BIDS: MICA structural processing
=================================
Preprocessing workflow for structural T1w.
This workflow makes use of ANTS, FSL and Freesurfer
Atlas an templates are avaliable from:
https://github.com/MICA-MNI/mi... |
ecffdfd9286c1e0e6ddd006c6ec24279ee7b85248a0f26c101b0fece99e730bf | Python | 1,925 | 55 | import os
import argparse
import logging
import autogluon.core as ag
from gluoncv.auto.tasks import ImageClassification
from gluoncv.auto.estimators import ImageClassificationEstimator
from d8.image_classification import Dataset
os.environ['MXNET_ENABLE_GPU_P2P'] = '0'
if __name__ == '__main__':
# user defined ... |
54c1347fb224b01c3253b9bc5df4ec57e33f7d4500f8feafa67a4ec327ceaa13 | Python | 1,926 | 67 | import argparse
from chembl_structure_pipeline import checker, standardizer
def load_mols(input_file):
"""
Returns a list of strings, each a molblock
"""
with open(input_file) as f:
mols = [
"".join(("\n", mol.strip())) for mol in f.read().strip().split("$$$$\n")
]
ret... |
a2b1291f49f6c3057adfa158f63f1ea236e7208817e4d3ae63cbc02e314457da | Python | 1,926 | 55 | """Detection Dataset from LST file."""
from __future__ import absolute_import
import os
import numpy as np
import mxnet as mx
from mxnet.gluon.data import Dataset
from ..recordio.detection import _transform_label
class LstDetection(Dataset):
"""Detection dataset loaded from LST file and raw images.
LST file i... |
beb349406ffa8a6f61449d9937cd19ccf0e571e907b117a0dfbc7eb6ef641c60 | Python | 1,926 | 52 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
05fb6456e9ee36cb236cbdbade391acc0438dc53d8e1b72a60d910b958f17a63 | Python | 1,927 | 60 | import numpy as np
import pytest
from CEST.src.CEST import CEST
from Metrics.src.CEST import mtr_asym
from test_support_function.src.CEST import generate_Z_3D
def test_OF_0():
# Test CEST with and water offset of 0 ppm
wassr_img = generate_Z_3D(img_size=(2, 2), dyn=21, ppm=1, a=1, b=0, c=1)
cest_img = ge... |
150102c6869d99ce6d9b45ee031ad152e97c0f4f5071961e4b4833a3e88cd01e | Python | 1,927 | 53 | """Smoke tests for default queue type fallback behavior.
Tests that Celery respects task_default_exchange_type and
task_default_queue_type when using implicit routing.
"""
from __future__ import annotations
import pytest
import requests
from pytest_celery import RESULT_TIMEOUT, CeleryTestSetup
from requests.auth impo... |
35a182900387b94f159dc644c65c16d1e51d6f68467992a519e3ba407dbd79d0 | Python | 1,927 | 74 | """Discover within a submodule before choosing a finer selector.
What this demonstrates
----------------------
Use ``tl.in_module("block")`` to restrict discovery, then compose it with
``tl.func("relu")`` for the intervention target.
How to run
----------
``python examples/intervention/09_submodule_discover_first.py`... |
b462bed70434c8452ec29b9dca7acd366d21d97227e17b0b8bb93d14e758fc49 | Python | 1,927 | 52 | import pandas as pd
import numpy as np
def aa_encoding_tensors(germline_cdr3_csv, aa_order, v_gene_list, j_gene_list, max_cdr3_len):
"""
Build tensors that one-hot-encode the germline sequences that extend into the CDR3.
V genes are left-aligned, while J genes are right-aligned.
"""
aa_list = list... |
b797ad2e4b2b53727c88437439af04f05d077f9954a83d64143a8e83b768fd26 | Python | 1,927 | 53 | import sys
# setting path
sys.path.append('../TM2_segmentation')
import os
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
from dateutil import parser
from datetime import datetime
from scripts.preprocess_utils import find_file_in_path, register_to_template
# load metadata file ... |
eb84e124f099d4a012092ac5fae915238f36ebf44d2d734b4772d84f6e6ae05a | Python | 1,927 | 64 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
211061b4b5fed20981083e7ecf59a04fe26515975cf21c272da32b4a628c21ab | Python | 1,928 | 69 | import torch
import torch.nn as nn
from einops import rearrange
from SRDTrans.SpatioTemporalTrans import SpatioTemporalTrans
from SRDTrans.MainFrame import MainFrame, SingleConv
class SRDTrans(MainFrame):
def __init__(
self,
img_dim,
img_time,
in_channel,
... |
6965f21912a009088963f75bc9d99c381229ab4604dc23e28086dba84ca4bb4b | Python | 1,928 | 46 | # -*- coding: utf-8 -*-
"""
Created on Tue Sep 20 14:35:19 2022
This is the Updated Version for easy access for multiple choses.
23/09/22 -> Starts Parallel Processing of Multiple Scripts
-> "_ImpPlot=True" Plots the Impedance Plot
-> "_SRT_Plot=True" Plots the Semi Real Time Data
... |
99cd8eba4f7e735b2277d36b7b7d15b7ec66862197a4323c3f1b4bdc35c57b29 | Python | 1,928 | 58 | import numpy as np
import time
from getUi import getUi
from noiseless_PutM import noiseless_PutM
def manfit_km17(data, d, r, X, opts):
# This code is to implement the LPCA method in
# Mohammed, K. and Narayanan, H. (2017). Manifold Learning Using Kernel Density Estimation and Local Principal Components... |
fed91a82d28750fe1bc46f629ef4018e41453c676995b8672658b9ff88d73b8b | Python | 1,929 | 48 | """Bounding-box helpers: validate/normalise a box, reduce it to its bounds, flag in-box
coordinates, and read back the in-box node ids spilled per shard."""
import numpy as np
from pyrosm.utils import validate_bounding_box
def _normalize_bounding_box(bounding_box):
"""``bounding_box`` checked and normalised as `... |
0b8f42caa6fcae31118f45da36542cfa62f93887caf3cf319e21f25d124c1895 | Python | 1,930 | 59 | #!/usr/bin/env python
'''Given desired set of parameters, generates all configurations
obtained by enumerate each parameter individually (continuous are discretized).
'''
import sys, re, MySQLdb, argparse, os, json, subprocess
import pandas as pd
import makemodel
import numpy as np
from MySQLdb.cursors import DictC... |
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