sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
329cfae870a5b9d637e9b5245aa6cd1b69662c0c27237acb502c105bed4bc314 | Python | 1,071 | 42 | """Add not-null constraints to group_role_association
Revision ID: 25b092f7938b
Revises: 9ef6431f3a4e
Create Date: 2024-09-09 16:17:26.652865
"""
from alembic import op
from galaxy.model.migrations.data_fixes.association_table_fixer import GroupRoleAssociationNullFix
from galaxy.model.migrations.util import (
a... |
4bb7b47e769362b74698630fd3becf36c0067e69cdc258738fd52f117f7accdc | Python | 1,071 | 46 | import csv
import re
import h5py
import urllib3
import urllib
import gzip
from io import StringIO
import requests
import numpy as np
import time
from utils_rbns import create_datapoints
rbns_data = 'metadata_rbns.csv'
protein_idx = 0
protein_list = list()
protein_dict = {}
with open(rbns_data, 'r') as f:
f.read... |
4d9cf35b5d7c73f3bcdb6f9b90cef1173360a3c0e39f2b672aff66011ca8d72a | Python | 1,071 | 36 | 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"]["motif_width"] = 17
msp.architecture["motif_model_spec"]["motif_feature... |
6d7c40fa06634c8acd9e68b75c9bb076e29a88bf8d43cca786281b9642e34e58 | Python | 1,071 | 36 | 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"]["motif_width"] = 17
msp.architecture["motif_model_spec"]["motif_feature... |
94701e07b0dca626574e26e2e1ad35ead77a74d651dd4f98489f9ebb7e0b410c | Python | 1,071 | 36 | 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"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
d5562e616cb3a361bf47219d2ebb0da23261757a70058e52fd55c770ca2d90aa | Python | 1,071 | 33 | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from drug_interactions.datasets.dataset_builder import DatasetTypes
from drug_interactions.models.AFMP import AFMP, AFMPConfig
from drug_interactions.models.CharSmiles import CharSmiles, CharSmilesConfig
from drug_interactions.models.DeepSmiles import DeepSmiles, Deep... |
db25a448324a6579bb2c4f27a60728a917e187f8af9958b47b4fbffc549ffbf3 | Python | 1,071 | 36 | 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"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
f0cb4004308782b224208b23fe345d6357f6e24ad23ae6b1ecdd0eb3bf2dde0f | Python | 1,071 | 36 | 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"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
0ff9605f69ad0bcc5fb8b9687c3b74e1100feaee0a2d6dfd8f7476cff0cf5e46 | Python | 1,072 | 34 | import sqlite3
from .db import DB
class DistrictTestingTable(DB):
def __init__(self):
super().__init__()
self.table_name = 'UK_testing'
self.table_desc = 'Uttarakhand district wise testing snapshot'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
... |
64198993940d098c5acfd05a819e6489e5f4943e710efda867a09b355de3d9b3 | Python | 1,072 | 42 | """Add not-null constraints to user_group_association
Revision ID: 13fe10b8e35b
Revises: 56ddf316dbd0
Create Date: 2024-09-09 21:26:26.032842
"""
from alembic import op
from galaxy.model.migrations.data_fixes.association_table_fixer import UserGroupAssociationNullFix
from galaxy.model.migrations.util import (
a... |
06b3bef8d7d30b5f661be27940d01a745768d6c6b921852d2d765c3ca707a53d | Python | 1,073 | 49 | from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
dev = torch.device('cuda:0')
class MultipleOptimizer:
def __init__(self, *op):
self.optimizers = op
def zero_grad(self):
for op in self.optimizers:
op.zero_grad()
def step(self):
for op in ... |
659cc65cf33d1f8639ade623900d227d49682f8c09dab4e4cfc5974cc960ef43 | Python | 1,073 | 19 | """
Copyright (c) 2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE BLUE BRAIN PROJE... |
af84b2c8a4b39aa8dc4135a03c7e12f00cb368b34ec981c8b179895593f2267c | Python | 1,073 | 38 | import torch
import numpy as np
def stack(items, axis=0):
# wrapps np.stack and torch.stack
if isinstance(items[0], np.ndarray):
# numpy interpolation
return np.stack(items, axis=axis)
elif torch.is_tensor(items[0]):
# torch interpolation
return torch.stack(items, dim=axis)... |
ebb6fddb4aa90960a274fbe748100209c2226e62bdda54282985704384f84b47 | Python | 1,073 | 36 | 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"]["motif_width"] = 11
msp.architecture["motif_model_spec"]["motif_feature... |
1ac5253972742f56a314e07a2e9bbe1c370b351eb970f3c496366b1a3204c84f | Python | 1,074 | 38 | import numpy as np
from .annotation import Annotation
class ExclusiveExons(Annotation):
"""
Represents a pair of exons, where exactly one
of the two must be included in a transcript.
"""
@classmethod
def patterns(self):
return ["ADAD"]
def compatible_with_overlapping_junction... |
237c9d3a87003b88248d4344d5e368b5b0ac864e86a535ac74c1897e516f8ce9 | Python | 1,074 | 24 | # coding=utf-8
# Copyright 2023 The Google Research Authors.
#
# 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 applicab... |
2a802d82586236459ef57bf72a6def921d62a7c062bb4ed7d555dacebe05bc7c | Python | 1,074 | 50 | #!/usr/bin/env python
import sys, os, re
def main():
usage = "\n\n\tusage: {} STAR.Log.final.out [STAR.Log.final.out ...]\n\n".format(sys.argv[0])
if len(sys.argv) < 2:
exit(usage)
tokens = ['sample_name']
processed_first = False
files = sys.argv[1:]
for log_filename in files... |
9b409836203545af2c4f0a091790a71d3a02fb0747d6aea14879ac7f9e6aecb9 | Python | 1,074 | 33 | import numpy as np
def select_independent_mutations(
saliency_map,
mech,
codon_table,
rna,
motifs_score,
*,
rng,
temperature,
negativity_bias
):
muts, map_back = saliency_map.sample_nonconflicting_mutations(
rna, codon_table, motif_width=mech.motif_width(), rng=rng
... |
b8c2fe01c1daf777cf899f111c060b954ebc451c7ddfd289925d5a53c7ab4c59 | Python | 1,075 | 32 | from typing import List, Tuple, Union
from PIL import Image
from lightly.transforms.rotation import (
RandomRotate,
RandomRotateDegrees,
random_rotation_transform,
)
def test_RandomRotate_on_pil_image() -> None:
random_rotate = RandomRotate()
sample = Image.new("RGB", (100, 100))
random_rota... |
4009878357e4b4237b58e5bf626eb4bd1e3445e9b05509e4795a278f40fa8d15 | Python | 1,076 | 26 | from ethoscope.stimulators.sleep_depriver_stimulators import MiddleCrossingStimulator
from ethoscope.hardware.interfaces.optomotor import OptoMotor
class OptoMidlineCrossStimulator (MiddleCrossingStimulator):
_description = {"overview": "A stimulator to shine light when animals cross the midline",
... |
5ff2cbb1931c2a15133690fe10ce70b6f64dda522006867aa9d4b9f12058f79b | Python | 1,076 | 32 | """
This module define a L1 loss with min max norm
"""
# pylint: disable=W0223
import torch
import logging
from utils.utils import select_output_lm
class L1LossMinSumNorm(torch.nn.modules.loss._Loss): # pylint: disable=protected-access
"""
This class define a specific loss : L1 loss with a min max normalisat... |
9769185c9b959c56394abb9a4f2c650c6ef766a20300a550334ebe51b1c49206 | Python | 1,077 | 32 | """
This module define a L1 loss with min max norm
"""
# pylint: disable=W0223
import torch
import logging
from utils.utils import select_output_lm
class L2LossMinSumNorm(torch.nn.modules.loss._Loss): # pylint: disable=protected-access
"""
This class define a specific loss : L1 loss with a min max normalisat... |
e7f58a99924b06421c70a715bec3243dd32f1dae19d65eead98f3c7299b3c332 | Python | 1,077 | 39 | """
net
"""
import torch
from torch import nn
import torch.nn.functional as F
class AttentiveNet(nn.Module):
def __init__(self, input_size, hidden_size) -> None:
super().__init__()
# self.liner1 = nn.Linear(input_size, hidden_size)
self.cov2 = nn.Conv1d(hidden_size, hidden_size, kernel... |
e4eb394543301c830907917d05fd6900b670fab127f6ebe08377f2e5e8cb7b60 | Python | 1,078 | 30 | """Phase 5(e): backward op module attribution unchanged by refactor."""
from __future__ import annotations
import torch
import torchlens as tl
def test_backward_pass_module_attribution() -> None:
"""Backward capture keeps forward-op module attribution available."""
torch.manual_seed(0)
model = torch.n... |
f32ba6aaefb1d95797d692ece1c036ed02a051bcfde292d4d2836fa9232db1c0 | Python | 1,078 | 42 | from sys import argv
from msp import MSP, rbnsp_split_90
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 172
msp.architecture["channels"] = 200
psam_model = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P") + tuple(rbnsp_split_90... |
38e9c7d508a33f24ee933e66cf725a6e8cd3b41f6a3e8bbeca7616d8ef4d9376 | Python | 1,079 | 32 | " Many losses code is from https://github.com/JunMa11/SegLoss/tree/master/losses_pytorch"
from losses.dice import *
from losses.distillation import *
__all__ = ['all_losses', 'get_loss']
all_losses = {
'CrossEntropyLoss': torch.nn.CrossEntropyLoss,
'MSE': torch.nn.MSELoss,
'BCE': torch.nn.BCELoss,
'Ex... |
61a3d10cd7997049be7b56e32327afea656c1c748665eadd154e2cf2681224f9 | Python | 1,079 | 26 | """ Keras loss functions will choke on NaN inputs, which we'll often have for
unspecified inputs. These are just a couple simple loss functions that mask NaN
values in both the test and predicted tensors when computing the cost. """
from nfp.frameworks import tf
def masked_mean_squared_error(y_true, y_pred):
mas... |
8df5156a4f796ade8f642dfb1e9615c37b727590ee3d6a85c0b1b5d9cf008aca | Python | 1,079 | 33 | import larvaworld
from larvaworld.lib import reg, param
from larvaworld.lib.util import AttrDict
from larvaworld.lib.param.custom import restore_param_class, _deserialize_param_value
# A=reg.LarvaworldParam.set_vparam(param.Number(default=1.0, bounds=(0.0, 10.0), doc="Custom v parameter"))()
# print(A.param.objects()[... |
a2f96032c8dfa5bf5989601d13011745cb219d117905f67a8066f4bb1b897359 | Python | 1,079 | 40 | def main():
import os
import pkg_resources
import sys
from PyQt6 import QtCore, QtGui, QtWidgets
from impose.gui.main import Impose
from impose._version import version as __version__
for arg in sys.argv:
if arg == '--version':
print(__version__)
sys.exit(0)... |
7ae8dad4a1560be10aa3443c0896214e8322659a59e37b30a0927272a7894524 | Python | 1,080 | 31 | import argparse
import os
import torch
import numpy as np
from transformers import *
from src.data.Batcher import Batcher
from src.adapet import adapet
from src.utils.Config import Config
from src.eval.eval_model import dev_eval
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument... |
eaaa5bdd73c4cd7be6fe76f78b507f1b18e3a19630b41eff93086bc3683039ae | Python | 1,080 | 33 | """AST guardrail tests for grad-preserving fastlog paths."""
from __future__ import annotations
import ast
from pathlib import Path
def _python_files() -> list[Path]:
"""Return files whose predicate-mode branches must avoid no_grad."""
return [
*Path("torchlens/fastlog").glob("*.py"),
Path(... |
25fdaffa8494879445e214da9025ed3ce7a980f4ac5f9906ab7b1ba7ba729a97 | Python | 1,081 | 38 | #!/usr/bin/env python
import argparse
import os
import shlex
import subprocess
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", help="Input file name")
parser.add_argument("-o1", "--output1", help="tabular output file")
parser.add_argument("-s", "--parameters", help="arguments")
args = parser.p... |
c73cf734ecdfc0705f2d1a44f47b0cf8280db9dab14f489d1c78c879fa9bfc98 | Python | 1,081 | 35 | """
A few ABFs were recorded with incorrect scaling factors.
This script can fix them.
"""
import os
import sys
import glob
import time
import numpy as np
import matplotlib.pyplot as plt
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.p... |
ffad9f07878cd8d05d9d1132af6a9ab55065866adea83528c242d756c7867345 | Python | 1,081 | 49 | #!/usr/bin/env python
# coding: utf-8
# In[4]:
import numpy as np
import argparse
import tempfile
import sys
from pathlib import Path
# In[6]:
def sample_from_bed(bed_filepaths, n):
total = 0
for bed in bed_filepaths:
with open(bed) as f:
for _ in f:
total += 1
... |
130a22323f0e4665e4f7da2cfd2d9af3359a1d853676387166abc5c33b7491f3 | Python | 1,083 | 44 | #!/usr/bin/env python
"""
Convert from GFF file to interval index file.
usage:
python gff_to_interval_index_converter.py [input] [output]
"""
import fileinput
import sys
from bx.interval_index_file import Indexes
from bx.intervals.io import GenomicInterval
from galaxy.datatypes.util.gff_util import (
conve... |
2d50df5585e7e1cb650a896dc6e1d5dd184e0333f67a49a5ae5b26ccd7927e43 | Python | 1,083 | 40 | import sys
import os
import re
import glob
import io
import time
import base64
import random
import tempfile
import subprocess
import nibabel
import json
import nrrd
import numpy
import cv2
import urllib.parse
import xml.etree.ElementTree as ET
import nibabel as nib
import numpy as np
import pickle a... |
ad2ae0586a1f1fc1acb3522b3b0b67448d2894fdc7f0dbd48cf193b6f74abe60 | Python | 1,083 | 29 | """
Ensure discontinuous sweeps (sweeps whose sweep length is less than their inter-
sweep interval) are loaded properly and work with comments.
I confirmed there is a problem on 2018_11_16_sh_0006.abf: print(abf) says total
length of 0.10 min when it should be 5 minutes.
Inter-sweep interval seems to be defined by ... |
7f1f222b5a653f0c72d1a404c74ae4a2a49cba3217c897a27f899b665a3f12f3 | Python | 1,084 | 37 | # Generated by Django 3.0.3 on 2020-02-27 10:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0051_upload_queue'),
]
operations = [
migrations.RemoveField(
model_name='upload',
name='scale_size... |
c114e7d2afa0b782a3603c6251c3f9acd2f906a44173b9d4f915d28505c8ab7f | Python | 1,084 | 35 | import os
import shutil
import torch
import yaml
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
def save_config_file(model_checkpoints_folder, args):
if not os.path.exists(model_checkpoi... |
d7648b96084c36875dfdbbbb590a248a51136de78c3eaa372949d27e757f61b6 | Python | 1,084 | 22 | from __future__ import annotations
from .bootstrap_count_curve import generate_bootstrap_count_curves # noqa: F401, E501
from .count_curve import count_point # noqa: F401
from .count_curve import generate_count_curve # noqa: F401
documentation = None
"""
This submodule provides functionality for generating count... |
14e40fc14edcf3551d2aaf120906a9c0cd293b061c8e4c4f3b80a49a18cc5af9 | Python | 1,085 | 38 | from urllib.parse import (
quote_plus,
urlencode,
urljoin,
)
from galaxy.util import commands
def count_special_lines(word, filename, invert=False):
"""
searching for special 'words' using the grep tool
grep is used to speed up the searching and counting
The number of hits is returned.
... |
1709d6ac3b6ddbf564c8d91d6d4530cdbfd192ac0562967925032178cc3f0f9d | Python | 1,085 | 39 | import pytest
from celery import states
class test_state_precedence:
@pytest.mark.parametrize('r,l', [
(states.SUCCESS, states.PENDING),
(states.FAILURE, states.RECEIVED),
(states.REVOKED, states.STARTED),
(states.SUCCESS, 'CRASHED'),
(states.FAILURE, 'CRASHED'),
])
... |
49c016ff9e08b5b4396fb48392157163e88bec9b41b0024f96c963c9bad77141 | Python | 1,085 | 37 | import logging
import time
from codecarbon import track_emissions
from codecarbon.external.logger import logger
@track_emissions(
measure_power_secs=5,
api_call_interval=3,
save_to_prometheus=True,
)
def train_model():
"""
This function will do nothing during (occurrence * delay) seconds.
The... |
72bb4f9f8ae76015ff4ae3d836ca6594f6c7a84def82a05e1292cc36749cd585 | Python | 1,085 | 31 | import os
from abc import ABC, abstractmethod
from typing import Any, Sequence, Dict
from bioconfpred.conf_ensemble import ConfEnsembleLibrary
class Evaluator(ABC):
"""Base class for evaluators that assess rankers/model on conformer
ranking/RMSD prediction
:param evaluation_name: Name of the current evalu... |
83f597f1ca67091c8f15b1923d0474a64bb00a4688733340c6ba12ad640a43ff | Python | 1,085 | 35 | import numpy as np
import pytest
import gym
from gym.wrappers import TransformObservation
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v1"])
def test_transform_observation(env_id):
def affine_transform(x):
return 3 * x + 2
env = gym.make(env_id, disable_env_checker=True)
wrapped_... |
a247116dd624be15b6f2d9beaf7452be938206549966890d6d4503afa838e126 | Python | 1,085 | 35 | """AST guardrail tests for fastlog detach behavior."""
from __future__ import annotations
import ast
from pathlib import Path
def _python_files() -> list[Path]:
"""Return files whose predicate-mode branches must avoid bare detach."""
return [
*Path("torchlens/fastlog").glob("*.py"),
Path("t... |
b596e08396c23a99f56c1e9fe36ecf0e7b66a5f972f42e4d342daf07d46314d3 | Python | 1,085 | 28 | '''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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 requir... |
df7c95e5075570e55ce3d8b9c56a83b393fb80bb23157b4c5c7ac557426aad33 | Python | 1,085 | 40 | 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["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
)
msp.architecture["sparse_spec"] = dict(type="S... |
4f6c28242fd11797a3d7b6ec5f9cd523c4ea3e6d22eadf6e35404cf34c971e1c | Python | 1,086 | 33 | from Bio import SeqIO
import sys
def split(rna_file,protein_file,prefix):
is_coding = lambda x: x.startswith('XM') or x.startswith('NM')
pc_storage = []
nc_storage = []
for record in SeqIO.parse(rna_file,'fasta'):
if is_coding(record.id):
pc_storage.append(record)
else:... |
343bddba52fb2e21bead0907a77166b03ef546f6d1d457c93ce342f6eef7604b | Python | 1,087 | 19 | import os
from os.path import dirname, join
# PYTHONPATH=/home/chansingh/llm-tree/experiments python /home/chansingh/llm-tree/experiments/01_train_model.py --use_cache 0 --subsample_frac 0.1 --refinement_strategy None
# PYTHONPATH=/home/chansingh/llm-tree/experiments python /home/chansingh/llm-tree/experiments/01_trai... |
42d78fbc6e120dd361860db214b8f206cc97dcbf6af57c796ecaf43266c324a5 | Python | 1,087 | 41 | """Conversion functions."""
import numpy as np
###################################################################################################
###################################################################################################
def dict_array_to_lst(in_dict):
"""Convert any numpy arrays presen... |
76db2c724a92fd78df794c198a7da65f9010c0646f74b2e49213f1c291c2ed13 | Python | 1,087 | 32 |
import numpy as np
import torch
def gabor_kernel(frequency, sigma_x, sigma_y, theta=0, offset=0, ks=61):
w = ks // 2
grid_val = torch.arange(-w, w+1, dtype=torch.float)
x, y = torch.meshgrid(grid_val, grid_val)
rotx = x * np.cos(theta) + y * np.sin(theta)
roty = -x * np.sin(theta) + y * np.cos(... |
afc6556667a03a6168de0012f2fb27daceda7949476a62c4fc731ea33b032c31 | Python | 1,087 | 35 | """
MetaCyto analysis datatypes.
"""
import logging
from galaxy.datatypes.sniff import FilePrefix
from galaxy.datatypes.tabular import Tabular
log = logging.getLogger(__name__)
class mStats(Tabular):
"""Class describing the table of cluster statistics output from MetaCyto"""
file_ext = "metacyto_stats.txt... |
e6de9ced0e7d6fb180dd64d839be8b23f32c280e2ba3916e8e69ef86171e84f6 | Python | 1,087 | 54 | """
test_ca.py
Unit tests for ca imaging processing.
"""
import unittest
import pathlib
from fused_org_ephys import FusedOrgCa
################################
class TestMUASpikes(unittest.TestCase):
def setUp(self):
# Calcium class
fpath = pathlib.PurePath('tests', 'test_data', 'gcamp', 't... |
fe3ab3996db1c93ea0abc13066864d2bcf58c9be3fb98c3a62f094049a05421a | Python | 1,087 | 34 | """Test functions for specparam.metrics.pointwise."""
from specparam.metrics.pointwise import *
###################################################################################################
###################################################################################################
def test_compute_poin... |
a68478b938a75bdf339c2d2461de0117f9902ccf909433331d30c97249d25e2d | Python | 1,088 | 38 | """
This module define an interface for the differents datasets
"""
import logging
import torch
class GenericDataset(torch.utils.data.Dataset):
"""
This class is an interface for the datasets
"""
def __init__(
self,
dataframe,
limit_size_input_prot,
config,
col... |
b80a57014df574fa253bb90474b16964226b855964ebb8873beaaa15a0861367 | Python | 1,088 | 36 | import csv
numb_to_letters = {'0': 'A', '1': 'B', '2': 'C', '3': 'D', '4': 'E', '5': 'F'}
def csv_processor(file_name_input, file_name_output):
access_mode_input='r'
access_mode_output='w'
f_input = open(file_name_input, access_mode_input)
f_output = open(file_name_output, access_mode_output, newline='')
... |
3af02b58f0864ae2a2f2a2e97f1f4d77909d72f34808e218c48641755895d69e | Python | 1,089 | 30 | import time
from types import SimpleNamespace
import pytest
from celery import states, uuid
from celery.result import AsyncResult, GroupResult
@pytest.mark.celery(result_expires=1, result_chord_expires=60)
def test_chord_expires(app):
if not app.conf.result_backend.startswith('cache+memcached'):
pytest.... |
cc7b9d31d0c74cee40092d4623a985dd0c87dd2f0a646ca7a986dbfff645b426 | Python | 1,089 | 44 | """Miscellaneous Helper Methods.
"""
import logging
from functools import wraps
logger = logging.getLogger(__name__)
class deprecated(object):
"""Mark a method deprecated
This method is used internally to mark methods as deprecated.
Deprecated methods will print a warning when used but will otherwi... |
f311726221c17d9c443e2c62222e720161c4ea2b3ca5c72fa34c5f01d3143f34 | Python | 1,089 | 47 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 32
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
3300613c9a56d43b7719f255dd5f15ea4800cbfa574f48c6ad9241d700935be1 | Python | 1,090 | 37 | from __future__ import absolute_import
from pip._internal.cli.base_command import Command
from pip._internal.cli.status_codes import SUCCESS
from pip._internal.exceptions import CommandError
class HelpCommand(Command):
"""Show help for commands"""
name = 'help'
usage = """
%prog <command>"""
su... |
52aaaf9a001d396d9696e6f9c4ef2665340b8b3d3b8335fb781288f9f0f78ad9 | Python | 1,090 | 41 | import sys
import os
import importlib
import anndata as ad
import pandas as pd
import numpy as np
from pyprojroot import here
proj_root = str(here())
sys.path.insert(0, os.path.join(proj_root, "package/python"))
from utils import set_file_logger
import cembav2env
importlib.reload(cembav2env)
rsc_dir = os.path.join(p... |
fca1b9013c86d4d1ea1af33a81c8c029e5273b5f1175f4987e894281baa8f8d9 | Python | 1,090 | 31 | # 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 complian... |
3216a4e698b4efb3b488f9d73df1e28c54d9ed52eb36625d03408728ae3233a3 | Python | 1,091 | 36 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._error_x import ErrorX
from ._error_y import ErrorY
from ._error_z import ErrorZ
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._line import Line
from ._marker import Marker
fr... |
67a4c5926a517863e20e89a68e82eb092e9fcdf5fcd731ae147c347a9acf89fc | Python | 1,091 | 41 | import re
import numpy as np
import pytest
from gym.spaces import Text
def test_sample_mask():
space = Text(min_length=1, max_length=5)
# Test the sample length
sample = space.sample(mask=(3, None))
assert sample in space
assert len(sample) == 3
sample = space.sample(mask=None)
assert ... |
a33a6c8493270d118db4154854018dc5d139a7591ecb074cbe5b0bff445c3f78 | Python | 1,091 | 37 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""Module provides classes for exceptions raised from DataProxy."""
class NoNextValue(Exception):
"""Raised when end of data file is met in self.get_next_value()."""
class NoNextTag(Ex... |
c7f40d14d415033c101514768ad447f91f02f1c31287d0a32c668c60c19c490e | Python | 1,091 | 38 | import sys
import collections
from collections import Counter
def get_AG(site_file):
c = collections.defaultdict(lambda :0)
with open(site_file) as f:
for line in f:
line = line.strip().split('\t')
if len(line) == 1:
continue
else:
chrID = line[0]
position = line[2]
ref_base = line[3]
... |
59b15a0771237140d3bf07821e24a96dfd2b64f64dce43b0a6b81eb7797c283c | Python | 1,092 | 38 | import logging
import time
from codecarbon import track_emissions
from codecarbon.external.logger import logger
@track_emissions(
measure_power_secs=2,
api_call_interval=2,
api_key="",
save_to_api=True,
)
def train_model():
"""
This function will do nothing during (occurrence * delay) seconds... |
650ca926dfa670495727adb786c88934eb0261a9082ee40549a532bf2a20efbb | Python | 1,092 | 40 | """Wrapper for flattening observations of an environment."""
import gym
import gym.spaces as spaces
class FlattenObservation(gym.ObservationWrapper):
"""Observation wrapper that flattens the observation.
Example:
>>> import gym
>>> env = gym.make('CarRacing-v1')
>>> env.observation_sp... |
b1d1b1b32f8548b2870bed3de112d26759681e03bc7b94ac34109332075a7556 | Python | 1,092 | 42 | from __future__ import annotations
import sys
from typing import TYPE_CHECKING
from loguru import logger
from loguru._colorizer import AnsiParser
if TYPE_CHECKING:
from loguru import Record
def _custom_format(record: Record) -> str:
"""Put together a context-aware custom format string.
Args:
r... |
4f529ca6ee85a88c487a488807ff30cb8e4672307b9017f348174a67a5984347 | Python | 1,093 | 42 | from werkzeug.routing import BaseConverter
from flask import request
from flask import session
from flask import url_for
def test_custom_converters(app, client):
class ListConverter(BaseConverter):
def to_python(self, value):
return value.split(",")
def to_url(self, value):
... |
c81a30f41bf262113726fd07bf248c1dd3ad776b85df98974118e7c7e64a6d32 | Python | 1,093 | 32 | import sys
import graph
import graphutils
import matplotlib.pyplot as plt
import utils
def Characteristics(filename1, label1, filename2, label2):
data1, celldata1, timedata1 = utils.LoadAllData(filename1)
data2, celldata2, timedata2 = utils.LoadAllData(filename2)
legendsize = 12
fontsize = 18
maj... |
8f1a100a9d66d0e0b04c67e58b77a4afb797963dc617c21ee54a22664ac1e7b8 | Python | 1,094 | 31 | import sqlite3
from .db import DB
class DistrictInfoTable(DB):
def __init__(self):
self.table_name = 'PB_mucormycosis_district_cases'
self.table_desc = 'Mucormycosis district-wise details for total cases, deaths, under treatment etc. Table on the last page of bulletins from June 2021'
sel... |
a4212b2d5615f7a1c0a62b693a60a698c8ee26a77ac31aa6f606d904a76bf513 | Python | 1,094 | 38 | ## Reading images and spectra exported as txt from Ntegra system
import numpy as np
def parse_txt_line(line):
splitted = line.strip().split(',')
floats = [np.float(el) for el in splitted]
x = floats[0]
y = floats[1]
nu = np.array(floats[2::2])
v = np.array(floats[3::2])
return x,y,nu,v
... |
a5a946522466181b6433db4d831e1d777aa910cbf4edb4ec8007f5abee47a5d8 | Python | 1,094 | 22 | #!/usr/bin/env python3
'''quick script for generating a real model and caffe time command'''
import argparse,sys
parser = argparse.ArgumentParser(description='Train neural net on .types data.')
parser.add_argument('-m','--model',type=str,required=True,help="Model template. Must use TRAINFILE and TESTFILE")
parser.add... |
b4dd40e17acccfd9e7bc46c7de75d80557ee72742032705bd0f739adbd947622 | Python | 1,094 | 34 | import numpy as np
from permacache import permacache, stable_hash
from ..direct_inference_experiment import directly_infer_output
@permacache("hsmm/analysis/validate_hsmm_directly", key_function=dict(sm=stable_hash))
def compute_hsmm(hsmm_spec, sm, config, limit, transformation_spec):
b_s, a_s, y_s = [], [], [... |
faeb2d0585331e66e2bd8e5ac5d4b261001dcc93de5ebf4c69b66a17a90eae38 | Python | 1,094 | 30 | import os
import numpy as np
from mirp.extract_features_and_images import extract_images
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
def test_dicom_segmentation_mask():
from mirp._images.ct_image import CTImage, GenericImage
image, seg_mask = extract_images(
image=os.path.join(CURRENT_... |
183cd62810527ba0d61948e487668da8a3291923767e90298364d2111fb97afd | Python | 1,095 | 31 | """
Double-check to ensure sweepUnitsY are always the right units for multi-channel
abfs that mix voltage-clamp and current-clamp. It passed right away, so I think
whatever bug used to be here got fixed a while back
"""
import os
import sys
import glob
import time
import numpy as np
PATH_HERE = os.path.abspath(os.pat... |
361fabc799e4913c4728ddc2e2d5e9b406def3edc348c38c7c4c6a579fa29cea | Python | 1,095 | 26 | '''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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 requir... |
8a9aca3728edcdd5a771d097e59c111580f3df04432b10d916e95302ba74fd18 | Python | 1,095 | 37 | import os
import sys
import numpy as np
import pandas as pd
import snapatac2 as sa2
clevel = "L1"
out_dir = f"sa2_dlt2_{clevel}_subsets"
os.makedirs(out_dir, exist_ok = False)
# load snap data into memory:
# - it consumes about 200G and loading process takes about 10 minutes.
# - tscc has slow IO, so use this to make ... |
b9ea11a707e2d326bf9f7cd58ed7b05778f2cd89c78985149c9a5eff3b06d358 | Python | 1,095 | 41 | from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark
from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark
from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior
from hobj.data.images import (
load_image,
list_image_ids,
get_image_path,
loa... |
d96a5dd1e808837b9014172b68e44b883a2825acc16c6da6e6a3cf874379cf5c | Python | 1,096 | 41 | """ Helper functions for views.py.
"""
import numpy
def getVal(form, element):
return getattr(form, element).data
def getData(form):
"""Retrieves the data from the form and places into dictionary."""
params = {}
form_elements = form.elements
form_fields = form.element_fields
for i, element ... |
df20b1fe769898718d6152cb6415bb9057bcdb23cc27b8e7c789e88be4a13a91 | Python | 1,096 | 38 | """
Get the evoked response
"""
import sys
sys.path.append('..')
import argparse
from config import fname, events_id
import mne
from pathlib import Path
# Be verbose
mne.set_log_level('INFO')
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', type=int... |
016afe287b6e7592cb135fc2a09af5d39a5ffe5101240671a68f813e056bcf01 | Python | 1,097 | 33 | from typing import Tuple
import torch
from torch import Tensor
from torch.distributions import Uniform
class AmplitudeRescaleTransform:
"""Implementation of amplitude rescaling transformation.
This transform will rescale the amplitude of the Fourier Spectrum (`freq_image`) of the image and return it.
A... |
50ddcc14fb69feed12fd8041fae64457bdc70c9188fbb1c324aac39826b1717e | Python | 1,097 | 41 | #!/usr/bin/env python
"""
Calculates the reverse complement of a sequence.
Usage:
reverse.py <sequence file> <output file> <sequence file format> <output file format> <alphabet: dna or rna>
"""
import sys
from Bio import SeqIO
from Bio.Alphabet import DNAAlphabet, RNAAlphabet
from Bio.SeqRecord import Seq... |
d73bbea7af3bd7f27300315bf7e09af958ccede73c5b65182aeaab2a51b0c6bb | Python | 1,097 | 35 | """The MISSING kwarg-omission sentinel.
Historical note: this module also carried the additive-deprecation helpers
(``warn_deprecated_alias``, ``TorchLensDeprecationWarning``, ``REMOVED_IN``).
The 2026-08-19 shim-removal lane deleted every deprecation shim outright per
the interim-phase ruling, so only the load-bearin... |
30ab573eb3fcf347f8399b3e6d6f5572395f3f7bac9892db0edee71e035900d8 | Python | 1,098 | 31 | import unittest
import numpy as np
from bdpy.ml import ensemble
class TestEnsemble(unittest.TestCase):
def test_ensemble_get_majority(self):
'''Tests of bdpy.ml.emsenble.get_majority'''
data = np.array([[1, 3, 2, 1, 2],
[2, 1, 0, 0, 2],
[2, 1, 1,... |
51f81bdf7bbc90a97b3bf39d8e4887e082844aa5e6af5184aad1277e2b2448c7 | Python | 1,098 | 40 | """
Refactor the AP detection module to provide a clean interface.
You can always refactor the core later.
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0... |
b1e1e909b48e8d7cb1b3b660fbf0e53abe6fafaa838874ae1810396b293f018a | Python | 1,098 | 35 | from torch import nn
class SSLEvaluator(nn.Module):
def __init__(self, n_input, n_classes, n_hidden=512, p=0.1):
super().__init__()
self.n_input = n_input
self.n_classes = n_classes
self.n_hidden = n_hidden
if n_hidden is None:
# use linear classifier
... |
d9705ea076ee190192092ae2db10bd2817b7d2468c3a32956efc3801dfbd7ce1 | Python | 1,098 | 21 | """
Copyright (c) 2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE BLUE BRAIN PROJE... |
4ce42e8ba6bae634e63472b50429d51e59911f6f70deb1fd355c53af82fad07a | Python | 1,099 | 49 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import tpot
import pandas as pd
import numpy as np
from tpot import TPOTRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import train_test_split
# In[2]:
features = pd.read_csv('Combined.csv')
features['Timestamp'] = pd.to_... |
645e16e1a233a5c7a42c0befc83b10e73a81e379f762857a4b89cfc02c76b3f1 | Python | 1,099 | 32 | import matplotlib.pyplot as plt
import torch
def visualize_test_inference(model_, data_loader, test_batch_size, num_classes, device):
X, Y = next(iter(data_loader))
Y = torch.nn.functional.one_hot(Y.long(), num_classes).permute(0, 3, 1, 2)
X, Y = X.to(device), Y.to(device)
Y_pred = model_(X)
Y_pre... |
bb9c662b3e0439120460dda4cdbe75b2358a1757ab143050b2104289c2fa808f | Python | 1,099 | 35 | import io
import gzip
import tempfile
import subprocess
import tqdm.auto as tqdm
import pandas as pd
from BCBio import GFF
# first row of https://www.gencodegenes.org/human/
ANNOTATION_FILE = "https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_40/gencode.v40.annotation.gff3.gz"
gz_path = tempfile.mkt... |
c13b96ce3fd9d1e618ba54b3ff7467ea0a41007c28e748121204eb618bc156c0 | Python | 1,099 | 29 | import torch
from imodelsx import explain_dataset_iprompt, get_add_two_numbers_dataset
if __name__ == '__main__':
has_gpu = torch.cuda.device_count() > 0
device = 'cuda' if has_gpu else 'cpu'
llm_float16 = True if has_gpu else False
# get a simple dataset of adding two numbers
input_strings, ou... |
37bb633838a32130d3895bdcb70e0d8818a43c5474a1170dfd7f31ddd59dab49 | Python | 1,100 | 35 | """
Get the grand averaged evoked response
"""
import sys
sys.path.append('..')
import mne
from config import fname, subjects
import argparse
from pathlib import Path
# Be verbose
mne.set_log_level('INFO')
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('event... |
07c82d5f9b384aa1c0c6b6675133c59b1c632b87b46f07d9d2ec8c137b935044 | Python | 1,101 | 29 | import numpy as np
import pandas as pd
import math
import pdb
# ------------------------------------------------------
# Correct non standard TypeIDs for soma
def set_standard_somaID(swc_matrix, nonstandard_somaID=None, swcPlus_remove=None):
updated_swc_matrix = swc_matrix
if not(nonstandard_somaID is None... |
4ab3a1e567edbfe1574650fe0e6cecf0dbee8e71ba0f0e2457c0ed80e1a21f12 | Python | 1,101 | 39 | from numpy import array, matrix, ndarray, result_type
np_float = result_type(float)
try:
import numba as nb
except ModuleNotFoundError:
guvectorize_compute = None
else:
_nb_float = nb.from_dtype(np_float)
def guvectorize_compute(target: str, *, cache: bool = True):
return nb.guvectorize([nb.v... |
aeba8c5d810068f8f29c5de7aeb41fb71295f18b1860049b52f42b1532b6e641 | Python | 1,101 | 32 | from typing import List
from uuid import UUID
from carbonserver.api.infra.repositories.repository_users import SqlAlchemyRepository
from carbonserver.api.schemas import User, UserAutoCreate
class UserService:
def __init__(self, user_repository: SqlAlchemyRepository) -> None:
self._repository: SqlAlchemyR... |
033d7dbd3f3874776824c2b47d058c756284299ddd77270cd3ab64eb2e9166e5 | Python | 1,102 | 40 | """Phase 4 multi-pass tests."""
from __future__ import annotations
import torch
import torchlens as tl
import torchlens.utils as utils
def test_trace_streaming_on_iterable_inputs() -> None:
"""Capture iterable inputs into one stacked root log."""
model = torch.nn.Linear(2, 2)
traces = utils.trace_stre... |
84acc4c7009e34c3fa3ac1e3984f9379c7606bd73bb0f3a88a86afc72c8817c1 | Python | 1,102 | 24 | from neurogym.wrappers.block import RandomGroundTruth, ScheduleAttr, ScheduleEnvs, TrialHistoryV2
from neurogym.wrappers.monitor import Monitor
from neurogym.wrappers.noise import Noise
from neurogym.wrappers.pass_action import PassAction
from neurogym.wrappers.pass_reward import PassReward
from neurogym.wrappers.react... |
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