sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f586b6dd91269d114c547d90dc70d6afe2f1a61bb504afae2cf389d38f5f19e5 | Python | 877 | 30 | import torch.optim as optim
def optimizers(model, config):
"""
Returns the optimizer based on the configuration.
Args:
model (torch.nn.Module): The neural network model.
config (dict): Configuration dictionary containing 'optimizer' and 'learning_rate' keys.
Returns:
torch.opt... |
d7ef6c498ec69bb7cc4fc322c55b35eaef3eece6d069df4e11e0020c451de773 | Python | 879 | 27 | from smac.env.starcraft2.maps import smac_maps
from pysc2 import maps as pysc2_maps
from smac.env.pettingzoo import StarCraft2PZEnv as sc2
import pytest
from pettingzoo import test
import pickle
smac_map_registry = smac_maps.get_smac_map_registry()
all_maps = pysc2_maps.get_maps()
map_names = []
for map_name in smac_m... |
ad44b84108571529d5bcfc63ca7766656d7be52c0d0baa90b3fdda64dd8be93a | Python | 880 | 36 | #!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import confu
parser = confu.standard_parser("clog configuration script")
def main(ar... |
c5295adf35aac62e878f8f04be761f0f75877c4d7cf55bc2e17b0abb8fd3fc8c | Python | 880 | 40 | """
Use cffi to access any of the underlying C functions from distributions.h
"""
import os
import numpy as np
import cffi
from .parse import parse_distributions_h
ffi = cffi.FFI()
inc_dir = os.path.join(np.get_include(), 'numpy')
# Basic numpy types
ffi.cdef('''
typedef intptr_t npy_intp;
typedef unsigned ch... |
74b7cbc8818f638d7aa8bd4746e01cb5afb38b49ef66b224266fa47b5afb2b8c | Python | 881 | 20 | import torch
class GrimaceCrossEntropyLoss(torch.nn.Module):
def __init__(self):
super(GrimaceCrossEntropyLoss, self).__init__()
self.eye_loss = torch.nn.CrossEntropyLoss()
self.nose_loss = torch.nn.CrossEntropyLoss()
self.cheek_loss = torch.nn.CrossEntropyLoss()
self.ear_l... |
95f27ee998973cb2633c5e0a26e3ff30af95d4d3b45f0f727b73c2e28ebd2836 | Python | 883 | 31 | from typing import Union
import numpy as np
import timm
import torch
class FrameQualityModel(torch.nn.Module):
def __init__(self, device="cuda"):
super(FrameQualityModel, self).__init__()
self.base_model = timm.create_model("mobilenetv3_large_100", in_chans=1, pretrained=False)
base_mode... |
7cefdf081e0cee9841138107bbd66b92900cbe3649354e7403382419b376303e | Python | 885 | 26 | import tensorflow as tf
from sklearn.metrics import roc_auc_score
import numpy as np
########### classification ##############
def cross_entropy(y_true, y_pred, MASK = -1):
mask = tf.cast(tf.not_equal(y_true, MASK), tf.keras.backend.floatx())
labels = y_true * mask
logits = y_pred * mask
cost = tf.nn... |
1d8569ca51cdd7d99eb28d0c97665d0fbb0a1f19de227289195a255894384426 | Python | 886 | 27 | import os
import sys
import glob
def pdbs_to_pdbqts(pdb_dir, pdbqt_dir, dataset):
for file in glob.glob(os.path.join(pdb_dir, '*.pdb')):
name = os.path.splitext(os.path.basename(file))[0]
outfile = os.path.join(pdbqt_dir, name + '.pdbqt')
pdb_to_pdbqt(file, outfile, dataset)
print(... |
5fc52a28f189c0d0740f4e1357ed4291841deb4b607308645b514b384f54933d | Python | 886 | 26 | # License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
# for test files, silence irrelevant and noisy pylint warnings
# pylint: disable=no-self-use,protected-access,missing-docstring
import os
import unittest
from antismash.... |
22681d1d2ba307647bfea8ca5ed5e4244caeb625a0bbf37ae22a35a2f0bf71b2 | Python | 887 | 34 | import random
_BLOCKS: set[str] = {
"baseline",
"isochronous",
"asynchronous",
"synchronous",
}
def generate_blocks_sequence(previous_blocks: list[str]) -> str:
"""Create a semi-random block sequence.
Parameters
----------
previous_blocks : list
List of previously generated b... |
a3b2f1203197670aa3f23c36503fd373d819dd60fed922ee2c67ea80d750036e | Python | 887 | 23 | """Overrides split strategies from schnetpack.data.splitting"""
from typing import List
from schnetpack.data import ASEAtomsData
from schnetpack.data.splitting import SplittingStrategy
class TestSplit(SplittingStrategy):
"""Splitting strategy that allows to put all dataset elements in test split without index p... |
efd77be72f39f1738b9722bb37eada8af7a8d1fd17b4e448e39e768599b89f53 | Python | 887 | 27 | import numpy as np
import torch
from bermol.utils import mol_to_sentence, smi_to_mol
from bermol.vocab import MolVocab
class BerMolTokenizer:
def __init__(self, vocab: MolVocab) -> None:
self.vocab = vocab
def encode(self, smiles: str) -> torch.Tensor:
mol = smi_to_mol(smiles)
if mol... |
38df84f99a924a1799f3c56b297d8cdcf5e915b18451464f31afc07f497ee1fd | Python | 890 | 33 | from abc import ABC
class RichRenderable(ABC):
"""An abstract base class for Rich renderables.
Note that there is no need to extend this class, the intended use is to check if an
object supports the Rich renderable protocol. For example::
if isinstance(my_object, RichRenderable):
con... |
65a2f4c1a591baaeb18f7d2c532c2d9e8499e4b7948f0bf01783b9fd39d062d9 | Python | 891 | 31 | import os
from pathlib import Path
import pytest
from tomography_python_programs._metadata_models.relion.tilt_series_set import RlnTiltSeriesSet
@pytest.fixture
def test_project_directory() -> Path:
return Path(__file__).parent / 'test_project_directory'
@pytest.fixture
def tilt_series_star_file(test_project_... |
15da0bcb1640713b00d9f94889c45701198b40f808cc6293cffc6c489c88e0c8 | Python | 893 | 33 | import os
import shutil
import random
random.seed(42)
source_dir = "data/raw"
output_dir = "data"
train_ratio = 0.7
val_ratio = 0.15
for class_name in os.listdir(source_dir):
class_path = os.path.join(source_dir, class_name)
images = os.listdir(class_path)
random.shuffle(images)
tr... |
cb1cda84e1cfd8feddb116df5c9642e21376fbbfbd20f6996cb0c83ebb828eda | Python | 893 | 46 | import torch
import numpy as np
import os
import sys
datadir = "./Dataset/Data"
def healthCheckOnRoiSignal(roiSignal):
"""
roiSignal : (N, T)
"""
# remove subjects with dead rois
if(np.sum(np.sum(np.abs(roiSignal), axis=1) == 0) > 0):
return False
return True
def abide1L... |
780e62fe7c98d8d10b7f482a642b9b54e68b8ff9d7ac274ad318017459c7c0b4 | Python | 894 | 24 | # License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
# for test files, silence irrelevant and noisy pylint warnings
# pylint: disable=no-self-use,protected-access,missing-docstring,too-many-public-methods
""" helper functio... |
1f73fe66a3ccadac3c0831650fee91f20edad7e3b6bac426eb8a2dbfae558452 | Python | 895 | 34 | """Generate an excel summary of parameters used in runs of ASCENT.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THIS ... |
e48bceac911458c6a959f10e0246e289b622d04a70e9d65fb923386940f82e8b | Python | 895 | 20 | from statistical_analysis import CrossCorrelationProcessor
main_path = 'Path/To/Your/Data/' # Update this to your actual data path
# create list of participants to be processed
# also works for subsample of participants
participants = [f'P{str(p).zfill(2)}' for p in range(1, 41)]
exclude = ['P13', 'P20'] # exclude p... |
2588fbe7e2b8769b4c9fe9ecfb23b98e1901bd9cf9758ef576bfd8700b0fd07b | Python | 896 | 37 | '''import pandas as pd
# Path to your pickle file
path = "data_false_negs/folds_with_rnacentral_info_full_v2_with_evidence_GPTLARGE.pkl"
# Load the DataFrame
df = pd.read_pickle(path)
# Show shape and first few rows
print("✅ Data loaded successfully!")
print("Shape:", df.shape)
print("\nColumns:", df.columns.tolist(... |
24b4988a738f3a6b28f5a4b8fbd25c3d1c27ebbe51ec32b696498a23089d78d6 | Python | 897 | 29 | import anndata as ad
import scanpy as sc
import torch
import pandas as pd
import scipy
import numpy as np
import sys
# Read in AnnData atlas object
adata = ad.read_h5ad(snakemake.input.merged_rna_anndata)
# Select for the most variable genes
sc.pp.highly_variable_genes(
adata,
layer='log-norm',
n_top_gen... |
47f85359ad5dd1c350fcc6404c4848d802924ab369c5e11dda6f06d7e7290e83 | Python | 897 | 32 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..counts._count_words import count_words
from ..counts._count_sentences import count_sentences
from ..counts._count_miniwords import count_miniwords
@typed_cache
def mcalpine_eflaw(text: str) -> float:
"""Calculate McAlpine EFLA... |
714a57c1a3d6b5d6c635777fd164e8e7fb68997be9fac21d613cd8fc207517c4 | Python | 897 | 36 | from .gridspec import gridspec
from .heatmap import heatmap
from .paga import paga
from .proportions import proportions
from .scatter import diffmap, draw_graph, pca, phate, scatter, tsne, umap
from .simulation import simulation
from .summary import summary
from .utils import hist, plot
from .velocity import velocity
f... |
88b4c9402e12552468ca02061875ef129d1df14773a5eaa6311a94705d09235e | Python | 897 | 28 | #
# This file is part of gunicorn released under the MIT license.
# See the NOTICE for more information.
# We don't need to call super() in __init__ methods of our
# BaseException and Exception classes because we also define
# our own __str__ methods so there is no need to pass 'message'
# to the base class to get a m... |
948c16586106a32fb6a2e88bb18beb357e60ba5d57a9209add97383c81670558 | Python | 898 | 41 |
datasetDetailsDict = {
"hcpRest" : {
"datasetName" : "hcpRest",
"targetTask" : "gender",
"taskType" : "classification",
"nOfClasses" : 2,
"dynamicLength" : 600,
"foldCount" : 5,
"atlas" : "schaefer7_400",
"nOfEpochs" : 20,
"batchSize"... |
a0db428319939a488cd43894c639f34de0310a3ffb8e379ec2d6f2fb781da28d | Python | 899 | 30 | from pandas import (
Categorical,
Index,
Series,
)
from pandas.core.arrays import BaseMaskedArray
def get_array(obj, col=None):
"""
Helper method to get array for a DataFrame column or a Series.
Equivalent of df[col].values, but without going through normal getitem,
which triggers trackin... |
b567b54162e54c03bbae321738b97f135671c0141d19ecf3d5f7f8d8068eef7b | Python | 902 | 40 | """ZX graph simplification wrapper."""
from typing import Any
import pyzx_param as pyzx
def full_reduce(g: Any, param_safe: bool = True) -> None:
"""
Apply PyZX full_reduce to the graph.
:param g: The ZX graph.
:type g: Any
:param param_safe: Whether to use parameter-safe simplification, default... |
e803e5d1e03e2f25be2d421fb5d10ae64c384b0e6023a770b9b97515eae20fa7 | Python | 902 | 39 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ._words_per_sentence import words_per_sentence
from ._syllables_per_word import syllables_per_word
@typed_cache
def flesch_kincaid_grade(text: str, lang: str) -> float:
r"""Calculate the Flesh-Kincaid Grade for `text`.
Para... |
6389d445de2114cd10dda5444f538efb3d75a59cf36741c57e5dacfbd3967a6c | Python | 903 | 26 | from pandas import Categorical
import pandas._testing as tm
class SubclassedCategorical(Categorical):
pass
class TestCategoricalSubclassing:
def test_constructor(self):
sc = SubclassedCategorical(["a", "b", "c"])
assert isinstance(sc, SubclassedCategorical)
tm.assert_categorical_equa... |
2e2adc581d153c24d99672b4387618be61088f7166beb8b465d90f34d050ba91 | Python | 904 | 34 | """Generate an excel summary of parameters used in runs of ASCENT.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THIS ... |
ee34bfa352104415498a1188fa2011ae7962737323660eec13103b7e59a370fd | Python | 904 | 29 | from pathlib import Path
import pytest
import numpy as np
from tomography_python_programs._utils.file import basename, match_filenames
@pytest.mark.parametrize(
"input, expected",
[
(Path('test/test2/test3.mrc'), 'test3'), # multiple directories
(Path('test.mrc.mdoc'), 'test'), # multiple s... |
03b7ea0eecef9ede4f1d884eeaf69bc50a8a6c40b2b0f9cd7bcaa6648ac4fd45 | Python | 906 | 32 | def get_acc_predictor(model, inputs, targets):
if model == 'rbf':
from acc_predictor.rbf import RBF
acc_predictor = RBF()
acc_predictor.fit(inputs, targets)
elif model == 'carts':
from acc_predictor.carts import CART
acc_predictor = CART(n_tree=5000)
acc_predict... |
244001226e8b3724826d252ae872d2fbfa9318adc78157127125cba43b2b27ca | Python | 908 | 28 | from pandas import (
DataFrame,
DatetimeIndex,
date_range,
)
import pandas._testing as tm
def test_isocalendar_returns_correct_values_close_to_new_year_with_tz():
# GH#6538: Check that DatetimeIndex and its TimeStamp elements
# return the same weekofyear accessor close to new year w/ tz
dates ... |
373c668d3434c15dd359dff7b01eb41608b1912581a947367aad8a0c496565a9 | Python | 908 | 30 | # main.py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from rulebasedpolicy.random_map import *
from rulebasedpolicy.a_star import *
from env.env3_train_env01 import FalseBelief_env1
def Find_a_way(size, board, start_x, start_y, end_x, end_y):
map = RandomMap(size... |
0adacb2c3abd77e10a7a3e692ad301564982922aeb708cebf98fc69624531400 | Python | 909 | 21 | import torch
from torch import nn
class MicePoissonLoss(nn.Module):
def __init__(self, log_input: bool = False, full: bool = False, eps: float = 1e-8):
super().__init__()
self.poisson = nn.PoissonNLLLoss(log_input=log_input, full=full, eps=eps, reduction="none")
def forward(self, inputs, targ... |
c29369e739b458611b1de8ea3ef01a84a1c5ee98404e7c9968d4a45ff927fd05 | Python | 910 | 25 | import os
import sys
import music21 as m21
import numpy as np
firstnotes = np.array([[m21.pitch.Pitch('b-4').midi],
[m21.pitch.Pitch('d4').midi],
[m21.pitch.Pitch('f3').midi],
[m21.pitch.Pitch('B2').midi]])
print(firstnotes)
input_path = "../xmlfiles/... |
0e19a287311814881071da98f2974ab760f431dda61fe653eda1d96a9fc19f3a | Python | 915 | 37 | """Returns thresholds from the selected sample/model/sim combos as a dataframe.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascen... |
f369890eafcc45ee87f50e6f522534c8381e023701bfb2760b0f58774c614b94 | Python | 916 | 29 | from __future__ import annotations
from stimuli.time import sleep
from ..detector import _BUFSIZE
from ..utils._checks import check_type
from ..utils.logs import logger
from ._config import TRIGGER_TASKS
from ._utils import create_trigger
def baseline(duration: float) -> None:
"""Baseline block corresponding to... |
2c3fdbd5b468a6bace384aead95f03bd8f327456ba2802f28b9fdf9d6ca60b17 | Python | 920 | 25 | import os
from distutils.command.install_headers import install_headers as old_install_headers
class install_headers (old_install_headers):
def run (self):
headers = self.distribution.headers
if not headers:
return
prefix = os.path.dirname(self.install_dir)
for header ... |
24a8d80a5b5b8c3891b5d0d2997aa333b603422aee2a295f5a7adcd08dfd879d | Python | 921 | 40 | from __future__ import annotations
import re
from ..utils._typed_cache import typed_cache
from ..selections._list_words import list_words
@typed_cache
def count_arabic_long_words(text: str, rm_apostrophe: bool = True) -> int:
"""Counts long arabic words (>5 letters) without short vowels (tashkeel).
Param... |
8be664e2c7ed2b970c4154368ea4b14cca5523e818c97375ebea794ef9a35117 | Python | 921 | 25 | import sys
from setuptools.command.egg_info import egg_info as _egg_info
class egg_info(_egg_info):
def run(self):
if 'sdist' in sys.argv:
import warnings
import textwrap
msg = textwrap.dedent("""
`build_src` is being run, this may lead to missing
... |
92a7be1bda0481d013ecddf88bc938c56932647baaf0f226ae0ec72d6950e333 | Python | 921 | 37 | """Tools for detecting collective events."""
__author__ = """Benjamin Graedel"""
__email__ = "benjamin.graedel@unibe.ch"
__version__ = '0.3.3'
from ._binarize_detrend import binData
from ._cleandata import clipMeas, interpolation, remove_image_background
from ._detect_events import (
DataFrameTracker,
ImageTr... |
e5f935f09378dd183a607bda9fa42376b39e3ae2ed4652d598fe8c69d0442550 | Python | 923 | 21 | import warnings
def _raise_warning(attr: str, submodule: str | None = None) -> None:
new_module = "numpy._core"
old_module = "numpy.core"
if submodule is not None:
new_module = f"{new_module}.{submodule}"
old_module = f"{old_module}.{submodule}"
warnings.warn(
f"{old_module} is... |
28a9c068b9bcf8e3d49613188cf063690e56b91789c8da3619dec6d5df2ef805 | Python | 924 | 34 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..counts._count_chars import count_chars
from ..counts._count_words import count_words
@typed_cache
def chars_per_word(text: str, ignore_spaces: bool = True) -> float:
"""Calculate the average word length in characters.
Thi... |
4a0212a641c01d29e0c4638575f0ab0ff7a374dc0fe4574c67736ff9d9039eb7 | Python | 924 | 27 | from operator import mod
from .training import Train
from .FBNettraining import FBNetTrain
from omegaconf import DictConfig
from typing import List
import torch
from source.components import LRScheduler
import logging
import torch.utils.data as utils
def training_factory(config: DictConfig,
model... |
6264e73c5eaa5f0bd863cdb065f43c5c5c15c0e618b0896cdcb4ab7400c35bee | Python | 924 | 30 | import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas._testing as tm
def test_astype(idx):
expected = idx.copy()
actual = idx.astype("O")
tm.assert_copy(actual.levels, expected.levels)
tm.assert_copy(actual.codes, expected.codes)
assert actual.name... |
0cb0e27661de54a3b8feb04cbad4894489f6605cd17930e92e70b59c3afbb577 | Python | 925 | 23 | from morphologist_wrapper import *
from sulcal_phenotype_wrapper import *
import sys
n_args = len(sys.argv) - 1
if n_args == 3:
fs_subject_name = sys.argv[1]
fs_dir = sys.argv[2]
output_dir = sys.argv[3]
run_morphologist_from_freesurfer(fs_subject_name, fs_dir, output_dir)
#give default values for... |
c4c29475a77625d0886149bba7d33f52823ea43f286c888a0ee42f7ca9c5040e | Python | 925 | 44 | from neurodesign import geneticalgorithm, generate, msequence
import sys
design_i = sys.argv[1]
EXP = geneticalgorithm.experiment(
TR = 0.68,
P = [0.5, 0.5],
C = [[0.5, -0.5]],
rho = 0.3,
n_stimuli = 2,
n_trials = 96,
duration = 212,
resolution = 0.136,
stim_duration = 1.... |
9988f19707a5a315a62b5fd61738cd28f00879033ace16c6bcbf8a5fe6abff13 | Python | 926 | 28 | import os
from itertools import permutations
import numpy as np
import torch
import torch.nn as nn
"""
Helper class that stores Clebsch-Gordan coefficients
"""
class ClebschGordan(nn.Module):
def __init__(self):
super(ClebschGordan, self).__init__()
tmp = np.load(
os.path.join(os.pat... |
bcf70d0646381f6471b32916d5b8d383e152953950d87d722dbf99b187dfb048 | Python | 926 | 34 | import numpy as np
from pandas import (
TimedeltaIndex,
timedelta_range,
)
import pandas._testing as tm
class TestRepeat:
def test_repeat(self):
index = timedelta_range("1 days", periods=2, freq="D")
exp = TimedeltaIndex(["1 days", "1 days", "2 days", "2 days"])
for res in [index.... |
a0097d256b5b9ba9c2cebdb4dc340e6a98ff8a28279b53ab01cc7dfce6eb155d | Python | 928 | 25 | import scanpy as sc
import pandas as pd
import numpy as np
import sys
# Import AnnData files
adata = sc.read_h5ad(snakemake.input.merged_rna_anndata)
filtered_adata = sc.read_h5ad(snakemake.input.hvg_rna_anndata)
# Save the .obsm['X_scvi']
adata.obsm['X_scvi'] = filtered_adata.obsm['X_scvi']
# Convert the cell barco... |
92020413fd4dce36712c1482fafe6ceb8b8e15995512ddda3eb8d3b4beb1d976 | Python | 929 | 33 | from __future__ import annotations
from typing import TYPE_CHECKING
from pip._internal.distributions.base import AbstractDistribution
from pip._internal.metadata import BaseDistribution
if TYPE_CHECKING:
from pip._internal.build_env import BuildEnvironmentInstaller
class InstalledDistribution(AbstractDistribut... |
1aa424c98d67e9319e97ff008a3a0dfd819608abe88eff9f3463a1186de9a0df | Python | 932 | 23 | ## ...........................................................................
##
## PIMMS (Polymer Interactions in Multicomponent Mixtures)
## Alex Holehouse, Pappu Lab, Holehouse Lab
## Copyright 2015 - 2026
## ...........................................................................
import numpy as np
from . ... |
2c1926ebf9a80e3b94f5163816cce06a49ce8f72320a9dedf848f71c97eaa5d9 | Python | 932 | 31 | import numpy as np
from numpy.testing import assert_, assert_equal, assert_raises
class TestRegression:
def test_kron_matrix(self):
# Ticket #71
x = np.matrix('[1 0; 1 0]')
assert_equal(type(np.kron(x, x)), type(x))
def test_matrix_properties(self):
# Ticket #125
a = n... |
7c9b21a630842f084e5843c944a76640273563e1a35b68776c4198d78947bb67 | Python | 932 | 35 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 1 18:04:35 2019
@author: charleshen
@note: Information Content descriptors: http://mordred-descriptor.github.io/documentation/master/api/mordred.InformationContent.html#mordred.InformationContent.InformationContent
"""
from mordred import Calcul... |
efbc34cb2b64af19586d29fefa50d53d431a6d45d147d237941bffbd0454a946 | Python | 932 | 43 | """
Common code used in multiple modules.
"""
class weekday(object):
__slots__ = ["weekday", "n"]
def __init__(self, weekday, n=None):
self.weekday = weekday
self.n = n
def __call__(self, n):
if n == self.n:
return self
else:
return self.__class__(... |
440ef1fe9f1950c7453fb7713b9854a9badd31c5382c56974156f70081226b96 | Python | 934 | 43 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..counts._count_long_words import count_long_words
from ..counts._count_words import count_words
from ._words_per_sentence import words_per_sentence
@typed_cache
def lix(text: str) -> float:
r"""Calculate the LIX for `text`
... |
1ee6fb47008524822e3d2973fd171160d61ccee11ccb634b3751954c0acf6985 | Python | 935 | 32 | #!/usr/bin/env python3
import os
import sys
import os.path as osp
import loompy as lp
import numpy as np
pth = sys.argv[1]
odir = sys.argv[2]
ds = lp.connect(pth)
celltype_1 = np.array([ '_'.join([str(x).replace(',','_'),str(y)]) for \
x,y in zip(ds.ca['Class'],ds.ca['Clusters']) ])
celltyp... |
a38c6d24d21ae4ad7c0cf25a695f2fcae8c5b3f20f96798c33416e8422a83766 | Python | 935 | 31 | #!/usr/bin/env python
# Set the path to the avg_q python installation here if you
# didn't add it to the global python path
import sys
sys.path.append("R:/avg_q/python")
import avg_q
# The avg_q_ui.exe binary will be found automatically in the directory
# above python/
a=avg_q.avg_q(avg_q='avg_q_ui')
import glob
# ... |
11f070c4922a91d76578904eebd38a4c261ccbf28ac9a6224e812c618f364e34 | Python | 936 | 34 | import cedalion
import cedalion.dataclasses as cdc
import trimesh
import numpy as np
def test_conversion():
vertices = np.asarray([
[0.,0.,0.],
[1., 0. ,0.],
[1., 1., 0.],
[0., 1., 0.]
])
faces = np.asarray([
[0., 1., 2.],
[1.,2.,3.]
])
trimesh_surfa... |
91bba60d4c54d600235f6dac06abd540136530bea39c7abd89f70d9cc1f7a04a | Python | 936 | 31 | #!/usr/bin/env python3.7
"""Plots the time and location where an action potential occurred.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmgl... |
9875df0601d448d651d867000c5b0c2a9313ae6d522a8a21c33a4a600c5c5461 | Python | 937 | 52 | """
Interplay between jit, vmap, randomness and backend
"""
import tensorcircuit as tc
K = tc.set_backend("tensorflow")
n = 10
batch = 100
print("tensorflow backend")
# has serialization issue for random generation
@K.jit
def f(a, key):
return a + K.stateful_randn(key, [n])
vf = K.jit(K.vmap(f))
key = K.get... |
de07bc5638574f534f74275d0d5c8aed74dafb0708630b348cd977aac34ddb54 | Python | 937 | 44 | from neurodesign import geneticalgorithm, generate, msequence
import sys
design_i = sys.argv[1]
EXP = geneticalgorithm.experiment(
TR = 0.68,
P = [.6,.4],
C = [[0.5, -0.5]],
rho = 0.3,
n_stimuli = 2,
n_trials = 125,
duration = 310,
resolution = 0.136,
stim_duration = 1.85... |
6f79ea184a843546dff22958810201b510a21e327a246f6fe474e1eb942e3b6c | Python | 938 | 40 | from data_provider.data_loader import Dataset_PEMS
from torch.utils.data import DataLoader
data_dict = {
'PEMS': Dataset_PEMS,
}
def data_provider(root_path, flag,seq_len,label_len,pred_len,batch_size):
Data = data_dict['PEMS']
if flag == 'test':
shuffle_flag = False
drop_last = True
... |
9a3bb7be3c875e0f2a447f397112d6bd12fc9459c5ec719d1138edd9efe92bb3 | Python | 938 | 34 | import numpy as np
import pandas as pd
from pandas import (
Categorical,
Series,
)
import pandas._testing as tm
class TestSeriesCount:
def test_count(self, datetime_series):
assert datetime_series.count() == len(datetime_series)
datetime_series[::2] = np.nan
assert datetime_seri... |
8ee56e99d8d86b46e25bfc3aad6fd766cd9d5b7f4b187dcd55c1e35c1ff0570e | Python | 940 | 33 | #!/usr/bin/env python2.5
import sys
import os
import showmovie
cwd = os.path.abspath(os.path.split(__file__)[0])
types = dict(val=("val%03d_3min", "valseq3minby10_%02d.index"), trn=("trn%03d", "trnseq.index"))
seq = [("trn", 1), ("val", 1), ("trn", 2), ("val", 2), ("trn", 3), ("val", 3), ("trn", 4), ("val",5)]
### ad... |
9867566fc3795d01b46b2585b824399f3921886d8074f73795eb7a942ff4d50c | Python | 940 | 33 | #!/usr/bin/env python2.5
import sys
import os
import showmovie
cwd = os.path.abspath(os.path.split(__file__)[0])
types = dict(val=("val%03d_3min", "valseq3minby10_%02d.index"), trn=("trn%03d", "trnseq.index"))
seq = [("trn", 1), ("val", 1), ("trn", 2), ("val", 2), ("trn", 3), ("val", 3), ("trn", 4), ("val",4)]
### ad... |
83f345fff3c1c2b1ca739ee2730d4facc6e5410df6616b2495600546acd393c5 | Python | 941 | 34 | from __future__ import annotations
from ..selections._list_words import list_words
from ..utils._get_cmudict import get_cmudict
from ..utils._get_pyphen import get_pyphen
from ..utils._typed_cache import typed_cache
@typed_cache
def count_syllables(text: str, lang: str) -> int:
"""Estimate the total number of sy... |
45401923f1abc2e9e9b0a18462bab9212c7963278ebe5540c1e9e07c019d018a | Python | 942 | 38 | r"""
It's used to check basic rnn features with cuda.
For example, it would throw exception if some components are missing
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class SimpleCNN(nn.Module):
def __init__(self):
super().__init__()
self.con... |
831f0ee8a882e4d870a0b83b7eb8e2f2dd70f53951c3938edb13056eff63fed7 | Python | 942 | 44 | from neurodesign import geneticalgorithm, generate, msequence
import sys
design_i = sys.argv[1]
EXP = geneticalgorithm.experiment(
TR = 0.68,
P = [0.5, 0.5],
C = [[0.5, -0.5]],
rho = 0.3,
n_stimuli = 2,
n_trials = 48,
duration = 620,
resolution = 0.136,
stim_duration = 10... |
e143516ce589b8ed4d54a3678060bb0595ad0b2a9f0f16d4e822e19970651bbd | Python | 943 | 35 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
with open("fm/version.py") as infile:
exec(infile.read())
with open("README.md") as f:
readme = f.read... |
fa16f88049ea1f38faac76b87e88115439200cdc583584731e0fa503ecf896de | Python | 943 | 40 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..counts._count_sentences import count_sentences
from ..counts._count_polysyllable_words import count_polysyllable_words
@typed_cache
def smog_index(text: str, lang: str) -> float:
r"""Calculate the SMOG index.
Parameters
... |
d298ff1068538c6c132c01d7541a46975b0a6446c6fb18c1b756f499eb51cbeb | Python | 945 | 36 | #!/usr/bin/env python3.7
"""Generate an excel summary of parameters used in runs of ASCENT.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmgl... |
7a247948904f38547171881615fac2faede8bc67d3ea5c9d65f0043545a71cc3 | Python | 946 | 30 | from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from m6anet.scripts import dataprep, inference, train, \
compute_norm_factors, convert
modules = ['dataprep', 'inference', 'train', 'compute_norm_factors', 'convert']
__version__ = "2.1.0"
def main():
parser = ArgumentParser(prog='m6ane... |
8d83ba69569f485248a2da07a441595a70242de59e08ed4f67d17aef7be07d63 | Python | 946 | 41 | #!/usr/bin/env python3.7
"""Returns thresholds from the selected sample/model/sim combos as a dataframe.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://gi... |
7f7795b51c76b4aac9c716af64d7bf7f52e7fbfd6c8614a57de4591034e5c615 | Python | 947 | 25 | from numpy._core import _internal
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, sha... |
c0a88fcea547605d5fcf0e144eeabf1eda60685043665ac21194bc577e5f8a44 | Python | 948 | 29 | """Contains errors raised for data handling using SQLite"""
class DBClosedError(Exception):
"""Thrown when the database is closed. This means the table was closed after creation
or was never created in the first place
"""
def __init__(self) -> None:
super().__init__(
(
... |
dec67055ad06b51972404ccccbf96afa7772bab27285e1234fb0a2dfcd99586e | Python | 948 | 36 | """Contains code to calculate the Jaccard index of two BGCs"""
# from python
from typing import Any
# from other modules
from big_scape.comparison.record_pair import RecordPair
def calc_jaccard_sets(set_a: set[Any], set_b: set[Any]) -> float:
"""Calculates the Jaccard index between two sets of objects
Args... |
4d7ad3c39df539a85f26e47170421ccf04181139987bc1978f72cb512e125a39 | Python | 950 | 33 |
import sys
sys.path.append('.')
from plotting.utils import load_results, mesh_name, sim_name, m3topl
import matplotlib.pyplot as plt
import numpy as np
import yaml
if __name__ == '__main__':
sim_name = sys.argv[1]
mesh_name = sys.argv[2]
results = load_results(mesh_name, sim_name)
with open(f"config_files/... |
0f498387494ca7d298a46ff087783edf090a1623141493894d652c69345cf5c9 | Python | 955 | 46 | from ._anndata import (
clean_obs_names,
cleanup,
get_df,
get_initial_size,
get_modality,
get_size,
make_dense,
make_sparse,
merge,
set_initial_size,
set_modality,
show_proportions,
)
from ._arithmetic import clipped_log, invert, multiply, prod_sum, sum
from ._linear_mode... |
ab11ac48ef2f078696deb9447ef001ae8432b0ee4c0dbde361bac863d5294f7b | Python | 955 | 44 | from neurodesign import geneticalgorithm, generate, msequence
import sys
design_i = sys.argv[1]
EXP = geneticalgorithm.experiment(
TR = 0.68,
P = [.5,.25,.25],
C = [[0.5, -0.25, -0.25],[0, 0.5, -0.5]],
rho = 0.3,
n_stimuli = 3,
n_trials = 240,
duration = 660,
resolution = 0.13... |
dbc43171bf1c5141049cf8d49cf2e96f9d725aa59dc5bc666dc9422b73152f3d | Python | 955 | 33 | #!/usr/bin/env python2.5
import sys
import os
import elshowmovie as showmovie
cwd = os.path.abspath(os.path.split(__file__)[0])
types = dict(val=("val%03d_3min", "valseq3minby10_%02d.index"), trn=("trn%03d", "trnseq.index"))
seq = [("trn", 1), ("val", 1), ("trn", 2), ("val", 2), ("trn", 3), ("val", 3), ("trn", 4), ("... |
8e3433e35258bc8862187c7fdb82b57a9ec97446a8883ab1ce546166e2174195 | Python | 958 | 36 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..utils._get_lang_cfg import get_lang_cfg
from ..counts._count_words import count_words
from ..counts._count_difficult_words import count_difficult_words
from ._words_per_sentence import words_per_sentence
@typed_cache
def gunning_... |
f3c77b31c132adfd235c841c7012bef0c51b2f506f477e86503ab6168901bb87 | Python | 958 | 26 | import os
from protgps.utils.registry import register_object
from protgps.utils.classes import ProtGPS
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
# TODO: add args for various callbacks -- currently hardcoded
@register_object("checkpointer", "callback")
class Checkpoint(ModelCheckpoi... |
280ed9868641ad74f97d6d0d9aab1ea38ec8bee3d54ab2dab1fe18ca243cbf8e | Python | 959 | 34 |
import sys
sys.path.append('.')
from plotting.utils import load_results, mesh_name, sim_name, m3topl
import matplotlib.pyplot as plt
import numpy as np
import yaml
if __name__ == '__main__':
sim_name = sys.argv[1]
mesh_name = sys.argv[2]
results = load_results(mesh_name, sim_name)
with open(f"config_file... |
c24a153bf28ff0be01e7ab95059ea53d7730bc9d70ab9069bfcc77a29471477b | Python | 960 | 25 | # License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
# for test files, silence irrelevant and noisy pylint warnings
# pylint: disable=no-self-use,protected-access,missing-docstring
import unittest
from antismash.modules.cl... |
d3f74a141a1c374155dd74f384e9433f9e53a1e1c2adedb644829ee9dead459b | Python | 961 | 34 | from __future__ import annotations
from typing import Callable
from pandas.compat._optional import import_optional_dependency
import pandas as pd
def _arrow_dtype_mapping() -> dict:
pa = import_optional_dependency("pyarrow")
return {
pa.int8(): pd.Int8Dtype(),
pa.int16(): pd.Int16Dtype(),
... |
fe0e0105dca21c20c13f934e7e89049e14675d96b17dcea490305100c8941575 | Python | 961 | 31 | import pickle
from pathlib import Path
from czifile import imread
from multiprocessing import Pool
from tqdm import tqdm
from crystal_tracer.algorithm.detection import frame_detection
out_dir = Path('../../data/case2')
flood_dir = out_dir / "flood"
csv_dir = out_dir / "detection"
def main(args):
stack, slice = ... |
78f12ab44191d0ca7f178c69b5b72ec583d2dc224cb5e64d5cdd541e15b5fe0a | Python | 962 | 38 |
# This code is written for readability rather than efficiency.
#
# - It is aimed to give a set of logical steps explaining how to run the analysis
# in the manner we ran it for this study.
#
# Read and inspect data
df <- read.csv("your_file.csv")
df <- df[ , -1] # Drop the first column (e.g., index)
head(df)
s... |
bbf7f8d72ae66f46acbab9a9ba43b122dfe9c179e2d550040112a1aefe2ecddc | Python | 962 | 40 | """For testing measures.py file."""
import numpy as np
import pytest
from phyid.measures import (
local_entropy_mvn,
)
@pytest.mark.parametrize(
"test_input,expected",
[
((0, 0, 1), 0.9189385332046727),
((0, [0], [1]), 0.9189385332046727),
(([0, 0], [0, 0], np.identity(2)), 1.8378... |
f2a51a621f101ce265cfb30dbe4b8937261d08e0a2bee5b4a5b99fff638f66ed | Python | 962 | 26 | from distutils.unixccompiler import UnixCCompiler
class ArmCCompiler(UnixCCompiler):
"""
Arm compiler.
"""
compiler_type = 'arm'
cc_exe = 'armclang'
cxx_exe = 'armclang++'
def __init__(self, verbose=0, dry_run=0, force=0):
UnixCCompiler.__init__(self... |
16612cfec29a53da31fb97f4354dff4d122fd17c4b415119f2ba2ef5535e85be | Python | 964 | 131 | import numpy as np
i8 = np.int64(1)
u8 = np.uint64(1)
i4 = np.int32(1)
u4 = np.uint32(1)
b_ = np.bool(1)
b = bool(1)
i = int(1)
AR = np.array([0, 1, 2], dtype=np.int32)
AR.setflags(write=False)
i8 << i8
i8 >> i8
i8 | i8
i8 ^ i8
i8 & i8
i << AR
i >> AR
i | AR
i ^ AR
i & AR
i8 << AR
i8 >> AR
i8 | AR
i8 ^ AR
i8 &... |
8f19d3c095f9f6aa81bc8297f10c82439e9d0b581df7dddb41e0224375e2ea59 | Python | 967 | 28 | import logging
import redis.asyncio as redis
from app.utils.environment import CONFIG
# Configure logging
logger = logging.getLogger(__name__)
# Note: Configure logging in the application entry point instead of here.
async def get_redis_connection():
"""Creates and returns an asynchronous Redis connection pool... |
9021343e4b8f9350b19191ce0dedda679e15f877350221e4c8d3558cde511083 | Python | 967 | 28 | import numpy as np
import pytest
from pandas import (
TimedeltaIndex,
Timestamp,
)
import pandas._testing as tm
class TestSearchSorted:
def test_searchsorted_different_argument_classes(self, listlike_box):
idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
result = idx.searchsorted(listl... |
9c1de91d618d525a16a600b627aec9806413bb130f5ea696ac86b7c4695d7695 | Python | 968 | 35 | """
H6 molecule VQNHE with code from tc.application
"""
import numpy as np
import tensorcircuit as tc
from tensorcircuit.applications.vqes import VQNHE, JointSchedule, construct_matrix_v3
tc.set_backend("tensorflow")
tc.set_dtype("complex128")
h6h = np.load("./h6_hamiltonian.npy") # reported in 0.99 A
hamiltonian =... |
410636fade3b38b1d438612caebee5e22be49d0ce0a63c2a539fc6a6419afa3c | Python | 972 | 30 | import numpy as np
from yass.augment import noise
from yass.batch import RecordingsReader
def test_can_kill_signal(path_to_standardized_data):
recordings = RecordingsReader(path_to_standardized_data,
loader='array')._data
noise.kill_signal(recordings,
... |
5df2eff619c6b153c8b00d4bc161222d61d93c8d80ec8184935b85390e5c85c5 | Python | 972 | 27 |
import sys
sys.path.append('.')
from plotting.utils import load_results, m3tofl, set_plotting_defaults, efcolor, pvscolor, ecscolor, dpi
import matplotlib.pyplot as plt
set_plotting_defaults()
import matplotlib.pyplot as plt
if __name__ == '__main__':
sim_name = sys.argv[1]
mesh_name = sys.argv[2]
results =... |
4c6a5a6ae42affcd6cb958e77aa93306584c82ef93634fa09becc59d865808ad | Python | 975 | 29 | import random
import numpy as np
from typing import Tuple
class ReverseComplement():
def __init__(self, p=0.5) -> None:
self.p = p
def __call__(self, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
if random.random() >= self.p:
return reverse_complement(x, y)
... |
88cfc545832dbef744a4cb4b512b8177f5b0e671ebdbce2fd9f4711dac9dbc83 | Python | 975 | 27 | import pandas as pd
import pandas._testing as tm
class TestUnaryOps:
def test_invert(self):
a = pd.array([True, False, None], dtype="boolean")
expected = pd.array([False, True, None], dtype="boolean")
tm.assert_extension_array_equal(~a, expected)
expected = pd.Series(expected, ind... |
8362fd715b3a34ae341b5ea43382ccc986643a29d3a715c5f6d2dfd058c32fab | Python | 977 | 37 | #!/usr/bin/env python3
"""
BiG-SCAPE
PI: Marnix Medema marnix.medema@wur.nl
Maintainers:
Arjan Draisma arjan.draisma@wur.nl
Catarina Loureiro catarina.salesesantosloureiro@wur.nl
Nico Louwen nico.louwen@wur.nl
Developers:
Arjan Draism... |
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