sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
797d730ea99ca2b4be2c0ffa8cca6efd3e8af4f5c0f053dad7408bdb012964f6 | Python | 520 | 17 | #python calc_perplexity.py < infile > outfile
# computes the perplexity of a grammar on a dataset
# infile: line-delimited log-probs of observations from a test dataset
# outfile: the perplexity computed from the observation probabilities in infile
import math
import sys
norm = 0
xent = 0
for line in sys.stdin.readli... |
d17aac0a396e5a0072c2547c8844ede135cf37e443ee85c5619c27834b379619 | Python | 520 | 18 | from wordfreq import tokenize, word_frequency
import pytest
def test_tokens():
assert tokenize('감사합니다', 'ko') == ['감사', '합니다']
def test_combination():
gamsa_freq = word_frequency('감사', 'ko')
habnida_freq = word_frequency('합니다', 'ko')
assert word_frequency('감사감사', 'ko') == pytest.approx(gamsa_freq /... |
d2a0ea17947cca4612f0202c2e06f7fc9b2be1743ba60710465b46c72a56f209 | Python | 520 | 27 | from pandas.core._numba.kernels.mean_ import (
grouped_mean,
sliding_mean,
)
from pandas.core._numba.kernels.min_max_ import (
grouped_min_max,
sliding_min_max,
)
from pandas.core._numba.kernels.sum_ import (
grouped_sum,
sliding_sum,
)
from pandas.core._numba.kernels.var_ import (
grouped_v... |
c83aa8c9059c5d5a2a06148fcf0dbd096c8ebbde506e134f05d1e80f879d5717 | Python | 521 | 10 | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and relat... |
b78d3962a50a38a8da056ce625abf0962fe318107d98e3f813f29074c7569fd9 | Python | 524 | 19 | # Replaces leaves from a linetrees file with the string 'LEAF'
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
def replaceLeaves(t):
for x in t.ch:
if len(x.ch) == 0:
x.c = 'LEAF!'
else:
r... |
7ad6dc74886e92cae6c7da865604ea45baa210ed35784292ead791330b720a6a | Python | 526 | 22 |
class Controller(object):
registered_callbacks = []
def __init__(self):
self.registered_functions = []
def register(name):
def reg_decorator(func):
Controller.registered_callbacks.append((func, name))
return lambda *args: func(*args)
return reg_decorator
... |
54dd2aef1a352e8a138c6549001a844f36adeadf14331a8ea06b8fa82a301bf3 | Python | 528 | 21 | import sys
import pandas as pd
if __name__ == "__main__":
data = pd.read_csv(sys.stdin, sep=' ', skipinitialspace=True)
evids = []
for i in data.index:
subject = data["subject"][i]
docid = data["docid"][i]
time = int(data["time"][i])
fROI = data["fROI"][i]
evid =... |
b1faa018fdd5ad5f74de02e9321217a5d093cd7fd60e992995fbb3ea86f78f33 | Python | 528 | 23 | import numpy
import pandas
import h5py
f = h5py.File('data/Smartseq2_Multisample_human_single_2021-12-15T21-07-41.loom', 'r')
cellIds = numpy.array(f['col_attrs']['CellID'])
ensambleId = numpy.array(f['row_attrs']['ensembl_ids'])
geneName = numpy.array(f['row_attrs']['gene_names'])
countTable = numpy.array(f['layers'... |
2fd84bea364615abac03a5686d6dafaca1947ed14b045e15012f89e419d5a220 | Python | 530 | 18 | import os
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--folder', type=str, default="./")
args = parser.parse_args()
import glob, os
os.chdir(args.folder)
for stl_fileName in glob.glob("*.stl"):
conversion_command = "meshlabserver -i " + stl_fileName + " -o " + stl_fileName[:-3] + "o... |
a9dd21e96c245e1f0b72e249b518b525eb4815d711a7aaf780a2cc424fbfa0c2 | Python | 530 | 25 | import sys
import tree
def annotDepth(t,b='L',d=1):
if len(t.ch)>0:
t.c += '-b'+b+'-d'+str(d)
if len(t.ch)==1:
annotDepth(t.ch[0], b, d)
if len(t.ch)==2:
if b=='L':
annotDepth(t.ch[0],'L',d)
annotDepth(t.ch[1],'R',d)
else:
annotDepth(t.ch[... |
a8f5afe1657db5c9bff2574e779d88f6b90408130d23fb980efbe63889aa0925 | Python | 534 | 17 | """Functionals, e.g. norms and loss functions."""
from mrpro.operators.functionals.L1Norm import L1Norm
from mrpro.operators.functionals.L1NormViewAsReal import L1NormViewAsReal
from mrpro.operators.functionals.L2NormSquared import L2NormSquared
from mrpro.operators.functionals.MSE import MSE
from mrpro.operators.func... |
12c7457de2ef2d40006aef0571cde81e7617d1b34942f5d648ae5d94798ad891 | Python | 535 | 28 | #! /usr/bin/python3
# -*- coding: utf-8 -*-
# @FileName: template.py
# @Author: Li Chengxin
# @Time: 2023/6/30 22:26
REGIONS_TMP = {
"shape_attributes":
{
"name": "polygon",
"all_points_x": [],
"all_points_y": []
},
"region_attributes":
{
... |
a7382feeefff85efe2503536591a2218f6f0c5527c7bacb18abfc40e0142ecd0 | Python | 535 | 23 | import sys
import pandas as pd
groupnames = sys.argv[1:]
df = pd.read_csv(sys.stdin, sep=' ')
groups = df.groupby(groupnames)
# Take means of numeric columns
means = groups.mean()
# Take first entries of all columns
first = groups.first()
nwords = groups.agg('count')['word']
mean_cols = set(means.columns)
keep_col... |
af5a7f7e1e5d26de0aeb42ee9b172a20091df8e1ea19262450c6108874adec82 | Python | 535 | 19 | import pytest
import pandas as pd
import pandas._testing as tm
class TestFreq:
def test_freq_setter_deprecated(self):
# GH#20678
idx = pd.period_range("2018Q1", periods=4, freq="Q")
# no warning for getter
with tm.assert_produces_warning(None):
idx.freq
# Per... |
6f72433f00002dcfcdf5554105e3c1058e55f696aa3811fe1863a91e5949b16e | Python | 536 | 26 | # pyaging/utils/__init__.py
from ._feature_ranges import get_feature_ranges, resolve_feature_ranges
from ._utils import (
cite_clock,
download,
find_clock_by_doi,
get_clock_metadata,
load_clock_metadata,
print_model_details,
progress,
show_all_clocks,
)
__all__ = [
"cite_clock",
... |
ab130376cc048f35ea8a40b336cc7127099fa6d4fdf7a8b28aa887c3388aa185 | Python | 538 | 20 | import os
from pathlib import Path
PROJ_CODE = 'AG'
BASE_DIR = Path(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)))
PROJ_DIR = BASE_DIR / 'proj'
MODEL_DIR = PROJ_DIR / 'model'
CACHE_DIR = PROJ_DIR / 'cache'
DATA_DIR = PROJ_DIR / 'data'
FIG_DIR = PROJ_DIR / 'fig'
SAVE_DIR = PROJ_DIR / 'save'
OUTP... |
0a76b45284b7c5c092bec600b2117e522c073f6c0162cf84a6b4bcaad354a8cd | Python | 539 | 21 | import dclab
import matplotlib.pylab as plt
# dataset
ds = dclab.new_dataset("fluorescence/fluorescence.rtdc")
fig = plt.figure(figsize=(7, 3))
ax1 = plt.subplot()
ax1.plot([ds["time"][0], ds["time"][-1]],
[ds["index"][0], ds["index"][-1]],
label="constant slope",
lw=3)
ax1.plot(ds["time"]... |
73d3a65e842b5dd6b8567fa7831f174f11efc06a78927723e2dece20dc0e7f56 | Python | 539 | 21 | __all__ = ["batched"]
from itertools import islice
from typing import Iterable, Tuple, TypeVar
T = TypeVar('T')
def batched(iterable: Iterable[T], n: int) -> Iterable[Tuple[T, ...]]:
"Batch data into tuples of length n. The last batch may be shorter."
# batched('ABCDEFG', 3) --> ABC DEF G
# itertools 3.1... |
8a990b48c3b143829e068f9acfe4f3b7e663cc030b952e102726c8d92f47ca8c | Python | 539 | 27 | from __future__ import annotations
from typing import TYPE_CHECKING
import numba
if TYPE_CHECKING:
import numpy as np
@numba.jit(
numba.boolean(numba.int64[:]),
nogil=True,
parallel=False,
)
def is_monotonic_increasing(bounds: np.ndarray) -> bool:
"""Check if int64 values are monotonically incr... |
fa1d609e4ebe7012cc9425ebff2b02976ab43487ce12f7a0a769d4ffba4e77bb | Python | 539 | 28 | import sys
from itertools import chain, combinations
def powerset(iterable):
xs = list(iterable)
return chain.from_iterable(combinations(xs,n) for n in range(len(xs)+1))
preds = sys.argv[1:]
if len(preds) == 1:
preds = preds[0].split('_')
pset = powerset(preds)
out = []
for s in pset:
out_cur = []
... |
5f11e18a9fe49e86d0b9d87c998b42cce7c04fc53e528ffc3e9837f54730a9d7 | Python | 541 | 26 | '''
Contains utility functions
'''
import shlex, subprocess
#from subprocess import Popen
import warnings
warnings.filterwarnings("ignore")
DIGITS = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
def shlex_cmd(command, print_cmd=False):
'''
A convinience function that submits a command using shlex and
... |
9cb96083e9ebdfc8cbf415ee9c32cff69b75895b354066bff768edee4f74270e | Python | 541 | 21 | import numpy as np
import pandas as pd
class TestSeriesCount:
def test_count(self, datetime_series):
assert datetime_series.count() == len(datetime_series)
datetime_series[::2] = np.nan
assert datetime_series.count() == np.isfinite(datetime_series).sum()
def test_count_categorical(... |
e19547605a81b98139ce067cc608d849974a447061a71d94881006279d4b4814 | Python | 541 | 16 | import sys
for linenum,line in enumerate(sys.stdin):
if linenum==0:
measures = line.split()
for i in range( 0, len(measures) ):
ctr = 1
for j in range( i+1, len(measures) ):
#sys.stderr.write( measures[i] + ' ?? ' + measures[j] + '\n' )
if me... |
71ff9287637ab97d40505da2ce5f94375c5fcd895ba2b121008dfcf8308cde9f | Python | 543 | 16 | from typing import List, Tuple, Union
import seaborn as sns
import matplotlib as mpl
Palette = Union[str, List[Union[str, Tuple[float, float, float]]], None]
def get_colormap(palette: Palette) -> mpl.colors.Colormap:
"""Generate a matplotlib Colormap from a `Palette`"""
if palette is None:
return sn... |
bc0da5ff8be172ebadb4a1fc31d1e344b54cdf18fe053d5f46b5a4928336e2b3 | Python | 543 | 20 | """Test the models package-level functions."""
from lightning_pose.models import check_if_semi_supervised
def test_check_if_semisupervised():
flag = check_if_semi_supervised(losses_to_use=None)
assert not flag
flag = check_if_semi_supervised(losses_to_use=[])
assert not flag
flag = check_if_sem... |
f264e253a9678486d1369aafa3fa2e0d69f0c21b5ece9db5a1ae6c9b09148a86 | Python | 543 | 22 | from flask import jsonify, g, current_app
from flask_httpauth import HTTPTokenAuth
from .models import AuthKeys
auth = HTTPTokenAuth('siteGUID')
@auth.verify_token
def verify_token(auth_guid):
g.auth_key = AuthKeys.query.filter_by(guid=auth_guid).first()
if g.auth_key is None:
return False
retu... |
1a4a4b0dcdb436b2d074209994d0aa9919c4e02d171c40c317012a0c015882bb | Python | 545 | 17 | def get_cgcnn_input(struct):
"""return the wren input string
TODO update wren to use a more standard convention
Args:
struct (Structure): input structure to get inputs for
Returns:
cgcnn inputs as a lattice matrix and list of sites of the form [f"{el} @ {x,y,z}", ]
"""
elems =... |
2efdb5a6a224aecdea27ae6522da1ae6cecaa071df4054119fbff8e76c8c66ac | Python | 548 | 11 | # Population Based Training (PBT) for rl_games.
# Ported from the Isaac Lab rl_games integration (isaaclab_rl); original DexPBT
# implementation from NVIDIA-Omniverse/IsaacGymEnvs (https://arxiv.org/abs/2305.12127).
# Copyright (c) 2022-2026, The Isaac Lab Project Developers.
# SPDX-License-Identifier: BSD-3-Clause
fr... |
99c6aafd1b61fd2d9f28c493f6f17aaa8cd489debb4b8a3aeaaca9186fa1f319 | Python | 548 | 18 | import darts.functional as F
from darts.api.metrics.average import MultitaskAveragingSupervisedMetric
class MultitaskAccuracy(MultitaskAveragingSupervisedMetric):
""" Multitask Classification accuracy """
def __init__(self, scope="train"):
super().__init__("accuracy", scope=scope)
def _value_fun... |
a3b63eb344b9adbdf574352f02b093353cdf15c3cb3b0876f356bb69fbcee775 | Python | 548 | 12 | """Removed — use the litpose CLI instead."""
raise SystemExit(
"scripts/predict_new_vids.py has been removed.\n"
"Please use the command line interface instead:\n\n"
" To predict on one or more video files:\n"
" litpose predict <model_dir> <video_file1> <video_file2> ...\n\n"
" To predict on a... |
9b037cfba4f7641e8219acefa3f3ca9933c60afbcefa6e469954c9dbb095228f | Python | 550 | 21 | """
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 .losses import SSIMLoss
from .utils import save_reconstructions
from .coil_combine import rss, rss_complex
from .fftc import fft2c_new a... |
bb8803177d93819cb32158d284b64fd3f1f5cc283c571242f815dbab66216a02 | Python | 552 | 18 | import io
import pytest
from rich.console import Console
import pyaging.logger._live as live_module
@pytest.fixture(autouse=True)
def non_interactive_display_console(monkeypatch):
"""Keep the live display off by default so test behavior does not depend
on whether pytest runs attached to a TTY (e.g. `pytest ... |
e4b157f601ba0ee944bdc54949d00f8a057c94215db5d93c0a87f61be80c5453 | Python | 557 | 16 | """Time-series forecasting task — PyCaret 4.0.
PyCaret 4.0 is OOP-only; the 3.x functional API was removed.
The 3.x class name ``TSForecastingExperiment`` was renamed to the cleaner
``TimeSeriesExperiment`` in 4.0 to match the task's module name.
from pycaret.time_series import TimeSeriesExperiment
exp = Tim... |
879a751810fc4f8901f27daa4566e7a29cb3d631501a69da62159efcaeab75c6 | Python | 559 | 25 | import sys
header = True
ROI = sys.argv[1:]
if 'AllROI' in ROI:
ROI.pop(ROI.index('AllROI'))
for i in range(len(ROI)):
ROI[i] = 'bold' + ROI[i]
ROI_cols = []
for line in sys.stdin:
row = line.strip().split()
if header:
for h in row:
if h in ROI:
ROI_cols.append(row.... |
c05f2adc7c1703a5d75cb02de2d3470606f35ff5eb8a073f3eea310821ab6cb0 | Python | 560 | 27 | """
Tests for behavior if an author does *not* implement EA methods.
"""
import numpy as np
import pytest
from pandas.core.arrays import ExtensionArray
class MyEA(ExtensionArray):
def __init__(self, values) -> None:
self._values = values
@pytest.fixture
def data():
arr = np.arange(10)
return M... |
99b67ce70d566024c225e1dfe910a5dd6de2d93f134963b9f98c7bf52b2e73ae | Python | 561 | 22 | import re
import sys
''' take stdin, sub any ==>.*<== lines with (U !ARTICLE). if none, add (U !ARTICLE) at top '''
lines = sys.stdin.readlines()
for line in lines:
if re.match(r'==>.*<==', line) is not None:
delims_exist = True
break
delims_exist = False
if delims_exist == True:
for li... |
a98173670339613c0df73c45399087be9f2eae494633fcffc7e2be992867ac68 | Python | 562 | 22 | """Test segmentation module."""
import numpy as np
import pytest
from gradec.segmentation import KMeansSegmentation, PCTSegmentation
@pytest.mark.parametrize(
"segmentation",
[
(PCTSegmentation(10)),
(KMeansSegmentation(10)),
],
)
def test_segmentation_smoke(segmentation):
"""Smoke te... |
b54df1b2ec3db26f387987c7ab5b006cf5e6652072103d64fbb530836840999c | Python | 563 | 15 | import sys, re, argparse, itertools
argparser = argparse.ArgumentParser('''
Compares an input linetrees file to a GCG-reannotated "target"
linetrees file, replacing all trees with newlines that failed
to reannotated in the target.
''')
argparser.add_argument('target', type=str, nargs=1, help='GCG-reannotated "target" f... |
1d74692b8a839909b8aa35c6a8b2cad238ebb8ced6dc230df89ae8effd7b6453 | Python | 565 | 21 | import numpy as np
import pytest
import pandas as pd
@pytest.mark.parametrize(
"data, index, expected",
[
({"col1": [1], "col2": [3]}, None, 2),
({}, None, 0),
({"col1": [1, np.nan], "col2": [3, 4]}, None, 4),
({"col1": [1, 2], "col2": [3, 4]}, [["a", "b"], [1, 2]], 4),
... |
e2f4ac3b872dc791e2d9b0093265b16ca92485ac78d1822420348330532be389 | Python | 565 | 22 | import sys
print('word sentid sentpos discid discpos corpus')
# 'id's start from 0, 'pos'es start from 1
discid = -1
sentid = 0
discpos = 1
corpus = sys.argv[1] if len(sys.argv) > 1 else 0
for line in sys.stdin:
line = line.strip()
if line == "!ARTICLE":
discid += 1
discpos = 1
... |
2a32e42d4382edc5eb6268505dabe0f7e09ed0cde07063d00026bbe021720c65 | Python | 566 | 21 | import torch
import torch.utils.data as Data
def Weighted_Sampling(train_label = None):
'''
:param train_label: Input train label (in tensor) to calculate sampling weight
:return: Pytorch Weighted Sampler
'''
class_sample_count = torch.tensor(
[(train_label == t).sum() for t in torch.uni... |
678a296176acc54fc885180e579522223add091dc48ffd383c1b8bdabae7d756 | Python | 566 | 17 | """Test functions for non-linear optimization."""
import torch
from mrpro.operators import Operator
# TODO: Consider introducing the concept of a "Functional" for scalar-valued operators
class Rosenbrock(Operator[torch.Tensor, torch.Tensor, tuple[torch.Tensor,]]):
def __init__(self, a: float = 1, b: float = 100)... |
6968062e828883ff9641f14dd51c8213a8ddd98889b3569789106c49f6f7a679 | Python | 566 | 26 | import sys, os, re, collections
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
import gcgtree
import semcuegraph
RELABEL = False
for a in sys.argv:
if a=='-d':
gcgtree.VERBOSE = True
semcuegraph.VERBOSE = True
if a=='r':
RELABEL = True
####... |
ac2f5cea062edd21fefc8f6178d604ae1a87da4a2b34591475a34942941c52b9 | Python | 566 | 22 | #!/usr/bin/env python
from setuptools import setup, find_packages
setup(
name="msmfcode",
version="0.1",
description="Package containing code for the simulation, evaluation and optimization of multi-scale, multi-field place codes.",
url="https://github.com/dietriro/msmf-code",
author="Robin Dietric... |
6b12a209c3910501557721414eef43350c6d5991e0942ac5f561a93268670631 | Python | 567 | 20 | """Calling cards data analysis in Python"""
from ._call_motif import call_motif, compare_motif
from ._find_fastq import find_fastq
from ._find_related_genes import (
pair_peak_gene_bulk,
pair_peak_gene_sc,
pair_peak_gene_sc_mu,
)
from ._footprint import footprint
from ._GWAS import GWAS, GWAS_adata_bulk, G... |
5d440d3e8b3cb49743018bfb56f6d9361bb56aa664575db0c6c4cc22c0f7baf5 | Python | 568 | 16 | import pandas as pd
import sys, argparse
argparser = argparse.ArgumentParser('''
Extracts RT tokenization from table of experiment data.
''')
argparser.add_argument('cols', type=str, nargs='+', help='Column(s) that define(s) a unique key for each RT token.')
args, unknown = argparser.parse_known_args()
def main():
... |
072293579f1a9c86ff06614e686c2486fdeb69e65dbb017b7faad17dcf73c3c1 | Python | 569 | 25 | import uuid
import pytest
from pandas.io.pytables import HDFStore
tables = pytest.importorskip("tables")
# set these parameters so we don't have file sharing
tables.parameters.MAX_NUMEXPR_THREADS = 1
tables.parameters.MAX_BLOSC_THREADS = 1
tables.parameters.MAX_THREADS = 1
@pytest.fixture
def temp_h5_path(tmp_path... |
a4bb2da0d40bbdf75997fa8125360722ecc68363fbea31b9521bb0957708a761 | Python | 569 | 24 | import vegas
import numpy as np
from ffcn_cffi import ffi, lib
@vegas.batchintegrand
def batch_f(x):
n, dim = x.shape
ans = np.empty(n, float)
_x = ffi.cast('double*', x.ctypes.data)
_ans = ffi.cast('double*', ans.ctypes.data)
lib.batch_fcn(_ans, _x, n, dim)
return ans
def main():
integ =... |
8684eaeeb3a34eb66464d40e82f594a0d7108f64459a6b6577cebdd59e7dc59c | Python | 570 | 23 | import numpy as np
import pytest
import pandas as pd
@pytest.mark.parametrize(
"data,exp_size",
[
# see gh-16362.
([[pd.NaT, "a", "b", 0], [pd.NaT, "b", "c", 1]], 8),
([[pd.NaT, "a", 0], [pd.NaT, "b", 1]], 6),
],
)
def test_maybe_infer_to_datetimelike_df_construct(data, exp_size):... |
583ed1c350a714f0d5c1e8a7917500ef434b4caed2187b93a3f3f0f165f63106 | Python | 572 | 21 | # ----------------------------------------------------------------------------
# Copyright (c) 2016-2026, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... |
112ae6dea0f8d19e1d4d7c45f7097b84f39557e8a3d6f308806514c4807d51bd | Python | 574 | 17 | import torch
def to_device(tensor, device: torch.device):
""" Convert tensor-like object to given PyTorch device """
if tensor is None:
return tensor
elif isinstance(tensor, torch.Tensor):
return tensor.to(device)
elif isinstance(tensor, dict):
return {k: to_device(v, device) f... |
fe4e3101b15017cb018cf9fe580140128044fad159cb3ca6592fc200215b027c | Python | 574 | 16 | from distutils.core import setup
import os
version_file = open(os.path.join(os.path.dirname(__file__), 'VERSION'))
version = version_file.read().strip()
setup(
name = 'gpmhc',
packages = ['gpmhc'],
version = version, # Ideally should be same as your GitHub release tag varsion
description = 'Inference f... |
4ac38075e8e65a95e487b243d046e6a3c7da06817945a3df900130923a617bb9 | Python | 575 | 25 | SIZE = 256
LABELS = 139
FILTERED_LABELS = 6
LABEL_MAP = {
"CSF": 0,
"WM": 1,
"GM": 2,
"BS": 3,
"CWM": 4,
"CGM": 5
}
OUTPUT = "."
ADNI_DATASET_DIR = "/home/ivanitz/Data/adni"
ADNI_TRAIN = "/home/ivanitz/deepbet/data/adni_seg_train.tfrecord"
ADNI_VAL = "/home/iv... |
0498b54e86ea33282a109eef2608f6275aa03c4eb99f6470b26e061a91668ab9 | Python | 577 | 17 | print(
"""
##################################################################################
~ MM2SF ~
(v 1.0)
An Unsupervised Machine Learning Approach for the Automatic Construction
of Local Chemical Descriptors
T... |
2a970d6e16c958a458283d8dd129538fa97cdbf5dac66d8f62cf05ee81938e8b | Python | 577 | 20 | import os, sys
import numpy as np
from sequence_models import pdb_utils
ex_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "examples"
)
assert os.path.isdir(ex_dir)
orig_coords, orig_atoms, orig_valid = pdb_utils.parse_PDB(
os.path.join(ex_dir, "gb1_a60fb_unrelaxed_rank_1_mod... |
5e1e2cae24174a37075429388e1762685e9377609cd631df4a6dc917ff811640 | Python | 577 | 24 | # -*- coding: utf-8 -*-
# Authors: Gianvittorio Luria <luria@dima.unige.it>
# Sara Sommariva <sommariva@dima.unige.it>
# Alberto Sorrentino <sorrentino@dima.unige.it>
#
# License: BSD (3-clause)
class Dipole(object):
"""Single current dipole class for SESAME.
Parameters
----------
... |
e3b94ce3654ff350726e7d1a2e6abc7d1a397cc2c8c49458f8e2007e5bf038ac | Python | 577 | 22 | #!/usr/bin/env python
__author__ = 'etseng@pacb.com'
import os, sys
import subprocess
def run_cmd(cmd):
if subprocess.check_call(cmd, shell=True)!=0:
raise Exception("Error cmd:").with_traceback(cmd)
input = sys.argv[1] # ex: test.sam
if not input.endswith('.sam'):
print("Only accepts files ending ... |
94c6ee2cbcefc46c33e07a0d29dc5025a6373b00068132c565bf08c5677b1398 | Python | 578 | 13 | """Module defining decoders."""
from onmt.decoders.decoder import DecoderBase, InputFeedRNNDecoder, \
StdRNNDecoder
from onmt.decoders.transformer import TransformerDecoder, TransformerLMDecoder
from onmt.decoders.cnn_decoder import CNNDecoder
str2dec = {"rnn": StdRNNDecoder, "ifrnn": InputFeedRNNDecoder,
... |
6dbcac44ff2c293cdeb81603e5b0aa4a1dcfae41e9f20881e247ab93a4c89452 | Python | 579 | 16 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class SimpleGNN(nn.Module):
def __init__(self, num_features, hidden_size, num_classes):
super(SimpleGNN, self).__init__()
self.conv1 = GCNConv(num_features, hidden_size)
self.c... |
85aec6a22c10b6b2b69c597ae472893e793505a535ef9478f3ddbe77079869dc | Python | 579 | 22 | from __future__ import annotations
import pytest
from pandas.compat import (
is_platform_windows,
)
from pandas.compat.pyarrow import pa_version_under22p0
pa = pytest.importorskip("pyarrow")
def _require_timezone_database(request):
if is_platform_windows() and pa_version_under22p0:
mark = pytest.ma... |
f8dc2ed08aa3a3cf31495f2454126563cd9008238f07583cf4922864e7132a3a | Python | 579 | 18 | """Re-export façade for the backbones package.
All backbone type constants and the ``build_backbone`` factory are defined in
``factory.py``; this module re-exports them. Nothing should be defined here —
add new symbols to ``factory.py`` and extend the import list below.
"""
from lightning_pose.models.backbones.factor... |
fd62d443848aaf7e86e77102779d30318a6be95234ebe911edde0a2f1377dd9e | Python | 580 | 19 | # License: see [LICENSE, LICENSES/legged_gym/LICENSE]
import numpy as np
import os
from datetime import datetime
import isaacgym
from legged_gym.envs import *
from legged_gym.utils import get_args, task_registry
import torch
def train(args):
env, env_cfg = task_registry.make_env(name=args.task, args=args)
pp... |
dcf92aacb35a6afac219a2d2de9eabe7b4fc0b2e9c15bb427ae10ce489199cc0 | Python | 581 | 28 | import matplotlib.pyplot as plt
def set_axis(ax, x, y, letter=None):
ax.text(
x,
y,
letter,
fontsize=25,
weight='bold',
transform=ax.transAxes)
return ax
plt.rcParams.update({
'xtick.labelsize': 15,
'xtick.major.size': 10,
'ytick.labelsize': 15,
... |
072de404f2264926c61454a09721aa7aae88801cbcffaefdc6715afcc6266839 | Python | 587 | 19 | import os
inputDir = os.getenv("INPUTDIR")
count = 0
# Nifti files are expected to be placed within INPUTDIR.
# They will be renamed to match the naming pattern.
print("Start renaming ...")
for filename in os.listdir(inputDir):
old_path = os.path.join(inputDir, filename)
if (os.path.isfile(old_path)):
... |
2ce956c7c51920e3d3be8109febf2421566e706a6b424f851ab65ce6db1efbff | Python | 587 | 22 | # __init__.py
from .substructurevocab import *
from .molgraph import *
from .molgraphdisplay import *
from .utilsonehot import *
from .utilsmol import *
from .utilsgraph import *
from .dataset import *
from .experiment import *
from .graphmodel import *
from .gineconv import *
from .attentivefp import *
from .training ... |
6d7cb7388fbeb24c56b31e5749e8acbd1948ad78b236636f5193f34d26bef36b | Python | 587 | 23 | import numpy as np
import pandas as pd
def is_object_or_nan_string_dtype(dtype):
"""
Check if string-like dtype is following NaN semantics, i.e. is object
dtype or a NaN-variant of the StringDtype.
"""
return (isinstance(dtype, np.dtype) and dtype == "object") or (
dtype.na_value is np.na... |
1da598b771869d9142c32329afa7b962c17779e9568e1ede77d7235e81877728 | Python | 588 | 26 | """
.. module:: gfx_manager
:platform: Linux, Windows
:synopsis: Manager for handling OpenGL resources and other graphics-related things
.. moduleauthor:: Fredrik Nysjo
"""
import glm
class GfxManager:
def __init__(self):
self.window = None
self.width = 1000
self.height = 700
... |
224e7c1939808bd603b7396bfd190ddc35bd3edef8169b356d360fa85d540b50 | Python | 588 | 27 | import sys
sentid_prev = 0
first_line = True
first_word = True
for line in sys.stdin:
row = line.strip().split()
if first_line:
word_ix = row.index('word')
sentid_ix = row.index('sentid')
first_line = False
else:
word = row[word_ix]
sentid = row[sentid_ix]
i... |
bc68747f9bd898744c070d3ead3895a3072e166e1e21672febfb0c460857b55d | Python | 588 | 15 | import sys
import os
import numpy as np
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
from app.models import Condition
def get_ppmi_condition(diagnosis):
all_diagnoses = ["Prodromal PD", "Healthy C... |
aab30481fcb31a4a292dfcc46499660c9e0d4f7acac591f1a32ba7a43825d266 | Python | 589 | 23 | class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, ... |
e4700a8a7a7cd236259faa1a414103cf4c9476ff38c981237303c14ccd8d0797 | Python | 589 | 18 | import functions.functions_torch as ft
def weights_and_sparsity(model, tol=1.0e-3):
"""
It extracts the weights and calculate their spasity (using the tol as the threshold to judge zero element)
respectively from the model, and return two dict type results.
"""
state = model.state_dict()
weigh... |
461f782ef008f4efde719dab86aacd5eb8965e0afd310db43254b635f5ec2262 | Python | 592 | 19 | from dissect.PropsSimulator.simulator import simulate
from dissect.prepare_data import dataset
from dissect.dissect_frac import run_dissect_frac
from dissect.dissect_expr import run_dissect_expr
def run_dissect(config):
print("\033[1mStarting simulations\033[0m")
simulate(config)
print("-----\n")
print("\033[1mPr... |
14b4d3d1b3ec7b6d14236ebe1595413112d121669385e48d2deb300f55163263 | Python | 593 | 30 | import sys
import pandas # noqa: F401
blocklist = {
"bs4",
"gcsfs",
"html5lib",
"http",
"ipython",
"jinja2",
"lxml",
"matplotlib",
"openpyxl",
"py",
"pytest",
"s3fs",
"scipy",
"tables",
"urllib.request",
"xlrd",
"xlsxwriter",
}
# GH#28227 for some ... |
808fc66e4e3ebb82298bfefe147558bbd8cf9d805ccb31f6c287ec27356910a1 | Python | 593 | 22 | import torch
import numpy as np
import scipy.fftpack
def random_orthogonal(dim3, dtype=None, device=None):
q, _ = torch.linalg.qr(torch.randn(dim3, dim3), mode='complete')
q = q.to(dtype=dtype, device=device).squeeze()
return q
def dct_matrix(dim, dtype=None, device=None):
"""
Form orthogonal dc... |
a8a96d2fb96128ce8497310179cb22d6ae0c77c0ec245892c0890a9c613a5010 | Python | 593 | 25 | import numpy as np
from abc import ABC, abstractmethod
class Transformations(ABC):
"""
Parent class for transformations, abstract methods are
defined here.
"""
__slots__ = "gadgetron_array"
def __init__(self, gadgetron_array: np.ndarray) -> None:
"""
Constructor... |
8a0c605c7800446a698d0bbdd184cec4ed445cf1f9f348265f9f401dacdf9cbd | Python | 594 | 29 | import sys
import glob
import pandas as pd
import numpy as np
import h5py
file_list = sys.argv[2:]
print(file_list)
total_data = pd.DataFrame()
for ff in file_list:
data = pd.read_csv(ff, sep=',', index_col=0, header=0)
total_data = total_data.append(data)
print(total_data.shape)
labels = total_data['ass... |
95bf7006bac736009da529703aa791809964238bc71e2fbeb7a294d9c84f2d21 | Python | 594 | 16 | import sys, re, argparse, pandas as pd
argparser = argparse.ArgumentParser('''
Reads x-fabp parser output and appends a summed FJProb field.
''')
args, unknown = argparser.parse_known_args()
def main():
data = pd.read_csv(sys.stdin, sep=' ', skipinitialspace=True)
data['fjprob'] = data['F-L+']
data['noFpr... |
3205b8162942e05e59dd031c867760bce936a3ebdbe56b4f88058b17d0239f69 | Python | 597 | 30 | __all__ = ["MassSpectrumReaderBase"]
import abc
from typing import Optional
from .spec import MassSpectrum
class MassSpectrumReaderBase(abc.ABC):
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def __next__(self) -> MassSpectrum:
spe... |
06284ec550bfcf9c5c84d2fd44c67b95b9105454f0e44feda8cbfa8cc6ffc3cd | Python | 598 | 21 | from datetime import datetime
import pandas as pd
class TestSetName:
def test_set_name(self):
ser = pd.Series([1, 2, 3])
ser2 = ser._set_name("foo")
assert ser2.name == "foo"
assert ser.name is None
assert ser is not ser2
def test_set_name_attribute(self):
ser... |
da7e8c3b9342b075fc4b1082c3f844e2d63a24b5d9e3840e876f1d8ead651163 | Python | 599 | 18 | import h5py
import numpy as np
import pandas as pd
import rdkit
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric
from rdkit import Chem
from torch_geometric.nn.aggr import GraphMultisetTransformer
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.typing imp... |
3a4ae958845a40ce73a3b4a127096b70b7a80cf97b99708356aae54cd32e9be7 | Python | 600 | 20 | import os
from cptools2 import job
def test_job_add_plate_autodetect_layout_old():
"""Job.add_plate should auto-detect old IX layout regardless of Job.is_new_ix"""
current_path = os.path.dirname(__file__)
exp_dir = os.path.join(current_path, "example_dir")
plate_name = "test-plate-1"
# Intention... |
5c9597a920bb96327357d3f5e408ec3cfe85f0a5c07ab260d15b7ef8bc08bc84 | Python | 600 | 17 | import numpy as np
from sequence_models.data import SortishSampler, ApproxBatchSampler
n_samples = np.random.randint(100, 500)
lengths = np.random.randint(100, 200, n_samples)
bucket_size = np.random.randint(10, 20, dtype=int)
sampler = SortishSampler(lengths, bucket_size)
assert len(sampler) == n_samples
assert len(... |
3d3c0268cce3db0316b174cb8d6bf08be3d55aec4b1d40b0a2e79a71433e2cc9 | Python | 601 | 21 | import sys
import collections
MINCOUNT = 100
Items = [ ]
XFeatCount = collections.defaultdict(int)
YValCount = collections.defaultdict(int)
for line in sys.stdin:
XFeats = line.partition(' : ')[0].split(',')
for xf in XFeats:
XFeatCount[xf] += 1
y = line.partition(' : ')[2][:-1]
YValCount[y] += 1
Item... |
e0e03097c4295326227e45a8e98bd82593b453aa96a0653fcfb30664b69204b7 | Python | 601 | 16 | # Due to some werid bug, this would always end up errors so didn't actually use it for bert tuning
from subprocess import check_output
# Hyper-parameters
table = "bert_tuning"
target = "dspln_PSYCHIATRY_60"
weight_decays = [0, 0.0001]
lrs = [0.0001, 0.00005, 0.00001]
for lr in lrs:
for weight_decay in weight_dec... |
ea49f82f3da2345d95b065b657f0f3e0497dc7b00ddc0bb29549c7229364bd6b | Python | 602 | 32 | import tube2
import cine
import random
import glob
from skimage import transform, util
from skimage import io as skio
videonames = glob.glob("data2/*.cine")
fcount = 0
for name in videonames:
vid = cine.Cine(name)
count = vid.image_count
frame_indexes = random.sample(range(0, count), 20)
for idx ... |
627c8fa362e29911c5a0f914461d180d6bcdbf72a4551bee23b23c3b585b10bf | Python | 603 | 39 | import sys
#python removeAt.py inputfile
def init(l):
outstr = ""
pctr = 0
pstr = ""
tag = False
skip = False
for cix in range(0,len(l)):
if l[cix] == "(":
pctr += 1
tag = True
elif l[cix] == ")":
if pstr[-1] == "-":
pstr = pstr[:-1]
else:
outstr += ")"
pstr = pstr[:-1]
elif ta... |
a305093944da94ec1fca2cd3238ede1fd38ac37d3a3c95bdf09176736f40d6ea | Python | 603 | 10 | from nnunet.training.loss_functions.ND_Crossentropy import CrossentropyND
from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
class nnUNetTrainerCE(nnUNetTrainer):
def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
un... |
256881050227738809fa4af1c117208aac74baca44dd2e415c2f3ed18ca23e61 | Python | 604 | 11 | # # # WARNING # # #
# This list must also be updated in doc/_templates/autosummary/class.rst if it
# is changed here!
from .smc_utils import (compute_neighbours_matrix, compute_correlation_distance_matix,
compute_neighbours_probability_matrix,
initialize_radius, estimate... |
7a557dbd9c5e45785dda05f6fad7f5d42bc5f06895a767c1e6d3f5fca193c55a | Python | 605 | 21 | # -*- encoding: utf-8 -*-
# JN 2015-01-06
# refactoring
from __future__ import print_function, division, absolute_import
from PyQt5.QtGui import QPixmap
class Cluster(object):
"""
represents a cluster
could as well be a dictionary
can objects vanish from memory, so that the
'value' problem of dict... |
a7b80b516e462cae6b267c6b0b80d5714a7fe0630f6d6015956e194d6a46b29a | Python | 606 | 14 | import numpy as np
from sklearn.mixture import GaussianMixture
def rejection_step(q_X_N_rej, q_Y_gm, mm):
q_Y_N_rej = mm(q_X_N_rej)
q_Y_rej_gm_pars = {"n_components": 40, "reg_covar": 1e-3, "tol": 1e-3, "max_iter": 100}
q_Y_rej_gm = GaussianMixture(**q_Y_rej_gm_pars).fit(q_Y_N_rej)
q_Y_log_prob = q_Y_g... |
b2e68ba23c3aedd50248f23522ba526c768b36acf2fe98fdeaffbf8e2ac5eb82 | Python | 606 | 17 | import pandas as pd
import json
import os
import pdb
template_json_file = None
for subdir, dirs, files in os.walk('../../temp/unzip_files/unzipped'):
for file in files:
if file.endswith(".json"):
with open(subdir+'/'+file) as f:
image_metadata_json = json.load(f)
ima... |
b607afb72e198a2a05ff16873d14d9df9a9b49de1860acf7aec3549ce81b29b5 | Python | 607 | 24 | from abc import ABCMeta, abstractmethod
from EventController import EventController
class BasicAppProcess:
def __init__(self) -> None:
pass
@abstractmethod
def initial(self, controller, viewcontainer, messenager):
self.controller = controller
self.messenager = messena... |
a6c1e5d3d4cc24ffbf14fae203bf806781e91c0db418b1a9616c0768eaa843af | Python | 609 | 14 | """
Tests for python_module
"""
from scanometrics.utils.stats import fdr
import numpy as np
def test_fdr():
"""Tests scanometrics.utils.stats.fdr() function, compared to octave output"""
p = np.array([0.01, np.nan, 0.003, 0.2, 0.3, 0.004, 0.2, 0.1, 0.02, np.nan, 0.04, 0.1, 0.003, 0.0004, 0.07])
pID, pN =... |
eba0e5b13fa9601b2ee505e936bac231c154072674229878a5a96b58f4d5be9c | Python | 610 | 20 | import pandas
import numpy
simulataniousLigands = numpy.array([2, 3, 5])
trainingSamples = numpy.array([10, 50, 100, 400, 800])
allConditions = numpy.zeros((len(simulataniousLigands)*len(trainingSamples), 3), dtype=int)
k=0
for i in range(len(simulataniousLigands)):
for j in range(len(trainingSamples)):
... |
789c4d6a3949fa06f043360b20bf39f15e717ea784194343b8cb7adea8831883 | Python | 611 | 28 | from .gcn import GCN
from .mpnn import MPNN
from .schnet import SchNet
from .cgcnn import CGCNN
from .megnet import MEGNet
from .gatgnn import GATGNN
from .deep_gatgnn import DEEP_GATGNN
from .super_cgcnn import SUPER_CGCNN
from .super_megnet import SUPER_MEGNet
from .super_schnet import SUPER_SchNet
from .su... |
7e33c3738a05e90f9852424070973ffcff9f4dd6ec5cda484ddb7fb58f8b75c2 | Python | 611 | 18 | #! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this wo... |
d7007b032af635abcc4a721078daf7ad83a229a2eced0a393be9d2ad0f75111a | Python | 611 | 25 | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import sys
import csv
id = sys.argv[1]
data = open('results_%s.loop'%(id),'r')
heads = data.readline().strip().split('\t')
sum_sig = 0
sum_es = 0
with open('results_%s_loop.csv'%(id), 'w') as f:
writer = csv.writer(f)
writer.writero... |
d0df643b811422f9055a32cdbc2a813c33a6cda4cfce5bcb541940865871c059 | Python | 612 | 31 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.16.0
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# %%
from pathlib import Path
import sys... |
f37754249f1df58d896b4bc3f45c56d4e7c3e448442aab65d8e5b1530557cbd2 | Python | 612 | 19 | #! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this wo... |
0fb8ccdc3df9ff422300c31fd7696eb074a9311472b3a9d7da860bf7015d0134 | Python | 613 | 19 | """ Main entry point of the ONMT library """
# import onmt.inputters
import onmt.encoders
import onmt.decoders
import onmt.models
import onmt.utils
import onmt.modules
from onmt.trainer import Trainer
import sys
import onmt.utils.optimizers
onmt.utils.optimizers.Optim = onmt.utils.optimizers.Optimizer
sys.modules["onmt... |
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