sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e1234138b9823cac201972a8e38c9164b60abb1b82b827b9fd58e74e3d5f097a | Python | 689 | 17 | from datetime import datetime
import sys
import enum
sys.path.append("..")
from app import db
from sqlalchemy.dialects.postgresql import JSON
class PreprocessTask(db.Model):
__tablename__ = 'preprocess_task'
id = db.Column(db.Integer, primary_key=True)
description = db.Column(db.String(255))
preproces... |
0cd6bd4d6bdf9771f88bed0cf44240d5d8a443383cd6db2c97526df299d04b93 | Python | 691 | 20 | import os
def configure_path_to_parameters(
DOCKER: bool, DOCKERPATH: str, MRLPATH: str, model_name: str
) -> str:
"""
Provides the right path depending on whether Gadgetron is being run
for local/offline development (i.e., DOCKER=False) or online/real-time
deployment (i.e., DOCKER=True).... |
9c482577f4413e4426eb33e1d9f6d365d946557e541e472781d82d26c87b802d | Python | 691 | 25 | import sys, os, collections
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
def makedepterm ( t, DepDict, w=0 ):
for st in t.ch:
w = makedepterm ( st, DepDict, w )
if len(t.ch) == 1 and len(t.ch[0].ch) == 0:
w += 1
t.ch[0].c = '_'.join(DepDict... |
ef3368fa89603527c4f230d8104b6c9489a3f3c1b09aa92b3b50ad44e44bb4e5 | Python | 692 | 28 | import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import Callback
import wandb
def cosine_schedule(base_lr, total_steps, warmup_steps):
def step_fn(epoch):
lr = base_lr
epoch += 1
progress = (epoch - warmup_steps) / float(total_steps - warmup_steps)
progr... |
31faa5f73165f42a988239a7dc9eadbd6a4fe6b983d960b561840755751ffd41 | Python | 693 | 27 | #python scripts/filterLexicon.py wordcounts
# flags all words that are not in the wordcounts file
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
from model import Model
lexicon = Model('L')
with open(sys.argv[1],'r') as lexFile:
for line in lexFile.re... |
65ddcdf3d2a4633386decf03c970cdd64fd379149f948bed7b2d29132f0d3efe | Python | 694 | 32 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.16.0
# kernelspec:
# display_name: ol-connectome
# language: python
# name: ol-connectome
# ---
# %%
from pathlib import Path
import sys
... |
c10bcf8ad471a66bb935e08ed47bc913242378d1f7362d6f382de0e42519bdec | Python | 694 | 25 | import gymnasium as gym
import numpy as np
class ExampleEnv(gym.Env):
'''
Just example empty env which demonstrates additional features compared to the default openai gym
'''
def __init__(self, **kwargs):
gym.Env.__init__(self)
self.use_central_value = True
self.value_size = 2... |
f5841bf0bd7a84a5da318e5ca69d895cf46c72bca758d8140a9b4491b6526dff | Python | 695 | 31 | import os, sys, re
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import model
R = model.Model('R')
for line in sys.stdin:
R.read(line)
proj = {}
proj_norms = {}
for P in R:
if P[0] in proj_norms:
proj_norms[P[0]] += R[P]
else:
proj_norms[... |
0a8b867856c40352fc4da2a4d41b664653df5ee2fe0262423353c6d137acf0c1 | Python | 696 | 23 | """Fill tensor in-place along a specified dimension with increasing integers."""
import torch
from mrpro.utils.reshape import normalize_index
def fill_range_(tensor: torch.Tensor, dim: int) -> None:
"""
Fill tensor in-place along a specified dimension with increasing integers.
Parameters
----------... |
2ea3773ca2f129c1ab4993d087b7d2fb1697f5205c1431938008ae7dd4036345 | Python | 697 | 28 | # -*- coding: utf-8 -*-
try:
import scipy
except ImportError:
scipy = None
import pytest
from emcee import moves
from .test_proposal import _test_normal, _test_uniform
__all__ = ["test_normal_kde", "test_uniform_kde", "test_nsplits_kde"]
@pytest.mark.skipif(scipy is None, reason="scipy is not available")
... |
44aad40ef7bf46485c97ae1b3edced49c31983f00dd3dbc496fde3e68e4540bc | Python | 697 | 27 | from setuptools import setup
dependencies = [
'seaborn',
'statsmodels',
'scipy',
'patsy',
'matplotlib',
'pandas',
'numpy'
]
VERSION = "0.3.4.2"
setup(
name='pymatch',
packages=['pymatch'],
version=VERSION,
description='Matching techniques for Observational Studies',
... |
79eae430628aed3cc745ec436b15f792cb3047553c7815143d61caecd0e06409 | Python | 697 | 22 | __all__ = ["SpectralAngleLoss"]
import torch
from torch.nn.modules.loss import _Loss
from ...util.math import spectral_angle
from .data import PeptideMS2DataBatch
class SpectralAngleLoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction: str = "mean"):
super().__init__(size_average=si... |
8409f6a5ce57e974972eaa691af855d2bb0fc2372587b8ae2ad90e1590a70ecd | Python | 697 | 26 | """Mixin for smarter tensor attributes."""
from typing import Any
import torch
class TensorAttributeMixin(torch.nn.Module):
"""Create tensor attributes as buffer."""
def __setattr__(self, name: str, value: Any) -> None: # noqa: ANN401
"""Set attribute.
Set tensors not requiring gradients ... |
8d875f364eadabee8d911894062062870690c406a4097b388b1320f0ea75e10d | Python | 698 | 22 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.tseries import offsets
class TestPickle:
@pytest.mark.parametrize("freq", ["D", "M", "Y"])
def test_pickle_round_trip(self, freq, temp_file):
idx = pd.PeriodIndex(["2016-05-16", "NaT", pd.NaT, np.nan], freq... |
268fdb544a7712025409d6c88e56e5d20ed1576af178a54658fb7ddd647b1b22 | Python | 699 | 30 | import numpy as np
import scipy.io as io
import sys
tagmap = io.loadmat('esns/ESN.mat') #location of trained ESNs
sents = []
sent = []
for line in sys.stdin.readlines():
sline = line.strip()
if sline == '':
#end of sentence
sents.append(np.array(sent)) #print ' '.join(sent)
sent = []
else:
#new ... |
e48a64cd0063e4117704f1295fbc424bd1b4247c77821fe8746b2657ad1b56f8 | Python | 699 | 18 | #!/usr/bin/env python3
# coding: utf-8
from .version import __version__ as version
__author__ = (
"Cyril Matthey-Doret, Lyam Baudry, Axel Breuer, Remi Montagne, ",
"Nadège Guiglielmoni, Vittore Scolari, Etienne Jean, Arnaud Campeas, ",
"Philippe-Henri Chanut, Edgar Oriol, Adrien Meot, Laurent Politis, ",
... |
ad98e6e475221b1c7cdcf4751e7102a05b375c5a6238df4ed7fe686bcc83c132 | Python | 700 | 28 | from pathlib import Path
from os import environ
from pprint import pprint
from generate_predictions import generate_inference
if __name__ == "__main__":
model_dir = Path("../models")
data = Path("../data/norbelladine_4OMTase/boxes/4OMTase_dataset.pkl")
out_file = None
model_glob = "*"
use_cpu = ... |
02aa8dd06d7c94db4e8c277bc54141e99149575ba8a79d0990bc4cdcb8d7869c | Python | 705 | 21 | import os
import cv2
image = cv2.imread("figure/1.jpg",cv2.IMREAD_GRAYSCALE) # imput origin image data
res_image = None
res_image = cv2.resize(image, dsize=None, fx=1, fy=1, interpolation=cv2.INTER_LINEAR) # resize image
blur_image = cv2.GaussianBlur(res_image, (13, 13), cv2.BORDER_DEFAULT) # blur image
bl... |
24c749bb1dcb9a7b3b38383c77424e449698628eeaa02a02f8400fbe2cdd1e81 | Python | 706 | 18 | def compute_fluorescence_density_fl1(rtdc_ds):
"""Compute fluorescence area divided by cell volume [a.u./µm³]"""
fl1_density = rtdc_ds["fl1_area"] / rtdc_ds["volume"]
return {"fl1_density": fl1_density}
info = {
"method": compute_fluorescence_density_fl1,
"description": "fluorescence density 1",
... |
62bd730c5db13717142ca6e006fe10bd405e11a2d1c777f510d9b64be0a6a7f4 | Python | 706 | 33 | import pytest
# The various methods we support
downsample_methods = [
"min",
"max",
"first",
"last",
"sum",
"mean",
"sem",
"median",
"prod",
"var",
"std",
"ohlc",
"quantile",
]
upsample_methods = ["count", "size"]
series_methods = ["nunique"]
resample_methods = downs... |
7fba51085d42aff3c1be43c63fa17369f962bd8ca03a7a134dfa65f41496c886 | Python | 706 | 20 |
# For reference, see <https://github.com/quinlan-lab/hts-python/blob/master/hts/htsffi.py>, even though it uses deprecated .verify()
import cffi
import os.path
cxx_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'x.cpp')
with open(cxx_path) as f:
src = f.read()
ffibuilder = cffi.FFI()
ffibuilder... |
bf2f06c7e71af7c58c5427beeedcb561e0748e9dbccb964a7a8614e1deb610fc | Python | 706 | 32 | """Define constant values used across the project."""
class DefaultTokens(object):
PAD = '<blank>'
BOS = '<s>'
EOS = '</s>'
UNK = '<unk>'
MASK = '<mask>'
VOCAB_PAD = 'averyunlikelytoken'
SENT_FULL_STOPS = [".", "?", "!"]
PHRASE_TABLE_SEPARATOR = '|||'
ALIGNMENT_SEPARATOR = ' ||| '
... |
a02efa8719757ca3fbccd3aa39a4a0bae32b9236632d697f6b0caf94cffc110f | Python | 708 | 18 | import pandas as pd
import pandas._testing as tm
def test_astype_str_from_bytes():
# https://github.com/pandas-dev/pandas/issues/38607
# GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively
# did a .decode() on the bytes object. In 2.0 we go through
# ensure_string_array which ... |
0f29415d6e0f3508e2443461c1da2e10b542af135d84c3392f62515aa0a04073 | Python | 709 | 35 | # JN 2016-02-12
"""
create channel_names.csv
"""
from __future__ import division, print_function
import os
import sys
import csv
from combinato import NcsFile
FNAME_OUT = "channel_names.csv"
def main(fnames):
"""
for each file in fnames, get entity name
write everything to a csv file
"""
fnames.... |
27711d61399dfe7f8ff0259a7995149cf6d2952e3fcf78fc75d478b3592880ce | Python | 709 | 28 | #!/usr/bin/env python
"""This script is used to get the midpoint of region"""
import re
import sys
from FEICP import open_file
try:
bed_file = sys.argv[1]
except:
sys.exit(__doc__)
mid_size = 200
with open_file(bed_file) as fh:
for line in fh:
if re.search('^#', line):
... |
391969cda6709b1d85b747954fb4c958d4815889792f74e45d04e2ac169eaf99 | Python | 709 | 27 | import re
class Dependency:
def __init__(self, label='',head="", child=""):
self.label = label
self.head = head
self.child = child
def __str__(self):
return self.label+"("+self.head+", "+self.child+")"
def read(self, s):
m = re.search("^([^\(]*)\(([^\,]*), ... |
7a07a2b2003032fbedb548a98ecfe91cb757c607868bfe048d55ecc29e6cedf5 | Python | 711 | 34 | import os
import nibabel as nib
import numpy as np
import random
import re
from skimage.transform import resize
from pathlib import Path
from const import *
def process_dataset(prob=0.9):
_dir = ADNI_DATASET_DIR
brain_masks = Path(os.path.join(_dir, "masks", "brain_masks"))
originals = list(Path(os.path... |
842ae856358e53bc207aa9bd37760b26a3672f0dfacd358faa6cb67e251ad6bf | Python | 711 | 22 | import torch
import pandas
import numpy
ligandInput = pandas.read_csv('data/macrophage-Ligands.tsv', sep='\t', low_memory=False, index_col=0)
inName = ligandInput.columns.values
ligandInput = ligandInput.loc[:,inName]
sampleName = ligandInput.index.values
X = torch.tensor(ligandInput.values.copy(), dtype=torch.double)... |
f4ad8543eeeebf21522af5933b57f601e9a68b6381b8d15b4b404fb9eb7b7866 | Python | 711 | 20 | """Task-specific PyCaret 4.0 experiments.
Each task subclass pre-configures its `TaskType` and `__sklearn_tags__`. The
legacy import paths (`pycaret.classification.ClassificationExperiment` etc.)
re-export from here so backward-compatible docs still work.
"""
from pycaret.tasks.anomaly import AnomalyExperiment
from p... |
28bcc2693a200072e31baf9a6a115f4810e726a6b85d269732aa6d6af8088975 | Python | 712 | 19 | import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
def plot_lda_confusion_matrix(lda_predictions, group_vals, groups, ax=None):
"""Plot a confusion matrix for LDA classifications."""
if ax is None:
fig, ax = plt.subplots(1, 1)
confusion = confu... |
7dbc0d7486b99d69b38f6014e667cd86548a617c930018741fad3cc934fc3bce | Python | 712 | 21 | 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_openpain_condition(diagnosis):
all_diagnoses = ['healthy', 'subacute',... |
8cf07694ddd943602934d41e348f224b2d1801af41e8e58cf7aac4995d03d7fc | Python | 713 | 26 | import sys
base_id = 0
last_id = 0
header = True
for line in sys.stdin:
if line.strip() != '':
row = line.strip().split()
if header==True:
sentid_col = row.index('sentid')
header = False
else:
try:
cur_id = int(row[sentid_col])
... |
91928b7d0bc129bd3de228c12383e64a96270e7b15a07c616b970bf815be2d75 | Python | 714 | 17 | import sys
import pandas as pd
import csv
spr = pd.read_csv(sys.stdin)
spr = spr.rename(lambda x: 'fdur' if x == 'time' else x, axis=1)
spr = spr[['subject', 'word', 'code', 'fdur']]
itemmeasures = pd.read_csv(sys.argv[1], sep=' ')
del itemmeasures['word']
spr = spr.merge(itemmeasures, on='code')
spr = spr.sort_values... |
1b45ad5a9da02f1df08efed5d20f169633dde12216cfff976e92023dcea9f216 | Python | 716 | 34 | import sys
linetoks_path = sys.argv[1]
with open(linetoks_path, 'r') as linetoks:
h = sys.stdin.readline().strip()
print(h)
h = h.split()
wix = h.index('word')
l = sys.stdin.readline().strip()
s = linetoks.readline().strip()
i = 0
out_cur = ''
while s and l:
w = l.split(... |
72a1e5770e1859bdfb9489c2791c1685dbb5c5c68dd8b7f92659664548112e13 | Python | 716 | 23 | import pytest
import pandas as pd
import pandas._testing as tm
class TestAstype:
@pytest.mark.parametrize("ordered", [True, False])
def test_astype_categorical_retains_ordered(self, ordered):
index = pd.IntervalIndex.from_breaks(range(5))
arr = index._data
dtype = pd.CategoricalDtype... |
8267cc23864869710ece2af5dca770f11ad9e1ea50a97bac825b5ba14012d2ac | Python | 716 | 26 | import requests
def get_downloads_from_pepy(package_name):
response = requests.get(f"https://api.pepy.tech/api/v2/projects/{package_name}")
response.raise_for_status()
return response.json()["total_downloads"]
def create_combined_badge_url(count):
base_url = "https://img.shields.io/badge/total%20dow... |
022e9d78388697050747d995be59086d76335990031ee3cb95a0630cad11db05 | Python | 717 | 27 | import pytest
from pandas._libs.tslibs.parsing import get_rule_month
from pandas.tseries import offsets
@pytest.mark.parametrize(
"obj,expected",
[
("W", "DEC"),
(offsets.Week().freqstr, "DEC"),
("D", "DEC"),
(offsets.Day().freqstr, "DEC"),
("Q", "DEC"),
(offs... |
f1b7241fe70c236769edc3448befb595b75d437540d9b7adf329b25275c41da1 | Python | 717 | 17 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by Roberto Preste
import os
DATADIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data")
HUMAN = os.path.join(DATADIR, "human.vcf")
HUMAN_ANN = os.path.join(DATADIR, "human_ann.vcf")
HUMAN_ANN_CSV = os.path.join(DATADIR, "human_ann.csv")
HUMAN_ANN... |
57b1db8439e855699fe88fe1266eaf86971dc8d89e9cd681dee84eaedc82659d | Python | 721 | 23 | import numpy as np, matplotlib.pyplot as plt
plt.rc("xtick",labelsize=20)
plt.rc("ytick",labelsize=20)
plt.rc("axes",labelsize=20)
a = np.loadtxt("fpbase_spectra.csv").T
l = a[0]
ex = a[1]
em = a[2]
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(l,ex,c="C0",lw=4,label="absorption")
ax.plot(l,em,c="C2",lw=4,la... |
1b33434422650185a7b98b388c4967b1ead88ccb2a7135d73762f6e11a47c609 | Python | 723 | 31 | #!/usr/bin/env python
import json
from pathlib import Path
from neuronbridge.model import *
ROOT_DIR = "schemas"
def print_schemas():
for model in [DataConfig, ImageLookup, Matches]:
print(model.schema_json(indent=2))
print()
def write_schema(schema_name, schema_obj):
filepath = Path(ROOT_D... |
cb6fdcdbbc355863d77b738c042b6bdcd8fe414011f67fc5023bf4cb082f5873 | Python | 723 | 25 | import pytest
from emcee.pbar import _NoOpPBar, get_progress_bar
try:
import tqdm
except ImportError:
tqdm = None
def test_display_false():
assert isinstance(get_progress_bar(False, 100), _NoOpPBar)
@pytest.mark.skipif(tqdm is None, reason="tqdm not available")
def test_tqdm_modes():
assert isinst... |
ee88a105496b6d88c97a0e4024331af8f83ff5b01fcabb46b6f9fd6a93d5bb86 | Python | 723 | 28 | import numpy as np
from skimage import util, restoration
def subtract_background(image: np.ndarray):
inverted = util.invert(image)
inv_bg = restoration.rolling_ball(inverted, radius=45)
# imshow(inv_bg)
# pyplot.show()
inv_dif = inverted - inv_bg
dif = util.invert(inv_dif)
return dif
if ... |
cb1ab1ce16b969985f0cdee70cc79c2eee955681e9f5247443850e272ec24c6d | Python | 724 | 21 | import os
# import cloudpickle
import compatibility
import pandas as pd
from . import parent_classes
def check(obj):
'''checks wherther obj can be saved with this dumper'''
return isinstance(obj, (pd.DataFrame, pd.Series)) #basically everything can be saved with pickle
class Loader(parent_classes.Loader):
... |
4fc986a611897e03953d2d24f016c63d59ccc00d896386a8ec4a5f4e75907df9 | Python | 725 | 22 | #! /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... |
95c6df1179bef79ce2e305ce2df6e763545af497d704daf3f392ff6395b8019d | Python | 725 | 26 | import reverb
import tensorflow as tf
def make_reverb_dataset(server_address: str, table: str, batch_size: int):
def _make_dataset(unused_idx):
return reverb.TrajectoryDataset.from_table_signature(
server_address=server_address,
table=table,
max_in_flight_samples_per_wo... |
b1ed6d391eb9088f58afe77b2756469023a67afd91736014dc08cebf81c05c5e | Python | 725 | 22 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestCategoricalComparisons:
def test_categorical_nan_equality(self):
cat = pd.Series(pd.Categorical(["a", "b", "c", np.nan]))
expected = pd.Series([True, True, True, False])
result = cat == cat
tm.assert_ser... |
f2649a12daa0ff52855026ab288ada248ca659e80e676f8aec5999240787d335 | Python | 725 | 11 | from aigarmic.plate import Plate, PlateSet, plate_set_from_dir
from aigarmic.model import Model, BinaryModel, BinaryNestedModel, SoftmaxModel
from aigarmic.file_handlers import (create_dataset_from_directory,
predict_colony_images_from_directory,
s... |
4facd50c0b9fb0cc00e4d3a12a0d5b1ad111a92fd529122bedb51eaefab48ce5 | Python | 727 | 28 | import sys
header = True
line = sys.stdin.readline()
sentid_base = 0
sentid = -1
docid_last = None
while line:
if line != '':
if header == True:
h = line.strip().split()
docid_col = h.index('docid')
sentid_col = h.index('sentid')
print(' '.join(h + ['sampl... |
9870bb24894e64866dec79ac69be5dcd4fa0873042b960f72e28c355be590a71 | Python | 727 | 27 | import os
import pysam
input_bam = "/data/DATA/CellLine/HCC1395/HiFi_2023Pacbio/HCC1395.GRCh38.bam"
# input_bam = "/data/DATA/CellLine/HCC1395/HiFi_2023Pacbio/HCC1395-BL.GRCh38.bam"
output_bam = "/data/DATA/CellLine/HCC1395/HiFi_2023Pacbio/HCC1395.GRCh38.nanomonsv.bam"
# output_bam = "/data/DATA/CellLine/HCC1395/H... |
820bf36da8a3c59b2be8ae5d4666e58646f6b12b94389bb031f7f38f18f05d85 | Python | 728 | 27 | import sys, json
itemmeasures = open(sys.argv[1])
# word time
time_info = open(sys.argv[2])
words = list()
start_times = list()
# header
time_info.readline()
for l in time_info:
word, time = l.strip().split()
words.append(word)
start_times.append(time)
header_cols = itemmeasures.readline().strip().split(... |
ead4d94fde065ee25b934e085e7f453e539666219ee6046b00718aef0e669658 | Python | 728 | 31 | """cptools2 package initializer.
Expose top-level modules and package metadata.
"""
from . import filelist # noqa: F401
from . import splitter # noqa: F401
from . import commands # noqa: F401
from . import parse_yaml # noqa: F401
from . import utils # noqa: F401
from . import job # noqa: F401
from . import colo... |
3bb0c59f4ff61ca19e3a3fdac754c0ab521b5cef479536eda477d8cf3ebc57ef | Python | 729 | 28 | # -*- coding: utf-8 -*-
"""
Pycaret
Author: Mavs
Description: Module containing all constants of pycaret.
"""
from typing import Union
import numpy as np
import pandas as pd
from scipy import sparse
# Group of variable types for isinstance
SEQUENCE = (list, tuple, np.ndarray, pd.Series)
# Variable types for type ... |
5c560c6b2543f19efbac76e79625205d52f6f90919b311bb0f7596c5c2c3786f | Python | 729 | 19 | import os
import models.args
def get_args():
parser = models.args.get_args()
parser.add_argument('--max-tokens', type=int, default=5000, help='Number of features to use in our BoW Model')
parser.add_argument('--use-idf', type=bool, default=True,
help='True, uses term frequency an... |
7617a945907335daf0ba0316ddc8d73bd0214e1317e3a11f22c715146aca4f3c | Python | 730 | 28 | import torch
import numpy as np
from abc import ABC, abstractmethod
class Pretransformations(ABC):
"""
Parent class for pretransformations, abstract methods are
defined here.
"""
__slots__ = "gadgetron_array", "device"
def __init__(self, gadgetron_array: np.ndarray, device: torc... |
a477df1c9bfb1d3f875290bc683cec7d18563ec46cb0ab67d84d3fd5645d3c7a | Python | 730 | 29 | # STA Movie Maker
# Lloyd Russell 2017
import sys
import os
import ctypes
import json
import numpy as np
import difflib
import glob
import colorsys
import scipy
from scipy import io as sio
from skimage.external import tifffile
import time
import sta, PrairieLink, paq2py, ThorLink
from skimage import ex... |
1e2f84333c4ebf4db28a0bfe8241597cd0e3d96d8551d2fcf841ae21da2a28b7 | Python | 734 | 23 | #! /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... |
b6cc93bebc0415508345e9bf3552047bfde1b51d7fa3530e5b8529ee593a5ba0 | Python | 734 | 25 | import subprocess
def run(model, attention, oversample):
att = "-a" if attention else ""
ov = "-o" if oversample else ""
subprocess.run(["make", "train_eval",
f"MODEL={model}", f"ATTENTION={att}", f"OVERSAMPLE={ov}"])
def run_lod(model, attention, oversample=False):
att = "-a" if ... |
62d75c90ef63dcdfffb2af5eaf3581e2e05387ed097697cc6de34616d4b3bf30 | Python | 736 | 20 | # Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# 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 applicable law or... |
0a755c8b116e99e075d545e470f1919d6af1d1d2e417c7c4c4ed6adb94827f0c | Python | 737 | 32 | """
Helpers for sharing tests between DataFrame/Series
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas import DataFrame
if TYPE_CHECKING:
from pandas._typing import DtypeObj
from pandas import Series
def get_dtype(obj: DataFrame | Series) -> DtypeObj:
if isinstance... |
5a3b451e3a27695baa6a3d171ee15210791397b851323c97c9dc2a01a0ba34e3 | Python | 738 | 33 | import matplotlib.pyplot as plt
import sys
data = sys.argv[1]
with open(data) as d:
d = d.read().split("\n")
allData = d[43:]
x = [float(allData[i].split('\t')[0]) for i in range(0,len(allData)-1)]
y = [float(allData[i].split('\t')[2]) for i in range(len(allData)-1)]
#filter by time
tota... |
b068ddc69ad1d4d206b53176dab9aa7bb735107e93cf23a8bf800dca83a95297 | Python | 738 | 20 | import pandas as pd
import sys
names = ['subject','word','sentid','sentpos','fdur','wlen', 'correct', 'resid', 'startofsentence', 'endofsentence']
data = pd.read_csv(sys.stdin,sep='\t',skipinitialspace=True)
data.rename(columns={
'subj_nr':'subject',
'sent_nr':'sentid',
'word_pos':'sentpos',
'RT':'fdu... |
b4d244b9113fdcfade54d2b875eb01bb532a4f9c98f4ab4361b429ce244cd5b5 | Python | 738 | 18 | # Copyright 2018-2023 Xanadu Quantum Technologies Inc.
# 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 applicable law or... |
e233f5bf0001a934bf5c4eed25dcba133c85345b678386398b5a09c3e75f19ed | Python | 738 | 28 | #!/usr/bin/env python3
import os
from glob import glob
from combinato import NcsFile
def to_original():
ncsfiles = glob('*.ncs')
for nf in ncsfiles:
n = NcsFile(nf)
oldname = n.header['AcqEntName'] + '.ncs'
if os.path.exists(oldname):
print('Not renaming {} to {}, file exi... |
89168431bd698aa38d1e82434385ad3a0a5fbbe439053747da2c927c1dbffac4 | Python | 739 | 32 | import numpy as np
import pandas as pd
class TimeLogicalOps:
def setup(self):
N = 10_000
left, right, lmask, rmask = np.random.randint(0, 2, size=(4, N)).astype("bool")
self.left = pd.arrays.BooleanArray(left, lmask)
self.right = pd.arrays.BooleanArray(right, rmask)
def time_... |
1f0380e06639f5f117d42c80edf3bb86ecad7fd46fa8d05012400c80e1585bb2 | Python | 740 | 25 | import sys, re
docids = sys.argv[1:] # yc: doesn't seemed to be using this?
headers = sys.stdin.readline().strip().split() + ['sampleid', 'tr', 'splitVal15']
docid_ix = headers.index('docid')
subj_ix = headers.index('subject')
time_ix = headers.index('time')
print(' '.join(headers))
for l in sys.stdin:
row = l.... |
6ca37482f818c686b9d35da3c3c9978093a4c746b6e7027a19257e26d0227336 | Python | 740 | 24 |
import json
from loess import loess_1d
from sklearn.ensemble import IsolationForest
import real_unit_convert as ruc
import aggregate_figures as af
import pandas
s_tables, l_tables = af.load_all()
united_s_table = pandas.concat(s_tables)
united_l_table = pandas.concat(l_tables)
l_groups = united_l_table.groupby('file... |
bb0613009558b91c41b1c9acb241eb1ec627daa4c00ac87f58d0d377e40f319a | Python | 741 | 29 | import pytest
import pandas as pd
@pytest.mark.parametrize(
"timedelta_val,expected",
[
("1 day", "day"),
("1 hour", "hour"),
("1 minute", "minute"),
("1 second", "second"),
("1 millisecond", "millisecond"),
("1 microsecond", "microsecond"),
],
)
def test_t... |
13d1574adf63043d354d27b0c464eebc7e0b0c2dccbf5f3a10aaea4db25d579d | Python | 743 | 20 | import warnings
import single_cell_parser as scp
errstr = "The cell_modify_function synaptic_input is experimental! Make sure synapses "
errstr += "are beeing activated as you expect and have the effect you expect!"
warnings.warn(errstr)
def synaptic_input(cell, network_param = None, synapse_activation_file ... |
f83a2d9ac88c1b1c0e8902719b50f64ad00102c1ca930285206853418fd322e2 | Python | 743 | 21 | import torch.nn as nn
from tnn.layers import tLinearLayer
class tFullyConnected(nn.Module):
def __init__(self, layer_widths, dim3, M, activation=None, bias=True, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
super(tFullyConnected, self).__init__()
self.M =... |
9fcc5e538a1db963b923eb1d845330174f7d7c1215f605bcc2469a2e5765981f | Python | 744 | 16 | # Copyright 2018-2023 Xanadu Quantum Technologies Inc.
# 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 applicable law or... |
fc177340b627c1bc0b3f52fee2e354121582ad5850589334413e13039259f561 | Python | 745 | 23 | from matplotlib.colors import to_hex
import numpy as np
def hex2rgb(value):
value = value.lstrip('#')
lv = len(value)
return [int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
def rgb_to_hex(rgb):
# Normalize RGB values to be within 0-1 range
r, g, b = [val / 255.0 for val in rgb]
#... |
07fcac48fb811aab0bbda6b84f6321976bb9b553cf43d6b8a58b8cc69be6242a | Python | 746 | 23 | #! /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... |
1d37e6a56fde242a9662db1b789f0e340ee7df398e14666fc249756bf0c3a0a2 | Python | 746 | 26 | import torch
from torch import nn
class DatasetTransform(nn.Module):
def __init__(self):
super().__init__()
def forward(self, dataset):
return dataset
class ImageDatasetTransform(DatasetTransform):
def __init__(self, **kwargs):
super().__init__()
import kornia
sel... |
d0df72e1bbcd5292b00c794a9a948b69c7885895d2668a127f5324fd8a4198b9 | Python | 746 | 23 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
class TestRepeat:
@pytest.mark.parametrize("use_numpy", [True, False])
@pytest.mark.parametrize(
"index",
[
pd.period_range("2000-01-01", periods=3, freq="D"),
pd.period_range("2001-01-01... |
ecdeee86fd7d7c43e678b4776f6ef68a8130397d355ad1424b35fc891dccf96f | Python | 746 | 26 | import ase
import os
from ase.io import read
import numpy as np
import csv
mofs = ase.io.read('opt-geometries.xyz',index=':')
refcodes = np.genfromtxt('opt-refcodes.csv',delimiter=',',dtype=str)
properties = np.genfromtxt('opt-bandgaps.csv',delimiter=',',dtype=str)
print(len(mofs), len(refcodes), properties.s... |
9e699baee422a461f640999ac941d5f261483ed398e8f2d40ef7f72aae451191 | Python | 748 | 40 | import sys
def correct_parens(s):
s = s.strip()
l = 0
r = 0
for c in s:
if c == '(':
l += 1
elif c == ')':
r += 1
paren_diff = l-r
if paren_diff < 0:
# print('Too many right parens!')
# print(l)
# print(r)
# print(s + '|')
s = s[:paren_diff]
# print(s + '|')
# pri... |
ac3cd92e2b47ed6d0bb486beedcc43d63e4a17ef571bbfe04977efffb7b49fa6 | Python | 748 | 30 | from __future__ import print_function # makes this work for python2 and 3
import vegas
import ctypes
# import library
cfcn = ctypes.CDLL('cfcn_ctypes.so')
# specify argument types and result type for cfcn.fcn
cfcn.fcn.argtypes = (ctypes.POINTER(ctypes.c_double), ctypes.c_int)
cfcn.fcn.restype = ctypes.c_double
# ... |
f39dff7ad7db931e3e1f1040e2ba369ac88dce28cf32bf7824bc7ba7489b44ad | Python | 749 | 35 | import matplotlib.pyplot as plt
def set_axis(ax, x, y, letter=None, fontsize=20):
ax.text(
x,
y,
letter,
fontsize=fontsize,
weight='bold',
transform=ax.transAxes)
return ax
def pval(pvalue):
if pvalue<0.05: nap = '*'
else: nap='n.s.'
if pvalue<0.01: ... |
28db6b1528c6258c4849367dc8ac0b0027b6af81b2c7ebd036a80148d8ac63b4 | Python | 750 | 31 | # flake8: noqa
from .aggregation import (
aggregate_counts,
calculate_mutation_rates,
merge_aggregates,
read_aggregates,
read_rates,
)
from .bam import (
check_bam_contains_duplicate,
check_bam_contains_secondary,
check_bam_contains_unmapped,
get_tags_from_bam,
parse_all_reads,
... |
40228c2914116af666325a5e46341143e03eccc3aa5a829cef0cb59465017414 | Python | 751 | 20 | """Module defining inputters.
Inputters implement the logic of transforming raw data to vectorized inputs,
e.g., from a line of text to a sequence of embeddings.
"""
from onmt.inputters.inputter import get_fields, build_vocab, filter_example
from onmt.inputters.iterator import max_tok_len, OrderedIterator
from onmt.in... |
8ec31062badc1fd5c1f04f0a75b7ca71b20dddce138700498c7ff19e5e73980e | Python | 751 | 30 | from abc import ABC, abstractmethod
class Structmaker(ABC):
"""
This base class is responsible for preparing the struct
to be sent through a socket (e.g., to the MLC tracking
software).
"""
@abstractmethod
def __init__(self, acquisition_data: dict, connection_data: dict, *args)... |
c4c92e1d08acb2939e1a9584758d59c8c2b624dd73be20afca8df33735f3abb4 | Python | 751 | 20 | from tests import TestCaseNeSVoR
import torch
import scipy
from scipy.sparse.linalg import cg as cg_scipy
from nesvor.svr.reconstruction import cg
class TestCG(TestCaseNeSVoR):
def test_cg(self):
A = torch.tensor(
scipy.linalg.hankel([1, 2, 3, 4, 5], [4, 7, 7, 8, 9]), dtype=torch.float32
... |
6cc12728ab1baed2464fe74c13b77b9036b4b7dd13cc9d459c3e474fa1593bb5 | Python | 752 | 26 | import imp
import os
#os.chdir('/gscratch/spe/mpun/protein_holography/hologram')
import Bio.PDB as pdb
from protein_holography.hologram.protein_dir_parse import get_proteins_from_dir
from protein_holography.hologram.sample_protein import sample_amino_acid_from_protein as sample
import protein_holography.hologram.pdb_... |
b01c8989c0d2f55f49e2d5fe839c2122c69c77b0b6b62c94d5884c4828d98b3a | Python | 752 | 29 | """https://stackoverflow.com/questions/39304951/"""
from functools import wraps
from PyQt6 import QtCore
class Runner(QtCore.QThread):
"""Runs a function in the background"""
def __init__(self, target, *args, **kwargs):
super().__init__(*args, **kwargs)
self._target = target
self._ar... |
f4d7b1f98fe010ce6585dffb26c9e6e51f46e6cbdf137124e3b0873b792e973e | Python | 753 | 26 | from pathlib import Path
from typing import List, Dict, Tuple, Union
import pickle as pkl
import numpy as np
def load_dataset(pkl_file: Path) -> Tuple[List[Dict[str, Union[str, int]]], np.ndarray]:
"""Snapshots provide the residue order of the boxes"""
assert isinstance(pkl_file, Path)
assert pkl_file.i... |
5da8b6cb4cfb047cae42e11b8b9174f1efd356e72d242b0ae3f32c2a582fa2a4 | Python | 754 | 23 | #! /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... |
ab1d55dcb4043a4a59f9e0f48acdd0cf2b1ded6ba6b86c94cdc1d6a63336fc63 | Python | 754 | 24 | import os
import pandas as pd
gwas_dir = "/cluster/projects/p33/users/alexeas/most_mental/new_start/gwas_merged/touchscreen"
outf = "gwas_n.touchscreen.csv"
outf_max = outf.replace("gwas_n", "n_max")
series_list = []
for de in os.scandir(gwas_dir):
if de.name.startswith("orig"):
trait = de.name.split('.')[... |
beeb9f521dd4f7f62af5e2c43ad92bb75a160224596ff78b8cb216582b9ed4ab | Python | 755 | 36 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.16.0
# kernelspec:
# display_name: ol-connectome
# language: python
# name: ol-connectome
# ---
# %%
# %load_ext autoreload
from pathlib ... |
df9d1a65f28b81a97ee71ae216df76ba7d0e2eb26f8c8903432960d4cfeb606f | Python | 755 | 27 | import pytest
@pytest.mark.filterwarnings("ignore:The inplace keyword:pandas.errors.Pandas4Warning")
@pytest.mark.parametrize(
"func",
[
"reset_index",
"_set_name",
"sort_values",
"sort_index",
"rename",
"dropna",
"drop_duplicates",
],
)
@pytest.mark... |
797798bc921ea8a235373eeec2eff2c0fdb7126e8f9d66249b578ccfa0c39f88 | Python | 756 | 23 | #! /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... |
9677c2416909638b9f33fdcd1f80608ee82c03bffeafe6753dcfbf17b1bd2038 | Python | 757 | 25 | import pytest
import pandas as pd
import pandas._testing as tm
def test_divmod_bool_raises(box_with_array):
# GH#46043 // raises, so divmod should too
ser = pd.Series([True, False])
obj = tm.box_expected(ser, box_with_array)
msg = "operator 'floordiv' not implemented for bool dtypes"
with pytest... |
bc4d61e72bac7073c7e681421595a713972735df84de6197e7f169fb440bc052 | Python | 758 | 19 | """PyTest fixtures for the csm tests."""
from mrpro.data import IData, SpatialDimension
from mrpro.phantoms.coils import birdcage_2d
def multi_coil_image(n_coils, ph_ellipse, random_kheader):
"""Create multi-coil image."""
image_dimensions = SpatialDimension(z=1, y=ph_ellipse.n_y, x=ph_ellipse.n_x)
# Cr... |
dc41c33c6f728e8821a4f9a24298f15ed857bc0c1fc8a41bc324b1b0a4dc472d | Python | 758 | 24 | # ----------------------------------------------------------------------------
# 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.
# ------------------------------------------------... |
6453579cc34cf66b474bd59a3772aed0b837951112272d7097c9fd549caa00a5 | Python | 760 | 31 | from values import *
def NCC(cell,i,memb_id,xNCC,area):
if cell.segment == 'DCT':
LzD=i-1
x1=LzD+1
xn=cell.total
xdct2=2.0/3.0
if x1/xn<xdct2:
NCCexp=1.0
else:
NCCexp=2*(1-(x1/xn-xdct2)/(1-xdct2))
else:
NCCexp = 1.0
alp=cell.conc[0,memb_id[0]]/dKnncc
alpp=cell.conc[0,memb_id[1]]/dKnncc
betp=... |
72bf335eec009f4ae4f7d46f75b7b0bc8611eba15f98e986402cbe2f0e6f1ab7 | Python | 760 | 30 | import numpy as np
import pandas as pd
from pyod.models.cblof import CBLOF
def convert_to_fp64(X):
if isinstance(X, pd.DataFrame):
X = X.astype(
{
col: np.float64
for col in X.columns
if X.dtypes[col] in (np.float32, np.float16)
}
... |
8d1d68a35c566d0d642050714b0278c2b1c99f16b246943689d83a04b8e62e3b | Python | 764 | 26 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import twixtools
import datetime
def main():
parser = argparse.ArgumentParser(description="Output the acquisition start time of the first scan in the datfile in iso format.")
parser.add_argument("datfile", type=str, help="Path to .dat file")
ar... |
1d3a7ff72c7c14e261306960b108f9344668410697835c398ce6a9c7adbc169b | Python | 765 | 29 | from pathlib import Path
from pandas import read_sas
ROOT = Path(__file__).parents[3] / "pandas" / "tests" / "io" / "sas" / "data"
class SAS:
def time_read_sas7bdat(self):
read_sas(ROOT / "test1.sas7bdat", encoding=None)
def time_read_xpt(self):
read_sas(ROOT / "paxraw_d_short.xpt", encodin... |
58c25f9877a404999f239de08d7e34962aba4bccc8c7bf85878e5a9150f70a0f | Python | 765 | 42 | """NiMARE: Neuroimaging Meta-Analysis Research Environment."""
import logging
import warnings
from ._version import get_versions
logging.basicConfig(level=logging.INFO)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("ignore")
from . import (
annotate,
base,
corr... |
72dea2e937dfa6acf2e0667860b7a38b0e7d35bec7326e97f4da58ba6e15d22a | Python | 765 | 20 | import sys, pandas as pd
data = pd.read_csv(sys.stdin, sep=' ', skipinitialspace=True)
## Remove non-numeric columns because they can't be reduced
data = data._get_numeric_data()
del data['sentpos']
dataGrouped = data.groupby('sentid')
dataSentID = dataGrouped[['sentid']].agg('max').rename(columns = lambda x: 'sent... |
f57512a25d38085c3ff3d2b45db0cd182d15ad8832052273726e6c103ac770a3 | Python | 766 | 26 | # Copyright 2024 Xanadu Quantum Technologies Inc.
# 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 applicable law or agre... |
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