sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
35904f15d53bd9fe5748625b127f05d8b93b5cc94b4f6f1d3c0e378d01d22732 | Python | 4,203 | 116 | import importlib.resources
from PyQt6 import uic, QtWidgets, QtCore, QtGui
from ... import meta_tool
from ... import pipeline
class MatrixDataset(QtWidgets.QWidget):
active_toggled = QtCore.pyqtSignal()
enabled_toggled = QtCore.pyqtSignal(bool)
option_action = QtCore.pyqtSignal(str)
modify_clicked =... |
38dfe0fd7fd407a31025b4d102bb06bf9bc5152e792f021697580cd7b7c2c213 | Python | 4,204 | 121 | """Tests for Rearrange Operator."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.PatchOp import PatchOp
from mrpro.utils import RandomGenerator
from tests import autodiff_test, dotproduct_adjointness_test
TESTCASES = pytest.mark.parametrize(
('input_shape', 'arguments', '... |
eb9586a8cf2f6502929198ff5ecf9de7ef83c1e5af0a3d0d28e2c9f6f4c9530f | Python | 4,206 | 129 | '''
Created on Mar 30, 2012
@author: regger
'''
from neuron import h
from collections import Sequence
import numpy as np
class Synapse(object):
'''
Synapse base class
Contains information about pre- and postsynaptic cell type,
branch ID of postsynaptic cell, branch pt ID,
and xyz-coordinates of sy... |
ec5c49aaa071b00a43ef065ff42ff8e369438f4c46a2466fb11b84d56cac30d1 | Python | 4,206 | 133 | import gzip
import numpy as np
import scipy
from scipy.spatial.distance import squareform, pdist
from sequence_models.constants import IUPAC_CODES
def get_dihedrals(a, b, c, d):
b0 = -1.0 * (b - a)
b1 = c - b
b2 = d - c
b1 /= np.linalg.norm(b1, axis=-1)[:, None]
v = b0 - np.sum(b0 * b1, axis=-1... |
e9aac48cdf0e08d13407db8337d7bf55b6017a2e85f62296279bb68270a4b514 | Python | 4,207 | 114 | import os
import numpy as np
import pandas as pd
def allign_alleles(df):
"""Look for reversed alleles and inverts the z-score for one of them.
Here, we take advantage of numpy's vectorized functions for performance.
"""
d = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
alleles = []
for colname in ['A1_x',... |
03416f453bceccb4686d7bde80f98e05c050575086ed028c88882f37c7bcdb07 | Python | 4,212 | 152 | """
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
from io import StringIO
import pytest
import pandas as pd
import pandas._testing as tm
def test_iterator(all_parsers):
# see gh-6607
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,... |
22e98a8fd66180c0b9c9a0d0650ef0ed710f2c4b73c0c32eeb3c9c6b79aa26a3 | Python | 4,212 | 121 | import numpy as np
import pandas as pd
from sklearn.mixture import GaussianMixture
def footprint(
peak_data: pd.DataFrame,
qbed_data: pd.DataFrame,
fixed_number: float = 50,
compress_number: float = 0,
min_insertions: int = 15,
delete_unfound: bool = False,
resultname: list = ["Chr_footpri... |
35ca99794654a9afcc610ef2e06b84d6eee761eff000074c767f06b3e8963765 | Python | 4,212 | 137 | from __future__ import annotations
import functools
from typing import TYPE_CHECKING
import numpy as np
from pandas.compat._optional import import_optional_dependency
if TYPE_CHECKING:
from collections.abc import Callable
@functools.cache
def generate_online_numba_ewma_func(
nogil: bool,
parallel: boo... |
a13d32b436f42b37a7501afa5c640a47965dae139bf8351934f448bf6df138d1 | Python | 4,212 | 96 | import os, sys, re
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
from tree import Tree
SS = [ ] ## derivation fragments (partial trees) derived at each storestate
IDENTIFY_EXTRACTIONS = True #False
## attach children to base category of derivation fragment...
def a... |
b398a975470dba1f53fdef47ef5fe2a43ce645169e6220b6d1f828aed2250e63 | Python | 4,213 | 139 | import multiprocessing as mp
import numpy as np
import pandas as pd
import scipy as sp
import time
import datetime
from scipy import stats
from .esw_star import esw_star
from ..utils.compute_pvalues import compute_pvalues
from ..summarydata import SummaryData
def _nsi(mean: pd.DataFrame, verbose: bool=False):
"""C... |
19a0dd16b43b04b4edf9e9f6c29544d8de95954532cf2998f3e0b9324730ee54 | Python | 4,214 | 107 | import hashlib
from pathlib import Path
import pandas as pd
import pytest
import torch
import pyaging as pya
pytestmark = pytest.mark.full_catalog
WEIGHTS_DIR = Path(__file__).resolve().parents[2] / "clocks" / "weights"
def _beta(feature):
digest = int(hashlib.sha256(feature.encode()).hexdigest()[:8], 16)
... |
85bf555169af3e7535ff5cfe947023c687b041ac015d6e3318af854f0b68c548 | Python | 4,214 | 94 | from pathlib import Path
from typing import Iterable, Tuple
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.widgets import Slider, Button, RadioButtons
import torch
from .emulator import load_default_emulator_model, APEmulator
import pandas as pd
from functools im... |
ae9c8318a018d97211e5d5cbaea6af29619cb5e50c94f9b7652fa92dcad2be96 | Python | 4,216 | 144 | import numpy as np
import pandas as pd
import scipy
def maximo_comun_divisor(a, b):
temporal = 0
while b != 0:
temporal = b
b = a % b
a = temporal
return a
def minimo_comun_multiplo(a, b):
return (a * b) / maximo_comun_divisor(a, b)
def f_loss_Corr(x, stim, y, a... |
4d8c18d7f2dd70a1f7176ffec768c0ddd64678f510f6d78d6adede91bd83e024 | Python | 4,217 | 112 | """Native allocation failures through PeakModel.build(), isolated from pytest.
The session build takes roughly 15 seconds; each small child run takes about
one second. Run explicitly with --run-slow. Cython and a C compiler are needed.
"""
import importlib.util
import json
from pathlib import Path
import shutil
impor... |
f0c0165544c95d6cc4cc996ff63f5ecc63aa84103acb2ddb9742fc0c5de27626 | Python | 4,217 | 129 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Federica.Magnabosco@mrc-cbu.cam.ac.uk
based on semnet/decoding by setare10
"""
import sys
import os
import mne
import time
import pickle
import numpy as np
import sn_config as C
from joblib import Parallel, delayed
from SN_semantic_ROIs import SN_semantic_RO... |
9cb1b8ace26b8195eef28d3a25345f6be279355401acdf7deceef84bfd96a9dd | Python | 4,218 | 137 | import os
import shutil
from pathlib import Path
from tempfile import gettempdir
import numpy as np
import pytest
from test.fixtures.data_fixtures import (
generate_batch_effects,
generate_covariates,
generate_response_vars,
)
"""This file contains pytest fixtures for file paths in the PCNtoolkit.
The f... |
fb44ee54c598c962ec32bbf1048453eea00b75c87c0d2d2c686813e608173ebd | Python | 4,219 | 132 | from pathlib import Path
import numpy as np
import pandas as pd
from scipy.spatial import distance
from fmriprep_denoise.dataset.atlas import fetch_atlas_path, ATLAS_METADATA
from nilearn.image import index_img
from nilearn.plotting import find_probabilistic_atlas_cut_coords
def get_atlas_pairwise_distance(atlas_na... |
0bb6cd620b5c5463297ee05dccd757dfd3bbe8f6be4c58a697d4beb8c3706d0e | Python | 4,220 | 120 | # Copyright 2026 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... |
dde761836a1225fc2791d0522918e3447acda2ae3c5065810c9cb433e8412043 | Python | 4,220 | 121 | import functools
import itertools
import logging
from multiprocessing import Pool, TimeoutError
import os
import signal
import time
import warnings
from Bio.PDB import PDBList, PDBParser
from Bio.PDB.mmtf import MMTFParser
from Bio.PDB.PDBExceptions import PDBConstructionWarning
import h5py
import numpy as np
import p... |
e7bc4842a875bf3bc2774f313c43f1e302d7ce40c8086cbcceec631b197fd221 | Python | 4,223 | 112 | #!/usr/bin/env python3
import numpy as np
import csv
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True,
"font.family": "serif",
#"font.serif": ["Palatino"],
"font.size": 16})
import seaborn as sns
import sys, getopt
import os
... |
b824e43828e28750c067e8ffb6ec568ccb6111b67cf82cca9c5941f689e65fbf | Python | 4,224 | 89 | import torch
import torch.nn as nn
class DPMSolverDiffuser:
"""Specialized DPM implementation based on the paper titled
'DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps'.
Reference: https://arxiv.org/pdf/2206.00927.pdf
"""
def __init__(self, schedule='l... |
51322e4ec03e9050a16118261ee859dac1ddcc21b921f55720703b965767db6b | Python | 4,225 | 162 |
from tensorflow.keras import activations
from tensorflow.keras.initializers import HeNormal
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (
Input,
Dense,
Flatten,
BatchNormalization,
Conv3D,
Activation,
Add,
MaxPool3D,
)
from tensorflow.keras.regularizers... |
b50af0c8d0b37996f28c776e7f574d8f01b942cf2183131a2bff93fd082fa462 | Python | 4,225 | 91 | #import numpy as np
#import logging
from base.surfaces import Surface
from base import gaussianDiffeons as gd
from base import conjugateGradient as cg, diffeo, kernelFunctions as kfun, pointEvolution as evol, bfgs, loggingUtils
from base.gaussianDiffeonsSurfaceMatching import *
from base import examples
def compute(cr... |
c70cb50a8d8488362197818590ae17595e4cfdf24110fa5b83629d7bfc503d86 | Python | 4,225 | 86 | import os
import pandas as pd
import sys
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 config.globals import ENVIRONMENT
from app import create_app, db
from app.models import SourceDataset, Subject,... |
32a5d13a353f64289ab0e650385102222b6d10232ee00911ecb73a666b78485d | Python | 4,227 | 98 | import os
import logging
import argparse
import numpy as np
from scipy.io import loadmat, savemat
from pathlib import Path
from collections import defaultdict
from local.common import EnergyBasedVad
from local.common import SelectElectrodesFromBothGrids
from local.common import SelectElectrodesOverSpeechAreas
logger =... |
860a10ab2b0c3111afcd927ab62a550a6b40e881b4b7b177af586e82aa43a53c | Python | 4,227 | 109 | import pytest
import pandas as pd
@pytest.mark.parametrize(
"td, expected_repr",
[
(pd.Timedelta(10, unit="D"), "Timedelta('10 days 00:00:00')"),
(pd.Timedelta(10, unit="s"), "Timedelta('0 days 00:00:10')"),
(pd.Timedelta(10, unit="ms"), "Timedelta('0 days 00:00:00.010000')"),
... |
1888ab72c3958f13eb0c034b681c2f4a61e6bafa749d238620cb50b8cb7543f2 | Python | 4,229 | 133 | # Copyright 2018-2024 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 ... |
d9e36a45d34dfbf3b33329b6657c131d9474017fc5a91e4676fdd91892d7754d | Python | 4,229 | 93 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchtext
"""
From the Kim CNN model.py from the Hedwig repository
"""
class CNN(nn.Module):
def __init__(self, config):
super().__init__()
dataset = config.dataset
output_channel = config.output_channel
ta... |
416d5dac316d60373a6dbbfe9d7c20d17f8206b30e707f9b00480c7d46e5c8c5 | Python | 4,230 | 91 |
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
plt.rcdefaults()
from matplotlib.backends.backend_pgf import FigureCanvasPgf
matplotlib.backend_bases.register_backend('pdf', FigureCanvasPgf)
if __name__ == "__main__":
data_dir = '/Users/eghbalhosse... |
b7a1e0ff9e8f72088d84c9080551e068e1fb9e1b53a3e0832fbeb8cffd051b6e | Python | 4,232 | 84 | #! /usr/bin/env python
from multiprocessing.util import get_temp_dir
from sys import argv, stdout, stderr, exit
from subprocess import check_output,call
import argparse
from os.path import basename, dirname, splitext,realpath,join,normpath,isdir,isfile,exists
from os import mkdir,getcwd,rmdir,listdir
from copy import d... |
c6962d55364402d9ffe0fb454fbd5bd413a9189a4f702be73bb126e775ba1356 | Python | 4,232 | 110 | # Copyright 2026 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... |
c0706389616b60a0abee87a9561820b958ffe6feba95dd832709a816533c7b1e | Python | 4,233 | 141 | '''
A data structure for easy inserts and lookups from a given refseq file
For use in quickly determining what genes intersect a large set of genomic regions
Author: Rebecca Riley
Date 6/7/23
'''
import numpy as np
import sys
PRESORTED = True # presorted uses linear search, so it's faster
get_chrom = l... |
f9b2fbe52f89edda48baf2d329b1eba9f005e4dcbff602701a8e58850e446967 | Python | 4,233 | 129 | from __future__ import annotations
from typing import TYPE_CHECKING
import weakref
from pandas.util._decorators import set_module
if TYPE_CHECKING:
from pandas.core.generic import NDFrame
@set_module("pandas")
class Flags:
"""
Flags that apply to pandas objects.
“Flags” differ from “metadata”. Fla... |
112d3e8991d60653878939adfc08e668f1316eb9e6ec236af1dbb41fedb8aab7 | Python | 4,234 | 106 | # emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
"""
.. _metas_ibma:
=========================
Image-based meta-analysis
=========================
Collection of NIDM-Results packs downloaded from Neurovault collection 1425,
uploaded by Dr. Cam... |
0b199f904e391fb33129e12d4d9cb6aa0debb61a7558a8bee5a4706c5a460c76 | Python | 4,235 | 111 | """Tests for Interval-Interval operations, such as overlaps, contains, etc."""
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
@pytest.fixture(params=[IntervalArray, pd.IntervalIndex])
def constructor(request):
"""
Fixture for te... |
6e8056292babe5a9ddcdf57a39b50e6aa000a4d5fc7c51e31982e10c8b2233e7 | Python | 4,235 | 118 | from .emulator import APEmulator
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.optim import Adam
from tqdm import trange
def match_Vm(emulator : APEmulator, t : np.ndarray, traces : np.ndarray,
weights=None,
x0=None, lambda_x0=10., x_init=None,
... |
9539224160da35b948f83f161e38b38145640c9e9a1cf1b82b4de4a5260016f4 | Python | 4,236 | 110 | import numpy as np
from scipy import sparse
from umap import UMAP
from umap.utils import csr_unique
# ===================================================
# Spatial Data Test cases
# ===================================================
# Use force_approximation_algorithm in order to test
# the region of the code tha... |
8d57a6d5d81f5ca16a64c21a32ed75e029990f7d0c1504c58328e5f1b4821a52 | Python | 4,241 | 167 | import matplotlib.pyplot as plt
import matplotlib
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
import matplotlib.ticker as ticker
import pandas as pd
import seaborn as sns; sns.set()
from statistics import mean, stdev
import matplotlib.transforms as transforms
import matplotlib.font_manager ... |
84caddd892377b74de8b61f1fdf2437de9dc025c2f3bc73a981edcb0605e4f9a | Python | 4,242 | 117 | """Byte pair encoding utilities"""
import os
import json
import regex as re
from functools import lru_cache
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # o... |
b3332152eb2ade26a48355d0007e645bf6cdf7d934032572113a88735b0e00e9 | Python | 4,242 | 119 | import torch
import numpy
import matplotlib.pyplot as plt
import bionetwork
import pandas
from sklearn.decomposition import PCA
#from sklearn.manifold import TSNE
import umap
#Load network
networkList, nodeNames, modeOfAction = bionetwork.loadNetwork('data/KEGGnet-Model.tsv')
annotation = pandas.read_csv('data/KEGGnet... |
ef947fab4160a18ee5a3e8318963aaf2f84036bf438d6b9336354e1b69c68610 | Python | 4,243 | 101 | # 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... |
a85f89e0cea3495b0ef36aad8c84bf08823fd65e03e0401fc4390ebbb4e86680 | Python | 4,245 | 111 | """Tests for `multimodal_ad.data.labeling`."""
import polars as pl
from multimodal_ad.data.labeling import (
classify_diagnosis,
classify_diagnosis_row,
label_nearest_visit,
smooth_temporal_labels,
)
def test_classify_diagnosis_positive_cases() -> None:
assert classify_diagnosis("AD dem/Vascular... |
fe347375abd0d6f3ec0f028103580764e295bcdca23842acdcf8e5482fa647ce | Python | 4,245 | 129 | """Shared Plotly figure utilities for ``pycaret.plots``.
Centralizes the design tokens (color palette, font, layout defaults) so
every chart looks consistent in the dashboard. Aligned with the React
UI's Tailwind tokens (`ink-*`, `accent-*`, `success-*`, `danger-*`,
`warn-*`).
"""
from __future__ import annotations
... |
e78444ef451a8e3ef34b3e3cf4d3014797f8909e07db0cba9b48597c67e72e4d | Python | 4,246 | 123 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestGetLevelValues:
def test_get_level_values_box_datetime64(self):
dates = pd.date_range("1/1/2000", periods=4)
levels = [dates, [0, 1]]
codes = [[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]]
index =... |
be46d37e3166c17e3efae2914e1e5c7e1d24500e0330e546027701dcbb89eb47 | Python | 4,247 | 128 | import numpy as np
import random
import os
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
import time
# import sklearn.linear_model
# from sklearn.model_selection import train_test_split
# from sklearn.linear_model import Perceptron, LogisticRegression
... |
55dccfc55a60f61baf4985cc044e7cdc3b918c58e24056c866009500e5c6aa6d | Python | 4,248 | 130 | import logging
import os
import pickle
from pathlib import Path
import re
import click
import numpy as np
import pandas as pd
from dotenv import find_dotenv, load_dotenv
def norm_func(x):
def std_norm(x):
return (x - x.mean()) / x.std()
return np.apply_along_axis(std_norm, 1, x)
def main_data_filte... |
3417af2643824df8e20a68a5c805b0f7ba69ac16408d31d0833531f9288fff5f | Python | 4,249 | 147 | """Conversion between different units."""
from typing import TypeVar
import numpy as np
import torch
__all__ = [
'GYROMAGNETIC_RATIO_PROTON',
'deg_to_rad',
'lamor_frequency_to_magnetic_field',
'm_to_micrometer',
'm_to_mm',
'magnetic_field_to_lamor_frequency',
'micrometer_to_m',
'mm_to... |
c6d4932114d5839fe8b8e8ddd304022535a376f0f91a1345a148969d9d55f7d0 | Python | 4,249 | 165 | """
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 typing import List, Optional
import torch
import torch.fft
def fft2c_new(data: torch.Tensor, norm: str = "ortho") -> torch.Tensor:
... |
b95df77128613ca36d89726e06a5d131b5b96333441e30b7fef25607c4a4a3eb | Python | 4,252 | 106 | # Copyright 2018-2025 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... |
261db0035d8c893ac4413a2fade8aac10ae775e167161eb00a2a995de45d82ae | Python | 4,253 | 129 | """
Computes the values of the last hidden layer (or the predictions) of the
discriminator for regions of real data along the genome.
Authors: Rebecca Riley, Sara Mathieson
Date: 12/14/22
"""
# python imports
import math
import numpy as np
import os
import random
import sys
import tensorflow as tf
sys.path.insert(1, ... |
a4e87a1fcec94f7fd9d1c69e377f7c0bd6d7f7c406701d16a80dfc48e91fae13 | Python | 4,253 | 141 | # 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... |
b64e556ff452ab749cd83f20026f8d8e171d28137f3d2a1a5c47df4b2d0c5e0e | Python | 4,253 | 121 | from onmt.utils.logging import logger
from onmt.transforms import register_transform
from .transform import Transform, ObservableStats
class FilterTooLongStats(ObservableStats):
"""Runing statistics for FilterTooLongTransform."""
__slots__ = ["filtered"]
def __init__(self):
self.filtered = 1
... |
c63e52551425997b07ca5e6efc47ba0137606580c67631b0495197134f15d79d | Python | 4,253 | 103 | import os, csv, math
import scipy.stats as ss
import pandas as pd
import numpy as np
def correct_p_values(pvalues, method = 'BH'):
"""Corrects p-values for multiple testing using various methods.
Arguments
---------
pvalues : array
List of p values to be corrected.
method : str
Optional method t... |
4f1de2460696c64e1c19cc1dde5df4d7e195e4db6badfea8b00b29e9b41b200e | Python | 4,255 | 72 | import numpy as np
layers = [
'X', 'X_Dense', 'X_MaxPool2D', 'X_Conv2D', 'X_BatchNorm',
'g', 'g_Dense', 'g_Conv1D', 'g_BatchNorm',
'M_Concatenate', 'M_Dense', 'M_BatchNorm', 'outputs'
]
layer_transitions = [
# X X_Dense X_MaxPool2D X_Conv2D X_BatchNorm g g_Dense g_Conv1D g_Batc... |
50b1c795d79b5946ca7f17d1d866230c89b1456895d0a11eee0c2a821f38fc14 | Python | 4,255 | 153 | import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.initializers import Orthogonal, TruncatedNormal
from tensorflow.keras.layers import (
Add,
Dense,
Dropout,
GlobalAveragePooling1D,
GlobalMaxPooling1D,
Lambda,
Layer,
LayerNormalization,
MultiHeadAttentio... |
8a7ec8650ab4c5bc92be34efbac7a2e484498db6f3c9cf00a5337c688a4dd99e | Python | 4,255 | 154 | """
Tests nproc>1.
N.B. nproc>1 slows things down here because integrand not sufficiently costly.
"""
import numpy as np
import vegas
import gvar as gv
numpy = np
DIM = 3
NPROC = 2
N = 10
NEVAL = 3e3
NITN = 5
NS = 1_000
@vegas.lbatchintegrand
def fl(x):
ans = 0.0
x0list = np.linspace(0.4, 0.6, N)
for x0... |
e6472304142af1cf4d8baac2ecd0069650d64ce4765b7b2e7ed85e0b02f349c9 | Python | 4,255 | 113 | from typing import List, Optional, Any, Dict
from multiprocessing import Pool
import importlib.util
import numpy as np
import torch
import logging
from ..image import Stack
def n4_bias_field_correction(
stacks: List[Stack], n4_params: Dict[str, Any]
) -> List[Stack]:
if importlib.util.find_spec("SimpleITK") i... |
6d7f185e3252b7f87111b3fba03db9e1b795ec755da58af0f6e622fbaadaa6f1 | Python | 4,256 | 138 | """
Functions for writing concatenated sequence alignments
Authors:
Anna G. Green
"""
from collections import OrderedDict
import numpy as np
from evcouplings.align import Alignment, write_fasta, parse_header
def write_concatenated_alignment(id_pairing, alignment_1, alignment_2,
tar... |
17e46ca769fdaf49f33b3087e6c1e56f0dfb6cd9ec7ce5dd20161d2da349732e | Python | 4,258 | 133 | import numpy as np
import sys
import scipy.io as scio
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import os
import matplotlib
from tqdm import tqdm
import math
matplotlib.use('agg')
class frameLoader():
def __init__(self) -> None:
self.frameSet = None
se... |
023ae5e12007dad7c19f1f27eaeb821dbace3b027e193b6772c6649dfec40110 | Python | 4,259 | 113 | from queue import Queue, Empty
from threading import Thread, Timer
class EventManager:
"""
事件管理器
"""
def __init__(self):
"""初始化事件管理器"""
# 事件对象列表
self.__eventQueue = Queue()
# 事件管理器开关
self.__active = False
# 事件处理线程
self.__thread =... |
9fbfe1833423510099a102a5b682b6b75fe0eff070d8fa95005fc74d215fd7b9 | Python | 4,260 | 129 | #!/usr/bin/env python
def scale_tensor(tensor, tensor_channels, scale, params):
'''
Scale a tensor according to the scaling parameter in the params dict.
Assumes the tensor has either 5 [batch, channels, x, y, z]
or 4 [channels, x, y, z] dimensions.
Parameters
----------
tensor : torch.Te... |
6cdf77defee6278cebfbe36af4a833109ebd84db97fbd1dfd3df83551d0cf0b6 | Python | 4,262 | 160 | #small_multiples.py
from initialization.config import *
from initialization.initialization import *
import plotly
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def hist_sm(df, factor='mean_intensity'):
'''
Plotly graph object histogram,
minimalist: intended for small multip... |
7994fc1d1eb52a6f4d2364411d48e18d5f2e7e46edf5f6aafa65cbcb982c5f49 | Python | 4,264 | 111 | """
===========================================================
Explore SESAME alternative inverse solutions on evoked data
===========================================================
In this example we shall show that SESAME is able to provide alternative solutions
with non-negligible probabilities and how the user c... |
c88966437c21c1a455b9702c41e60bbaad30577381f49090adcf0b4b848b70a1 | Python | 4,265 | 108 | """
.. _datasets_databases:
=========================
Neurosynth and NeuroQuery
=========================
Neurosynth and NeuroQuery are the two largest publicly-available coordinate-based databases.
NiMARE includes functions for downloading releases of each database and converting the databases
to NiMARE Datasets.
... |
3bade20a60e9e0954aefe366be48a2fb900c36a5a76c1cc739aa65912a632512 | Python | 4,269 | 149 | """Common utilities for Numba operations"""
from __future__ import annotations
import inspect
import types
from typing import TYPE_CHECKING
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.errors import NumbaUtilError
if TYPE_CHECKING:
from collections.abc import Ca... |
8fe20c7e41d0d4179ab9d03cf5d131b8d266e256b6ba1f2c652dd811840338ee | Python | 4,269 | 144 | # code from https://gitee.com/LynnHg/pytorch-medical-image-segmentation
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
activation = nn.ReLU
def initialize_weights(*models, a=0):
for model in models:
for module in model.modules():
if isinstance(module, nn.Conv2d)... |
d59ca92a59770b4afa9bb9812aeb8a8d5e266fdbc5722ef8a7aec57d02da55c1 | Python | 4,272 | 104 | """Regression coverage for singleton-strand accounting (issues #805 and #757)."""
from collections import Counter
import pytest
from MACS3.Signal.FixWidthTrack import FWTrack
@pytest.mark.parametrize("maxnum", [-1, 1, 2])
@pytest.mark.parametrize("reads", [
[],
[(b"chr1", 100, 0)],
[(b"chr1", 100, 1)],... |
373bed207dd3c60b29138fbc914f734f68c98515d6403c6f3d0bd8bf341391ac | Python | 4,273 | 114 | """
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 typing import List, Optional, Union
import torch
import torch.distributed as dist
from data.mri_data import SliceDataset
from torch.uti... |
b91d106c8dd2c54682e042a0d2cbb21312be092714190717faf1f34a820dfd3c | Python | 4,273 | 124 | """Utility functions for testing nimare."""
import os.path as op
from contextlib import ExitStack as does_not_raise
import nibabel as nib
import numpy as np
import pytest
from ..meta.utils import compute_kda_ma
# set significance levels used for testing.
# duplicated in test_estimator_performance
ALPHA = 0.05
def ... |
f02dc2f09e78e4f88a2255e899617fd041d9d70ea53ff99d3e6d66365aaf2b82 | Python | 4,273 | 173 | import numpy as np
import collections
solute_id = {
'Na' : 0,
'K' : 1,
'Cl' : 2,
'HCO3' : 3,
'H2CO3' : 4,
'CO2' : 5,
'HPO4' : 6,
'H2PO4' : 7,
'urea' : 8,
'NH3' : 9,
'NH4' : 10,
'H' : 11,
'HCO2' : 12,
'H2CO2' : 13,
'glucose' : 14,
'... |
4d5c59018c009bca97ce8b6afe7b5ea2ba0237a55cffb786572abac25b592e82 | Python | 4,274 | 110 |
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 16 17:09:27 2021
@author: Daniel Hulsey
this script aligns a ccf to an individual session, and extracts dff data for each indicated region.
Running it fully requires:
1. saved dff trace for the session being analyzed
2. saved points to align CCF to multim... |
6167351f143d0d6b59ee8210d78304771e80e9ef7e636781a791ff9cf6fb2cab | Python | 4,274 | 118 | """Define RNN-based encoders."""
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import pad_packed_sequence as unpack
from onmt.encoders.encoder import EncoderBase
from onmt.utils.rnn_factory import rnn_factory
class RNNEncode... |
bff0f1a85fda9e553260d5a7648545a98586047c037a4e03ee95f17c2040e826 | Python | 4,275 | 112 | # Copyright 2018-2025 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... |
019f2abad00b51efe4a498bfdabe5ed35cb617b693a25e6f6b056e7c9d489771 | Python | 4,280 | 143 | import numpy as np
from pandas import (
DataFrame,
HDFStore,
Index,
date_range,
read_hdf,
)
from ..pandas_vb_common import BaseIO
class HDFStoreDataFrame(BaseIO):
def setup(self):
N = 25000
index = Index([f"i-{i}" for i in range(N)], dtype=object)
self.df = DataFrame(... |
3c8eba9497c5d200ecf019a9171c691979c125c39835c99b89a364ea36d15a1a | Python | 4,280 | 119 | import math
import os
import tempfile
from typing import Dict, Optional
from . import utils
from .logging import logger
def STAR_genomeGenerate(
fasta_path: str,
gtf_path: str,
index_dir: str,
n_threads: int = 8,
memory: int = 16 * 1024**3,
temp_dir: Optional[str] = None,
) -> Dict[str, str]:... |
7dbc3bedf44fba0988dade046fbb2767d534d093ea7e66b222d7f92c740e0101 | Python | 4,280 | 129 | from __future__ import annotations
from datetime import (
date,
datetime,
time,
timedelta,
)
from typing import (
TYPE_CHECKING,
Any,
TypeAlias,
)
from pandas.compat._optional import import_optional_dependency
from pandas.io.excel._base import BaseExcelReader
if TYPE_CHECKING:
from p... |
804937067a1b4d18e49155de8963ff3b33cc96a9a55ff25748f40123ab1519ef | Python | 4,280 | 138 | # Copyright 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 agre... |
af68f6c297c50e796777a0c7c7ea7c530b30dbb5f59692d05363b1cb4c88f5e9 | Python | 4,281 | 156 | import os
import numpy as np
import torch
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split as split
from roost.roost.data import CompositionData, collate_batch
from roost.roost.model import Roost
from roost.utils import results_multitask, train_ensemble
torch.manual_seed(0) ... |
bca8b35fc5360ce69b31e6083cb12213560397c5511413d43b2c091b31511fde | Python | 4,282 | 140 | import numpy as np
import pandas as pd
class BooleanArray:
def setup(self):
self.values_bool = np.array([True, False, True, False])
self.values_float = np.array([1.0, 0.0, 1.0, 0.0])
self.values_integer = np.array([1, 0, 1, 0])
self.values_integer_like = [1, 0, 1, 0]
self.... |
d248cbf757e9f55dcc02b1d49a8a4443c078d10e989c151ce4e6ddba95a84b9f | Python | 4,283 | 120 | #!/usr/bin/env python
__author__ = 'etseng@pacb.com'
"""
Input: fasta, each sequence is an individual gene
given an input transcript sequence ---
-> randomly select N positions to have a SNP, N = <length> / 300
-> generate two haplotypes, one is the original, one is mutated
(store this in Haplotypes)
-> for each hapl... |
382f17f7a86e16696749966ee4e27552124a999eba50e907babf01773a359e02 | Python | 4,284 | 150 | """
Assertion helpers for arithmetic tests.
"""
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import (
BooleanArray,
NumpyExtensionArray,
)
def assert_cannot_add(left, right, msg="cannot add"):
"""
Helper function to assert that two objects... |
e16c1ce7373a7d84da7f3d6f5684d3310d7fae63425b4bf3c3b2e46a81833da8 | Python | 4,284 | 99 | from esmologs.ESM2_to_3Di import ESM2_to_3Di
import argparse
import torch
from Bio.SeqIO import FastaIO
import sys
import re
from tqdm import tqdm
from pathlib import Path
torch.set_grad_enabled(False)
def convert_batch(model, seqs, device="cpu"):
lengths = [len(seq) for seq in seqs]
x = model.encode_seqs(seq... |
db89554ae33eb430b0c1d6d720d7471f04c0834a98524b9d6b12dd9fe7721057 | Python | 4,286 | 113 | from typing import Dict, List
import torch
from ..utils import gaussian_blur
from ..transform import RigidTransform, transform_points
from ..image import Volume, Slice
class PointDataset(object):
def __init__(self, slices: List[Slice]) -> None:
self.mask_threshold = 1 # args.mask_threshold
xyz_a... |
2d0889e42942b5cac67e3253508e87c9f5664bee0cde8fa95c68d5fc880c2c8e | Python | 4,289 | 132 | # Copyright 2025 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... |
03436d9134faf5bae845a13b79e64c3cedccce1c82e757f4b7df7a1579e79bbb | Python | 4,290 | 115 | import numpy as np
import pytest
import pandas._config.config as cf
import pandas as pd
class TestTableSchemaRepr:
def test_publishes(self, ip):
ipython = ip.instance(config=ip.config)
df = pd.DataFrame({"A": [1, 2]})
objects = [df["A"], df] # dataframe / series
expected_keys = ... |
e526889a5d7aeeae9983e30ad129d8e422188bb8b510e05e4928d139acba806e | Python | 4,290 | 125 | import tensorflow as tf
from util.loss import gradient_reversal,loss_domain
class feature_extracter(tf.keras.layers.Layer):
## 输入的每个sample为二维张量
def __init__(self):
super().__init__()
self.conv_1_1 = tf.keras.layers.Conv2D(256,kernel_size=(1,5),strides=1,padding='same',activation='relu')
... |
511d27f024c0bdb48b2015a99b5a1cb07bf799de2bf43dd159c0fd85dfabe252 | Python | 4,293 | 126 | # -*- coding: utf-8 -*-
"""
preprocess of T1w-CE
"""
import os
from app.utils import cast_to_type, end_execution, get_exitcode_stdout_stderr, wrap_string
import numpy as np
import nrrd
from skimage import morphology, filters
import logging
logger = logging.getLogger(__name__)
def mypdf(data):
pdf = np.zeros(len(... |
01cffd125b06e5a4862d34787afe514718a4e40c064f0417f6f5044fa9a1e058 | Python | 4,298 | 110 | import importlib.resources
import json
from pygments import highlight, lexers, formatters
from PyQt6 import uic, QtCore, QtWidgets
class BasinsPanel(QtWidgets.QWidget):
"""Tables panel widget
Visualizes tables stored in the .rtdc file
"""
def __init__(self, *args, **kwargs):
super(BasinsPan... |
33dd870c45646f231ff0f7998b89b8bc31829a90af3c4eafe0ce16d0f3f185ef | Python | 4,299 | 139 | import operator
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.fixture
def data():
"""Fixture returning boolean array with valid and missing values."""
return pd.array(
[True, False] * 2 + [np.nan] + [True, False] + [np.nan] + [True, False],
dtype="... |
c6079f8e8cd55dc7fcac11c990f8913a063e89be9e6f2936ee17e5fcf609f7d5 | Python | 4,301 | 82 |
from ..utils import get_phenolist, PheWebError
from .. import conf
from ..file_utils import VariantFileWriter, write_json, get_generated_path, get_filepath, get_pheno_filepath
from .read_input_file import PhenoReader
from .load_utils import parallelize_per_pheno, indent, get_phenos_subset
import itertools
import argp... |
cc15b07051237cf02aeb06fa05c526f13e898dbd8b83d6f1172bce42d8ed3ef3 | Python | 4,301 | 126 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import ResNet, Bottleneck
from torchvision.models.squeezenet import SqueezeNet, Fire
num_classes = 2
class ModelParallelSqueezeNet(SqueezeNet):
def __init__(self, gpu_number, *args, **kwargs):
super(ModelPara... |
0f14cd36220d610686c56a82229692b870ffc5ab792ac02d2e3294e9b54aa7dc | Python | 4,304 | 132 | """Interpolation of data tensor."""
from collections.abc import Callable, Sequence
from typing import Literal
import torch
from mrpro.utils.reshape import normalize_indices
def interp(x: torch.Tensor, xp: torch.Tensor, fp: torch.Tensor) -> torch.Tensor:
"""One-dimensional linear interpolation for monotonically... |
bab9fdeeb0973028477cb8903f43537b8e1e11d7bf5b932f7c92e418162011c8 | Python | 4,305 | 149 | # Copyright 2026 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... |
ac46a17a5f74717e8a9f49c94a4bd8ea801f06657a0c6e981bf2fb2a4269513c | Python | 4,306 | 102 | """Tests for spoiled gradient echo signal model."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.models import SpoiledGRE
from mrpro.utils import RandomGenerator
from tests import autodiff_test
from tests.operators.models.conftest import SHAPE_VARIATIONS_SIGNAL_MODELS
def tes... |
637d455fe7aea1333a831eaf21955721ab520dcaee3b6aa7ad421467306f6a53 | Python | 4,308 | 121 | import os
import numpy
import pandas
import skimage.io
import tensorflow as tf
import matplotlib.pyplot as plt
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
exec(open("unet_model.py").read()) # load U-Net model
### functions ---------------------------------------------------------
def list_files(catalog, wd = os.getc... |
a76f459ca41cdd9424e6d9694d23e366642c32beba6f4b55b2b408b40432c303 | Python | 4,308 | 127 | """Test views."""
# ------------------------------------------------------------------------------
# Imports
# ------------------------------------------------------------------------------
import numpy as np
import pytest
from phylib.io.array import _spikes_per_cluster
from phylib.io.mock import artificial_features,... |
97e85fa4a3bccddec6f61176cc6b1d073a8508eab7272c852fb4dceda2db21ef | Python | 4,309 | 76 | import scipy.io as scio
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from scipy import sparse
# 封装归一化函数
def Normalize(filename,normalize_file_list):
i=0
for per_file in filename:
feature=scio.loadmat(per_file)['permission']
scaler = MinMaxScaler()
m1 = sc... |
98f085d7239746d932857edd8328b914a5aaa7a354fc8cde5f5a3a9fb909eb1c | Python | 4,310 | 115 | import sys
import os
from distutils.spawn import find_executable, spawn
import shutil
import subprocess
from kcsd import sample_data_path
if find_executable('nrnivmodl') is not None:
for path in ['x86_64', 'i686', 'powerpc']:
if os.path.isdir(path):
shutil.rmtree(path)
spawn([find_executable... |
af16bce79c8285c86c71d60e0e3ced10d602f0135b915c77b2726cdb3c926948 | Python | 4,311 | 155 | """
Module for containing graphql functionality for interacting with the Datasets Service API.
"""
import os
from typing import Any
from requests import post
GRAPHQL_URL = os.getenv("DATASETS_ENDPOINT_URL", "https://cloud.pennylane.ai/graphql")
class GraphQLError(BaseException):
"""Exception for GraphQL"""
d... |
1a921220730f71e21e9e8d17af5de797ff49edb5acc21b4deb3a16b7ff60e10a | Python | 4,312 | 134 | #
# File to train networks built from the hnn.py class.
#
# This file establishes the clebsch gordan coefficients, sets up an hnn with given parameters,
# loads holograms from .npy files, and then tests the network via a function call.
#
from argparse import ArgumentParser
import logging
import os
import sys
import ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.