sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2fe7c75714b8fdb579f32bec2bba1b14c5eae0d703a589bcf49e1d5b3e0fe846 | Python | 3,413 | 113 | import numpy as np
try:
from skimage.measure import marching_cubes
except ImportError:
from skimage.measure import marching_cubes_lewiner as marching_cubes
import matplotlib.markers as mmarkers
import matplotlib.pyplot as plt
import vtk
import pyvista as pv
from .cache import cache_return
@cache_return
de... |
663929e29811b83c71c114cf598230be32e6fdb956a3da763e21a52b24e3a2a1 | Python | 3,415 | 100 | #!/usr/bin/env python
'''
Testcases for the neural network prediction
'''
import windseer.utils as utils
import windseer
import numpy as np
import os
import unittest
windseer_path = os.path.dirname(windseer.__file__)
testdata_folder = os.path.join(windseer_path, 'test', 'testdata')
test_file = os.path.join(testdata_... |
be1be7dff9b72d10cd7c83576739e68854f4a04ab152844e1a1cad2094b0e539 | Python | 3,415 | 82 | from tests.conftest import TRAIN_ANNOTATIONS_PATH, TEST_ANNOTATIONS_PATH
from aigarmic.file_handlers import create_dataset_from_directory, predict_colony_images_from_directory, \
save_training_log
from aigarmic._img_utils import get_image_paths
import tensorflow as tf
import pytest
import csv
from os import path
... |
39ae7a48e94fdd10ee84428d20afceca595155cee9dfad20b4c652f479d0a151 | Python | 3,416 | 80 | #
# Nonlinearity class for use in holographic neural networks
#
# L:ayer takes inputs g^i of shape l x c(l,i) x m(l) and produces all squares
# in the irreducible basis via the Clebsch_Gordan coefficients
#
# In Einstein notation, this layer is summarized by the equation
#
# f^{i}_{l,c(l,i),m(l)} = C^{l,m(l)}_{l_1,... |
46592ee942f7766274a0e9fadb8acfb4fc933900ab7a2d381a74e5666def293e | Python | 3,419 | 94 | """Tests for Rearrange Operator."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.RearrangeOp import RearrangeOp
from mrpro.utils import RandomGenerator
from tests import (
dotproduct_adjointness_test,
forward_mode_autodiff_of_linear_operator_test,
gradient_of_linea... |
05b15b6887e46d3c33e8dc56c054ed73721c2d9a9fda19a2d6704ab9d29eb677 | Python | 3,421 | 90 | """Tests for the mono-exponential decay signal model."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.models import MonoExponentialDecay
from mrpro.utils import RandomGenerator
from tests import autodiff_test
from tests.operators.models.conftest import SHAPE_VARIATIONS_SIGNAL_M... |
1e3d8f6002cc262c0633d5f6a05bb389ff09daa29cc5e6428bac7b5388919e3a | Python | 3,421 | 103 | ''' backup_published_versioned.py
Create a backup of a published_versioned DynamoDB table
'''
import argparse
import sys
import time
import boto3
from botocore.exceptions import ClientError
import inquirer
from inquirer.themes import BlueComposure
import jrc_common.jrc_common as JRC
#pylint: disable=broad-excepti... |
44707c6ca327180511c5a1c0ac7a0ab74de042c1b779cf5360d90f622ece37a7 | Python | 3,421 | 87 | import pickle
import argparse
import numpy as np
from scipy.integrate import odeint
from numpy.random import random
###################################################################
def coupled_lorenz_96(xy, t, N, F1, F2, epsilon):
"""
Unidirectionally coupled Lorenz96 systems X->Y with constant forcing
... |
32bb9f93aecfcb9834648a66ec012ffec0d4da97f501f53a1272f64b8226646e | Python | 3,422 | 106 | from indices.base import BaseComparisons
class SokalMichner(BaseComparisons):
"""Class to calculate the Sokal-Michner index.
n=2 formula:
(a + d)/p
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The fingerprints mu... |
65af1eca0ac1de4d766cc45b67e3bf9f1243ae7fee6e9b1dfd48abb9a25102b1 | Python | 3,425 | 104 | # 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... |
d4bff6e162b47a44740281fbe6256aa08e74c50c9d3a01faa7316e7e7ff840f9 | Python | 3,425 | 76 | from losses.sl1loss import SL1Loss
from .render_models import *
from .renderer import *
from .render_utils import *
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import CosineAnnealingLR
import torch.nn as nn
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Renderi... |
ec5bf46c5893f9381c36d90ec959e5c17613ec7bac61b8872321fe0290bae3c2 | Python | 3,426 | 81 | """
Taken from: https://github.com/castorini/hedwig/blob/master/models/reg_lstm/weight_drop.py
BSD 3-Clause License
Copyright (c) 2017,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistribution... |
4788aec2dc5cb4d6ee0bdfc1029665cfa0ca97d1ec6bdee3a741c54e21f79cdd | Python | 3,428 | 114 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
def test_nlargest():
a = pd.Series([1, 3, 5, 7, 2, 9, 0, 4, 6, 10])
b = pd.Series(list("a" * 5 + "b" * 5))
gb = a.groupby(b)
r = gb.nlargest(3)
e = pd.Series(
[7, 5, 3, 10, 9, 6],
index=pd.MultiIndex... |
6752731bc364dad5cea41572c01ad114378dfe0cfcfb467f3d88d64bd6cb8aa4 | Python | 3,428 | 83 | '''format for reduced models.
spike times and lda values are kept with an accuracy of .01.
The index of the spike times is reset, i.e. the sim_trial_index is not kept.
The attribute `mdb_list`, which contains instances of ModelDataBase, is replaced
by a list of strings that only contain the unique_id of each database.... |
7938917834a1beb4c764bae76ccb87dd2516a0c3bfbe2d6382648eb6acd42a6a | Python | 3,428 | 135 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.core.interchange.dataframe_protocol import (
Buffer,
DlpackDeviceType,
)
if TYPE_CHECKING:
import numpy as np
import pyarrow as pa
class PandasBuffer(Buffer):
"""
Data in the buffer is guarante... |
485c42820cedb57cda418b6322323f70d915a6e17e43f10df6ac3e0f0f9047f6 | Python | 3,432 | 98 | from onmt.utils.logging import logger
from onmt.transforms import register_transform
from .transform import Transform
from onmt.utils.alignment import subword_map_by_joiner, subword_map_by_spacer
import re
from collections import defaultdict
@register_transform(name='filterfeats')
class FilterFeatsTransform(Transform... |
6875b7611d934a91cfbe5b32aa67ab11e082b3b7f1a890e4341bc005bf5ab369 | Python | 3,432 | 94 | import sys
import os
import numpy as np
import torch
import torch.utils.data as du
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import util
from util import *
from drugcell_NN import *
import argparse
def predict_dcell(predict_data, gene_dim, drug_dim, model_file, hidden_f... |
94fe5efac142b4de7140a8cbf85aa28dc71726802968d1f341ccbb194812a801 | Python | 3,432 | 97 | import os
import pandas as pd
# open file
path_nii1 = '/mnt/kannlab_rfa/Anna/PING/Package_1207895/niftis/IRSPGR_PROMO/'
path_nii2 = '/mnt/kannlab_rfa/Anna/PING/Package_1207895/niftis/MPRAGE/'
input_annotation_file = 'data/csv_files/ping.csv'
import sys
# setting path
sys.path.append('../TM2_segmentation')
import o... |
d39c6966367e67a36d990cb6c6d5b93e2d279906f08c28984681af1ffaa831d0 | Python | 3,432 | 124 | import pathlib
from functools import cached_property
from typing import Optional
import anndata as an
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
from sklearn.preprocessing import LabelEncoder
from .data import generate_dataloaders
from sklearn.utils.class_weight import compute_... |
f8ac2370d9a656deadec4ace683fcbcb8ebfd33ae8958b8c736416a99f2a5fc9 | Python | 3,432 | 102 | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of condit... |
898c8ec722a0ece713a4d85375295b688ce81b0a2e3c24108bec08535133d155 | Python | 3,433 | 116 | import os
import sys
from types import ModuleType
from unittest.mock import MagicMock
# Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# Ensure the repository root i... |
a4893ba292bba2d5c38aae1f1494a39343e452166f25413739d1b323e4d696fe | Python | 3,433 | 101 | import argparse
from Bio.SeqIO import FastaIO
import sys
import re
import numpy as np
def accuracy(pred, actual):
"""
pred: predicted sequence
actual: reference sequence
returns: mismatches, total_characters
"""
if len(pred) != len(actual):
raise ValueError("Pred... |
c1eea3b4735dec1411af1a1567f57fbd8afcee955238b79a2b88f35a42f92ec7 | Python | 3,433 | 108 | from indices.base import BaseComparisons
class RussellRao(BaseComparisons):
"""Class to calculate the Russell-Rao index.
n=2 formula:
a/p
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The fingerprints must be also... |
433a024dbb1ce5fbbc8758c5f8687d5814440b6f3b85dc55b78a9fad0977127f | Python | 3,434 | 88 | import argparse
import os
import ete3
import pandas as pd
from ete3 import NCBITaxa
def get_args():
"""
Get arguments from command line with argparse.
"""
parser = argparse.ArgumentParser(
prog='Convert_kreport_to_mpa.py',
description="""Convert a kraken report file into mpa (metaphlan)... |
b14ba26cefb053d641b59f73f24283c0bbab5cfb556ee517b057536afb5d998e | Python | 3,434 | 122 | import torch
TRANSFORM_EPS = 1e-6
def axisangle2mat_torch(axisangle: torch.Tensor) -> torch.Tensor:
theta2 = axisangle[:, :3].pow(2).sum(-1)
small_angle = theta2 <= TRANSFORM_EPS
theta = torch.clamp(theta2, min=TRANSFORM_EPS).sqrt()
ang_x = axisangle[:, 0] / theta
ang_y = axisangle[:, 1] / theta
... |
283aabc8875ad01a4f202d09e3de0ae2447c8a7e4c3cbfe754e98966cd750422 | Python | 3,435 | 111 | from keras.preprocessing import sequence
import numpy as np
from sys import argv
train_iso_ids = np.load('../data/id_lists/train_isoform_list.npy')
test_iso_ids = np.load('../data/id_lists/test_isoform_list.npy')
train_prot_id = np.load('../data/id_lists/train_swissprot_list.npy')
aa_num_dict = {}
aa_num_dict['F'] =... |
b811341f34eb1cddf3c130028dc2d01aaed5f76a89215fffedf4994d90ba0deb | Python | 3,435 | 116 | # Tests aimed at pandas.core.indexers
import numpy as np
import pytest
import pandas as pd
from pandas.core.indexers import (
is_scalar_indexer,
length_of_indexer,
validate_indices,
)
def test_length_of_indexer():
arr = np.zeros(4, dtype=bool)
arr[0] = 1
result = length_of_indexer(arr)
as... |
4333cd0f47e42270e3843c0cc8e31d96b858b916d5940b22e9c097605fece78f | Python | 3,439 | 99 | """Structured event types emitted by PyCaret 4.0 operations.
Events are the single source of truth for "what happened during this run":
- Every `Experiment` method emits at least one event.
- Events are immutable dataclasses that can be serialized to JSON.
- A React UI subscribes to the stream (via `BaseLogger.subscr... |
564c2c906e04f97649175575e3c233fe14c2dcffb79672d3181b57ed6663f14d | Python | 3,439 | 100 | import os
import random
import pandas as pd
import logging
from sbrnet_core.utils import (
normalize_psf_power,
full_read_tiff,
full_read,
lsi_fwd_mdl,
pad_3d_array_to_size,
write_tiff,
sbrnet_utils,
)
logger = logging.getLogger(__name__)
def make_synthetic_dataset(config: dict) -> None:
... |
0995c17233e8dc2da2d574dad3abb6a003437d1e8c9abf662acbe8f3e439c921 | Python | 3,441 | 103 | import sys
import io
import numpy as np
import pandas as pd
def get_cloze(df):
sel = df.Word == df.Response
df = df[sel]
if len(df):
return df.Response_Proportion.sum()
return 0.
def get_count(df):
return df.Total_Response_Count.max()
stdin = io.TextIOWrapper(sys.stdin.buffer, encoding='l... |
28e5c058c77278351431e1bc4ed2a6c7c685ae02a9fa95f0a987371dab005640 | Python | 3,442 | 78 | import sys
import re
import pandas as pd
import argparse
def filter_names(names, filters):
"""
Return elements of **names** permitted by **filters**, preserving order in which filters were matched.
Filters can be ordinary strings, regular expression objects, or string representations of regular expressions... |
0f2b232d7458db9baa193d418e5e6aaaf7903d49a6328085a148a6ba96fd435f | Python | 3,444 | 84 | from scipy.cluster.hierarchy import linkage, fcluster
# from sklearn.cluster import DBSCAN
def iterative_cluster(partition, hyper_cigar, options, ignore_op=False, ignore_length=False):
"""
perform iterative cluster, given a partition and a target hyper_cigar, determine if the hyper_cigar belongs to this parti... |
35c6cf6cc07c442af3696ab178f86e87e5768744618e5b623b10e941bafab2ac | Python | 3,445 | 128 | from __future__ import division
import collections
import math
import numbers
import random
import types
import warnings
import cv2
import numpy as np
from PIL import Image, ImageOps
class CropCenterSquare(object):
def __init__(self):
pass
def __call__(self, img):
# assert img.width == ma... |
39639540b8ee9fd1d38246310273954da07f9f8b85db38240b55def9cf7f4cdb | Python | 3,448 | 79 | import matplotlib.pyplot as plt
from msmfcode.core.config import *
from msmfcode.evaluation.plot import plot_error
from msmfcode.evaluation.data import load_experimental_data, load_experimental_config
from msmfcode.models.cann import DMSMF, FMSMF, Grid
ERROR_LABELS = {Metric.POS_ERROR_MEAN: 'Mean positional error (m)'... |
a7d54b400034f3c6d6adaec05c42b6749632ba8ef613562dc91b628f89af226f | Python | 3,449 | 97 | from numba import njit, types
from numba.typed import Dict
import numpy as np, sys
from Wavenet.evaluation import evaluation
from configuration import config
from Wavenet.training import training
@njit(cache=True, fastmath=True, nogil=True)
def Wang_3D(dic, Iapps, noise, v0, h0, n0):
n_integrations = int(dic['int... |
d1f9abfb67a6ead84ad15e2be9da56b25d80d2a45c2c133e0f5345b8a0baf2ad | Python | 3,449 | 111 | import argparse
from matplotlib import pyplot as plt
import numpy as np
import random
import scipy
import torch
import windseer.data as data
import windseer.utils as utils
import windseer.plotting as plotting
parser = argparse.ArgumentParser(
description='Analyze the noise of the wind measurements'
)
parser.a... |
f60499bd788bba3e117cc48b5c9fd26138bebedfbc4e18e12d7166a70d9f8d4e | Python | 3,451 | 106 | from indices.base import BaseComparisons
class Faith(BaseComparisons):
"""Class to calculate the Faith index.
n=2 formula:
(a + 0.5 * d)/p
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The fingerprints must be als... |
a0e3ad5ba8065baa49f21db340498c09ffb4376c86c1b4cbc8949c85ef1fad0e | Python | 3,454 | 127 | # JN 2015-01-13
"""
collect all functions that have to do with distances
between clusters, groups etc
"""
# pylint: disable=E1101
from __future__ import division, print_function, absolute_import
import logging
import numpy as np
# import sys
from .. import options, CLID_UNMATCHED
logger = logging.getLogger(__name__)
... |
89231b2f608b13c41a10ce4494d15a27faf3425722a43d0e9cad9af0a30a9e7d | Python | 3,455 | 95 | """Tests for priors.
Covers the parts of a prior that need no fitted model. Tests that compile a
prior into a PyMC model live in ``test/test_regression_models/test_hbr.py``,
because they need a fitted model to compile against.
"""
import numpy as np
import pytensor.tensor as pt
import pytest
from pcntoolkit.math_fun... |
1d31caf663888bb031b08594d9052fea7f7cf422ce6d67caf31845e621665d3c | Python | 3,456 | 91 | """Test for the adjoint as backward option in the LinearOperator class."""
import pytest
import torch
from mrpro.operators import LinearOperator
from mrpro.utils import RandomGenerator
from torch.autograd.gradcheck import GradcheckError
from tests import dotproduct_adjointness_test
class NonDifferentiableOperator(L... |
f9d114b2e7886c1f66c726edefe2ba3d29cb77f66b9224710119f61c073b7b26 | Python | 3,460 | 114 | import numpy as np
from .curves import Curve, remesh
class Rectangle(Curve):
def __init__(self, corners = None, npt = 25, spacing = None):
super().__init__()
if corners is None:
self.corners = ([0,0], [1,0], [0,1])
else:
self.corners = corners
ul = np.array(... |
1af9eda67692294110ce2034bfafb43d40925b27eb471cdaee863b1b7214be4b | Python | 3,461 | 99 | """Utils for applying RBF interpolation.
Adapted from code for Jain et al., 2020 https://proceedings.neurips.cc/paper/2020/hash/9e9a30b74c49d07d8150c8c83b1ccf07-Abstract.html
"""
import numpy as np
import time
from multiprocessing.pool import ThreadPool
from sklearn.linear_model import Ridge, RidgeCV
def phi(t, tau... |
0507cffa820b63372d504a8c462c42b7037ad7c9f2506e49379ac7f6c103956d | Python | 3,463 | 74 | import re
import sys
import codecs
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='''
"Rolls" tokens from a space-delimited input table (typically tokens used by a parser) into a target tokenization (typically tokens used in a reading time study, defined by a gold tokenization file) f... |
4ac5e021a5a5603f91c8917720bf1fb1513258f710131023dbf44ecd9dc3ab38 | Python | 3,463 | 105 | # 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... |
595e36d347979dfe4b04b4266fc7d2fffe7c2c1d63e4722386d3035fa4a398a7 | Python | 3,463 | 88 | import torch
import numpy as np
# Typing
from torch import Tensor
from typing import List, Tuple, Optional
####################### Model #######################
from torch import nn
from graphite.nn import MLP
from graphite.nn.basis import GaussianRandomFourierFeatures
class Encoder_dpm(nn.Module):
def __init__... |
0f923fa95758cd0806799b7471b9fc254ce5b173eb13ee91dfe5b881607f2006 | Python | 3,465 | 138 | # JN 2015-01-11
# refactoring
from __future__ import print_function, division, absolute_import
import os
import time
import subprocess
import logging
import numpy as np
# pylint:disable=E1101
logger = logging.getLogger(__name__)
from .. import options
# Debugging options
DO_CLEAN = True
DO_RUN = True
DO_TIMING =... |
e46d23a24a007cdfa38eae01412dd1158ccaba8c9e8ed6016654859ad53680bb | Python | 3,466 | 99 | import pytest
import pandas as pd
import pandas._testing as tm
pytestmark = pytest.mark.single_cpu
def test_retain_index_attributes(temp_hdfstore, unit):
# GH 3499, losing frequency info on index recreation
dti = pd.date_range("2000-1-1", periods=3, freq="h", unit=unit)
df = pd.DataFrame({"A": pd.Series... |
2024a02c495f6a06e6a20d131fd7a7d8d43b1a8a9d2f680d1a356e7119181799 | Python | 3,467 | 87 | """config.symmetry_loss: mirror-consistency loss on the continuous policy mean."""
import pytest
import torch
from tests.test_ppo_masking import make_ppo_agent, _rollout_batch
def mirror_maps(obs_dim=None, act_dim=None):
# reversal is an involution: a valid mirror on any layout
obs_dim = obs_dim or mirror_ma... |
eb70281958e8609860ae5b09c76c8c2893c2a32c289e7255394160f38e174f6e | Python | 3,467 | 116 | import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import Model, backend
from tensorflow.keras.initializers import Constant
from tensorflow.keras.layers import Dense, Lambda
from rl_toolkit.networks.layers import MultivariateGaussianNoise
class Actor(Model):
"""
Actor
====... |
24b222377436b15c9952520a55221879f7336c50a120f5bf3c2da819cfdb6657 | Python | 3,471 | 198 | """
Useful values and constants for all of package
Authors:
Thomas A. Hopf
"""
# amino acid one-letter code to three-letter code
AA1_to_AA3 = {
"A": "ALA",
"B": "ASX",
"C": "CYS",
"D": "ASP",
"E": "GLU",
"F": "PHE",
"G": "GLY",
"H": "HIS",
"I": "ILE",
"K": "LYS",
"L": "LE... |
dc20aa4b5644579912e44a154d622f0f08c4202189785a9ba5d9f1afebf50577 | Python | 3,471 | 99 | from math import isclose
import numpy as np
import mantel
condensed_X = [0.2, 0.4, 0.3, 0.6, 0.9, 0.4]
condensed_Y = [0.3, 0.3, 0.2, 0.7, 0.8, 0.3]
condensed_Y_with_nan = [0.3, 0.3, 0.2, 0.7, 0.8, np.nan]
redundant_X = [
[0.0, 0.2, 0.4, 0.3],
[0.2, 0.0, 0.6, 0.9],
[0.4, 0.6, 0.0, 0.4],
[0.3, 0.9, 0.4... |
dfe4d70bb810a27d87d6883359c0e9e5960ba0b23683ea4732a3e506fd8e7d23 | Python | 3,471 | 79 | #!/usr/bin/env python
__author__="etseng@pacb.com"
"""
Given a pooled input GFF + demux CSV file, write out per-{barcode group} GFFs
If input fasta/fastq is given, optionally also output per-{barcode group} FASTA/FASTQ
"""
import os, re, sys
import cupcake.io.GFF as GFF
from csv import DictReader
from collections impo... |
1118ec90b5f66c30804979b9dc61049c80818832e689b45b3554fa6912fd58c2 | Python | 3,472 | 101 | __all__ = ["GlycoBranchSpectralAngleLoss"]
from typing import Dict, cast
import torch
from torch.nn.modules.loss import _Loss
from ...util.math import spectral_angle
from ..common.data import GlycoPeptideDataBatch
from .data import (
GlycoPeptideBranchMS2DataBatch,
GlycoPeptideBranchMS2Output,
adjust_fr... |
fe27acaa0cb8f56101743cefce8820ef084fc220b6f222217dba5f1d25456d71 | Python | 3,472 | 104 | # -*- coding: utf-8 -*-
import torch
CONSTANT = 0.25 # base float used for the weights
def initialize(method="uniform"):
"""
Returns the desired function for the initialization of the weights for a linear layer.
:param method: Name of the method to be used.
:return: Function that is used.
"... |
594487c11711106ef37bba4cbcf1c6e4e63476dc3df55281f8b528cc784cc67f | Python | 3,474 | 100 | from pathlib import Path
from kilosort.gui.logger import XStream
from PyQt5 import QtCore, QtGui, QtWidgets
class MessageLogBox(QtWidgets.QGroupBox):
def __init__(self, parent):
QtWidgets.QGroupBox.__init__(self, parent=parent)
self.setTitle("Message Log Box")
self.gui = parent
se... |
43292967bcbb42e0885604be1a6759ac8b995fc8534786d39ba2ad0750a01719 | Python | 3,476 | 123 | """Logging infrastructure for the SAM algorithm.
This module provides a centralized logging configuration for the samalg package.
All logging throughout the package should use the logger obtained from get_logger().
"""
from __future__ import annotations
import logging
import sys
from typing import TYPE_CHECKING
if ... |
e10a819fc022260722f93c9270fc844d99045c4198dbaa79bb4b8bf294792dc5 | Python | 3,476 | 91 | # 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... |
62f85cab80d213e3a4d4b142a6eca4b6e17b785a9e20e8af364cbc3028534d9d | Python | 3,478 | 125 | #!/usr/bin/env python
import numpy as np
def apply_transform(g, data):
import scipy.ndimage
data = resample(g, data)
a = _augment_matrix_dims(g.rps_to_xyz(), len(data.shape))
A = np.array(np.linalg.inv(a))
output_shape, offset = get_newshape(data.shape, a)
return scipy.ndimage.affine_transfor... |
a7119b81f782444c90753a949d9fd175177bb790d9f698852c4fb29e8aaa7c3e | Python | 3,479 | 78 | """Public track contracts sensitive to NumPy sorting and native allocation."""
import numpy as np
import pytest
from MACS3.Signal.PairedEndTrack import PETrackII
from MACS3.Utilities.Execution import CPUAvailability, ExecutionPolicy
@pytest.mark.parametrize('size', [1, 2, 15, 16, 17, 63, 64, 65, 1001,
... |
a5b7bddc01c0eba97f352964344ff4c9923139b2cbd82b820220566bbe71108c | Python | 3,480 | 101 | #!/usr/bin/env python
from solo import hashsolo
import scanpy as sc
import matplotlib.pyplot as plt
import argparse
import pandas as pd
parser = argparse.ArgumentParser(description="Parser for HashSolo - Demultiplexing")
parser.add_argument(
"--hto_data",
help="Input directory containing hashing count matrix ... |
d019c0cb7554a4ba4fb60a0441713b085dfe45bfd5f44721710b4678a349121e | Python | 3,482 | 107 | import numpy as np
import pytest as pytest
import scipy.sparse
from numba import njit
from umap import UMAP
@pytest.fixture(scope="session")
def all_finite_data():
return np.arange(100.0).reshape(25, 4)
@pytest.fixture(scope="session")
def inverse_data():
return np.arange(50).reshape(25, 2)
@njit
def nan_... |
dfbbcc2f0d89f5c6bc5805a5791592f943b85861fd0c1843e4bbc450fd3b7eba | Python | 3,483 | 108 | #!/usr/bin/env python
""""Getting exon-intron junction from exon bed file
Usage: sort -k1,1 -k2,2n exon.bed | uniq | %prog - >exon_intron.bed"""
# ======------------------------------------====-------------------------------====
# | | | | | | | |
... |
35baabafea64bd106b2838eef4d475d1dc5711d3558296569346168016486f92 | Python | 3,485 | 89 | from tests import TestCaseNeSVoR
from nesvor.image import load_slices, save_slices, Slice, Volume, load_volume
from nesvor.transform import RigidTransform
import torch
import os
import shutil
class TestImage(TestCaseNeSVoR):
@staticmethod
def get_image_test_data(is_volume=False):
data = []
for... |
4fb6f3cc337bf202ceb0077363529a102cfc00883c6befe91679ae10636033f2 | Python | 3,485 | 122 | import tensorflow as tf
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 int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def int64_list_feature(value):
... |
60367863a6305a97593c49e4aed8c21dd3020f2c11f5765da78098be0577ea4a | Python | 3,485 | 85 | #!/usr/bin/env python
'''
Testcases for loading measurements
'''
import windseer.data as data
import windseer.utils as utils
import windseer
import os
import torch
import unittest
windseer_path = os.path.dirname(windseer.__file__)
model_config_filename = os.path.join(
windseer_path, 'test', 'testdata', 'model', ... |
eb83485a5c2c0e314f1ce7d5b7b420cb9c5add8ee3c7da20411ab9bb352aa4d6 | Python | 3,487 | 110 | import sys
import re
import numpy as np
import pandas as pd
# This script extracts read information from the fastq out of the bbduk run (which combines R2 lineage barcode reads with R1 cellbc and umi information)
# and saves into 3 useful formats for subsequent pipeline analysis.
# INPUTS:
EXTRACTED_FASTQ = str(sy... |
e79c7d6f51472db12d93d37932fb6b06fcea20800bcceddbafec7d0eddfeba50 | Python | 3,488 | 104 | import logging
import time
import numpy as np
import torch
from modules import (
dimensions,
model,
pretransformations,
posttransformations,
transformation_utils,
)
class BicubicDimensions(dimensions.Dimensions):
"""
Bicubic dimensions class. Store data here.
"""
... |
c5f488751bf7f913cd341b1c3b4e2f06e5ce647ba95766279fdf896eec5024bf | Python | 3,489 | 120 | from typing import Optional
import torch
import torch.nn.functional as F
from .misc import gaussian_blur
def ncc_loss(
I: torch.Tensor,
J: torch.Tensor,
mask: Optional[torch.Tensor] = None,
win: Optional[int] = 9,
level: int = 0,
eps: float = 1e-6,
reduction: str = "none",
) -> torch.Tenso... |
73bafcb75a13837d95102c2e82afe6d60b7ff933f95b7cafcd886fe62c3d1d02 | Python | 3,490 | 97 | import sys, re, string
lineitems = sys.argv[1]
textgrid_dir = sys.argv[2]
onset = re.compile(' xmin = ([^ ]*)')
offset = re.compile(' xmax = ([^ ]*)')
timestamp = onset
word = re.compile(' text = \" *([^ "]*)')
doc_list = ['Boar', 'Aqua', 'MatchstickSeller', 'KingOfBirds', 'Elvis'... |
bb5ed94de9bdb11d2f76398e25c2b3da27fcb295d63fe0aeffa992c08a629807 | Python | 3,490 | 106 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 9 11:28:16 2023
@author: Elida
"""
import numpy as np
import random as rn
import generate_splines as sp
from generate_tubular_model import getRandomVect
import os
import random as rn
def generate_radio_list(r1_range, r2_range, r3_range, r4_range, r5... |
747575683ef7531af1a8fc00d81daac4e4f96b9b85c7b7075991cb686fb8d9a3 | Python | 3,491 | 102 | from fontTools import ttLib
from pathlib import Path
import pytest
import re
NAME_ID_FAMILY = 1
NAME_ID_UNIQUE_ID = 3
NAME_ID_FULLNAME = 4
NAME_ID_POSTSCRIPT_NAME = 6
def test_consistent_version():
for sub_dir in [Path("2D/fonts"), Path("3D/fonts")]:
assert sub_dir.is_dir()
name5_re = re.compil... |
77ef18f9cc4bf40554e5bd6cd1c2e5f571d0aadaaa1c0a88dcf1fcd85bd860cd | Python | 3,491 | 96 | #!/usr/bin/env python3
import numpy as np
from scipy import interpolate
import tensorflow as tf
def forward_solution(data_input, n_samples, state_0, n_steps, num_outputs, dt, rhs):
x = state_0
y_history = tf.TensorArray(tf.float64, size = n_steps)
y_history = y_history.write(0, x[:, :num_outputs])
fo... |
bb566e29c1c25e3faf51417b1d29d7b4098266f8b6458fb63c40571144049f62 | Python | 3,495 | 106 | from indices.base import BaseComparisons
class SokalSneath1(BaseComparisons):
"""Class to calculate the Sokal-Sneath 1 index.
n=2 formula:
a/(a + 2 * b + 2 * c)
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The fi... |
c60a838bdd1c3414176ac61e59b07c9ffdd9305c66e44ccc8e8148bb7d132838 | Python | 3,495 | 139 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by Roberto Preste
import os
import unittest
from unittest.mock import Mock, patch
import pandas as pd
import pandas.testing as pdt
from mtoolnote.classes import Field
from mtoolnote.nonhuman import NonHumanAnnotator, NonHumanVariant
from mtoolnote.tests.constant... |
15f5d073df6c7111059755fd3a97422817a833ce59e6563a462cfb808c5b67e1 | Python | 3,496 | 106 | from indices.base import BaseComparisons
class RogersTanimoto(BaseComparisons):
"""Class to calculate the Rogers-Tanimoto index.
n=2 formula:
(a + d)/(p + b + c)
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The f... |
ac3e0cfd2842ff9769d235108c7a3bca315b7025fb190b212fe6c4ab522ed60e | Python | 3,496 | 81 | from unittest.mock import Mock, call
import pytest
import torch
from pyaging.predict._pred_utils import load_clock
from pyaging.utils._hf import PyAgingDownloadError, PyAgingResourceNotFoundError
from pyaging.utils._utils import load_clock_metadata
def test_load_clock_downloads_lowercase_hf_file_and_prepares_model(... |
87d3cc0f7cef4f084fc06a3a74f9bb69ed8c9efc239a80c2e0966ae9155a1d3d | Python | 3,497 | 111 | import pytest
import pandas as pd
import pandas._testing as tm
class TestDelete:
def test_delete(self, unit):
idx = pd.date_range(
start="2000-01-01", periods=5, freq="ME", name="idx", unit=unit
)
# preserve freq
expected_0 = pd.date_range(
start="2000-02-... |
3824265c308f187dafd8a564e7f7961b38285c165b9dc6d1a37d374c52d050f5 | Python | 3,498 | 87 | import scipy.ndimage as ndimage
from random import randrange
import skimage
import random
import numpy as np
def augment_image_diffusion(b1500_in, adc_in):
"""
Augments diffusion-weighted images by applying random transformations.
Parameters:
- b1500_in (numpy.ndarray): Input diffusion-weighted imag... |
809cfe782d91372b6f1c8da9ccd4f7610e1c955b500b2de9fbf2763eb9f57f1f | Python | 3,500 | 142 | # MOFTransformer version 2.2.0
import os
from sacred import Experiment
from moftransformer import __root_dir__
from moftransformer.utils.validation import _set_load_path, _loss_names
ex = Experiment("pretrained_mof", save_git_info=False)
@ex.config
def config():
"""
# prepare_data
max_num_at... |
9416e5c41f9d3d6115d1310ac055b2d1960716382e1e5acc312af525c44ee187 | Python | 3,501 | 84 | """Pose estimation model classes, re-exported at the package level.
**Four model types**, each available in a supervised and a semi-supervised variant
(8 concrete classes total):
- ``regression`` — direct (x, y) coordinate regression.
``RegressionTracker`` / ``SemiSupervisedRegressionTracker`` → ``regression_tracke... |
a5e29bf4a8cd8bcc27fae1ceb1ce056b28b72eafec25d6352cb1dce1d6b76a82 | Python | 3,501 | 87 | #coding=utf-8
from math import sqrt
float_err=1e-10
def inSegment(p,line,line2):
if line[0][0] == line[1][0]:
if p[1] > min(line[0][1],line[1][1]) and p[1] < max(line[0][1],line[1][1]):
if p[0] >= min(line2[0][0],line2[1][0]) and p[0] <= max(line2[0][0],line2[1][0]):
... |
e0376f9071f5d1ac0c16525f19dc9c624810bbd23ddee4b48b2d44193083db8c | Python | 3,502 | 89 | # 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... |
ade5cbc735d6fbdc7117ca9b892898e346e150a750dd87c01fbcbf6ffbca1173 | Python | 3,503 | 53 | """Prepare reusable native-grid population weights for all study years."""
from pathlib import Path
import json,os,sys
os.environ.pop('PROJ_LIB',None);os.environ.pop('PROJ_DATA',None)
os.environ['OPENBLAS_NUM_THREADS']='1'
sys.stdout.reconfigure(encoding='utf-8')
import numpy as np,pandas as pd,rasterio
from shapely.ge... |
dcc6b8fd4ce189364615f5ac299fa57ff00f60c72aa0e83414003d8a98fb2f86 | Python | 3,503 | 93 | # 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... |
b9abe6c0823d366fc99088fee6e3b1426411207db9bcb533896b51ee5ab34c7e | Python | 3,504 | 92 | """Session 34 — fix sklearn 1.8 ``squared=False`` deprecation in the
regression metric registry.
The legacy ``RMSEMetricContainer`` passed ``args={"squared": False}`` to
``mean_squared_error``. sklearn 1.6+ deprecated that path in favor of a
dedicated ``root_mean_squared_error``. Every regression CV run was
emitting ~... |
475ab8b2bf49c1e5cd1e1e6030723c0f051345b07d753dfeb940428cb6d6faa0 | Python | 3,505 | 97 | #!/usr/bin/env python3
from .knead import Knead
from argparse import ArgumentParser
from os.path import isfile
from pathlib import Path
import logging
import sys
def has_extension(path):
suffix = Path(path).suffix
return suffix != ""
def get_parser():
parser = ArgumentParser(
description = "Fluent... |
28a4488df62a9fd951dbde9ee12bb7b628bbf909278451f377e69e61d32279d8 | Python | 3,508 | 118 | import copy
import pathlib
import tempfile
import dclab
import numpy as np
from shapeout2 import pipeline
def test_apply_filter_ray():
path = pathlib.Path(__file__).parent / "data" / "calibration_beads_47.rtdc"
# initiate the pipeline
pl = pipeline.Pipeline()
slot_id = pl.add_slot(path=path)
# ... |
f2d27c742a91309767d4b449088caa7ef45f66759812d5f4021e325259785123 | Python | 3,508 | 98 | """Tests for saturation recovery signal model."""
from collections.abc import Sequence
import pytest
import torch
from mrpro.operators.models import SaturationRecovery
from mrpro.utils import RandomGenerator
from tests import autodiff_test
from tests.operators.models.conftest import SHAPE_VARIATIONS_SIGNAL_MODELS
@... |
0f87ff7d38d1fc186ef3cc4403527f449ca1b59794f581cfa7968baa5f3944ac | Python | 3,509 | 97 | """normalize_input_init_count: warm-started running-stat count.
With the legacy count of 1, the first update batch outweighs the unit prior
thousands to one, so the stats jump straight to the batch stats — recomputing
early train-time policies far from the rollout policy that collected the data
(observed as a huge fir... |
55e96d675b6215cb623bc28ff10b7d6b304cc23856cc43377437d3df39a90f86 | Python | 3,509 | 87 | # 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_macm:
============================================
Meta-analytic coactivation modeling analysis
============================================
Meta-analytic coactivation modeling (MA... |
be484828c41924233cb542934917dce08a7e6adfd2d70219aa08c47d9f76332b | Python | 3,510 | 105 | import pytest
import pandas as pd
import pandas._testing as tm
from pandas.tseries.offsets import (
BaseOffset,
BDay,
Day,
Hour,
)
class TestFreq:
def test_freq_setter_errors(self):
# GH#20678
idx = pd.DatetimeIndex(["20180101", "20180103", "20180105"])
# setting with an... |
fda1cd0e19f65ba4945c6b967f3da8c91b3d571872ef62de7f946172492d867f | Python | 3,511 | 84 | """
Converts pairs of 3Di and amino acid fasta files into foldseek databases.
"""
import argparse
import sys
from esmologs import __version__
from esmologs.utils import iter_fasta
def fasta2foldseek(aa_fasta, tdi_fasta, output_basename):
# write the dbtypes
#TODO: do I need to make the .lookup file?
... |
4217683f24bb150da2057d70b1a54940c1dd6573f749a6e13acd29fed76a4517 | Python | 3,512 | 120 | import queue
from viewContainer import viewContainer
from StimulationSystem.ApplicationProcess.AppdisplayProcess import AppdisplayProcess
from StimulationSystem.ApplicationProcess.AppinitProcess import AppinitProcess
from psychopy import visual
import copy
import math
import os
from tqdm import tqdm
class Ap... |
ee9d189aa9eeb375c51d02c026b58ea1e032f06700da5a37ba808545dc6b8b87 | Python | 3,513 | 108 | from indices.base import BaseComparisons
class JaccardTanimoto(BaseComparisons):
"""Class to calculate the Jaccard-Tanimoto index.
n=2 formula:
a/(a + b + c)
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that will be compared.
The finge... |
dc997865f93f9d540013e5ba6e81f1bec63d82c0008e9714dc240b571ea454d4 | Python | 3,514 | 110 | """K-space trajectory base class."""
from abc import ABC, abstractmethod
import torch
from einops import repeat
from mrpro.data.KTrajectory import KTrajectory
from mrpro.data.SpatialDimension import SpatialDimension
class KTrajectoryCalculator(ABC):
"""Base class for k-space trajectories."""
@abstractmeth... |
2d9daec30f171d9fd504b34620fd9044f8b2f1deebf4f3c3841841e936e28372 | Python | 3,516 | 140 | import numpy as np
import pytest
import pandas as pd
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
"""
Several variants of integer value 1. The zero-dim integer array
behaves like an integer.
This fixture can be used to check that datetimelike indexes handle
addition... |
3a2d8a54794800fe96969d7c8d85150d5a8aa77747a1463475de18aa8e97beec | Python | 3,516 | 140 | import numpy as np
import pytest
import pandas as pd
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
"""
Several variants of integer value 1. The zero-dim integer array
behaves like an integer.
This fixture can be used to check that datetimelike indexes handle
addition... |
95713eb006702c48983e28f662f60a625accff58272e8ad35518677af930a05b | Python | 3,516 | 116 | # Copyright 2018-2020 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... |
7b64df7d14f3aa7e1a68488e6a4c49be2cebf0c5dbca4847ebba26806fed902f | Python | 3,518 | 151 | # 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... |
f84d8c9c63517482d59d9c24d9e05fbd0bb02bf5686d98d124f80c58d2cbed55 | Python | 3,519 | 133 | from __future__ import annotations
import gzip
import io
import tarfile
from typing import (
TYPE_CHECKING,
Any,
)
import zipfile
from pandas.compat._optional import import_optional_dependency
import pandas as pd
if TYPE_CHECKING:
from collections.abc import Callable
from pathlib import Path
fr... |
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