sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1f90c09b025b1555dcb0ede982453d1683865b945593821bb9b1483e8df64404 | Python | 3,642 | 127 | """
frozen (immutable) data structures to support MultiIndexing
These are used for:
- .names (FrozenList)
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
NoReturn,
Self,
)
if TYPE_CHECKING:
from collections.abc import Iterable
from typing import SupportsInde... |
8a2157522cd10558ed3c4888eb21bb5da82e4c3c595f0192ef5017b22d5c5fd8 | Python | 3,644 | 108 | #
# This file computes the atomic spherical coordinates in a given set of
# neighborhoods and outputs a file with these coordinates.
#
# It takes as arguments:
# - The name of the ouput file
# - Name of central residue dataset
# - Number of threads
# - The neighborhood radius
# - "easy" flag to include central res... |
2cb23d69b8685c2f44e8ffae34a364ba938ac08897896b89823c001f069fb29e | Python | 3,646 | 95 | import numpy as np
import scipy.signal # type: ignore
from sklearn.decomposition import FastICA # type: ignore
from sklearn.preprocessing import StandardScaler # type: ignore
def detrend(signal: np.ndarray, degree: int) -> np.ndarray:
"""Detrend a single signal"""
x = np.arange(len(signal))
coeffs = n... |
e5574f3ada2e66ad4ca58f0ba221f4be72f190bd8a56069b27ac23a1d52988d3 | Python | 3,650 | 71 | import pandas as pd
from rdkit import Chem
import numpy as np
from utils import GraphCreator
import torch
from sklearn.preprocessing import MinMaxScaler
import pickle
import os
# create folder structure
os.mkdir("data")
os.mkdir("feature_importances")
os.mkdir("logs")
os.mkdir("data")
os.mkdir("plots")
os.mkdir("train... |
c564b2953cab4115bdddc16a618e82aa833fbac3a83489c30bdf4bedff767227 | Python | 3,651 | 123 | """
_
.__(.)< (KWAK)
\___)
~~~~~~~~~~~~~~~~~~~~
"""
import socket
import time
import threading
import numpy as np
class Pose_Streamer:
BUFER_SIZE = 2048 # bytes (max size of a pose string is 1037 bytes)
def __init__(self, sfreq, host, port):
"""
sfreq: sending fr... |
b89b2c4432d1c2ddc9ec0d88e536151650fa1a9ffff20fcc9e1fa49eb031a6c8 | Python | 3,652 | 113 | import sys
import random
import numpy
import collections
import math
numY = int(sys.argv[1])
NUMITERS = int(sys.argv[2])
alpha = float(sys.argv[3])
beta = float(sys.argv[4])
refstart,refend = (sys.argv[5],('_'+sys.argv[6])) if len(sys.argv) > 6 else (sys.argv[5]+'_','_')
sys.stderr.write( 'numY = ' + st... |
c346466efeec76e249c1f103a2dd43637f1719669b96f6366173384452b002fa | Python | 3,653 | 117 | from pathlib import Path
from typing import Set, List
import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow.keras as K
from tensorflow.keras.models import Model
from MutComputeX.data_loader import load_dataset
from MutComputeX.post_process import (
prediction_df,
average_prediction... |
11308ec352a583073afb9addd023ca3ca8ef43447052b1e6933d826f8b970432 | Python | 3,654 | 125 | import numpy as np
import pyqtgraph as pg
from kilosort.gui.logger import setup_logger
from PyQt5 import QtCore, QtGui, QtWidgets
logger = setup_logger(__name__)
class ProbeViewBox(QtWidgets.QGroupBox):
channelSelected = QtCore.pyqtSignal(int)
def __init__(self, parent):
super(ProbeViewBox, self)._... |
e1ddb6041279e8cd0fc86b67e4a77313ca87115d5c7a8be56f7ea3a7dba7db0b | Python | 3,654 | 124 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
class TestDataFramePctChange:
@pytest.mark.parametrize(
"periods, exp",
[
(1, [np.nan, np.nan, np.nan, 1, 1, 1.5, np.nan, np.nan]),
(-1, [np.nan, np.nan, -0.5, -0.5, -0.6, np.nan, np.nan, np.... |
217b48a1f01e9e5135bcbea2df27fff5ea5caa51eb808f8dceab0feb067d3c33 | Python | 3,655 | 137 | from collections.abc import Generator
import contextlib
import weakref
import pytest
from pandas.errors import Pandas4Warning
import pandas as pd
import pandas._testing as tm
from pandas.core import accessor
def test_dirname_mixin() -> None:
# GH37173
class X(accessor.DirNamesMixin):
x = 1
... |
ad02c14f3b286a4563cecd4977d6e00136cf8f08bbad4f4555f0ebe68beeff20 | Python | 3,656 | 91 | import os, sys, re, copy, argparse
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
argparser = argparse.ArgumentParser('''
Transforms unlabeled dependency graphs into unlabeled phrase structure trees, following the
procedure in Collins et al (1999). Resultant... |
0613dc36bf12e43888d46514eb780c479bf09c74fb4b44e150b9c680c0b17ecc | Python | 3,657 | 117 | __all__ = ["match_spectrum_mz", "MS2SpectrumAnnotatorBase"]
import abc
from typing import Generic, Literal, TypeVar, Union
import numpy as np
from ...chem.common.mz import MzAnnotationProto
from ...specio.spec import MassSpectrum
from ...util.config import Configurable
from .spec import MS2SpectrumProto, MzArray
d... |
7c778ef877a7874cb7c2db613bd4d244184ba3e432104816e259542462dde7ed | Python | 3,658 | 131 | import numpy as np
from src.nexi.models.functions.nexi_functions import (
M,
nexi_signal,
nexi_jacobian,
nexi_hessian,
nexi_signal_from_vector,
nexi_optimized_mse_jacobian,
)
def test_M():
x = 0.5
b = np.array([1, 2, 5])
t = np.array([20, 30, 40])
tex = 30
Di = 2
De = 1... |
b75dc47d1e1759a4a02ad8edc56dbf985f4641c6ffc11fc0f245ca905c95e8d4 | Python | 3,658 | 110 | """L1 Norm."""
import torch
from mrpro.operators.Functional import ElementaryProximableFunctional, throw_if_negative_or_complex
class L1Norm(ElementaryProximableFunctional):
r"""Functional class for the L1 Norm.
This implements the functional given by
:math:`f: C^N \rightarrow [0, \infty), x \rightarro... |
0b32f5d3f4f7f7c3adf0a365625c0528a0606a2e07a90ce5f3f6c539fdf10b78 | Python | 3,659 | 94 | # 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... |
7e0bd175c285affe8eaabbe1ea3ac16509bceba0fdb8f27df71710b415305b0c | Python | 3,659 | 95 | import os
import pandas as pd
from cptools2 import splitter
from cptools2 import filelist
# need to have an image list
CURRENT_PATH = os.path.dirname(__file__)
TEST_PATH = os.path.join(CURRENT_PATH, "example_dir")
TEST_PATH_PLATE_1 = os.path.join(TEST_PATH, "test-plate-1")
# just use a single plate
IMG_LIST = filelist... |
9578533e969080c1cf5f7ceda2a3639339df309c722f221639ef456c63f20e10 | Python | 3,660 | 91 | import torch
import torch.nn as nn
from gvp import GVP, GVPConvLayer, LayerNorm
class GVPEncoder(nn.Module):
'''
GVP-GNN encoder.
Takes in protein structure graphs of type `torch_geometric.data.Data`
or `torch_geometric.data.Batch` and returns an embedding.
Should be used with `g... |
2df0096ed4f259902189374e2629c5ba5a92941209edc56f0b5c12420219f6e5 | Python | 3,662 | 91 | """Local validation and sidecar selection for per-clock Hugging Face sync."""
import importlib.util
import json
from pathlib import Path
import pytest
ROOT = Path(__file__).resolve().parents[1]
_spec = importlib.util.spec_from_file_location("hf_repo_sync", ROOT / "clocks" / "hf_repo_sync.py")
hf_repo_sync = importl... |
c1b63dde9447cf6ab229fff70a88ef750995016427e5a47a261c8f43cfe1e9b4 | Python | 3,665 | 92 | import argparse
import configparser
import logging
import os
import sys
import numpy as np
from datetime import datetime
from typing import Tuple
from local.common import BCI2000MatFile
from prepare_corpus import get_feature_extractor
logger = logging.getLogger("baseline_offline.py")
def get_default_session_name() ... |
3d1259915669d0d6f1d226c2a8e33fa6519a179fb03ded31c27f388691c438a3 | Python | 3,666 | 104 | import sys
import os
import re
from bs4 import BeautifulSoup
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
info_dict: dict = dict()
with open(os.path.join(os.path.dirname(__file__), "../nesvor/version.py")) as fp:
exec(fp.read(), info_dict)
print("post processing starts")
BASE = sys.argv[1]
cmd... |
6cc3a670db7d1fcf117b968f529efeff7bb8f4d44fc8cea287fce303eacd402a | Python | 3,668 | 134 | # coding: utf-8
from pytest import raises
from atropos.adapters import (
Adapter, Match, ColorspaceAdapter, FRONT, BACK, parse_braces, LinkedAdapter
)
from atropos.io.seqio import Sequence
def test_issue_52():
adapter = Adapter(
sequence='GAACTCCAGTCACNNNNN',
where=BACK,
max_error_rate... |
cb99a1f7962bcf38cdb604189469954a6125625a633ff108b896e504bb367a93 | Python | 3,669 | 69 | import json
# 优势在于从json文件层面把废物样本信息给剔除了，而不是从系统层面把文件删除。
# 该函数用于删除废物样本
# file1是所有实体打标文件
# file2是需要删除的样本名称文件
# final是最终的样本数据文件
# 需要删除7次
def delete_trashsample(file1,file2,final):
final_samples = []
with open(file1, 'r') as f:
data = json.load(f)
with open(file2, 'r') as f:
da... |
e159b6121a0d38c297989859cae4c4398b78fc56ed9b577d6537578e1b2f56ad | Python | 3,669 | 102 | #!/usr/bin/env python
"""This script is used to count the unique reads spanning exon-exon junction from the alignment
======------------------------==========------------------------=========================
======|==========|=========================
The input file can be generated in Lin... |
54ae621d087e1dc981dc25ad7a59877a29f3996622b1685c530759e134f1bf02 | Python | 3,670 | 94 | #
# 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.
#
import os
import numpy as np
import tensorflow as tf
print("Num GPUs Available:... |
b6bab0c80abf4e864a4b2fc30bbcec96a016ea56c3ed40738e5bf383b95ab050 | Python | 3,670 | 98 | """Envpool wrapper guard: MyoSuite tasks on envpool < 1.2.6 are refused
(deterministic resets, envpool#432) unless env_config sets
allow_deterministic_resets: true.
The stub-module tests run without envpool installed (CI has none); the
real-path test skips unless envpool is importable.
"""
import sys
import types
impo... |
d4d7abaf423e6ba88d12ce717a07b8ab35387e3d5db50bba2a3a2185a0994073 | Python | 3,671 | 97 | """Zero functional."""
import torch
from mrpro.operators.Functional import ElementaryProximableFunctional, throw_if_negative_or_complex
from mrpro.utils.reshape import normalize_indices
class ZeroFunctional(ElementaryProximableFunctional):
"""The constant zero functional."""
def __call__(self, x: torch.Ten... |
d4e8acda09a59121b7ef9e11ef2ebcae28968003ce9d3c004e2303fab8640476 | Python | 3,671 | 81 | import numpy as np
output_labels = ["pLA_min", "pLA_max", "dpLA_dt_min", "dpLA_dt_max",
"VLA_min", "VLA_max", "dVLA_dt_min", "dVLA_dt_max",
"pLV_min", "pLV_max", "dpLV_dt_min", "dpLV_dt_max",
"VLV_min", "VLV_max", "dVLV_dt_min", "dVLV_dt_max",
"pRA_mi... |
bcbaac6602dd38f9a292d8a8016281e27b247773e70629d067226e0c0a6af04b | Python | 3,674 | 92 | import torch
import torch.nn as nn
import torchtext
from models.lstm.embed_regularize import embedded_dropout
from models.lstm.weight_drop import WeightDrop
# Define the model
class LSTM(nn.Module):
def __init__(self, config):
super(LSTM, self).__init__()
# self.V = n_vocab
self.D = config... |
5a9245ca9ae81dd8cc40aedfa1c47f9ab729dd55a195153c888bc0172c8703c9 | Python | 3,677 | 100 | import os
import pandas as pd
from matplotlib import pyplot as plt
import math
import os
import numpy as np
import scipy as sc
import pylab as pl
import seaborn as sns
from scipy.stats import ranksums
import random
# def plot_sim_denovo_accuracy(sim_hifi, sim_ont):
#
# fig = pl.figure(figsize=(1.8, 5))
# ax... |
6a5b0b5fc4ee19fc29f3ed21620dfc5b671527bea42c6a1749590cc00735a08a | Python | 3,677 | 104 |
from ..utils import get_phenolist
from .. import conf
from ..file_utils import get_tmp_path, get_dated_tmp_path, get_pheno_filepath
from .load_utils import PerPhenoParallelizer
from boltons.fileutils import mkdir_p # to make tmp directory
import sys, argparse
from boltons.iterutils import chunked
from typing import L... |
af1366bc248a62d1720b26f7c9d46ee9891d74047d25d8ecb98f941a83dec035 | Python | 3,677 | 84 | from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from functions.OneDCNN_Utils import read_pkl
from functions.OneDCNN_Utils import change_bar_width
camPath = 'data/OneDCNN/CAM/'
resultPath = 'data/OneDCNN/savedResult/'
figPath = 'fig/'
epochTypes = ['Feedback', '... |
ff79977998bb8aae98afe1eceac7abd6d8a5ca87bfb0c2c384e36474127f2d3a | Python | 3,678 | 101 | import torch
class PenaltyBuilder(object):
"""Returns the Length and Coverage Penalty function for Beam Search.
Args:
length_pen (str): option name of length pen
cov_pen (str): option name of cov pen
Attributes:
has_cov_pen (bool): Whether coverage penalty is None (applying it
... |
a7a60a9b87abbd09e2b4ff2179efd0875a3813f954f61a3704daadfe84cc0c02 | Python | 3,679 | 124 | """Collect relevant icons to icon theme subdirectories
The icons used rely on the KDE breeze theme.
apt install breeze-icon-theme
This script must be run on a linux machine. Please make sure
that `icon_root` is correct.
"""
import pathlib
import shutil
# The key identifies the theme; each list contains icon nam... |
7d06564fab0d31102f17a236c052f85caf63cd8a79774a8d121c8e064ea0410d | Python | 3,680 | 108 | #!/usr/bin/env python
# Filename: manual_annotator.py
# Author: Alessandro Gerada
# Date: 2023-01-28
# Copyright: Alessandro Gerada 2023
# Email: alessandro.gerada@liverpool.ac.uk
"""
This script shows random images from 96-well plates, and waits for user input to
annotate the image. Supports up to 10 growth lab... |
d6813fdec1f25a03974d8ea6bcfa2b2f1209397fd9ad34f81cebdfb8ace5e80c | Python | 3,681 | 83 | import pandas as pd
import os
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__), "..", "..")))
import logging
from app import create_app, db
from load_db.PPMI import get_ppmi_condition
from app.models import Sub... |
447cfed2c24267ff2e01988b35beb22a9e2715d21bbcb304d145b04b2ecc23f0 | Python | 3,682 | 127 | """
frozen (immutable) data structures to support MultiIndexing
These are used for:
- .names (FrozenList)
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
NoReturn,
Self,
)
if TYPE_CHECKING:
from collections.abc import Iterable
from typing import SupportsInde... |
127b5d38cd4fae0c9032e0796e1df5e428b3548352dab70e186fc5ee3fe7230f | Python | 3,683 | 88 | """
@author: Hongao ZHU
@description:
read from the .itemmeasures and the .csv files, integrate the information within,
and output a comprehensive .csv file
@example usage:
python3 integrate.py -C <input_csv_path> -S <input_itemmeasures_path> -O <output_csv_path>
"""
import argparse
import pandas as pd... |
89d9bae00e6fcf9848a483ab472536f0a8421871e1a0d9ff34aa535fddbde097 | Python | 3,685 | 117 | import torch
import numpy as np
import torch.nn as nn
import time
import torch.utils.data as Data
from scNovel.scNovel_classifier import classifier
from scNovel.weightsampling import Weighted_Sampling
from scNovel.score_functions import get_ood_scores
def scNovel(test = None, reference = None, label = None, proces... |
1f7d6a5b7462a2459948b394f5443fc45878d9395af28cfc601765f179663e61 | Python | 3,686 | 97 | # Copyright (c) 2018-2023, 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... |
b46b283fef8d6c96dca5637dffd5a180b1f3d1f3c345a6d6f56c0f0a72ccc708 | Python | 3,686 | 120 | import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import os
import argparse
import matplotlib as mpl
label_size = 16
#mpl.rcParams['xtic... |
20bdc22fce69dfeeab5dace54bfdcec75cec9dbbe34e38da825b5c056b1f7229 | Python | 3,687 | 106 | from indices.base import BaseComparisons
class BaroniUrbaniBuser(BaseComparisons):
"""Class to calculate the Baroni-Urbani-Buser index.
n=2 formula:
(sqrt(a * d) + a)/(sqrt(a * d) + a + b + c)
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that ... |
87b91ca96d11dc7bae4c5725b873bbe82260c20f95be01fdd107f042a77c1644 | Python | 3,687 | 79 | import json
import pandas as pd
import numpy as np
import fire
import text_extensions_for_pandas as tp
import gpmhc.data as data_utils
import gpmhc.learner as learner_utils
import os
#Only one GPU inference is currently suppported, cpu inference not supported
def infer(input_model='./models/baseline_model/', sub_model... |
689fc14ae8a70694f0ce5b253e099b27f73c1fea4fc5afd443e86147cc5dc948 | Python | 3,688 | 106 | from indices.base import BaseComparisons
class GoodmanKruskal(BaseComparisons):
"""Class to calculate the Goodman-Kruskal index.
n=2 formula:
(2 * min(a, d) - b - c)/(2 * min(a, d) + b + c)
Attributes
----------
fingerprints : np.ndarray
Numpy array with the fingerprints that wil... |
a217ffc57d9344cb65c0fd5ef7759ac01f5094b5b4701c8836417a1cde84befe | Python | 3,689 | 102 | from datetime import datetime
import pytest
from pandas.errors import Pandas4Warning
import pandas as pd
import pandas._testing as tm
class TestTruncate:
def test_truncate_datetimeindex_tz(self):
# GH 9243
idx = pd.date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific")
s = pd.Se... |
f5a7876efe0765b0698211c1383e73bda402cebd5ffb96bbb63b1d45739ffe01 | Python | 3,689 | 109 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code curated for GCPNet (https://github.com/BioinfoMachineLearning/GCPNet):
# ----------------------------------------------------------------------------------------------... |
a15f25ecd98afbac2757d5b6afca45a613eb3f9622fcb542b0a1841a0fd406f0 | Python | 3,691 | 97 | __all__ = ["AutomaticWeightedLoss"]
from typing import Any, Mapping
import torch
import torch.nn as nn
class AutomaticWeightedLoss(nn.Module):
"""automatically weighted multi-task loss
Params：
num: int，the number of loss
x: multi-task loss
Examples：
loss1=1
loss2=2
... |
91455274bcab382422e0879d61fb432e4d1304439734da5f90a69f3fb700cbdb | Python | 3,693 | 127 | # some codes are 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,... |
c5c18c121eb22fd4daebc96a5f76d6a1f232685ec797a36eb71edf1240220a43 | Python | 3,693 | 73 | import json
# 传入总体的json文件，读取json文件的键值数据，一个一个遍历扫描
# 然后字典统计频次
def text_save(filename,allfilename, data, data1):
file = open(filename,'a',encoding='utf8')
for i in range(len(data)):
s= str(data[i])+"\n"
file.write(s)
file.close()
print("保存频率文件成功")
with open(allfilename, 'w',en... |
cf1821922eb6260ea4897dd7b041a4a3ace0b3f59f931e2993c3ada875e20ede | Python | 3,693 | 112 | import torch
import numpy as np
def ecdf(p):
"""Estimate the cumulative distribution function.
The e.c.d.f. (empirical cumulative distribution function) F_n is a step
function with jump 1/n at each observation (possibly with multiple jumps
at one place if there are ties).
For observations x= (x_1, ... |
c3c207bae7a1a99adb1e9460328a77c171076d9f710cd88621b0056afd1fdec2 | Python | 3,694 | 92 | from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from pyaging.preprocess._tage import _map_to_mouse_entrez
REPO_ROOT = Path(__file__).resolve().parents[2]
FIXTURES = REPO_ROOT / "tests/data/tage"
MAPPING_ASSET = REPO_ROOT / "clocks/weights/tage_gene_mapping.csv.gz"
MAPPING = pd.DataFram... |
198bd96db7f4c588653499c07f6214fa7e426cc5b8e01f31bd86a5e3d8319360 | Python | 3,695 | 121 | from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas.core.dtypes.cast import (
maybe_box_native,
maybe_unbox_numpy_scalar,
)
import pandas as pd
@pytest.mark.parametrize(
"obj,expected_dtype",
[
(b"\x00\x10", bytes),
(4, int),
(n... |
5d373c1fde54d7d123db2a53220b619943a406210d83c0155984f8e5d24acbfa | Python | 3,695 | 121 | import torch
import bionetwork
import matplotlib.pyplot as plt
import numpy
import plotting
import time
import pandas
batchsize = 5
activationFunction = 'MML'
parameters = bionetwork.trainingParameters(iterations=150, clipping=1)
networkSize = 10
batchsize = 5
seed = 15
networkList, nodeNames = bionetwork.getRan... |
7a35f4e8aedf2bf3ab6af73e3015987527ff4892f2abeecbd04f5d3fb3cf01a7 | Python | 3,696 | 110 | import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import pytest
from umap import UMAP
import umap.distances as dist
def _data(dtype=np.float64):
return np.array([[0, 2, 5], [6, 1, 0], [2, 4, 3], [1, 0, 8]], dtype=dtype)
def _expected(X, Y, metric, value):
if metric == "minkow... |
103952e2e0550181fe9be7a90fc5752d207c5ecbed118dee17e2d0c429251136 | Python | 3,700 | 99 | # Copyright (c) 2018-2023, 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... |
7ac0e0b467320641279af9150ae7ca8f1cbf069da2e4e69f984eb04a756e06c3 | Python | 3,700 | 143 | """Test the initialization and training of context heatmap multi-head crnn models."""
import copy
import pytest
pytestmark = pytest.mark.gpu
def test_supervised_heatmap_mhcrnn(
cfg,
heatmap_data_module_context,
video_dataloader,
trainer,
run_model_test,
):
"""Test the initialization and tra... |
cac08c391a435a0316b0c284e55f3769d747b58de3b98ef28ca46566f6eaf35b | Python | 3,700 | 100 | import ntpath
from sys import path as sys_path
sys_path.append('..')
sys_path.append('../base')
import os
from base import loggingUtils
import multiprocessing as mp
from multiprocessing import Pool
from glob import glob
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import imageio.v3 as iio
impo... |
ead98f662c745253dce31341a0fbeac36fc7882d7a1c0f510147670b59282d30 | Python | 3,700 | 68 | #!/usr/bin/env python3
"""
Description:
Directly convert transcript counts to Isoform Fractions (IF). Transcripts with no non-zero counts will still be filtered out. This module is useful when skipping pre-filtering step.
Usage:
./transform_tx_counts_to_ifs.py -i <tx_counts.txt> -m <tx2gene.txt> -F <filtered_i... |
f1bf199c09e0af4ec60c8efe1290560940a9904841998a71a506f1df51a72384 | Python | 3,700 | 143 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 17 16:08:44 2020
@author: reisertm
"""
import sys
sys.path.append("/home/reisertm")
sys.path.append("/home/reisertm/nora/src/python")
import numpy as np
import tensorflow as tf
import math
from tensorflow.keras import Model
from tensorflow.keras ... |
9739bc33da9fb53a52363967038ddc1e1085827f2a31643b2925034c3b027c3d | Python | 3,703 | 128 | # some codes are 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,... |
d86e988c6e077351a2cfbd4a06dfbbc37d2f80b0a6a6a66077ac1662b1d6c055 | Python | 3,703 | 93 | # -*- coding: utf-8 -*-
"""
Created on Sun Dec 20 17:02:31 2020
@author: Wladek
"""
import numpy as np
import matplotlib.pyplot as py
import matplotlib as mpl
from matplotlib.colors import LogNorm
from figure_properties import *
import matplotlib.patches as mpatches
from scipy.signal import welch, argrelmin, butter, ... |
c1ed426d812baba5f8f49a607bfe1e943b894e37576c28658398c04053754a62 | Python | 3,705 | 107 | # MOFTransformer version 2.0.0
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
DEFAULT_FIGSIZE = (8, 8)
DEFAULT_VIEW_INIT = (0, 0)
def get_default_cbar_kwargs(figsize):
return {
"num_ticks": 6,
"decimals": 4,
... |
bb8a03b94745017c343c723c4900286b6840e51c99bc253e1f467a7424e1b4ac | Python | 3,707 | 91 | import pandas as pd
import matplotlib.pyplot as plt
import os, 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 app import create_app, db
from app.models import Image, Subject, SourceDataset, Prep... |
5e283a59d3c814223eb05f6c288d3a2ba003292bac26767d3dd48f45147dc09f | Python | 3,708 | 78 | from botocore.client import Config
import ibm_boto3
from io import StringIO
import types
import os
import pandas as pd
import sys
import json
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__), "..", "..")))
import pdb
from... |
b294dd50c94702f02275a3442bb11dceb4ff58187ed56c6a1379d297cbffb67c | Python | 3,708 | 114 | """Custom exception classes.
CLASSES
-------
ProcessingOrderError : Exception
- Class for raising exceptions/errors associated with performing processing
steps in an incorrect order.
UnavailableProcessingError : Exception
- Class for raising exceptions/error associated with trying to perform
processing st... |
e8ca5e7e1bd43574230e2d0a1874cc2f726e903f16cd2a73560d950e7e788387 | Python | 3,708 | 84 | import numpy as np, matplotlib.pyplot as plt, sys
from scipy.stats import kstest
import pumpprobe as pp
plt.rc("xtick",labelsize=18)
plt.rc("ytick",labelsize=18)
plt.rc("axes",labelsize=18)
ds_list = "/projects/LEIFER/francesco/funatlas_list.txt"
matchless_nan_th = 0.5
matchless_nan_th_added_only = True
matchless_na... |
0062bdd2bcc04fb86e0e5649e565200d3c8c6b5d14cf0422c92433a4c2f86bd4 | Python | 3,712 | 115 | import importlib.resources
from PyQt6 import uic, QtCore, QtWidgets
from ... import pipeline
class MatrixFilter(QtWidgets.QWidget):
active_toggled = QtCore.pyqtSignal()
enabled_toggled = QtCore.pyqtSignal(bool)
option_action = QtCore.pyqtSignal(str)
modify_clicked = QtCore.pyqtSignal(str)
def _... |
62b46a0c48b879443f3c2f581a8ca331a445b17785ce222d7a7ed9eb2e96b60a | Python | 3,712 | 131 | from typing import Callable, List, Any, Optional
import torch
from argparse import Namespace
import logging
import traceback
import sys
import io
class LazyLog(object):
def __init__(self, func: Callable[..., Any], *args, **kwargs) -> None:
self.func = func
self.args = args
self.kwargs = kw... |
3b9cfdc1128bc346f8945b9333162d5222f49f9ea9e8aba12f3435bd17d39ef2 | Python | 3,713 | 88 | #! /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... |
62fe2841bfd9602db4ff74b17b8d85de69af875d5377f7da99b11a699e305fc8 | Python | 3,713 | 113 | """
This code incorporates modifications to software originally developed by the Turaga Lab, available at https://github.com/TuragaLab/DECODE.
The original work is licensed under the GNU General Public License version 3.0 (GPL-3.0).
We have adapted and extended this code under the same GPL-3.0 license.
"""
import t... |
0c8475c213b7b6abedaf6b1edba254c58c2237714d0435918ca8b8727d360598 | Python | 3,716 | 103 | """Structural checks for the thin notebooks under `notebooks/` (not `notebooks/legacy/`).
Parses `.ipynb` files as plain JSON (no `nbformat`/Jupyter dependency, so
these tests always run in the default `just test` job): every new notebook
must stay a thin orchestration layer over `multimodal_ad` -- no function/class
d... |
b71d090beb70eb1e53fdbe39c7fcfe4d9246b57da13a58876999eb92406eaa61 | Python | 3,717 | 119 | #! /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... |
e8c7e39ce4b3c01b84e1c9d5bc02224c4bab0a9b6ead56fa118a0c0099c19c49 | Python | 3,717 | 150 | #!/usr/bin/python3
from draw_backbone import draw_backbone
def read_aln(file):
import sys
aln_dict = {}
f = open(file)
for line in f:
line = line.rstrip()
tmp = line.split('\t')
scaf1, strand1, bg1, ed1, scaf2, strand2, bg2, ed2 = tmp[0:8]
if bg1 == '-' or ed1 == '-' or bg2 == '-' or ed2 == '-': continu... |
7fef9c91c5c5eb2a1a977005e930f58a790c9369c47a110c337958040308e36f | Python | 3,719 | 141 | import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score, average_precision_score
import torch
def train(
model,
train_loader,
criterion,
optimizer,
):
total_loss = 0
total_sample = 0
y_list = []
prob_list = []
for static, dynamic, y in train_loader... |
9e2586adaf47e674527461d257010a41c5a039aac5b50f95b8fc27cf61d66724 | Python | 3,719 | 135 |
import unittest
import torch
import math
from tnn.regularization import TikhonovRegularization, SmoothTimeRegularization, BlockRegularization
from tnn.utils import extract_data, insert_data
class TikhonovRegularizerTest(unittest.TestCase):
# setup tnn network
def setup_network(self):
torch.set_defau... |
b18d1f7fd3167a571491d22b2a0db31ff041bc7ca6377794e2195c5450cc3df6 | Python | 3,721 | 124 | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.ticker import MaxNLocator
import matplotlib.transforms as transforms
import sys
import argparse
sys.path.append('../')
from plotting_functions import *
#create figure
fig, ax = plt.subplots()
#set font
# plt.rcParams['font.fam... |
2bf2a558eb6f162ffc30b26d2b38fcceda19afed5f21be10b6c636cb520b667a | Python | 3,722 | 62 | import sys
import pandas
import csv
import re
X = pandas.read_csv( sys.stdin, sep=' ', index_col=False, quoting=0, converters={'word': str} ) #, na_values=default_missing ) #quotechar='|' ) # quoting=csv.QUOTE_ALL ) #, quotechar=None ) #, quoting=csv.QUOTE_NONE )
#X.to_csv( sys.stdout, sep=' ', quoting=0 )
## Find ... |
e5ce76de8bcb866de589409ca5abdc129caa3ad4ab5e2b2cf99647df3c804c12 | Python | 3,722 | 124 | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.ticker import MaxNLocator
import matplotlib.transforms as transforms
import sys
import argparse
sys.path.append('../')
from plotting_functions import *
#create figure
fig, ax = plt.subplots()
#set font
# plt.rcParams['font.fam... |
479095add1e23085e99fb4a6814315e55a3c86dc9ea8cdafa48553be8a313de9 | Python | 3,723 | 121 | # 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... |
5d5e2a6af063b07f4927b11fbcd2e356f71d01ff71fde6bf3a78726429e190c7 | Python | 3,725 | 124 | # 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 agree... |
a299cb14b81c3a18eb38fe768df63f6cec553159a67d7a534829ee75a3a9d969 | Python | 3,726 | 120 | #!/usr/bin/env python
'''
Testcases for the measurements binning
'''
import windseer.utils as utils
import numpy as np
import torch
import unittest
class TestBinLogData(unittest.TestCase):
def setUp(self):
self.grid_dimensions = {
'n_cells': 2,
'x_min': 0.0,
'y_min':... |
5b3610a02671d577d25327780d5f96aba534cf595db8ae57a0912dca9649fab1 | Python | 3,727 | 128 | import os
import argparse
import zmq
from collections import defaultdict
from model_data_base import distributed_lock
import sqlite3
import cloudpickle
import yaml
config_path = os.environ['ISF_MDB_CONFIG']
with open(os.environ['ISF_MDB_CONFIG'], 'r') as f:
config = yaml.load(f)
# we here assume backend is sqlite... |
5f47bec77f419e53d87ce1f44e854892485b0d485d21e7f515d90a9bba1fa94c | Python | 3,727 | 106 | import json
import math
import os.path
import numpy as np
import cv2
class DetectionParser:
def __init__(self, fname: str = None, py_data: dict = None):
if fname:
if fname.endswith('.json'):
with open(fname) as jf:
self.__data = json.load(jf)
elif p... |
2935b2ab7e82a5a8005253c20ac0ea5100222eb3cfa4b40a7d32308f65d74816 | Python | 3,731 | 114 | import numpy as np
import pytest
from pandas.compat import is_platform_arm
import pandas as pd
import pandas._testing as tm
from pandas.util.version import Version
pytestmark = [pytest.mark.single_cpu]
numba = pytest.importorskip("numba")
pytestmark.append(
pytest.mark.skipif(
Version(numba.__version__)... |
6382d039b180f5da14c5b650294805dca9de30a83a6a643b7ecd8903c6d725a1 | Python | 3,732 | 122 | #adapted from Jacques Lerousseau
#this script expects the following input variables with those names:
# sfreq: the sampling frequency
# trial_duration
# prestim_duration:
# poststim_duration:
# condition_freq:
# EVOKED:
#import required packages
import sys
import os
import numpy as np
import mne
import scipy.signal
i... |
05313bc2d04caf1d426be8d7bfa8234c605a7c1c019c3a3d1b758be8280cf246 | Python | 3,734 | 133 | #!/usr/bin/python3
import sys
import argparse
# author: zhouyang zhouyang@genomics.cn
from draw_gene import draw_gene_v1, read_gff_v1, draw_gene_model
from draw_aln import draw_aln, read_aln
from draw_backbone import draw_backbone
def read_conf(conf):
f = open(conf)
gene = {}
aln = []
rang = {}
flag = 0
orient... |
a5cf5119c958ff6e612ff4bde5244fa20cd2aa64d0199a10634c6d2da157ea3f | Python | 3,737 | 108 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
class TestIntervalIndexRendering:
# TODO: this is a test for DataFrame/Series, not IntervalIndex
@pytest.mark.parametrize(
"constructor,expected",
[
(
pd.Series,
("(0.... |
5aef1b8eec6cd8d7ea0f2cc423a69fdd475425d5526e167d547004b368508fdd | Python | 3,739 | 106 | from pandas import read_parquet
import torch
import torch.nn as nn
import torch.nn.functional as F
# types imports
from torch import Tensor
from torch.nn import Module, Conv2d, Sequential
from sbrnet_core.utils.constants import view_combos
RSQRT2 = torch.sqrt(torch.tensor(0.5)).item()
class SBRNet(Module):
def ... |
6cb1f971c997ca9585904fed263985c0441ee7559b91977804fdffd1b31f6f64 | Python | 3,739 | 81 | import argparse
import os
from cdr.config import Config
from cdr.model import CDREnsemble
from cdr.util import filter_models, stderr
if __name__ == '__main__':
argparser = argparse.ArgumentParser('''
Generate summary of saved model(s)
''')
argparser.add_argument('paths', nargs='+', help='Path(s) to... |
02cc6f31a50147e1c24b8bc5639b4e9fb9dd31d1362f32de1425276ff6b04b06 | Python | 3,740 | 162 | """
Period benchmarks that rely only on tslibs. See benchmarks.period for
Period benchmarks that rely on other parts of pandas.
"""
import numpy as np
from pandas._libs.tslibs.period import (
Period,
periodarr_to_dt64arr,
)
from pandas.tseries.frequencies import to_offset
from .tslib import (
_sizes,
... |
ec7c567272216f18f820c6479a63874a47d92fec4661783707522c7e21987cdb | Python | 3,743 | 94 | """
.. module:: tool_smartbrush
:platform: Linux, Windows
:synopsis: Smart brush tool (2D or 3D) based on the SmartPaint method
.. moduleauthor:: Fredrik Nysjo
"""
from tool_common import *
import numpy as np
import scipy as sp
import scipy.sparse
import scipy.sparse.csgraph
import scipy.ndimage
import glm
c... |
1f1e18f125c1884ae6c3cf9efa11c718c064d688832624ad1f807ad89075536e | Python | 3,744 | 108 | import pathlib
import numpy as np
from shapeout2 import pipeline
def test_get_heredity():
path = pathlib.Path(__file__).parent / "data" / "calibration_beads_47.rtdc"
# initialize
slot = pipeline.Dataslot(path)
ds = slot.get_dataset()
ray = pipeline.FilterRay(slot)
# come up with a few filte... |
85083706e5b7170de1675d402fc29b3335cc8559a90364a9d2480c3822df3575 | Python | 3,744 | 113 | 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... |
bcf7ec0c83dcd485fae4987675066d1d06b77da538fdf08e3f5a59f1fcc9061a | Python | 3,745 | 82 |
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... |
0ff0cdd7bef9a86c4b5092203301ef810e65f4e55c59fa2f38e8b9113cda539e | Python | 3,746 | 75 | from botocore.client import Config
import ibm_boto3
from io import StringIO
import types
import os
import pandas as pd
import sys
import json
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__), "..", "..")))
import pdb
from... |
81ce19793063debb3c66bbafb5d100fe38cd9cf2953e7276f14b5155fc489f24 | Python | 3,747 | 109 | """Tests for the nimare.diagnostics module."""
import os.path as op
import pytest
from nilearn.input_data import NiftiLabelsMasker
from nimare.diagnostics import FocusCounter, Jackknife
from nimare.meta import cbma, ibma
from nimare.tests.utils import get_test_data_path
@pytest.mark.parametrize(
"estimator,meta... |
121daee026919293af42626df29033c99994f44669701b746ee066543060d818 | Python | 3,752 | 108 | #!/usr/bin/env python
__version__ = '1.0'
import os, sys
import random
from collections import namedtuple, defaultdict
from Bio import SeqIO
simType = ['sub', 'ins', 'del', 'match']
simTypeSize = 4
def throwdice(profile):
dice = random.random()
for i in range(simTypeSize):
if dice < profile[i]:
... |
ba0e8243e37d0865337e4647cb04aa52464bd006e1e9d5a71f9d7e44f5bf9bbe | Python | 3,752 | 98 | # -*- coding: utf-8 -*-
import math
class GCContent(object):
"""
Class is primarily used to determine the error probabilities based on the GC-content.
"""
@staticmethod
def _default_error_function(gc_percentage):
"""
Default/Fallback function to calculate error probabilities base... |
d96e13cc3d471205fd7cf65a0c77ae432b5eadb0cb498c76b24477acd648fcd8 | Python | 3,753 | 81 | """Iterative SENSE Reconstruction."""
from __future__ import annotations
from collections.abc import Callable
from mrpro.algorithms.reconstruction.RegularizedIterativeSENSEReconstruction import (
RegularizedIterativeSENSEReconstruction,
)
from mrpro.data.CsmData import CsmData
from mrpro.data.DcfData import DcfD... |
6390f8673f85727e1f7ab3d1887293654f8d70e1fddd57c9f2d03e2d41952946 | Python | 3,754 | 87 | #!/usr/bin/env python
version = "v0.1.0.20220721"
import os,os.path,sys,re
from argparse import ArgumentParser
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '/home/hongxiaoning/pipeline/bin'))
import myc
import time
if __name__ == '__main__':
#Parameters to be input.
parser=Argume... |
e6f75c4295f7aab5a34f0b44f1d147f77fc8c4a50bb62de886a7d443fb4e9c2e | Python | 3,754 | 113 | import torch
import bionetwork
import matplotlib.pyplot as plt
import numpy
import plotting
import time
import pandas
import saveSimulations
batchsize = 5
activationFunction = 'MML'
parameters = bionetwork.trainingParameters(iterations=150, clipping=1)
networkSize = 100
batchsize = 5
#networkList, nodeNames = bionet... |
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