sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bbb01b6b38f1481ffef8b03e63f1f9cead4581c76e767f1eef89797df3330042 | Python | 1,406 | 50 | """Demonstrates how to use the tracer module, independent of autodiff, by
creating a trace that prints out functions and their arguments as they're being
evaluated"""
import autograd.numpy as np # autograd has already wrapped numpy for us
from autograd.tracer import Node, trace
class PrintNode(Node):
def __init... |
9493034e3157826d3a34ef8009e3986db807cf9da3078b29027b32c9fe9ab196 | Python | 1,407 | 40 | #!/usr/bin/env python
from perception_tools.perception import Perception, set_pose
from pybullet_tools.utils import ClientSaver
from perception_tools.common import is_fixed, is_item
class PBPerception(Perception):
def __init__(self, world, state={}):
super(PBPerception, self).__init__(world)
for n... |
c493a22065980de01c49e7670e9c616cd03d8b19519179e0d6da85b016686ec7 | Python | 1,407 | 31 | import torch
from typing import Callable
from .trainingwrapper import TrainingWrapper
class ShallowLinear(TrainingWrapper):
"""Single-layer linear network."""
def __init__(self, num_inputs: int, num_outputs: int, loss_fun: Callable,
metric: Callable, lr: float=0.01, weight_decay: float=1e-4,
... |
d58bc573595de45d63cc904fd2328f1a3108a2f0a2f384f6d47a7909511cc043 | Python | 1,407 | 49 | import subprocess
import sys
import tempfile
from pathlib import Path
def install_locked(extra):
"""
Install the versions pinned in uv.lock into Blender's Python, so tests in Blender
run against the same dependencies as `uv sync`.
Args:
extra: Optional dependency group to install alongside th... |
500a8c0e2ce4d551c757356ce391de4467107acfccfb10d2d016655d1fb05691 | Python | 1,408 | 26 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
f55034a11837b6296ec3e76a7d9c37b2cb242c29dbab8c18516f567cf360842d | Python | 1,408 | 38 | #!/usr/bin/env python
import sys, os, subprocess, platform
from glob import glob
def execute(message, command_line, return_='status', until_successes=False, terminate_on_failure=True, silent=False):
print(message); print(command_line)
while True:
p = subprocess.Popen(command_line, bufsize=0, s... |
23f36b953aeae5bc3811407b88dc8084ceff44db074ce3e6bac2b8ecc3ee043b | Python | 1,409 | 55 | import pysam
import sys
fn=sys.argv[1]
rfh=open(sys.argv[1]+'_multi_split_aligned_temp.txt','w')
IDs=[]
align=[]
splits=['1','1-1','1-2','1-3','2-3','2-2','2-1','2']
count=0
rfh.write('ID\t'+'\t'.join(splits)+'\n')
dfh=pysam.AlignmentFile(fn, "rb")
pre_ID=''
count=0
locs=[]
for i in splits:
locs.append('na')
for... |
578b93fbfb180202d3d27395f26f9e1d24ecaac3aafc2d3aa0220896ce8e0b5f | Python | 1,409 | 50 | from typing import Dict, List, Union
from ..base_transform import LabelTransform
class FixCategory(LabelTransform):
r'''
Returns a pre-set label for all samples, usually used to supplement the dataset with new categories.
.. code-block:: python
transform = FixCategory(value=0)
transform... |
335d16ab81ad25e7313633fb2be529686d27e7d4f75a6b4bafdf8e3297f569f2 | Python | 1,410 | 47 | import torch
from scipy.io import loadmat
from .utils import AtomsData
from ..utils import progress_bar
from ase import Atoms
class QM7b(AtomsData):
def __init__(self, root: str, cutoff: float = 4.0, device: str = "cpu") -> None:
self.cutoff = cutoff
self.device = device
super().__init__(r... |
ab773a89236cfa5389a4cb9ee2f550bf3aa8e290149d975d17d345b637f56e16 | Python | 1,410 | 45 | from bnnsrc.utils import *
import numpy as np
import torch
class BaseNet(object):
def __init__(self):
return
# cprint('c', '\nNet:')
def get_nb_parameters(self):
return np.sum(p.numel() for p in self.model.parameters())
def set_mode_train(self, train=True):
if train:
... |
ee2758749fde9db77f8dec8e443feea1d705fb9d24c923fdf0cb6e963300c1b2 | Python | 1,410 | 55 | from __future__ import annotations
from enum import Enum
from ord_schema import reaction_pb2
WORKUP_TYPES = list(reaction_pb2.ReactionWorkup.ReactionWorkupType.keys())
class MessageType(str, Enum):
""" type of ord messages defined for message level comparison """
REACTION_WORKUP = "REACTION_WORKUP"
""... |
24635f73e1525309d5393f967cce313baae959918708c8f33bf8f06c5c555d2d | Python | 1,411 | 30 | # Create a list of all possible pairs of participants,
# and indicate if the pair is real or pseudo based on the real pair list.
import pandas as pd
from itertools import combinations
# Load the CSV file containing real pairs
real_pairs_df = pd.read_csv("/project/3011157.03/Simon/proj_2022_CABB_movie/MRI/real_pair_lis... |
5a002ee0912bd16849f6bd43c59e7860e98b8085de14988543917e93feaf9fa5 | Python | 1,411 | 45 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 1 18:16:05 2017
@author: Carlos Atico Ariza, PhD
"""
import matplotlib.pyplot as plt
import numpy as np
import itertools
def plot_confusion_matrix(cm1, classes, normalize=False,
title='Confusion matrix', cmap=plt.cm.Blu... |
c21d8c0298e99fe293661d6a6104d47f94f0c22eeb958605e15bc5cb29aec220 | Python | 1,411 | 52 | from nibabel import load as nload
from wonambi.attr import Channels, Freesurfer
from wonambi.attr.chan import (find_channel_groups,
create_sphere_around_elec,
)
from .paths import (chan_path,
fs_path,
template_mri_pat... |
d8f8c8a3131e00b87d42ec0642f71b352ec08ed5189ef1e153af9b294a4cc704 | Python | 1,411 | 35 | import numpy as np
import hmp
from hmp.models import EventModel
from hmp.patterndata import PatternData
from hmp.visu import plot_topo_timecourse
from test_io import init_data
def test_plot():
_, _, epoch_data, positions, sfreq, n_events = init_data()
hmp_data = hmp.basedata.default(epoch_data, n_comp=2,dura... |
6ced79a0ab68acb36e9ef17851de6c987da808f0be355948ef2dc2c1b88c5fed | Python | 1,412 | 37 | import numpy as np
import xarray as xr
import hmp
from hmp import simulations
from hmp.models import EliminativeMethod, EventModel
from hmp.patterndata import PatternData
from test_io import init_data
def test_backward_simple():
""" test a simple fit_transform on perfect data and compare to ground truth."""
... |
be43c9e66ef60f76ca701ec508b1a372702626e2ad594cfdea319bc688d83367 | Python | 1,412 | 40 | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
def load_data(file_path):
return pd.read_csv(file_path)
def preprocess_data(df):
# Example preprocessing steps
df = df.dr... |
18aba68732b498cfdbcb671371a26192077221959c3b20169b32851e460e186e | Python | 1,413 | 40 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
18d95fe7695fcbb80833a625b61664b9bba58e3ff267cb4c2ad2191adbc65194 | Python | 1,413 | 40 | import numpy as np
from utils import in_sa_dir_stats, pickle_dump
from joblib import Parallel, delayed
from joblib.externals.loky import set_loky_pickler
set_loky_pickler('pickle')
import os
os.environ['OPENBLAS_NUM_THREADS'] = "1"
os.environ['MKL_NUM_THREADS'] = "1"
NNULLS = 10000
drosophila = np.load('../../data... |
01154bb106f525436212fff70d989bb8662b916391b6d36da5a2bcb37f733794 | Python | 1,414 | 44 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
03f7122fb7692cc9b0de880f3c14a462fded0b2b5f7cb39a50ec26b94e1dd990 | Python | 1,414 | 35 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Free Software Found... |
918bf7cbaf67b63745e231565484df951580f2467d670b467b384a61da5f280e | Python | 1,414 | 44 | # ----------------------------------------------------------------------
# Copyright (C) 2014, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or mo... |
539e95f05fdf06ada683ddb7701b26187b042e1a84a6a94c3e4637773ce1c2a9 | Python | 1,415 | 59 | # Node-group asset "Force" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
from bp... |
580b2b2c28f19559f7faea3d20deef4da1dfe3a92231ebdbcdde858212511e40 | Python | 1,415 | 48 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
9d824edb7555a54703077753a2a0e8c546de1bf725e4ddc2deb20571c2d21531 | Python | 1,415 | 45 | import scanpy as sc
import pandas as pd
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from copy import copy
vir = copy(mpl.cm.viridis)
vir.set_under("lightgray")
import numpy as np
sc.settings.verbosity = 3
sc.logging.print_header()
sc.settings.set_figure_params(figsize = [10,8],dpi... |
48875d2f7a359b4969d73b1b2f665425ec56bde5fa0f7c0cf11147d530346600 | Python | 1,416 | 59 | #!/usr/bin/python3
'''
Changing the call
to string.atol() to string.atof() and changing
the format "let %c%d = %d' to 'let %c%d = %g'
solved the problem.
'''
import sys
import string
if len(sys.argv) < 2:
print("Usage: %s infile [outfile] [delimiter_char]" % sys.argv[0])
sys.exit(1)
filename_in = sys.argv[1]... |
23df3fde4e81dfc0edbfb50e53327177cc734b8be250bd34b133931e1f70d111 | Python | 1,417 | 42 | from pathlib import Path
from segm.data.base import BaseMMSeg
from segm.data import utils
from segm.config import dataset_dir
ADE20K_CONFIG_PATH = Path(__file__).parent / "config" / "ade20k.py"
ADE20K_CATS_PATH = Path(__file__).parent / "config" / "ade20k.yml"
class ADE20KSegmentation(BaseMMSeg):
def __init__(... |
51ae6f9183f93fae92f3db71e7d803ec5da5fbbbb6b4b2acaad763634c7ffae3 | Python | 1,417 | 44 | from function import *
file_path = ['c_new_mini_train_data.csv',
'c_new_test_data.csv', 'c_new_train_data.csv']
for path in file_path:
df = pd.read_csv(path)
reproduce_model = GetReproduceModel('xception_1d', 'finish_model/06.23.14.01')
p_li, t_li = reproduce_model.get_predict_expression(
... |
c8eb681face9024a0a60452dafc161ceb62790d1d0690063590d8761a7b53108 | Python | 1,417 | 46 | import logging
import os
from typing import Optional
from pip._vendor.pyproject_hooks import BuildBackendHookCaller, HookMissing
from pip._internal.utils.subprocess import runner_with_spinner_message
logger = logging.getLogger(__name__)
def build_wheel_editable(
name: str,
backend: BuildBackendHookCaller,
... |
0d1cc589efc3dff5e03c5a7f5ae6cdf77ccda32777237385cfba50163c05e02d | Python | 1,418 | 42 | # -*- coding: utf-8 -*-
"""Numpy dataset"""
def _numpy_generator(data_dict, subset):
for i in subset:
datum = {k: v[i] for k, v in data_dict.items()}
yield datum
def load_numpy(dataset, splits=None, shuffle=True, seed=0):
"""Loads dataset from numpy array
It is assumed that the numpy ar... |
2ebbe64cb513ac875a105b0903e46070d1f099d5afa418eb79b76d5855ea9b16 | Python | 1,418 | 36 | # import subprocess
#
# with open('src/visualization/runs_to_draw.txt', 'r') as f:
# lines = f.readlines()
# for line in lines:
# run_select, tag, epoch = [x.strip() for x in line.split(' ')]
#
# proc = subprocess.Popen(
# [f'python', './src/visualization/draw_video.py', f'{run_select}', f'{tag}'... |
18fb6f36670fa28304b9b24229546d27f4ccb3859896ead52b41f102a02bcb0f | Python | 1,419 | 31 | import os
# os.environ["PATH"] = "E:\\Histology\\WSIs\\vips-dev-8.11\\bin" + ";" + os.environ["PATH"] # Use this while working on windows
import pyvips as vips
import openslide
print("Pyips: ",vips.__version__)
print("Openslide: ",openslide.__version__)
from histolab.slide import Slide
import matplotlib.pyplot a... |
aced857fc59073da63971bd25c8a4c1010b655161090300ea77c40223b0ea3b0 | Python | 1,419 | 30 | """xlsx_read.py 2026-09-29: read the first sheet of an .xlsx with the standard library only (an .xlsx is zipped XML)."""
import zipfile, re, xml.etree.ElementTree as ET
NS = {'m': 'http://schemas.openxmlformats.org/spreadsheetml/2006/main'}
def read(path, sheet=1):
z = zipfile.ZipFile(path)
shared = []
if... |
fa222cb3f4039f70370ece9623c39fa898b10e20fa4d57d091601f8e197b012a | Python | 1,419 | 42 | """
This is a small collection of helper functions to debug the installation / imports.
"""
def main():
import subprocess
import os
#Script to check if all requirements are installed
to_check = {}
#to_check['java'] = (['Java', '-version'], None) #command, desired output
to_check['R'] = (['R'... |
fe76b18e5210edd328f5643ac75d0a196c56c35048ea7bfede183083a3dde7c5 | Python | 1,419 | 44 | # Librairies
import os
import sys
code_path = os.path.split(os.path.split(os.getcwd())[0])[0]
sys.path.append(code_path)
from joblib import load
import argparse
from setting import *
# Arguments
argParser = argparse.ArgumentParser()
argParser.add_argument("-n", "--name", type=str, help="dataset name")
argParser.add_a... |
39da019fa8474e463fd5296b5da2121ddddb9b32bed50d68f024a80b0c47bfc7 | Python | 1,420 | 44 | import random
import unittest
import torch.nn as nn
import torch
from torch.utils.data import Dataset
from torch.utils.data.dataloader import DataLoader
from torcheeg.trainers import GlowTrainer, CGlowTrainer
from torcheeg.models import BGlow, BCGlow
class DummyDataset(Dataset):
def __init__(self, length: int = 1... |
70ca301cbc2220b941afa01c9cb957f7cfd5239b9a54b7561f7ce469b6b6daab | Python | 1,420 | 49 | from elasticai.creator import ir
from elasticai.creator.ir2vhdl import factory
from elasticai.creator_plugins.lutron.lutron import lutron
def test_can_generate_vhdl_for_lutron():
impl = factory.graph(
ir.attribute(
{
"input_size": 2,
"output_size": 2,
... |
90e624e7ccbf5420324fff95c24d1ccd849a1331d98ede0178669e52f7764140 | Python | 1,420 | 51 | # pylint: disable=too-many-function-args, invalid-name
""" HCP ICA dataset loading script"""
import numpy as np
import pandas as pd
from omegaconf import DictConfig
from src.settings import DATA_ROOT
def load_data(
cfg: DictConfig,
dataset_path: str = DATA_ROOT.joinpath("hcp/HCP_AllData_sess1.npz"),
lab... |
956a8578349a5bd6550a486bbcc600aaefcefa9878abfc83d9d1f2a5114f35ee | Python | 1,420 | 49 | """Known error codes as reported in the HED specification."""
known_error_codes = {
"hed_validation_errors": [
"CHARACTER_INVALID",
"COMMA_MISSING",
"DEF_EXPAND_INVALID",
"DEF_INVALID",
"DEFINITION_INVALID",
"ELEMENT_DEPRECATED",
"PARENTHESES_MISMATCH",
... |
a829f6cfd2280394a217d9d7f53ff4786a921b6d1afbba250c8fb91b903cebe5 | Python | 1,420 | 39 | # Material "Fresnel Outline", dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# The class is a recipe for the material's shader tree — build recreates the material and runs it into material.node_tree.
from bpy.types import ShaderNodeTree
from nodebpy impor... |
c42f776e32870b2b1a464017a38d6d460f87f3d3c47e720a460bed7570e9f01a | Python | 1,420 | 36 | ###############################################################################################
# Emma Bailey, 17/06/2024
###############################################################################################
import numpy as np
from Functions.import_data_rs import import_data
if __name__ == '__main__':
... |
eafbf6bc6e62ec5e23f123afa89c3abeca4f8a1d92f421a6fcac6121f2891b0d | Python | 1,420 | 42 | """
Optuna example that optimizes a classifier configuration for Iris dataset using sklearn.
In this example, we optimize a classifier configuration for Iris dataset. Classifiers are from
scikit-learn. We optimize both the choice of classifier (among SVC and RandomForest) and their
hyperparameters.
"""
import optuna... |
efe22a5167e916cbe5bd3f58c4cbd5fcb3764293c462d8040962a2ef794c3b3a | Python | 1,420 | 55 | import os.path
from src import settings
import src.data.handler as data_handler
import torch
import itertools
import numpy as np
def sigmoid(z):
return (1 / (np.exp(-z))) - 1
def calc_weigths(fmri_data, min_weight=0.0, max_weight=2.0):
weight = sigmoid(np.abs(fmri_data)) # calc 0 based sigmoid
weight... |
7fa6477ced93cff49ab1fb3beed92b0a6b8749274d84aeb778d94947626c8d53 | Python | 1,421 | 19 | """POST HOC (8 Oct 2026, after the twin B verdicts). Twin B's channel test (changes from rest) found the published signs
"rescued" and v2LN30 harmless. Does the sign set show up at rest instead? Mean uPN resting rate (receptor neurons at their
Hallem resting rates, 1 s x 3 trials, seed 3000) for every sign set the twin... |
a8a3465dbb375fa36ffda29695cc3faaa68fe7470e33d949cb269a4235fbe454 | Python | 1,421 | 39 | import tensorflow as tf
import numpy as np
class Logger(object):
def __init__(self, log_dir):
"""Create a summary writer logging to log_dir."""
self.writer = tf.summary.create_file_writer(log_dir)
def scalar_summary(self, tag, value, step):
"""Log a scalar variable."""
with se... |
b35ab8610b39f4500f08c5117214420584bdb9ec3a4e6ce1aa63e90ded2f17f2 | Python | 1,421 | 63 | #============================================================
#
# Deep Learning BLW Filtering
# Metrics
#
# author: Francisco Perdigon Romero
# email: fperdigon88@gmail.com
# github id: fperdigon
#
#===========================================================
import numpy as np
import pywt
from sklearn.metrics.pai... |
108c2b3732eaf3f133edb9bf83ea3100a5caf5f0b4ae442437a32e75634111ea | Python | 1,422 | 38 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
2136fa1dd280dbe1eaf6b623e2cf4c8b5f31117b99f8bf2723c81d2828f80264 | Python | 1,422 | 46 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
54155fdec8058fa06a377ea6c84c2fc753c220621efddb592f30e547827bd01f | Python | 1,422 | 35 | #*******************************************************************
# Python Script for Selective File Copying Based on Filename Criteria
# Date: December-2023
#*******************************************************************
# This script is designed to selectively copy files from an input
# directory to an outpu... |
9194c3a01cfb3414a86716c7b222d0421fc9e87ffec0cef3f41a0786ffd33f04 | Python | 1,422 | 43 | from dimension_encoding.glm import PModCVCLIP
from dimension_encoding.utils import load_clip66_preds
import argparse
def parse_arguments():
parser = argparse.ArgumentParser(
description="Run variance partitioning based on a cross-validated regression to compare a categorical and dimension model"
)
... |
b2c3891f72e63bc64758d24bb32006f43a6d2195001e62fc50f32f4c6681a92d | Python | 1,422 | 44 | __all__ = ["ParseError", "parse_nested_list"]
class ParseError(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return self.value
# Basic functions for parsing PDDL (Lisp) files.
def parse_nested_list(input_file):
tokens = tokenize(input_file)
next_token = n... |
b747e093064a0d3d26e0ed269c305befa062e3e6542ad13c4a057e2dd8639912 | Python | 1,422 | 46 | from unittest import TestCase
from pyseg.vtk_ext import vtkClosestCellAlgorithm
import vtk
__author__ = 'martinez'
class TestVtkClosestCellAlgorithm(TestCase):
def test_Execute(self):
# Read reference
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName('../data/border.vtp')
r... |
eb058782cf1d0681c98b523d9b0b3bb6f9aea2efdba80efe300ae7e821b2509c | Python | 1,422 | 46 | #!/usr/bin/env python
# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This metric filters the useful information of the criteria (sucess / fail... |
f52d02503f14dd0a99797a7e672b7c1f1c14f74944e10ae760382ba990f30677 | Python | 1,422 | 39 | """Metadata generation logic for source distributions.
"""
import os
from pip._vendor.pyproject_hooks import BuildBackendHookCaller
from pip._internal.build_env import BuildEnvironment
from pip._internal.exceptions import (
InstallationSubprocessError,
MetadataGenerationFailed,
)
from pip._internal.utils.sub... |
becc8b720dfee86dbeac6f499eb309d7e0bc11eeb64568b26b409f7f6a438eba | Python | 1,424 | 48 | """HDL (Hardware Description Language) Generator Abstraction.
This module provides a unified interface for generating VHDL and Verilog code.
It abstracts away the differences between the two languages, allowing you to write
code generation logic once and target either HDL.
The abstraction uses a Protocol to define th... |
cab48f295b958bce8468c2f8d04e6adfac2bb1f336ea81c5f182a9709369a638 | Python | 1,424 | 30 | from __future__ import division, print_function
import argparse
from topaz.utils.conversions import file_coordinates_to_boxes
name = 'coordinates_to_boxes'
help = 'convert coordinates table to .box format files per image'
def add_arguments(parser):
parser.add_argument('paths', nargs='+', help='path to input c... |
0157fbd53a3708ebabd20818d75964d025e424e635c81b07e3eee7cb9209ceb9 | Python | 1,426 | 42 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimplePReLU(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int, init: float):
super().__init__()
se... |
3b8ad547a916d441bf1759a26f2035992dd5f74809d9f3ed9d8d5b0a0912370b | Python | 1,426 | 30 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
a6008fc7b2839c0280ba19ad0aa22dad6dc5abff46db98951c539a9863f4dec7 | Python | 1,426 | 59 | import sys
from pylearn2.gui.patch_viewer import make_viewer
from pylearn2.utils import serial
model = serial.load(sys.argv[1])
generator = model.generator
final = generator.mlp.layers[-1]
success = False
i = -1
success = False
to_search = generator.mlp
while not success:
print "while loop ", i
final = to_sea... |
b5dce55d85ba48a86f8fd5c1853a8e2b0ffc10f058624c9f7ff5fc1656464981 | Python | 1,426 | 59 | from pathlib import Path
from datetime import datetime
import yaml
import pickle
import __main__ as main
class RenamingUnpickler(pickle.Unpickler):
def find_class(self, module, name):
remap = {"hippo.models.cscg_untouched": "hippo.models.cscg"}
module = remap.get(module, module)
return su... |
fc959ca1fc6178e84e8c72f021bf265cc2176ba9f6bc5050b5d23149cc3e4d7c | Python | 1,426 | 50 | import os
from matplotlib import pyplot as plt
import pandas
import merlin
import seaborn
import numpy as np
from typing import List
from merlin.core import analysistask
from merlin.analysis import filterbarcodes
from random import sample
import time
from merlin import plots
plt.style.use(
os.sep.join([os.path... |
0895bc8e9cd07613c9ea98b4238ee18d2abb8ca9be0ecc8bf96827cd9dbfe515 | Python | 1,427 | 55 |
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
template_txt = str(snakemake.input)
fasta_file = str(snakemake.output)
aminoacids = {
"ALA": "A",
"ARG": "R",
"ASN": "N",
"ASP": "D",
"CYS": "C",
"GLN": "Q",
"GLU": "E",
"GLY": "G",
"HIS": "H",
"... |
619ab0001dd2b9419d7ff0181c43c564f9e768bacb2fe9b21ead82d91e7f31e9 | Python | 1,428 | 35 | """ Example usage of Python interface of scanphyslog2bids.
This shows how to set up a parallelized conversion workflow. """
import numpy as np
import os.path as op
import nibabel as nib
from glob import glob
from scanphyslog2bids.core import CouldNotFindThresholdError, PhilipsPhysioLog
from joblib import Parallel, del... |
919bcf511ac1d1cb6b2fea40a584d27298237f7f3ea6b6e7303567f3db826c12 | Python | 1,428 | 45 | from __future__ import annotations
import pytest
jf = pytest.importorskip("jobflow")
from pathlib import Path
from quacc import flow, job
from quacc.utils.files import copy_decompress_files
def test_copy_files(tmp_path, monkeypatch, jobflow_output):
monkeypatch.chdir(tmp_path)
@job
def create_file(na... |
e78e6fac4a6a0945a53f264a8c46f686de0877edd15f3256e14986a500c19ce2 | Python | 1,428 | 44 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleHardSigmoid(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int):
super().__init__()
self.mode... |
081966ea2500ef6bdc1ee529e2192e3ac9176052ebac94032c9f0a854e81188f | Python | 1,429 | 45 | import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import grad, jacobian
from autograd.test_util import check_grads
npr.seed(1)
def test_jacobian_against_grad():
fun = lambda x: np.sum(np.sin(x), axis=1, keepdims=True)
A = npr.randn(1, 3)
assert np.allclose(grad(fun)(A), jacobi... |
2a54056d398c9d8df248018d3a82bd4900732390aa863dcfbdd962e69f523d1b | Python | 1,429 | 66 | import skfeature.utility.entropy_estimators as ee
def information_gain(f1, f2):
"""
This function calculates the information gain, where ig(f1,f2) = H(f1) - H(f1|f2)
Input
-----
f1: {numpy array}, shape (n_samples,)
f2: {numpy array}, shape (n_samples,)
Output
------
ig: {float}
... |
5c672d43c196bb4341d1f9c29f36725eb2433f16e7bff2e48d59a53ec5711c9d | Python | 1,430 | 42 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimplePReLU2(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int, init: float):
super().__init__()
s... |
692a899f067461613c88413d3c823a3266420a3b08f9c5a9babf17370a2fcd51 | Python | 1,430 | 47 | """Standalone launcher for the Version & Update dialog (`-m regtools.updater`)."""
import os
import sys
import traceback
from regtools import get_project_root
_project_root = get_project_root()
if _project_root not in sys.path:
sys.path.insert(0, _project_root)
def _write_crash_log(exc):
"""W... |
8d87306aef161bef31388e35dae721cca3e579cade088a35154e838e4c2c3971 | Python | 1,431 | 26 | """PASS D: random sample of FlyWire segments, with coordinates, for the ESS correction."""
import pyarrow as pa, pyarrow.ipc, numpy as np, pandas as pd, os
ROOT=os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CLS=['gaba','acetylcholine','glutamate','serotonin','octopamine','dopamine','neither']
PC=['nts_11.... |
5627094ac12faea82f7279952e88629fa97ce0fa7af16db67e9abbfa55db0714 | Python | 1,432 | 45 | rest_api_version = 99
extensions = dict(
required_params=['training_frame', 'x', 'destination_key'],
validate_required_params="",
set_required_params="""
parms$training_frame <- training_frame
if(!missing(x))
parms$ignored_columns <- .verify_datacols(training_frame, x)$cols_ignore
if(!missing(destination... |
fc02024e8e699c8491b3a617ec4f0922503ef114db9456e62dd7d1ca52abd0e3 | Python | 1,432 | 54 | """I/O utilities for the Vasp calculator."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc.utils.files import load_yaml_calc
if TYPE_CHECKING:
from pathlib import Path
from typing import Any
def load_vasp_yaml_calc(yaml_path: str | Path) -> dict[str, Any]:
"""
Load... |
50c458a2d79b8ab9888774bf3abdb8efc1c5d540a56bbb2b4962a0024c7146dd | Python | 1,433 | 49 | import torch
import numpy as np
import argparse
from src.config.configloading import load_config
from src.render import run_network
from src.trainer import Trainer
def config_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="./config/CCTA.yaml",
help... |
e6a747de8c59d7a52a69f8a97e1026176905abeaf1ef3c9695c29cb257f765c5 | Python | 1,433 | 51 | """Global definition of system settings to be imported by sequence constructors."""
from pypulseq.opts import Opts
# The definitions below may be adjusted to the individual specifications of different scanners
system = Opts(
# Set raster times to spectrum card frequency (timing checks)
grad_raster_time=1e-6,
... |
1e34a68ea33fe0b05cb83fba33e3f6a763e6511f5a59e9e82d2eecd4110c5cd8 | Python | 1,434 | 30 | import matlab.engine
import numpy as np
import matplotlib.pyplot as plt
import pickle
import pdb
eng = matlab.engine.start_matlab()
targetPSTH = eng.load('samplePSTH.csv')#np.array([2, 4, 6, 4, 8, 3, 2, 4, 3, 5, 2, 3, 1, 5, 5, 2, 4, 4, 3, 5, 3, 2, 1, 1, 5, 4, 3, 4, 2, 3, 4, 5, 4, 5, 4, 5, 3, 5, 5, 6, 7, 4, 6, 1, 7, ... |
afcc82bbb327b6bede39848a4385504199aee694e2aa8864d03971711f6af99a | Python | 1,434 | 32 | import torch
from typing import Callable
from .trainingwrapper import TrainingWrapper
class MLP(TrainingWrapper):
"Non-linear multi-layer perceptron"
def __init__(self, num_inputs: int, num_hidden: int, num_layers: int,
num_outputs: int, loss_fun: Callable, metric: Callable,
lr: float=0... |
08dc10f1812f54abb95dc4742cf5f11709480977734df44b4cbeb9ae57ca0b0e | Python | 1,435 | 42 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleLeakyReLU2(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int, init: float):
super().__init__()
... |
53bb55b7068e43ed3bd81052cf2976d2133c22bc4cbd66ed51fc636c571e921b | Python | 1,436 | 48 | ######################################################################
#
# minSimple.py: minimize a molecule
#
######################################################################
from __future__ import print_function
from openbabel import openbabel
import sys
# Make sure we have a filename
try:
filename = sys.ar... |
8491052fc2fdb3532258b21a4683244edc1134cc65a871cfe3919169ff8718c8 | Python | 1,436 | 45 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
CONFIGS = {
'astar_merge_and_shrink_bisim': [
'--search',
'astar(merge_and_shrink('
+ 'merge_strategy=merge_linear(variable_order=reverse_level),'
+ 'shrink_strategy=... |
24ddbecd8809e314d48a217e28dd402cbb5b08299457131389437dc23caa7658 | Python | 1,437 | 48 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Tong He and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import numpy as np
class config:
BASE_DIR = '../../../../../../data/fmri_predict_behavior'
CUR_DIR = os.getcwd()
INTER_DIR = os.path.... |
7419305ddec93806d4a2f83f1a0870726584d63937be439ad56f0f9d2c35909d | Python | 1,437 | 45 | import numpy as np
def imTrimBlack(I, return_ind=False):
# I>thres
# trim the black pixels on the border
ind = np.zeros(6, int)
for cid in range(3):
if cid == 0:
tmp_max = I.max(axis=1).max(axis=1)
elif cid == 1:
tmp_max = I.max(axis=0).max(axis=1)
elif c... |
123c09d1c01982dd5355902c67cac325dd10f07a3d9ed1ac44077c1a094f356a | Python | 1,438 | 34 | from setuptools import setup, find_packages
VERSION = '1.0.0'
DESCRIPTION = 'm6ATM - m6A Transcrtipome-wide Mapper'
LONG_DESCRIPTION = 'm6ATM is a deep learning based m6A detection model using Nanopore Direct RNA sequencing data.'
# settings
setup(name = 'm6atm',
version = VERSION,
author = 'Boyi Yu',
... |
1b288535c021fa9aca7916901829a252eb2b20d0668559711132a801ddc59f57 | Python | 1,438 | 44 | import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def simulate_risk_factors(
dfG: pd.DataFrame,
var_geno: float,
num_rfs: int,
num_shared_qtls_rf: int,
) -> pd.DataFrame:
sim_weights = lambda n: 0.3 + 0.7 * np.random.rand(n)
# simulate betas
beta... |
8fcb6f324bd2220945ad93fadf196b5a4fbe67fe4caf0eff82087670724c6d49 | Python | 1,438 | 36 | import torch.nn as nn
class ResNetBlock(nn.Module):
def __init__(self, c_in, act_fn, subsample=False, c_out=-1):
"""
Inputs:
c_in - Number of input features
act_fn - Activation class constructor (e.g. nn.ReLU)
subsample - If True, we want to apply a stride insid... |
bb504bd264b6c32502fe292602ca80b5ff1ad042c16508f1a91c72a580c5cd2a | Python | 1,438 | 42 | # 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 agreed to in writing, software
# distributed under th... |
cc9e260181ea791cb31b52143fc71cf40305d0b7c1531e648a22465cd6afb61f | Python | 1,438 | 42 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleLeakyReLU(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int, init: float):
super().__init__()
... |
18a0b98b995e2073f3e9fbf7e31824098f4c665e0de4765e9680c3139f454524 | Python | 1,439 | 46 | import numpy as np
from matplotlib import pyplot as plt
from merlin.plots._base import AbstractPlot
from merlin.plots._base import PlotMetadata
class TestPlot(AbstractPlot):
def __init__(self, analysisTask):
super().__init__(analysisTask)
def get_required_tasks(self):
return {'test_task': '... |
349d17272d19b09be711c9fa40e6f21a9a93f9645715c6f6368a394b401d709e | Python | 1,439 | 60 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import configs, suites
from downward.reports.scatter import ScatterPlotReport
import common_setup
from relativescatter import RelativeScatterPlotReport
SEARCH_REVS = ["issue529-v1-base", "issue529-v1"]
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = ... |
6a3907573f1a48c5095e93537a1407c6b064a55221e98aef3e26bd263fa3dcd5 | Python | 1,439 | 39 | import numpy
from mahotas.features.texture import haralick_labels
import GLCMFeatures
import ExtendGaborFeatures
import FirstOrderFeatures
def TextureExtraction(Rowcoord,Colcoord,dicomImageBrain,dicomImageRaw,Distances,Gaborkernel_bank):
windowsize=5
subImageBrain = dicomImageBrain[Rowcoord - 2:Row... |
8a83651a92185be490b1637229d479c728cc0dab17ca75c4a2a2e50a9ab2fe03 | Python | 1,439 | 61 | import operator
def subvals(x, ivs):
x_ = list(x)
for i, v in ivs:
x_[i] = v
return tuple(x_)
def subval(x, i, v):
x_ = list(x)
x_[i] = v
return tuple(x_)
def func(f):
return f
def toposort(end_node, parents=operator.attrgetter("parents")):
child_counts = {}
stack = [... |
9bb6c0488d8651b2c51be3db01e7dd1edaeb5969492c58614816f0ef4829d936 | Python | 1,439 | 44 | # -*- coding: utf-8 -*-
# Copyright (c) 2012 Giorgos Verigakis <verigak@gmail.com>
#
# Permission to use, copy, modify, and distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS P... |
a8f04d2ef6d8e2d1e465239ed99d72e469c97f12d7d3ae2a263869bdeb1b4758 | Python | 1,440 | 44 | import array
import gzip
import os
import struct
from urllib.request import urlretrieve
import numpy as np
def download(url, filename):
if not os.path.exists("data"):
os.makedirs("data")
out_file = os.path.join("data", filename)
if not os.path.isfile(out_file):
urlretrieve(url, out_file)
... |
89cf1f24431c8e6b5dcbfb0149b5cd63bea682d6fbe54872ee3ab7e5cd80a28a | Python | 1,441 | 41 | import torch
import torch.nn.functional as F
import yacs.config
def onehot_encoding(label: torch.Tensor, n_classes: int) -> torch.Tensor:
return torch.zeros(label.size(0), n_classes).to(label.device).scatter_(
1, label.view(-1, 1), 1)
def cross_entropy_loss(data: torch.Tensor, target: torch.Tensor,
... |
a0bde5fbbdba03e49a750f54163bd709be6534b8cfbbe1c5b1fa717ca81d6a5f | Python | 1,441 | 52 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 31 22:11:51 2020
@author: usingh
"""
import sys
import datetime as dt
from colorama import Fore,Style
#color print
def orfipy_print(color,*args,stderr=False,**kwargs):
if stderr:
print(color,file=sys.stderr,end="",**kwargs)
pr... |
c147875d20961e2aa35bbdad3e1463624f20f544035cb0505b93d0cce40085d0 | Python | 1,441 | 37 | import colorsys
from math import floor
import numpy as np
class ColorSet:
def __init__(self, number_of_classes: int, hues_per_class: int = 2):
self.number_of_classes = number_of_classes
self.hues_per_class = hues_per_class
self.number_of_hues = (number_of_classes * hues_per_class)
h... |
e0a75c8904544157e2493f7f54f9411321ce74b01996cf545698e70d69d461b9 | Python | 1,441 | 45 | from matplotlib import pyplot as plt
import numpy as np
from merlin.plots._base import AbstractPlot
class SegmentationBoundaryPlot(AbstractPlot):
def __init__(self, analysisTask):
super().__init__(analysisTask)
def get_required_tasks(self):
return {'segment_task': 'all'}
def get_requir... |
eae37dc7af79278aa6d45d8a31b23a701151fca932ebec983d0f2c7cf693eeda | Python | 1,441 | 50 | import sys
import os
out_dir_path = sys.argv[1]
models = ["default", "TINYX0111", "TINYX011", "TINYX01", "TINYX2", "TINYX3"]
use_cases = ["sepsis", "viral", "covid"]
execution_times = []
for model in models:
execution_times.append([])
for use_case in use_cases:
execution_times[-1].append(0.0)
for i... |
697fd04ddbec441e1ca6b2f6888ad628c75c500ca5cecfefe19ac7c1e18f21d1 | Python | 1,442 | 43 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
1241b1672d420f8ed70e181d09442a32238e9f84e231bb1dcf8986ca3546c667 | Python | 1,443 | 75 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pancancer_evaluation.config as cfg
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
... |
5c85713e905da7a08226fdddb86d112731954db4ab2b3bd2e3ee85a07c7b813d | Python | 1,443 | 37 | from __future__ import print_function,division
import argparse
from topaz.utils.data.train_test_split_micrographs import train_test_split_micrographs
name = 'train_test_split'
help = 'split micrographs with labeled particles into train/test sets'
def add_arguments(parser=None):
if parser is None:
parse... |
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