sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0f2aa681beea539a204306fc880885af7b7a7926836ed5cd66b1ce9ee04e167e | Python | 1,909 | 44 | #!/usr/bin/env python3
import argparse
import subprocess
import os
import tempfile
parser = argparse.ArgumentParser(description='Run METASOFT.')
parser.add_argument('metasoft_jar', help='metasoft.jar')
parser.add_argument('metasoft_input', help='')
parser.add_argument('prefix', help='Prefix for output file: <prefix>.m... |
3c23115ec3e09c4fbd363594c2aca7bbb22b58228c157273fb1333f1e3aa8f3e | Python | 1,909 | 53 | import scipy as s
import scipy.special as special
import scipy.stats as stats
from .basic_distributions import Distribution
from mofapy2.core.utils import *
class Binomial(Distribution):
"""
Class to define Binomial distributions
Equations:
p(x|N,theta) = binom(N,x) * theta**(x) * (1-theta)**(N-x)
... |
58133fee531bd4ac660be545eee5120f12f42b185e87329e8854a51026b00446 | Python | 1,909 | 61 | #!/usr/bin/python
#
## @file
#
# Coherent CUBE 405 laser control.
#
# Hazen 7/10 (com port modified Josh 6/26/13)
#
import cube
## Cube405
#
# Controls a Coherent Cube 405 laser. This is sub-class of cube.Cube.
#
class Cube405(cube.Cube):
## __init__
#
# Initiate RS-232 communication, ... |
c56374cbee8e5ac22d4dd4685ecc86ce4a4919d0c7ccb4abbb4246de55aca6f4 | Python | 1,909 | 45 | def rpn_model(file):
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import ElasticNet
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.feature_selection import f_regression
import numpy as np
import json
with... |
40b4b68125193ad453f36cc700e90f2d62a4c1f7557935a9ad5a1e62d09f50de | Python | 1,910 | 54 | import os
import numpy as np
import pandas as pd
from impedancefitter.RC import RC_model
from impedancefitter.readin import (readin_Data_from_collection,
readin_Data_from_TXT_file,
readin_Data_from_csv_E4980AL)
f = np.logspace(1, 8, num=20)
omega ... |
e68ae1a0f28bf587e85b94450c6915ab819c066c6926b631a49fc862fbd91c8f | Python | 1,910 | 51 | from collections.abc import Callable
from functools import partial
from pathlib import Path
from eir.experiment_io.input_object_io import load_serialized_input_object
from eir.setup.input_setup_modules.setup_array import set_up_array_input_object
from eir.setup.input_setup_modules.setup_bytes import ComputedBytesInput... |
603515081868e34dd7c5eb3369dc32e03b00b160985f1bc4f862207696a56ff6 | Python | 1,911 | 46 | # cifar10 dataloader
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
def Cifar_Data_Load(args, is_training=True):
args=args['dataloader']
if is_training:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.... |
7c825bfbe07772e4b1170744f85bd24631ae1944568fe43ff48fec6b9767343b | Python | 1,911 | 49 | import h5py
from glob import glob
import os
import numpy as np
from scipy.stats import scoreatpercentile as sap
import matplotlib.pyplot as plt
srcdir = 'ZENODOPATH/quiet_active_detection/quiet_active_stats'
figdir = 'FIGDIR/quiet_active_stats'
if not os.path.isdir(figdir): os.makedirs(figdir)
myfiles = glob(os.path... |
ceaa9764eca4840f51c9be5051d28114e851e1d0bda50ac2e33e1273833c8471 | Python | 1,911 | 40 | """Explicit byte-verified restoration of tracked inputs into a fresh checkout."""
import argparse,json
from datetime import datetime,timezone
from pathlib import Path
from urllib.request import Request,urlopen
from src.acquisition.download import ROOT,MANIFEST,sha256
def restore_record(record,root=ROOT):
target=r... |
0285f480e97bcc98fbbe835b3378ec585eba14ce0f16c575881460ee97b2edaa | Python | 1,913 | 74 | #! /usr/bin/env python
# Copyright 2014 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import os
import argparse
impor... |
2e87d0ee3c2457ab3c5f6c1e77a40f580eb70eeab3886434a0aeebe6283694bc | Python | 1,913 | 60 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Restore a project from a backup zip as a new project.
Pair with project_backup.py to migrate or clone a project.
Steps:
1. Restore --backup as a brand-new project.
2. Optionally delete the restored copy (--cleanup) — useful when testing a
... |
5811225a22bdd74c46e7f07605458bd601345287364464d7ad4ec9a6d169475e | Python | 1,913 | 63 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
"""
def compute_downsample_size(down_size, resolution_wh):
"""
Compute the downsample size based on the given input size and original resolution.
This function determines the downsample size based on different formats of `down_size`:
1. If `down_siz... |
595bce43be9051338bd55f8dd1fe9d6f25e634ed726afe79baa12e3b093011e3 | Python | 1,913 | 66 | from enum import Enum
class Parameter(Enum):
EMBEDDINGS = "embeddings"
HIDDEN_SIZE = "hidden_size"
USE_CRF = "use_crf"
USE_RNN = "use_rnn"
RNN_LAYERS = "rnn_layers"
DROPOUT = "dropout"
WORD_DROPOUT = "word_dropout"
LOCKED_DROPOUT = "locked_dropout"
LEARNING_RATE = "learning_rate"
... |
1d2f112ce93c37b45538c71d3c321fa62786a3195ed6ae834cf76c6476a3b8c8 | Python | 1,914 | 60 | #!/usr/bin/env python
# coding: utf-8
# In[3]:
import scvelo as scv
import numpy as np
import torch
import matplotlib.pyplot as plt
import pyrovelocity as pv
from pyrovelocity.tasks.data import download_dataset
from pyrovelocity.tasks.preprocess import preprocess_dataset
from pyrovelocity.tasks.train import train_d... |
449a753bb9fc2fbb2d3d8d9c3ed014db669b3206515f54aa2974732a0240a067 | Python | 1,914 | 49 | # SPDX-FileCopyrightText: Copyright © Timo Koch
# SPDX-License-Identifier: GPL-3.0-or-later
import matplotlib.pyplot as plt
import matplotlib as mpl
from overlay import overlay_images, niifile_to_ndarray
from map_to_mri import TIME_STAMPS, VTK_FIELDS, PREFIX
import numpy as np
mpl.rcParams['savefig.pad_inches'] = 0
... |
1a798a8655827b5168fc23abc7df18ec78dc73334a4607ce645deaf57b08ded6 | Python | 1,915 | 68 |
import ntac
import os
from os.path import join
from ntac import Ntac, FAFBData
# first download the flywire data we'll need for this example. This will be cached in ~/.ntac/flywire_data
ntac.download_flywire_data(verbose=True)
# SeededNtac is happy to accept an aribtrary CSR adjacency matrix and a list of labels
#... |
4bcd52ca417b959eca431e4ab84d16eb794b8ef0a200cf59c041f32c5b6f65e0 | Python | 1,915 | 45 | # import libraries
## general libraries
import pandas as pd
## brain-score libraries
import brainscore
## domain-transfer specific libraries
from helpers_background_id import loading_brain_model_activation
##### NOTES ON THIS SCRIPT #####
# This script is used to create a stimulus set with the background-id inclu... |
6a19e260e9ed678c3890495034ab8d861d845404b52aa63207c3ff686a082309 | Python | 1,915 | 41 | # The ImpedanceFitter is a package to fit impedance spectra to equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020 Julius Zimmermann, julius.zimmermann[AT]uni-rostock.de
#
# This program is free ... |
d0bd107d3e772acf10043f609d0bd63df24ab663fc16c30ff09998857f5c8f7a | Python | 1,915 | 61 | """Cross-day cell tracking for CAT-NAP.
Follows the same cells through a culture's DIVs by matching suite2p ROIs with
ROICaT, and says how much each match can be trusted.
Design and the evidence behind every constant here: ``python/CATNAP_CELLTRACKING_PLAN.md``.
The short version of what that document establishes, b... |
e83f3db1a07a4c7f822b68df9ff245496a85a969842e5cf63e5e4a5fb0f7efcb | Python | 1,915 | 70 | import argparse
from training import text_pretrain, train_hdc_mlp
from inference import lhdm_infer
def main():
parser = argparse.ArgumentParser(
description="LHDM: text pretraining, visual training, and inference."
)
parser.add_argument(
"--stage",
type=str,
required=True,
... |
92b1df576dbfc8a62f12aa4d8c3ceffdbc2da7bef04ba5b274d64e546151ef9e | Python | 1,917 | 48 | #@SNTService snt
snt.requireVersion("5.0.5") # SNT version required to run this script
"""
Exemplifies how to plot a two-dimensional bivariate histogram in SNT.
Documentation Resources: https:#imagej.net/plugins/snt/scripting
Latest SNT API: https://javadoc.scijava.org/SNT/
"""
from sc.fiji.snt import Tree
from s... |
eaa3afa3f44f5d79e6f79c931e16a39187cf227fc5ccf09ba455d6bdf892c8c0 | Python | 1,917 | 44 | import numpy as np
from brainscore_vision import load_metric, load_ceiling, load_dataset
from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition
from brainscore_vision.data.sanghavi2020 import BIBTEX_SANGHAVIMURTY
from brainscore_vision.utils import LazyLoad
VISUAL_DEGREES = ... |
0138a504a9d7f73e88816057cd80c4050d47ff5382f0b1bd81d321da369a53e0 | Python | 1,918 | 68 | # -*- coding: utf-8 -*-
"""Various matlab functions for high-resolution fMRI data."""
import os
from .. import execute_command
class MatlabCommand:
"""Class for running Matlab commands.
Parameters
----------
fname : str
Name of the Matlab script to run.
*args
Variable length arg... |
e9d7eaaac73855155233eb79b77287405766cb46d94477a3eb9bdbbae704e7d7 | Python | 1,918 | 51 | # -*- coding: utf-8 -*-
# @Author : LG
from PyQt5 import QtWidgets
from ISAT.ui.text_prompt_dock import Ui_Form
class TextPromptDockWidget(QtWidgets.QWidget, Ui_Form):
def __init__(self, mainwindow):
super(TextPromptDockWidget, self).__init__()
self.setupUi(self)
self.mainwindow = mainw... |
5a8354ce1e37d5407331153d3b76b3a36885f41c91f96abf1b58d143818f7d50 | Python | 1,919 | 52 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from pathlib import Path
fr... |
79f2dddf96d90abe7cacebc9abe5644b522438d060f89f41d2f7ac9f87a20fe1 | Python | 1,919 | 64 | import numpy as np
from nibabel import load as load_mri
from multiprocessing import Pool
from subprocess import run
inp_paths = None # to define
out_paths = None # to define
N_PROCS = None # to define
def indicesCrop(mri_path):
mri = load_mri(mri_path).get_fdata()
d1_1=0
while d1_1<mri.shape[0] and np.... |
0e49f8b02dbb23545e62319e7237c1db165eb9a9d2f6385abcf34abd6b7c65c2 | Python | 1,920 | 66 | """Train Chemprop models on all the Therapeutics Data Commons (TDC) ADMET datasets"""
import subprocess
from pathlib import Path
from typing import Literal
from tqdm import tqdm
from tdc_constants import (
DATASET_TO_TYPE,
DATASET_TYPE_TO_METRICS_COMMAND_LINE,
ADMET_ALL_SMILES_COLUMN,
)
def train_tdc_a... |
d1fc5b8e841feb5730c538359812f564f1fb087861fb16a46b50a20a18bba740 | Python | 1,921 | 55 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import glob
import os
import tempfile
from dipy.io.stateful_tractogram import Space, Origin
import h5py
from scilpy import SCILPY_HOME
from scilpy.io.hdf5 import reconstruct_sft_from_hdf5
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already... |
e59ca74b635199c7b774a7e596bde20c95232a5dbc1d8a4ae92a8ac5aa8ec552 | Python | 1,921 | 59 | import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
file_path = r"C:\featur... |
613f3aa3e95280ec9a9e5e41a2970c980632e4bfc8e70e02ad54f693795e94e3 | Python | 1,922 | 73 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import argparse
import json
import os.path
from typing import Any
from attr.converters import to_bool
from cvat_sdk.core.proxies.tasks import ResourceType
def parse_resource_type(s: str) -> ResourceType:... |
ee2618026c135d0498acbd002147fba331dbef9f0f80d8845b435e5d9267e440 | Python | 1,922 | 74 | """Public lowercase API for the Waven package.
The implementation currently lives in :mod:`Waven` for historical reasons.
This package provides the import style users expect after installation:
import waven
from waven import PipelineConfig
"""
from __future__ import annotations
import importlib
try:
fro... |
2d6e65e72780af42c0a6f260b580179854e4d06a1fcdc64338dca1b71092d971 | Python | 1,923 | 56 |
import tensorflow as tf
import csv
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from numpy import argmax
import pandas as pd
import tensorflow.keras.backend as K
from tensorflow.keras import initializers
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.layers import ... |
2113e0881f526736b587b65b3c4d471c817f9718aa9e1ecf04c34660d6a59a33 | Python | 1,924 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Create "files.csv" files in the directories downloaded from BioImage Archive
"""
import os
import glob
import pandas as pd
import mbflim.utils as ut
config = ut.load_config()
source_dpath = config['paths']['source_dpath']
lists_dpath = os.path.join(source_dpath, ... |
6fa71ded304fbd2e84ad2418e756d65982c92e5edf6de2e8da2059bf27f991e9 | Python | 1,924 | 54 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import numpy as np
from dipy.io.surface import load_surface
from dipy.io.utils import Space
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (ch... |
c6e7b17d840e04acfa3acda9ba5f354f8887066c963a5613cb145e23b612a73a | Python | 1,924 | 56 | """Evaluate predictions from TDC ADMET Benchmark Group models."""
from pathlib import Path
import numpy as np
import pandas as pd
from tdc.benchmark_group import admet_group
from tdc_constants import ADMET_GROUP_SEEDS, ADMET_GROUP_TARGET_COLUMN
def tdc_admet_group_evaluate(data_dir: Path, preds_dir: Path) -> None:... |
2a2e1cdd93b196378c1973dd0109d7ea5bc32c9abdad84dc58f831cd650b5aae | Python | 1,925 | 28 | from testsetclass import TestsetClass
class RG18(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['ne4', 'hfNe', 'c2h2', 'c2h2Ne', 'hfKr', 'ar3', 'hfAr', 'bzNe', 'c2h6Ar', 'ar', 'kr', 'kr2', 'ne2', 'ar4', 'c2h2Ar', 'bzAr', 'bz', 'c2h6Ne', 'ne3', 'ar2', 'kr3', 'hf', 'ne', 'ne6',... |
d14a4c5433a1103c20f45065e4f0526235019a9b558d42bec27b6770aaa66333 | Python | 1,925 | 38 | from .cornets import CORnetCommitment, _build_time_mappings, CORNET_S_TIMEMAPPING
import logging
from .cornet_helpers import vonecornet
_logger = logging.getLogger(__name__)
class VOneCORnetCommitment(CORnetCommitment):
def start_recording(self, recording_target, time_bins):
self.recording_target = record... |
a49bc770fb7d324363a9bad81e535dc96e4758284edff8c009191c878b8b4f83 | Python | 1,926 | 44 | import numpy as np
from brainscore_vision import load_ceiling, load_metric, load_dataset
from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition
from brainscore_vision.data.sanghavi2020 import BIBTEX_SANGHAVIJOZWIK
from brainscore_vision.utils import LazyLoad
VISUAL_DEGREES =... |
cb2bebd064841579301050b33eea363511cd705be059253867ac575cb2a59b66 | Python | 1,927 | 70 | """parse_case
Returns:
[int]: which data case we are in.
"""
import os
__all__ = ["parse_case"]
def parse_case(sourcespace, targetspace, tdir):
"""parse_case
Args:
sourcespace (string): space in which the source data lies.
targetspace (string): space to interpolate the source data to.
... |
f8f81f272f8d8b60f0d270085a9496c82e5e221428e96e79240fb46404b1531d | Python | 1,927 | 70 | # Import Packages, Libraries and Files
from psychopy import visual, core, event, __version__, gui, monitors,constants
import os
import glob
def start_movie(win,moviename):
videos=glob.glob(f"{os.path.dirname(os.path.abspath(__file__))}/videos/**/*.mp4",recursive=True)
if len(videos)<1:
print('ER... |
1faceb9719b433f8b6ef92a199b1b67569a9d2f75d72a2bb20e6e8c25b33c269 | Python | 1,928 | 49 | from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_images, load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
import ssl
from transformers import MobileViTForImageClassification... |
ec4865e9ab8ce2fbdc7eeec20cdd721a10deed9c56eeb2c3fa9d52053d6cb47f | Python | 1,928 | 86 | #!/usr/bin/env python
import sys
import gzip
import argparse
import logging
from Bio import SeqIO
logger = logging.getLogger("cut_indrop_spacer")
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[
logging.FileHandler("cut.log"),
... |
10c5fa13547f8171685f006a32ab7abc0f77d262b4ba7a85bb9ebe05051ff5a6 | Python | 1,929 | 55 | from typing import List, Union
from torch_geometric.data import Data, HeteroData
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.utils import coalesce
@functional_transform('remove_duplicated_edges')
class RemoveDuplicatedEdges... |
d1cd17162b9bd725c0b4458a50dff7498ff2a6fec66c1f3643bd53c5cd4104a0 | Python | 1,929 | 57 | from pathlib import Path
import click
import nibabel as nibabel
import numpy as np
import pyvista as pv
import simple_mri as sm
def mri_data_to_ndarray(
grid: pv.StructuredGrid, data_name: str, shape: tuple[int, ...], mask: bool = True
) -> np.ndarray:
data_array = grid.point_data[data_name]
if mask:
... |
f37b21e1bdcfd94caea64eee7e9775f47702d038b1f86a88c561b82cdcde1372 | Python | 1,929 | 58 |
from matplotlib import gridspec
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
cur_fold = 6
cur_path = Path.cwd() / "data" / "txt_barchart_"f"{cur_fold}_fold_effect_erc_handdraw_g12_2025.txt"
with open(cur_path) as f:
lines = [line for line in f]
num_each_row = len(... |
2ba5485227e4460f177e7d566be97842dbec9777d9f4b714afb48732d248b1a2 | Python | 1,930 | 52 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from pathlib import Path
fr... |
cb1897bc025c2b3399c4e9383541b2060d905b0d539a6dbf3041aa148f7595c1 | Python | 1,930 | 64 |
#%%
import numpy as np
from scipy import stats
# turn off warning messages
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
#%%
def omega2(data, trialInfo, iv):
ssTotal = ((data - data.mean(axis=0))**... |
2cdd9d041245ed3f61f6b88f35c370c1f5e1c2bcdfc2ab3f61636e67993e5049 | Python | 1,932 | 63 | import pytest
import torch
from eir.models.model_setup import get_model
from eir.models.model_setup_modules.meta_setup import al_meta_model
from eir.setup import config, input_setup
from eir.setup.output_setup import set_up_outputs_for_training
from eir.train_utils.optim import maybe_wrap_model_with_swa
from eir.utils... |
c1f0b83a725ffbefc3ce065e39a7de21cdcab7f6a84134919af864d18f8b602b | Python | 1,932 | 78 | import numpy
class Highlighter(object):
def __init__(self):
self.color_map = [
"#ff0000",
"#ff4000",
"#ff8000",
"#ffbf00",
"#ffff00",
"#bfff00",
"#80ff00",
"#40ff00",
"#00ff00",
"#00ff40... |
5a4c35a5c26380ad13a8e6d36fd6c567150063626b5da0d05084dd6d7e7a634a | Python | 1,934 | 59 | from ogb.utils.features import (allowable_features, atom_to_feature_vector,
bond_to_feature_vector, atom_feature_vector_to_dict, bond_feature_vector_to_dict)
from rdkit import Chem
import numpy as np
def smiles2graph(smiles_string):
"""
Converts SMILES string to graph Data object
:input: SMILES string (s... |
5daa9a4fd539db7e1417d2436dc858fc1c740a6153ce2a10b5777f6ca046b31c | Python | 1,934 | 61 |
from matplotlib import gridspec
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
cur_fold = 3
cur_path = Path.cwd() / "data" / "txt_barchart_"f"{cur_fold}_fold_effect_erc_handdraw_g12_2025.txt"
with open(cur_path) as f:
lines = [line for line in f]
num_each_row = le... |
b983c821323d7420a543ebb57730db4ac5aa2a880fd03c72c3e6eed0b99b9868 | Python | 1,934 | 59 | import pandas as pd
import time
import json
import content_feature as cf
import csv
def get_content_vector(tweets_text):
content_vector = {
"characters":cf.count_characters(tweets_text),
"hashtags":cf.count_hashtags(tweets_text),
"words":cf.count_words(tweets_text),
"hashtags_to_wor... |
de4b31b599c4db31779c5e8897948c398d1e11658b07d3d45eb3304ba9469534 | Python | 1,934 | 49 | # -*- coding: utf-8 -*-
# @Author : LG
from functools import partial
from PyQt5 import QtWidgets
from ISAT.ui.visual_prompt_dock import Ui_Form
class VisualPromptDockWidget(QtWidgets.QWidget, Ui_Form):
def __init__(self, mainwindow):
super(VisualPromptDockWidget, self).__init__()
self.setupUi(s... |
e89ec14733bb9311318d8e6e26d193faed56821810606d0ef951ea3718c4c40e | Python | 1,934 | 59 | import pandas as pd
import time
import json
import content_feature as cf
import csv
def get_content_vector(tweets_text):
content_vector = {
"characters":cf.count_characters(tweets_text),
"hashtags":cf.count_hashtags(tweets_text),
"words":cf.count_words(tweets_text),
"hashtags_to_wor... |
98d1f4b3af846f7ddee9b15f203fafac6a316772b1eea1ca9e81ccfecd87f3cb | Python | 1,935 | 63 | from __future__ import annotations
from pathlib import Path
import pytest
from PySide6.QtCore import Qt
from PySide6.QtWidgets import QApplication
from pytestqt.qtbot import QtBot
from ..conftest import close_or_pause
from .conftest import MainWinFactory
from .conftest import show_window_and_wait_for_imagedata
@py... |
9f5f66a81c79fb8db6c29cbea82ffca30af8f58ed01e123c96c3af82ad1e9cfb | Python | 1,937 | 60 | """R² (coefficient of determination) metric for Keras models."""
import tensorflow as tf
class R2(tf.keras.metrics.Metric):
"""Streaming R² metric for regression models.
Accumulates residual and total sum of squares across batches
and computes R² in ``result()``.
"""
def __init__(self, name: st... |
13da9324f65587034aa45bf2a564521854eaef4142da234a5f5fd8497a14a831 | Python | 1,938 | 51 | import numpy as np
from PIL import Image
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
class ModelLoader:
def __init__(self, model_path):
self.session = None
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
... |
e5ef1baa932d63426b24c5ccfad9575f328e503aec2235462aa5cffb49c87e42 | Python | 1,938 | 23 | from testsetclass import TestsetClass
class DC13(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['ISO_E36', 'ch2n2', 'c2h2', 'c20bowl', 'ISO_C8H8_1', 'c2h4', 'CL2', 's2', 'C6Cl6', 'c20cage', 'carbooxo2', 'o3', 'o3_c2h4_add', '13dip', 'be4', 'ISO_E35', 'heptatriyne', 'ISO_P36',... |
fd0e0ce54611b276f71be10b189377e3a3b8f3fc49cd83616e5fa632e0e1f425 | Python | 1,938 | 60 | import os
import pandas as pd
import numpy as np
from nilearn.connectome import ConnectivityMeasure
from sklearn.preprocessing import StandardScaler
def scrub(ts, fd, scrub_threshold, frames_before=0, frames_after=0):
frames_out = np.argwhere(fd > scrub_threshold).flatten().tolist()
extra_indices = []
for... |
0741fd8cae2f6c9cc9eb763da56ff62650a570ffb6eabfbd4a0f384b64c6785d | Python | 1,939 | 78 | from .aepicker import BasicPhaseAE
from .base import GroupingHelper, SeisBenchModel, WaveformModel, WaveformPipeline
from .cred import CRED
from .das_base import (
DASAnnotateCallback,
DASModel,
DASPickingCallback,
FKFilter,
InMemoryCollectionCallback,
MultiCallback,
PatchCoordinate,
Pat... |
3b941c81ec351563be64b728a1b4b37177d96008303c2185c9eb9ff89911efad | Python | 1,939 | 75 | """
Sklearn-compatible model factories for score prediction.
Supported models: logreg, xgboost, random_forest, vit
The ViT model requires the [vit] extra: pip install kinescope[vit]
"""
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClas... |
5454b60ad09064c363c8974f3049bcb2d620ff55570bb5b0dc889e30cec82991 | Python | 1,939 | 56 | import numpy as np
from scipy.stats import mannwhitneyu
from tqdm import tqdm
import pandas as pd
def load_matrix(file_path):
return np.loadtxt(file_path, delimiter="\t", dtype=float)
def mann_whitney_u_per_gene(matrix, group_a_idx, group_b_idx):
p_values = []
u_stats = []
for gene_row in matrix:
... |
a913e9eafb8f48ecd374501133d2f75d5d80051d73d4a59357f0af1c9f1c9ae4 | Python | 1,940 | 55 | import numpy as np
from tqdm import tqdm
from argclz import AbstractParser, argument
from neuralib.io import csv_header
from rscvp.util.cli.cli_output import DataOutput
from rscvp.util.cli.cli_stimpy import StimpyOptions
from rscvp.util.cli.cli_suite2p import Suite2pOptions
from rscvp.util.util_trials import TrialSign... |
1e1d68b9ac6d810279fa2c0b322c655addeda5d6626289dfcb3093053061ed37 | Python | 1,941 | 73 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
from nibabel.freesurfer.io import read_geometry, write_morph_data
from gbb.normal import get_normal
# local inputs
from ..io.affine import read_vox2ras_tkr
from ..io.get_filename import get_fi... |
49b0636ecab52248439a288d9361cc8ca1df1f8a562383bccbcaa0ab773bd3e6 | Python | 1,941 | 64 | """Example script for applying hierarchical clustering to match state time courses
from different runs.
The method is described here: https://www.biorxiv.org/content/10.1101/2023.01.18.524539v2
"""
import pickle
import matplotlib.pyplot as plt
from scipy.cluster import hierarchy
from osl_dynamics.inference import mo... |
727c3281ee5d91fb219e6382c52b36c364c6fa4ba4d7dbbf190d5c97f2d768cb | Python | 1,941 | 68 | import matplotlib.pyplot as plt
import pandas as pd
cases = 'Reymond_99_heart_ref3_02'
# plot for the arterial system
models = 'arterial'
elements = ['A5','A1','A8','A52','A49']
title = ['Carotid','Aorta','Radial','Femoral','Anetrior Tibial']
start = [0,1,1,1,1,0]
lit_folder = 'literature_results/charlton_data/'
li... |
8ce3e9a3067b785a113a4d6b12ef3cc21ffa491fb037cd0326afd7991bc5439a | Python | 1,941 | 76 | """Pydantic configurations for SFCN model variants."""
from abc import ABC
from typing import Annotated, Any, ClassVar, Literal, cast
from pydantic import BaseModel, Field
from ..models.sfcn import SFCN, BinarySFCN, CategoricalSFCN, RegressionSFCN
class BaseSFCNConfiguration(BaseModel, ABC):
"""Shared configur... |
9c46ff55b4a38ca08c0c0dffaffba65bb07664651fd05141e7d868ba0aa87da8 | Python | 1,941 | 70 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
# %%
f = h5py.File('/home/atuin/b1... |
e2ed35f5e65c389b4e0d9d45b20e4639a1d1645deeca5b450648d3af8920836b | Python | 1,941 | 53 | import cv2
import numpy as np
import os
from tqdm import tqdm
input_root = 'E:\peiguoquan_data\Tea_Origin\Tea_Origin' # 注意路径斜杠方向
output_root = 'E:/peiguoquan_data/Tea_origin_keying'
os.makedirs(output_root, exist_ok=True)
# 茶叶颜色极限范围 (基于提供的RGB数据)
lower = np.array([20, 30, 0]) # 更低更宽，避开背景颜色
upper = np.array([170, 1... |
434a4f338a6fa03fd0a5178b9111951d5f552b2b7e8813503941c39ae4d73160 | Python | 1,942 | 72 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
# local inputs
from ..io.get_filename import get_filename
def crop_coordinate_mapping(file_in, pad=0, overwrite_file=True,
path_output=""):
"""Crop coordinate... |
84e3e6f620a59b17cf740c13302bd2887a3ac32a5c78cbb95e3bd8879d924103 | Python | 1,942 | 70 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
# %%
f = h5py.File('/home/atuin/b1... |
2792b6d6f8585f406afd9690ad6f0b9aedba1e36a86836e38a0088b0a97ae5ca | Python | 1,943 | 61 | from pydantic import BaseModel, Field
from typing import Union
from chemgraph.models.atomsdata import AtomsData
class VibrationalFrequency(BaseModel):
"""
Schema for storing vibrational frequency results from a simulation.
Attributes
----------
frequency_cm1 : list[str]
List of vibrationa... |
5bf922700f26888df966f0b3fb5759424b28250135ae3ebd182daf6edabf556b | Python | 1,943 | 50 | import tensorflow as tf
from typing import Any, Dict, List, Tuple
from loss_function import loss_function
class LossAndGradientCache:
"""
A class to cache and compute loss and gradients using TensorFlow.
"""
def __init__(self, num_params: int, data_all: Dict[str, Any],
fixed_params: ... |
a5be86c7211a4052c7d84bd7db8d5239735a68c24c4be442ddc379d990ba7d02 | Python | 1,943 | 68 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from enum import Enum
from typing import Any
import attrs
from cvat.apps.engine.models import Job, Location, Project, Storage, Task
class StorageType(str, Enum):
TARGET = "target_storage"
SOURCE = "source_storage"
def __str__(self):
... |
473fd3ffc5ddab1a6e048e6c9890f71f0ea0e81ba0b4aca64c675050060ebd71 | Python | 1,944 | 60 | import torch
import torch.nn.modules.batchnorm
import skoots.lib.utils
from torch import Tensor
from typing import Dict, Optional, Union, List
import matplotlib.pyplot as plt
from torchvision.utils import flow_to_image, draw_keypoints, make_grid
from torch.utils.tensorboard import SummaryWriter
from yacs.config import ... |
bde07f8071ba25285cf80df9267a0dffde8c57d34a63e857cafa88644b4c0a2d | Python | 1,945 | 59 | # Copyright 2024 DeepMind Technologies Limited
#
# 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... |
35197d177b90efad87fd37c9f78cf15022cc0ed01af763187d95907fdd0fb301 | Python | 1,946 | 59 | import os,csv,re
import pandas as pd
import numpy as np
import scanpy as sc
import math
import numba
@numba.njit("f4(f4[:], f4[:])")
def euclid_dist(t1,t2):
sum=0
for i in range(t1.shape[0]):
sum+=(t1[i]-t2[i])**2
return np.sqrt(sum)
@numba.njit("f4[:,:](f4[:,:])", parallel=True, nogil=True)
def pairwise_distanc... |
662a7bae6fef19a0d51212eb4c0ce919f4e12acfe9ad3d29401f8d0d44b86ab2 | Python | 1,946 | 77 | import os
import h5py
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
DATA_DIR = DIR.rsplit('/', 1)[0]
DATA_DIR = DATA_DIR.rsplit('/', 1)[0] + '/data'
# %% b-values and vectors
with h5py.File(DATA_DIR + '/0.7mm_21-dir_R1x3_dvs.h5', 'r') as f:
bvals = f['bvals'][:]
bvecs = f['bvecs'][:]
... |
be8565f5015a88b08c7b04654d44abe8176f3fda67bb150917d8a4cb471478f0 | Python | 1,946 | 83 | #!/usr/bin/env python
"""
Extract ranges of scores from a sorted file in which each line contains a
position followed by a score.
TODO: The finder class might actually be useful, it strides through a file
and builds an index based on the first line. Maybe move it into the
library and get rid of this very ... |
fedf6830e87ed42f877858a43a1995ed50416bad5ad67d6b9f24c7a5db4b5d27 | Python | 1,946 | 63 | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(
self,
feature_maps: int,
image_channels: int,
depth: int = 5,
activation: str = "Sigmoid",
dropout: float = 0.0,
):
super().__init__()
asse... |
8ccd8adfb40292be6de6780eea2cf23744e36c3b3085645169b179020d868f3a | Python | 1,947 | 70 | import random
from utils import decode_one_hot, one_hot
from rules import (
ATOMIC_NUMBER,
ATOMIC_SUBSTITUTIONS,
CHARGE_SUBSTITUTIONS,
HYBRIDIZATION_SUBSTITUTIONS
)
def atomic_rules(atom_one_hot):
atom_num = decode_one_hot(
atom_one_hot,
ATOMIC_NUMBER()
... |
c6b3e8e2d17d32acaf5f08cd4319cb3e4ea7ecfae15c6243dad8905bb74c0459 | Python | 1,947 | 77 | """Predict a continuous value using neuroimaging features.
Note, this script doesn't require osl-dynamics, it uses scikit-learn
to train a machine learning model.
This script should achieve an R2 value of ~0.55.
"""
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardSc... |
d3586fa67837535f1a6e9c102aff95e8e05b53bb497488d47405311432eb7afd | Python | 1,947 | 78 | import argparse
import h5py
import numpy as np
# %%
parser = argparse.ArgumentParser(description='create dvs h5 file for 3-scan trace.')
parser.add_argument('--bvals', nargs='+', type=int,
default=[50, 100, 200, 300, 400, 500])
parser.add_argument('--bavgs', nargs='+', type=int,
... |
e135d58a559c26ee55e1e7504af17da4c1680e26d3f0a01495dc9c4fbaa01d20 | Python | 1,947 | 52 | """Setup file for dannce."""
from setuptools import setup, find_packages
setup(
name="dannce",
version="1.2.0",
packages=find_packages(),
install_requires=[
"six",
"pyyaml",
"imageio==2.8.0",
"imageio-ffmpeg",
"numpy<1.19.0",
"scikit-image",
"matp... |
3b1b12564361bab43be2ad9fa8ad90c0b7132b13e0f7650035cd4f8e9809fc17 | Python | 1,948 | 47 | # Parts taken and modified after https://github.com/MIC-DKFZ/nnUNet/
import psutil
import torch
from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor
def get_torch_device(device='gpu'):
assert device in ['cpu', 'cpu-max', 'gpu',
'mps'], f'-device must be either cpu, cpu-max, g... |
3cd38515fec6f21b7e1703f2bb96313a4b74fbd423416aae509450acb125b4e5 | Python | 1,948 | 57 | #!/usr/bin/env python
"""
This is part of the work-around for the lack
of a 64 bit version of the AotfLibrary.dll file.
Hazen 12/13
"""
import socket
import sys
import AOTF
my_aotf = AOTF.AOTF()
my_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
my_socket.connect(("127.0.0.1", 9001))
e... |
8a1f0ed31eb1a978b62b4dbb7052d853290d2aa50c9ce1eaf40a9cd1891a206f | Python | 1,948 | 68 | import click
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from cycler import cycler
purple = [.5, 0, .5, 0.9]
purple_light = [.5,0,.5, 0.3]
green = [0, 1, 0, 0.9]
green_light = [0,1,0,.3]
blue = [1,.3,0, .9]
blue_light = [1,.3,0,.3]
colors = [purple,green, blue]
colors_light = [purple_l... |
118167be7be858d422e3e90b7985575764d6e148801149b20f3188cae2f93d92 | Python | 1,949 | 64 | import tkinter as tk
from tkinter import filedialog
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import librosa.display
import numpy as np
def open_files():
# show dialog to select CSV file
csv_path = filedialog.askopenfilename(
title="Select CSV(acceleration) file",
filet... |
55346071b9d96e3a8e0a0c1f78b23fec962e1885154b7a53776e5286cf31c5fd | Python | 1,949 | 62 | # Generated by Django 4.2.26 on 2026-05-06 19:53
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0103_alter_default_chunk_type"),
]
operations = [
migrations.AlterField(
model_name=... |
01457c6acdc54e1a6b471e9c1da96dda6f5a3e84241a2881094c7d253313b1e5 | Python | 1,950 | 57 |
import torch
import torch.nn as nn
import torchvision
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
from collections import namedtuple
from torch.utils.data import Dataset, DataLoader
#import spacetorch.analyses.rsa as rsa
import argparse
from urllib.request import url... |
2174762c14de16d7a398242f637a56bcf313661951ffa6f05668c5ae85404b48 | Python | 1,950 | 67 | """
A dense decoder
"""
from functools import partial
from torch import nn
from .utils import (get_norm_layer3d,
get_activ_layer)
class Conv3dBlock(nn.Conv3d):
"""
3d convolution block with optional normalization
and activation for the dense decoder below.
"""
def __init__(s... |
53a4257d496f80205e22a91b0753e2ec61ca873ee95fdbacc5f7e2d5f758701b | Python | 1,950 | 81 | # -*- coding: utf-8 -*-
"""
Interface to write csv files of cell coordinates / intensities
The module utilizes the csv file writer/reader from numpy.
Example:
>>> import os, numpy
>>> import ClearMap.IO.CSV as csv
>>> import ClearMap.Settings as settings
>>> filename = os.path.join(settings.ClearMapPa... |
9b7dc707a4e825008e01f17b9f439f69beeda73c2583a781e443886ffeaffd07 | Python | 1,950 | 63 | """ MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... |
550f6be01e8faae2c0205c874ca25c639de3ddd34a6a14a3bf78858fad0fa45e | Python | 1,951 | 86 | import sys
import os
import glob
import gzip
from optparse import OptionParser
USAGE = """
count-kmer-maps.py --kdir <dir of kmer fasta files>
counts kmer mapping positions
"""
###############################################################################
parser = OptionParser(USAGE)
parser.add_option('--kdir'... |
a942bea99ebd2b6ad3ad01a3c2812cd622ddbb3d5a5052039ddb52cf2c8bf7c5 | Python | 1,951 | 55 | # Copyright (c) 2026 Rohit Jena. All rights reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its use, reproduction, or... |
24568fedaeaa967e35c3a4ebcb3d1c2b28ece6cb1b65713f00bda35090beacba | Python | 1,952 | 45 | # ==============================================================================================================================
# Author: Lucas Berg
# ==============================================================================================================================
import os
import sys
import numpy as np
i... |
3cbf5fcbecf14721a0f2793e1e946c13556531b821c28348b8e5269e5998ef17 | Python | 1,952 | 61 | """
Tests for `bx.seq.seq`.
"""
import unittest
import bx.seq
from bx.seq import (
fasta_tests,
nib_tests,
qdna_tests,
)
test_fa = "test_data/seq_tests/test.fa"
test2_fa = "test_data/seq_tests/test2.fa"
test_nib = "test_data/seq_tests/test.nib"
test_qdna = "test_data/seq_tests/test.qdna"
valid_fasta = f... |
7b023f51d98b70f784728697d0f48ea3165d5830970544fdc68c2728d1707b96 | Python | 1,953 | 66 | import typing
from collections import OrderedDict
from collections.abc import Hashable, Sequence
from dataclasses import dataclass
from typing import Literal
from eir.setup import schemas
from eir.setup.input_setup_modules.common import get_default_sequence_specials
@dataclass
class ComputedBytesInputInfo:
input... |
8644e5e7e95519f803fb49c1dba5b7ce92e067d4647fb725d3a56541ddb7ee94 | Python | 1,953 | 70 | import glob
import os
import pandas as pd
from psychopy import event
from psychopy.constants import FINISHED
def learn_anc(win, vidstim):
vidstim.play()
while vidstim.status != FINISHED:
vidstim.draw()
win.flip()
keys = event.getKeys()
if "escape" in keys:
# rese... |
f69c54bb049bd64b94db245830725fff039318328f0f6a0df9bf9442071be37d | Python | 1,953 | 46 | import torch
from distances import DotProductSimilarity
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.base_metric_loss_function import BaseMetricLossFunction
from losses.mixins import WeightRegularizerMixin
class NormalizedSoftmaxLoss(WeightRegularizerMixin, Base... |
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