sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ab8be39baf562eb7662aa9f4defff0ad4cc32bf9b622ccb1d6fbc744e9e5db86 | Python | 2,517 | 89 | #!/usr/bin/env python3
import sys
import os
import signal
from optparse import OptionParser
USAGE = """
extract-from-bam.py --in <BAM file>
--rg <read group ID extract, leave blank if all >
--reference <reference fasta file, needed for CRAM support>
"""
par... |
e814a31997164012c6d9081bb484793853714a1fda6fcc5bf5d7123b1d1016e4 | Python | 2,518 | 69 | import drop
from drop.config.DropConfig import DropConfig
import subprocess
from pathlib import Path
from snakemake.logging import logger
def setupPaths(projectRoot):
# repository paths
repoRoot = Path(drop.__file__).parent
repoPaths = {
"template": repoRoot / "template",
"Scripts": repoRo... |
0bfbbe6aa7e0d30be43bf6a09fe1577eb8157fec155d1da0105177203bdfe2fa | Python | 2,519 | 80 | import numpy as np
import matplotlib.pyplot as plt
import librosa
import librosa.display
import tkinter as tk
from tkinter import filedialog
def plot_audio_analysis(wav_file):
# Load the audio file
y, sr = librosa.load(wav_file, sr=None, mono=False)
print(f"Sample Rate: {sr}")
# If stereo, process eac... |
9c8044d0c9f71785651e5c83f02d45f70287314424d3e74ed15ec9dc5a1c01db | Python | 2,519 | 48 | from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_stimulus_set_from_s3, load_assembly_from_s3
from brainscore_core.supported_data_standards.brainio.assemblies import NeuroidAssembly
BIBTEX = """@article{hebart_thin... |
23423f494aaf734ceb987d4b0a20db6a4a2f98c6957ac44cc40dd5dafe3648a2 | Python | 2,520 | 89 | import unittest
from ogo.cli.CreateDatasetJSON import (
printFiles,
getOnlyNIFTI,
getSortedFileList,
appendSubdirectory,
CreateDatasetJSON
)
class Test_PrintFiles(unittest.TestCase):
def setUp(self) -> None:
""" Use this method to prep data or files for other tests, if necessary. """... |
7900a58d57e07143949d820fc2966cc7a12dfa9cf2d5a91b044812ab9ae85198 | Python | 2,520 | 80 | import numpy as np
import pandas as pd
from typing import Union, Callable
from types import SimpleNamespace
import re
def read(f_in: str, index_by: int = 0, properties: Union[list, None] = None, terms: Union[list, None] = None) -> SimpleNamespace:
'''
'''
# helper function for legalisation of labels
... |
e40b70cd26b42286ee92a4b250f07243f6373aa4b79ac9835830b94184b5fc01 | Python | 2,520 | 83 | """
Preprocess raw audio files into mel-spectrograms.
Usage:
python scripts/preprocess_audio.py \
--input_dir data/raw_audio/ \
--output_dir data/audio/ \
--n_workers 8
"""
import argparse
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
... |
28cdec941aaf78fc46b6a486425ae310a4b32e90356e2365995012610f37e8b0 | Python | 2,521 | 75 | from typing import Union, List, Dict
class DeviceInfo:
def __init__(self,
name: str,
pretty_name: str,
default: bool = False,
availability_func: callable = lambda: True):
self.name = name
self.pretty_name = pretty_name
sel... |
5ce90b3757c048434a3f97b5911a589f9b270d33a4bbaf543cf29cc84727d10d | Python | 2,521 | 77 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
from collections import defaultdict
from dataclasses import fields, is_dataclass
from typing import Any, Mapping, Protocol, runtime_checkable
import torch
def _is_named_tuple(x) -> bool:
return isinstance(x, tuple) and hasattr(x, "_asdict"... |
8d71475f8868d9ee2fa4494662c3e4a0d3ab36b0cec1b28b9fc1b95bb44f5301 | Python | 2,522 | 58 | from nipype.interfaces.fsl import Reorient2Std
from PUMI.engine import BidsPipeline, NestedNode as Node
from PUMI.pipelines.anat.anat_proc import anat_proc
from PUMI.pipelines.func.compcor import anat_noise_roi, compcor
from PUMI.pipelines.anat.func_to_anat import func2anat
import os
from PUMI.pipelines.func.func_pro... |
ff0a26bdaf646052ad36784f4724789308161a17c7105e5e712f39cacda350e2 | Python | 2,522 | 98 | #!/usr/bin/env python3
from pathlib import Path
import numpy as np
QSIPREP_ROOT = Path("/mnt/f/BIDS/WCH_AF_Project/derivatives/qsiprep")
FLIP_AXIS = 1 # y axis
SUFFIX_OUT = "_flipped"
OVERWRITE = False
TARGET_STR = "-0.00000000" # strict trigger condition
def load_bvec_as_3xn(bvec_path: Path) -> np.ndarray:
... |
3aa801432fe6e5140bc0ae3c925b0faf341ce4cdf67fa6c79ba33280b7546d05 | Python | 2,523 | 82 | # vessap open graph benchmark dataset
import os
from ogb.io import DatasetSaver
from ogb.linkproppred import LinkPropPredDataset
# step 1
# Create a constructor of DatasetSaver. dataset_name needs to follow OGB convention
# and start from either ogbn-, ogbl-, or ogbg-. is_hetero is True for heterogeneous graphs,
#... |
b0d5fdb0212359bf4a2116f297a84d8c3c713197e846f34cef0f87c37ad7ca38 | Python | 2,523 | 67 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from ... |
c3e384d74ef99dee1f619e4cec1e70e3ba377fea7d84fd7f6b9868c2557c9b5b | Python | 2,524 | 67 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from unicore import metrics
from unicore.losses import ... |
b13697121d442bf1c8ceb19e154ace40e15604757224d9c7378d64cca78216bb | Python | 2,525 | 91 | import math
import numexpr
from langchain_core.tools import tool
from langgraph.types import interrupt
@tool
def calculator(expression: str) -> str:
"""Evaluate mathematical expressions safely.
This function provides a safe way to evaluate mathematical expressions
using numexpr. It supports basic mathem... |
bfe3d304dd6362d02b6356a08994fb77a72879aa28d2b3a2d7f6b5b055562b61 | Python | 2,525 | 70 |
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from pathlib import Path
performance_array = np.load(Path.cwd() / "data"/ "model_performance_array.npy")
# fft
fft_array = np.zeros((performance_array.shape[0], 9))
thres_array = np.zeros((performance_array.shape[0], 9))
for ith_folder in list(rang... |
d6c66adc08facecac4274b16cbe7609e2ba2110a28bfb762edab615799f4651f | Python | 2,525 | 63 | import os
import subprocess
import shutil
import nibabel as nib
import time
import numpy as np
import pandas as pd
from nipype import Node, Workflow
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Direc... |
df82bca3667b23064bcb9bba20b1b7507539254dd26cf6cfe7fc28b6fe3fb42e | Python | 2,525 | 74 | """
Description:
CLI for sequence prediction using the Sei deep learning model,
given an input FASTA or BED file.
Outputs Sei chromatin profile predictions.
Usage:
1_sequence_prediction.py <seq-input> <output-dir> [--genome=<hg>] [--cuda]
1_sequence_prediction.py -h | --help
Options:
-h --help... |
4628972a20b0fcf7b013d964ae7a1ee4345774a696384d578ce830e3620cc8f4 | Python | 2,528 | 67 | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 31 22:23:35 2019
@author: czz
"""
from gluoncv import model_zoo, data, utils
#from matplotlib import pyplot as plt
import mxnet as mx
import cv2
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='Train YOLO networks with ra... |
4de868f3ebbe9b66cf85fe3c2bc515747cd94c01f9294da3f0d7bf9518156224 | Python | 2,528 | 76 | import torch
import torch.nn as nn
from torch import Tensor
from monai import transforms as mo_trans
from model.base_model import LocalAttention, MLP
from model.transformer3D import ViT3D
from utils.datasets import get_transform
class BrainAgeEstimator(nn.Module):
def __init__(self, args, device, chan... |
2ec2cae5499c78e78654bee25a612597005537520317107d50912afac1806014 | Python | 2,529 | 74 | import numpy as np
from scipy.ndimage import convolve1d
# TODO - incorporate other filters?
# From supplement doc: "apply a range of standard filters to data (smoothing, high-pass, etc)"
def smooth(x,window_len=11,window='hanning', axis=-1):
"""Smooth data using a window with requested size.
This method is b... |
7f723cefa61de03ae5ac0b5d66c1232b1d4afb95be62e694ead1d44ee6afd1c5 | Python | 2,531 | 87 | """ntac package initialization."""
import numpy as np
from .visualizer import Visualizer
from .data import download_flywire_data
from scipy.sparse import csr_array
from .graph_data import GraphData, FAFBData
from .seeded import Ntac
def sbm(n, k, p_in_range=(0.1, 0.9), p_out_range=(0.1, 0.9), seed=None):
"""
... |
d455e042a4cc032fa371ae37a305713958bf3077a98060caeee2926f1e38871a | Python | 2,531 | 66 | import user_features as ufis_verified(user):
import csv
import ijson
import pandas as pd
import time
def get_user_vector(user):
user_vector = {
"user_id": uf.get_user_id(user),
"profile_description": uf.has_description(user),
"profile_location": uf.has_location(user),
"profile_url"... |
e45d9de56b1c7e69693911144c33f03a9af5cf0d57387fe04ce6dd95beb6d088 | Python | 2,532 | 66 | import glob
import os
import scipy
from nilearn.image import load_img, new_img_like, smooth_img
from nilearn.plotting import view_img, view_img_on_surf
from nilearn.glm.thresholding import threshold_stats_img
import numpy as np
import pandas as pd
import fire
from nilearn.glm.second_level import SecondLevelModel
b... |
16e197d795229d9855bd4988c270c66c616c8dc2321142e20e485ad2853fe611 | Python | 2,533 | 58 | import mne
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mne_bids import BIDSPath
shapes = ['rectangle', 'square', 'isoTrapezoid', 'parallelogram', 'losange', 'kite', 'rightKite', 'rustedHinge', 'hinge', 'trapezoid', 'random']
theory_df = pd.read_csv("./symbolic_model_simple_ref.csv")
cp... |
aea4c4d3a7c3338854a55def5994376435568a28b392d8b158c30b8a2e5ccb0e | Python | 2,533 | 69 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compress tractogram by removing collinear (or almost) points.
The compression threshold represents the maximum distance (in mm) to the
original position of the point.
"""
import argparse
import logging
import nibabel as nib
from nibabel.streamlines import LazyTracto... |
1c08063153c2de247490db52d3a95f6ea018568a9ddbe67f3699cfd1c4d266bb | Python | 2,534 | 91 | """LangChain ``@tool`` wrappers for cheminformatics functions.
Each tool delegates to the pure-Python implementation in
:mod:`chemgraph.tools.cheminformatics_core`.
"""
from __future__ import annotations
from typing import Literal
from langchain_core.tools import tool
from chemgraph.schemas.atomsdata import AtomsD... |
9637234601056edf123aba3a5d85ae10c81c5c1c325677d2217406a0a755034b | Python | 2,534 | 59 | import torch
from dgd import utils
class ExtraMolecularFeatures:
def __init__(self, dataset_infos):
self.charge = ChargeFeature(remove_h=dataset_infos.remove_h, valencies=dataset_infos.valencies)
self.valency = ValencyFeature()
self.weight = WeightFeature(max_weight=dataset_infos.max_weigh... |
08c07da38b2fe553502b848c2f7ecf76fc3b671ac30a1a99a3c738ed4d488597 | Python | 2,535 | 73 | # This file is part of Mooney computational modeling project.
#
# Mooney computational modeling project is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) an... |
2b8c9ef7b9c037b9d18895d34ff9dd19e596790f0ae7ce7e0e2370694aa75d6b | Python | 2,535 | 79 | import torch
from torch.autograd import grad
from knn_cuda import KNN
import torchsort
def gradient(inputs, outputs, create_graph=True, retain_graph=True):
d_points = torch.ones_like(outputs, requires_grad=False, device=outputs.device)
points_grad = grad(
outputs=outputs,
inputs=inputs,
... |
26c67e8695ed654057c3e8de5c0742df1e844ac016572e9920efb46ff560b349 | Python | 2,536 | 102 | """Example script for training Single-Channel DyNeMo on simulated data.
This script should achieve a dice coefficient of ~0.8.
"""
print("Importing packages")
import numpy as np
from osl_dynamics.data import Data
from osl_dynamics.inference import modes, metrics
from osl_dynamics.simulation import HMM
from osl_dynam... |
c9773b300e201fcec80d30e1264b4537ecae886a35c6ccccbf74561b8b629c9d | Python | 2,536 | 92 | import os
from pymol import cmd
import numpy as np
one_letter = {
'VAL':'V', 'ILE':'I', 'LEU':'L', 'GLU':'E', 'GLN':'Q',
'ASP':'D', 'ASN':'N', 'HIS':'H', 'TRP':'W', 'PHE':'F', 'TYR':'Y',
'ARG':'R', 'LYS':'K', 'SER':'S', 'THR':'T', 'MET':'M', 'ALA':'A',
'GLY':'G', 'PRO':'P', 'CYS':'C'
}
def read_pdb( ... |
38c95e1f53d6b257825bc210757764531deec77bc3844cca587c65fd61cbc9c5 | Python | 2,537 | 77 | import unittest
import logging
import numpy as np
import pandas as pd
from batchglm.api.models.numpy.glm_nb import Simulator
import diffxpy.api as de
class TestExtremeValues(unittest.TestCase):
def test_t_test_zero_variance(self):
"""
Test if T-test works if it is given genes with zero variance... |
58d550cc9b768bdb89116319d626e9ee60a8dbd09fb0e1fc04ba200cba187f13 | Python | 2,537 | 74 | from brainvoice.configuration.configuration import Configuration
import numpy as np
import os
import torch
from torchsummary import summary
import argparse
import shutil
def init_from_config(device, experiment_subdir, experiment_name):
experiment_name = f'experiments/{experiment_subdir}/{experiment_name}'
e... |
9bff4a3d30613c144b7bb2e5d5447abb8c19d95950038d3186b3c5f7a9c4c8bb | Python | 2,537 | 54 |
import numpy as np
import xarray
from marques_utils import gen_sample
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
ASSEMBLY_NAME = 'devalois.DeValois1982b'
SPATIAL_FREQUENCY_STIM_NAME =... |
779f764e9fda04f4a67fdda4b29cac5d9db48574cb23bf2261b9b950fbbb204f | Python | 2,538 | 88 | # -*- coding: utf-8 -*-
import numpy as np
import vtk
import nibabel as nib
from nibabel.freesurfer.io import read_geometry
def convert_freesurfer_into_polydata(surface_to_polydata, reference):
"""
Convert a freesurfer surface into a polydata surface with vtk.
Parameters
----------
surface_to_vt... |
07bd5ac0ced8101c70cbe0535be16179234b6732897e01b941e520facded7e63 | Python | 2,539 | 75 | import torch
import torch.nn as nn
import torch.nn.functional as F
class DihedralFeatures(nn.Module):
def __init__(self, node_embed_dim):
""" Embed dihedral angle features. """
super(DihedralFeatures, self).__init__()
# 3 dihedral angles; sin and cos of each angle
node_in = 6
... |
99ffc9b4712de2e7e690041f2a2d565d7f2e5b5ab23c44b3681e290d0999a97c | Python | 2,539 | 83 | from PUMI.engine import FuncPipeline, QcPipeline, NestedNode as Node
from PUMI.pipelines.func.info.get_info import get_repetition_time
from nipype.interfaces import afni, utility
from PUMI.pipelines.multimodal.image_manipulation import timecourse2png
@QcPipeline(inputspec_fields=['in_file'],
outputspec_fi... |
dad7ed61f89036b2cad0d3165fcc2cf08cb91a8aa460c6dfea172c74910c44a8 | Python | 2,539 | 87 | #!/usr/bin/env python
import os
import sys
sys.path.append('.')
import click as ck
import numpy as np
import pandas as pd
import gzip
import logging
from deepgo.utils import NAMESPACES, Ontology
from collections import Counter
import json
import os
import math
import torch as th
from deepgo.models import DeepGOModel
... |
f69eca08b047e4b84d22152299905ee418a7d7ebf683c890622e09481d0808ec | Python | 2,539 | 95 | import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def evaluate_predictions(
labels_path: str,
predictions_path: str,
visualize: bool = False,
destination: str | None = None,
):
labels = pd.read_csv(labels_path)
predictions = pd.read_csv(predictions_path)
... |
5108146ac58130650e1cecfd915788616270dab603c9e0419d971a16ce4e2119 | Python | 2,540 | 80 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Merge a list of Spherical Harmonics files.
This merges the coefficients of multiple Spherical Harmonics files by taking,
for each coefficient, the one with the largest magnitude.
Can be used to merge fODFs computed from different shells into 1, while
conserving the m... |
5e91ebcd89776cbb4a60a53b8ebb16d886a616a3d6e20c1f738abe0e213456a6 | Python | 2,540 | 78 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Modify PFT maps to allow PFT tracking in given mask (e.g edema).
"""
import argparse
import logging
import nibabel as nib
import numpy as np
from scilpy.io.image import get_data_as_mask
from scilpy.io.utils import (add_overwrite_arg, add_verbose_arg,
... |
b5f8e226fb0bf110a297ed0c7a484eb604b6395f0cfadf8e40a01ee15da4ef4c | Python | 2,540 | 73 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from contextlib import suppress
import django_rq
from django.conf import settings
from rq.exceptions import NoSuchJobError
from rq.job import Job
from cvat.apps.engine.log import ServerLogManager
from cvat.apps.engine.utils import get_rq_lock_for_j... |
1aeb24f022f63bf59d47677f09cea892cc6581bc2c3610338abb9f83e17fc421 | Python | 2,541 | 62 | import json
import pandas as pd
import torch
from torch.utils.data import Dataset
import random
class SATARDataset(Dataset):
def __init__(self, dataset_name, split, data, padding_value,
max_tweet_count=128, max_tweet_length=64, max_words=1024,
random_seed=20220401):
rando... |
31f9c7e3ddebca2156e57da6e96c650484760e652f9188d96ce9c7030b1285a8 | Python | 2,542 | 71 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Henrik Zunker, Maximilian Betz
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complia... |
0d03addfb44bad6c83508ed5bd5c7b4b11309e33e6538a22b30e535bae4d16bd | Python | 2,543 | 91 | from __future__ import annotations
from ._core import data as _data
# expose internal functions as API
from ._core.data import (
crop,
stitch,
normalize,
show
)
def load(filepath: str) -> numpy.ndarray:
"""
Load data from a file into a NumPy array using a format-specific loader.
The func... |
2be508a2b746f3d698efad91c04aadbb0e0c4630afa655d78f307149ac474d13 | Python | 2,543 | 79 | from typing import Union, List, Dict
class ModeInfo:
def __init__(self,
name: str,
pretty_name: str,
folds: List[Union[int, str]],
model_plans: List[str],
model_config: str,
suffixes: Dict[str, str],
... |
431660115684457bcf950d1ed0769b956ca1a02bc139cde08a1cbe5187f8a8ce | Python | 2,543 | 53 | from pathlib import Path
import numpy as np
import xarray as xr
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import pandas as pd
DATASETS = ['inlab-instructions', 'inlab-no-instr... |
4cbc6592e5e7c73be1ee92d68666c63344288856c0f12c46b65eb4e4647b83f3 | Python | 2,543 | 80 | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 19 12:23:50 2023
@author: gligorov
"""
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
def perform_PCA(matrices, n_components):
"""
Perform PCA on the dataset and return feature importance
"""
#Extract rows from all ... |
689bef671a2930fc7296ce31586dfd0d08617d3257aa1466e2108001b35f9956 | Python | 2,543 | 82 | #!/usr/bin/env python
# coding: utf-8
import argparse
import pandas as pd
from math import ceil
# Use BioPython - yes even though we can just write it out using normal fwrite.
# Just so it's more conforming to standards.
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq
from Bio import SeqIO
def split_re... |
a404c67875a7c9f53e5e8059a155257a5d3af5f0dbe687ea19085584f74902e4 | Python | 2,546 | 80 | from __future__ import absolute_import
from .resnet import *
from .resnet_ibn import *
#from .vision_transformer import * #RWM added
from .ensemble_resnet import * #RWM added
from .xception import * #RWM added
from .xxception import *
from .hexception import *
from .sam import *
from .unet2 import * #change betwe... |
0383c54b940da40d3a27f41a1cf0f4b95fc062e9f9c2464ecf1202228db8ff21 | Python | 2,547 | 74 | import os
from pathlib import Path
import traceback
import sys
import socket
from flask import Flask
from flask import send_from_directory
from flask_cors import CORS
import dtiplayground
import dtiplayground.dmri.common as common
from dtiplayground.config import INFO as info
logger=dtiplayground.dmri.common.logg... |
11690748257bfba9dd83a3044d420535894d760c8b9c2b1fa4cf3cd7a9593f7e | Python | 2,547 | 79 | import torch.utils.data as data_utils
from PIL import Image
import numpy as np
import random
import glob
import os
from Utils import utils
utils.seed_torch(seed=0)
Map = {
'NETU': 1,
'ELSE': 0
}
class ZSData(data_utils.Dataset):
def __init__(self, path, transforms=None, padding=0, bi=True):
... |
3663271279f051541641f2d9271c3836ac83fb4e42d13dedaf8be7c514c0d055 | Python | 2,547 | 80 | import os
import sys
import numpy as np
import difflib
import time
from typing import List, Dict
# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.llm_orchestrator.self_correcting_agent import SelfCorrectingAgent
def calculate_similarity(code1: str, code... |
578b749b67919e34fc627ce4f9ad5f9f4d5169783553e2a2bd4dab3f0682a80b | Python | 2,547 | 70 | """Tests for the pure fitting helpers in measure_thickness (no pycurv/graph-tool)."""
import numpy as np
from surface_morphometrics import measure_thickness as mt
def test_find_mins_locates_left_and_right_minima():
x = np.linspace(-10, 10, 41)
# two wells: minima near -5 and +5
y = -np.exp(-(x + 5) ** 2)... |
6c6583c7179700a8ef9eb6cbbf1ff125ad8bddacdc403826f4c021e201f4a155 | Python | 2,547 | 79 | # Copyright 2014-2015 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.
"""Tests scripts.create_new_detector for creating... |
c451f5d0958f8c06dd17a76a0f7bb095a0ec5bf0df2db9455b0b3a48111f99ea | Python | 2,548 | 69 | from nab.detectors.base import AnomalyDetector
import numpy as np
import math
class KnncadDetector(AnomalyDetector):
def __init__(self, *args, **kwargs):
super(KnncadDetector, self).__init__(*args, **kwargs)
self.buf = []
self.training = []
self.calibration = []
self.scor... |
35a3418cfc96418c24c41197219d632d36f65c3f585ead725efff17ab055409d | Python | 2,549 | 78 | import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
from cann import CANN1D
#set default ramndom seed for reproducibility
np.random.seed(0)
#set backend to cpu
bm.set_platform('cpu')
#create and run the network
cann = CANN1D(num=256, mbar=0, tau=3, tau_v=144)
v_ext = cann.... |
61d3adb950413c5022888a4142083c74577b9645c552fc6ef024f9c49cd45f73 | Python | 2,549 | 92 | 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_csv_E4980AL,
readin_Data_from_dataframe,
readin_Data_from_TXT_file,
)
f = np.logspace(1, 8, num=20)
omega = 2.0 * np.pi * f
R = 1... |
d7f4dd5b0ae9f5b1fe24cfb8b36ec9bf3076a8fd3ac79f4c7f2a78a54bb9ed70 | Python | 2,549 | 91 | import numpy as np
import scipy as sp
def Z_ECIS_Lo_Ferrier(omega, aecis, Lecis, Wecis, Rb, Cm, Rm, Zn=None):
r"""ECIS impedance model as described in Lo and Ferrier, 1998.
Parameters
----------
omega: :class:`numpy.ndarray`, double
list of frequencies
aecis: double
:math:`alpha` ... |
1ab6659e1965bb8374666984797475b3c51a65ec551f1022254dbc3357a84098 | Python | 2,552 | 86 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from depth.datasets import build_dataloader, build_dataset
from depth.models import build_depther
def par... |
1ec0c9d53489748be7aa7e6584e1aef94d2ab804653230b48ea3feaae22c9250 | Python | 2,552 | 104 | import sys
sys.path.insert(0, '../opt_linear/')
import numpy as np
import tensorflow as tf
from read_data import read_data
from multistart_optimizer import multistart_optimization_parallel
L2_B97 = 0.00
TESTSETS = {
"ACONF": 0.309569196,
"ADIM6": 0.06744186,
"AHB21": 0.035265286,
"AL2X6": 0.0063156... |
51481b2d4ae44f75f8fd0046b1b41db86a4aac24804c3edfc58151f85e9f7fdb | Python | 2,552 | 69 | import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from scipy.io import loadmat
from scipy.interpolate import interp1d
plt.rcParams["font.family"] = "Noto Sans"
plt.rcParams["font.size"] = 6.6
# Find prev dataset if exist
TANK_PARENT_PATH = Path('D:\Data\Lobster\Lobster_Recording-200319-161008\... |
9ccefe3ba40d18382b67cc575ac615919689c09d9694b1da1fa95e75a279ffa0 | Python | 2,552 | 68 | import numpy as np
import nibabel as nib
import os
import sys
src_dir = os.path.dirname(os.path.abspath(__file__))
metab = sys.argv[1] # one of 'glu', 'gaba', 'glu_gaba'
n_jobs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
# Base derivatives directory; set DERIV_ROOT to anonymize local paths.
DERIV_ROOT = os.envi... |
e8979d0398c1d784026d9ed0f83dd5bc658a2a25997b49c089ae79f9379aa3e2 | Python | 2,552 | 86 | import subprocess
import sys
import yaml
import os
from glob import glob
import pandas as pd
import numpy as np
print ('Hey psth!')
pathpath = 'PATHS/general_paths.yml'
with open(pathpath,'r') as f: pdict = yaml.safe_load(f)
genpath = pdict['metricsextraction']
script = 'metrics_extraction/response/extract_psth.py'... |
12a3776d3c95ff0a2dd6b48d8551d5937aa737b2105279b25d963c2d961475cc | Python | 2,553 | 63 | import sys
import importlib
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
import itertools
import train_it_mnist as ted
importlib.reload(ted)
if __name__ == '__main__':
device = 'cpu'
difficulities = ['easy', 'difficult']
sats = ['accura... |
22af8aa5f7957988eb404edd3f13ca85f2842bcf73eff1240ae16f2532574384 | Python | 2,554 | 75 | from collections.abc import Callable, Sequence
from functools import partial
from typing import Union
from eir.experiment_io.input_object_io import load_serialized_input_object
from eir.predict_modules.predict_tabular_input_setup import (
setup_tabular_input_for_testing,
)
from eir.setup import input_setup, schema... |
d6eb62f193f82de5d41677650ea318a0993e925808995241bd3fe96cb63ed137 | Python | 2,554 | 76 | #!/usr/bin/python
#
## @file
#
# Active X based APT piezo & strain gauge control.
#
# Ke 12/09
#
from PyQt5 import QtCore, QtGui, QAxContainer
import sys
import time
class APTPiezo(QAxContainer.QAxWidget):
def __init__(self, parent = None):
QAxContainer.QAxWidget.__init__(self, parent)
self.setC... |
afd0fe18dd58d61f5cf58cd90950e079cd9241febf4a2e0262cfc5199c671b07 | Python | 2,555 | 62 | """
DirectoryContext: Separates input (read) and output (write) directories.
Allows MuscleX to read images from one directory and write all outputs
(caches, results, settings, logs) to a different directory. When both
paths are the same the behaviour is identical to the legacy co-located
layout.
Copyright 1999 Illin... |
0f85ff5005b32641b1861c548ad801f307cc67ec1a7da9f51b0f1fc13f624da0 | Python | 2,556 | 79 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
def load_pvalues(file_path):
"""
Load p-values from the gene-based results file.
"""
df = pd.read_csv(file_path, sep='\t')
pvalues = pd.to_numeric(df['pvalue'], errors='coerce').dropna()
# pvalues = df['pvalue']... |
3dd5b5eb2232bdc99b39ad3b28655e61f54c51457a86d74208b80e9821eee8b9 | Python | 2,556 | 72 | """
This module is an example of a barebones numpy reader plugin for napari.
It implements the Reader specification, but your plugin may choose to
implement multiple readers or even other plugin contributions. see:
https://napari.org/stable/plugins/guides.html?#readers
"""
import numpy as np
def napari_get... |
5747774ebd1ac85f3eb62923b17ae7ed9f8b7bd8ae428f9a2d00b17374ffe229 | Python | 2,556 | 138 | import numpy as np
from impedancefitter.elements import (
Z_C,
Z_CPE,
Z_L,
Z_R,
Z_stray,
Z_w,
Z_wo,
Z_ws,
parallel,
)
omega = 2.0 * np.pi * np.logspace(1, 8, num=20)
R = 100
alpha = 0.5
def test_Z_R_type():
"""Test return type."""
Z = Z_R(omega, R)
assert isinstance(... |
a9bc37932e38f99f210d474654530fbaa114b5657fb8945dbc3f4cd7922bd26c | Python | 2,556 | 59 | """
Concatenate sets of intervals.
Preserves format of the first input -- it is possible to concat two files that
have different column orders. Of course, the meta-data of the second will be
lost (and filled with a "."). If all of the files (GenomicInteralReaders) are
the same format, sameformat=True will preserve all... |
f0fa79142eecd4b31d6ef43da11cc3fe0db4990b234d20eb60f1dd14ae6ad86c | Python | 2,556 | 72 | from brainvoice.configuration.configuration_instantiator import construct_from_kwargs
from brainvoice.modules.weight_init import apply_weight_init
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class VAEAutoencoder(nn.Module):
def __init__(self, encoder, bottleneck,
... |
2406968f8b70dd9618dc2407b2aaef3b5f38763a316a9f0ad77575c68c3fe312 | Python | 2,557 | 61 | import os
import numpy as np
import torch
import time
from tqdm import tqdm
import argparse
from Feature import getData
from RNArankmodel import RNAranknet1, RNAranknet2
def RNArankpredict(args):
device = torch.device('cpu')
torch.set_num_threads(4)
os.makedirs(args.output_dir, exist_ok=True)
model_cl... |
402ec14be456a1172c68e994fad8c7dc459414e3a9ddf0f218f8020629b70f79 | Python | 2,557 | 59 | # 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... |
45cb8f8ccdb34106e8edbdde6992cefc239e1f91d5cae1dbd34160dddb628634 | Python | 2,557 | 72 | #!/usr/bin/env python
"""
Class for storage of a single frame of camera data
or the data from a feed and it's meta-information.
Notes:
(1) The numpy data field (np_data) is expected to
be of type numpy.uint16.
Hazen 3/17
"""
class Frame(object):
"""
Class for the storage of a single fra... |
5eb4146bf0abfb72f3fcde51f81114835483fbdcaeb19cc29673c81694af494d | Python | 2,557 | 63 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
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 (check md5sum)
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.T... |
dbe667f7c5694404427ccb49fe0ce8c54f38e8326b6af96f02dcde2ae14d4228 | Python | 2,557 | 75 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Batch-advance completed jobs to the next workflow stage
Find every job whose state is 'completed' at --from-stage, move each one to
the next stage, and print the list of modified jobs. Optionally restrict the
sweep to a single task with --task-id... |
dda49f5c24b85a34f2672a053534fd5c0a2d2fdac7fadcfd0dfac75339b00b06 | Python | 2,557 | 68 | """
Complement a set of intervals.
"""
from bx.bitset import MAX
from bx.intervals.io import (
BitsetSafeReaderWrapper,
GenomicInterval,
)
from bx.intervals.operations import bits_set_in_range
def complement(reader, lens):
# Handle any ValueError, IndexError and OverflowError exceptions that may be throw... |
1ff04a24bd75389b7880eccdc0feea62a31a8536bbbc753d0840201a2162818c | Python | 2,558 | 74 | # -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 21:12:47 2019
@author: Yoshi
"""
import os
import shutil
import numpy as np
import matplotlib.pyplot as plt
def sum_grapher(path,data,framerate,key,frequencies,decibels):
# Creates and changes to output directory
graph_output_path = path + "/Output/Deb... |
484568cfdc297d6b14a954ed28041dff814f8929e4f4136303bf0ca48d590397 | Python | 2,560 | 67 | import functools
import importlib
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
import torch.hub
import ssl
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from torch.nn im... |
56981cb9f7a50d08be7a6e0c3f362c4bb50295a83618d834a66377eb14b6701e | Python | 2,560 | 68 | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
#for multicollinearity feature importance
from scipy.cluster import hierarchy
from scipy.spatial.distance import squareform
from scipy.stats import spearmanr
def plot_dendrogram(df):
fig, (ax1, ax2)... |
8968425392373a2287e48aeb9dbf54b2cada081c95a105889cf65f585f785df4 | Python | 2,560 | 64 | import os
import sys
import argparse
import options
from model import *
from model.get_location import *
from model.neighbor_graph import *
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default='murine_cellline', help='The name of benchmarking datasets')
args = parser.parse_args()
def ... |
b9eb9ed0481dc9aaa0931e3c48650f351331bd62f32e13798ff2cc94534ee192 | Python | 2,560 | 82 | # vessap open graph benchmark dataset
import os
from ogb.io import DatasetSaver
from ogb.linkproppred import LinkPropPredDataset
# step 1
# Create a constructor of DatasetSaver. dataset_name needs to follow OGB convention
# and start from either ogbn-, ogbl-, or ogbg-. is_hetero is True for heterogeneous graphs,
#... |
13502091d6fe302d4e1e575ecc5b8970ca1f33aa65547c57f8977ce78d8c629d | Python | 2,561 | 101 | """Example code for making plots.
"""
import numpy as np
from osl_dynamics import simulation
from osl_dynamics.utils import plotting
#%% Basic plots
# Line plot
x = np.arange(100)
y1 = np.random.normal(size=100)
y2 = np.random.normal(size=100)
plotting.plot_line([x], [y1], filename="line.png")
# Plot a figure wi... |
7c7a32d97a6fb943ca12cc9a34f6bded7cd87548761830f588d0cf85b0e77a53 | Python | 2,562 | 73 | from numpy.random import seed
seed(42)
import tensorflow as tf
tf.random.set_seed = 42
import random
random.seed(42)
import numpy as np
np.random.seed(42)
import pandas as pd
import tensorflow as tf
import SimpleITK as sitk
from tensorflow.keras.utils import to_categorical
import sys
from tensorflow.keras.... |
47babfbd4fb1504e5871d95e31fe207203f1c23105781039d63ba3a6ccbdc74f | Python | 2,563 | 75 | # -*- coding: utf-8 -*-
from distutils.core import setup
LONGDOC = """
jieba
=====
“结巴”中文分词：做最好的 Python 中文分词组件
"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to
be the best Python Chinese word segmentation module.
完整文档见 ``README.md``
GitHub: https://github.com/fxsjy/jieba
特点
====
- 支持三种分词模式：... |
45ed6f46ade8989297f815e8eee41edb73d274a0c8a39561f96840fcbc6802bf | Python | 2,564 | 81 | """Step 1: Preprocessing."""
from pathlib import Path
import mne
import matplotlib
matplotlib.use("Agg")
from osl_dynamics.meeg import parallel, preproc
# ----------------------------------------------------------------------------
input_dir = Path("BIDS")
output_dir = Path("derivatives")
plots_dir = Path("plots")
lo... |
a01c026d32d56c697c176bed4328819db2dedcb94c86cb93add804484aa6486b | Python | 2,566 | 75 | """
===============================================================================
File Name : predictor.py
Author : Alex Bui
Created : 2024-11-13
Description : Include prediction heads
===============================================================================
"""
import sys
sys.path.append('..')
from ... |
fc60a9496917bf5d633d1f29b7843cc75b19bb7f2be92e79aa8686c6d4b82607 | Python | 2,566 | 79 | # 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 ... |
40a111d323145d55d254651b7b7ca9d87c709634cb721466c96ea92f1ed56b66 | Python | 2,567 | 70 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from http import HTTPStatus
from typing import Any
import pytest
from shared.utils.config import get_method, make_api_client, put_method
@pytest.mark.usefixtures("restore_db_per_class")
class TestGetServer:
def test_can_retrieve_about_unauth... |
62566b663e8f4fee91c13d5565d285cfa911533e3a85d4207c18da736c6773fa | Python | 2,567 | 67 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
import torch
from ultralytics.utils import ARM64, IS_JETSON, LOGGER, YAML
def torch2paddle(
model: torch.nn.Module,
im: torch.Tensor,
output_dir: Path | str,
metadata: di... |
9067df376f50a434545d5d8fbb6dd8145f1d3a02f155728c7ece90c6e2e472a8 | Python | 2,567 | 69 | # Copyright (C) 2020-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os
import os.path as osp
import shutil
from glob import glob
from datumaro.components.dataset import Dataset
from cvat.apps.dataset_manager.bindings import (
GetCVATDataExtractor,
detect_da... |
d4ed3cbfaf44e66dadaffbfb16c7e9f8f28747be380f94291fffb5d24e459e40 | Python | 2,567 | 101 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
from pathlib import Path
HERE = Path(__file__).parent
sys.path[:0] = [str(HERE.parent), str(HERE / 'extensions')]
on_rtd = os.environ.get('READTHEDOCS') == 'True'
# -- General configuration ------------------------------------------------
nitpicky... |
93237307a3be76811aa530167a2648503ae4ea4baa6b4bcf4dbf4d47ab0a542a | Python | 2,569 | 90 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of labels using laplace solves and landmarks")
# Add the arguments
my_parser.add_argument("target_path", metavar="path", type=str, help="... |
56a4894200c1a6ed1ae3c2d156440f9da70afebbe21325694fd7c76cd068a813 | Python | 2,570 | 84 | import torch
from torch import nn
from torchvision.models import resnet50
class SliceEncodeTransformerBaseModel(nn.Module):
def __init__(
self,
num_class: int,
encoder: nn.Module,
n_feature: int,
n_token: int,
trans_heads: int = 8,
trans_blocks: int = 4,
... |
aa53481170ef170fabf3bfb7a8e526252d36665618696ba57b4113a1a17c5e88 | Python | 2,570 | 97 | from io import StringIO
from numpy import allclose
from bx.motif.io import transfac
sample = """
VV TRANSFAC MATRIX TABLE, Rel.3.2 26-06-1997
XX
//
AC a
XX
ID V$MYOD_01
XX
DT 19.10.92 (created); ewi.
DT 16.10.95 (updated); ewi.
XX
NA MyoD
XX
DE myoblast determination gene product
XX
BF T00526; MyoD; Species:... |
4e04d260f48722e86a28809b472c79ac9ec8304d8422ebbe84a39a48cc2ffb20 | Python | 2,571 | 92 | from pathlib import Path
import shutil
import re
import pydicom
ROOT_DIR = Path(r"/mnt/f/SVD/樊焱0034077070_20250408")
MOVE_FILES = True
OVERWRITE = False
def sanitize_name(text):
text = str(text).strip()
text = re.sub(r'[<>:"/\\|?*]', "_", text)
text = re.sub(r"\s+", "_", text)
text = text.strip("._ "... |
60013cb43553033f5dd12ceebb6ec694a1ed89cdbd6e1f36d1336aab6e1c6eb0 | Python | 2,571 | 90 | from __future__ import annotations
import numpy as np
import pytest
from labelme._shape import Shape
from labelme._shape_clipboard import ShapeClipboard
def _make_polygon() -> Shape:
return Shape(
shape_type="polygon",
points=np.array([(0.0, 0.0), (10.0, 0.0), (10.0, 10.0)], dtype=np.float64),
... |
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