sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2ba40add5b3325d60bc63f6e82330c4615aa14df9da0b4fc4209e244ab890b14 | Python | 1,833 | 55 | """Customed activations."""
from typing import Any, Union
import torch
from spikingjelly.activation_based import surrogate
from spikingjelly.activation_based.neuron import LIFNode
surrogate_dict = {
'sigmoid': surrogate.Sigmoid,
'atan': surrogate.ATan,
'leaky_relu': surrogate.LeakyKReLU,
}
class LIF(LIF... |
b249a224f7c3eb35511f30b6113a0fb5c21b3d6177c84379e5d7300c1bafe13a | Python | 1,833 | 73 | import argparse
import base64
import os
import requests
def upload_weights_to_github(filename: str, token: str, user: str, repo: str):
with open(filename, 'rb') as f:
bytes = f.read()
bytes = base64.b64encode(bytes).decode()
if os.path.isfile(token):
with open(token, 'r') as f:
... |
efa08eadfbea5af29ce1e94e162bb1970b8e04e3d29b694af6b44ff23041bccb | Python | 1,833 | 50 | #this script with assistance from ChatGPT3.5 20231204
# the function is to create a mean of all the normalized images:
# python nii2meanT1.py
import os
import nibabel as nib
import numpy as np
import fnmatch
def create_mean_image(folder_path, output_path):
# Ensure the output folder exists
os.makedirs(os.pat... |
d8c53627b8cbde8714fa54c3538ab933b79926d5a6a0d22e027dd4db704d5981 | Python | 1,834 | 60 | from abc import abstractmethod
import tensorflow as tf
class Backbone(tf.keras.Model):
"""
Interface class for backbones.
"""
def __init__(
self,
image_size: tuple,
num_channel_initial: int,
out_kernel_initializer: str,
out_activation: str,
out_channel... |
0d18905e5704e180d75358a374b4eb5bb0d85e194bd1f87ce92c3b6db5d5cc1a | Python | 1,835 | 60 | #!/usr/bin/python
#
## @file
#
# Coherent CUBE 445 laser control.
#
# Hazen 7/10
#
import cube
## Cube445
#
# Controls a Coherent Cube 445 laser. This is sub-class of cube.Cube.
#
class Cube445(cube.Cube):
## __init__
#
# Initiate RS-232 communication, verify that the laser is respondi... |
c3d380549b1421e1cbb72b8495fb492c84ca0163b23a4b3a5135bd139cf90ca7 | Python | 1,835 | 55 | ###################################################################
### Script to merge data from multiple files into a single file ###
###################################################################
### Copyright 2020-2021 Jannik Luboeinski
### licensed under Apache-2.0 (http://www.apache.org/licenses/LICENSE-2.0... |
d08c498c693c6a1e061db24e90b4f8995bf9873cea03fec0e54e55b6a2d392e3 | Python | 1,836 | 67 | #!/usr/bin/env python
"""
Reads a list of intervals and a set of indexed mafs. For each interval print
the amount covered by each species other than the reference.
usage: %prog maf_files [options] < interval_file
-s, --src=s: Use this src for all intervals
-p, --prefix=p: Prepend this to each src before... |
f0c4d6ae474fe3c6fe7a687663de1420fe0bd70a21e31c7a2c7a1b0e251651df | Python | 1,836 | 48 | #!/usr/bin/env python
# Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk 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 Foundation, either version 3 of the
... |
26e0d044e0d63648dbc1e5a2f9ea51b7729bc5bef770643cd4605190cf585f81 | Python | 1,837 | 58 | # -*- coding: utf-8 -*-
import sys
sys.path.append('..')
def test_get_entity():
from seqeval.metrics.sequence_labeling import get_entities
seq = ['B-PER', 'I-PER', 'I-PER', 'O', 'B-LOC', 'I-LOC', 'O', 'O']
r = get_entities(seq, suffix=False)
print(r)
# [('PER', 0, 2), ('LOC', 4, 5)]
assert r... |
b6d137a07034f9d61300c232fab110899ef4c1ecbe0f21d1264aed2cb4e3764c | Python | 1,837 | 58 | #!/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... |
171b0d453df19ce8fc2e55a179e1fb0595832b898d715e16dbcf3979854a97db | Python | 1,838 | 85 | import argparse
import uvicorn
from fastapi import FastAPI
from eir.serve_modules.serve_api import create_info_endpoint, create_predict_endpoint
from eir.serve_modules.serve_experiment_io import (
ServeExperiment,
load_experiment_for_serve,
)
from eir.utils.logging import get_logger
logger = get_logger(name=... |
2a6e900f1c46aec7511fb3e29e09edf1917e920d526f834f2e808fcc9b11e9b2 | Python | 1,838 | 44 | import torch
import torch.nn as nn
from models import *
class Net2(nn.Module):
def __init__(self, my_pretrained_classifier, context_dim, feature_dim=512):
super(Net2, self).__init__()
self.pretrained_clf = my_pretrained_classifier
self.clf_fc1 = nn.Sequential(nn.Linear(feature_dim, ... |
3ed7f71dd12b08b4f0f942984e037c6dbb7a8cf6184add39008293b1f1552f3b | Python | 1,838 | 79 | # %%
from data_loading import load_dataset_gino
import torch
from deepcardio import Trainer
from deepcardio.losses import LpLoss, H1Loss
dltrain, dltest, data_processor = load_dataset_gino(
folder_path='./data',
train_batch_sizes=[10], test_batch_sizes=[10], query_res=[32, 32])
device = 'cuda' if torch.cuda... |
a8ece44c94479cae776440ecf9d3a635adcc36ca97ff79fde4f101b026cce90d | Python | 1,838 | 60 | import os
import numpy as np
import pytest
from deepreg.warp import main, shape_sanity_check
image_path = "./data/test/nifti/unit_test/moving_image.nii.gz"
ddf_path = "./data/test/nifti/unit_test/ddf.nii.gz"
@pytest.mark.parametrize(
("out_path", "expected_path"),
[
("logs/test_warp/out.nii.gz", "l... |
c7a4a8b224ba201fc3495c6b906f7e9e2cc293e1f790f164269b8c636f6db137 | Python | 1,838 | 52 | import scipy as s
import scipy.special as special
from .basic_distributions import Distribution
from mofapy2.core.utils import *
class Beta(Distribution):
"""
Class to define Beta distributions
Equations:
p(x|a,b) = GammaF(a+b)/(GammaF(a)*GammaF(b)) * x**(a-1) * (1-x)**(b-1)
log p(x|a,b) = log[Ga... |
c8b6170b200b3aa7a8348a8a31c16c259fdecff05ee06f5652580461c9849866 | Python | 1,839 | 46 | import os
import sys
import argparse
def check_and_link_symbolic_file(source_file, symbolic_file, link_mode):
"""Check the input, manage directories, and handle links based on mode."""
# Check if input file exists
if not os.path.isfile(source_file):
print(f"Error: The input file {source_file} ... |
0a9fbd2d13cf71ac065c3fdda38bd3bcc77a7e3597a74a32f162ab648ac704f0 | Python | 1,840 | 73 | import os
import random
import torch
import numpy as np
def set_seed(seed=42):
"""
Set random seeds for reproducibility.
Parameters:
- seed: Random seed
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_s... |
0b00812c17a2841d3a3e99ff9e1e79e75c8ced3508da03c91e567aa8cf576d92 | Python | 1,840 | 67 | #!/usr/bin/env python
"""
Reads a list of intervals and an axt. Produces a new axt containing the
portions of the original that overlapped the intervals
usage: %prog interval_file refindex [options] < axt_file
-m, --mincols=10: Minimum length (columns) required for alignment to be output
"""
import sys
import bx... |
67702d20282c52ded525ac5701d76d74a5a974f0c9aa1bce9dfbfc5f20a7d7b4 | Python | 1,841 | 47 | """Split each echo to prepare for registration.
NOTE: This script is unnnecessary if the scanner exports each echo separately.
"""
import os
import subprocess
import numpy as np
import nibabel as nb
# =============================================================================
NII_NAMES = [
"/home/faruk/DATA-P... |
89b386d521494b8ac131fe48bd528c3c8fe7af4c9f50439a599de7dda57be83a | Python | 1,841 | 52 |
"""
Load an LSM stack into Lasagna
"""
import os
import tifffile
from lasagna.plugins.io.io_plugin_base import IoBasePlugin
from lasagna.utils import preferences
class loaderClass(IoBasePlugin):
def __init__(self, lasagna_serving):
self.objectName = 'LSM_reader'
self.kind = 'imagestack'
... |
beb13ba8c6190fb7013ce664d62a931e44ab2b7ac4c8305c996bd09ccb21a975 | Python | 1,841 | 67 | #!/usr/bin/env python3
import argparse
import numpy as np
import nibabel as nib
def extend_mask_x(
mask_nii: str,
out_nii: str,
extend_part_nii: str,
n_vox: int = 3,
direction: str = "plus",
) -> None:
img = nib.load(mask_nii)
data = img.get_fdata() > 0
out = data.copy()
extend_pa... |
79ed4ae0d2745edbf945298f1daed1af8d94ef26f1370165348f11f7a12a27d8 | Python | 1,842 | 53 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import numpy as np
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'])
t... |
902cff04040a2df8c7fd51525b8537411273d4664628dba20148fd52e908e0e0 | Python | 1,842 | 70 | # -*- 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 get_tsnr(file_in, tsnr_max=200, write_output=False, path_output=""):
"""Get tSNR.
This function computes the tsnr o... |
f7651c6793cabbec6636be5544940817cb4f719928b68c8341f8ff0c7fb508f4 | Python | 1,842 | 56 | import os
import matplotlib.pyplot as plt
import torch
def visualize_segmentation(test_loader, model, device):
model.eval()
batch = next(iter((test_loader)))
inputs = batch['image']
labels = batch['label']
print("############################")
print(inputs)
inputs = inputs.to(dtype=torch.... |
2f3acd5ddee80b6c78f7ba36fd8223a6defab812b70f39f2bc20ae39ce7dcb79 | Python | 1,843 | 66 | import os
import sys
import time
from unittest.mock import patch
import pytest
from magicgui._util import debounce, user_cache_dir
from magicgui.widgets import FunctionGui
def test_user_cache_dir():
ucd = user_cache_dir()
import sys
from pathlib import Path
home = Path.home().resolve()
if sys.p... |
31711d187a1be4170e56187d3d9f69e07c5a2835e65cb6b8b90c0b7caccd3482 | Python | 1,843 | 52 | import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[2])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import os
from os.path import join
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from libs.pytorch_utils import AdaptiveConcatPool2... |
53fa995114c6baf9ae71693d67aa10dea19cf15826a899a785fec97abab81a2d | Python | 1,843 | 57 | """Example code for plotting connectivity maps using the coherence
from state/mode spectra.
The spectra can be calculate with a multitaper (in the case of a state time
course) or regression (in the case of a mode time course).
See examples/spectra/hmm_multitaper.py for how to calculate a multitaper
"""
import numpy ... |
707adcb362d0d7a67430a97c656a164e25274bc21848acc0da2cebeeb7344958 | Python | 1,843 | 51 | import torch
from distances import LpDistance
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
class NCALoss(BaseMetricLossFunction):
def __init__(self, softmax_scale=1, **kwargs):
super().__init__(... |
b4f711b129ef15d9ea5f3a2d6c0efbff8f1a6598d05c430a3714072682e14388 | Python | 1,843 | 49 | """Guard: chat clients are constructed only in the protocol builders.
After PR 2, no module under ``chemgraph/models`` or ``chemgraph/agent`` may
construct ``ChatOpenAI``, ``ChatAnthropic``, or ``ChatGoogleGenerativeAI``
except the corresponding ``protocols/*`` builder. The agent layer now routes all
model loading thr... |
1ec2202f26a23e2a2f99645b055ef89f703eb782cefe8e2acf9cf0f763d39738 | Python | 1,844 | 69 | # Figure 3
import numpy as np
from scipy.constants import epsilon_0 as e0
import impedancefitter as ifit
from impedancefitter.single_shell_wall import eps_cell_single_shell_wall
from impedancefitter.suspensionmodels import bhcubic_eps_model, eps_sus_MW
Cm = 0.01
dm = 7e-9
em = Cm * dm / e0
print("Membrane permittivi... |
997d514bf2aad4147476ff42466a74d294d19b0af075311b15885de81fae8348 | Python | 1,844 | 57 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 16:03:07 2023
@author: rl05
"""
import os
import os.path as op
import shutil
import mne
import config
data_dir = op.join(config.project_repo, 'data')
subjects_dir = op.join(data_dir, 'mri')
os.environ['SUBJECTS_DIR'] = subjects_dir
subject =... |
ac50a5708135d2918e9174ebaa45eaa5fb7e526057f20dd31717c0e146f2e5ee | Python | 1,844 | 51 | import numpy as np
def color_log_remap(ori_cmap, lo_lev, up_lev):
"""
colorLogRemap: Lookup of the original color map table according to a "log-like" curve.
The log-like curve contains a linear part and a logarithmic part; the size of the parts
depends on the range (lo_lev, up_lev).
Arguments:
... |
2aea2cf199ef1d80df119128b3e087315d7d95485862674fe0b0250fef217836 | Python | 1,845 | 47 | import numpy as np
from dipy.reconst.shm import anisotropic_power
__all__ = ["anisotropic_index", "anisotropic_power"]
def anisotropic_index(shm):
"""
Calculates anisotropic index based on spherical harmonics coefficients.
Code from Dmipy
---------------
The MIT License (MIT)
Copyright (c) ... |
6c0341342e38d3f524ae35980306041d8a1ebc5c92c2b5fb62065877d1ddd74f | Python | 1,846 | 61 | """
Import helper methods
Gregory Way 2018
Usage:
from utils import apply_classifier
from utils import shuffle_columns
"""
def apply_classifier(coef_df, rnaseq_df):
"""
Apply the logistic regression classifier coefficients to the input gene
expression matrix. The function will perform the gene s... |
8d0068ab89f756ac94cc3baf26ab9e64f523ed3010d9a7c6cf5370ec88695314 | Python | 1,846 | 47 | #!/usr/bin/python
"""
GamepadF310 HAL module.
Hazen 04/18
Jeff 06/21
"""
import storm_control.sc_hardware.baseClasses.joystickModule as joystickModule
import storm_control.sc_hardware.logitech.gamepadF310 as gamepadF310
class GamepadF310Module(joystickModule.JoystickModule):
def __init__(self, **kwds):
... |
b86f8c23beb3b6f5f87c2194d40ca261fd83d29a34803f1c6861a98551aa100c | Python | 1,846 | 50 | #!/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=['others.zip'])
tmp_dir = tempfile.Tempo... |
1433c3ac998ffa971d8575637f3fe2adf2ee45c362ad9d34b80eda31c3aea853 | Python | 1,847 | 66 | """Groq endpoint (own-client protocol).
Groq keeps its existing client (``load_groq_model``), which owns ``groq:``
prefix stripping, ``GROQ_API_KEY`` handling, and retry-on-auth. This spec routes
``groq:`` models to that loader via the ``groq`` protocol builder.
"""
from __future__ import annotations
from chemgraph.... |
9e5677d4cac51d77f3a8a751c68c8474383de1484961db229c65e67c29eb2fbb | Python | 1,847 | 45 | import logging
import sys
import xnat
class XNATConnectionProvider:
"""Provides the Connection to the XNAT-server with the given alias, secret and address."""
def __init__(self, xnat_alias: str, xnat_secret: str, xnat_web_addr: str):
self._xnat_alias = xnat_alias
self._xnat_secret = xnat_sec... |
c3013462bd74eccb016365f8db19bfb90896450a4299a793640d68d3819ec17c | Python | 1,847 | 57 | import glob, os, re, shutil, subprocess, pgzip, time, traceback, tifffile, numpy as np, numba as nb, \
scipy.stats, joblib as jl
from itertools import chain
from skimage.measure import label, regionprops
import skimage.filters
from tqdm import tqdm
def findTransformix():
try:
v = subproc... |
d52a3fe2710d34fed97f20935fc3948ed9d4b88c4d51feb62c774edfcac2b45a | Python | 1,847 | 34 | import json
import pandas as pd
import os
import yaml
def aggregate(directory):
maps = pd.DataFrame(columns=["train", "human breast cancer", "canine lung cancer", "canine lymphoma",
"canine cutaneous mast cell tumor", "human neuroendocrine tumor",
... |
235f5eda20090d15fada36377b7ce0413481ce26f8b8b5725e7553183aa34ce5 | Python | 1,848 | 53 | #!/usr/bin/env python3
"""
filter_frags.py
Ro3 fragment-like filter: MW 120¨C250, cLogP ¨C1~2.5, HBD¡Ü3, HBA¡Ü4, Rot¡Ü3, rings¡Ü2
+ salt removal + neutralization + PAINS removal
"""
import sys
from rdkit import Chem
from rdkit.Chem import Descriptors, rdMolDescriptors, Crippen, Lipinski
from rdkit.Chem.Filte... |
b45d940a1a5592a1f5be733966df6de611e78c92a5189e9b0822bef8cef795ac | Python | 1,848 | 52 | #!/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=['tractometry.zip'])
tmp_dir = tempfile.... |
ed42ec8575c7ce83b9ee59b12d6048f73640e9f344596ee89dcf6ec963a85dad | Python | 1,848 | 54 | import sys
import getopt
import json
import pandas as pd
import numpy as np
def affine_gem_usage():
print("""
Usage : affine_gem.py -i <input.gem> \\
-o <output> \\
-B [backward (reverse) affine matrix]
-F [forward affine matrix]... |
b631dc0c1c0639a539eddc7e11b42b7cf1b3765e7009dd083543686c288b71aa | Python | 1,849 | 68 | #-*- coding : utf-8-*-
import csv
import pandas as pd
import time
def caculate_feature(df ,length, bias):
data = df.loc[bias:bias+length-1]
characters = sum(list(data["characters"]))
hashtags = sum(list(data["hashtags"]))
words = sum(list(data["words"]))
mentions = sum(list(data["mentions"]))... |
641ffcbd135f51232494a1f678c41ffe24a88c404114ea2c7a0c41dea2f61bb4 | Python | 1,850 | 49 | import os
import re
import click
def rename_files(directory):
for filename in os.listdir(directory):
if 'complete_optimized' in filename:
print(f"Skipping: {filename}")
continue
if filename.endswith('.pkl'):
match = re.match(r'(.*?)_(\d{5})_(\d{3})_complete(.*)\... |
ba8e754b83fec8bf0ed75051b7abcc07dbd51b290ad08cea49f745c3c320b212 | Python | 1,850 | 50 |
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def getplot_corrmat_clusters(enrmat,corrfnstr='pearsonr',**kwargs):
'''enrmat is enrichment-mat like in nregs x nclust'''
corrfn = getattr(stats,corrfnstr)
nregs,ncl = enrmat.shape
corrmat,pmat = np.zeros((ncl,ncl))*np.nan,np.z... |
dda5e5a6c43ac538091e09a39b82616698ac22e0edd9e831bd89f799f57ecf54 | Python | 1,850 | 62 | ########################################################################
### Routine to compute the frequency spectrum of spike raster data ###
### from multiple data files ###
########################################################################
### Copyright 2019-202... |
5bb20e60a4ed4b2575c9779f5d7c5f18732aa69d519f435d21ee5775a2e5ebaf | Python | 1,851 | 48 | import torch
import torch.nn as nn
from .modules import CausalCNN, Softplus
class CausalCNNVDecoder(torch.nn.Module):
"""
Variational decoder.
"""
def __init__(self, k, width, in_channels, channels, depth, out_channels,
kernel_size, gaussian_out, softplus_eps, dropout):
super(C... |
5c4eb92447fd345dab06e841b89331f58a1289e870b16a55cc97306fb55dfca9 | Python | 1,851 | 57 | import jellyfish
import unittest
import sys
import os
from collections import Counter
class TestMerFile(unittest.TestCase):
def setUp(self):
self.mf = jellyfish.ReadMerFile(os.path.join(data, "swig_python.jf"))
def test_histo(self):
histo = Counter()
while self.mf.next_mer():
... |
7826936b277fc6888b7d05b32e0c2bafe8a4648ae362c5ef3d03d1ad3004a2c3 | Python | 1,851 | 75 | #!/usr/bin/python3
#
from functools import reduce
from uac.edge import Edge
class Element:
num = 0
def __init__(self, t, n, reg=0):
self.type = t
self.index = Element.num
self.n = list(map(int, n))
self.nset = frozenset(self.n)
self.isolated = False
self.region... |
8990cbd2c5339aaa520e561f0d2b279eec34c8a504df92cd52fd9a8cf4aa6cce | Python | 1,851 | 68 | #-*- coding : utf-8-*-
import csv
import pandas as pd
import time
def caculate_feature(df ,length, bias):
data = df.loc[bias:bias+length-1]
characters = sum(list(data["characters"]))
hashtags = sum(list(data["hashtags"]))
words = sum(list(data["words"]))
mentions = sum(list(data["mentions"]))... |
d579d0bd167146e8a07378c92f3f52ca4f77e26fa5f4f2263b52b6a1ae5da89d | Python | 1,851 | 24 | from brainscore_vision import benchmark_registry
from .benchmark import Allen2022fmri, Allen2022fmriRSA
# Default: 8 subjects, 515 images
benchmark_registry['Allen2022_fmri.V1-ridge'] = lambda: Allen2022fmri('V1', 'ridge')
benchmark_registry['Allen2022_fmri.V2-ridge'] = lambda: Allen2022fmri('V2', 'ridge')
benchmark_r... |
e26576c94147c891132a311c5f4c9d457167d114d22501d06451d4502e7f796f | Python | 1,851 | 51 | """A minimal Thalamus Task Controller task.
This is the smallest useful behavioral task: it draws a square, waits for it to be
touched, logs the trial, and reports success. It is meant to be loaded by the Task
Controller (it is not a standalone script) -- either drop it into
``thalamus/task_controller/`` and register... |
0439664c024f6df08813bb4a632dc5b181581a6122eef600be6ad1e4f4f5fff3 | Python | 1,852 | 79 | """Train an HMM on time-delay embedded/PCA data.
"""
from sys import argv
if len(argv) != 3:
print("Please pass the number of states and run id, e.g. python 1_train_hmm.py 8 1")
exit()
n_states = int(argv[1])
run = int(argv[2])
#%% Import packages
print("Importing packages")
import pickle
from glob import... |
519d9784d8a6f25acbdb63328c3ae19a40d5a85918b3b889530db5217b9de31b | Python | 1,852 | 64 | """Post-hoc analysis.
"""
import os
from sys import argv
if len(argv) != 2:
print("Please pass the number of states and run id, e.g. python 5_calc_post_hoc.py 8")
exit()
n_states = int(argv[1])
output_dir = f"results/inf_params/{n_states:02d}_states"
import pickle
import numpy as np
from glob import glob
... |
465a063536655f019926d0c2cf0dc130ba4ddf5e46d3011709e4a25f365e970d | Python | 1,853 | 69 | import re
"""
Module to infer data type from string or convert a string to a data type.
"""
def data_type_from_string(input_string):
"""
Returns the data type which string appears to be: int, float, or str
"""
# if all integers return int
if re.match('^[0-9]+$', input_string) is not None:
... |
c399faa0d20eb6e12e354736a875f3cbb3cd13eef02dead75be3de59efb41b2c | Python | 1,853 | 61 | from python.segmentation.train import ManagedList
class TestManagedList():
# Initialize ManagedList with values and verify they are stored correctly
def test_initialize_with_values(self):
# Given
initial_values = [1, 2, 3, 4, 5]
max_size = 10
# When
managed_list = ... |
c70ece9db5285aa20dd2c89ee41b4eea9b9dc244f441507469538e97d50721f0 | Python | 1,853 | 60 | """Contains global variables for use in the ADMET-AI website."""
import time
import pandas as pd
from admet_ai.web.app import app
USER_TO_PREDS: dict[str, pd.DataFrame] = {}
USER_TO_LAST_ACTIVITY: dict[str, float] = {}
def get_user_preds(user_id: str) -> pd.DataFrame:
"""Gets the user's predictions.
:par... |
29d6d22eee727a0c8d12129a0c243a8f04b3a4fe0a2d087b2abe99bba6773b5f | Python | 1,854 | 62 | ########################################################################
### Routine to compute the frequency spectrum of spike raster data ###
### from multiple data files ###
########################################################################
### Copyright 2019-202... |
3a02f06818cd5c2ed640424e3b8d6c40c16bc325eca7dca9be1f4a8cc47d1360 | Python | 1,854 | 69 | """Post-hoc calculation of mode-spectra using a GLM regression.
"""
from sys import argv
if len(argv) != 3:
print("Please pass the number of modes and run id, e.g. python 4_calc_regression_spectra.py 6 1")
exit()
n_modes = int(argv[1])
run = int(argv[2])
#%% Import packages
print("Importing packages")
impo... |
9f19dee056433bb735eda060c55441a007f4fc9120a03989e7a6499a895a5676 | Python | 1,854 | 58 | #!/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... |
a53667bf840948deb8f31edad8e0a1a51a0c28235a3ad725b28818967ccae4d1 | Python | 1,854 | 54 | import os
import numpy as np
from scipy.spatial.distance import pdist
from scipy.stats import spearmanr
from mapdata.nsd_datalocation import nsd_datalocation
from meadows.meadows import (get_matask,
get_dragrate,
load_json_data,
mead... |
fefb52d3a4fc5ac3820365ebc03ba612994576432d9731cbb6d6f850e57fc002 | Python | 1,854 | 48 | # 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 torch.optim
from . import UnicoreOptimizer, register_optimizer
@register_optimizer("adadelta")
class ... |
0ced11557bda88c09f8b324ba6eabc8d060b03f1d98d71547af540c38da07e8b | Python | 1,855 | 61 | from dynamont.segmentation.train import ManagedList
class TestManagedList():
# Initialize ManagedList with values and verify they are stored correctly
def test_initialize_with_values(self):
# Given
initial_values = [1, 2, 3, 4, 5]
max_size = 10
# When
managed_list ... |
17465cc9edae9595a404402bf090f02c5018d5a7956cc9dfa2f560c54dba3c26 | Python | 1,855 | 66 | #!/usr/bin/env python
import click as ck
import numpy as np
import pandas as pd
import time
import math
from utils import Ontology, NAMESPACES
import gzip
from extract_esm import extract_esm
from pathlib import Path
import torch as th
from Bio import SeqIO
@ck.command()
@ck.option(
'--data-root', '-dr', default='... |
3bf9a63c2c8e10158469f3b1a6fb3d2a6b40c049e4a4fc4e68310589dcd38d9a | Python | 1,855 | 56 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
__version__ = "8.4.171"
import importlib
import os
import sys
from typing import TYPE_CHECKING
# Set ENV variables (place before imports)
if not os.environ.get("OMP_NUM_THREADS"):
os.environ["OMP_NUM_THREADS"] = "1" # default for reduced CPU ut... |
877ad405af808964cbc750346cd09efd8f1d1ce558af1fad40bd1f76fe90e980 | Python | 1,856 | 61 | import numpy as np
import pandas as pd
import logging
def set_log(lgr, verbose):
if verbose==True:
lgr.setLevel(logging.INFO)
return True
elif verbose==False:
lgr.setLevel(logging.CRITICAL)
return False
else:
lgr.setLevel(verbose)
return True
def remove... |
af5b03451da4b7aba2b1efad5d87d34d98de8b40fa177e22eb5eca07c976c3b5 | Python | 1,856 | 51 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 21 11:01:06 2023
@author: Guido Meijer
"""
import json
from os import mkdir
from os.path import join, dirname, realpath, isdir, isfile
from datetime import date
# Get data folder path
with open(join(dirname(realpath(__file__)), 'settings.json'), 'r') as openfile:
se... |
d82bace8233c6e1885a38c3debecbe0a7d380758b783b943a783a296fc82403f | Python | 1,856 | 47 | import torch
import torch.nn.functional as F
from typing import List
def consistency_regularization_loss(
img_logits: torch.Tensor, # shape: [B, 1, H, W]
ins_logits_list: List[torch.Tensor] # list of B tensors, each of shape [Nᵢ, 1]
) -> torch.Tensor:
"""Compute the consistency regulariza... |
dcd98ac4a9952ea28559952bc18454fc879cefb4785c739902b25bd766cbc807 | Python | 1,856 | 44 | #!/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=['atlas.zip'])
tmp_dir = tempfile.Tempor... |
38b13ff93d64a42d8d13c08272e89d0e3face805e3444d459a15348dd2540f5a | Python | 1,857 | 70 | import numpy as np
from scipy.constants import epsilon_0 as e0
import impedancefitter as ifit
from impedancefitter.single_shell_wall import eps_cell_single_shell_wall
from impedancefitter.suspensionmodels import bhcubic_eps_model, eps_sus_MW
em = 5.0
Rc = 5e-6
dm = 7e-9
km = 0.0
kcp = 0.1
ecp = 60
kmed = 0.1
emed = 8... |
4a22552314e284aac203f40d9b90d5f0a5a93b700860e5c13d94b9c09bd82720 | Python | 1,857 | 61 | #!/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... |
e185534777c9b8fa15597e0737961e9008b151c32a85eeae0a9749513d63cbe0 | Python | 1,857 | 59 | import os
import torch
from torch import autograd
from torchdrug import utils
path = os.path.dirname(__file__)
if torch.cuda.is_available():
spmm_ext = utils.load_extension(
"generalized_spmm",
[os.path.join(path, "generalized_spmm.cpp"), os.path.join(path, "generalized_spmm.cu")],
ext... |
12f8552edac987fac2d5e7d1561167814f351c6604a3f62404e86b608c8ec749 | Python | 1,858 | 35 | """Running one analysis across several computers through a shared folder.
Most of a run is per-recording work that nothing else depends on — spike
detection, thresholding, the null models — and most people have more than one
computer. A *shared run* splits the recordings between the machines you have,
lets each work t... |
c005f4f695467bb9a959f6b36045f184cad978580141059ede31d67f9aaad1ec | Python | 1,858 | 48 | import numpy as np
from tqdm import tqdm
from argclz import AbstractParser
from neuralib.io import csv_header
from rscvp.spatial.util import prepare_si_data
from rscvp.util.cli import SelectionOptions, PositionShuffleOptions, DataOutput, NeuronID
__all__ = ['SparsityOptions']
class SparsityOptions(AbstractParser, P... |
cc10190041d1ebf0bcc239671a8fc015b01fe56638daa6d4111bc1aa6ddb1683 | Python | 1,858 | 37 | """
The purpose of this file is to provide benchmarks based on publicly accessible data that can be run on candidate models
without restrictions. As opposed to the private benchmarks hosted on www.Brain-Score.org, models can be evaluated
without having to submit them to the online platform.
This allows for quick local ... |
69080e8817ca00a9f9ca32cd34bfd2f4576f017c38dbd26a0dbaf0b54ab0807d | Python | 1,859 | 56 | from __future__ import annotations
from pathlib import Path
from labelme import _config
def test_load_retired_release_keys(*, tmp_path: Path) -> None:
config_file = tmp_path / "labelmerc"
config_file.write_text("""\
instance_label_auto_increment: true
auto_save: false
shortcuts:
edit_line_color: Ctrl+L
... |
dd7de3f794d2fa45bf5e84d6c196ac89f313a5dbcdb1e1476a7a0d921479a393 | Python | 1,859 | 38 | import os
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, Directory, File, TraitedSpec, traits
from cvdproc.config.paths import get_package_path
recon_all_external_mask_script = get_package_path("pipelines", "bash", "freesurfer", "freesurfer_reconall_external_mask.sh")
class ReconAllExternal... |
a771a0b3c470286a169197309843cc2ac56ecbc3d6281eedca27dc53dfbf0e05 | Python | 1,860 | 69 |
from re import L
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
#load features
dataset='Twibot-22'
#nlp features
nlp= np.load('Twibot-22/nlp_22.npy')
id_include=np.load('Twibot-22/id_include.npy',allow_pickle=True)
id_include=list(id_include.item())
#profile features
nlp=nlp
p=np.load('Tw... |
b79860b4ecc9e572b143c7e39de32f63e82655d3202b8c707d870964f051e5f9 | Python | 1,860 | 73 | #!/usr/bin/env python
"""
Base class / functionality for a filter wheel.
Hazen 06/17
"""
from PyQt5 import QtCore
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.hardwareModule as hardwareModule
class FilterWheelWorker(hardwareModule.HardwareWorker):
pa... |
87f61658f8de5f8a795bbd3ade1410ff3714d2988ff3e1c1974965c00dfa5b05 | Python | 1,862 | 50 | import pandas as pd
import pytest
from eir.setup.input_setup_modules import setup_omics
@pytest.mark.parametrize(
"indices_to_subset,expected",
[
([0, 10, 20, 30, 40, 50, 60, 70], [0, 10, 20, 30, 40, 50, 60, 70]),
([0, 10, 20, 30, 40, 50, 60, 70, 120, 999], [0, 10, 20, 30, 40, 50, 60, 70]),
... |
b96180a048a5404e3e827ba19c5ec9f598f7f3dbb8a2699f4bba6fdd68abb8ad | Python | 1,862 | 66 | """ Step Scheduler
Basic step LR schedule with warmup, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import torch
from typing import List
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.... |
170cee120f7c5472c96ff9d80e9d8783d997a98840e5303523f9de6a0085b5b1 | Python | 1,863 | 47 | from argclz import AbstractParser
from neuralib.io import csv_header
from neuralib.registration import CellularCoordinates
from neuralib.suite2p import get_s2p_coords
from neuralib.util.verbose import publish_annotation
from rscvp.util.cli import StimpyOptions
from rscvp.util.cli.cli_sbx import SBXOptions
from rscvp.ut... |
7f8bc206c282b5954261151ee4d59289ae4ae1a19ff749bfafe845d3bf4ae427 | Python | 1,863 | 50 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
b936d231d25b1399c4ea8da977b5413e1969b507daa991c211281f1d8ae110ba | Python | 1,863 | 49 | import os
import nibabel as nib
import numpy as np
from nipype.interfaces.base import (
TraitedSpec, BaseInterfaceInputSpec, BaseInterface,
File, traits, Str
)
class FlipBTableInputSpec(BaseInterfaceInputSpec):
in_bvec = File(exists=True, mandatory=True, desc='Input bvec file to be flipped')
out_bvec =... |
35079b2c6c73065c4272bdbf53a299c835848226e3e5ef6a8566d904a978c560 | Python | 1,864 | 65 | from collections import OrderedDict
import numpy as np
from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper
try:
from .descriptor import GaborEntropyConfig, GaborFilterEntropyExtractor
except ImportError:
from descriptor import GaborEntropyConfig, GaborFilterEntropyExtrac... |
a5d1c0074fad7665e20491f4ce9881b26154787169fd4a828922897ca7e516e0 | Python | 1,864 | 57 | import os
import numpy as np
# generate batch-iterbale dataloader
class BatchLoader:
def __init__(self, data, batch_size: int = 32, apply_noise: bool = False):
self.data = data
self.batch_size = batch_size
self.apply_noise = apply_noise
self._label_in = "low"
self.... |
067684ae83a8368822655af2f8558bfedd5bd86d902cbb4f4594298f1cf24ac6 | Python | 1,865 | 54 | # SPDX-FileCopyrightText: Copyright © Timo Koch
# SPDX-License-Identifier: GPL-3.0-or-later
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
import click
from pathlib import Path
def overlay_images(overlay_data: np.ndarray, background_data: np.ndarray, ax, slicer_op, min_val=None, max_val=Non... |
08dc86551075dbf1eef387f3098a4e481d75ff68d36371f61c825e0db0ac222e | Python | 1,865 | 60 | """
Created on Aug 30 2022
Script to demonstrate usage of standalone DWI functions:
Generate_ADC
Generate_IVIMmaps
@authors: s.zijlema, p.vhoudt, k.baas, e.kooreman
Group: Uulke van der Heide - NKI
"""
import numpy as np
from DWI_functions_standalone import generate_ADC_standalone, generate_IVIMmaps_standalone
impo... |
33038261c31bfce391b6573d17bdc5aa0c654733c17b6145c4040506e70b0afc | Python | 1,865 | 48 | from torch_scatter import scatter_softmax, scatter_sum
import torch
import torch.nn as nn
from .activation import get_activation
class AttentionPooling(nn.Module):
"""Attention-based pooling layer supporting batched graphs."""
def __init__(self, input_dim: int, dropout: float = 0.2, activation: str = 'gelu'):... |
5faaf87018cd4758449f2bf9811b24579f70fdf2b6246ae579d9c4f9b5be6b00 | Python | 1,865 | 47 |
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
angle_list = np.linspace(0, 350, 1000)
fold3 = np.cos(3*np.linspace(-np.pi/3, np.pi/3, len(angle_list), endpoint=False))
fold6 = np.cos(6*np.linspace(-np.pi/3, np.pi/3, len(angle_list), endpoint=False))
fold3 = (fold3-np.min(fold3))/(np.max(fold3) -... |
d71c7a795a6f04500b75befac0975c1ae89aa9707965bb234911d47dd73c3e1e | Python | 1,865 | 57 | #!/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... |
34462ba9eff9440f53b74363ad6f85398ffe638056cfd11e0c1bce19d961dddc | Python | 1,866 | 47 | import socket
from psychopy import visual, monitors, core
from VideoCallStimYale import activeblock, restblock, runtask
# Initialise psychopy, parameters of monitor from psychopy monitor tool
win = visual.Window([1280, 720], screen=0, monitor="Main_Monitor", units="deg")
#initial_screen = visual.TextBox(window=w... |
ba3fa1de680ad60c68b98e321f4349235f17a26379e078b83c4ba5903fb20270 | Python | 1,866 | 57 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef, auc, roc_curve
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.mo... |
e6d9c2903af265be7adb9fd384caafdaad5ce7790cc70c3a9a15e0c3f45bb4d1 | Python | 1,866 | 69 | # -*- coding: utf-8 -*-
"""Python package for the analysis of high-resolution fMRI data."""
import os
import subprocess
import sys
# meta infos
__author__ = "Daniel Haenelt"
__license__ = "GPL v3"
__version__ = "2.0.0-alpha"
__status__ = "Development"
__all__ = ["SoftwareName", "check_installation", "execute_command... |
0939e729b0f06f5dcf59c6818bcdcf3dc29e5f82870e9b1f12eb2b49166df72d | Python | 1,867 | 65 | # -*- coding: utf-8 -*-
"""Python package for for analysis of high-resolution fMRI data."""
import os
class MatlabCommand:
"""Class for running Matlab commands.
Parameters
----------
fname : str
Name of the Matlab script to run.
*args
Variable length argument list to pass to the ... |
a4cb4ab78174fd9ca86b0c87639a798805038ca8a2db2215d03df36d1dbda391 | Python | 1,867 | 55 | """
This module contains utility functions for the MSI project.
"""
import os
import torch
import json
# Load the trained model from a checkpoint
def load_checkpoint(model, checkpoint_path):
model.load_state_dict(torch.load(checkpoint_path, weights_only=True))
model.eval()
def extract_paths(path_dir, type=... |
c320dbe5f0726a6dbace737e9bfec856eafcde4fcfa2ac0cdd6ffa6dbd60beac | Python | 1,867 | 55 | import json
import base64
import io
from PIL import Image
import yaml
from model_loader import ModelLoader
def init_context(context):
context.logger.info("Init context... 0%")
model_path = "/opt/nuclio/faster_rcnn/frozen_inference_graph.pb"
model_handler = ModelLoader(model_path)
context.user_data.mo... |
28cbc1ed2370529de068533bc857013d42a74776040940d1131937018a68cbf0 | Python | 1,868 | 48 | import chromatics
import json
import os
import pandas as pd
def add_enhancer_distance_to_promoter(df, bin_count = None, bins = None):
df['window_start'] = df[['promoter_end', 'enhancer_end']].min(axis = 1) + 1
df['window_end'] = df[['promoter_start', 'enhancer_start']].max(axis = 1) - 1
df['enhancer_distan... |
ac1381e533754f56781c68ca87f45f600aa3b0089eb2562f11c9590a36e4792d | Python | 1,868 | 59 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import os
import numpy as np
import pytest
import torch
from PIL import Image
from sam3.perflib.masks_ops import masks_to_boxes
class TestMasksToBoxes:
def test_masks_box(self):
def masks_box_check(masks, expected, atol=1e-4):
... |
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