sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2fd6eb7562615d35ba5d8d34b24d5fd91c06510a6544d197c45b302885ad60c5 | Python | 2,245 | 50 | #TO BE UPDATED
from NeuronModel.NeuronClass import neuronClass
import numpy as np
import matplotlib.pyplot as plt
hsv = plt.get_cmap('hsv')
def colors(n):
return hsv(np.linspace(0., 1.0, len(n)+1))
class MSN(neuronClass) :
parameters = {'save': True, 'spike': True, 'tau': 10., 'R': lambda x:10., 'u_rest': -6... |
e4ee7ef9b4d51e6771dbcdb829490c9a52ea45d6d2fa1b1cace2ab9dfc1a554c | Python | 2,245 | 72 | """There are three configurable things: predictors/models, data, training/evaluation.
Only first two require factories.
"""
import os
import pandas as pd
import logging
from src.domain_chop import PairwiseDomainPredictor
from src.models.rosetta import trRosettaNetwork
from src.domain_assignment.assigners import Spa... |
04e6b16973755bc49ea42027f6690b9cfaeae9b8ad042a0009910c77a65b113e | Python | 2,246 | 76 | """
In this file the testing for the GCAPS for the 123 bus network is performed
"""
import numpy as np
import gym
from stable_baselines3 import PPO
import torch
from stable_baselines3 import A2C, PPO
from stable_baselines3.common.callbacks import CheckpointCallback, BaseCallback
from stable_baselines3.comm... |
a0f6c38e9d339f9be4a564fe4bcff360a2fd46bffb24fce8cf1f55b395962ac2 | Python | 2,246 | 66 | import argparse
import json
from pathlib import Path
def open_json(fp):
"""
Open a JSON file and return the object
:param fp: The path to the object
:return: The dictionary object
"""
with open(fp, mode="r", encoding="utf-8") as open_file:
obj = json.load(open_file)
return obj
d... |
75b902945a8080263826c252a0e8992d03808a221887ae7231586f1a38d87a9b | Python | 2,247 | 67 | import logging
import warnings
import torch
from torch import nn
from torch.nn import ModuleDict
from .base import Shifter
logger = logging.getLogger(__name__)
class StaticAffine2d(nn.Linear):
def __init__(self, input_channels=2, output_channels=2, bias=True):
"""
A simple FC network with bias ... |
59f1d68bf5a562a039146f2610b0977d4f1e8a45df4e2fe3596122640d7426f6 | Python | 2,248 | 58 | """
openff-evaluator
Report results for free energy simulations.
"""
import sys
from setuptools import setup, find_namespace_packages
import versioneer
short_description = __doc__.split("\n")
# from https://github.com/pytest-dev/pytest-runner#conditional-requirement
needs_pytest = {'pytest', 'test', 'ptr'}.intersecti... |
c7f97694233e7ccb3d22fa11aca6cb46f698c841d9d61d43f174826afa6d8eba | Python | 2,249 | 68 | import sys
sys.path.append(".")
import argparse
from tqdm import tqdm
import gzip
from crystallm import (
extract_formula_nonreduced,
extract_space_group_symbol,
extract_volume,
extract_formula_units,
)
try:
import cPickle as pickle
except ImportError:
import pickle
import warnings
warnings.f... |
390e9f62bc198930019280647fd250abdb26a7a9ecedf0d997a3d919fcd9219b | Python | 2,252 | 77 | from tqdm import tqdm
import hashlib
import os
import argparse
import pandas as pd
def formatter(x):
try:
x = float(x)
return '{:.1f}'.format(x)
except:
pass
try:
x = pd.to_datetime(x)
return str(x)
except:
return x
def main():
parser = argparse.A... |
6daadac701a85029a92780f405284c563ee4d35a8cc76b4f6b06b62541db77d3 | Python | 2,252 | 69 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.github.toolkit import (
BranchName,
CommentOnIssue,
CreateFile,
CreatePR,
CreateReviewRequest,
DeleteFile,
Dire... |
a14731a3a0b7af6a17ac11fe1913974ea5fd4d1b56d156ef2e452fcff5d19b4c | Python | 2,252 | 64 | from collections.abc import Hashable
import numpy as np
from pgmpy.structure_score.log_likelihood_gauss import LogLikelihoodGauss
class BICGauss(LogLikelihoodGauss):
r"""
BIC structure score for Gaussian Bayesian networks.
This score penalizes the Gaussian log-likelihood to discourage overfitting. The ... |
1cde379d53fdb117e8f8916dc8a6c2836304179536be66c9eefe6d24bb186186 | Python | 2,255 | 83 | #!/usr/bin/env python3
import os
import warnings
import argparse
from pathlib import Path
import pandas as pd
from src.drop_width.preprocessing import DropDataProcessor
from src.drop_width.model import build_lstm_model
# Suppress TensorFlow and warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.filterwarnings(... |
8884e63ddb7f0fa84bd89bfd956fbefa10db687fa0cfc3380b85d06837be4474 | Python | 2,255 | 60 | import argparse
from pathlib import Path
import torch
from openbabel import openbabel
openbabel.obErrorLog.StopLogging() # suppress OpenBabel messages
import utils
from lightning_modules import LigandPocketDDPM
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', ... |
c329e9321687a314852a5b21e98160c416d1f9f6f35c6342299ad53ac42701d8 | Python | 2,256 | 63 | # -*- coding: utf-8 -*-
####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# S... |
c69ccee98fa546228a5c71a5eea659a92eed054c7bf5f832f028254387a8038c | Python | 2,256 | 67 | import glob
import numpy as np
from scipy.stats import iqr
import matplotlib.pyplot as plt
from paths import TEST_DATA_PATH
test_data_path = TEST_DATA_PATH + "/baseline/"
ground_truth = np.squeeze(np.load(test_data_path + "baseline.npz")["oxygenations"])
wls = []
results = []
est_oxy = []
names = []
for filename in... |
359c945c0fe23cc6cc191fa11c854b01425a553a8ec138915e269c14d747b992 | Python | 2,257 | 80 | # direct prediction using iComFormer
import sys
sys.path.append("/yourpath/Comformer")
import pickle as pk
import os
import torch
import numpy as np
import pandas as pd
from jarvis.core.atoms import Atoms
from comformer.graphs import PygGraph, PygStructureDataset
from torch.utils.data import DataLoader
from comformer.m... |
381d4540e55715142ef62a64e8529f275e87a79d1b57a752e8fc9236b96ad912 | Python | 2,260 | 67 | # Load in pre-trained model weights and evaluate its goodness (ECE, MCE, error) also saves logits.
import keras
import pickle
from keras.models import Model
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
from load_data_birds import load_data_birds
from keras.layers import Global... |
285ea900cd57eb961c426a78381f293d716ca2a9d26f707fb7523cf81c777a84 | Python | 2,261 | 58 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
f9e8f56f5630286c3fc7bf3ecc58f0436f1b0498a0fc9177802250eb57f1e356 | Python | 2,262 | 69 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
0abb07f11a1b34ea881536e40a055489c191aff7e6de13422c30228b915e264a | Python | 2,265 | 31 | """Page distance between each float and its in-text citations in main.pdf (or the PDF given as argv[1]).
Columns: kind, number, float page, first citation page, float page - first citation page, nearest citation page (either
side, earlier page on ties), float page - that page. Body floats are flagged when the nearest... |
ec107b1d83fc805376ed593719b3bae9b89aaef812afd48d1a6d8e8136c3924d | Python | 2,266 | 36 | '''
This datageneratormust be defined before training the network. Custom Keras datagenerators are elaborately described and illustrated here, https://towardsdatascience.com/implementing-custom-data-generators-in-keras-de56f013581c
The generators must return (EEG, PMA) or (EEG, PMA, sample_weights) where:
- EEG is... |
1c400471a5f9a83fdd7e0f5060bd61ab3d37b01f6e76e3dd1c8e6709aee1b945 | Python | 2,267 | 69 | import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from typing_extensions import override
from langchain_classic.output_parsers.format_instru... |
fe55f0fe26e51a4e43749d0901953ba6678e87e9ad2d140dfd17460ca5d0d17b | Python | 2,267 | 77 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Prune a neural network defined in Keras
#'################################################################################... |
0259f06a4a066ec5f64db39855581200f796368b0a451fabb19a9a10c63c7739 | Python | 2,268 | 66 | # Import necessary modules of astroCAST
import os
import pathlib
from pathlib import Path
from astrocast.denoising import Network, SubFrameGenerator
root = Path("/tf/data/")
model_path = root.joinpath("model")
save_path = root.joinpath("model_retrain")
train_paths = root.joinpath("pub_data/InVivoSuppRaw.h5")
infer_i... |
18daf4e88fb8bf633e4676a9bff315c861b72d2132021605dc54d2a82302736d | Python | 2,268 | 64 | from queue import Queue
import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from functions.batchnormp import BatchNormPFunction
class BatchNormSync(Module):
sync = True
checking_mode = False
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
... |
e38a9f91eea60e5e4b39e0e445cf91c66dca770e5714ff47328e454d1d876a13 | Python | 2,268 | 48 | import hyperopt
import numpy as np
import os
from src import utils
from src import data_loader
from src import constants
from src import model
i = 0
def optimize(config, ranges, num_evals, output_name="best_hyperparameters.json"):
params = config.copy()
# Define hyperparaments and ranges
params["hid... |
3757b6f74c1d859eb90bb07b52ce056dd16545cb7c2aa125e26224c623f375a9 | Python | 2,269 | 69 | import sys
import math
import argparse
import re
from itertools import combinations
def dist(p1, p2):
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
dz = p1[2] - p2[2]
return math.sqrt(dx**2 + dy**2 + dz**2)
def read_atoms(file, chain=".", model=1):
pattern = re.compile(chain)
current_model = model
... |
5f3f60bfcb0f41539fe61546b22a17e4121742d4e68294b712f2cd17cad121ff | Python | 2,269 | 66 | import numpy as np
import copy
import itertools
def neurons_initialization(neuron_init, correlation, MDC_data, n_neurons, data_min, data_max, depth, rng, ord):
def masked_norm(a, b, ord=2):
mask = ~np.isnan(a) & ~np.isnan(b)
if not np.any(mask):
return np.inf
return... |
ba2ac6066c453097481108b001c93a2e7baefef9a29bb42d5c90d70fecea6e6d | Python | 2,269 | 92 | import numpy as np
if __name__ == '__main__':
'''
path = './acm/labels.npy'
x = np.load(path).astype('int32')
print(x)
print(len(x))
print('max = ', max(x), 'min = ', min(x))
'''
database = 'aminer'
path = './{}/train_20.npy'.format(database)
x = np.load(path)
print(x)
... |
7acb113181f2308a7c1ce2b075183ce7741db55eb417c126e4bd4b9ac71d8b16 | Python | 2,271 | 72 | # Load in model weights and evaluate its goodness (ECE, MCE, error) also saves logits
import numpy as np
import collections
import pickle
from resnet_sd import resnet_sd_model
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.optimizers import SGD
from keras.datasets impor... |
95749c9c6bedbafbb1e5a9c3deeff593bbab1b6b93762b0f072c6893968da2c5 | Python | 2,271 | 74 | import logging
def createFormattedStrings(fields, values, widths=None, pad=2, alignment='<', float_format='.3f'):
""" automatically create a formatted string based on fields and values """
if widths is None:
widths = [max(len(f)+pad, 6) for f in fields]
header_format = ""
values_format = ""
... |
76ece9e6e4e81faa4a8da27e087817f2c851039c8d66780f17e5ee42f309ce71 | Python | 2,273 | 60 | import jax
import jax.numpy as jnp
import jax.random as jrng
import jax.tree_util as jtu
import flax.linen as nn
from flax.core.scope import DenyList, union_filters
from flax.core.frozen_dict import freeze
from projectlib.utils import grow_to
class LayerwiseModule(nn.Module):
def init(self, rngs, *args,
... |
ec0aba983a707a17efeca75f0ed1d3b2eff4dd27b799981e456f0d94145fc03c | Python | 2,273 | 60 | import logging
import os
import socket
import sys
import pandas as pd
from configs.configs import get_config_dict
from helpers.brain_states import extract_number_of_brain_state_switches
def _save_number_of_brain_state_switches(
config_dict: dict,
num_basis_states: int,
connectivity_metric: str = 'correl... |
6c29bb3a6ca9590ffacd936e1231c91a19a09befeb8fba27d23643d352aecab1 | Python | 2,274 | 62 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 24 16:46:26 2020
@author: gritti
"""
import os
# import sys
# sys.path.append(os.path.join('..'))
from orgseg.GUIs import inspection
from orgseg.DatasetTools.segmentation import io as ioSeg
from orgseg.DatasetTools import io as ioDT
####################################... |
7494ba2b0f7a37a2eca1b170b49ec5cdf23373e475b8faad36f97c45040d1ddc | Python | 2,274 | 36 | '''
This datageneratormust be defined before training the network. Custom Keras datagenerators are elaborately described and illustrated here, https://towardsdatascience.com/implementing-custom-data-generators-in-keras-de56f013581c
The generators must return (EEG, PMA) or (EEG, PMA, sample_weights) where:
- EEG is... |
84479d26a7ca35e574deb4e185794830b51c344918e0ab907bfa57c4937fefb3 | Python | 2,274 | 56 | import torch.nn as nn
import torch.nn.functional as F
from models.pointnet2_utils import PointNetSetAbstractionMsg,PointNetFeaturePropagation
class get_model(nn.Module):
def __init__(self, num_classes):
super(get_model, self).__init__()
self.sa1 = PointNetSetAbstractionMsg(1024, [0.05, 0.1], [16,... |
f9957fcc6b09c769c13096f751745c00a2da9e94179c68ce0c8ce1e474f6bef3 | Python | 2,274 | 65 | from collections.abc import Hashable
from pgmpy.structure_score.log_likelihood import LogLikelihood
class AIC(LogLikelihood):
r"""
AIC structure score for discrete Bayesian networks.
AIC balances discrete log-likelihood against model complexity using a sample-size independent penalty. The local
scor... |
56f4f4b0865d9156bc905a0f1d65c7227078cf59d68e625e2851e89de96b0f21 | Python | 2,275 | 61 | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from .functionals import softplus_inverse, cutoff_function
import e3nn.o3 as o3
def check_irrep(irrep):
if not isinstance(irrep, o3.Irreps):
irrep = o3.Irreps(irrep)
return irrep
def Shifte... |
e395e284a386157e4ddff60869a7b3e77f34f43ab583d01e34953422534dc417 | Python | 2,276 | 66 | import os
import sys
import torch
import argparse
import numpy as np
from tqdm import tqdm, trange
from genie.utils.model_io import load_model
def main(args):
# device
device = 'cuda:{}'.format(args.gpu) if args.gpu is not None else 'cpu'
# model
model = load_model(args.rootdir, args.model_name, args.model_ver... |
10b57b5700eec1da77cff02604681d216e1ca5fc8267d3374f4f4d4fe9581c70 | Python | 2,277 | 78 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSelfAttn(nn.Module):
def __init__(self, F):
super().__init__()
self.conv1 = nn.Conv2d(
1, F, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True
)
self.conv2 = nn.Conv2... |
694b6fec65dc486b69bcbc5de3d30a9d82a4cec13f4fa9930328c5f22b6bb7d2 | Python | 2,277 | 85 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import nibabel as nib
import numpy as np
import os
import pylab as plt
DESCRIPTION = 'Fit Rayleigh Distribution to noisemap data. Cornelius Eichner 2020'
np.set_printoptions(precision=2)
def rayleigh_dist(x, sigma):
f = (x / sigma**2) * np.exp(- ... |
87dbe8e8d2f0ca5b9d14a07d1c02dbbda35493ed4259562f7695546e7a4ce98a | Python | 2,277 | 63 | """
Script for updating NDStorage/AcqEngJ/NDViewer to latest version
It runs on the *-update branch and is called by an action in NDStorage/AcqEngJ/NDViewer repo
"""
import xml.etree.ElementTree as ET
from pathlib import Path
import sys
import os
import requests
import time
dep_name = sys.argv[1]
if len(sys.argv) == ... |
cab6c46dfcb513dd85e13d033bd527d581000ce832800b793e7467fa4e715a86 | Python | 2,277 | 64 | import json
import logging
import os
import socket
import sys
import pandas as pd
from configs.configs import get_config_dict
from helpers.hcp import get_human_connectome_project_subjects
if __name__ == "__main__":
data_split = 'all'
experiment_dimensionality = 'multivariate'
model_name = 'SVWP_joint'
... |
f915e9fcf5317e333dc594c9255b8bb1120cea4c1a6bfe7947d296050b70b060 | Python | 2,277 | 96 | """Example04."""
# pylint: skip-file
import numpy as np
import torch
import compare_models
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dtype', help='dtype', default='float64')
args = parser.parse_args()
keys = ['EMB3 (PyTorch)', 'EMB4 (PyTorch)']
torch.jit.optimize... |
c6650083bb0c708426d5875a19cdf930c53c7b4ec9a85903b40ba40a7b4e6336 | Python | 2,278 | 73 | # Load in model weights and evaluate its goodness (ECE, MCE, error) also saves logits.
import numpy as np
import collections
import pickle
from resnet_sd import resnet_sd_model
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.optimizers import SGD
from keras.datasets impo... |
95226eac101ce069db5773f6ca9919bd1eed2ad92a9ba5d1e8ec210e8baf6f78 | Python | 2,280 | 79 | """ Nuclei of the auditory brainstem model provided by Verhulst et al. (2018).
This script is downloaded from https://github.com/HearingTechnology/Verhulstetal2018Model.
Reference
---------
Verhulst, S., Altoè, A., & Vasilkov, V., (2018). Computational modeling of the human auditory periphery: Auditory-nerve
response... |
fd2601e776d71f0b14520e23514967f53a999c6d1fb29be08f1314dc2d194f2b | Python | 2,281 | 71 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Export tcga model results
#'#################################################################################
#'###########... |
3859b361ce87c1362b1431cc3e8d9b37d45f5732f33f2955bb14e2641bf8483d | Python | 2,282 | 118 | TORCHVISION = [
"vgg11_bn",
"vgg13_bn",
"vgg16",
"vgg16_bn",
"vgg19_bn",
"densenet121",
"densenet169",
"densenet201",
"inception_v3",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"shufflenet_v2_x0_5",
"mobilenet_v2",
"wide_resnet50_2",... |
37c0e92d355a569cbf6acecd09f1ccb969ba6363505a3718d9dbd8926b57ac34 | Python | 2,284 | 87 | #! /usr/GenNet/env_GenNet/bin/python
#'#################################################################################
#'#################################################################################
#' Export tcga model results
#'#################################################################################
#... |
4fc4fc16cec6a358c865239780d90830a059661981a89a07cedd18649fafaa93 | Python | 2,285 | 67 | from collections.abc import Hashable
import numpy as np
from pgmpy.structure_score.log_likelihood_cond_gauss import LogLikelihoodCondGauss
class BICCondGauss(LogLikelihoodCondGauss):
r"""
BIC structure score for Bayesian networks with mixed discrete and continuous variables.
This score penalizes the co... |
38c95d17d62fd9c8b2c7a9039c9b778ec083850e81b90c18711ce0ffe6c30efa | Python | 2,286 | 67 | from pdearena.configs.config import Config
from pdearena.configs import (
Coal,
Blast
)
from importlib import import_module
import os
config_prefixes = {
Coal: "coal",
Blast: "blast",
}
CONFIGS = dict()
CONFIG_PATH = "pdearena.configs"
def get_config_names(path: str, configs: dict):
... |
afcc16bb87b41d5f280089bfd22334f6b2a0a7bd6526bfd5e9173b64638b0acd | Python | 2,286 | 79 | """Chain that runs an arbitrary python function."""
import functools
import logging
from collections.abc import Awaitable, Callable
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from pydantic import Field
from typing_extensions imp... |
f540aa864cd13974ec48b6759a838340268993cea3746e9bbf3147cc5f00d33c | Python | 2,287 | 62 | """ Import a General data.
Import data from any source, even transcriptomics/ metabolomics/etc.
To construct its own data, user should prepare an Excel file with 3 sheets.
1. The 1st sheet must be the assay data, in which the first row is the
sample name and furthers are the expression levels of each
protein/metabo... |
2cf8d476adbd34fadb9a565254103c3aa01e93933340ba3ced283d783cb83106 | Python | 2,288 | 95 | '''
cgatflow.py - Computational Genomics Analysis Workflows
=======================================================
:Tags: Genomics
To use a specific workflow, type::
cgatflow <workflow> [workflow options] [workflow arguments]
For this message and a list of available keywords type::
cgatflow --help
To get... |
c6a4f022cc3ee521229722547c9c999d85fa39259a95d56dafd9078a94cf0c94 | Python | 2,288 | 87 | from __future__ import print_function
import argparse
import sys # for commanad line
import os # for file paths
import re
import itertools
import cPickle as pickle
import numpy as np
def main():
'''Read reactions'''
global v
found_counts = []
times = []
candidate_counts = []
# Read from file
print('Readi... |
f238a276a4171329e9e151e3921a0c2bba8ee270e128932f79c0efea599aaaf5 | Python | 2,288 | 58 | # class for collecting statistical tests for analysing cellsdata
import numpy as np
import scipy.stats as stats
import statsmodels.api
import statsmodels as sm
from packerlabimaging import AllOpticalTrial
"""STATISTICS FOR TESTING FOR SIGNIFICANCE OF PHOTOSTIM RESPONSES"""
def runWilcoxonsTest(array1, array2):
... |
880151c72792d9572adbe384cf2d3d318cbf0169414fce6cc46aa78709cedf7d | Python | 2,289 | 70 | from collections import OrderedDict
import copy
import torch
import torch.nn as nn
from openfold.utils.tensor_utils import tensor_tree_map
class ExponentialMovingAverage:
"""
Maintains moving averages of parameters with exponential decay
At each step, the stored copy `copy` of each parameter `param` is
... |
192db55a69fca568d583b6cdfebf0b6323b2d8c0beb97f286beded6f1befd08c | Python | 2,291 | 66 | from mimic3models.metrics import print_metrics_binary
import sklearn.utils as sk_utils
import numpy as np
import pandas as pd
import argparse
import json
import os
def main():
parser = argparse.ArgumentParser()
parser.add_argument('prediction', type=str)
parser.add_argument('--test_listfile', type=str,
... |
373d34fcbf4eaffc98cfd4ccbc3bae034701b5b7341e7680f755e90da61aeb36 | Python | 2,293 | 74 | import dataclasses
import pathlib
import pickle
@dataclasses.dataclass
class CiliaAnalysisDataset:
cilobjs_array: list
cn_to_cilobj: dict
cn_to_skelcd: dict
cn_to_mpskel: dict
cn_to_bbox_mesh: dict
@property
def cellnames(self):
return (
self.cn_to_cilobj.keys(... |
ad64772551c1689ed3a505807267fcb8d2779f4108c16085f1bd81649150f14a | Python | 2,293 | 67 | import os
import mne
import numpy as np
from audio_utils import WaveProcessor
from tqdm import tqdm
def create(wav_folder: str, tg_folder: str, out_folder: str, segments_list: list, config: dict) -> None:
""" Create feature from audio snips.
This function creates a feature from audio snips. The feature is a ... |
e896c1e2e8371d0c836801c7ad93bea8997ac75268c1fda5de90e0b5d8fc342a | Python | 2,293 | 55 | """Class registry for models, layers, optimizers, and schedulers.
"""
optimizer = {
"adam": "torch.optim.Adam",
"adamw": "torch.optim.AdamW",
"rmsprop": "torch.optim.RMSprop",
"sgd": "torch.optim.SGD",
"lamb": "src.utils.optim.lamb.JITLamb",
}
scheduler = {
"constant": "transformers.get_const... |
00e103eb43b52943747653ea0ec8aec4518d6c3ebe58d17cfa8e677f9cce2623 | Python | 2,297 | 51 | import tempfile
from pathlib import Path
from mrQA import monitor, logger
from mrQA.config import status_fpath
from mrQA.tests.conftest import THIS_DIR
from mrQA.tests.simulate import copy2dest
from mrQA.utils import send_email
# @settings(max_examples=10, deadline=None)
# @given(args=dcm_dataset_strategy)
def run(f... |
86b4af5021a05a58544ac05f525b376d74ac3983f011025d821747f743f59f06 | Python | 2,297 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import numpy as np
import nibabel as nib
import pylab as plt
import os
DESCRIPTION = """
Align Shifted LR and RL Data for TopUp using only axis roll.
This step is required due to a shift on Bruker scanners if the PE direction is reversed.
Cornelius E... |
bd45d2c5a6fac01d3d52ec6a851e41aea260f9585a5ea7c30c3ee80b0dcb1db8 | Python | 2,297 | 80 | import argparse
import numpy as np
from scipy.signal import filtfilt
def lp_filter_zero_phase(x, fc):
"""
lp_filter_zero_phase - Second-order zero-phase Lowpass filter.
Syntax: y = lp_filter_zero_phase(x, fc)
Inputs:
x: Vector or matrix of input data (channels x samples).
fc: -3dB cut-off... |
60da01c03ea5b1157580baff65e9a1d99029e4b3219709fa038513eb2f577fe6 | Python | 2,298 | 71 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
4c15df25ebf1b24a59fffd12b928696d10eac74fbfd6e923d7c2599259deaed7 | Python | 2,299 | 75 | # Copyright 2022 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... |
2357e7f0e34a1d48379fa92afe16b92b5ac53c97d50f4df55d3298324150f72a | Python | 2,301 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 8 23:20:18 2023
@author: weihan
"""
import torch
import torch.nn.functional as F
from torch_geometric.nn import ChebConv, LayerNorm, GCNConv
class ChebConvConvolutional(torch.nn.Module):
"""
Graph convolutional network by ChebConv method.
... |
ee7fa0f552779d63c4d3c2bccb3810f4e12adf1b8008c9590fb228cabf59f8fa | Python | 2,301 | 64 | from openff.interchange.drivers.all import get_openmm_energies
from openff.toolkit import ForceField, Molecule, Topology
from openff.toolkit.typing.engines.smirnoff import LibraryChargeHandler, vdWHandler
from openff.units import Quantity, unit
sage_21 = ForceField("openff-2.1.0.offxml")
sage_21_uc = ForceField("openf... |
1c7f5c6c2027045d082f5e30994ca977ec2fda218f27fa51f18c2ab57dedd702 | Python | 2,302 | 60 | """Evaluation protocols for battery SOH prognostics.
P1 random-row split (leaky: cycles of the same cell in train and test)
P2 within-cell chronological (first `train_frac` of each cell's cycles -> train, rest -> test; leaky w.r.t. cell identity)
P3 cell-disjoint (GroupKFold by cell inside each dataset; the realist... |
a4a24570ef0a35739178caabb66bf1fe84c75ecffefe233fbf110732472f7055 | Python | 2,303 | 82 | from bindsnet.encoding import bernoulli
from bindsnet.environment import GymEnvironment
from bindsnet.learning import MSTDP
from bindsnet.network import Network
from bindsnet.network.nodes import Input, LIFNodes
from bindsnet.network.topology import Connection
from bindsnet.pipeline import EnvironmentPipeline
from bind... |
89df467f662aed27e0b64483a54b689abf4d795ce3a766fe28b6205f1ebbd9f3 | Python | 2,304 | 77 | import sys
import argparse
from yolo import YOLO, detect_video
from PIL import Image
def detect_img(yolo):
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
... |
be499ce327090f5d1e3845e49b9e1c37689a3a09c76f12ec7659c953c7413e9e | Python | 2,304 | 78 | import matplotlib.pyplot as plt
from dival.util.plot import plot_image
from dival.measure import PSNR, SSIM
from dival import DataPairs
from dliplib.utils import Params
from dliplib.reconstructors.dip import DeepImagePriorReconstructor
from dliplib.utils.helper import load_standard_dataset, set_use_latex
set_use_la... |
cbc467447dfbabb7e6951e0f5bf5a51c35a4d624a3766ca5ce22884aac01665f | Python | 2,304 | 60 | import torch.nn as nn
import torch.nn.functional as F
class View(nn.Module):
def __init__(self,shape):
super(View, self).__init__()
self.shape = shape
def forward(self, input):
return input.view(*self.shape)
class Net(nn.Module):
def __init__(self, dims):
super(Net, self)._... |
add062713190d0344d141cac9f21ded39022ae2c452dd0b821d43e5d8c295b8b | Python | 2,306 | 73 | import argparse
import numpy as np
def baseline_sliding_window(x, l, approach):
"""
baseline_sliding_window - Baseline wander extraction from biomedical recordings using a single stage of median or moving average filtering.
Syntax: b = baseline_sliding_window(x, L, approach)
Inputs:
x: Vector ... |
2f7181ad446ede924d77dabba02f21d06e0e9d57fc1bd545c9a15ff7e6807854 | Python | 2,307 | 61 | """Process-global default `TracePolicy` for agent middleware.
`TracePolicy` (from langgraph) shapes what a middleware hook span records. This module
adds a process-wide default that individual middleware override via their own
`trace_policy` attribute. Set it once at startup with `configure_trace_policy`; the
default ... |
07ab4f2ec1ee0cf301e37b3e74e329af5ed4840c57d1a369f8a6ae4920ead104 | Python | 2,308 | 90 | #!/usr/bin/env python3
import argparse
import pathlib
from typing import Optional
import yaml
import pandas as pd
def parse_arguments(
key: Optional[str] = None, stage: Optional[str] = None, method: Optional[str] = None
):
"""
Parses command line arguments
"""
parser = argparse.ArgumentParser(de... |
813b25b19745c295f881016dde50d2fb011db2d54054b60b0af5ab7d8d18211c | Python | 2,310 | 53 | #!/usr/bin/env python
# Imports
import unittest
import os
import sys
import tempfile
import shutil
import filecmp
PATH_TO_DATA = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data')
sys.path = [os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')] + sys.path
from enrichm.generate import Genera... |
93df83307e085bccf7f759c8ceee49a7aef79d8343b949e9b4c2611b15f34c58 | Python | 2,310 | 68 | from logging import Logger
from pathlib import Path
from typing import List
import re
import numpy as np # needed in eval
from ..data import (AnnotationTable, WorldlineTable)
from ._utilities import default_args
@default_args("_this, _now, *")
def delete_annotations(dataset: Path, annotations: AnnotationTable,
... |
a19fcd97e9f7f1261d7cfb3758aa2368ed8fc268af9bc252512c0ab8acd18e80 | Python | 2,310 | 57 | import unittest
import torch
from EPI_MRI.LinearOperators import Conv3D, FFT3D, getLaplacianStencil
from EPI_MRI.utils import m_plus
class TestLaplacians(unittest.TestCase):
def setUp(self):
""" Initialize any common data or setup needed for the tests. """
self.m = torch.tensor([128, 192, 256]) ... |
343053f9143cdff2b0f58eaccbbc3a05b6c52f8c6c7317735defaf1b3e8a37c1 | Python | 2,313 | 89 | """
Functions used for the scaden model
"""
import logging
import collections
from anndata import read_h5ad
import numpy as np
import tensorflow as tf
from sklearn import preprocessing as pp
import pandas as pd
logger = logging.getLogger(__name__)
def dummy_labels(m, labels):
"""
Create dummy labels needed f... |
4dbda64ff0b1b7e21f76983dc40fefbab707e6bdbf83f655fbab275c135a2eb9 | Python | 2,313 | 80 | from tqdm import tqdm
import os
import argparse
import pandas as pd
def formatter(x):
try:
x = float(x)
return '{:.1f}'.format(x)
except:
return x
def get_all_csv_files(dir):
csv_files = []
for subdir, _, files in tqdm(os.walk(dir), desc='Getting all csv files'):
for... |
55f2932bf45c7ef470b8954961adb6cee54cc1b66a91505ca9764edf65e7a42e | Python | 2,313 | 69 | """
This script runs tensorQTL in python.
"""
import pandas as pd
import argparse, session_info
from functools import lru_cache
from tensorqtl import cis, read_phenotype_bed
from tensorqtl import calculate_qvalues, genotypeio
@lru_cache()
def get_genotypes():
plink_prefix_path = "../../plink_format/_m/genotypes"
... |
6e5dae50b856b38e4c1432bcd277d89dc43b7047d24c8621f7304621a0444459 | Python | 2,314 | 74 | import pickle
import copy
import numpy as np
import scipy
import scipy.stats
class networkClass:
parameters = {}
def __init__(self, populations, weights, inputs):
self.populations = populations
self.weights = weights
self.inputs = inputs
def subclass(self, params, **kwargs):
... |
e6366d5efc61d928481df0a0eca7d33e24baa9fdf1d54eaaa13682b3e44ef50f | Python | 2,314 | 56 | """
Classes that wrap instance of known java objects for ease of use
"""
from pyjavaz import JavaObject, DEFAULT_BRIDGE_PORT, DEFAULT_BRIDGE_TIMEOUT
from mmpycorex.core import ZMQRemoteMMCoreJ # Don't delete this, its called by other code
class Magellan(JavaObject):
"""
An instance of the Micro-Magellan API
... |
10f2af9decd2c974853239c3c7c454151c2edf171e4eefeb6acc85117857ef05 | Python | 2,315 | 72 | import hydra
from hydra.core.config_store import ConfigStore
from config import Scenario
from model import Net
import os
from utils import *
from matplotlib.ticker import FormatStrFormatter
from tqdm import tqdm
import matplotlib as mpl
from matplotlib import font_manager
from scipy.stats import pearsonr
import yaml
fr... |
6e6f3a3015554ac835b8a83cee8378309434e0e146cdfeaae2c585f3361d009e | Python | 2,315 | 50 | import numpy as np
import torch
from torch.utils.data import Dataset
import os
import pandas as pd
import networkx as nx
from networkx.algorithms import tree
from math import pi
from rdkit import Chem
from rdkit.Chem.rdchem import BondType
from Bio.PDB import PDBParser
import warnings
from Bio.PDB.PDBExceptions import... |
af42fcf58259ceebb6a39e315403f0b975b68d5487f4ef0a815411ad3cc072e3 | Python | 2,315 | 54 | from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
import numpy as np
import time
from edNEGmodel.edNEGmodel import *
from solve_edNEGmodel import solve_edNEGmodel
start_time = time.time()
I_stim = 36e-6 # [uA]
alpha = 2
t_dur = 30e3 # [ms]
stim_start = 10e3
stim_end = 20e3
sol, my_cell = so... |
f9cf5565ca8033abb2bc006a4de3f4d0824dfc79bf0a9d867371bc76510e1149 | Python | 2,316 | 73 | import torch
import torch.nn as nn
from enformer_pytorch import from_pretrained, seq_indices_to_one_hot
from typing import Optional, Union
from transformers import PretrainedConfig, PreTrainedModel
from enformer_pytorch import Enformer, seq_indices_to_one_hot
class EnformerConfig(PretrainedConfig):
model_type = "... |
d2e3d53cb830ce95002597709990037695646dc16c0730ec8f5f7b532bab37ed | Python | 2,317 | 77 | from OCC.Extend.DataExchange import STEPControl_Reader
from OCC.Extend.TopologyUtils import TopologyExplorer
from OCC.Core.StepRepr import StepRepr_RepresentationItem
from get_information import get_brep_information
from write_h5_file import write_h5_file
def check_face_in_map(topo, face_label):
# 用来检查是否所有的B-rep面... |
6da65cca718fa4951f943ff654f2037e8b76e6b5407ed8ef95ed2314d798dd0f | Python | 2,318 | 70 | # -- coding: utf-8 --
# @Time : 2022/10/16 9:11
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : predict.py
import numpy
import torch
from utils import load_data, set_params, evaluate
from module import HeCo
import warnings
import datetime
import pickle as pkl
import os
import random
from utils.logger... |
d40d9575317ebe34b16afe59115c088a087e0471a136bccb43a7c2d970ab2983 | Python | 2,319 | 68 | #### This script compares GENCODE version 25 with version 41.
import session_info
import polars as pl
import seaborn as sns
from pyhere import here
import matplotlib.pyplot as plt
from scipy.stats import spearmanr
def load_annotation(fn, comp):
return pl.read_csv(fn, separator="\t")\
.filter((pl.col(... |
3b5845c022f1e3d74b497df13e46809deecc5426b04f491499ebe2855407ed33 | Python | 2,320 | 65 | import tempfile
import typing as tp
from pathlib import Path
from typing import Tuple
from MRdataset import DicomDataset
from hypothesis import strategies as st
from hypothesis.strategies import SearchStrategy
from mrQA.tests.simulate import make_compliant_test_dataset
from mrQA.tests.utils import download
param_str... |
3d36eca5e96b74c46b392bf0c002fee888e687efb575fd07a25b03279e66a061 | Python | 2,320 | 55 | from __future__ import annotations
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.utils.json import parse_json_markdown
from langchain_classic.agents import AgentOutputParser
from langchain_classic.agents.conversational_chat.p... |
d55132f092f39066d0e10e12fa688e5f23d89c9e5053909dd40fafd50b1d0147 | Python | 2,320 | 56 | import torch
import torch.nn as nn
class EstBN(nn.Module):
def __init__(self, num_features):
super(EstBN, self).__init__()
self.num_features = num_features
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register... |
c0254484e09b263e235f835904e9b5b5c5f17bac949e8871758f71dd079f6d8c | Python | 2,321 | 46 | from src.PID import continuous_functions, format_data, discrete_functions
from src.PID import PID_utils
def compute_continuous_measures(X, src_idx, tgt_idx, args):
if src_idx is None or tgt_idx is None or X is None:
return {}
measures = dict()
measures['immi_syn'], measures['mi'] = continuous_funct... |
49e80d2383c4c0bc1fa764b8b3381840c174729721978aa04f9312be586a2766 | Python | 2,322 | 73 | import torch
import numpy as np
def thresh_func(mask, thresh=0.5):
mask[mask >= thresh] = 1
mask[mask < thresh] = 0
return mask
def dice_loss(pred, target):
pred = torch.sigmoid(pred)
pred = pred.contiguous().view(-1)
target = target.contiguous().view(-1)
intersection = torch.sum(pred... |
ac98f4eb38edf8ce588a294758389fc85c34194c33d20b60bcaa876ec4d18083 | Python | 2,324 | 79 | import argparse
import pathlib
from typing import Optional
import numpy as np
import pandas as pd
import yaml
import networkx as nx
import itertools
def parse_arguments(
key: Optional[str] = None, stage: Optional[str] = None, method: Optional[str] = None
):
"""
Parses command line arguments
"""
p... |
dda81ebfeb9f2d6f1b37c7da9386df40b293cfa1e8ff3a71f0d30fc653fa9a25 | Python | 2,324 | 52 | import tensorflow.compat.v1 as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
class EmbeddingLayer(layers.Layer):
def __init__(
self,
atom_size,
atom_embedding_size,
bond_embedding_size,
name='embedding_layer',
activation=None,... |
1dd8992fd00cfd3ff6aeb5e2e29db2a3747967ac880f6c1c6aa15dc023adcec3 | Python | 2,326 | 72 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import torch.nn.functional as F
def scaledlp_loss(input: torch.Tensor, target: torch.Tensor, p: int = 2, reduction: str = "mean"):
assert input.dim() == 5 and target.dim() == 5 # B, T, C, S, S
input = input.flatten(3)
ta... |
6d0fd1bf3e4eae04159f08709bcef87930c7955fc3116797b778e83db3083d27 | Python | 2,326 | 69 | """Benchmarks for tool-to-OpenAI schema conversion.
Agent loops convert every bound tool on every model call (token counting and
`bind_tools`), so conversion cost is paid per step. The warm benchmark measures
the steady state with the `tool_call_schema` memo populated; the cold benchmark
measures first-time conversion... |
9102b180c552e2d0e3cea88e01146f5b17d88920b724bd0284ea7383a9c6ef37 | Python | 2,328 | 76 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
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