sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fb0fb6f16f23d87ea26b5bd2abc532dab0393a3ea381cc4b5b825cad163adb7e | Python | 2,034 | 39 | import numpy as np
import torch
import argparse
from omegaconf import OmegaConf
from dipy.io import image
from utilities import instantiate_from_config
from inference import swin_denoise, swin_denoise_low_mem
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Inference with swin")
parse... |
671910729d088e85c0caad4cf931cf04f993dc66f5ef52f40632c311aca5fd19 | Python | 2,035 | 47 | from typing import Any
from typing import Dict
from torch.utils.data import DataLoader
def get_io_dims(data_loader: DataLoader) -> Any:
"""
Returns the shape of the dataset for each item within an entry returned by the `data_loader`
The DataLoader object must return either a namedtuple, dictionary or a p... |
aabe1ea6cd9f361a95a4132f2ed9ecec9c0523debae5dc70763a83a78668d73f | Python | 2,035 | 54 | #!/usr/bin/env python
import os
import tempfile
from ..model.summarystatistics import FormattedSummaryStatistics
from ..model.ld import LDScores
from ..model.frequencies import Frequencies
from ..model.weights import Weights
class PartitionedHeritability:
@staticmethod
def partitionHeritability(summaryStati... |
7be14eed4b20ed56f0228345c07a81410654036428054aad9f01ae558ab9931d | Python | 2,036 | 49 | import argparse
from model import twostage_Unet_TF1 as twostage_Unet
import tensorflow as tf
import os
from keras.backend import get_session
import tempfile
import shutil
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--load_weights_path", type=str)
parser.add_argument... |
4ff850200c3320c5c0a6dd5a6a4b3d35c98ef4a487197ce81ee1556e64da16c3 | Python | 2,038 | 68 | from typing import Tuple, List
import numpy as np
from scipy.signal import butter, filtfilt
from mne.decoding import CSP
from mne.filter import filter_data
from sklearn.base import BaseEstimator, TransformerMixin
def butter_bandpass_filter(x, low, high, sfreq, order):
b, a = butter(order, [low, high], btype="ban... |
e3f92e2d1ad26b0851c3743b2dae3755e55e4fb7530df389c8dc3f48c7a110aa | Python | 2,038 | 52 | """
Compute time series of event segments (with start time and end time),
for all data files in the provided group and save it into a separate
with one event segment file for each of the original data file.
These segments include:
- trial events (data: index of the trial)
- slider movement events (data: value of the e... |
f2b5d98951ddc8350033e2a67801e50cea0dc9ec6e6c1e262a3160e872f5dabe | Python | 2,041 | 54 | 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 stim(neuronClass):
parameters = {'save': True, 'spike': True, 'noise': None}
def __init__(self, **kwargs):
ne... |
d764b78548b6f641fefea1df1a9dc723f7500bd42784ab8993b14f7051b4ee9b | Python | 2,042 | 61 | import concurrent.futures
import importlib
import subprocess
import sys
from pathlib import Path
def test_importable_all() -> None:
for path in Path("../core/langchain_core/").glob("*"):
module_name = path.stem
if (
not module_name.startswith(".")
and path.suffix != ".typed... |
530e0fd50999c5a93979fbfcc7f477357350141e2e51ba1949006adb27abfa24 | Python | 2,043 | 61 | """Usage utilities."""
from collections.abc import Callable
from typing import Any
def _dict_int_op(
left: dict[str, Any],
right: dict[str, Any],
op: Callable[[int, int], int],
*,
default: int = 0,
depth: int = 0,
max_depth: int = 100,
) -> dict[str, Any]:
"""Apply an integer operatio... |
e6952d4b91ddb2cb286a2b6ce46cbd1ce0bd4562dbcd7d1f949666b5d6e808c0 | Python | 2,043 | 47 | import torch
import torch.nn as nn
from torch_geometric.utils import to_dense_batch
class Combine(nn.Module):
def __init__(self, target_model, d_model=256, nhead = 8, num_encoder_layers=3, num_decoder_layers=3, dropout_rate=0.15):
super(Combine, self).__init__()
self.target_model = targe... |
5f95743d5784d513e3dfb463dc5d5a71556a754eff2c71328d209bfc672c343f | Python | 2,044 | 91 | """
Vector linear algebra operations for numba accelerated code..
"""
import numba as nb
import math
import numpy as np
@nb.njit(fastmath=True)
def add(vec1, vec2):
result = np.zeros(3)
result[0] = vec1[0] + vec2[0]
result[1] = vec1[1] + vec2[1]
result[2] = vec1[2] + vec2[2]
return result
@nb.... |
60e5d6edb48bdb4731298014fa54b4ef60ccb9324f72008a2df7edbbdb9d1e36 | Python | 2,045 | 85 | """collect scripts from a directory and create documentation
stubs.
Files starting with lower case characters are put into
the directory :file:`scripts`, while those starting with
upper case characters are put into :file:`modules`.
"""
TEMPLATE_SCRIPT = '''
.. automodule:: %(prefix)s
.. program-output:: python ../sc... |
e6ce59ef35b1efff7a189652f870a905f92d6934376beb3e1caae0e96024427e | Python | 2,045 | 66 | import argparse
import skimage.io
import numpy as np
from numpy.ma import masked_array
import matplotlib.pyplot as plt
from matplotlib import cm
parser = argparse.ArgumentParser()
parser.add_argument('-left', type=str, required=True, help="left hemisphere aligned data, mhd")
parser.add_argument('-right', type=str, r... |
04f16190af7a34fe76096e2569c7ede79da3635bc4a4430b24f817e7acf4d4fb | Python | 2,047 | 61 | #!/usr/bin/env python
# Purpose: Find avg. DVARS value for each session and for each participant overall.
#
# Input: Single-column txt file DVARS for each bold image. These were generated using the CBIG2016 preproc pipeline.
# Output: Avg. DVARS value for each participant's session an deach participant overall.
#
# Wr... |
62f7bc1885b2d9fc88dc01cc63f30667a6a9c5fdcdc52265e43ac7c1e3086d01 | Python | 2,050 | 71 | # Copyright 2020 DeepMind Technologies Limited and Google LLC
#
# 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 applic... |
8c76f09459f5549eed4e0f94d63f2392bf242d25a65342779bc3a627e538e2f3 | Python | 2,050 | 62 | ####################################################################################
#
# 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... |
9a9382f2a9561b10dd0b58804a25525c7369a113492d25f0bf4262fe5986ea9c | Python | 2,054 | 49 | """Validation of scalar images, transforms, and detached NRRD payloads."""
from pathlib import Path
import nibabel as nib
import numpy as np
import nrrd
import SimpleITK as sitk
def related_files(path):
path = Path(path)
files = [path]
if path.suffix.lower() == '.nhdr':
try:
header = ... |
de3bd5bd0ce7593873c646979c814cadae376770189cd07369a158a881eb7820 | Python | 2,054 | 50 | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from scipy import stats
impedances = pd.read_csv('256ch_device2_impedance_spectroscopy.csv')
phases = pd.read_csv('256ch_device_phase_spectroscopy.csv')
non_broken_phases = phases.drop(labels = impedances.index[impedances['1000'] > 5e6].toli... |
83a6338f71f384536d7d9798d002e0b67a3fbc54ea4ce03f86071258a1fb2ccc | Python | 2,055 | 61 | import torch
import joblib
import matplotlib.pyplot as plt
from torch_net_utils import plot_hebb
from data_utils import generate_recognition_data, recog_chance
from neural_net_utils import plot_train_perf, Synapse
#%%
d = 25
P = 0.5
intlv = True
for R in [1,2,5,10,20]:
for i in [2,3]:#,2,3]:
# ... |
83499944783aaa9d662a56064b84f02ea36901b9f20a714c4d0f4d8edb611e41 | Python | 2,057 | 72 | from __future__ import annotations
from typing import List
import networkx as nx
import numpy as np
from networkx.algorithms.community import louvain_communities, modularity
from libs.function.utils import shortest_path
class Graph:
def __init__(self) -> None:
self._cluster = np.nan
self._comm... |
31c5ff3749caba4adcfb8e7ef618007de920bbe4f8ec1ef769cc41f9fbd4f193 | Python | 2,058 | 59 | import os
import pickle
import re
import time
class WideFieldImaging:
"""
WideField imaging cellsdata object.
"""
def __init__(self, tiff_path, paq_path, exp_metainfo, pkl_path):
self.tiff_path = tiff_path
self.paq_path = paq_path
self.metainfo = exp_metainfo
# set an... |
58c4b47cca1d0a85cba1b0d9e51b6c11421725bf6bba4083e8268a077abef270 | Python | 2,058 | 111 | # GIF-Parameters as similar as possible to lif model
import numpy as np
# default values for parameters
eps = np.finfo(float).eps
# number of units
N_E = 4000
N_I = 1000
# connection probabilities
ps = np.array([[0.2,0.5],[0.5,0.5]])
# connections strengths
# weights are js/sqrt(N)
# nan means they are calculated
js... |
5daf5729ff84db1d457abf8e396c45364e3ef4d07cfc5a1024e83b1100967d45 | Python | 2,058 | 72 | import numpy as np
import pandas as pd
import voxcell as vc
from lmfit import Parameters, Model
from tqdm import tqdm
def linear_volume_increase(h, a, b, H):
"""
Calculate the volume increase using linear density assumption.
Args:
- h (array_like): Array of heights.
- a (float): Coefficient for t... |
da9eed96fb21f55d01630afc074031c183e889a98770a6d8ebdc65e5c39f532c | Python | 2,059 | 60 | import logging
import os
import socket
import sys
import numpy as np
import pandas as pd
from configs.configs import get_config_dict
from helpers.test_retest import compute_tvfc_summary_measure_test_retest_scores
if __name__ == "__main__":
data_split = 'all'
experiment_dimensionality = 'multivariate'
m... |
873211cfdfe1f90e09e2651100a77f701e4cb98f1f49ff995881e1ebf9bda992 | Python | 2,060 | 65 | from contextlib import contextmanager
import numpy as np
import torch
from .training import eval_state
def get_module_output(model, input_shape, use_cuda=True):
"""
Return the output shape of the model when fed in an array of `input_shape`.
Note that a zero array of shape `input_shape` is fed into the m... |
892d2350e1073530621344fc4a7b86b564aa85cfd8dfd50e6180110a113dee4c | Python | 2,061 | 42 | import mne
import os.path as op
from config_FaceName import MEG_data_path,group_name,Ids,bids_root
from mne.preprocessing import read_ica
import numpy as np
import pandas as pd
from mne_bids import BIDSPath
for subject_id in Ids:
subject = group_name+"%02d" % subject_id
print("processing subject: %s" % s... |
22502df41773a163c796c72ae0d1abec61a7c6d98a477d38580419917dc522f8 | Python | 2,062 | 69 | # Stock Cube Parameters
stock_min_x = 10.0
stock_min_y = 10.0
stock_min_z = 10.0
stock_max_x = 50.0 #50
stock_max_y = 50.0 #50
stock_max_z = 50.0 #50
stock_dim_x = None
stock_dim_y = None
stock_dim_z = None
# General Feature Parameters
min_len = 2.0 #2
clearance = 1 #1
inner_bounds_clearance = 2
# Round Parameters
... |
e7cfa82662d9b61ea0785f67f002a500531af1c9f20b06328f37907a6873557e | Python | 2,064 | 59 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
798e7dac1e69c8af7fabbf6a1c2edd12b9b965afb0fe906132858b61a75537a5 | Python | 2,067 | 50 | import os
import logging
import datetime
from functools import partial
from utils.distribution import is_master_process, sync_barrier_re1
from utils.my_containers import singleton
import re
@singleton
class LoggerManager(object):
def __init__(self, log_dir=None):
self.logger_dictionary = {}
ts = ... |
b04cea4533453992569b43fbf86860512be54721fd006654bf990fd62183a21f | Python | 2,069 | 78 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 11 17:31:43 2023
@author: weihan
"""
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, LayerNorm
class GraphConvolutional(torch.nn.Module):
"""
Graph convolutional network.
Default three layers a... |
819036aef7ec81aa840aca509bf41dc643c44be28674914d16ac86d32ad4b0f3 | Python | 2,070 | 58 | import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import random
import seaborn as sns
import torch
import matplotlib.pyplot as plt
import h5py
from sklearn.preprocessing import MinMaxScaler,StandardScaler
import argparse
def get_attention_weight(modal_all_path,save_path):
data_label=np.loa... |
92fcd90b3c4adf6090be32e5271fc9927cb3430cae9a89f61dfeb4c92a3afb68 | Python | 2,070 | 58 | import pytest
from skbase.lookup import all_objects
from pgmpy.base import DAG, PDAG
from pgmpy.metrics import (
SHD,
AdjacencyConfusionMatrix,
BaseSupervisedMetric,
BaseUnsupervisedMetric,
CorrelationScore,
FisherC,
OrientationConfusionMatrix,
get_metric,
)
def test_subclass_inherits... |
5065e44dabd9a0582c021bbec4d84723e49b43569f23eef5b6fc594094174102 | Python | 2,071 | 66 | import inspect
import logging
import os
import sys
class SingleLevelFilter(logging.Filter):
def __init__(self, passlevel, reject):
super().__init__()
self.passlevel = passlevel
self.reject = reject
def filter(self, record):
if self.reject:
return record.levelno != ... |
d9413e50e026ad19464b2d8d6972a8ee623bcc44e92aca8e0ec3fcd7682de413 | Python | 2,074 | 75 | import torch
import torch.autograd as autograd
from torch._vmap_internals import _vmap
def jacobian_dense(fun, x, vectorize=True):
x = x.detach().requires_grad_(True)
return autograd.functional.jacobian(fun, x, vectorize=vectorize)
def jacobian_linop(fun, x, return_f=False):
x = x.detach().requires_grad... |
31fb18d6e41cf59778338786eeb97928141c8d092a4e4599597a14ed3c059018 | Python | 2,075 | 73 | from skimage.io import imread
import os
import pandas as pd
import numpy as np
"""
COMPUTATION OF ACCURACY OF CLASSIFICATION USING GROUNDTRUTH
"""
images_folder = "/home/cazorla/Images/Test papier svetlana/tube neural 3d/Test_depth"
groundtruth_path = "/home/cazorla/Images/Test papier svetlana/tube neural 3d/gt_new.... |
608abefba7290486a52bd42d6bbd0dbbc87a62707ae6ebe0967cbb0ffd923c5a | Python | 2,075 | 49 | import argparse
from model import twostage_RCAN3D_TF1 as twostage_RCAN3D
import tensorflow as tf
import os
from keras.backend import get_session
import tempfile
import shutil
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--load_weights_path", type=str)
parser.add_argu... |
eaa83440105e9a0cf98cb7b902bcb7a2fd54837bddf50be19525e881a4f616b0 | Python | 2,075 | 83 | """**Schemas** are the LangChain Base Classes and Interfaces."""
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.caches import BaseCache
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_co... |
815962c2ce73a6aead5a1d39d47d3d70e5bbd41bd317a0a6a0678b3015fa3e65 | Python | 2,076 | 46 | ##################################################################################
# DNN gene expression autoencoder pathways network 5
##################################################################################
##################################################################################
from tensorflow.k... |
ad80c8f255a81ad5815438ce1123d8997f4231c2abd7e0a922c347034e042aed | Python | 2,077 | 46 | import os
import sys
import subprocess
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
dir_base = '/home/yyasumizu/nanoporemeth/data/hWGBS_ICC/hIC... |
76d49b79c0153db6645d20e861b7b7ec2a079af0c679a4699a33c42b052ff732 | Python | 2,078 | 77 | """
Script recorder for the SMdRQA UI.
Every action taken in the UI appends an equivalent block of plain Python
to a :class:`ScriptRecorder`, so the whole interactive session can be
downloaded as a standalone, seeded, reproducible ``.py`` script.
"""
import datetime
class ScriptRecorder:
"""Accumulates Python c... |
3bc7eb8afe6d05a28779a6ca64fab62b9ac0e16d5300cfd6e1d6cf541cf31f62 | Python | 2,079 | 66 | """
2023.08.30
author: femke.hurtak@epfl.ch
Helping script for working with multiprocessing
"""
import time
import multiprocessing as mp
def queue_wrapping(function, result_queue, **kwargs):
"""
Wrapper function to allow a function to be called and add the result to a queue.
This allows to calla function... |
7e2161c384085999c675c9fe5506f2f43b33a4638d21edaeebba9d5ca3e67adf | Python | 2,079 | 64 | ####################################################################################
#
# 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... |
52f05ab6df09db11d6aacb431cf7677fa25f7ec2c0027cca8928d19998fc9a0f | Python | 2,082 | 82 | import math
GR = (1.0 + math.sqrt(5)) / 2.0
# custom colors
colors = {
"prior": "#909090",
"priorweak": "#D0D0D0",
"V": "#346ebf",
"Vgray": "#698dbf",
"Vweak": "#b8caec",
"Valt": "#b8e5ed",
"T": "#2faf41",
"Tgray": "#60af6b",
"Tweak": "#b7edc0",
"VT": "#ee1d23",
"VTalt": "#... |
70726861aa48bc6f5c6714e0049b243bd2d821024955f28ac308b7aa9292b028 | Python | 2,084 | 51 | import torch
from tqdm import tqdm
class normalizer(torch.nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.normalize = False
self.hparams = kwargs['hparams']
self.pde = kwargs['pde']
self.level = self.hparams.noise
self.size = self.pde.n_scalar_comp... |
26e25aa408e601842f72aee653a0dd3d8e7e2eea2c2103c649b525a3678dd849 | Python | 2,085 | 66 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import sys
import timeit
from functools import partialmethod
from pytorch_lightning.utilities.rank_zero import rank_zero_only
import sys
class Timer:
def __enter__(self):
self.t_start = timeit.default_timer()
... |
779d438206097cb833652af8f5d4ecdae7d36d866cb3be3e4d0add8f6ef2ad3b | Python | 2,089 | 55 | import os
from BasicSetting import *
from LSTM import predict, evaluate, preprocess
from DirStrategy import get_settings
import numpy as np
from utils import *
from logger import ResultLogger
def dir_predictor(setting):
resultLogger = ResultLogger()
settings = get_settings(setting)
cur_dir... |
9b56721f00066247b14df19b97e76b262741c4aec3cc7844636ca527b36f7224 | Python | 2,089 | 53 | import pandas as pd
import numpy as np
import re
import argparse
def get_data(save_path):
data_up_max=pd.read_csv(save_path+'/data_up_max.txt',sep='\t')
data_up_min=pd.read_csv(save_path+'/data_up_min.txt',sep='\t')
data_down_max=pd.read_csv(save_path+'/data_down_max.txt',sep='\t')
data_down_min=pd.rea... |
d19e74bb653556ce3447a87094186dcc4cac7ca9685e3980b3be6a826ea65e26 | Python | 2,089 | 75 | """
Downloaded from:
https://github.com/zavolanlab/zgtf/blob/master/zgtf/zgtf.py
(20.06.2020)
Modifications: Maciej Bak
"""
import os
import sys
import operator
import HTSeq
def gtf_to_transcript_exons(gtf, transcript_type):
"""
Parse gtf and return a dictionary where
key: trancsript_id
value: list of... |
63992fa3c576b2ff2c7b7fec50080b490f9a8a2aacf7a7833fca517cc0a38c07 | Python | 2,090 | 66 | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
e15626991564cbcc8588e18ad0ab010222acacf3977e6f8a955a81c76de54ca7 | Python | 2,090 | 65 | """Check version consistency between `pyproject.toml` and `version.py`.
This script validates that the version defined in pyproject.toml matches the `VERSION`
variable in `langchain_core/version.py`. Intended for use as a pre-commit hook to
prevent version mismatches.
"""
import re
import sys
from pathlib import Path... |
6e1c13a650c28f3f8b8c099ad4504143a31a0a87ad4e543815c3489ce72c758f | Python | 2,092 | 82 | """Handle chained inputs."""
from typing import TextIO
_TEXT_COLOR_MAPPING = {
"blue": "36;1",
"yellow": "33;1",
"pink": "38;5;200",
"green": "32;1",
"red": "31;1",
}
def get_color_mapping(
items: list[str], excluded_colors: list[str] | None = None
) -> dict[str, str]:
"""Get mapping for... |
0377d6abc85b48f20b417edd7de672a8e90a51fe103b7f92f5e3f4bb776aaf9a | Python | 2,094 | 68 | """
Tests for zip_extract.py
"""
from pathlib import Path
import zipfile
from click.testing import CliRunner
from .zip_extract import run
def test_run_no_args():
runner = CliRunner()
result = runner.invoke(run, [])
assert result.exit_code == 2
assert 'Missing option' in result.output
def test_run_mi... |
15faf4bbc13e85ece899c855bdda55a156a9211e02a8ddcd94130b8d818d270c | Python | 2,094 | 71 | import pathlib
import re
from langchain_core.callbacks import CallbackManagerForChainRun
from typing_extensions import override
from langchain_classic.callbacks import FileCallbackHandler
from langchain_classic.chains.base import Chain
class FakeChain(Chain):
"""Fake chain class for testing purposes."""
be... |
46591c1511161a00259c3d29b0e3967369106bbc1567e909065fa68be50871dc | Python | 2,094 | 62 | """Function Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class FunctionMessage(BaseMessage):
"""Message for passing th... |
13aa431a8729bf7de9ef1bf17cfafe77be1ef4dcf1ed6947c4812dbb1010e434 | Python | 2,095 | 59 | import jax
import jax.numpy as jnp
from projectlib.utils import grow_dims, grow_to, flatten
def sq_euclidean_dist(x, y):
assert x.ndim == y.ndim == 1, "sq_euclidean_dist expects two vectors"
return jnp.sum((x - y) ** 2)
def rbf_kernel(x, sigma):
return jnp.exp(x / (-2 * sigma ** 2))
def _kernel_matrix(... |
71dc33e5047d38b74118022e5fb017680e7a5aede07eb9bcd5a2440e26cecdb7 | Python | 2,096 | 48 | # -- coding: utf-8 --
# @Time : 2024/02/01 11:30
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : run_came.py
import sys
sys.path.append('../')
from analysis_utils import ttest_plot_utils
from analysis_utils import homo_random_config as config
import os
if __name__ == '__main__':
cfg = config.... |
73cf9c94c656c4f3ef008e524effce69b98b3f57827afdf8fe2cecafdf7baff5 | Python | 2,096 | 73 | from typing import List
import torch
import torch.nn as nn
from e3nn.util.jit import compile_mode
import hienet._keys as KEY
from hienet._const import AtomGraphDataType
@compile_mode('script')
class Rescale(nn.Module):
"""
Scaling and shifting energy (and automatically force and stress)
"""
def __i... |
7e2938f1153ea678edeeb82ab28d3d5269fd27c97af742645e28d35b372dd071 | Python | 2,096 | 74 | '''collector for stacks to be processed.
'''
import os
import glob
import itertools
import logging
import luigi
import pandas
from .ioutils import parse_meta
# Typical file extensions of stacks that need to be processed.
DEFAULT_FNAME_PATTERNS = [
'*confGFP*stk',
'*conf488*stk',
'*.czi',
... |
9619fecb5db1da58db18150b9db8cb0fe7d7b0cf8a07b03cee132910f2cd9b9c | Python | 2,097 | 76 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import nibabel as nib
import numpy as np
import os
from scipy import interp
DESCRIPTION = 'Drift Correction of dMRI Data, Based on Linear Interpolation Between b0s. Cornelius Eichner 2021'
def buildArgsParser():
p = argparse.ArgumentParser(descri... |
ccb2b3eda2ddeabdde700f33905b6782bf271d5e4a8df7760606c44a7bcdb306 | Python | 2,100 | 64 | import functools
import torch
import torch.nn as nn
from networks.resnet import resnet50
from networks.base_model import BaseModel, init_weights
class Trainer(BaseModel):
def name(self):
return 'Trainer'
def __init__(self, opt):
super(Trainer, self).__init__(opt)
if self.isTrain and ... |
56c5b2a7d59211d99614c6d9f5c66a27ef52137c9df3181cd79aeba12bcaaa1c | Python | 2,101 | 57 | # Move this into train
import configparser
def read_arguments_from_file(fp):
"""Reads run arguments from file"""
config = configparser.ConfigParser()
config.read(fp)
settings = config['settings']
return _convert_arguments(settings)
def _convert_arguments(settings):
converted_settings = {}
... |
c661bff533166a178de5ca045ae7b247f41d621b87660d02865a1bc02bf35862 | Python | 2,102 | 80 | import json
import sys
class Message:
def __init__(
self,
message_id,
message_text,
author,
timestamp,
parent_message=None,
child_message=None,
):
self.message_id = message_id
self.message_text = message_text
self.author = author
... |
2ae4cb9fc19e645c7c69db1a7e349637bac1972d8838eabef05c232b344e1f78 | Python | 2,103 | 62 | ####################################################################################
#
# 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... |
9902e3d4c2d3e88fa4cda466f964d0f535781b4f276edc57de3d0c1301f08433 | Python | 2,104 | 62 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
fafe838beade24cb79855154d427b826df7e5ae95075f721fa4c0d3f39a6a351 | Python | 2,104 | 54 | from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable
from langchain_core.utils.function_callin... |
bf2b554aeaa9f74fe8f82ec5505ddda425ebfed6eca6a2c25b7ce151cfbbc025 | Python | 2,105 | 63 | #!/usr/bin/env python
import logging
import os
import random
import time
import urllib.request
from http.client import RemoteDisconnected
# Setting logging level config
LOGLEVEL = os.environ.get("LOGLEVEL", "DEBUG").upper()
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=LOGLEVEL... |
c18cb71d6745fde5b96440757b3ec54fbfbef48c4ef95b1641f854e94000f37a | Python | 2,105 | 81 | import os, sys, re, types
import matplotlib.pyplot as plt
import sqlalchemy
from SphinxReport.Renderer import *
from SphinxReport.Tracker import *
# for trackers_derived_sets and trackers_master
if not os.path.exists("conf.py"):
raise IOError( "could not find conf.py" )
exec(compile(open( "conf.py" ).read(), "co... |
4b415b75279553eff9777a5d36832336562e1a6c33c164cb7701ebb6bb1da73a | Python | 2,106 | 60 |
from typing import Optional, List
from torch.utils.data import DataLoader
from scdp.common.pyg import Collater
class ProbeCollater(Collater):
def __init__(self, follow_batch, exclude_keys, n_probe=200):
super().__init__(follow_batch, exclude_keys)
self.n_probe = n_probe
def __call__(self, ba... |
6c87af77914d21edd6e5940ac301e6b299ff3557c43940777fc8b176aec02af3 | Python | 2,106 | 77 | """**Prompt** is the input to the model.
Prompt is often constructed
from multiple components. Prompt classes and functions make constructing and working
with prompts easy.
"""
from typing import TYPE_CHECKING, Any
from langchain_core.example_selectors import (
LengthBasedExampleSelector,
MaxMarginalRelevanc... |
65dc0b66699711606bf3b9267052f10bfa1d844280d161465169dc45631997c3 | Python | 2,107 | 51 | from boundingbox import BoundingBox
import cv2
import numpy as np
def preprocess(img, input_shape, letter_box=True):
if letter_box:
img_h, img_w, _ = img.shape
new_h, new_w = input_shape[0], input_shape[1]
offset_h, offset_w = 0, 0
if (new_w / img_w) <= (new_h / img_h):
... |
8e7ed4b77a349719b8a4e88a6240e69c1c5c0c4da41152e351b37bbbd109afd8 | Python | 2,107 | 67 | import os
import cv2
import torch
import numpy as np
import datetime
# Additional Scripts
from train_transunet import TransUNetSeg
from utils.utils import thresh_func
from config import cfg
class SegInference:
def __init__(self, model_path, device):
self.device = device
self.transunet = TransUNe... |
aaf5446291a50e003bafbb1f14cb46b5339bbfa7209befabb92061203e5a016e | Python | 2,107 | 67 | import pandas as pd
from .power_divergence import PowerDivergence
class ChiSquare(PowerDivergence):
"""
Pearson chi-square test for conditional independence on discrete data.
This class is a thin specialization of :class:`PowerDivergence` with ``lambda_="pearson"``.
For the contingency-table constru... |
34cd1f242b018cfdc7236f40e1d1176c28f2d9624467d3a322847b32b1bc09e4 | Python | 2,110 | 60 | import sympy as sym
import tensorflow as tf
from tensorflow.keras import layers
from dipgnn.models.layers.envelope import Envelope
from dipgnn.models.model_utils import bessel_basis, real_sph_harm
class SphericalBasisLayer(layers.Layer):
"""
This code is extracted from dimenet (https://github.com/gasteigerjo/... |
59859f068afbbddb71e0bbf32ffa737d4cf5e006145fbae812240c1ea990d4ce | Python | 2,113 | 64 | import pandas as pd
from .power_divergence import PowerDivergence
class ModifiedLogLikelihood(PowerDivergence):
"""
Modified log-likelihood ratio test for conditional independence on discrete data.
This class is a thin specialization of :class:`PowerDivergence` with
``lambda_="mod-log-likelihood"``.... |
3b7f714402da063e74de3f6d40241803fc1cc7c745a73a593f48e273a6d16e30 | Python | 2,114 | 71 | from typing import Any
from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel, __all__
from langchain_core.outputs import LLMResult
from langchain_core.prompt_values import PromptValue
EXPECTED_ALL = [
"BaseLanguageModel",
"BaseChatModel",
"SimpleChatMo... |
9b789aaf2efc8c06516f556d6b366978ea5483d5ecab7c2d0942c365f734a8cd | Python | 2,115 | 81 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#import pykrige.kriging_tools as kt
from pykrige.ok import OrdinaryKriging
import random
#plt.rc('font', family='Times New Roman')
Data5_100 = pd.read_csv('P_5_100.csv')
Data5_150 = pd.read_csv('P_5_150.csv')
Data5_200 = pd.read_csv('P_... |
f79b99f21569a33266ca309bffa0aaf7458886cdafc9354f49e8578d5c2a64a1 | Python | 2,115 | 63 | #!/usr/bin/env python
import logging
import os
import random
import time
import urllib.request
from http.client import RemoteDisconnected
# Setting logging level config
LOGLEVEL = os.environ.get("LOGLEVEL", "WARNING").upper()
# logging.basicConfig(
# format='%(asctime)s %(levelname)-8s %(message)s',
# level=... |
441a689c5ef08b9affca0cba24daafb5c0c891b08f9998e238c2e62c909e3ace | Python | 2,116 | 72 | """LangSmith evaluation utilities.
This module provides utilities for evaluating Chains and other language model
applications using LangChain evaluators and LangSmith.
For more information on the LangSmith API, see the
[LangSmith API documentation](https://docs.langchain.com/langsmith/home).
**Example**
```python
f... |
367d9b6e4034c6dea880c1efbd212092dd9d2c6cd96b36d71827695ab4fea7af | Python | 2,117 | 66 | """Output classes.
Used to represent the output of a language model call and the output of a chat.
The top container for information is the `LLMResult` object. `LLMResult` is used by both
chat models and LLMs. This object contains the output of the language model and any
additional information that the model provider... |
bdea9c2cce3d8c87ef3e7fca519fdec0080fb76f3de75d19bc295c12f5e2a1c5 | Python | 2,117 | 66 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import numpy as np
import os
# from __future__ import print_function
DESCRIPTION = 'Reordering bvecs file i.e., swapping and inverting dimensions. Cornelius Eichner 2018'
np.set_printoptions(precision=2)
def buildArgsParser():
p = argparse.Argume... |
7fec805d6adac0d9a1d0c83f921d9be273dffae6c662fadbdddc2506d03bedc6 | Python | 2,120 | 81 | """
scaden Main functionality
Contains code to
- process a training datasets
- train a model
- perform predictions
"""
# Imports
import logging
import tensorflow as tf
from anndata import read_h5ad
from scaden.model.architectures import architectures
from scaden.model.scaden import Scaden
logger = logging.getLogger... |
128bbc42a8133e98a48b2762535149a76dc64e1d820047cd1fa779b653fb2a1e | Python | 2,121 | 58 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
1548bb3b0accf7d96c50581b5f8cf6fdd5fd283239a8242c81a3a49b7ba85638 | Python | 2,121 | 65 | #!/usr/bin/env python
# coding: utf-8
import os
from os.path import abspath, join, isdir
import sys
# for relative paths in nn4dms code to work properly, we need to set the current working
# directory to the root of the project
# we also need to add the code folder to the system path for imports to work properly
pr... |
1d4c7156a0fb994b3bfac928643c5024ac4fb9db5d2d045be3705cc991c57a6f | Python | 2,122 | 68 | from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch
class _BatchInstanceNorm(_BatchNorm):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
super(_BatchInstanceNorm, self).__init__(num_features, e... |
58314ca3e31dfbc8bb95da65b9d58742d78b17dc7bc2fdde534898f0f974863f | Python | 2,124 | 61 | """
Wrapper for glmnet_python to work with the experiments script.
Authors: George H. Chen, Lexie Li, Ren Zuo
"""
import numpy as np
import pandas as pd
import pickle
from sklearn.preprocessing import StandardScaler
import glmnet_python
from glmnet import glmnet
from glmnetCoef import glmnetCoef
class CoxWrapper:
... |
a5dcd8e50926b16e635191bfad949da99c90308f3b6b0748ee3537c6e8030eab | Python | 2,124 | 61 | # coding=utf-8
import torch
import torch.nn.functional as F
from alg.algs.ERM import ERM
class CORAL(ERM):
def __init__(self, args):
super(CORAL, self).__init__(args)
self.args = args
self.kernel_type = "mean_cov"
def coral(self, x, y):
mean_x = x.mean(0, keepdim=True)
... |
aa1b327ad1df5d99173cc93f16b76ee6579442817d2e33021249e5ca8e0b7ba0 | Python | 2,124 | 72 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Transfer learning using features from TCGA model
#'#######################################################################... |
cd71cc1c38558c156b512b1d88b01c7d708e84960d57f56ef28ae603bb5e7bf3 | Python | 2,124 | 76 | import numpy as np
from scipy.stats import skew
all_functions = [min, max, np.mean, np.std, skew, len]
functions_map = {
"all": all_functions,
"len": [len],
"all_but_len": all_functions[:-1]
}
periods_map = {
"all": (0, 0, 1, 0),
"first4days": (0, 0, 0, 4 * 24),
"first8days": (0, 0, 0, 8 * 24... |
42e15f453f42a65c8a8d67928c4593b24f39e2daeb5600d0bd010c9e310c3f12 | Python | 2,125 | 68 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 13 14:53:06 2022
@author: athanasiadis
"""
import numpy as np
def computer(sig_res):
"""
computer
______________________
This is a function that computes the results of the permutation test.
Input:
- t... |
30238fb13272cd1f36b25b1f77d605de7684d0c803a175bdd5af645680e4ddd0 | Python | 2,126 | 72 | """Test Base Schema of documents."""
from collections.abc import Iterator
import pytest
from typing_extensions import override
from langchain_core.document_loaders.base import BaseBlobParser, BaseLoader
from langchain_core.documents import Document
from langchain_core.documents.base import Blob
def test_base_blob_... |
7f81c7f562436bc1d2536ae352e80003e9be346528201f752d03ee5e444b3bd2 | Python | 2,126 | 70 | # Custom pytorch modules
from typing import Sequence
import torch
from torch import nn
class Unnormalize(nn.Module):
"""
Helper class for unnormalizing input tensor
"""
def __init__(
self,
mean: Sequence[float] = (0.0,),
std: Sequence[float] = (1.0,),
inplace: bool = ... |
9bee07de25acd75ebb36213df1af6d76c0f1f223b99720c54f3231124d3e1632 | Python | 2,127 | 63 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
import torch
from nequip.data import AtomicDataDict
from nequip.nn import GraphModuleMixin, scatter, AvgNumNeighborsNorm
from typing import Optional, Union, Dict, Sequence
from math import sqrt
cla... |
31dffe9f86d1831a0c2b004d868f50bd6084508b364cd13fb6b5947b215e8453 | Python | 2,128 | 43 | from django.shortcuts import render
from django.contrib import messages
from django.views.generic.edit import FormView
from django.core.exceptions import ValidationError
from .forms import ClusteringForm
from .models import Cluster
from .clustering_code import cluster_data
class IndexView(FormView):
model = Clus... |
49c1e0d5f37e7989040840570f420781847f3fed3a66b20d19ecb05d41f7085c | Python | 2,128 | 46 | import numpy as np
import h5py
import argparse
import os
def main(files, dsets, metrics):
for file in files:
assert os.path.isfile(file), f"either {file} doesn't exist or path is wrong"
h5df = h5py.File(file, mode="r")
for i, key in enumerate(dsets):
assert f"{key}_coverage" i... |
b541457e503b6c7dadf2ef16fde478ab31acd694e42ed96f30697555749a81cb | Python | 2,132 | 58 | from datetime import datetime, timedelta, timezone
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import comma_list
class DatetimeOutputParser(BaseOutputParser[datetime]):
"""Parse the output of an LLM call to a dat... |
095a3f613cd4df18b75f577e7df659e2594b11cbf570335400a7af93cf15f3e4 | Python | 2,133 | 45 | #!/usr/bin/env python
import os
import tempfile
from ..constants.constants import HUMAN_SOMATIC_CHROMOSOMES, BASELINE_WEIGHTS_URL
from ..utilities.utilities import untar
class Weights:
SUFFIX = ".l2.ldscore.gz"
@staticmethod
def validateDirectory(directory, chromosomes = HUMAN_SOMATIC_CHROMOSOMES, pref... |
a88e63f58d78b5f892df6388a5a5b9b1eb25551e08a4012d43add626ec0a901d | Python | 2,134 | 61 | # Imports
import sys
import os
import argparse
import inspect
from datetime import datetime
import numpy as np
from tqdm import tqdm
from math import ceil
from time import perf_counter, process_time
import torch
import torch.optim as optim
try:
from torchdiffeq.__init__ import odeint_adjoint as odeint
except Impo... |
3fa3fae9c305bb57baa02e687578959dc7d441c59bab08cc36cdd0bc74fce110 | Python | 2,137 | 58 | ####################################################################################
#
# 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... |
568508dcec8043e6aa307e003e52ed1a349a54bdc54025b633b7dab34a80516b | Python | 2,137 | 69 | import numpy as np
from voxcell import VoxelData
from os import cpu_count
import sys
from tqdm import tqdm
import scipy.ndimage as nd
import flatmap_util as fmutil
flatmap_nrrd = sys.argv[1]
pixel_res = int(sys.argv[2])
output_nrrd = sys.argv[3]
ncpu = cpu_count() // 2
if len(sys.argv) > 4:
ncpu = int(sys.argv... |
66612c70c423aebb16d0a7919b2987ef74f8a606fe67c0caa098df4ce578a029 | Python | 2,137 | 54 | import math
import torch
import torch.nn.functional as F
"""
IMPORTANT NOTE: The cutoff and the switch function are numerically a bit tricky:
Right at the "seems" of these functions, i.e. where the piecewise definition changes,
there is formally a division by 0 (i.e. 0/0). This is of no issue for the function
... |
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