sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a29e4c3746d188852c36fbedd0c01d13e0a72e6ecbef847ab48b292033f75639 | Python | 1,865 | 60 | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from Actor1 import init_weights
class Critic(nn.Module):
def __init__(self, OBS_DIM, ACTION_DIM, TARGET_DIM, RNN_SIZE, FC_SIZE, RNN):
super().__init__()
self.OBS_DIM = OBS_DIM
self.AC... |
e5ff811d3858ea61ba00f4aadc2470d8c2f670e34e9eae837e890345fecabbd1 | Python | 1,866 | 60 | # Copyright 2023 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 applicable law or agreed to in writing, sof... |
69ee28d5c212627c595bdd331714444f7f91e0ef5d969a80f40705073deabf8c | Python | 1,867 | 47 | import numpy as np
def reformat_syn_parameters_eglif(CELL, params, M):
"""
valid if CELL is specified in cell_library
"""
# M : row = connected cells("from"), column post synaptic target ("to"_cell).
if CELL == 'RS':
# synapsi to_RS
params['Qe'], params['Te'], params['Ee'] = M[0... |
7d8db1230df44a83e05139e920ec4719c44899eddc218fc635b27e2f24497520 | Python | 1,867 | 76 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Creates a genetic relatedness matrix (GRM) from biallelic sites in an input VCF
"""
import argparse
import sys
import pysam
def get_allele_dosage(record):
ads = []
for s in record.samples:
GT = list(record.samples[s]['GT'])
if len([a for a ... |
3195cb043a718c48b07bc211f88920e3b75b1d8a46fd01c9b9da7fcc9d9d23f8 | Python | 1,868 | 44 | import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from src.ops.vandermonde import log_vandermonde, log_vandermonde_fast
@pytest.mark.parametrize('L', [3, 17, 489, 2**10, 1047, 2**11, 2**12])
@pytest.mark.parametrize('N', [4, 8, 16, 32, 64, 128, 256])
# @pytest.mark... |
57a5c9e2c4fd6d710a290f28aa4d72e8ea1195ef2fb80ba63d1c2174796f3997 | Python | 1,868 | 56 |
from deepsith import DeepSITH
from torch import nn as nn
import numpy as np
import torch
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white', palette='colorblind', context='poster')
class DeepSITH_Classifier(nn.Module):
def __init__(self, out_features, layer_params, dropout=0):
su... |
4feb5296f36492f19c9257d840d3486dcc64acf629e0f2554c9305a043235004 | Python | 1,870 | 51 | from unittest.mock import patch
import numpy as np
import pytest
from skorch.utils import to_numpy
class TestGradientNormClipping:
@pytest.fixture
def grad_clip_cls_and_mock(self):
with patch('skorch.callbacks.regularization.clip_grad_norm_') as cgn:
from skorch.callbacks import Gradient... |
c098381fb9bc3017dff2a4143a01410bcf29fc29049c6238f7bac63568a7be62 | Python | 1,870 | 55 | #!/usr/bin/env python
import numpy.testing as npt
import os
from caiman.source_extraction import cnmf
from caiman.paths import caiman_datadir
def demo():
fname = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')]
fr = 10 # frame rate (Hz)
decay_time = .75 # a... |
fcec4982beb68f75651d528f6c817ea586d42e2be733ea6d204ffaff1faf8915 | Python | 1,870 | 53 | import torch
from torch.nn.parameter import Parameter
import numpy as np
from torch import sigmoid
import copy
class SoftConcrete(torch.nn.Module):
def __init__(self, threshold = 0.5, remove_key_parts = False, use_top_k = False, k = 0.05):
super(SoftConcrete, self).__init__()
self.loc_bias = 3
... |
bb313841e9f00df6d4a67528ef672971e773a75edfc6f3bc46f594b4e6ab04e7 | Python | 1,872 | 56 | from Bio import SeqIO
import random
import os
import sys
from pathlib import Path
import pandas as pd
# /ccb/cybertron/khchao/splam-analysis-results/train/results/tool_benchmark
SEQ_LEN = 800
project_root = '/ccb/cybertron/khchao/splam-analysis-results'
test_junc_out_f = f'{project_root}/train/results/tool_benchmark/{... |
3af3201ef9602701f47f88d064224bf9bc5e1053f8745109fbd7b17c8d7f708d | Python | 1,873 | 62 |
import numpy
import random
import sys
sys.path.insert(0, "..")
from sklearn.preprocessing import PolynomialFeatures
from matplotlib import pyplot
import algorithms
# numpy.random.seed(42)
# random.seed(42)
class Sampler:
def __init__(self, degree=3):
self.degree = degree
self._min, self._max = ... |
472a555e3c7511a77400540fe1af8d1b029c37198564211edd2ae5116b41e252 | Python | 1,873 | 50 | import numpy as np
import scipy.stats as stats
from hic3defdr.util.scaled_nb import logpmf, fit_mu_hat
def lrt(raw, f, disp, design, refit_mu=True):
"""
Performs a likelihood ratio test on raw data ``raw`` given scaling factors
``f`` and dispersion ``disp``.
Parameters
----------
raw, f, dis... |
795bbb5f0426f685ec3e0e3275623f5234cd48874070dca7f7ca1ec06ec1dc59 | Python | 1,873 | 54 | import kaggle
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.paths import get_data_path
# The dataset in this example is obtained using the `kaggle` api.
# If you haven't done so already, you should set up the api credentials:
# https://github.com/Kaggle/kaggle-api#api-... |
c82f502799384b287c86b117016d9f4f3d8f69390bf600e600bb38adc5a7505c | Python | 1,873 | 39 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from io import StringIO
# for log in [False, True]:
for log in [False]:
for thresholds in range(2, 101):
neg_pred_file = f'/ccb/cybertron/khchao/splam-analysis-results/src_tools_evaluation/splam_result/RELEASED/neg_{thresholds}/LOG/_... |
650392c63abc9ba33016a0b90408088af34b093db18adcbbdf90078af27cc906 | Python | 1,874 | 71 | from dataclasses import dataclass
import numpy as np
from .node import NIRNode
@dataclass(eq=False)
class Affine(NIRNode):
r"""Affine transform that linearly maps and translates the input signal.
This is equivalent to the
`Affine transformation <https://en.wikipedia.org/wiki/Affine_transformation>`_
... |
f0882a1fd8529cfb88845e9568eb07a3af30289a09ec36134a391f35aae8396d | Python | 1,874 | 104 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache
# In[2]:
# this path determines where downloaded data will be stored
manifest_path = os.path.j... |
34db9020072fca86dad26ce09ae4230e5a03e43863c4c0269de051aa2ae5ccaf | Python | 1,875 | 47 | import torch
import torch.nn.functional as F
def multiresolution_spatial_basis_application(raw_frames: torch.Tensor,
full_scale_basis: torch.Tensor) -> torch.Tensor:
'''
* big_height must be an integer multiple of small_height
* big_width must be an integer m... |
207e572024b53fd2469dc5df31616648c24cb249d9a599171154ed04cf7ebc08 | Python | 1,876 | 70 | #!/usr/bin/env python
"""
Collect sample list metrics. Writes stats to stdout.
Metrics:
sample_list_count : Number of samples
"""
import argparse
import sys
import svtest.utils.IOUtils as iou
KEY_PREFIX = "sample_list_"
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
... |
8413cf5558393391512ecb4df77ded2c99555b916e6fa3adf1b40b89982e87ee | Python | 1,876 | 55 | import os
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from utils import padded_corr
from utils import EventLabel, TrainValidSplit, HumanBondaries, DataLoader
sns.set(style='white', palette='colorblind', context='talk')
tvs = TrainValidSplit()
evlab = Even... |
bf7b35a224cebb1a8a0417965341493db87655799a3e4c6278b9fd9db7e6a7cf | Python | 1,877 | 62 | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from Actor1 import init_weights
class Critic(nn.Module):
def __init__(self, OBS_DIM, ACTION_DIM, TARGET_DIM, RNN_SIZE, FC_SIZE, RNN):
super().__init__()
self.OBS_DIM = OBS_DIM
self.AC... |
1ac8865ec4a5ca5042fd343456ef7dad016a8cdb70b48b9e2bf057cbf42b458d | Python | 1,878 | 57 | #!/usr/bin/env python
# Copyright (c) 2017-present, Facebook, Inc.
#
# 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 a... |
a63e9695c66605d4958832c80ae4c07e2c25f3937afd28a0c68c9e55d0f3b589 | Python | 1,879 | 51 | import os
import nibabel as nib
import numpy as np
import IVIMNET.deep as deep
from hyperparams import hyperparams as hp
output_root = '/mnt/d/fc-ivim-estimation/results'
subs = [sub for sub in os.listdir(output_root) if sub.startswith('sub')]
pars = ['D','f','vd']
times = []
for sub in subs:
# Gather input file... |
238dbc9cbf523ed306281c2f1003f04a46a6df78196533420ced3eb4ed412c98 | Python | 1,880 | 47 | import argparse
def main(xyz_filepath: str, dataset: str, memory: str, num_threads: int, verbose: bool = True):
if dataset == "qm9":
import psi4
# Set memory and number of threads
psi4.set_memory(memory)
psi4.set_num_threads(num_threads)
# Set computation options
... |
663b26657ddfb7b1c5596b78b34b55ad93bb710291045046a3439866d4fee784 | Python | 1,880 | 116 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
Atomic radii in picometers
NaN stored for unavailable parameters.
"""
from __future__ import annotations
ATOMIC_RADII = {
0: float("NaN"... |
bf8e8e195cbf8f3a43358dbe572b49bad6a46d520b191d7c9cc8fac022df4b7c | Python | 1,880 | 69 | '''
Example for visualizing the fitness transition throughout the evolutionary process.
'''
import os
import pickle
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.colors import Normalize
path = os.path.join(os.path.dirname(__file__), 'examples/xor/resu... |
cfdfa6964682408d0acf3cd95ee6882b720b01cf4f5ba1c91bd051990cd645e4 | Python | 1,880 | 59 | import os
import shutil
import tempfile
import gym
import pytest
from gym.wrappers import TimeLimit
import pfrl
@pytest.mark.parametrize("n_episodes", [1, 2, 3, 4])
def test_monitor(n_episodes):
steps = 15
env = gym.make("CartPole-v1")
# unwrap default TimeLimit and wrap with new one to simulate done=T... |
29118da47d0f0a4a1e3e1ad1e8cefdef598a31e7d3bda11b98d652d4784c2ee7 | Python | 1,881 | 73 | """OmicVerse-style console output helpers for the vendored gsMap pipeline."""
from __future__ import annotations
from datetime import datetime
class Colors:
"""ANSI color codes for terminal output styling."""
HEADER = "\033[95m"
BLUE = "\033[94m"
CYAN = "\033[96m"
GREEN = "\033[92m"
WARNING... |
5c93ce64cfd989109076c961bf3082dd01322746310e6e7bd1ed164cab8b8478 | Python | 1,881 | 51 | #!/usr/bin/env python
from distutils.core import setup
from distutils.command.install import INSTALL_SCHEMES
import os
for scheme in INSTALL_SCHEMES.values():
scheme['data'] = scheme['purelib']
data_files_list=[]
data_path="mozaik/stimuli/vision/textureLib/"
for root, dirs, files in os.walk(data_path):
for f ... |
92c624eefb22a3d6ecdee093c39efd2d32a3c62a2e5c57167c37542af2f3bd0f | Python | 1,882 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 30 11:28:50 2019
@author: sam
"""
import numpy as np
import scipy.signal
import pywt
def haar_wavelet(X,a,mode='same'):
if a==0:
return X
haar = np.ones(2*a)
haar[a:] = -1
return np.convolve(X,haar,mode=mode)/a#[a:-a+1]
... |
919165e49407b22d002eaa503e74041e1ed0b399b872b9f4f66ed65f9fdf2761 | Python | 1,883 | 53 | """ Creates a ukbb_dev dataset which is a miniature version of a typical ukbbs.
NOTE: dataset names, tables to be copied are currently hardcoded,
will fail if dev dataset already
exists.
"""
from google.cloud import bigquery
# CONSTANTS
ORIGINAL_DATASET = 'ukbb7089_201904'
DEV_DATASET = 'ukbb_dev'
EXACT_TABLES = ['c... |
cbfda16f2e0d9ff48cb587785f2add425290477c74bfff7a3f9b5321c0ea774b | Python | 1,884 | 56 | import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def convert_ldm_original(checkpoint_path, config_path, output_path):
config = OmegaConf.load(config_path)
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
keys = lis... |
47dea5c294fd8655fb8b87de2ebe365811fc738e92d95bfd5315a089fcc43641 | Python | 1,885 | 52 | """build_session(dynamic_shapes=True) must accept inputs in shapes other than declared ones."""
import numpy as np
import pytest
from deepface.commons import onnx_utils
onnx = pytest.importorskip("onnx")
ort = pytest.importorskip("onnxruntime")
def make_model():
# (1, 3, 4, 4) declared conv graph, which works ... |
50dc742d07f5a5e5db879e5711e7f9ce44db450cdecabe99857d9e12f06145eb | Python | 1,885 | 55 | import numpy as np
import scipy.special as special
from .basic_distributions import Distribution
from ..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[GammaF(a+b)/... |
8ced045bfa80767cd7b64a7853d1f3c371e30465aa9b58ad7694036370076ae0 | Python | 1,885 | 51 | """
Date: Oct 25 2019
Author: Shaoju Wu
Email: swu3@wpi.edu
see also:
https://github.com/Jilab-biomechanics/CNN-brain-strains
"""
import numpy as np
import scipy.io
import tensorflow as tf
import keras
from keras.models import load_model
from scipy.io import loadmat
from scipy.io import savemat
from sklearn.metrics i... |
9bfdc97eec2c24607ff76c363756fe32b1ab37ae3b1b68079e58e471d3383bdd | Python | 1,885 | 50 | import torch
from projectmetis.proto import metis_pb2
from projectmetis.python.logging.metis_logger import MetisLogger
from projectmetis.python.models.model_dataset import ModelDataset
from projectmetis.python.models.model_ops import ModelOps
class PyTorchModelOps(ModelOps):
def __init__(self, model_filepath="/... |
cf10c0e23fa52b4cda8d4205c8aa6f72f02cda108e28927742d7054708286cd1 | Python | 1,885 | 49 | import sys
from ray._private.utils import open_log
from ray._raylet import StreamRedirector
def redirect_stdout_stderr_if_needed(
stdout_filepath: str,
stderr_filepath: str,
rotation_bytes: int,
rotation_backup_count: int,
):
"""This function sets up redirection for stdout and stderr if needed, b... |
9e3ae3d51e34e37387734914e725f3a8acd00e71c1e5d0100da61749fb452e1f | Python | 1,886 | 64 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import warnings
from .annotate_intersection import split_gencode_fields
def annotate_nearest_tss(sv, gencode):
"""
Annotate each variant record with its nearest TSS.
Parameters
----------
sv : pbt.BedTool
columns = (chrom, start, ... |
a735f23a5ba955494aacfd254680ef4aaa16dc3f524af207fb276d0b201c3c98 | Python | 1,886 | 47 | import tensorflow
import keras.optimizers
import keras.initializers
from sak.losses import *
def select_loss(loss):
switcher = {
'dice' : DiceLoss,
'jaccard' : JaccardLoss
}
return switcher.get(loss.lower())
def select_optimizer(optim):
switcher = {
... |
0da0f754e943a9fb1f15ae5b48f74f68d54b00356411f51b5bb4a43bb23ecdba | Python | 1,887 | 42 | import math
import torch
from scipy import ndimage, spatial
def rotate_vol_around_axis(vol, rot_angle, rot_axis, output_shape=None, order=3):
"""
Rotates the 3D tensor 'vol' by 'rot_angle' degrees around 'rot_axis'. The rotated tensor, which is typically larger than the original one, is center-cropped such t... |
38744c479502481311d1074726a0ae4abdf96dd36305bbac6610f4bc47e68f38 | Python | 1,887 | 64 | import torch
import torch.nn as nn
import numpy as np
class conv_encoder(nn.Module):
def __init__(self, n_components=2):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(
in_channels=1, out_channels=64, kernel_size=3, stride=2, padding=1,
),
... |
f504d5c2e9706c81b0f6e5659023adc4ce8397e682dc5aa1207564c882f6d147 | Python | 1,887 | 52 | import pfrl
from pfrl.agents import categorical_dqn
from pfrl.agents.categorical_dqn import _apply_categorical_projection
from pfrl.utils.recurrent import pack_and_forward
class CategoricalDoubleDQN(categorical_dqn.CategoricalDQN):
"""Categorical Double DQN."""
def _compute_target_values(self, exp_batch):
... |
4d054440a5985d148f5dc84166c5af45299f1062ef61db36aa265ff417b3fc9e | Python | 1,888 | 75 | # -*- coding: utf-8 -*-
"""
Created on Wed May 11 12:46:10 2022
@author: gagan
"""
import numpy as np
from scipy.stats import wilcoxon as wx
from scipy.stats import ttest_rel as tr
import matplotlib.pyplot as plt
def get_pval(output,keys=None,verbose=1):
nexps = len(output)
if 'var' in output:
nexp... |
c5478c5b9c6b9bd82bd29a30e5de39f17dea0c0cb4627b9d79461fdddf4e6c00 | Python | 1,889 | 81 | '''fitting a periodic function using an rnn
this is the basis of a bunch of learning hierachical seqs with crp-rnn
'''
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import seaborn as sns
from utils import to_np, to_pth
from model import CRPLSTM_REG as Agent
sns.set(style='white'... |
227ad22b577cde5d972101bc68acefaee166d70b177afdac7307a1595c8c2cd6 | Python | 1,890 | 49 | # -----------------------------------------------------------------------------
# I/O
# learning data directory, train.bin and valid .bin are expected. You should prepare them using tokenize.py
data_dir = 'directory_containing_train.bin/val.bin'
out_dir = 'output_directory' # output directory
log_dir = os.path.join(o... |
444e3cc614e6c5203f85f415e5f6b4a624dc3d3cb85e113eefe39f7b130bb0cc | Python | 1,890 | 57 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 applicabl... |
bbff971c49b9049931f25ac6e37df735386e99debae220e3c571d1b261941e03 | Python | 1,890 | 55 | from dataclasses import dataclass, field
from typing import Any, Dict, Optional
import numpy as np
from .node import NIRNode
from .typing import Types
from .utils import calc_flatten_output, parse_shape_argument
@dataclass(eq=False)
class Flatten(NIRNode):
"""Flatten node.
This node flattens its input tens... |
ba94e3813e918bf4f01b3740724771b698e70f7c3382c83203f50af11c4fd661 | Python | 1,891 | 64 | import unittest
import numpy
import torch
from pfrl.nn import to_factorized_noisy
def names_of_parameters(module):
return set([name for name, tensor in module.named_parameters()])
class TestToFactorizedNoisy(unittest.TestCase):
def test_modulelist(self):
model = torch.nn.ModuleList([torch.nn.Linea... |
1a07b531cdbea76b66c45348295973cfed8922d4303c95a74802e7a330766a11 | Python | 1,892 | 40 | import nibabel as nb
import numpy as np
BBOX = {'sub-05': {'RH': {'xlower': 435, 'xrange': 162, 'ylower': 55, 'yrange': 162, 'zlower': 95, 'zrange': 158},
'LH': {'xlower': 263, 'xrange': 162, 'ylower': 35, 'yrange': 162, 'zlower': 79, 'zrange': 158}},
'sub-06': {'LH': {'xlower': 271, 'xrange... |
0738d841608eb3101002dd1a1f7d455b9f8ee1fcd8b215aeeb0d6d07e3f71eab | Python | 1,893 | 68 | # -*- coding: utf-8 -*-
"""
Created on Mon Jul 17 12:27:40 2023
@author: gagan
"""
from identification import get_exp_data, compare_ARX_params, get_best_model
from identification import compare_switch, compare_bilin, regret
import os
from tqdm import tqdm
import pickle as pkl
from glob import glob
import numpy as np
... |
6a41899605e13ad700e336072dfc8f5ee325cf88c79290594b1d0ad0349c6c5a | Python | 1,893 | 49 | #!/usr/bin/env python
from __future__ import print_function
import argparse
import gzip
import sys
import os
def parse_args():
parser = argparse.ArgumentParser("Produce a BED based on VCF calls")
parser.add_argument('--input_vcf', '-i', dest='input_vcf', required=True, type=str,
help='V... |
ed388b0d66228458c8deebac82f88a9efbb3fb34c35eadefa34d90bada2912bc | Python | 1,893 | 39 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
1679c24e5dff3fd77859d69b74b51915ed2eb74dd76595f0666c7ed1a03c5bb9 | Python | 1,894 | 32 | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: i_output_file.proto
"""Generated protocol buffer code."""
from google.protobuf.internal import builder as _builder
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor... |
19e9a2a0690d45fa2327909fcb41e7a046b3a7a85aec77a711da748301e54e1b | Python | 1,894 | 59 | from splam_utils import *
from splam_dataset_Chromsome import *
# from splam_Conformer import *
from SPLAM import *
import numpy as np
import torch
import torch.nn as nn
import sys
from torch.optim import Optimizer, AdamW
from torch.optim.lr_scheduler import LambdaLR
from tqdm import tqdm
import math
import os
def mai... |
c0d9099ebbc53983f36bb1d41b618f47d466093f959e712baf0c85ac527ff6e6 | Python | 1,894 | 53 | #Imports relevant modules
import os
import nibabel as nb
import numpy as np
#Defines the pipeline ID.
pipeline = '0'
#Defines the path in which FEAT output is stored for this pipeline.
pipeline_dir = '/<directory root>/FEAT/Pipeline_{}'.format(pipeline) #Full path removed for purpose of public sharing.
#Creates an e... |
85b6896f0f253ee38b6a0cecd9434a89330d0256d77454767605d1fbf3e7554d | Python | 1,896 | 55 | import sys
import torch
import shutil
from pathlib import Path
from rdkit import Chem
from tqdm import tqdm
basedir = sys.argv[1]
structure_dir = Path(basedir, 'crossdocked_pocket10')
test_set = torch.load(Path(basedir, 'split_by_name.pt'))['test']
receptor_dir = Path(basedir, 'receptor_pdbs')
receptor_dir.mkdir(e... |
254776c59cc2649554b3fd28abf2eb13c8f78faf60e6cb15379f0f645661e210 | Python | 1,897 | 51 | #this script with assistance from ChatGPT3.5 20231204
# the function is to create a mean of all the normalized images:
# python nii2meanFLAIR.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.... |
0c86bc1e4c3be49c90528a8c96ca9fc567d4c92728801f08442c81ea8d3ff536 | Python | 1,898 | 62 |
import numpy
import random
import sys
sys.path.insert(0, "..")
from sklearn.preprocessing import PolynomialFeatures
from matplotlib import pyplot
import algorithms
# numpy.random.seed(42)
# random.seed(42)
class Sampler:
def __init__(self, degree=3):
self.degree = degree
self._min, self._max = ... |
8bc9ad116a4be8a834ae54002113e208b545761610398a2a791f0d719d1008de | Python | 1,898 | 51 | import csv
import code
# code.interact(local=dict(globals(), **locals()))
import os
file = "score_sum_indiv_F1.profile"
dir, _ = os.path.split(file)
with open(file, 'r') as f, open(file + '.reformat', 'wt', newline='') as f2, open(os.path.join(dir, "PRS_MetS_F1.csv"), 'wt', newline='') as f3:
reader = csv.reader(... |
9bad73901f54557d97d15c7a8344def7454a387a2e8279db4ca35ccfab9fce69 | Python | 1,899 | 50 | from loguru import logger
import os
import argparse
import torch
import copy
import pandas as pd
from deltasplice.constant import default_model_paths, model, Fapath, EL, SeqTable, repdict, IN_MAP
from pyfasta import Fasta
def main():
# load config file
parser = argparse.ArgumentParser()
parse... |
a854cc0cfa903995a8331d169d1f49c3f1b44155940e92c885fb53f3994d47b3 | Python | 1,899 | 55 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
0e3915c902fb9d1a2d217bc6ad5331729a84b1b7abcaca37b88d115069485484 | Python | 1,901 | 55 | import torch, math
class Embeddings(torch.nn.Module):
def __init__(self, d_model, vocab, pad):
super(Embeddings, self).__init__()
self.lut = torch.nn.Embedding(vocab+2, d_model, pad)
self.d_model = d_model
def forward(self, x):
return self.lut(x) * math.sqrt(self.d_model)
... |
3a32b7805facc4fe67a95cf7cc5ccffef7e320e2d50cb843f7a2910a897f9fb6 | Python | 1,901 | 49 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.cache import cache_file
if __name__ == "__main__":
file_name = cache_file("auto2.dta", "http://www.stata-press.com/data/r15/auto2.dta")
df = pd.read_stata(file_name)
# In case that ... |
f113a65db51bee552f7511d3c51f1034a1afa296819c085bd29d95061e288226 | Python | 1,901 | 74 | import requests
# __doc_import_begin__
from ray import serve
from ray.serve.handle import DeploymentHandle
from ray.serve.gradio_integrations import GradioIngress
import gradio as gr
import asyncio
from transformers import pipeline
# __doc_import_end__
# __doc_models_begin__
@serve.deployment
class TextGeneration... |
f94c75f91b1ff087a16ab2ebc98cd76b18228023c8f3c499c400aac74d5e3e9e | Python | 1,901 | 57 | # -*- coding: utf-8 -*-
"""
This is implementation of model of self-sustained activitity in balanced networks from:
Vogels, T. P., & Abbott, L. F. (2005).
Signal propagation and logic gating in networks of integrate-and-fire neurons.
The Journal of neuroscience : the official journal of the Society for Neuroscience,... |
2671be80f18a49f10c32953507ea220b7fbe4830f42d18c28c57bb7ab9157fe8 | Python | 1,902 | 68 | #!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_extension import CUDAExtension
requirements = ["torch", "torchvision"]
d... |
0c73c079b417ca2658284629c722af6e03d5ea4e0576de757f3913b8a2c49322 | Python | 1,904 | 53 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Download helper for SVHN from the Hugging Face hub, for aihwkit tests.
The original host of the dataset (``ufldl.stanford.edu``) used by torchvision
is not a... |
c245db8d9f18bb00ee805e2217b861573292d8f75d92ff12cfce8fb668f0de46 | Python | 1,907 | 47 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
825ed82850b2a9116d9e6d972fa3ac4276c283b16f14a696356b7cb490cf8e23 | Python | 1,908 | 60 | import warnings
from io import BytesIO
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union
import numpy as np
from ray.data.block import Block, BlockAccessor
from ray.data.datasource.file_based_datasource import FileBasedDatasource
if TYPE_CHECKING:
import pyarrow
class NumpyDatasource... |
8a3b35a4ab5419017a9afbad3c8363648aff57a3c05d8b8f1c598f1d685f8716 | Python | 1,909 | 62 |
import numpy
import random
import sys
sys.path.insert(0, "..")
from sklearn.preprocessing import PolynomialFeatures
from matplotlib import pyplot
import algorithms
# numpy.random.seed(42)
# random.seed(42)
class Sampler:
def __init__(self, degree=3):
self.degree = degree
self._min, self._max = ... |
ba1e457ef7d28f95d777d5fa2ff33007b274ff8df4925a9dfbaf880c1b57b84e | Python | 1,909 | 39 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
19270eea312569a459fd2569cb4a8945efbb757d775b87d56df5faf8e76c07b1 | Python | 1,910 | 37 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
996ee5e48d059a109ca160dcb553913524785ad72c7a5d5e8093144ad0aaba48 | Python | 1,910 | 63 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
4c8e680eac605a644e530359880d7f74ab268bf807aea0c6001e820100836621 | Python | 1,912 | 73 | # import GNNSubNet
from GNNSubNet import GNNSubNet as gnn
# import ensemble_gnn
import ensemble_gnn as egnn
# Synthetic data set ------------------------- #
loc = "data/synthetic"
ppi = f'{loc}/NETWORK_synthetic.txt'
feats = [f'{loc}/FEATURES_synthetic.txt']
targ = f'{loc}/TARGET_synthetic.txt'
# Read in the synthet... |
7bddad8a037f3c9f877e7db597fffaa45839e25aa6157c915c98e265a6f64be9 | Python | 1,914 | 57 | from typing import List, Tuple
from ipywidgets import GridspecLayout, VBox, widgets
from data_profiling.report.presentation.core.frequency_table import FrequencyTable
def get_table(
items: List[Tuple[widgets.Label, widgets.FloatProgress, widgets.Label]]
) -> VBox:
table = GridspecLayout(len(items), 3)
f... |
2b8ed949805cd0f1cceccb00030b014996ab3082f10d979369ce6dbded048734 | Python | 1,915 | 54 | from exputils.gui.jupyter.multi_selection_widget import MultiSelectionWidget
import exputils as eu
import numpy as np
class RepetitionIDsSelectionWidget(MultiSelectionWidget):
'''
MultiSelectionWidget to select experiment ids in a experiment_data dict.
'''
@staticmethod
def default_config():
... |
59269f531d72117d1356f7c77c6821480a42d8bf5e533c4cf2d4091f39f06cfc | Python | 1,915 | 62 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax",... |
6180d48a074a0049f9eb14b74cd63824ebfa5c57010d1ca0c31b4f0d0b01d224 | Python | 1,916 | 59 | import argparse
import functools as ft
import swap_errors.analysis as swan
def create_parser():
parser = argparse.ArgumentParser(description="fit linear models")
parser.add_argument(
"data_file",
type=str,
)
parser.add_argument("--region_str", default="no_pfc", type=str)
parser.ad... |
64a3a5a137b9cf71907fca7ed383835027483bc4b121341c543d18e2fd832133 | Python | 1,916 | 51 | import csv
import code
# code.interact(local=dict(globals(), **locals()))
import os
file = "score_sum_indiv_F1.profile"
dir, _ = os.path.split(file)
with open(file, 'r') as f, open(file + '.reformat', 'wt', newline='') as f2, open(os.path.join(dir, "PRS_MetS_F1.KoGES.csv"), 'wt', newline='') as f3:
reader = csv.r... |
be859700bd0721bb6241527ea601619ff9cf6c046be28aa423368aca85173718 | Python | 1,916 | 71 | import numpy as np
__all__ = ["correlation", "quick_correlation_one_to_many"]
def add_noise_to_constant_rows(x, epsilon=1e-15):
"""Adds noise to rows that are constant to avoid division by zero.
Args:
x: m x n values
Returns:
m x n values
"""
if epsilon is not None:
x = ... |
c3696f263eb804d26284cbbe5de424026acfbde93bfce6fb1f963ced79e5ae48 | Python | 1,916 | 49 | import argparse
import os
import easygrid
import re
if __name__ == '__main__':
parser = argparse.ArgumentParser('Script to gather stats from log and results file from an LD score regression run.')
parser.add_argument('results_file_list', help='List of results files to gather statistics from. Note that the cor... |
f9d62b873bc496adf196ea818c7fd2049be27653af27be320f275435998f8c8f | Python | 1,916 | 72 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import argparse
import errno
import os
import ase
import ase.io
from ase.io.trajectory import Trajecto... |
45af3f22ddd24deb6b2ea749ad82fa883973674632aff5e18cc3502e8c3286ad | Python | 1,919 | 44 | import logging
from datetime import datetime
from agent.data.entities.config import ROOT_LOGGER_ID
from agent.nlp.spo_extractor import extract_spos_from_sentences, match_tokens_cuis_in_sentences
from agent.nlp.spo_extractor import extract_spos_from_sentences_given_predicate, clear_spos
from agent.nlp.spo_extractor imp... |
a8fa82087f359fe68e07a7672904f81e997321d6cf63d75ccefa29aeb883e7dd | Python | 1,919 | 66 | import snntorch as snn
import numpy as np
import torch
import matplotlib.pyplot as plt
from snntorch import spikeplot as splt
V_THR = 2.0 # TODO: STRANGE BEHAVIOR FOR *V_THR = 2.5*
class TestNet(torch.nn.Module):
def __init__(self, reset_after=False) -> None:
super().__init__()
self.lif = snn.S... |
ed30365c8a7406fe8da4f21de9f18e6e6c058663f3336b8573aa24f9da83ca08 | Python | 1,919 | 63 | # stdlib dependencies
from typing import Any, List, Union
# 3rd party dependencies
from numpy.typing import NDArray
# project dependencies
from deepface.models.demography.DemographyUtils import GENDER_LABELS
from deepface.models.demography.onnx.OnnxDemography import OnnxDemography
from deepface.commons.onnx_utils imp... |
28e9376cb1c4d30c5fdde772eed50c139999054395b010ad847b9520e1e3e4bf | Python | 1,920 | 75 | from spinnaker2 import snn, hardware
def run_model(n_pre, n_post, timesteps):
neuron_params = {
"threshold":100.,
"alpha_decay":0.9,
}
stim = snn.Population(
size=n_pre,
neuron_model="spike_list",
params={0:[1,2,3], 5:[20,30]},
... |
ec8872e7368b8a814fd453743e6a600302de9755c9330ea548c5716c930d1d47 | Python | 1,921 | 47 | from .model_dummy_1 import batch_size as batch_size_1
from .model_dummy_1 import expected_output as expected_output_1
from .model_dummy_1 import input_shape as input_shape_1
from .model_dummy_1 import snn as snn_1
from .model_dummy_2 import batch_size as batch_size_2
from .model_dummy_2 import expected_output as expect... |
50f35393c9eb95d78e0e350984102f8ab9f50aeae5ed5d86218c0abd14ac37ed | Python | 1,922 | 63 | from setuptools import setup, find_packages
from hic3defdr._version import version_scheme, local_scheme
with open('README.md') as fobj:
long_description = fobj.read()
extras_require = {
'evaluation': ['scikit-learn>=0.20.3'],
'test': [
'nose>=1.3.7',
'nose-exclude>=0.5.0',
'doctes... |
566e9a87a69629afb34e98d1186f117e44a745c561adb56f07b485597fc9aa2b | Python | 1,922 | 49 | import logging
from datetime import datetime
from agent.data.entities.config import ROOT_LOGGER_ID
from agent.classifiers.data.liwc_management import export_sentence_data_for_liwc_features, get_sentence_liwc_features
from agent.classifiers.tree_classifiers import tree_classifier
logging.basicConfig(format='%(levelna... |
8c534e0511b28484208f90d5b2bd7d4f9e456a16e67cb0f94b898154c11b3322 | Python | 1,922 | 68 | from datamate import Namespace, set_root_context
from flyvis.task.tasks import Task
def test_task(mock_sintel_data, connectome, tmp_path_factory):
task_config = Namespace(
type="Task",
dataset=Namespace(
type="MultiTaskSintel",
tasks=["flow"],
boxfilter=dict(ex... |
7269b137ef4f7e927c74ed4ef7827dedca245b8c0e5f607d8375a52be57de2bd | Python | 1,924 | 56 | import os
import argparse
def main(args):
fin = os.popen(r'''zcat %s''' % (args.vcf))
header = []
body = {}
SVID_key = []
for line in fin:
pin = line.strip().split()
if pin[0][:2] == '##':
header.append(pin)
else:
body[pin[2]] = pin
SVID_... |
adf85d4e98a00742ad51ade4c7a156c016b4b3014b29ca86b559a6ae1163bfac | Python | 1,925 | 53 | import argparse
import os
def replace_in_markdown_files(folder_path, old_path, new_path):
# Check if the folder_path exists and is a directory
if not os.path.exists(folder_path) or not os.path.isdir(folder_path):
print(f"Error: {folder_path} does not exist or is not a directory")
return
#... |
e4e12f5b063a05290d7c4b9fa153c0ce90ca6552ae12889e1ce04f7cda3c641f | Python | 1,926 | 58 | import torch
import numpy as np
from kornia import create_meshgrid
from einops import rearrange
@torch.cuda.amp.autocast(dtype=torch.float32)
def get_ini_position(H, W, device='cpu', flatten=True):
grid = create_meshgrid(H, W, False, device=device)[0]
i, j = grid.unbind(-1)
ini_position = \
tor... |
bdbed8b427e244c9d08463049a9385bfea3ce7e2d51740591f6e1809b57f5146 | Python | 1,927 | 69 | # -*- coding: utf-8 -*-
#
"""
std_wham.py
Standardize WHAM record.
"""
from .standardize import VCFStandardizer
@VCFStandardizer.register('wham')
class WhamStandardizer(VCFStandardizer):
def standardize_info(self, std_rec, raw_rec):
"""
Standardize Wham record.
"""
# Add placeh... |
c11bf4670845f6a59b6eac6256368fe9c9630b5b807f9de1b9565a440fd82454 | Python | 1,928 | 72 | import json
import logging
import subprocess
from contextlib import contextmanager
from functools import lru_cache
from pathlib import Path
from typing import Any
@lru_cache(100)
def warn_once(logger: logging.Logger, msg: str) -> None:
"""
Log a warning message only once for a given logger and message combina... |
10a172057edf3f6377c6183cd77a5765249efb31d11b841de2103fd70997b473 | Python | 1,929 | 43 | import torch
import math
def sparse_diag(x):
indices = torch.arange(len(x), device=x.device).unsqueeze(0).repeat(2, 1)
values = x
return torch.sparse_coo_tensor(indices, values, (len(x), len(x)), device=x.device)
def sparse_normalize(x):
size_factor = sparse_diag(1. / (torch.sparse.sum(x, dim=1).to_de... |
a2980b551f00253a5bdf5908b884bb3d7523d2c8f404fd5b7c083df8a85381d6 | Python | 1,929 | 62 | """Average echoes for before registration."""
import os
import nibabel as nb
import glob
# =============================================================================
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti'
DIRECTIONS = ['AP', 'PA', 'RL', 'LR']
# Set subs to work on
SUBS = ['sub-07... |
8876dc0403a6a957e43a745958a4a6d3ad5825f09111f9dc5221f8ad842872ad | Python | 1,930 | 57 | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gcn.layers import GConv
class GCN(nn.Module):
def __init__(self, channel=4):
super(GCN, self).__init__()
self.channel = channel
self.model = nn.Sequential(
GConv(1, 10, 5, pad... |
0d1675a616fd6d40d1c1ff403870aaecf95688ba2f9c7a05e6c0c152cba90df9 | Python | 1,931 | 58 | import torch
import torch.nn as nn
from ..utils import create_activation
from ..layer import CosformerLayer, PerformerLayer, VanillaTransformerLayer, FlowformerLayer
from ..utils.pe import select_pe_encoder
class TransformerEncoder(nn.Module):
def __init__(self,
num_hidden,
nhead,... |
8be3388663031412e49ee5215a5cd6141d9d0ef6b96dd7bb706499c4fb5b0696 | Python | 1,931 | 63 | import os
from typing import Tuple
import pandas as pd
from data_profiling.config import Settings
from data_profiling.model.summary_algorithms import describe_path_1d
def path_summary(series: pd.Series) -> dict:
"""
Args:
series: series to summarize
Returns:
"""
# TODO: optimize usin... |
9b446c0f13e545a9f77ea777bc23fd8e7345c9583c042c4d907cdbe5bc3dd568 | Python | 1,931 | 57 | # __begin_class_based_autoscaling_policy__
import asyncio
import json
import logging
from pathlib import Path
from typing import Any, Dict, Tuple
from ray.serve.config import AutoscalingContext
logger = logging.getLogger("ray.serve")
class FileBasedAutoscalingPolicy:
"""Scale replicas based on a target written ... |
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