sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0831de1ebad00b4a09c9d15cd60cdd21d3170b19e2f4d9c17141b1ddbcf30604 | Python | 3,280 | 111 | '''
Keras implementation of B networks
'''
import tensorflow as tf
def b_layer(x, filters, kernel, layer_num, pooling=True):
'''Base layer for B models
'''
if pooling:
x = tf.keras.layers.MaxPool2D(
pool_size=(2, 2),
name='MaxPool_Layer_{}'.format(layer_num))(x)
x = t... |
105485824e45517d56cc114a93e01a6389be97007f99bca5357fcc5f52be7753 | Python | 3,281 | 75 | """
Library of single-neurons configurations.
Write here the single cell parameters.
If you need another type of cell just add an "elif" case
"""
from __future__ import print_function
#NB: Trefrac = 2ms (from Marrasco et al)
# Ie = Ith!!!
# A2 = Iadap update and A1 = Idep update. FOR THE MOMENT SAME VALUE AS... |
99712e8fb4dc138e333aabd0e59851242cc79c7e226d76924c355d9b0b97be96 | Python | 3,281 | 83 | import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
import itertools
from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv(in_planes, out... |
19e2e6d81088754e41feebc62630fec24502ca359653c47452a07f7dfd7921f7 | Python | 3,282 | 138 | import os
import sys
import time
import errno
import time
import timeit
import numpy as np
from numba import jit
import warnings
warnings.simplefilter("ignore")
@jit(nopython=False)
def VEP2Dmodel(params, constants, init_conditions, SC, dt, ts):
nt=ts.shape[0]
nn=SC.shape[0]
#parameters
eta=pa... |
973b8280a82cafbd2a012d9d3ddb90291477aacdb179c91f9579761e364f89d9 | Python | 3,282 | 95 | import logging
import nltk
import copy
from nltk.tokenize import word_tokenize
from nltk import word_tokenize, pos_tag, ParentedTree
from agent.data.entities.config import ROOT_LOGGER_ID
logger = logging.getLogger(ROOT_LOGGER_ID)
NN_NN_RULE = r"NP: {<NN.?>+<NN.?>}"
JJ_NN_RULE = r"NP: {<JJ>+<NN>}"
DT_JJ_NN_RULE = r"... |
33d671eb00f78f564682f33b75960ca0502e00342b33b7d676407efb78d1da9e | Python | 3,283 | 72 | import os
import nibabel as nib
import numpy as np
import ivim
import pandas as pd
import time
output_root = '/path/to/results'
subs = [sub for sub in os.listdir(output_root) if sub.startswith('sub')]
pars = ['D','f','vd']
times = {}
for method in ['seg','nlls','bayesu','bayess']:
times[method] = []
for sub in s... |
5f00147c23b185dfe9ec226802d3452cf77433d06c6168c7ca9b0970539310d0 | Python | 3,283 | 147 | from dataclasses import dataclass
from typing import Any, List
#################
@dataclass
class divideopt:
divide_type: str
zslice: str
param_alloc: str
param_size_thres: float
exception: dict
module_7z: bool
@dataclass
class sampleropt:
name: str
cube_count: int
cube_len: List[int... |
57a9496fa9e303ec451d5b102021392f8c96c3308489edbd24d86e61cc318dc6 | Python | 3,285 | 99 | import collections
import pickle
from typing import Optional
from pfrl.collections.random_access_queue import RandomAccessQueue
from pfrl.replay_buffer import AbstractEpisodicReplayBuffer, random_subseq
class EpisodicReplayBuffer(AbstractEpisodicReplayBuffer):
# Implements AbstractReplayBuffer.capacity
capa... |
74168d132b96f46727b6d6350e345a67d15237ababca5dfe1ec00de2126da1d4 | Python | 3,286 | 75 | import os
import time
from subprocess import call
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sc
from matplotlib.colors import ListedColormap
import scipy.sparse as sparse
import importlib
import copy
import ... |
25b4c89d8f929bcb667e2753674baf5fb53e28aed3e069778eb13075c5083930 | Python | 3,287 | 88 | import torch
import torch.nn as nn
from random import sample
from torch.nn.modules.linear import Linear
import torchvision.models as models
import math
import torch.nn.functional as F
from torch.nn.parameter import Parameter
# encoder
class MLPEncoder(nn.Module):
def __init__(self, num_genes=10000, num_hiddens=12... |
728ed3df55f3457a2d88c17b191fe82cf6b9ca50c83fbd0933c13baf259394c6 | Python | 3,288 | 101 | """
const.py
Helpful constants for working with IGS data, and methods to retrieve them.
"""
import numpy as np
import matplotlib.colors as pltcolors
"""
Define constants (e.g. chromosome sizes, colormaps).
"""
SIZES_HG38 = np.array([0, 249250621, 243199373, 198022430, 191154276, 180915260, 171115067,
... |
aa399686452c2b21f4e6b21b5877c4b6528a72a3458424e8dba9dc10b5731314 | Python | 3,289 | 91 | r"""FlowSOM for cytometry — ``ov.flow.flowsom``.
The same algorithm ``ov.single.ev.flowsom`` runs, on the same code
(:mod:`omicverse.flow._som`), writing into ``uns['flow']`` instead of
``uns['ev']`` and defaulting to the layer a cytometry workflow actually
produces.
Automated clustering is a complement to gating, no... |
b15b61e430d94c2af6d70e7beb10f9e0ad7b26decc6ec56e91060d4212229dbf | Python | 3,289 | 89 | # coding=utf-8
'''
Created on May 5, 2016
@author: jivan
'''
import cPickle
import itertools
import re
import requests
from web_utilities import HTMLTableStripper
def get_valid_hla_class_i_allele_names():
resp = requests.request('GET', 'http://hla.alleles.org/alleles/class1.html')
ts = HTMLTableStripper()
... |
511794883f823dc14d9accff12b6f54730fe384b2810fcae106c47f6a00d0516 | Python | 3,290 | 85 | ####################################################################################################
# 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... |
8b3c2d99908535a11ae5e9538d87d7c1691b22c46aa3768ba32f59720ec6f178 | Python | 3,291 | 94 | import torch.nn as nn
import torch
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels) -> None:
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding='same')
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=... |
0aa732ac8ef630bf74b516ec32356131c44827d91cadc1676d77bc5a89f325f2 | Python | 3,292 | 116 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""aihwkit example 13: Custom experiment training (3 fully connected layers).
Example of customization of a Basic Training experiment, adding the use of
a sched... |
0be6222ed97edb08a1590189c2cfe77b495f191bc0c51f6bf4f7c75b46e5ec56 | Python | 3,292 | 25 | from unittest import TestCase
from web_utilities.HTMLTableStripper import HTMLTableStripper, HTMLParseError
class HTMLTableStripperTests(TestCase):
def basic_tests(self):
html_results = '''
<tr class="odd"><td>HLA-DRB1*01:01</td><td>1</td><td>2</td><td>16</td><td class="sequence">IIGKKDKDGEGAPP... |
6d5541ec952d783d226e63b242374cd4f26b1c48311788cb8ecb2e83a0061e10 | Python | 3,293 | 111 |
import pdb
import numpy as np
import torch
from data_assimilation.oracle import ObjectStorageClientWrapper
from data_assimilation.particle_filter.base import BaseForwardModel
from latent_time_stepping.utils import (
load_trained_AE_model,
load_trained_time_stepping_model
)
class NNForwardModel(BaseForwar... |
e71b5ea2790e37157586a2358687810df081e50fdf22acfd33a06fca179fa981 | Python | 3,293 | 111 | """
"""
import re
from urllib.request import urlopen
def show_method_list():
print( """
* All available method names:
-----------------------------
1) Chou-Fasman
2) Emini
3) Karplus-Schulz
4) Kolaskar-Tongaonkar
5) Parker
6) Bepipred-1.0
7) BepiPred-2.0
""")
def show_detail_help():
print( """
detail help... |
0b801524bc7a5a01fd08eaa6625f68fc8426f7fd8701aaa590a58beb5549e252 | Python | 3,294 | 106 | """Plotting for single-extracellular-vesicle (single-EV) proteomics.
Only the EV-specific plot lives here:
* :func:`misev_marker_plot` — MISEV2023 positive vs contaminant marker
levels (the EV-preparation purity view).
Generic single-cell plots — embedding scatters, marker dotplots,
mean-signal heatmaps and stacke... |
3919041ae1ee7160aa0773f521af1726c3eda4302e8024d2a3b070af7c55a731 | Python | 3,295 | 102 | from typing import Tuple
import torch
from torch import nn
class Img2SpikeLayer(nn.Module):
"""Layer to convert images to spikes.
Args:
image_shape: tuple image shape.
tw: int Time window length.
max_rate: maximum firing rate of neurons.
layer_name: string layer name.
... |
e5dfddef330cf2f83c0ac2936757e65bc7c63f06cdb0e062d66f19a47b151f60 | Python | 3,296 | 88 | import tensorflow as tf
from models.deep_learning.deep_learning_models import DLRegressor
from utils.constants import is_spline
class FCNRegressor(DLRegressor):
"""
This is a class implementing the FCN model for time series regression.
The code is adapted from https://github.com/hfawaz/dl-4-tsc designed f... |
6ca1bd67c2ee8af4c3f5742970b37ce91bdda3e45984c70fb7eb2d1b72d60dd2 | Python | 3,297 | 108 | #!/usr/bin/env python3
import torch.nn as nn
import torch
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(
self,
latent_size,
dims,
dropout=None,
dropout_prob=0.0,
norm_layers=(),
latent_in=(),
weight_norm=False,
xyz_i... |
c7cd9ad7e6c99b73ed64d2dd0dc6dbafc1ea3ec2de040c6ca86d86f7dfe92db2 | Python | 3,297 | 108 | #!/usr/bin/env python3
import torch.nn as nn
import torch
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(
self,
latent_size,
dims,
dropout=None,
dropout_prob=0.0,
norm_layers=(),
latent_in=(),
weight_norm=False,
xyz_i... |
0b8d5d0653fe6180c22dea21fa7a93eb0d5bf6c692a761bb09e56b9356d411e4 | Python | 3,298 | 106 | import numpy as np
from torch.utils.data import DataLoader, Dataset
import scanpy as sc
import torch
import sys
sys.path.append('..')
from ..VAE.VAE_model import VAE
from sklearn.preprocessing import LabelEncoder
def load_VAE(vae_path, num_gene, hidden_dim):
autoencoder = VAE(
num_genes=num_gene,
... |
53d27206af05e7360374ca37b79fd5b67ae21679622099c3ccf7e58808e5af1c | Python | 3,298 | 91 | import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
import pandas as pd
def calculateConfusion(filename):
data = pd.read_excel(filename, sheet_name=None)
data_list = []
for k, df in data.items():
# Example true and predicted labels
true_labels =... |
bb6aa341564d80cc4fa93ca42da1dad0938567e21ccfec0cf061bec11cdea6ca | Python | 3,298 | 86 | import os
import pprint
SAMPLE_SIZE = 1000
INPUT_DIR = '../../../train/results/train_test_dataset/'
NEG_DATASETS = [f'{INPUT_DIR}input_neg_1/800bp/test_neg_1.shuffle.fa', f'{INPUT_DIR}input_neg_random/800bp/test_neg_random.shuffle.fa']
POS_DATASETS = [f'{INPUT_DIR}input_pos_alts/800bp/test_pos_alts.shuffle.fa', f'{INP... |
180671ebb843949a32a4d6218fef218224983d2cdf944667bf0d28c727317fbf | Python | 3,301 | 113 | import numpy as np
from pokeformer.vocabs.prot_atom_names import atom_names, res_names
from pokeformer.vocabs.prot_atom_vocab import ProtAtomFeature
def _get_pdb_atom_lines(pdb_file: str):
with open(pdb_file) as f:
raw = f.readlines()
return list(
filter(
lambda x: x[:6] == "ATOM ... |
6b749645f4a8509473f05d1f7903d7296fc5cef84f5dfd38bb4e11374872afd0 | Python | 3,302 | 83 | import requests
from .__config import base_url
from .__aux_funcs import process_response
def get_all_gene_panels(direction="ASC", pageNumber=0, pageSize=10000000, projection="SUMMARY", sortBy=None):
"""
Get all gene panels from cBioPortal. \n
:param direction: Direction of the sort. \n
Possible val... |
667d02129b18ee1568268d66085749c75d0c8fd6115d8772e2b9e01d03b78c18 | Python | 3,303 | 108 | import numpy as np
from copy import deepcopy
from utils import to_pth
class Polynomial():
def __init__(
self, n_terms=5, max_coeff_val=.5, max_degree=3, max_shift=3,
xrange=1, xstep=.01, noise_level=0,
):
self.n_terms = n_terms
self.max_coeff_val = max_coeff_val
... |
ed8af9856da42377383e64a314a25566dd14f2631403dc9de1fef57e719496f8 | Python | 3,303 | 113 | import time
from logging import getLogger
try:
from gym.wrappers import Monitor as _GymMonitor
except ImportError:
class _Stub:
def __init__(self, *args, **kwargs):
raise RuntimeError("Monitor is not available in this version of gym")
class _GymMonitor(_Stub): # type: ignore
... |
2cef0f9418d0f93c8e029755570e28106c0fc04cdb5b7f94e86b8b4e91597370 | Python | 3,306 | 87 |
# %%
import pandas as pd
from UpSetPlot.upsetplot import plot
from matplotlib import pyplot as plt
from UpSetPlot.upsetplot import generate_counts, plot
import matplotlib.patches as mpatches
from UpSetPlot.upsetplot import UpSet
from UpSetPlot.upsetplot import from_contents
from matplotlib import cm
import numpy as np... |
14ce1c0a069494d8ee0905fb42fde58a2893190446db96bd19c3491970b032f5 | Python | 3,307 | 95 | import dataclasses
import logging
import time
from concurrent.futures import ThreadPoolExecutor
from queue import Queue
from typing import List
from ray.autoscaler.v2.instance_manager.instance_manager import (
InstanceUpdatedSubscriber,
)
from ray.autoscaler.v2.instance_manager.instance_storage import InstanceStor... |
bceded8ef978f977d98b645b68a4bc96e9edf19f22ccd7b59e9eb46f1eb10e3f | Python | 3,307 | 97 | """Script to download the SeizeIT dataset doi:10.48804/P5Q0OJ.
The script requires a valid API key and depends on the requests library.
"""
import hashlib
import json
import logging
import os
from pathlib import Path
import requests
from requests.adapters import HTTPAdapter, Retry
def downloadSeizeit(destination: s... |
58ee74c4e35a2f48a671ee07a8091053b71debce9fb881fdc8c9db709650cb5d | Python | 3,308 | 72 | ####################################################################################################
# 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... |
eecf2979a15d94099be584e2c2b40ebf6d69c01f6b25d492c91b495c27ceeb1c | Python | 3,308 | 76 | ####################################################################################################
# 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... |
8bdbc6c389dc0ca87040ee563511e17241eaa05051e6a30eba2c9f5321d1dc0e | Python | 3,309 | 93 | """Random-partition baseline.
Uniformly assigns each cell to one of ``n_metacells`` buckets, optionally
stratified by an ``obs`` column (e.g. cluster) so that within-stratum
random sampling is what gets compared against principled metacells.
This is the **honest baseline** users should sanity-check against —
see Bilo... |
6d2587df273226de81dc8bdd908cdd26638f9b40abf39984efd85d66f7b90167 | Python | 3,310 | 78 | import numpy as np
import random
def scramble_timeseries(timeseries):
"""Expects a dense NxTx3 array of three-channel timeseries data,
with N samples of duration T.
If the three channels are: heartrate, stepcount, nonmissingness,
returns two new datasets.
The first returned value is the 'scramble... |
94748e6f9cff43a36d3c270ca86ac1c5b807774034ac23f7f5b10ec398a93414 | Python | 3,310 | 76 | from typing import Any
import math
import numpy as np
from tqdm import tqdm
from .countdata import CountMatrix
from . import Colors, EMOJI
def log_fold_change(old, new, base=2):
return math.log(new/old, base)
def expression_modulo_metadata(count_matrix: CountMatrix,
header: str
... |
820e12d1ce73fce4038c0989d34305150878ad2757132c1b7f6f37d6f6990902 | Python | 3,313 | 112 | """
Minimal working example for shotgun VAE using mouse USVs.
0) Define directories and parameters.
1) Tune preprocessing parameters.
2) Warp song renditions & train a generative model.
3) Plot.
4) The world is your oyster.
"""
from itertools import repeat
from joblib import Parallel, delayed
import numpy as np
impo... |
1ff7c8857a6a074cc690ddc949fe757144ea6058f2789777b9371d35c72e4fd7 | Python | 3,315 | 208 | import matplotlib.pyplot as plt
import numpy as np
from spinnaker2 import brian2_sim, helpers, s2_nir # , hardware
import nir
np.random.seed(123)
timesteps = 50
# load NIR model
nir_model = nir.read("lif_norse.nir")
print(nir_model)
# patch nir model
LIF_node = nir_model.nodes["1"]
assert isinstance(LIF_node, nir... |
61b97c878279f4e407e4c64ae8800cbc848b7f9598b38e5cbfc6e016bb2695a9 | Python | 3,315 | 88 | """Register each run to one reference run."""
import os
import subprocess
import glob
# =============================================================================
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti'
# Set subs to work on
SUBS = ['sub-07']
# ===================================... |
3390ee3b5c3d894326c73d760427367334e529afe20b4ec2f396deda61a99197 | Python | 3,316 | 86 | import os
import h5py
import numpy as np
from typing import List, Tuple, Dict
from itertools import product
from ml4h.defines import TENSOR_EXT
from ml4h.TensorMap import TensorMap, Interpretation
CONTINUOUS_TMAPS = [
TensorMap(f'{n}d_cont', shape=tuple(range(2, n + 2)), interpretation=Interpretation.CONTINUOUS)... |
95af7651ce801c45a1acfdae2a3700bd54000c24955ed6044fb7a5a14b518638 | Python | 3,316 | 68 | import requests
from .__config import base_url
from .__aux_funcs import process_response
def fetch_gene_panel_data(molecular_profile_ids=None, molecular_prof_sample_ids=None):
"""
Fetch gene panel data from cBioPortal. \n
:param molecular_profile_ids: List of Molecular Profile IDs (e.g., ["brca_tcga_gistic... |
27eb1d7640c62163710f5399b60c604f9737e9be91369a1ef4d5a0b5ac4858f8 | Python | 3,318 | 114 | """
Compare the statistical values computed with nilearn versus fsl
"""
import pandas as pd
import numpy as np
import nibabel as nb
import glob
import matplotlib.pyplot as plt
import seaborn as sns
# Set data path
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti/derivatives'
# ============... |
94d6ea77c3f619e6df90cd75eb2595b983f65b21e9439bba9b14494066741ae6 | Python | 3,318 | 108 | #!/usr/bin/env python3
import torch.nn as nn
import torch
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(
self,
latent_size,
dims,
dropout=None,
dropout_prob=0.0,
norm_layers=(),
latent_in=(),
weight_norm=False,
xyz_i... |
1ffffbdf2622b83fa719465099ed678d16876a5d304438fe795c9990f24a9ce9 | Python | 3,319 | 87 | ####################################################################################################
# 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... |
bf275c6b38c57240712da3471f13906c984668b1da6da9c58e09b753e62cede4 | Python | 3,319 | 82 | import sys
def main():
output_dir = "./dataset/"
output_files = [output_dir+"pos/", output_dir+"pos_MANE/", output_dir+"pos_ALTS/", output_dir+"neg_1/", output_dir+"neg_random/"]
target = "splam"
for output_file in output_files:
for target in ["spliceai/spliceai.noN", "spliceai/spliceai.N", "s... |
013af86a66e1deb0f1b76db45f45abfb82a8cc38c91d4f773c906d47c051481c | Python | 3,320 | 101 | # flake8: noqa
# fmt: off
# __example_deployment_start__
import time
from ray import serve
from starlette.requests import Request
@serve.deployment(
# Each replica will be sent 2 requests at a time.
max_ongoing_requests=2,
# Each caller queues up to 2 requests at a time.
# (beyond those that are sent ... |
e2314afeac99d5d7f70438a8accd308b26a44a90f00ba167838d1401247d3915 | Python | 3,320 | 111 | #!/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... |
09c3207d7a2252b8552cf745768fbcd5a0dcbfb9ff6e2941adb9cdb932693acf | Python | 3,321 | 88 | """
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 logging
from typing import TYPE_CHECKING, Literal
import torch
from fairchem.core.units.mlip_u... |
242e8bf0cbe3e82256c05374ed843dd77aead985c82a96babc42afa87677a67b | Python | 3,321 | 90 | ####################################################################################################
# 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... |
d81c63b70db816c261566c8598cd1d144c577922fcfe59730230e87b38a72074 | Python | 3,321 | 104 |
import numpy
from pfrl import agent
from matplotlib import pyplot
def scale(x, space, key):
return x * (space[key]["high"] - space[key]["low"]) + space[key]["low"]
def rescale(x, space, key):
return (x - space[key]["low"]) / (space[key]["high"] - space[key]["low"])
class HumanAgent(agent.BatchAgent):
"... |
69bd434801231cc959ffcd873f90e1ecbfd0f6efc70a32db54cd5cca9a1e5aed | Python | 3,322 | 104 | '''
Calculates ALL 39203 combinations for Yi2014.
This takes VERY long.
'''
import moabbpatch
import moabbpatch.datasets
import moabbpatch.baseparadigm
import moabbpatch.mne_estim_output
SEED = moabbpatch.set_random_seed(1023)
import numpy as np
import pandas as pd
import swifter
import moabb
import mne
# to disable ... |
99af90c87f3c67d6c24102e95e30a3ebcc24c86d8e5af86cad15601e01852178 | Python | 3,322 | 88 | # Copyright (c) DP Technology.
# 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 absolute_import, division, print_function
import numpy as np
import torch
from torch.utils.data import Dataset
from .data import DataHub
fro... |
0ec8b1a410f10ae03f7762f269e52486d8fabb3d18b3d2074554a491a51f683c | Python | 3,323 | 127 | # flake8: noqa
# fmt: off
# __init_namespace_start__
import ray
ray.init(namespace="hello")
# __init_namespace_end__
# fmt: on
ray.shutdown()
# fmt: off
# __actor_namespace_start__
import subprocess
import ray
try:
subprocess.check_output(["ray", "start", "--head"])
@ray.remote
class Actor:
pass
... |
44cc8e86a1714754f6b4f4ee753c1d6f462dbeef0b34c4dbe093cb3fde40acb1 | Python | 3,323 | 86 | import pandas as pd
import os
def get_hg38_chrom_size():
f_chrs = open("../src/hg38_chrom_size.tsv", "r")
lines = f_chrs.read().splitlines()
chrs = {}
for line in lines:
eles = line.split("\t")
chrs[eles[0]] = int(eles[1])
return chrs
def main():
chrs = get_hg38_chrom_size()
... |
8443283062ea7bff3e8feb6084415f26336454a35883060a17bf884e9f976f37 | Python | 3,323 | 80 | import numpy as np
import struct
from spinnaker2.neuron_models.application import BaseApplication
from spinnaker2.configuration import PEConfig
from spinnaker2.neuron_models.common import add_log_memory_region, format_routing_table
from spinnaker2.coordinates import ByteAddr
class SpikesFromImagesApplication(BaseApp... |
0f823d610bfb074a9f9736394090af7cb00e144055dce881939aa4447a2a56f0 | Python | 3,324 | 80 | import torch
__all__ = ["local_pixel_shuffle", "random_inpainting", "random_outpainting"]
def local_pixel_shuffle(
data: torch.Tensor, n: int = -1, block_size: tuple = (0, 0, 0), rel_block_size: float = 0.1
) -> torch.Tensor:
batch_size, channels, img_rows, img_cols, img_deps = data.size()
if n < 0:
... |
09e1e7c74d413356caa13ab174bcbe1ba083156915c4319474bd71e02428ef9c | Python | 3,325 | 116 | # built-in dependencies
import os
from unittest.mock import patch
# 3rd party dependencies
import pytest
import psycopg
from deepface import DeepFace
# project dependencies
from deepface.commons.logger import Logger
logger = Logger()
connection_details_dict = {
"host": "localhost",
"port": 5433,
"dbname... |
3544ccc7d321b106d0970802f3af88839618f401e7d1a433c69c76101c51f265 | Python | 3,325 | 109 | import gc
from os.path import join
import anndata
import dask.dataframe as dd
import lightning.pytorch as pl
import numpy as np
import pandas as pd
import xgboost as xgb
from scipy.sparse import csr_matrix
from cellnet.estimators import EstimatorCellTypeClassifier
from cellnet.models import TabnetClassifier, LinearCla... |
63a5e4d25bbfd1967c285eaa5b0d80d3f98d1f818107a727bb71b2ba750b1fdd | Python | 3,325 | 74 | ####################################################################################################
# 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... |
fcb26dbe7a2508e839eb2f71d0cf32e270b7e3e2643379039328226500966c8f | Python | 3,325 | 92 | r"""``ov.epi.pl`` — epigenomics plotting (wraps :mod:`epione.pl`).
QC diagnostics, embeddings, differential-accessibility and peak-to-gene
plots, footprints and Hi-C contact visualisations, delegating to
`epione <https://github.com/aristoteleo/epione>`_. Each function imports
epione internally and returns the matplotl... |
500f08e7ff3c8d90f043b293a4890436c36850fb35dedf13b8cdaaabcac5f75a | Python | 3,326 | 94 | """Tests for toy.py."""
import numpy as np
import pytest
import torch
from torch import nn
class TestMLPModule:
@pytest.fixture
def module_cls(self):
from skorch.toy import MLPModule
return MLPModule
def test_one_hidden(self, module_cls):
module = module_cls()
parameters ... |
8f30021e9605366af8964b9bf3e538161b34bce4dc439160d2b63362b73985b0 | Python | 3,326 | 120 | from typing import Callable, Optional
from numpy.typing import ArrayLike
from jaxtyping import Key
import numpy as np
import jax
import jax.numpy as jnp
import jax.random as jr
from .modules import NoGradLayer
from ..global_defs import (
get_default_dtype,
get_params_dtype,
get_sites,
)
class Scale(NoGrad... |
9177e4e719fc38904ce67f37263a9451029938bf88d19ef217bca2fb341a4fc8 | Python | 3,326 | 95 | import torch.nn as nn
from network import resnet_d as Resnet_Deep
from network.nn.mynn import initialize_weights, Norm2d, Upsample
from network.nn.operators import ModuleHead, Aux_Module, ChannelReasonModule
class SRNet(nn.Module):
"""
Implement DeepLabV3 model
A: stride8
B: stride16
with skip c... |
91b2d0e870ad8271f007a20de8ff9ada9834fe873cc54ce0636b5f039d066e21 | Python | 3,326 | 89 | import numpy as np
import scipy.special as ssp
def get_tc(y, hd, bin_len=40): # compute empirical tunning curve of data
hd_bins = np.linspace(hd.min(), hd.max(), bin_len)
tuning_curve = np.zeros((len(hd_bins) - 1, y.shape[1]))
for ii in range(len(hd_bins) - 1):
data_pos = ((hd >= hd_bins[ii]) * (... |
a057a21c1b7b555c6fe093e498bf38fe9eb1e343ee11cc3dc74b25508d3ab0d8 | Python | 3,328 | 121 | """ Some handy utilities for working with vector geometries
"""
from typing import List, Tuple, Callable
import warnings
from shapely import geometry as geo
def get_ccw_vertices_from_two_lines(line1: List[Tuple], line2: List[Tuple]):
"""
Convenience method two do both get_vertices_from_two_lines()
... |
2718f7ba7416b2b194ad7b05fef28448a1f47c55e56604efb5b4cb23a9d6a56f | Python | 3,329 | 72 | #Imports relevant modules
import os
import numpy as np
import csv
#Defines the directory containing pipeline results.
feat_dir = '/<directory root>/FEAT/' #Full path removed for purpose of public sharing.
#Defines the pipeline we're interested in.
pipeline = '0'
#Defines the filepath of this pipeline.
pipeline_dir =... |
992e4794dc8968b0b86aefddfc8ff2a32723993c23d1972a0449d648753465f3 | Python | 3,329 | 83 | """Methods for Facets notebook integration."""
import base64
import os
import pandas as pd
from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator
FACETS_DEPENDENCIES = {
'facets_html': 'https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter... |
9fbb3ce4e47bc6aa84be0444912eb3b8d709674327d2280e78ae73b54b4e5775 | Python | 3,331 | 101 | __all__ = [
'Wasserstein2Loss'
]
import cupy as cp
import torch
from typing import Dict, Tuple
from .utility import torch_to_cupy, cupy_to_torch, BLOCKDIM, BLOCKSIZE
ker_blur = cp.RawKernel(r'''
extern "C" __global__ void blur(
const float* src,
int size,
int n,
float B,
float* dst)
{
int ... |
5494ae7eec942d9c3a442a6f90412e6ce125e62842bb7636acef0f640c5f64da | Python | 3,332 | 88 | """Validated inputs for the HiveServer2 read API.
This module defines the API boundary without opening a Hive connection or
creating a Ray read task. The public read_hive facade is defined in
ray.data.read_api.
"""
from __future__ import annotations
import re
from dataclasses import dataclass, field
from typing impo... |
81d6ea9d368509834e17cd1206f8de21d69c44a93719ab3c8925352d745fda51 | Python | 3,332 | 87 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Runner that executes Experiments locally."""
from typing import Dict, List, Optional
from torch import device as torch_device
from torchvision.datasets impo... |
caebca2febba273f5cad2801426aa6672912ad617bd7df5166e8ae4fae435b87 | Python | 3,332 | 67 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'thinbass.ui'
#
# Created by: PyQt5 UI code generator 5.15.2
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtG... |
e7d7cd21d7c94c8f78a303b1265cc9b1b4878e4bdf05dd99558e2762d9d95a68 | Python | 3,332 | 113 | # flake8: noqa
# isort: skip_file
# __xgboost_start__
import pandas as pd
import xgboost
# 1. Load your data as an `xgboost.DMatrix`.
train_df = pd.read_csv("s3://ray-example-data/iris/train/1.csv")
eval_df = pd.read_csv("s3://ray-example-data/iris/val/1.csv")
train_X = train_df.drop("target", axis=1)
train_y = trai... |
f617e984fd7536802627a537d6382cad3e3c7820dd691c82be7001934d8f7b2d | Python | 3,334 | 72 | import argparse
import json
import os.path
import keras
import glob
import numpy as np
import PIL.Image
from tensorflow import keras as k
import time
from utilities import extract_patches
from osgeo import gdal
PIL.Image.MAX_IMAGE_PIXELS = 500000000
def calculate_qs(tile_path, model, num_patches=9, us... |
218f335a7f0f13f0c5968ce189ee615c128e7f507db075615f1207f388ce4e0e | Python | 3,335 | 99 | """
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 os
from abc import ABCMeta
import hydra
import numpy as np
import omegaconf
import torch
from t... |
4cae92373ea761973145b3d1412fd83b9ba090761656c42748fd2e6db8e24e0d | Python | 3,335 | 117 | """
Synfire chain example
Reproduction of synfire chain implementation from SpiNNaker2 prototype chips.
References:
1. Implementation on Santos28 chip:
Höppner, Sebastian, et al. "Dynamic power management for neuromorphic
many-core systems." IEEE TCAS I(2019).
https://doi.org/10.1109/TCSI.2019.2911898
2. Impl... |
76d74d35297b290552c631731d8fc371a3965fb1b58a6e52fda49b9a0fc6dd4a | Python | 3,335 | 119 | from typing import Optional, Callable
import jax
import jax.numpy as jnp
from equinox.nn import Lambda
from .modules import Conv, Sequential
from .nqs_layers import ReshapeConv
from ..sites import TriangularB
from ..utils import neel, stripe
from ..global_defs import get_lattice, get_subkeys
def sign(neg: bool = Fals... |
8b78b46c164efa28605fc60830b95c3b2059d8d9b2b5f71ebfba547f064e42ac | Python | 3,336 | 74 | # 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... |
bab16644bf3f1d091a0f5a99c1a01512a77c93099e463937564cc3b8f21d8044 | Python | 3,336 | 97 | # -*- 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.
#... |
33846f81538868630f6ed798963ca72ea9e9cd5dfd34b2ea3c1268066684d5cf | Python | 3,337 | 108 | # 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... |
a2edb0167cb2e446cbc1013d93589b746f1a73e283beba4c2d126a08ee38ac64 | Python | 3,337 | 131 | import argparse
import torch
import random
import os
import numpy as np
import pandas as pd
from chromoformer import ChromoformerDataset
from chromoformer import ChromoformerClassifier
from tqdm import tqdm
from sklearn import metrics
def seed_everything(seed=42):
random.seed(seed)
np.random.seed(seed)
... |
ff5f68d49f573f5f2ff9042a6dd5039dafd79ba27a33455ee37f137c9c4df31e | Python | 3,338 | 98 | import os
import hashlib
from typing import Any, Set
import logging
from datetime import datetime
# pylint: disable=broad-except
class Logger:
"""
A Logger class for logging messages with a specific log level.
The class follows the singleton design pattern, ensuring that only one
instance of the Logg... |
e55a5c6e54cecac56f0cfd6ea527854cfd4da8fcdccbd5cf43bceb056049db2f | Python | 3,340 | 80 | import torch
from torch import nn
from monai.networks.nets import EfficientNetBN
class PrognosisModelD(nn.Module):
def __init__(self):
super(PrognosisModelD, self).__init__()
self.extractor_liver = EfficientNetBN(model_name="efficientnet-b0", in_channels=1, spatial_dims=2, num_classes=32)
... |
1d358dacd57782198e9fc003971e8a2901c8a92ac9c4bef12e1b82e19a7305e4 | Python | 3,342 | 93 | """Functions related to n_component inference from fit data."""
# Copyright (c) 2024 Valentin Goldité. All Rights Reserved.
# Modified from https://github.com/scikit-learn (BSD-3-Clause License)
# Copyright (c) Scikit-learn developers. All Rights Reserved.
from math import log
from typing import Union
import torch
fr... |
1fc7b4d2e2b9daf729372bab480e2751748f3b9ede7ccf7401827007229d754a | Python | 3,343 | 100 | import os
import sys
import time
import h5py
import numpy as np
from typing import Tuple, Optional, List
from ml4h.defines import TENSOR_EXT, StorageType
from ml4h.TensorMap import TensorMap, Interpretation
Shape = Tuple[Optional[int], ...]
DataDescription = Tuple[str, Shape, StorageType]
SYNTHETIC_DATA_PATH = os.... |
225bb97947a32b2593013bb53f93c30a2e61bf4178f6227eb9f9880ea665e0dd | Python | 3,343 | 138 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Access multiple TabixFiles from a single interface.
Fetching from the list of TabixFiles returns an iterator over merge-sorted
entries in the tabix files.
"""
import heapq
import pysam
from svtk.utils import is_smaller_chrom
class _TabixRow:
def __init__(self... |
9096815ef1d36126c51332167ff464bfb2defb9603bdacda04a124f78dbf400f | Python | 3,345 | 92 | # built-in dependencies
from typing import Any, List, TypeVar, Union, cast
# 3rd party dependencies
import numpy as np
from numpy.typing import NDArray
import torch
from torch import nn
# project dependencies
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons.logger import Logger
l... |
d304ff17fa36d538c0243c1221b8381b20e6fb21bb7f7ebebe835c860415ad13 | Python | 3,345 | 109 | """input/output functions."""
import os
import pickle
from pathlib import Path
def save_object(obj, folder=".", filename="./obj"):
"""Save general object in a pickle."""
if not Path(filename).parent.is_dir():
Path(filename).parent.mkdir()
pickle.dump(
obj,
open(os.path.join(folder,... |
dba1ef27a47fff46448791f7b2aa141f3df98551483405878dafe4793812d17f | Python | 3,345 | 89 | import json
import os
import pandas as pd
from anndata import AnnData
from sklearn.metrics import f1_score
def _dataframe_to_markdown(df):
# Create the header
pretty_columns = [
(
"Consensus Prediction"
if col == "popv_prediction"
else (col[4:-11].replace("_", " ")... |
586a96b08e4b1a126e9ddf9db8016c2190514ebdf24c3518cc8a69a423b42963 | Python | 3,346 | 108 | import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from lqit.registry import MODELS
def gaussian(window_size=11, sigma=1.5):
x = torch.arange(window_size)
gauss = (-(x - window_size // 2)**2 / (2 * sigma**2)).exp_()
return gauss / gauss.sum()
def create_window(window_si... |
6b42a61dd99526b6044ecbcfc7f23ac0cb3a9695962f58dddc67091a9bf3077e | Python | 3,346 | 90 | import numpy as np
from typing import List, Set, Dict, Tuple
import visionloader as vl
import spikebinning
SAMPLE_RATE = 20e3
#ACFT1 = 0.5e-3 # units seconds
ACFT1 = 0 # units seconds
ACFT2 = 1e-3 # units seconds
ACFT1_SAMPLES = ACFT1 * SAMPLE_RATE
ACFT2_SAMPLES = ACFT2 * SAMPLE_RATE
def count_refractory_violat... |
9576f817cc63c955fe0a64fb4a85253672b13326045db8165f928f689d870e63 | Python | 3,346 | 101 | import socket
import os
import sys
if socket.gethostname() == 'kelarion':
CODE_DIR = '/home/kelarion/github/'
SAVE_DIR = '/mnt/c/Users/mmall/Documents/uni/columbia/assignment_errors/'
LOAD_DIR = '/mnt/c/Users/mmall/Documents/uni/columbia/assignment_errors/server_cache/'
repler_DIR = 'repler/src'
else: ... |
59a05f33482c1e62db85d07df8e437efd3ded4b263f9fc8ac02b43d6fd00f0e2 | Python | 3,347 | 90 | import numpy as np
import nibabel as nb
from nilearn import image
from nilearn import datasets
def _load_aal():
aal = datasets.fetch_atlas_aal()
img_aal = nb.load(aal.maps)
mtx_aal = img_aal.get_fdata()
list_val = np.unique(mtx_aal)
return img_aal, mtx_aal, list_val
def _expand_parcels(img_aal, ... |
9886d1a18b31dea0052b9309b562fac2fa1fe0a47c7ca229c8875b2efde8ccf3 | Python | 3,347 | 91 | from typing import Callable, Optional
import torch
import torch.nn as nn
class Repeat(nn.Module):
"""Utility layer which wraps any nn.Module.
It flattens time and batch dimensions of the input before feeding it to the child module and
unflattens those dimensions to the original shape before passing it t... |
1257f0d077893b610716355b508aaf9c0e863c978845735a7dfea39622508228 | Python | 3,350 | 105 | #
# 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... |
907d2da2bceee304cd71006af6d2b13edcfa54051ba90fb59be84dc370920efe | Python | 3,350 | 88 | '''
Registering anatomical to functional image because that seemed to work better
than the other way round.
Then applying the inverse transformation to register func to anat.
'''
import glob
import os
import subprocess
subs = ['sub-07']
# Define data dir
DATADIR = '/Users/sebastiandresbach/data/neurovascularCoupl... |
daa370a63f458de4c4ada4c8a48aa869f6503c15d54ee2f31ce3666cffdcdeca | Python | 3,350 | 100 | """Enrichment dispatcher for ``ov.protein`` — thin wrapper over ``ov.es``.
For protein-level analysis the canonical input to ``ov.es`` is either:
* the AnnData itself (per-sample scoring), or
* a ranked statistic vector from ``ov.protein.de`` (one-shot GSEA / ORA).
This module re-exposes the relevant ``ov.es`` funct... |
ebbf463e521689f3fd90bbc52299c59dcbebf640884395e26f04f01bb70dab5a | Python | 3,352 | 103 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard imports
from pathlib import Path
# Third-party imports
import numpy as np
import pandas as pd
# Internal imports
from ..shared.kmer.common import *
from ..shared.co... |
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