sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b921de5c6cc97c0d021d0c4dee95285d997bc6c677578dafe50bf00326d5786d | Python | 4,017 | 91 | """
Module for opening and processing raw recording data from .h5 files.
This module contains a function to open and process raw recording data, particularly from
files generated by Multi Channel Systems (MCS). It handles large files by processing data
in chunks to avoid memory limitations.
Functions:
file_opener... |
d9d9c66be175521afb44543de859687d2221b4d5311a178e48ea308f34ef04c8 | Python | 4,017 | 159 | import pdb
from matplotlib import pyplot as plt
import numpy as np
import ray
import yaml
from yaml.loader import SafeLoader
import torch
from ray import tune
from ray.air import session
from ray.tune.search.hyperopt import HyperOptSearch
from latent_time_stepping.AE_models.VAE_encoder import VAEEncoder
from latent_... |
54a17eb62d63947fc69e5591cbd758e844dcf96f598ad7d0a0d8e6ac5010c6e5 | Python | 4,021 | 124 | # 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... |
4189249e7b176a62679bbc8f8bfc139a698d7c49edce6411f851141e7408b1bf | Python | 4,022 | 121 |
import numpy
import random
import sys
sys.path.insert(0, "..")
from scipy import stats, special
from sklearn.preprocessing import PolynomialFeatures
from matplotlib import pyplot
import matplotlib; matplotlib.use("MacOSX")
from banditopt import algorithms
# numpy.random.seed(42)
# random.seed(42)
class Sampler:
... |
f3b9cba2a981e1a95d38be14dd5b32bab9b19781e1d69b8f461cb3386049ad0a | Python | 4,022 | 110 | import torch
from torch import nn
import torch.nn.functional as F
class BatchDiscriminator(nn.Module):
def __init__(self, input_dim, hidden_dim, hidden_layers, dropout, target_classes):
super(BatchDiscriminator, self).__init__()
self.layers = nn.ModuleList()
for i in range(hidden_layers - 1... |
77ce9c6ac2b193ad0fab1059a112e691d7af4794f5bb79354209af478792722c | Python | 4,023 | 120 | #!usr/bin/python
"""scDRS on each cell type alone."""
# wget https://figshare.com/ndownloader/articles/19312583/versions/1
# ----------------------------------------------
# scDRS (v1.0.2) scores on the multiregion data:
# Updated: 06/21/23
# ----------------------------------------------
import os
import scdrs
import ... |
1d406d2548de1b4eb6cc9c4ee17b8702676c48bc2127d3f21ac2ff1eaa95f316 | Python | 4,024 | 95 | import argparse
import scipy.stats as sts
import numpy as np
import pickle
import functools as ft
from datetime import datetime
import general.utility as u
import swap_errors.circus as swc
def create_parser():
parser = argparse.ArgumentParser(description='fit several modularizers')
parser.add_argument('-o', '... |
250f94e9c401198752838bdf9813b54aea711ac2249222158222e51605eb1059 | Python | 4,024 | 116 | # coding=utf-8
# Copyright 2023 HuggingFace 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 applicable law or ag... |
899df8bac64c502b1056fd330c2f09d449aae79fd11e0e6883d0b6fa6a808edc | Python | 4,024 | 132 | from data_profiling.config import Settings
from data_profiling.report.formatters import fmt, fmt_bytesize, fmt_percent
from data_profiling.report.presentation.core import (
Container,
FrequencyTable,
FrequencyTableSmall,
Table,
VariableInfo,
)
from data_profiling.report.presentation.frequency_table_... |
06a89ba3c42b5fca72dfb15932ccbf857528137b350ab236007171750ba90ed5 | Python | 4,025 | 75 | import os
import argparse
import subprocess
from utils.tools import ROOT_PATH, absolute_path
"""
Train and test the SNN on a single run.
The input directory must contain the datasets (training, validation, test) to the format <X_{set}.bin>, <y_{set}.bin>. Please see README for more details.
The configuration paths mu... |
f97a00b8128f4a95e18e00dfddc4190cd42e3fcb58a447b6ad9e247925ed7c62 | Python | 4,025 | 118 | """
_*_ coding: utf-8 _*_
"""
import pandas as pd
import numpy as np
import scipy.stats as st
def hyper_geometric(distribution, sample_size, N_0, N_1, N_2, idx):
x_0 = distribution.loc[idx, 'cluster_0']
x_1 = distribution.loc[idx, 'cluster_1']
x_2 = distribution.loc[idx, 'cluster_2']
n = distribution.... |
a6a98980ffa4f1b9bf3f104508459415863130008d68b50de66b90de04e30748 | Python | 4,026 | 85 | import torch as th
import numpy as np
import scanpy as sc
import scipy as scp
from sklearn.model_selection import train_test_split
from .utils import normalize, sparse_mx_to_torch_sparse_tensor
from .dataloader_utils import handle_adata_view, ensure_csr_csc
class ANVIDataset(th.utils.data.Dataset):
def __init__(... |
135aaabf74547e5c7aa71e8a3e45b87ee4c68eda663cf8d99b55a23608f65461 | Python | 4,028 | 93 | """Columns a reader appends to each batch instead of reading them.
``include_paths=True`` adds a ``path`` column and ``include_row_hash=True``
adds ``row_hash``; a checkpoint ID column is the next one. Each used to be a
boolean on the reader plus four hand-written steps: advertise the column in
the schema, keep pyarro... |
2ae9ca8181f3a1e79c9f3e9c6471ca109b34669967ebc82c7ca3b40e106df01f | Python | 4,031 | 123 | from typing import List, Optional
from data_profiling.config import Settings
from data_profiling.model import BaseDescription
from data_profiling.report.presentation.core import Container, CorrelationTable, Image
from data_profiling.report.presentation.core.renderable import Renderable
from data_profiling.visualisatio... |
5f4b02de9784a6c6fd4c7298103d7e9aa981f37b55e5a1b46563a227bab3c890 | Python | 4,031 | 117 | r"""Sample normalization for metabolomics.
All methods scale each **sample** (row) to make inter-sample
comparisons valid despite differences in dilution, injection volume,
or total extracted material. They do NOT touch between-feature scaling
(that's `_transform.py`).
Methods
-------
- **PQN** (Probabilistic Quotien... |
2c7bcc38894e0a74fe775e558bf6297d69c795f504b836477a938b95946729d9 | Python | 4,032 | 153 | #! /usr/bin/env python
# altschulEriksonDinuclShuffle.py
# P. Clote, Oct 2003
# NOTE: One cannot use function "count(s,word)" to count the number
# of occurrences of dinucleotide word in string s, since the built-in
# function counts only nonoverlapping words, presumably in a left to
# right fashion.
import sys,rando... |
b7b6c0b01324449539a66bb988310106000e2e711e239fba78aa9fd583afa5cd | Python | 4,032 | 105 | from scipy.sparse import csr_matrix,issparse
import numpy as np
import os
from tqdm import tqdm
import pandas as pd
from .._registry import register_function
@register_function(
aliases=["CAST", "空间CAST", "CAST整合", "spatial_cast", "空间样本嵌入"],
category="space",
description="Learn shared spatial embeddings ac... |
f7d984a8565d4fe3ed5dd5835e62f4e3c54339d603b270ce6f63b9f1183ce0fa | Python | 4,033 | 110 | from pickle import FALSE
from datagenerator_pd import DataGenerator
from sklearn.metrics import confusion_matrix, accuracy_score
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import seaborn as sns
import SimpleITK as sitk
import pandas as pd
import numpy as np
import random
... |
45e7c04980f5b12b85556c3633a05be9dd59538063c177e6ebb35ae842f16fd9 | Python | 4,035 | 113 | # MNIST Hand written digit tensor maps
import os
import sys
import gzip
import pickle
import logging
from typing import Dict
import h5py
import numpy as np
import tensorflow as tf
from skimage.transform import resize
from ml4h.TensorMap import TensorMap, Interpretation
from ml4h.normalizer import ZeroMeanStd1
def i... |
c67cbc57b8394359296fe767cfebf9933b665099503943f122ffda3fe740a50c | Python | 4,035 | 137 | import unicodedata
import string
import re
SOS_token = 0
EOS_token = 1
class Lang:
def __init__(self, name):
self.name = name
self.word2index = {}
self.word2count = {}
self.index2word = {0: "SOS", 1: "EOS"}
self.n_words = 2 # Count SOS and EOS
def addSentence(self, ... |
753e378df366f18b00bd095f1984400e972c11d0275be54fcdf3cd6508dafad3 | Python | 4,036 | 103 |
# In[ ]:
import os
from numpy import interp
from scipy.stats import stats
import tensorflow as tf
from tensorflow.keras import initializers
from tensorflow.keras.layers import Layer, InputSpec
from tensorflow.keras import backend as K
from tensorflow.keras.layers import *
from tensorflow.keras.models import ... |
8b90922e468678a5969368e6664ea90a9d2be95938a01dc3db2f36da4b00c2c9 | Python | 4,036 | 102 | # __replica_rank_start__
from ray import serve
@serve.deployment(num_replicas=4)
class ModelShard:
def __call__(self):
context = serve.get_replica_context()
return {
"rank": context.rank.rank, # Access the integer rank value
"world_size": context.world_size,
}
ap... |
6174593707a1f0d46aa1ca79ae2378212f27c846993a79b476e7820973cb4118 | Python | 4,037 | 112 | import numpy as np
import pytest
from siapy.entities import Pixels, Shape, SpectralImage
from siapy.utils.signatures import get_signatures_within_convex_hull
from siapy.utils.plots import display_image_with_areas
def test_get_signatures_within_convex_hull(configs):
raster = SpectralImage.rasterio_open(configs.im... |
654f3083d4114af371fa610fefd706cebcff8afe63567a0896ae103e906ab421 | Python | 4,038 | 153 | import pdb
import numpy as np
import matplotlib.pyplot as plt
import ray
from data_assimilation.particle_filter.base import BaseForwardModel
from data_assimilation.test_cases.multi_phase_pipeflow_with_leak.PDE_model import PipeflowEquations
def compute_forward_model_PDE(
forward_model,
state: np.ndarray,
... |
6f57c7736c8869c5f745f30b03c38f81564e7494e12a40710caad9313b33a39d | Python | 4,041 | 88 | import os
import json
import numpy as np
import pandas as pd
import nibabel as nib
from permutations_stats.permutations import friedman
from scipy.stats import wilcoxon
tissues = {'WM':3,'CGM':2,'DGM':4,'CSF':1}
titles = {'WM':'White matter','CGM':'Cortical gray matter','DGM':'Deep gray matter','CSF':'Cerebrospinal fl... |
5163ec328863744e236476dddba95a55f9144850912c2cb9ce410616a70f63bf | Python | 4,043 | 104 | # -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals, absolute_import, division
from collections import OrderedDict
from warnings import warn
from ..utils import _raise
from ..utils.six import Path
from ..utils.tf import keras_import
get_file = keras_import('utils', 'get_file')
_MODELS = ... |
64744e4eac0e415d7e206aa7bcd9131f7cda7fa0ca12c115c2812ffe6217eeb8 | Python | 4,044 | 113 | """
Created on Fri Jan 14 19:26:23 2022
@author: trmabdelaal
"""
import scvelo as scv
import scanpy as sc
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity
import sys
sys.path.insert(1,'SIRV/')
from main import SIRV
# lo... |
8baa391e63340023a1aa6be7edccbab7a27b553ca15a40091752de7c498071c4 | Python | 4,045 | 111 | # coding=utf-8
# Copyright 2023 HuggingFace 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 applicable law or ag... |
d3245aacd26dc4c5a3d21d71cb8b5b03ba1b83d5dded0e004cce2353ac3ec998 | Python | 4,045 | 78 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 22 11:06:59 2022
@author: YaoMeng
script for fetching neuromaps images and transform to MNI 152 2mm space.
"""
from neuromaps import datasets, nulls, transforms
from neuromaps.datasets import fetch_annotation
from neuromaps.parcellate import Parcel... |
3085aad1da0785564f351268f9c2797e5cf132a7749479400665842ac6e21ce3 | Python | 4,046 | 125 | import os
import numpy as np
from spinnaker2 import hardware, snn
from spinnaker2.mla import conv2d, mla_helpers
def test_conv2d_if_neuron_rate_code():
rng = np.random.default_rng(42) # fails for seed = 12
# parameters of convolution layer
in_width = 28
in_height = 28
in_channels = 1
out_ch... |
d83d2ff2dcfa688fd12cfff164dfc87d0012c596bab1155a9e7d7ee5312c6e18 | Python | 4,048 | 127 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
# pylint: disable=too-many-instance-attributes
"""Forward / backward / update related parameters for resistive processing units."""
from dataclasses import dat... |
8dcc8caf85159b0a031b3ba0c23f4fceb409ef647efb6c1d53f9229c254fb8ec | Python | 4,049 | 171 | #!/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... |
bbc053eafcea2f82fe1ba3a6bc77aa32f152bb4932687835a44d0939c20b0018 | Python | 4,049 | 118 | from typing import Optional, List
import numpy as np
from neuron_morphology.feature_extractor.data import Data
from neuron_morphology.features.statistics.coordinates import COORD_TYPE
from neuron_morphology.feature_extractor.marked_feature import marked
from neuron_morphology.feature_extractor.mark import Geometric
... |
4f511857e9ff2d734af2d7d24eeafd7cc25b017f2dcd5af68a5086389f7ca690 | Python | 4,050 | 121 | import argparse
import numpy as np
import optuna
from optuna.integration import SkorchPruningCallback
import skorch
import torch
import torch.nn as nn
import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
device = "... |
2167f3127e2f25540b0c9b7c8dbfecd8fb0fb950a2ed5e384abfb0007110b81e | Python | 4,051 | 110 | import numpy as np
import torch
import argparse
import pickle
from eval_fns.eval import batched_compute_convolutional_masked_ms_ssim, Masked_MS_SSIM, \
batched_computed_convolutional_masked_LPIPS
from lib.data_utils.sta_metadata import compute_convex_hull_of_mask
from lib.dataset_config_parser.dataset_config_pars... |
94421706613d3890e66d3106b105523a756cf61f158710d9f0e0dbf1012dde41 | Python | 4,051 | 112 | import yaml
import scipy
import pickle
import argparse
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def bootstrapped_return(x, y, stride, total_steps, confidence_level=0.95, to_bootstrap=True):
assert len(x) == len(y)
num_runs = len(x)
avg_ret = np.zeros(total_steps // stride)
... |
4fd4cbf2c03633c2b77ed543fcb3dea7c4375e52d388ad02bf47a4566b3fb4ae | Python | 4,052 | 120 | #!/usr/bin/env python
from __future__ import print_function
# batteries
import sys
import gzip
import argparse
import logging
from time import time
# 3rd party
from lxml import etree
# argparse
desc = 'Convert UniRef XML to fasta. Mostly to rebuild previous UniRef databases'
epi = """DESCRIPTION:
A simple script to co... |
9872a39492a2c5e91ed4b7d7b1cc95c5d78ab9d99bb4331bcc20c95308e6d17c | Python | 4,052 | 135 | # -*- coding: utf-8 -*-
import numpy as np
import sys
import os
from pathlib import Path
from vtkplotlib.plots.BasePlot import ConstructedPlot
from vtkplotlib import nuts_and_bolts
class Surface(ConstructedPlot):
"""Create a parametrically defined surface. This is intended for
visualising mathematical funct... |
5ff2a424a75b3345f3b336f32192fd9520fa1fe5305c7cbd6f7d209c527eece8 | Python | 4,054 | 85 | from loguru import logger
import os
import numpy as np
from deltasplice.utils import parse_bed_line
import json
def annotate(Lines, WDict):
for line in Lines:
Fa, Chr = line[:2]
if Fa not in WDict:
WDict[Fa] = {}
if Chr not in WDict[Fa]:
WDict[Fa][Chr] = {}
... |
d5ca5f0e1a6535e0c6644e4ce33f7418025be668d9d382bd081d4d026b95223d | Python | 4,054 | 114 | from __future__ import annotations
import logging
from pathlib import Path
import numpy as np
import pandas as pd
import scanpy as sc
import scipy as sp
from ._config import CauchyCombinationConfig
from ._style import Colors, EMOJI
logger = logging.getLogger(__name__)
def acat_test(pvalues, weights=None):
"""... |
66db9281e6722aa1898adeeca296783f9d3cd95d9320d6b6b0be4e32d5e56228 | Python | 4,055 | 120 | import nibabel
import numpy as np
from numpy.random import default_rng
from nilearn.maskers import NiftiMasker
from numpy.testing import assert_array_almost_equal
def zero_mean_coefficient_determination(
y_true, y_pred, sample_weight=None, multioutput="uniform_average"
):
"""
Calculate ratio for y_true, y... |
366fde268f7990275fe3038837eb139139534883a2d8312a443458162c4abb11 | Python | 4,058 | 122 | # -*- 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.
#... |
25314abdde5575d07ba66274bf4d7961c2924130cd1750a0cd8fa7c8954c1d00 | Python | 4,060 | 91 | from multiprocessing import Pool
import os,time,random
import datetime
import numpy as np
python_file=r"/mnt/disk1/scfan/software/anaconda3/envs/ImputeHiFI/bin/python"
def run_with_one_function(title,args_list,p_num=1):
from datetime import datetime
# print('start run step one prepare data spatial_dist_bin_p... |
e5b8d89b1473255df3286bad4bdafb8038900d075ec38c16a5a5d0c205244651 | Python | 4,060 | 131 | # -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 14:49:57 2023
@author: Leyang Xue
"""
import sys
import numpy as np
import os
# Add package path
packagepath = '../kcorePercolation' # Manually specify the path to your script
sys.path.append(packagepath)
from utils import kcorePercolation as kp # Import custom util... |
ae6f168db14c9b35b315aadb43787dd285b61f720638670e26123840f9522939 | Python | 4,062 | 121 | _base_ = [
'../../_base_/datasets/urpc2020/urpc2020-validation_coco_detection.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FasterRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 10... |
2f6f0da3f1f4d140b186db89b678df7804f2d96c3f749502de50c88c9476d6d9 | Python | 4,063 | 117 | import pandas as pd
from typing import List
import copy
# func_name: func_name is process each element in each column, instead of the whole column
def process_col(input_df: pd.DataFrame, col_name: str, func_name):
if col_name in input_df.columns:
input_df[col_name] = input_df[col_name].map(func_name)
... |
6a748b5a83d0fede4269f20036d42c3fa25961b3a5ad5802601fc158dc036e9d | Python | 4,063 | 102 | import numpy as np
from typing import List, Callable, Tuple, Union, Dict
NSCENES_STIXEL_SIZE = 2
def make_upsample_wn_to_nscenes_noflip(upsample_factor: int) -> Callable:
'''
Makes a callable function that upsamples a filter matrix from STA
coordinates into nscenes stimulus coordinates
:param up... |
65e70122f41355b8a8313272734902cd020293df8f160a1df5115544f964317d | Python | 4,064 | 85 | import numpy as np
import oddt
from oddt.utils import is_openbabel_molecule
from oddt.interactions import close_contacts
from oddt.fingerprints import _ECFP_atom_repr
import torch_pgn.graphs.graph_utils as gu
"""
Functions to output basic atom representations.
"""
def featurize_atoms_ECFP_like(molecule, index_list,... |
764268c8d9a2954c2e3ca2e7929bf9134564dbe05a239bf3fc5d293e216a46eb | Python | 4,064 | 132 | """
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 importlib.util
from pathlib import Path
import pytest
# Import docs/check_links.py directly by... |
a3008af77294dfb0d3b7adc868f3fe570fcc84b268f24182d5702a02191cd20c | Python | 4,064 | 147 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import importlib
from copy import copy
from pathlib import Path
from typing import Any, Self
import rq
from rq import Retry
from rq.job import Job as RQJob
from rq.job import JobStatus
from cvat.apps.redis_handler import signals
class DetachedJob... |
f58b362c763be0a53116a5400a49ad930cd69b2f8ad3c425f635b8658ae08373 | Python | 4,064 | 117 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# freq
"""
"""
import argparse
from collections import deque, defaultdict
import numpy as np
import pandas as pd
import pysam
import svtk.utils as svu
def process_metadata(vcf, outlier_sample_ids):
n_samples = len(vcf.header.samples)
called_counts = dict()
... |
769ca3709cf2ebd7f29a8d01626198a81aadec078a2cbba6da1e25f2b7e893a3 | Python | 4,065 | 136 | """
Test for issue 1429:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/1429
"""
from typing import List, Optional, Tuple
import numpy as np
from pyspark.sql import DataFrame, SparkSession
from pyspark.sql import types as T
from data_profiling import ProfileReport
from data_profiling.config import... |
cf12b1c3b23c8be826e849b7c3743e1d1536de18d41e32706773bd4136ed12da | Python | 4,066 | 92 | import sys, os
import argparse
import concurrent.futures as cf
import numpy as np
import pandas as pd
import torch
os.environ.setdefault('PROJECT_DIR', ".")
sys.path.append(os.environ['PROJECT_DIR'])
from src.models.model_whole import prepare_model, add_model_args
from src.features.tokenizer import load_tokenizer
from ... |
19a0223482a0854dd1cbcf22d111693fca415f680638454b0cbaeac316886640 | Python | 4,068 | 128 | import numpy as np
import warnings
from ..utils import coms_list_to_matrix
__all__ = [
'symmetric_jaccard',
'overlapping_nmi',
]
def symmetric_jaccard(coms_1, coms_2):
"""Quantify agreement between two community assignments based on symmetric Jaccard similarity.
Computed as in the CESNA paper as
... |
6f445d69ec2e49e4ed9d8922369c8f5181d47245d9b892a365eaec99a9e701fa | Python | 4,068 | 127 | import subprocess
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple
from pybuildkite.buildkite import Buildkite
BRANCH = "master"
BLOCK_STEP_KEY = "unblock-me"
class GapFillingScheduler:
"""
This buildkite pipeline scheduler is responsible for scheduling gap fillin... |
eb8dbda3e8d5dbfb8b5864661ea525e1b5e16e81a4ae410faa0c5cffbe624517 | Python | 4,068 | 123 | # -*- coding: utf-8 -*-
# Development work, e.g. for higher level functions.
# These functions are not intended to work on any machine or pass the tests.
# They are early drafts (e.g. of higher level workflows) intended to be shared among select collaborators or multiple machines of one collaborator.
# Please don't ed... |
81c2382f3af6dae41437f1e9dd7f31c9a4d585347d62869cd6f223b2bf2a57a5 | Python | 4,069 | 121 | #
# 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... |
ee23639e79f5e8ec62fd7f0b2a3bf47dbe64b9a5e653c1d4f0b9e6ef5779442b | Python | 4,070 | 117 | import numpy as np
import torch
import torch.nn as nn
from .layers import *
from .modules import *
class RepeatedModule(nn.Module):
def __init__(self, n_layers, d_model,
n_head, d_k, d_v, d_inner, dropout=0.1):
super().__init__()
self.linear = nn.Linear(128... |
c58218a9daebccb8aa267b1337e539390b84611a8e2596f005fb5d4231de13e6 | Python | 4,071 | 131 | from unittest.mock import patch
import pytest
from flyvis_cli.flyvis_cli import (
SCRIPT_COMMANDS,
filter_args,
handle_help_request,
main,
)
def test_filter_args():
# Test filtering out commands from arguments
argv = [
"--ensemble_id",
"0001",
"train",
"--task... |
d333f15dc6dc55ce297d3aff240b3645bd31e8251270ec6b5b7e3557f9a2fc82 | Python | 4,071 | 111 | ####################################################################################################
# Copyright (c) 2020, 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 free softw... |
d602e900dd9946524ebb80862d4e781913019734ed2d9f1bedef1fafa20e50bc | Python | 4,072 | 116 | from keras.utils import Sequence
import numpy as np
from csbdeep.models import CARE
from csbdeep.utils import _raise, axes_check_and_normalize, axes_dict
import warnings
def augment(x, y):
rot_k = np.random.randint(0, 4, x.shape[0])
X = x.copy()
Y = y.copy()
for i in range(X.shape[0]):
if np... |
efb7adabfc09933249e05636fb11d934abd158e4c5e70c5105537f0f7e45dc3a | Python | 4,074 | 121 | #
# 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... |
68615c629a17d44c5f004880da935f9a8163c2d326b099678545af8a399abb44 | Python | 4,075 | 128 | import os
import numpy as np
import pytest
from scipy.special import logsumexp
from endstate_correction.smc import SMC
from .test_system import setup_ZINC00077329_system
@pytest.fixture
def _am_I_on_GH() -> bool:
# will return True if on GH, and false locally
if os.getenv("CI") == None:
return Fals... |
cf3b1660c8d610f60a69a48cb1b3db4f7cd60701bdc876118b23f18dc23b2627 | Python | 4,076 | 106 | from typing import Optional
import numpy as np
from ray.data._internal.datasource_v2.common.file_reader import FileReader
from ray.data._internal.datasource_v2.interfaces.file_manifest import FileManifest
from ray.data._internal.datasource_v2.interfaces.in_memory_size_estimator import (
InMemorySizeEstimator,
)
f... |
c7f4cec5ba0cf342b60353cadbf4a0675115c5030c4e0aaf369ec5264123b14a | Python | 4,077 | 122 | import nir
import torch
import torch.nn as nn
import sinabs.layers as sl
from sinabs import from_nir, to_nir
def test_iaf():
batch_size = 2
iaf = sl.IAFSqueeze(batch_size=batch_size)
graph = to_nir(iaf, torch.randn(batch_size, 10))
converted = from_nir(graph, batch_size=batch_size)
assert len(gr... |
a5896be0882dcc3547d5a318942ee18469ccbeb158b717b7fdbd13dd823479c8 | Python | 4,078 | 107 | # -*- 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.
#... |
1b6d84d43b5e60fa93fa807f6cc542775159fecde2c79c293711c54003535915 | Python | 4,079 | 176 | # flake8: noqa
# __begin_starlette__
import starlette.requests
import requests
from ray import serve
@serve.deployment
class Counter:
def __call__(self, request: starlette.requests.Request):
return request.query_params
serve.run(Counter.bind())
resp = requests.get("http://localhost:8000?a=b&c=d")
asser... |
ccea855446b5d95d43220747731fb741e5d4b6b64cddef05301586b4bd3c6c02 | Python | 4,079 | 101 | import torch
import logging
import re
from torch.utils.data import TensorDataset
from sklearn.model_selection import train_test_split
from agent.data.entities.config import FD_CLASS_VALUES, ITEMS_TABLE
from agent.data.entities.config import TRAIN_ITEM_FILTER, TRAIN_LABEL, TEST_ITEM_FILTER
from agent.data.entities.con... |
3bf2ed33241307be73d296643d6e6555082cccef95baf29d516628ff7cfb749f | Python | 4,080 | 114 | import numpy as np
def toUint8(data):
data=np.real(data)
data=data.astype(np.double)
ma=np.max(data)
mi=np.min(data)
data=(data-mi)/(ma-mi)*255
data=np.clip(data,0,255)
data=data.astype(np.uint8)
return data
def toUint16(data):
data=np.real(data)
data=data.astype(np.double)
... |
9899f09c1af8fbced269f51ffefadc868bd9966daf966ec7ae80a7e981d282e5 | Python | 4,082 | 111 | """MAFFT wrapper for multiple sequence alignment.
Wraps the real ``mafft`` CLI (https://mafft.cbrc.jp/alignment/software/).
Install via ``conda install -c bioconda mafft``.
Input: one FASTA with the sequences to align (typically ASV centroids
produced by :func:`omicverse.alignment.vsearch.unoise3` /
... |
a6d8354ac13ff108857d01f6198f764284821acb007ef26ba83968601b84af70 | Python | 4,083 | 110 | import rdkit
import rdkit.Chem as Chem
import numpy as np
elem_list = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr', 'Pt', 'Hg', 'Pb', '... |
9934556d06c552d1a8ce23dec6e72d3aa04eb040dc0d213ee9b84dd96c981d9e | Python | 4,084 | 116 | """An explicitly supplied zero threshold is distinct from the default."""
from types import SimpleNamespace
import cv2
import numpy as np
import pytest
from deepface.modules import modeling, recognition, verification
from deepface.models.FacialRecognition import FacialRecognition
@pytest.mark.parametrize("threshol... |
888efdcc4bd7280d2ba24fe223def9bb3d1198a54709e9d491a10eeefa1073f4 | Python | 4,085 | 124 | """
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 torch
EPS = 1e-7
class Safeacos(torch.autograd.Function):
@staticmethod
def forward(c... |
dbdc1a31dde4aaba2cef9641223066653c46c33fe2c8bd7786cafa2611e1a773 | Python | 4,085 | 135 | from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from torch import nn
from torch.profiler import record_function
if TYPE_CHECKING:
from fairchem.core.models.allscaip.configs import (
GlobalConfigs,
GraphNeuralNetworksConfigs,
MolecularGraphConfigs,
... |
11bec089a339f9298c7735726077643b02a19fd28501c1d61af7541794fbd3eb | Python | 4,086 | 110 | # -*- 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.
#... |
bcc160d9ddae34002b151921aaf29f9422539a621ab7a7bea42971f4bececcb1 | Python | 4,086 | 106 | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import os
def handle_duplicate_names(path):
# pre: path has '/path/to/file(optversion).ext'
filename, extension = os.path.splitext(path)
count = 1
while os.path.exists(path):
path = filename + '(' + str... |
014067f85df1b634984834f0f1cceb321b7f48c5bf327e9737a0cfffa7844afa | Python | 4,087 | 114 | #Test generate
from src import generate
import glob
import geopandas as gpd
import pandas as pd
from deepforest import main
import rasterio
import pytest
import os
import distributed
import numpy as np
def test_predict_trees(rgb_path, plot_data):
m = main.deepforest()
m.use_release(check_release=False)
box... |
8f45feac70ff960840f2b0136ea053c8358aad18d8b0b96dfeb6e10dce545a69 | Python | 4,088 | 117 | import numpy as np
def uot(mu, nu, c, epsilon,
niter=50, tau=-0.5, verb = 1, rho = np.inf, stopThr= 1E-7):
lmbda = rho / ( rho + epsilon )
if np.isinf(rho): lmbda = 1
mu = np.asarray(mu, float).reshape(-1,1)
nu = np.asarray(nu, float).reshape(-1,1)
N = [mu.shape[0], nu.shape[0]]
H1 ... |
24f2f426cd8aca981a7c47702e2557af957f0e274237b6a047df5b27f2974fc4 | Python | 4,089 | 139 | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import logging
import numpy as np
import plyfile
import skimage.measure
import time
import torch
import deep_sdf.utils
def create_mesh(
decoder, latent_vec, filename, N=256, max_batch=32 ** 3, offset=None, scale=None
):
start = t... |
9687d7510f5a64ceb61817cc3e06b567668de51a82816b8ee53155faed660f6e | Python | 4,089 | 152 | from __future__ import annotations
import warnings
from typing import Optional, Union
import numpy as np
import scipy.sparse as sps
from anndata import AnnData
from scipy.sparse import spdiags
from ._utils import sample_obs_key, spatial_connectivity_key, str2list
def _aggregate_mean(adj, x):
return adj @ x
d... |
e0337f92dd675a10ad0d9b225d0d2c8612ce713fbf4a8e40ea8deee395aae2c9 | Python | 4,089 | 115 | # modified from https://github.com/facebookresearch/deit
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
import utils
def train_one_epoch(model: torch.nn.... |
384d8c6fc919b94b781df9ec9f832d46c97cb2162bc1b851ec4de34e78a7c031 | Python | 4,091 | 130 | """
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
from dataclasses import MISSING, dataclass, field, fields, is_dataclass
from typing import Literal
@d... |
47b3ce26bf3447c6776cde607760f1aa7bc3b2441dfb0e5a1418fc379cfc06c8 | Python | 4,091 | 125 | #!/usr/bin/env python
"""
Collect bincov matrix metrics. Writes stats to stdout.
Metrics:
bincov_matrix_intervals : number of intervals (rows)
bincov_matrix_intervals_all_samples_zero : number of intervals that were 0 across all samples
bincov_matrix_intervals_at_least_one_zero : number of intervals with 0 in a... |
85a1776d918b466693f3fba3f4e4b3e09145c63801302ffb39b234d9fb18d285 | Python | 4,091 | 121 | '''
Created on Sep 15, 2015
@author: jivan
'''
import re
valid_amino_acid_chars = 'acdefghiklmnpqrstvwyACDEFGHIKLMNPQRSTVWY'
class FASTASequenceInput(object):
@staticmethod
def valid_sequence_text(text):
ret = True
if '>' not in text:
ret = False
sequences = text.split('>')... |
274b9cbca5fa4b547f56db677a4d59f5626aee8a489b7355d54d8aee64ff5718 | Python | 4,092 | 122 | import numpy as np
import os
from .loadNetworkForAnalysis import loadNetwork
from ..analysis import parse_activity
from ..io import SAVED_DIR
def spike_rates(activity, window_size=20, start_time=0, end_time=300):
n_units, n_trials = activity.shape
n_bins = int((end_time - start_time) / window_size)
firing... |
61dc7123b8388fb81d644ff52fbbb79dc01589b0e7a7f2b4554d0001869d229b | Python | 4,093 | 92 | from collections import deque
from typing import Dict, Optional
from ray._private.telemetry.open_telemetry_metric_recorder import (
OpenTelemetryMetricRecorder,
)
from ray.core.generated import events_event_aggregator_service_pb2
from ray.dashboard.modules.aggregator.constants import AGGREGATOR_AGENT_METRIC_PREFIX... |
807e0fc2d96e319dd00e3ada04105fe704ff6c778cd1db8fa50433401453a4bb | Python | 4,093 | 86 | import pandas as pd
import dill as pickle
from lib5c.util.system import check_outdir
from hic3defdr.analysis.core import CoreHiC3DeFDR
from hic3defdr.analysis.analysis import AnalyzingHiC3DeFDR
from hic3defdr.analysis.simulation import SimulatingHiC3DeFDR
from hic3defdr.analysis.plotting import PlottingHiC3DeFDR
cl... |
e1aad79e8a749ecf176df3d09d3c5b2ddf4afcd44e1e72fbad340b2f0cd3c9fd | Python | 4,093 | 99 | # Auxiliary functions
import pandas as pd
def process_response(response, fail_msg, ret_format=None, attribute_ids=None):
if response.status_code == 200:
if response.text and response.text != '[]': # Check if the response body is not empty
try:
data = response.json()
... |
4a53f22b0bb268bc47d4172305f102447cf2ff931e3a287c72953098b16ba560 | Python | 4,094 | 119 | """
This module contains functions to load and clean the human experimental data
from the data/paths directory.
"""
import os
import numpy as np
import scipy.io
import random
def load_data():
"""
Loads all available subject path data from the data/paths directory
and returns the data as two dictionaries, ... |
c3db119a5cb7fe57430e650339927f5488a5b48f4172577b4c45f8d5359f7df3 | Python | 4,095 | 119 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class EulerDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (EulerDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_config(self... |
3ce2b03af2af65ec1a35c4fdb15d22984c40424919d084625856a5cb4a438c55 | Python | 4,099 | 123 | import numpy as np
import torch
from torch import nn
class GaussianHeadWithStateIndependentCovariance(nn.Module):
"""Gaussian head with state-independent learned covariance.
This link is intended to be attached to a neural network that outputs
the mean of a Gaussian policy. The only learnable parameter t... |
47f800d76d9cfb366c3c06408bd8dcdf50de97b27e599db7d08b030d24f2d32c | Python | 4,100 | 130 | import importlib
import importlib.machinery
import sys
import types
from stl._compat import ascii_read, ascii_write, has_speedups
def test_has_speedups_returns_bool(speedups):
result = has_speedups()
assert isinstance(result, bool)
def test_ascii_read_type(speedups):
# ascii_read is either None (no spe... |
f0f5e449c366983142208e42dd2429a12de1c828530f8668db5b5d809564c7a8 | Python | 4,100 | 112 | import yaml
import scipy
import pickle
import argparse
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def bootstrapped_return(x, y, stride, total_steps, confidence_level=0.95, to_bootstrap=True):
assert len(x) == len(y)
num_runs = len(x)
avg_ret = np.zeros(total_steps // stride)
... |
9124485083ba4528848a5a86ffcdc2ed5612ace65aaf7c7c8b311ab401ff19c3 | Python | 4,101 | 145 | import argparse
from collections import defaultdict
import sys
from Bio.SeqIO import parse
import matplotlib.pyplot as plt
from os.path import *
import numpy as np
def main():
args = parse_args()
fqs = [(basename(fq),parse( fq, 'fastq' )) for fq in args.fastqs]
plot_fqs( fqs, args.output )
def plot_fqs( i... |
0264e03adb1bd02be952db7bc53444f4a866faddc4c25732589260214df527c9 | Python | 4,102 | 136 | import os
import boto3
import json
import configparser
from zipfile import ZipFile, ZIP_DEFLATED
from io import BytesIO
from argschema import ArgSchemaParser
from neuron_morphology.pipeline._schemas import InputParameters
def get_credentials(credentials_file):
"""
get credentisals from credentials_file
Pa... |
7c5e6cde8d433a1ad5e0f8cf86bdbb2af39513c742cb774e8de0c9ef68f0647a | Python | 4,102 | 150 | import nir
# import nirtorch
import torch
# import numpy as np
import lava.lib.dl.slayer as slayer
import snntorch as snn
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [10, 2]
plt.rcParams["figure.dpi"] = 200
dt = 1e-4
scale = 1 << 6
# general params
lif_size = 2
vthr = 1.0
# ... |
94a6bd9021799909d4a0eb8b1e8b1bdfe762e5b0fab211e2d24ce1a8bb7a360a | Python | 4,103 | 114 | import os
from importlib import resources as impresources
from pathlib import Path
from string import Template
import pandas as pd
from ... import bids
from ...bids.convert2bids import BidsConverter
from ...eeg import Eeg
from ...load_annotations.chbmit import loadAnnotationsFromEdf
BIDS_DIR = impresources.files(bid... |
0d41c617ada58f9ce4352569bb56b6416fd10db7f8c8c26ce407ffa870bd5b3b | Python | 4,104 | 114 | from __future__ import annotations
"""Training helpers for the vendored gsMap graph attention model."""
import logging
import time
try:
import torch
import torch.nn.functional as F
_TORCH_AVAILABLE = True
except ImportError:
_TORCH_AVAILABLE = False
from tqdm.auto import tqdm
from .._style import Co... |
0e5d92c6402704d98c3ecbd8a062fbafeff6a3b5df0eb3b36330b61f215519c4 | Python | 4,106 | 126 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code adapted from GCPNet (https://github.com/BioinfoMachineLearning/GCPNet):
# ---------------------------------------------------------------------------------------------... |
4aed9b3d17597a1b3ed105c49ac54da7318dd351ddc35bde92f8ec61a194b50e | Python | 4,107 | 116 | CODE_DIR = '/home/kelarion/github/'
SAVE_DIR = '/mnt/c/Users/mmall/Documents/uni/columbia/assignment_errors/'
import socket
import os
import sys
import pickle as pkl
import numpy as np
import scipy as sp
import scipy.linalg as la
import scipy.io as sio
from sklearn import svm, manifold, linear_model
from sklearn.mode... |
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