sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7f351852ac43ad27e6233f42f05abf36ca67f8522fa0f28390d38165ceec3ab8 | Python | 4,207 | 116 | ###########################################################################################
# Elementary tools for handling irreducible representations
# Authors: Ilyes Batatia, Gregor Simm
# This program is distributed under the MIT License (see MIT.md)
#################################################################... |
cee1e7762c071ab4547bd0d16615916fb1f2a0c63fe34d24a7b89c7fecd2e6b4 | Python | 4,207 | 93 | """IMGT's F / ORF / P verdict on a named segment (#634).
`imgt_alleles.tsv.gz` is a committed, reviewed authority and `functionality` was read by nothing:
`vdjdb qc` asks whether a call *looks* like a TRBV name and `curate.nomenclature` asks whether IMGT
*has* it, and neither asks whether IMGT thinks the gene is funct... |
1b85469eac40a2b3f038516811f0303c931ecc4d5e136e04c0c7c37e6184f4e5 | Python | 4,209 | 115 | import numpy as np
import os,glob
import shutil
from tifffile import imsave, imread
from PyQt5.QtWidgets import QFileDialog, QApplication
def get_meta(paramFile):
param_list={}
with open(paramFile) as f:
lines = f.readlines()
for l in lines:
try:
(val,p)=l.split(":")... |
3e7714146b9d7e9cd1fc30fa4500597bdb600456e995c29302fb8e8dba0c96a8 | Python | 4,212 | 93 | import pandas as pd
import os
import numpy as np
#Once you have the windows data, the last part missing will just be to split the dataframe in windows (and store each dataframe in a dictionary with the right window).
#Import the fingerprinting file with the fp columns
fp_file = pd.read_csv("~/Desktop/Fingerprinting_P... |
66e77d0ac959e46f8a5a747a704c88254fc2932238abceddb2f33b78ddb06128 | Python | 4,213 | 106 | from typing import Dict, List, Set, cast
from chromadb.types import LogRecord, Operation, Vector
class Batch:
"""Used to model the set of changes as an atomic operation"""
_ids_to_records: Dict[str, LogRecord]
_deleted_ids: Set[str]
_written_ids: Set[str]
_upsert_add_ids: Set[str] # IDs that are... |
77c8fcc911c7757bb683cfa1023c39e3689193270815b2843fd5787a4c0a7a78 | Python | 4,215 | 114 | from PymoNNto.Exploration.Network_UI.TabBase import *
from PymoNNto.NetworkBehavior.Recorder.Recorder import *
class individual_weight_tab(TabBase):
def __init__(self, title='Ind Weights', weight_attr='W'):
super().__init__(title)
self.weight_attr = weight_attr
def add_recorder_variables(self... |
1db049b651ea31f0919e5a2d5757127585bf991cd6f3b4ebff35a3705792ce44 | Python | 4,216 | 74 | import sys
import h5py
import pandas as pd
import numpy as np
def hdf5_to_csv(hdf5_path):
hdf5 = h5py.File(hdf5_path,'r+')
name = hdf5_path.split('.')[0]
first = True
for epoch in hdf5.keys():
for dataset in hdf5['{}'.format(epoch)].keys():
... |
e03cc7b79ada18aeeb20574ec34d3dca5bf3d94a5c11ccbb6104a14f45c0981b | Python | 4,217 | 102 | # Copyright 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import pytest
import logging
import os
from tests.end_to_end.utils.tr_common_fixtures import fx_federation_tr, fx_federation_tr_dws
import tests.end_to_end.utils.defaults as defaults
from tests.end_to_end.utils import federation_helper as f... |
5763e85e13c8db7ff9bc2c743106efa8466d67bb267c6f0a7184c9451097ad90 | Python | 4,219 | 114 | import community as cm
import networkx as nx
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
import phenograph
flags = tf.app.flags
FLAGS = flags.FLAGS
def louvain_adj_clustering(adj, s_rec):
"""
Performs community detection on a graph using the Louvain method
:param adj: sparse adj... |
b33e30ce9f79bd6cffe9f9261177bd8fb57ba054a1ec8812a635551fcc25124f | Python | 4,220 | 117 | from collections.abc import Sequence
from functools import lru_cache
from pathlib import Path
import cv2
import numpy as np
class VideoPathHolder:
"""Read frames from one or more video files with an LRU frame cache."""
cap: cv2.VideoCapture
current_captured_video_path: Path | None
_precomputed_frame... |
ec438b360d2302a5c8ad7767aecb521428987705b1494a45f8c02544ac676601 | Python | 4,220 | 112 | #!/usr/bin/env python
from wandb import Api
import wandb
import argparse
import numpy as np
def copy_run(
source_run, target_project, target_name=None, prefix="", entity="schroedinger_univie", convert=False, add_reference=False, verbose=False
):
source_run = source_run.replace("https://wandb.ai/", "")
if "... |
b20ab437f3313f164542f1fe38b6a1fff4ebbba8ff3cf4aacc787d3c1a5b0eb1 | Python | 4,222 | 110 | import logging
from collections.abc import Sequence
from pathlib import Path
import numpy as np
from scipy.optimize import linear_sum_assignment
from sklearn.metrics import confusion_matrix
from idtrackerai import GlobalFragment, IdtrackeraiError, Session
from ..network import IdentifierBase, get_predictions, load_i... |
5f42ecb1db7cf66eeda2b722667fbc3e972a673b9fde70f490d42d12ce8cb577 | Python | 4,231 | 102 | """Ordinary least squares on plain numpy arrays.
`regress` fits `Y ~ X` and returns coefficients, standard errors,
t-statistics, p-values, degrees of freedom, and residuals as arrays. Use it for
quick regressions on tabular or behavioral data; for voxel-wise models on
imaging data use `BrainData.fit(model='glm')`, whi... |
b4a2610cead3e9e149a7ef04ddb5a71202d71a81d69f2c2f09224a35c93d1dc9 | Python | 4,233 | 91 | """Does the shipped clustering still give a record its cluster-mates?
Nothing compared the motif tables before this: `vdjdb diff` keys `cluster_members.txt` on `cid`, a
cid carries a position in a sorted list, so the CI comparison names its five legacy members with
`--only` and the motif files were judged by purity an... |
0ee6b12a8b32a420c9eed3a2743ea1e0114f54767c8832728a421b69ba799996 | Python | 4,235 | 141 | import numpy as np
from sleeprnn.data import utils, stamp_correction
def find_envelope(x, win_size):
half_win_size = win_size // 2
shifts = np.arange(-half_win_size, half_win_size + 0.001).astype(np.int32)
shifted_signals = []
for shift in shifts:
shifted_signals.append(np.roll(x, shift))
... |
4e1b52a3b44bfff15ebd204b050c86200178ec4f7243ef9f944620304adae00e | Python | 4,235 | 118 | # Copyright (c) 2020 PaddlePaddle Authors. 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 appli... |
c4489bff4f21abf9256c73ed014f0f54d3ab5221e4c16d4fd9092257998bc4ff | Python | 4,240 | 92 | import io
import click
import pandas as pd
from uniprotparser.betaparser import UniprotParser, UniprotSequence
from sequal.sequence import Sequence
from curtainutils.common import read_fasta
if __name__ == "__main__":
fasta_file = r"C:\Users\Toan Phung\Downloads\20250113_Mouse_Reviewed.fasta"
inpu... |
65e85faac6e82e333a6ed00e88841af244e24c1b1c730c8e3a955ba97430385e | Python | 4,241 | 120 | # -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cogn... |
77e1592aa05c86a11c9733d7c943d331bb87c5c4f6609dd3e0b6a5a794c2a9e8 | Python | 4,241 | 113 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Adaptive aggregation module."""
from typing import List
import numpy as np
from openfl.interface.aggregation_functions.core.interface import AggregationFunction
from openfl.utilities.optimizers.numpy.base_optimizer import Optimizer
fr... |
1542f67090070bdb5aa485324ffb05dcf20b89eb680a43f6e19ad39e283eab44 | Python | 4,243 | 116 | from video_stream import ImageSource, AcquireException
import cv2
import time
from pathlib import Path
class VideoImageSource(ImageSource):
"""
VideoImageSource - an image source that reads images from a video file or a camera using openCV.
"""
default_params = {
**ImageSource.default_params,... |
002c1b750367ed6ce131cf3fcf54a062b9d9de4e420a785f0461005617638721 | Python | 4,244 | 118 | from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
from chromadb.utils.embedding_functions.schemas import validate_config_schema
from typing import List, Dict, Any, Optional
import numpy as np
class InstructorEmbeddingFunction(EmbeddingFunction[Documents]):
"""
This class is used t... |
a864fac7ffd7741670cce3feddc77ea6880383e4504fc588300c015afff9c701 | Python | 4,247 | 142 | import torch
from torch import nn
from torch.nn import functional as F
def swish(x):
return x * torch.sigmoid(x)
class SpectralNorm:
def __init__(self, name, bound=False):
self.name = name
self.bound = bound
def compute_weight(self, module):
weight = getattr(module, self.name ... |
45d7729ade4fafc18c4235f30eed7b4f1d860df28ecac60e4758c0b2b85d618f | Python | 4,251 | 125 | """LSTM (or GRU) RNN for the classical Reinvent de novo model"""
from typing import Tuple, Dict, Sequence, Any
import torch
import torch.nn as tnn
import torch.nn.functional as tnnf
class RNN(tnn.Module):
"""
Implements an N layer GRU(M) or LSTM cell including an embedding layer
and an output linear lay... |
d8e5db646555c329731dbb8bd31d2476d6e08565e203202009e94d4a75f5f5b4 | Python | 4,252 | 119 | from os import PathLike
from pathlib import Path
from typing import List
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
def make_prediction_plots(
preds: List[np.ndarray] = None,
true: List[np.ndarray] = None,
losses: np.ndarray = None,
descriptors: List[str] = None,
... |
ba88a1a74211cb9e3254610db4c6d456ddc7ec807910cb4297e3a83c89c3b3c8 | Python | 4,258 | 131 | import numpy as np
import torch
import torch.nn as nn
from model_angelo.models.common_modules import (
AttentionBlock,
GlobalLayer,
MaxNeighbour,
NormalizeStd,
RegularBlock,
ResBlock,
Upsample
)
class Resnet(nn.Module):
def __init__(
self,
in_channels,
out_chan... |
4227f2aa898f751d95856b7a253128430f9c108c2a18a368bcebc694860c946c | Python | 4,260 | 121 | """Mutable experimental metadata and its presentation definitions."""
from __future__ import annotations
import copy
from typing import Any, TYPE_CHECKING
from sanpy.sanpyLogger import get_logger
if TYPE_CHECKING:
from sanpy.bAnalysis_ import bAnalysis
logger = get_logger(__name__)
EXPERIMENTAL_METADATA_DEFIN... |
033e2510cf9b203e6ed7bf43e38fdc90bb088d48eb1d57bf7f9311c13cdc4be2 | Python | 4,261 | 149 | import numpy as np
# import scipy.signal
from PyQt5 import QtWidgets
import matplotlib.pyplot as plt
from sanpy.sanpyLogger import get_logger
logger = get_logger(__name__)
import sanpy
from sanpy.interface.plugins import sanpyPlugin
# from sanpy.bAnalysisUtil import statList
class exportTrace(sanpyPlugin):
"... |
37eb9b9913fb64bb91ca39b8fe518b5ffb1e2c6709e1746d33aa08e17c5643f8 | Python | 4,261 | 132 | """
For Fig 2B go to:
Z:\people\gritti\IRIR\supFig2_3
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
import pandas as pd
import seaborn as sns
from scipy.fftpack import dct
# import cv2
plt.rcParams.update({'font.size': 15})
rc('font', size=8)
rc('font', family='Arial')
#... |
dcaf0f02c9ee29b3d5759ccfbfe7cfff5eca95b9a27695f808dde24d20906167 | Python | 4,261 | 155 | """Multidimensional scaling (MDS)."""
import sklearn.manifold._mds as _mds
import sklearn.metrics.pairwise as mp
from sklearn.manifold import MDS as _MDS
import geomstats.backend as gs
from geomstats._sklearngs.manifold._classical_mds import ClassicalMDS
from geomstats._sklearngs.manifold._mds import smacof
from geom... |
500bf97be832f1f6861e102223921315edf67d52a692d8179361e7e04ce8059b | Python | 4,264 | 113 | """Test harness for the LAMMPS integration contract.
Reproduces, in pure Python, the data layout LAMMPS's pair style feeds to the
exported model: an open (non-periodic) cluster of LOCAL atoms plus GHOST
periodic images within the receptive field, and a local_or_ghost mask. This is
what lets us exercise the lammps_clas... |
64313e516ad57499efbe8d21abd537f43b436c1f57348317fc6f3065e32a5a48 | Python | 4,267 | 114 | import time
from math import ceil
from pathlib import Path
import numpy as np
from ppafm.common import eVA_Nm
from ppafm.ml.AuxMap import ESMapConstant
from ppafm.ml.Generator import InverseAFMtrainer
from ppafm.ocl.AFMulator import AFMulator
from mlspm.data_generation import TarWriter
from mlspm.datasets import down... |
68ec568a03f14bb1cf024e2ee0b16361ea684607a1a4ff56ad0360016d2f391b | Python | 4,268 | 99 |
# -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network models and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cognitiv... |
2dacd4bd374217c82916e18166eb1c309f318b231e03775e181113a1e124dd35 | Python | 4,270 | 117 | #This code takes binned CT image series from a specified directory, and segments the image data
#by running prediction using a specified tensorflow model.
#This code is run in a singularity container from a SLURM-script
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
import h5py
import getopt, sys
#... |
edac6f44c722d2413fbc12359223512e68fa92e34f972f8c2bddafba95bec549 | Python | 4,270 | 134 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# fairseq documentation build configuration file, created by
# sphinx-quickstart on Fri Aug 17 21:45:30 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... |
341931df6002c24d0858f311e06201eecf0d6368f3496b01d6bc3cb206e1f512 | Python | 4,271 | 137 | import torch as T
import torch.nn as nn
from Networks.network import Network
class UNet5_filter_teacher(Network):
def __init__(self, base, expansion):
super(UNet5_filter_teacher, self).__init__()
self._build(base, expansion)
def forward(self, image, segbox):
x_new = T.cat((image, segbo... |
8fc800dcefa1fa97b480c5eea130e70550f821481b86ac7a72b1885294a6420c | Python | 4,274 | 107 | import unittest
import torch.utils.data as tud
from torch import Tensor
from reinvent.models.linkinvent.dataset.paired_dataset import (
PairedDataset,
PairedModelVocabulary,
)
from reinvent.models.linkinvent.networks.decoder import Decoder
from reinvent.models.linkinvent.networks.encoder import Encoder
from t... |
7aa394dd60823ea201512d80069976fc35eab959cc6a2583267939fd5536fbe6 | Python | 4,276 | 107 | """`--plot` and `--plot_frequency`: the plots a training run draws for itself.
Different from `mace_plot_train`, which draws them afterwards from a results log.
These come from `TrainingPlotter` inside the run, and `--plot` defaults to True, so
every default training writes one.
Until the fix in this branch it wrote ... |
baf0afd8aa3b5e844b578b3721ed76053319adae0a7055b4e480057d8133a35b | Python | 4,276 | 141 | # saved as /n/groups/marks/users/david/res/model_save//RES_1638990570.3313198_32.pt
import gvp
from atom3d.datasets import LMDBDataset
import torch_geometric
from functools import partial
import gvp.atom3d
import torch.nn as nn
import tqdm, torch, time, os
import numpy as np
from atom3d.util import metrics
import skle... |
270d9e01f1c6c56a19dc60d3421ef95ea35bf5682ecb1b2d029d5a403e91c430 | Python | 4,277 | 135 | #!/usr/bin/env python
import json
import tempfile
from glob import glob
from pathlib import Path
from shutil import copyfile
import nibabel as nib
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
from nipype import Workflow
from sdcflows.workflows.fit.syn import init_syn_preprocessing_wf, init_sy... |
550f590cabdec5430707d9d6c729746c258ec132187a63f42e707da99fde67aa | Python | 4,277 | 145 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch.utils.data.sampler import Sampler
from typing import Optional
import torch.distributed as dist
import math
import argparse
class BaseSamplerDP(Sampler):
"""
Base class for DataParal... |
78a6f6e8c135506b1162120b3529090a0a285f5cb2f647569349623f0d56fbd1 | Python | 4,279 | 96 | """The three stages of a segment call stay distinguishable, and the engine's call is the shipped one.
`chains` reports `v.segm.submitted` (what the publication said), `v.segm` (what ships) and
`v.segm.arda` (what the markup engine aligned against). Collapsing any two of them loses the only
thing that tells a curation ... |
a03648d6c0b484218e080a9b3267e1dfb03e40ad9262d649d4c3eed05b67c585 | Python | 4,280 | 192 | import analysis_utils as au
def plot_mean(df):
df = au.nature_pre(df)
g = au.nature_relplot(
data=df,
x='pc_learning_rate',
y='Mean of test__classification_error',
hue='Rule', style='Rule',
col='Dataset',
row='energy_fn_str',
).set(xscale='log')
au.nat... |
ed12b67607f327d9f0fbba16a4b75bcde9a6f8f6cb86a045223fb53e8bf77876 | Python | 4,280 | 123 | # Copyright (C) 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import xml.etree.ElementTree as ETree
import json
import os
import math
import hashlib
def xml_to_json(input_base_folder, subfolders, output_folder, verify_hash=1):
if not os.path.exists(input_base_folder):
raise SystemExi... |
bfc83d8f4c14a9c8d80306ab630d8f6840c771bcd2be1301edd5191f7892503b | Python | 4,281 | 106 | '''
Module focused on the implementation of Averaged Higher Order SVD
(AHOSVD) technique.
'''
import torch
import numpy as np
from smithers.ml.layers.hosvd import HOSVD
class AHOSVD():
"""
Class that handles the construction of the functions needed
to perform the dimensionality reduction of a given tenso... |
93af6fa36f69203857472e06b7dfa1feaf1daa39766e0d9161a1f0a33857a34b | Python | 4,283 | 112 | # Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... |
9bec78f5eaf224fc15f2df79f38d64270d835591eb1ad21f9dd5aa1b33d95738 | Python | 4,283 | 115 | import torch
from torch import nn
from torchvision import models
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels, atrous_rates):
super(ASPP, self).__init__()
modules = []
for rate in atrous_rates:
modules.append(
nn.Sequential(
... |
d8c5deb36a76e781b2e2c3e53f4d7b11e37b7843cab795dd6a2ca7d77d628b2c | Python | 4,284 | 116 | from collections.abc import Iterator, Sequence
from typing import Literal
from . import Fragment, IdtrackeraiError
from .utils import deprecated
class GlobalFragment:
"""Represents a collection of N_animals coexisting :class:`Fragment` s."""
duplicated_identities: set
"""Temporary identities duplicated ... |
d32ed40115bfbb9aa27a5b2c5f504435eb842f2d70fb62bef9a9ad43cf68e690 | Python | 4,285 | 124 | import pandas as pd
import numpy as np
import scipy.stats
# AUC comparison adapted from
# https://github.com/Netflix/vmaf/
def compute_midrank(x):
"""Computes midranks.
Args:
x - a 1D numpy array
Returns:
array of midranks
"""
J = np.argsort(x)
Z = x[J]
N = len(x)
T = np.z... |
34745641320f7741cb7d6ec7bb4ccd18469df6e9d945cb3288568d0f06e49647 | Python | 4,286 | 130 |
from sleepy.processing.dataset import Dataset
import unittest
from unittest.mock import MagicMock
import numpy as np
import pdb
class DatasetTest(unittest.TestCase):
def standardScenario():
"""Standard test scenario creating a dataset with rudimental epochs
and labels. No filtered data... |
d2933eb75c8035ad94b7c3fee67a9c2e86cf9fd3e54938fc11a30f71df68048b | Python | 4,286 | 111 | # Copyright (c) 2021 PaddlePaddle Authors. 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
#
# Un... |
e57324866ffcd2918c52b0a21dfd4c4ebbdc4eef1d1588c7ec311e2370d7845a | Python | 4,287 | 125 | __all__ = ["TanimotoPairGenerator"]
from functools import partial
import logging
import numpy as np
import pandas as pd
from reinvent.chemistry import conversions
from reinvent.chemistry.similarity import calculate_tanimoto_batch
from reinvent.models.utils.parallel import parallel
from .pair_generator import PairGene... |
c6cdb947e75d586cde95875f4bcfbf32f8b5232558e60c762dd10fae528d1579 | Python | 4,288 | 152 | import copy
import itertools as it
from pathlib import Path
from typing import Any, List, Optional, Type
import hypothesis.strategies as st
from bids.layout import Config as BidsConfig
from hypothesis import assume
from snakebids.core.datasets import BidsComponent
from snakebids.core.input_generation import BidsDatas... |
4805597b8a32177723909238103b77bc9e27c4620f31aae332d63b5f2d67624c | Python | 4,289 | 85 | # =====================================================================================
# RE-MEASURE D-Fine T4 latency (clean, warmup+median). FRESH NOTEBOOK, T4 GPU.
# =====================================================================================
# HOW TO RUN:
# 1) T4 runtime. Run CELL 1 -> sets up D-Fine + ... |
9daec0bf36d476d169593d3eb291b6c8da61b062133e1017f1a48f445b6a205e | Python | 4,289 | 85 | import io
import click
import pandas as pd
from uniprotparser.betaparser import UniprotSequence, UniprotParser
import re
from curtainutils.common import read_fasta
reg_pattern = re.compile("_\w(\d+)_")
protein_name_pattern = re.compile("(\w+_\w+)")
def lambda_function_for_spectronaut_ptm(row: pd.Series, index_col: str... |
e4e8fbd18547b25942cc076343ab83771de71f72f667661be6de515a7a9ecdbb | Python | 4,289 | 134 | """The diversity filter is a memory for repeated SMILES
Depending on the concrete filter, scaffolds or SMILES that are repeatedly found
are memorized. Filtering happens on a given minimum score.
"""
from __future__ import annotations
from typing import List, Optional
from abc import ABC, abstractmethod
import logging... |
249335272f4a1e8dfb63f242f9c5628fc1cec1f9cd283f967cd69372799fc59c | Python | 4,290 | 138 | #!/usr/bin/env python
import json
import tempfile
from glob import glob
from pathlib import Path
from shutil import copyfile
import nibabel as nib
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
from nipype import Workflow
from sdcflows.workflows.fit.syn import (
init_syn_preprocessing_wf,
... |
e0e8a7f1459eb83cba4ee642126c375806a5d022f59ac0b65de03ccd6610f06f | Python | 4,291 | 95 | import logging
import jax
import jax.numpy as jnp
import kfac_jax
from deeperwin.configuration import ClippingConfig
from deeperwin.hamiltonian import get_local_energy
from deeperwin.utils.utils import pmean, without_cache
import functools
LOGGER = logging.getLogger("dpe")
def init_clipping_state():
return jnp.ar... |
923c22a77064acc5310ccca557e37516e096f2e0894d9571edc3e25cdeac779a | Python | 4,292 | 101 | """The ProductHPDMatricesAndSiegelDisks manifold.
The ProductHPDMatricesAndSiegelDisks manifold is defined as a
product manifold of the HPD manifold and (n-1) Siegel disks.
The HPD Siegel disks product has a product metric.
The product metric on the HPD Siegel disks product space is the usual
HPD matrices affine-invar... |
dc2e3480e40e5f55400500b27fab8734d0f7b3ed21fc33b22d69ca0234a1f2c1 | Python | 4,292 | 141 | import geomstats.backend as gs
from geomstats.test.data import TestData
from .base import VectorSpaceTestData
from .mixins import GroupExpMixinsTestData
from .riemannian_metric import RiemannianMetricTestData
class EuclideanTestData(GroupExpMixinsTestData, VectorSpaceTestData):
def exp_random_test_data(self):
... |
dd7e5980b24fa3a6beb9ec2788b4c1469e77c296c41451617c05b6dc5c924338 | Python | 4,292 | 113 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 12 15:25:17 2018
@author: ngritti
"""
from deeprest.rawDataClass import rawData
import time, glob, os, shutil
from tifffile import imread, imsave
from skimage.filters import threshold_otsu
import numpy as np
from raw2tiff import convert2tiff,fuse_il... |
ddc3fc15b952e6b60942e2a082270c216b48e280355f6bcc0ae7c7fe53ee4df7 | Python | 4,297 | 138 | # -*- coding: utf-8 -*-
import os
import numpy as np
import logging, argparse
from data_functions import load_data, get_windows
import h5py
import matplotlib.pyplot as plt
from data_functions import get_feature_vector
from mpi4py import MPI
import time
import configparser
import sys
def parse_args():
# Argume... |
9d504c681a1df6999b2063e0d9092ac46db859ed3407803f6b87716a50f1f995 | Python | 4,298 | 158 | """Recompile the bids function stub file based on latest specs."""
from __future__ import annotations
import inspect
import itertools as it
import re
from pathlib import Path
from types import ModuleType
from typing import Iterable
from snakebids.paths import _config, specs
from snakebids.paths._templates import spe... |
2db8d14edc482651d8da067eac914ccf4de64db7e8801cf941995397e38240e1 | Python | 4,299 | 77 | """The ``epitopes`` catalogue: one row per (epitope, species), with what supports it.
The proteome link (#632) is read from ``proofreading/epitope_proteome.tsv``, a committed reviewed
input, never resolved during a build - `mhcmatch` fetches the proteome from HuggingFace, so doing it
here would put a network call in t... |
42a370764cd04cfffcebfd722d8a5b04bc7b56f338fe71b1ba2d205cdb1a5f46 | Python | 4,299 | 116 | import os
from chromadb.utils.embedding_functions.openai_embedding_function import (
OpenAIEmbeddingFunction,
)
from chromadb.utils.embedding_functions.schemas import validate_config_schema
from typing import Dict, Any, Optional, List
from chromadb.api.types import Space
import warnings
class BasetenEmbeddingFunc... |
b35d8a57592b914072073f423d8ce316486c7d49b5a45d4dba6d2873d7067d50 | Python | 4,299 | 121 | from abc import abstractmethod
from typing import Callable, Optional, Sequence
from chromadb.types import (
OperationRecord,
LogRecord,
SeqId,
Vector,
ScalarEncoding,
)
from chromadb.config import Component
from uuid import UUID
import numpy as np
def encode_vector(vector: Vector, encoding: Scalar... |
60be52404b45a9088111a268f643b6ec191485561c04753c622dd67119e04408 | Python | 4,302 | 106 | from typing import List, Dict, Tuple
from rdkit.Chem.rdchem import Mol, AtomKekulizeException, Atom
from rdkit.Chem.rdmolops import FragmentOnBonds, GetMolFrags
from reinvent.chemistry import conversions, tokens
from reinvent.chemistry.library_design.dtos import ReactionOutcomeDTO
class BondMapper:
def convert_... |
061e6bf3b43e9a3c236a826bcf8dbcdd17f053b98f9dc4c3705d805d1c4f881e | Python | 4,304 | 110 | """
NeuroLearn Preferences
======================
"""
__all__ = ["MNI_Template", "resolve_mni_path"]
__author__ = ["Luke Chang"]
__license__ = "MIT"
import os
from nltools.utils import get_resource_path
MNI_Template = dict(
resolution="2mm",
mask_type="with_ventricles",
mask=os.path.join(get_resource_pa... |
fcbe730e729377867cd7452330936ca1c85ecd02718dcd699eca363421af8eb4 | Python | 4,307 | 88 | from ontoutils import RobotImportsWrapper
from ontoutils import RobotTemplateWrapper
from ontoutils.lucid_chart import ParseLucidChartCsv
from logging_conf import setup_logging
setup_logging()
def main():
# Get the imports and create the bcio_external.owl file
import os
os.chdir(os.path.dirname(__file__... |
09505f5abd9142e284aa2df8266fb58885a58279210e0c9de776b8546e42cd59 | Python | 4,314 | 149 | import numpy as np
import sys
import time
from shapely.geometry import Polygon
import paddle
import unittest
from ext_op import matched_rbox_iou
def rbox2poly_single(rrect, get_best_begin_point=False):
"""
rrect:[x_ctr,y_ctr,w,h,angle]
to
poly:[x0,y0,x1,y1,x2,y2,x3,y3]
"""
x_ctr, y_ctr, width... |
ebacf073225a8f4153801cff731b9021891fef430edb9f62c6c1e60c69a4e094 | Python | 4,316 | 122 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from ._tp_scatter_base import TensorProductScatter
def nequip_tp_desc(
irreps1,
irreps2,
irreps3,
):
"""Construct the NequIP version of channelwise tensor product descriptor.
sub... |
4e6ef1014c5024229e497347c9066c7eac12535521568704e9b8dff0347b3fd0 | Python | 4,322 | 113 | # -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cogn... |
7269b5b6ea186eebac9d3c9d196ea02328cee00572d23713d41b36f21ce4fbcf | Python | 4,322 | 119 | #!/usr/bin/env python3
## This script performs movie segment classification using 15 s segments,
## similar to Haxby et al. ([2011](https://doi.org/10.1016/j.neuron.2011.08.026))
## and Guntupalli et al. ([2016](https://doi.org/10.1093/cercor/bhw068),
## [2018](https://doi.org/10.1371/journal.pcbi.1006120)).
## Author... |
aa7801fc3670985b97e0aae19cc3483861bc7c6d2aab66ca8c2b222d6cdcbde9 | Python | 4,327 | 124 | # Taken from a PyTorch Lightning tutorial
# https://pytorch-lightning.readthedocs.io/en/stable/notebooks/lightning_examples/cifar10-baseline.html
import os
import argparse
import random
import torch
import torch.nn.functional as F
import torchvision
from pl_bolts.datamodules import ImagenetDataModule
from pytorch_lig... |
2cfc47a229fa722a96f368517264e573c107a8082626e3628e9fe72181434ff7 | Python | 4,328 | 122 | import math
import geomstats.backend as gs
from geomstats.test.random import RandomDataGenerator
from geomstats.test.test_case import TestCase
from geomstats.test_cases.geometry.manifold import ManifoldTestCase
from geomstats.vectorization import get_n_points
class SurfacesLocalRandomDataGenerator(RandomDataGenerato... |
c6e51703bbbfb592882a5f8a8f215100be66320e7df9748b8042401d1430b5f3 | Python | 4,329 | 129 | # Copyright (c) 2020 PaddlePaddle Authors. 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 appli... |
dc7934156dde3c55472330965f61323a647d8aba32ac7ecb43fe77ef38b5eae0 | Python | 4,331 | 136 | import geomstats.backend as gs
from geomstats.test.data import TestData
EULER = gs.exp(1.0)
class RiemannianMinimumDistanceToMeanSPDTestData(TestData):
def _get_X_train(self):
X_train_a = gs.array([[[EULER**2, 0], [0, 1]], [[1, 0], [0, 1]]])
X_train_b = gs.array([[[EULER**8, 0], [0, 1]], [[1, 0],... |
4123a881e1a5dce738df157b0a3ea8f0554cb17cfb8c14cd3960d79984ab8a61 | Python | 4,332 | 110 | """
Plot snapshots of the input data showing the detected waves.
"""
import os
import numpy as np
import quantities as pq
import argparse
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
CLI.add_arg... |
6dd1669f5f5da85bb745c7504d1a62e2135cc38bad1420f4fc17680b77a75aa9 | Python | 4,332 | 141 | """Geometric median."""
import logging
from sklearn.base import BaseEstimator
import geomstats.backend as gs
class GeometricMedian(BaseEstimator):
"""Geometric median.
Parameters
----------
space : Manifold
Equipped manifold.
max_iter : int
Maximum number of iterations for the ... |
3475a53a5ded4407b4cac3fca3dabb32e919b6dec17cc9486593adcd9a8cebab | Python | 4,334 | 144 | import numpy as np
import pandas as pd
from nltools.data import Design_Matrix
from nltools.external.hrf import glover_hrf
import pytest
def test_add_poly(sim_design_matrix):
matp = sim_design_matrix.add_poly(2)
assert matp.shape[1] == 7
assert sim_design_matrix.add_poly(2, include_lower=False).shape[1] ==... |
87ee6a37022dcfee47cd36f3dd44a168193ee7b79151344ceb428230d5578094 | Python | 4,335 | 142 | import geomstats.backend as gs
from geomstats.test.data import TestData
class FisherRaoMetricCmpUnivariateNormalTestData(TestData):
fail_for_autodiff_exceptions = False
def metric_matrix_test_data(self):
smoke_data = [
dict(base_point=gs.array([0.1, 0.8])),
dict(base_point=gs.... |
fc8fb3cae1dfbd24081a05a40e4711621a5b2e57022fc2b86a6c66a3401f37ec | Python | 4,335 | 126 |
from pathlib import Path
import pandas as pd
import os
import torch
from PIL import Image
from torchvision import transforms
# dataset for the raw vOPA images
class VOPADataset_img(torch.utils.data.Dataset):
def __init__(
self,
path_img: Path = Path("src/images"),
path_protocols: Path = Pa... |
671e39f1985677ee4dd830fe64667fb1690f356e2250461820b24e70580ec3e8 | Python | 4,336 | 130 | """
Generate AIZynthFinder route JSON files with specified reaction classes.
"""
import json
import argparse
import sys
def build_route(reaction_classes: list) -> dict:
"""
Build route with specified reaction classes.
Uses a nested linear structure where each reaction's classification
... |
c084165251a2b732758fdd371817fe063303b4e19602eccdfacb7da5885d763b | Python | 4,336 | 138 | import os
import argparse
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
import pandas as pd
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from model_tools.activations.pca import LayerPCA
from model_tools.brain_transformation.neural import Layer... |
e08b6661c355f58da13306326ae6709361ee3e4c500e24732e50db8f12f3e9f6 | Python | 4,336 | 125 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
import paddle.nn as nn
import typing
from ppdet.core.workspace import register
from ppdet.modeling.post_process import nms
__all__ = ['BaseArch']
@register
class BaseArch(nn... |
1241e30d6138966308d426b8b7bef2732ac96436b7623874edafcb5e4b9583b5 | Python | 4,337 | 97 | # -*- coding: utf-8 -*-
"""
Created on Fri Nov 12 15:51:52 2021
@authors: Maxim Filimonov, Irena Spasic (School of Computer Science & Informatics, Cardiff University, Cardiff, CF24 4AG, UK)
@contact information: spasici@cardiff.ac.uk, filimonovm@cardiff.ac.uk
"""
from cmath import inf
import json
import os
import arg... |
6ec9bccd23719986f379e03aa267a2da1a8206d55fc644070ae025a566928c57 | Python | 4,341 | 143 | # saved as /n/groups/marks/users/david/res/model_save//RES_1638990570.3313198_32.pt
import gvp
from atom3d.datasets import LMDBDataset
import torch_geometric
from functools import partial
import gvp.atom3d
import torch.nn as nn
import tqdm, torch, time, os
import numpy as np
from atom3d.util import metrics
import skle... |
9038356182bef70626035267220ebb00ff300a66eb44eb4adb2b316dc2475ecb | Python | 4,341 | 219 | """Kernels and kernel pairings using KeOps genred."""
import geomstats.backend as gs
from geomstats.varifold.base import Pairing
if gs.__name__.endswith("pytorch"):
from pykeops.torch import Genred
else:
from pykeops.numpy import Genred
from .._device import get_device, to_device
def GaussianKernel(sigma):... |
ffb936927ebf7c02d186a3c4035e3af8ab46bec867a8f98d38aef912a8e7434e | Python | 4,342 | 174 | from sklearn.metrics import roc_curve, auc
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import os
from sklearn.decomposition import KernelPCA
from config import RESULT_PATH
def plot_multiclass_roc(
y_test,
y_pred,
n_classes,
figsize,
name,
c... |
50a407b24ef5a50a286374183657c17a9bc84469a8e76ccf9e14825b57e2e494 | Python | 4,343 | 108 | ################################################################################
# Import, Set up MPI Variables, Load Necessary Files
################################################################################
import os
from os.path import join
import sys
import matplotlib
import matplotlib.pyplot as plt
from matp... |
3d78575924ec7f980f97ec9d5f55c11a183bef7fd0f70841d08003362785758e | Python | 4,344 | 112 | # ##############################################################
# This file is received from
# https://github.com/DmitryUlyanov/deep-image-prior/blob/master/models/skip.py
# It is the implementation of the following study:
# https://arxiv.org/abs/1711.09485
# The code is licenced under Apache 2.0 licence
# https://git... |
c956aa471134d4d3a30ed202aa8401d675c79fc81f6a56d30a5b7bb5eabb3e3e | Python | 4,344 | 128 | # Author: Guoqing Bao
# School of Computer Science, The University of Sydney
# Date: 2019-12-12
# GitHub Project Link: https://github.com/guoqingbao/Pathofusion
# Please cite our work if you found it is useful for your research or clinical practice
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="0"
import gc
gc.enable... |
1363cbc47184e13cdc5fb4614c12f7374fe920316094afe0d1e14d73c6522839 | Python | 4,346 | 99 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... |
59d104ad1465c309bf53a1dda3aa32e12452392224dc95099db9ffb18a44f9ae | Python | 4,347 | 117 | # Copyright (C) 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
import torch
import keras
class CNNModel(keras.Model):
"""
A custom Keras model for a Convolutional Neural Network (CNN) that overrides
the `train_step` ... |
5be799fb661a039642bad9f8716866dbaa6eb1976fec23e69878b7d266091b0f | Python | 4,348 | 117 | """
This file removes the need for torch_cluster, but it forces this operation to CPU.
For inference, this is ok. For training, we still need torch_cluster for the speed.
The code is copied from: https://pytorch-geometric.readthedocs.io/en/1.3.1/_modules/torch_cluster/knn.html
"""
import torch
from scipy.spatial impor... |
1f44354e5c0d21dd202621e11f04b7e3c6c526d1b124b80e6ca09decad6224af | Python | 4,350 | 119 | '''
Colorize logger messages depending on the log level.
Usage: just import this module.
This code was stolen and modified from:
https://stackoverflow.com/questions/384076/how-can-i-color-python-logging-output
'''
#!/usr/bin/env python
# encoding: utf-8
import logging
# now we patch Python code to add color support t... |
6ebb66cea98c300348179c227ec2e047584a077a1392c9b4612f9a26eb733718 | Python | 4,352 | 112 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Geometric median module."""
import numpy as np
from openfl.interface.aggregation_functions.core import AggregationFunction
from openfl.interface.aggregation_functions.weighted_average import weighted_average
def _geometric_median_obj... |
ef4642009aa49b52139e4482c2f66c16273b93420ec9a11a0512d482cafa79c6 | Python | 4,352 | 168 | """solves using petsc4py"""
import time
import numpy as np
try:
import petsc4py.PETSc
HAS_PETSC = True
except ImportError:
HAS_PETSC = False
def petsc_mtx_from_scipycsr(m):
"""construct a petsc matrix object from a scipy CSR sparse martrix
Parameters
----------
m : :cla... |
d032eeb260c0c298b0c02e951d34b09448f0978e476d185473e8522e74b17f3f | Python | 4,353 | 140 | import numpy as np
import os
import subprocess
import json
import argparse
import glob
def init_args():
parser = argparse.ArgumentParser()
# params for testing assert allclose
parser.add_argument("--atol", type=float, default=1e-3)
parser.add_argument("--rtol", type=float, default=1e-3)
parser.add... |
d02ab7e7572410e6f2f4aa598a4efa1a33d5a59c60fe3ac892fcb8bc935d2e97 | Python | 4,355 | 92 | from tqdm import tqdm
import numpy as np
import pandas as pd
from scipy.stats import ortho_group
from typing import Optional
from numpy.typing import ArrayLike
from theory_simulation.regression import regression_performance
def run_simulation(dim_nat: int = 30, dim_eco: int = 20, dim_exp: int = 3,
... |
10ef9292db687f0d98977a862f820f373cb7b42e3c0e77230b063f91ec26ead6 | Python | 4,357 | 105 | # Copyright (C) 2024 Intel Corporation
# Licensed subject to the terms of the separately executed evaluation license agreement between
# Intel Corporation and you.
from openfl.federated import PyTorchDataLoader
class TemplateDataLoader(PyTorchDataLoader):
"""Template dataloader for PyTorch.
This class should... |
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