sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3434772401e31969e3712803b2c8b0fe5d6e30568e9c8ba8f1fc7d899f300077 | Python | 11,194 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
b0998dc57b3de203b993d51816e435d01a732ee6929226830f7b35b218c0a62f | Python | 11,197 | 259 | """Contract tests for `nltools.models._Glm` (development/specs/glm.md).
`_Glm` is a numerical estimator over a precomputed `DesignMatrix` and a
one- or two-dimensional response. It delegates fitting to nilearn's
`run_glm` and contrast inference to nilearn's `Contrast`, so the numerical
references here are direct nilea... |
c9c789bf48a84f744d93991b9e4cf3290ae188def4ca33477176340025a11e0e | Python | 11,197 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
53cff4fbcf12e407f4717b93ecc420f080a77da5be27ad7d413802dd3b2a9f8f | Python | 11,198 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
a0f004cec4e780c693cd7eb8dfaef2a27a812c4151f1c92cd97c0906fe472e4c | Python | 11,198 | 303 | """Provides a child class ConvNet of torch.nn.module to allow
the construction of a convolutional neural network as well as
a function to load a network and to initialize values of a tensor
"""
import os
import torch
import torch.nn as nn
from matplotlib import pylab as plt
from sklearn.metrics import balanced... |
32bcdb1a58495cdac36a6eb90af5371996d76c2808c44bea426feba7f8fd1708 | Python | 11,206 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
b15a1aa3008b589c3a690793a72ffd260d78cbea579b9bd7e81cc0d3ad40807c | Python | 11,206 | 219 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
b980753c23db7bb6509ef31616c615936eaef47f9ad90de0924eddf1c3d56ad3 | Python | 11,207 | 344 | # /// script
# requires-python = ">=3.11"
# dependencies = [
# "marimo",
# "nltools>=0.6.0",
# ]
# ///
# Multivariate classification — marimo notebook. Source of truth for the docs page; rendered to the docs page by scripts/marimo_to_zensical.py.
import marimo
__generated_with = "0.24.0"
app = marimo.App(widt... |
62951637e29c320112d9470f17e57e750b42f475f92c13c59425fb1719e874a6 | Python | 11,213 | 392 | import numpy as np
import matplotlib.pyplot as plt
from scipy import signal as sig
import scipy.spatial.distance as dst
import csv
# Data processing functions
def my_FR(spikes: np.ndarray,
duration: float,
window_size: float,
overlap: float) -> (np.ndarray, np.ndarray):
"""
... |
7377389cb60216d77c07f2693f89e29e3a0bfedbe9a9b307155e43d0dd7cb4d9 | Python | 11,213 | 219 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
558332e6bf4cb79381bda324bf76c2b8d4c412cda8f122780743871f2761d787 | Python | 11,225 | 264 | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
import torch
from torch.utils.data import DataLoader,TensorDataset
from modelClasses import *
from utilityFunctions import *
from sklearn.metrics import confusion_matrix,classification_report,pre... |
f1e89a4d952a0658f9bc9b6fc41be5ce694460ccf04b2680de81cbe31a127eef | Python | 11,234 | 361 | from __future__ import print_function
import copy
import pprint
import torch
import numpy as np
import matplotlib.pyplot as plt
import utils
import analysis_utils
from dataset_learning_trainable import DatasetLearningTrainable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from to... |
e5596b0ece6fd8f1f1b2e23ac0a2fb818549b377e141765ea74426e5dca226d2 | Python | 11,235 | 292 | """One-sample permutation test (sign flipping).
Tests whether a mean differs from zero by randomly flipping the sign of each
observation — the permutation analogue of a one-sample t-test. Permutations run
on joblib workers; `n_jobs` sets how many, and a given `random_state` gives the
same result at any worker count.
"... |
c892c0915d85c96efa35a909dbeeae36c0b2dd34cf2a1b038f4259aac2e51213 | Python | 11,240 | 278 | # 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
#
# Unless required by appli... |
5157bd6a0e1ba51ec35bf89be59f1efed40f2a7c9bf81ba01d5a03904928c669 | Python | 11,241 | 382 | '''
Create CNN based on ResNet-50 to generate flatten representation of the 2D feature matrix
'''
import numpy as np
import pandas as pd
from pyparsing import And
import os
from tensorflow.keras import activations, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model, load_model... |
afce264453df54b85514a0f309dc4ea8a72a5c17decf868e0785006f48ced83b | Python | 11,241 | 316 | # system
# sci
import numpy as np
# ephys
# plotting
# own
import sssort.functions as sf
from sssort import sssio
def calc_update_final_frates(SpikeInfo, unit_column, kernel_fast):
"""calculate all firing rates for all units, based on unit_column. This is for after units
have been identified as 'A' or '... |
ed520513e391ef5818ad22ba2ec52d1390faacdfb43f4c81413e956712d54223 | Python | 11,245 | 332 | # /// script
# requires-python = ">=3.11"
# dependencies = [
# "marimo",
# "nltools>=0.6.0",
# ]
# ///
# Multivariate Pattern Analysis — marimo notebook. Source of truth for the docs page; rendered to the docs page by scripts/marimo_to_zensical.py.
import marimo
__generated_with = "0.23.9"
app = marimo.App(wid... |
8031b864f9cf38cb3dff5bf1b8350c765d0c8231810b7d039ff576cbb550e9b5 | Python | 11,253 | 317 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
"""AtomicDataDict
A "static class" (set of functions) operating on `Dict[str, torch.Tensor]`,
aliased here as `AtomicDataDict.Type`. By avoiding a custom object wrapping
this simple type we avoid unne... |
05c91bc24fa7b82c1899578b39af6108a1f3f47b949cf314c71787cfbe38ecc2 | Python | 11,256 | 300 | import torch
from torch import nn
from torch.nn import Linear, Embedding
from torch_geometric.nn.inits import glorot_orthogonal
from torch_geometric.nn import radius_graph
from torch_scatter import scatter
import torch.nn.functional as F
from math import sqrt
from .geometric_computing import xyztodat
from .features im... |
97af5b1178acf2be02fd65cd405c31f06f40dd4055c2e5d5ab9ef7b2d843f7a8 | Python | 11,258 | 288 | import pytest
import geomstats.backend as gs
from geomstats.test.vectorization import generate_vectorization_data
from geomstats.test_cases.geometry.manifold import ManifoldTestCase
from geomstats.test_cases.geometry.mixins import GroupExpTestCaseMixins
from geomstats.vectorization import repeat_point
class _LieGrou... |
9b68fd47243088700025777201a533833bf220c92a4c7ec11677518621bace0a | Python | 11,260 | 352 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from copy import deepcopy
from typing import Union, Sequence, Optional, Tuple, Dict, Iterable
import bindsnet.network.nodes as nodes
import bindsnet.network.topology as topology
from bindsn... |
afb462debb8e7ceca2601d56fdd6852b4e93f5e47463f6442642b82d64015a1b | Python | 11,263 | 247 | """Trains a model on a dataset and generates predictions at the end of the training.
This script supports running on a list of datasets and models. For each dataset
and model:
1. The dataset is loaded and split into train, validation, and test sets according
to the desired configuration in the dataset_configs list. S... |
12bed89bb13f065185a77c83f2827660c53f98e499be78959a4242c3de742f6b | Python | 11,264 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
20d3f6ce5d3e135a528ec24279d0718b9c761111d27044fa88ba77bfe27ea59c | Python | 11,267 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
3a6eb5d9f6da8e7cc0b8ec501fdf94a3104a4bc49acf5181c48ab5ca7f8819a9 | Python | 11,276 | 218 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
8e41950fc714edb8adfd5ced490092bfac89f33476e34a7373fea9b270cde40f | Python | 11,280 | 321 | import matplotlib.pyplot as plt
import numpy as np
import csv
def load2(filename):
sim = []
col = []
num = []
mes = []
with open(filename, newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for i, row in enumerate(reader):
if i>0:
sim.app... |
69aa903e78011a059a41d224c3483d2e16526b128d82d600685f53baa62e70af | Python | 11,287 | 293 | # /// script
# requires-python = ">=3.11"
# dependencies = [
# "marimo",
# "nltools>=0.6.0",
# ]
# ///
# GLM Analysis — marimo notebook. Source of truth for the docs page; rendered to the docs page by scripts/marimo_to_zensical.py.
import marimo
__generated_with = "0.23.9"
app = marimo.App(width="medium")
@a... |
58144efdde42b031477682ecb0830667afbfcabec811831370b37a56c1422684 | Python | 11,292 | 165 |
import numpy as np
from .SharedTrees import cstr, get_fmt, Description, get_data_kind, get_recording_mode, AmplifierState_v9, UserParamDescrType, LockInParams_v9
class TraceRecord(Description):
def __init__(self, n=1):
super(TraceRecord, self).__init__(n)
self.description = [
("TrMar... |
a7bc8424206ca2f9b903ac4e3c6af4bf61a8dc6fbb2147ebff4f9ac6ef82098d | Python | 11,301 | 252 | #########################################################################
# SlumberNet: Sleep Stage Classification using Residual Neural Networks #
#########################################################################
# This Python script implements SlumberNet, a deep learning model for classifying
# sleep stages ... |
8e87dce01fa07118e91e99fc86af6ead97756812c43e8a6686ddd2c7300c314c | Python | 11,312 | 205 | """Which alleles can present each epitope VDJdb records. Issue #372.
**This does not second-guess the allele a publication reported.** The restriction on a record is the
author's finding and the authority for it is the paper. What a presentation predictor adds is the
*other* alleles: given a donor's HLA type, which of... |
f885a6a38fc7bf7be0e60f7171af25af039f03d76f5c49a8d00f37e453b9bc7a | Python | 11,324 | 351 | # -*- coding: utf-8 -*-
#
# nltools documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 4 07:22:28 2015.
#
# 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
# autogenerated file.
#
# A... |
148989cc397ccf4caa1ee86ad1d5bdb4fb155209c03417425058b0a85107d2cc | Python | 11,330 | 280 | import contextlib
import numpy as np
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
from model_angelo.gnn.backbone_frame_net import BackboneFrameNet
from model_angelo.gnn.cryo_attention import CryoAttention
from model_angelo.gnn.gnn_output import GNNOutput
from model_angelo.gnn.sequence_... |
c05a1cfce998d8b141bd95e78c16c12bfa666cc6080cae27b923f3b5e68c0cff | Python | 11,332 | 357 | """Statistical Manifold of geometric distributions with the Fisher metric.
Lead author: Tra My Nguyen.
"""
from scipy.stats import geom
import geomstats.backend as gs
from geomstats.geometry.base import VectorSpaceOpenSet
from geomstats.geometry.euclidean import Euclidean
from geomstats.geometry.riemannian_metric im... |
59da62e4521d9a9338aa5255952fa2c15df484767e1393f572f83563714f7bdc | Python | 11,340 | 216 | import os
import torch
import hydra
from tqdm import tqdm
from omegaconf import OmegaConf
from sklearn.metrics import roc_auc_score
from benchmarks.xgraph.gnnNets import get_gnnNets
from benchmarks.xgraph.dataset import get_dataset, get_dataloader, SynGraphDataset
from benchmarks.xgraph.utils import check_dir, fix_ran... |
5422fbeca75e3786883a4aa84d05a91c15dc2faa7b638a966ecdae0de6a32217 | Python | 11,347 | 219 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
1632edad5c0128e9f6c575358272311e2cd9b53039b638314409ea5549d2d958 | Python | 11,350 | 314 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import logging
import os
from typing import Any, Dict, Iterator, List
import torch
from... |
a4d3b60fe7a1961ebe32ee5a76c3e5bb42a07272dab570157548440f4475f381 | Python | 11,356 | 348 | """
For Fig 2B go to:
Z:\people\gritti\IRIR\supFig2_3
"""
import os, glob
import matplotlib.pyplot as plt
from matplotlib import rc
import pandas as pd
import seaborn as sns
import numpy as np
from scipy.stats import binomtest
plt.rcParams.update({'font.size': 15})
rc('font', size=8)
rc('font', family='Arial')
# pl... |
00069873c4e1749c205b8d46d6706e9edb4d45aa5117dd9b7ae1c59dcecca669 | Python | 11,359 | 293 | # 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
#
# Unless required by appli... |
2ff7181c2ff0845b275cd80041aa97cf97791887a27ad7581d84afd8f4e0c59f | Python | 11,359 | 297 | from PymoNNto.Exploration.Evolution.Devices.Evolution_Device_Single_Thread import *
from PymoNNto.Exploration.Evolution.Devices.Evolution_Device_SSH import *
from PymoNNto.Exploration.Evolution.Breed_And_Selection_Module import *
from PymoNNto.Exploration.Evolution.Single_Evolution_UI import *
from PymoNNto.Exploration... |
8d93763c5891f2a30b1271b8f9e005be75582c16bd8f607e28ba2c04ceb21568 | Python | 11,360 | 318 | import os
aff = os.sched_getaffinity(0)
print(
"**********************before import torch******************************" + str(aff)
)
import torch
print(
"**********************after import torch******************************"
+ str(os.sched_getaffinity(0))
)
os.sched_setaffinity(0, aff)
from DeepPurpose... |
9ab0bb3ccb4ed15fc94578593adefa5de71e22a653843ffd5b85e1669b09f4e5 | Python | 11,363 | 310 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from scipy.signal import firwin
import tensorflow as tf
def apply_fir_filter_tf(signal, kernel):
"""For single signal, not batch"""
signal = tf.reshape(signal, shape=[1, 1, -1, 1])
... |
efba79028aa180fed1b87df9b60617039a5345112c0bbd8b71027b832bf3ca2d | Python | 11,373 | 370 | '''
@Description:
@Author: voicebeer
@Date: 2020-07-03 00:53:27
LastEditors: Please set LastEditors
LastEditTime: 2020-09-08 08:42:42
'''
# for SEED data loading
import copy
import os
import scipy.io as scio
# standard package
import numpy as np
import random
random.seed(0) # temporary
# normalization function
de... |
f8193bcb83106dc97cef7aeb6c2b31bbe7427c022a8fe81324c9f6fd17576fa4 | Python | 11,373 | 345 | # 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... |
9e2791e7f67be8b1f285759459845dea89d333488aa31438816cb82ba780e9c4 | Python | 11,384 | 289 | import logging
import os
import sys
import time
from contextlib import contextmanager
from typing import Dict, Tuple
import numpy as np
import torch
from ase.data import chemical_symbols
from e3nn.util.jit import compile_mode
try:
from lammps.mliap.mliap_unified_abc import MLIAPUnified
except ImportError:
cl... |
1c5783bb15597b1176bae298c6b910bd3ce8fc350e8879becca4bc6f80549b08 | Python | 11,386 | 283 | import pytest
import tempfile
import subprocess
import hydra
import math
import torch
from omegaconf import OmegaConf, open_dict
from nequip.utils.unittests.utils import _training_session, _check_and_print
try:
import schedulefree # noqa: F401
_SCHEDULEFREE_INSTALLED = True
except ImportError:
_SCHEDUL... |
dfa7624731951f223fe5a11f362f0960cbc7860f69d036f684f5754045cd0e2e | Python | 11,387 | 358 | import ast
from argparse import ArgumentParser, ArgumentTypeError, _ArgumentGroup
from collections.abc import Callable
from pathlib import Path
from idtrackerai import Session, __version__, conf
from idtrackerai.utils import resolve_path
def Bool(value: str) -> bool:
"""Argument type for booleans, accepts true/t... |
f470bd850a936aee05799d6a43adeb57142bd09db67b894cd22a52ab87c0b0e0 | Python | 11,391 | 352 | """
ModelAngelo evaluate model residue per residue
Compare a predicted model with a ground truth model. You need:
1) A predicted mmCIF file, passed to --predicted-structure/--p/-p
2) A target mmCIF file, passed to --target-structure/--t/-t
3) Output file, passed to --output-file/--o/-o
or
1) An input data file
"""
imp... |
d0c51936a7f9e8504f1973f054b68ad50751173f114fdb5cb3dc54d68979a180 | Python | 11,396 | 301 | """
Test suite for BrainData.plot() method.
Tests follow TDD approach: write tests first, then implement functionality.
Focuses on plotting functionality, brain-space integration, and user-friendly defaults.
"""
import pytest
import numpy as np
from nltools.data import BrainData
class TestBrainDataPlotting:
"""... |
2b186ad924f8d95605a1c345229ca9f6b244992083bd4def8b263677acc65e12 | Python | 11,403 | 323 | # Copyright 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""AggregatorGRPCClient module."""
import time
from logging import getLogger
from typing import Optional, Tuple
import grpc
from openfl.experimental.workflow.protocols import aggregator_pb2, aggregator_pb2_grpc
from openfl.experimental.w... |
766655e1016162a459e55c714c251f48d4a5b58f826d8e8d4a944ef8d993e422 | Python | 11,403 | 224 | import tensorflow as tf
import numpy as np
from variational.layers import DenseReparameterisation
from variational.activations import sigmoid_moments
class VariationalAttentionGlobalPooling(tf.keras.layers.Layer):
def __init__(self,
number_of_heads,
number_of_features,
... |
ddf07c6125033085a8ebaaf03484c898559390325504da4c440e174497486283 | Python | 11,411 | 216 | # %%
import sys
import os
import random
import time
import glob
import numpy as np
import math
import h5py
from scipy.io import savemat, loadmat
import multiprocessing as mp
sys.path.insert(1, '../..') # the path containing "suns" folder
os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ['CUDA_VISIBLE_DEVICES'] =... |
b24b715bc5931ab1bbc76638718aba1e22cdf5d3dbb5564459621e1c7620e30f | Python | 11,415 | 292 | """
Dispatch management for different machines (clusters, local, ...)
"""
import copy
import logging
import os
import re
import shutil
import subprocess
from pathlib import Path
from ruamel import yaml
from deeperwin.configuration import Configuration, to_prettified_yaml
from deeperwin.run_tools.available_gpus import ... |
18c4fc5617791166f1d2436872341ddbdfcec5c718c4212fca05a22d30030423 | Python | 11,421 | 294 | # Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
import os
import nibabel as nib
import numpy as np
import numpy.ma as ma
def parse_segments(seg, msk_modes):
"""Parse the label segments.
Each channel cor... |
557f90a4ad281b1e1f282342b01055d79f2ad6ed9b7fd21169b4d8867fc04d86 | Python | 11,426 | 206 | """Motif inference: which CDR3s recur against an epitope more than the repertoire explains.
Two methods, run side by side and shipped as two pairs of files.
**TCRNET** asks whether a clonotype has more one-substitution neighbours than a matched background
repertoire accounts for, then clusters what survives on the Ha... |
2b4d4a3b67de0bd60341e84860b7c43d414a815135bdbe7cf3d0a2c79c9a7acd | Python | 11,428 | 265 | # 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
#
# Unless required by appli... |
6fe1346b012f785fc03ecf6636de0e5716c62a7fc77e526fbda54dc4fccab30c | Python | 11,430 | 297 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""UnbalancedFederatedDataset module."""
from abc import abstractmethod
from typing import List
import numpy as np
from tqdm import trange
from openfl.utilities.data_splitters.data_splitter import DataSplitter
def get_label_count(label... |
5cc244f94da1940d364d2e76878f2b7490955829d4175a4a2fef09d7c21e5f76 | Python | 11,433 | 294 | """Tests for nltools.algorithms.alignment.procrustes — data alignment and Procrustes."""
import nibabel as nib
import numpy as np
import pytest
from nltools.algorithms.alignment.procrustes import (
align,
procrustes,
procrustes_distance,
align_states,
)
from nltools.data import BrainData
from nltools.... |
f68dba57b2e4422125d483ca2bfdd8fe7eac50b2944fef4584d83b640c4cdb95 | Python | 11,435 | 300 | """Advance notice for the surfaces MACE v1.0 removes or replaces.
MACE v1.0 is a rewrite. Its feature inventory carries a KEEP / MERGE / DROP
disposition for every enumerable surface of this package, and the non-KEEP rows
of it are reproduced in :mod:`mace.tools.deprecation_table`. This module turns
those rows into wa... |
3a4e68b67e50fc30703acf3844d1a2504ae1619ecbfbfb55be62554031d34fdf | Python | 11,441 | 281 | #!/usr/bin/env python3
#
# Copyright 2017 Martin Heusel
#
# 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 la... |
1dad081fb7895ef0e47afa697be239b9e409e26edd8fe41838ef60dffdbc3983 | Python | 11,442 | 269 | '''
Module focused on the implementation of VGG.
'''
import torch
import torch.nn as nn
import torchvision
from smithers.ml.utils_imagerec import decimate
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
class VGG(nn.Module):
'''
VGG base convolutions to... |
4e9cb50330205735a15a452b18b78d820cd2ead8e4b60c3c2d5cfe8e797f9930 | Python | 11,449 | 319 | # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 applica... |
bd2f9510932a83d446fcc042407a1dab9c8eae203c038434f40a006729fb1194 | Python | 11,459 | 263 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Model validation metrics."""
import warnings
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from ultralytics.utils import LOGGER
from ultralytics.utils.metrics import box_iou, mask_iou, plot_mc_curve, plot... |
434a41ec26f0c9a867a04f30b4648ff6a782d3c37b02963d3917eec2f0fc5f97 | Python | 11,474 | 186 | import scipy.io
import numpy as np
import matplotlib.pyplot as plt
def get_running_time(dataset,nRep):
# Provide a rough estimation of running time
if dataset=='nhp':
exp_time=np.ceil(22*4.3*nRep*np.array([10, 16])/60/60)
elif dataset=='rat':
exp_time=np.ceil(40*0.9*nRep*np.array([10, 16])/... |
d6ccfa663a4a0eea9b952af268cd302831c408ecda0cc4d484b66f596e3ac0f6 | Python | 11,482 | 394 | import unittest
import torch
import torch.utils.data as tud
from reinvent.models.transformer.core.dataset.paired_dataset import PairedDataset
from reinvent.models.transformer.core.vocabulary import SMILESTokenizer
from tests.models.unit_tests.linkinvent.transformer.fixtures import mocked_vocabulary
from tests.test_da... |
ca25641c5ade6fc29f27355edea8c617bc0ed1af05ce6e2c51c6b223b403fdcf | Python | 11,485 | 301 | # 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
#
# Unless required by appli... |
ae1832725e4922a7f672c6e3374b073d88f604ae826c5b626dffafceb7eadf0c | Python | 11,488 | 287 | #!/usr/bin/env python3
import math
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from mlspm.graph import MoleculeGraph
from mlspm.datasets import download_dataset
from ppafm.ocl.AFMulator import AFMulator
from ppafm.ocl.field import HartreePotential
from ppafm.ocl.oclUtils import init_e... |
044c340221e439be37120ae9213f0636a3f8cbbe8ed657a4a692facfbc862633 | Python | 11,495 | 314 | import os
import sys
import time
import zipfile
import warnings
from glob import glob
from urllib.request import urlretrieve
from typing import Optional, Tuple, List, Iterable
import cv2
import torch
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from bindsnet.datasets.preprocess import... |
666bc3f900f7e5f2feaa666bfcb081ca85c9d64e05179f1ddac6c8f7b8ed6a4b | Python | 11,496 | 263 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
import tempfile
import os
from pathlib import Path
from nequip.data import AtomicDataDict
from nequip.data.transforms.neighborlist import NeighborListPruneTransform
from nequip.nn import ... |
d8fa4c580ac614cebfc75d25f4442b5e5829743d7cbafa55941f184106e2d94a | Python | 11,501 | 332 | # -*- coding: utf-8 -*-
# Copyright 2014-2018 The ODL contributors,
# 2019 Johannes Leuschner
#
# This file was taken from ODL and modified by Johannes Leuschner to add
# random state support instead of seeds.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a... |
f955e2b9db00ffa4321c51eb114a869c1b231647aaeebda95507913d09141dfa | Python | 11,504 | 345 | import asyncio
from typing import Any, Awaitable, Callable, Generator, cast, Dict, Tuple
from unittest.mock import MagicMock, patch
import chromadb
from chromadb.config import Settings, System
from chromadb.api import ClientAPI
import chromadb.server.fastapi
from chromadb.api.fastapi import FastAPI
import pytest
import... |
98794e7683b79a1fc02ddd649c417a57942e6c2b36e4a99080d7a6c4ae664500 | Python | 11,505 | 343 | # Copyright (c) 2019 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
#
# Unle... |
0a9c8b23dcb7f6559d28df26d800b96690c8cf524e0682bbbee47a2425729d35 | Python | 11,520 | 357 | import os
import torch
import argparse
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms
from tqdm import tqdm
from time import time as t
from bindsnet.datasets import MNIST
from bindsnet.encoding import PoissonEncoder
from bindsnet.models import DiehlAndCook2015
from bindsnet.net... |
de5529659d09a155ccca86f98f3c8d37fa3fc4fbdb87285e994624166b891860 | Python | 11,526 | 300 | import os
from rdkit import Chem
from rdkit.Chem import AllChem
import time
import pickle
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
def get_pos_dict(idd=None,
cl_file=os.path.join("..", "data", "pretraining_data", "merge_cl.pt"),
yield_file=os.path.join("..", "data",... |
7f5287d377f77e979f29d23b109cb9cfba3613fc7a084d6e853b36a74b52970f | Python | 11,531 | 283 | import os
import random
import numpy as np
import torch
def setup(args):
"""
setup
- name: the name of dataset
- device: CPU / GPU
- seed: random seed
- n_clusters: num of cluster
- n_inputs: dimension of feature
- alpha_value: alpha value for graph diffusion
- lambd... |
f8079e8074b134baeb2c67e6fe74255c64ffc897c0af25ed27a1cc9747e99b12 | Python | 11,535 | 330 | # Copyright (c) 2022 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... |
d45e7d9023f382425136e38cf50332e2900c371737d0056382c2d817f3dbf2ab | Python | 11,540 | 326 | import copy
import io
import os
import random
import re
import time
from itertools import islice
from pathlib import Path
from typing import Any, Generator, Iterable, Optional, Tuple
import numpy as np
import torch
import webdataset as wds
from torch.utils.data import get_worker_info
from .graph._data_loading import ... |
2bb7bfc74ba00bcea2886d502f190e57f85a3b97f97d670ee0b5e9c17ba78ac1 | Python | 11,547 | 261 | import tensorflow as tf
import numpy as np
from crf_rnn_layer import crf_rnn_layer
def get_spatial_rank(x):
"""
:param x: an input tensor with shape [batch_size, ..., num_channels]
:return: the spatial rank of the tensor i.e. the number of spatial dimensions between batch_size and num_channels
"""
... |
752cf2f50193ef7f86f9f15fcf2b92719fc44fa782cd38391f03f6d6733fb498 | Python | 11,547 | 305 | # ==============================================================================
# Copyright 2023 Technical University of Denmark
# Author: Nikolas Borrel-Jensen
#
# All Rights Reserved.
#
# Licensed under the MIT License.
# ==============================================================================
import numpy as ... |
2d9b2d7c5fb7edcbdbd8d72148dcb39f5098f7ffc5e635ed69ee6015c1654f79 | Python | 11,558 | 330 | from collections.abc import Sequence
from pathlib import Path
import cv2
import numpy as np
from qtpy.QtCore import Signal # type: ignore[reportPrivateImportUsage]
from qtpy.QtCore import Qt, QThread, QTimer
from qtpy.QtGui import QIcon, QImage, QPainter, QPixmap
from qtpy.QtWidgets import (
QCheckBox,
QCombo... |
6f211fb28ee9ac403ae722e9319ed9e0664f8592e142fbaba59dafabb2f9a894 | Python | 11,558 | 293 | from keras.layers import Activation, Convolution2D, Dropout, Conv2D
from keras.layers import AveragePooling2D, BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.models import Sequential
from keras.layers import Flatten
from keras.models import Model
from keras.layers import Input
from... |
fd51ea29842ab5536487e9145d67e824331a5b8ac84333a9b580a72699fc75c3 | Python | 11,565 | 361 | import uuid
from django.contrib.auth.models import User
from django.db import models
from django.utils import timezone
from celsusdjango import settings
# Create your models here.
class ExtraProperties(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE, primary_key=True)
curtain_link... |
8043062614cc9e5e54a1eceafa39def6ee175ea45dff3ea8a80533a04c2c226d | Python | 11,569 | 230 | # -*- coding: utf-8 -*-
import os, sys
import numpy as np
import argparse
import logging
from data_functions import writeTbsFile
import copy
import subprocess
MSMOD_PATH = 'msmodified/ms'
import time
# this function creates an array for writing to text that has the ms parameters
# from a CSV file produced via boots... |
b55bf1ec3ccf87fe2fcd58e5b9b72cb81bdbd6e72c5a3d5502cb2f6dfea5abf5 | Python | 11,571 | 287 | # 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... |
63a51f305c047c13454740ef720513d3482de437d47bf8ddf24b75203563a8da | Python | 11,574 | 351 | # 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... |
9b7d1c7946cac85829cd2b88b5f3ca18ebd2fdb0882d8798f3c00c094a1b49ef | Python | 11,587 | 278 | import json
from copy import deepcopy
from typing import List
import pandas as pd
import requests
import seaborn as sns
from matplotlib import pyplot as plt
from curtainutils.common import curtain_base_payload
class CurtainClient:
def __init__(self, base_url: str):
self.base_url = base_url
def post... |
277208f7cc26a71706af2313b86c2808fd6cf996d4ee7e2502038948dfb26bd9 | Python | 11,590 | 272 | # Copyright (c) 2022 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... |
7170d126885267deb607c11b96fba2b084f549e33c825f726b38107c687e97ee | Python | 11,593 | 308 | import torch.nn as nn
import numpy as np
from collections import defaultdict
from torch.nn import Sequential as Seq, Linear as Lin, ReLU, BatchNorm1d as BN, Dropout
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from torch import autograd
from typing ... |
0ad86e7041e07c09f47cd7f09d03753bb2cdfcec0e37e43ba18d2a43f071664b | Python | 11,596 | 387 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
"""
Evaluate a trained pytorch-lightning model on the three tasks on CASF2016:
- Scoring => Spearman rho, R2
- Docking => top1, 2, 3 success rates
- Screening => Average EF, success rates
"""
from train import (
... |
6c5b22f27f8e6a121f7c898bbe66288c990145234dccabdb7e40894f795f9584 | Python | 11,601 | 324 | import itertools
from typing import Callable, Optional, Tuple, Dict
import torch
from tqdm import tqdm
from .base_pipeline import BasePipeline
from ..analysis.pipeline_analysis import MatplotlibAnalyzer
from ..environment import Environment
from ..network import Network
from ..network.nodes import AbstractInput
from... |
6478c3fc69d7cc8a12671f7471ad88ce9f054ab31d329820d480aea48f6a6e37 | Python | 11,611 | 214 | from DeepPurpose import utils, dataset
from DeepPurpose import DTI as models
import warnings
import itertools
import numpy as np
import pandas as pd
import wandb
from tqdm import tqdm
import sys
warnings.filterwarnings("ignore")
import random
import argparse
def main(num_samples, val_setting, cuda_id, num_workers, da... |
ef6eb21c7abf6815057ce8ad2b27e6b0c17bbacd33e29c0f65916084c123f8b1 | Python | 11,612 | 279 |
# -*- 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... |
1e1a79af9bf9c612b8c205be9f9e39633716609e3c7c28a835b614e8f6ba45b8 | Python | 11,614 | 282 | '''
Description:
Author: voicebeer
Date: 2020-12-10 08:47:27
LastEditTime: 2021-02-27 08:52:04
'''
# standard
import models
import utils
import argparse
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import copy
import random
import time
import math
from torch.utils.tensorboard ... |
b027edbce9fc7a02715365e5630f5963e196db582b5360e04d0a2d6baa43b5e9 | Python | 11,614 | 345 | # Copyright (C) 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Collaborator tests module."""
import numpy as np
import pytest
from pandas.testing import assert_frame_equal
from openfl.interface.aggregation_functions import AggregationFunction
from openfl.interface.aggregation_functions import We... |
fdf894cf231ee87fc007a7fed4b7470631857adf36ddf50aa8ed2c61b6d00623 | Python | 11,616 | 327 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# OS stuff
import os
import sys
import warnings
from pathlib import Path
# Computational stuff
import numpy as np
import matplotlib as mplb
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
from matplotlib import font_manag... |
3d96f296291a3f6a6765aec565839416638fc6d962562cb48d609ddfe44f19f4 | Python | 11,620 | 319 | from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def _get_padding(kernel_size: int | Tuple[int, ...], nd: int) -> Tuple[int, ...]:
if isinstance(kernel_size, int):
kernel_size = (kernel_size,) * nd
padding = []
for i in range(nd):
paddi... |
872e5640f300725c2cc3b085ae303bba3d85741db58863387831410f66b70b59 | Python | 11,622 | 274 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
827e6b6c3747ff51608598ed3d8b4bae24dfe8203e8c679e093eb1a439d3e9b0 | Python | 11,626 | 245 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
from torch.optim.swa_utils import _group_tensors_by_device_and_dtype
import lightning
from .lightning import NequIPLightningModule
from nequip.utils import RankedLogger
import warnings
fr... |
ca43c3f8cb7b282383eb9b5d6718f89ffe05bda117bd20c1077937827d33c97a | Python | 11,633 | 354 | '''
Alex Crits-Christoph
License: GPL3
Identifies closest GenBank prokaryote genomes by euclidean distance between tetranucleotide frequencies of query sequences.
'''
import json
from scipy.spatial import distance
import sys
from Bio import SeqIO
from marker_genes import meta_marker
import operator
import argparse
im... |
f88b643b8fcb6540559b209962edf66eb7c8fa94c1ec94808293c4b53aed7b0b | Python | 11,634 | 291 | """
Calculate the optical flow (vector field) signal
using the Horn Schunck algorithm.
"""
import argparse
from scipy.ndimage import gaussian_filter
from scipy.signal import hilbert
import neo
import numpy as np
from copy import copy
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, write_neo, save... |
fa6c6da2b908c95f78bd1fa33433e9b14c15390a1c3d2c405789026c104f0cb3 | Python | 11,642 | 305 | import numpy as np
import xarray as xr
from sklearn.base import BaseEstimator
from sklearn.model_selection import KFold
from numpy.testing import assert_allclose, assert_raises
from xarray.testing import assert_allclose as assert_xr_allclose
from unittest.mock import patch, create_autospec
from scipy.stats import pe... |
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