sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
eab1873d684a60e23253374cb0d587f750cfe9bdd60e4c91222963af666d4db4 | Python | 811 | 27 | from numpy.testing import assert_array_almost_equal
def assert_array_almost_equal_up_to_sign(actual, desired, decimal=6,
err_msg='', verbose=True):
"""Like numpy.test.assert_array_almost_equal, but modified to allow objects to have different signs
"""
msg = ''
try:
assert_... |
4f3c807786ef83b2d5a853f5dae0ad09d25cadd846dda543649124a62a61460f | Python | 812 | 43 | import autograd.numpy as _np
from autograd.numpy import (
array,
eye,
zeros,
)
atol = 1e-8
rtol = 1e-5
def is_array(x):
return type(x) is _np.ndarray
def to_ndarray(x, to_ndim, axis=0, dtype=None):
x = _np.asarray(x, dtype=dtype)
if x.ndim > to_ndim:
raise ValueError("The ndim cann... |
2036338f4941da50dce3b08563e93d246f15bca303499566bf5f76973a1e6cb0 | Python | 813 | 24 | from __future__ import absolute_import
import numpy as np
from numpy.testing import assert_allclose
from nose.tools import assert_equal, assert_true, assert_raises, raises
import pycircstat
def test_clustering():
x = np.asarray([6.10599459, 0.14824723, 3.11272268, 3.45703846, 5.88211171, 3.53760218,
... |
8439d06122a12d7256aac8f6081355d308123a5df4cec1a3a4f719c7fed67a5d | Python | 813 | 22 | import torch.nn as nn
from reinvent.models.transformer.core.network.encode_decode.clones import clones
from reinvent.models.transformer.core.network.encode_decode.sublayer_connection import (
SublayerConnection,
)
class EncoderLayer(nn.Module):
"Encoder is made up of self-attn and feed forward (defined below... |
de8df383d97aa12bb0e52b19cd1d734d12e7e18b406dbab3e4491f960b1abc7a | Python | 813 | 28 | import pytest
from corgie.cli.vote import compute_softmin_temp
@pytest.mark.parametrize(
"dist, weight, size, result",
[
(0, 0.4, 1, 0),
(0, 0.6, 1, 0),
(8, 0.99, 3, 8./6.), # SEAMLeSS params with MIP0 fields
(1, 0.99, 3, 1./6.), # adapting SEAMLeSS params for mipless fields
... |
f591b76664e77a902b50fd69818576a901ff7ee1f4b40c850aed375903262991 | Python | 813 | 25 | import random
from geomstats.geometry.poincare_polydisk import PoincarePolydisk
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.nfold_manifold import (
NFoldManifoldTestCase,
NFoldMetricTestCase,
)
from .data.poincare_polydisk import (
PoincarePolydiskMetr... |
18602725c1c2080aa819cc0eb0540589f54f454d9e271a581c712123d52a6056 | Python | 814 | 29 | """Shared runtime schema types for SanPy detection and analysis results."""
from enum import StrEnum
class DetectionParameterCategory(StrEnum):
"""Categories used to organize detection parameters for presentation."""
PRESETS = "presets"
DETECTION = "detection"
WINDOWS = "windows"
WAVEFORM = "wav... |
204e67a67625bedbe3222ab07bbd188ab823700a0b2aa68557a620b18b7a992e | Python | 814 | 31 | """Synthetic accessibility score
Synthetic Accessibility Score of Drug-like Molecules from P. Ertl & A. Schuffenhauer, J Cheminformatics 1:8 (2009)
Larger SA score = more difficult to synthesize
"""
from __future__ import annotations
__all__ = ["SAScore"]
from typing import List
import numpy as np
from .sascorer ... |
7fff038da0ff0a1d9ea687737f5bb4882e82ecd36d287598efd0cbadea62753f | Python | 814 | 23 | from PymoNNto.Exploration.StorageManager.StorageManager import *
import numpy as np
#convenience functions:
#add root project folder to import path
add_project_root_path()
#print numpy arrays with commas for easier copy paste
#np.set_string_function(lambda x: repr(x), repr=False)
from PymoNNto.NetworkCore.Network i... |
a74f363408daa6c156dfcbbfb243d71786ec64086b68fad21b776989a8ae4594 | Python | 815 | 24 | from PymoNNto import *
class CreateWeights(Behavior):
def initialize(self, synapses):
distribution = self.parameter('distribution', 'uniform(0.0,1.0)') # ones
density = self.parameter('density', 1.0)
synapses.W = synapses.matrix(distribution, density=density) * synapses.enabled
... |
f5c776c54d897b75e20da7c1a8ce311dce80e7139137bcb17b653c23b8124054 | Python | 815 | 26 | import collections
from pathlib import Path, PosixPath, WindowsPath
from typing import Any, OrderedDict
from ruamel.yaml import YAML, Dumper
def get_yaml_io():
"""Return yaml loader/dumper configured for snakebids."""
yaml = YAML()
# Represent any PathLikes as str.
def path2str(dumper: Dumper, data:... |
ff6e3364a5d8632fb002d9479f0e9d221dcc7c2837434e2f5460ec95e25363f8 | Python | 816 | 23 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
# Separate version utilities to avoid circular imports with __init__.py
import importlib.metadata
from typing import Optional
_ALL_PKGS = importlib.metadata.packages_distributions()
def get_version_s... |
0d05f119094029b3532224bd4df8d5a2f877e16f7567347fa7124ec1e3a91baa | Python | 817 | 28 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from .type_mapper import ChemicalSpeciesToAtomTypeMapper
from .dataset import DatasetIndexTransform
from .neighborlist import (
NeighborListPruneTransform,
NeighborListTransform,
SortedNeigh... |
24bf3a94e7a540830f40375292d9a374eec112306dd00fef43b3977208e9495c | Python | 817 | 29 | from video_stream import ImageObserver
import numpy as np
class HistogramObserver(ImageObserver):
"""
An observer that calculates the image histogram for each frame and stores it in the observer output.
"""
default_params = {
**ImageObserver.default_params,
"bin_count": 255,
}
... |
b1f0aacdf2fdbcec9debf8aa50a3e3968bafb2c10ad33b9335822585570c3875 | Python | 817 | 26 | import numpy as np
def allclose_sign(a, b):
"""True is all values are close, up to the same sign for all values."""
return np.allclose(a, b) or np.allclose(a, -b)
def allclose_trajs(trajs1, trajs2):
"""Check that all values of the trajectories are close."""
trajs1 = np.concatenate(trajs1, axis=0)
... |
1a06cdf984ddcb36d11d3ac7ab974f24229f8890e7a692d644fcd0902eb34b93 | Python | 818 | 33 | import torch
from .collate import time_aware_collate
class DataLoader(torch.utils.data.DataLoader):
def __init__(
self,
dataset,
batch_size=1,
shuffle=False,
sampler=None,
batch_sampler=None,
num_workers=0,
collate_fn=time_aware_collate,
pin... |
921bd2121ba7e66255ddac24eeaecc199fc2c2142bc15c72869325191087fa07 | Python | 818 | 20 | from reinvent.chemistry.standardization.filter_types_enum import FilterTypesEnum
from tests.chemistry.fixtures.test_data import CELECOXIB, BENZENE
from tests.chemistry.standardization.base_rdkit_standardizer import BaseRDKitStandardizer
class TestRDKitStandardizer(BaseRDKitStandardizer):
def setUp(self):
... |
d6f3b8c4e81e066ea120d4da92ab23bc0645344772905991bee548f958704a50 | Python | 818 | 29 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from torch.nn import functional as F
from typing import Optional, Tuple
from . import register_act_fn
@register_act_fn(name="hard_sigmoid")
class Hardsigmoid(nn.Hardsigmoid):
"""
... |
2c2b7e855766414975467f037ad93ccc42b52170e7a276a4d588e9192cd0ce79 | Python | 819 | 38 | # -*- coding: utf-8 -*-
def input_yes_no(default='y'):
"""
Demand user input y[es] or n[o].
The user is asked repeatedly, until the input is valid.
Parameters
----------
default : {``'y'``, ``'n'``}, optional
The output if the user enters empty input.
Returns
-------
inp... |
21e8cec2d6a7fad94efc78eac0a0dcdbecf1a2c55dbe25588622dd843904d6b8 | Python | 820 | 24 | # Generated by Django 4.1.1 on 2022-10-04 10:10
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('celsus', '0003_remove_author_project'),
]
operations = [
migrations.AlterField(
model_name='dif... |
a4c678938ec1de1573aa4c39d3fff4facb518671b6d0f9a1b753458fdf3c1a4a | Python | 820 | 30 | import os
from torch.utils import data
from torchvision import datasets
from torch.utils.data import Dataset
import torch
import numpy as np
def npy_loader(path):
sample = torch.from_numpy(np.load(path))
return sample
class my_Dataset(Dataset):
# Characterizes a dataset for PyTorch
def __init__(self, ... |
de36da5a1de5b87881311a1ab01028a47803d4e2a851f9858b77642cfbdf4e65 | Python | 820 | 27 | """Stacking data objects of one class into a single object."""
def concatenate(data):
"""Concatenate a list of `BrainData` or `Adjacency` objects.
Args:
data (list[BrainData] | list[Adjacency]): Objects to concatenate; all must
be of the same class.
Returns:
BrainData | Adjac... |
112cdf84abb8a2b43d4eadb1d4910a6582d39971e7b435f7bb6268fe8497c6fb | Python | 821 | 23 | import pytest
from nltools.data import BrainData
from nltools.data.braindata.utils import _check_brain_data
def test_check_brain_data_from_list_of_paths(sim_brain_data, tmpdir):
"""List of file paths is now accepted."""
p1 = str(tmpdir.join("a.nii.gz"))
p2 = str(tmpdir.join("b.nii.gz"))
sim_brain_dat... |
647cc86815bd7066e46b2419dd2a5c7b1810080ef206896a737a903b88bbfe56 | Python | 821 | 35 | import itertools as it
from typing import Iterable, Sequence, TypeVar
import more_itertools as itx
T = TypeVar("T")
def unpack(iterable: Iterable[T], default: Sequence[T]) -> Iterable[T]:
"""Return default if iterable has no elements
Allows safe unpacking of possibly empty iterables
Parameters
---... |
19e4aed6692edda7a379eea6d51eda5eca1c41a43f514473ef7acc491283d0eb | Python | 822 | 25 | from tqdm import tqdm
from data.dataset import BratsTest
from matplotlib import pyplot as plt
from data.utils import plot_img_and_target
dataset = BratsTest(None, None, None)
dataset.return_name = True
image_save_folder = dataset.root.parent / 'plots'
image_save_folder.mkdir()
fig_handle = plt.figure(figsize=(19.20... |
f5d191e4896cc82482b780e5b44e2f94362b37716c9bd014f7f0ede95ce37065 | Python | 822 | 37 | """
Exponential decay, or exp(-x).
For values x < 0, the output is 1.0 ("rectified" of "clamped" exponential decay).
"""
__all__ = ["ExponentialDecay"]
from dataclasses import dataclass
import numpy as np
from .transform import Transform
@dataclass
class Parameters:
type: str
k: float
def expdecay(x, k=... |
06eb945153a945f34f831af5aee65bc9c583cbde0038f84bab83a69ee348fa02 | Python | 827 | 33 | # Based on solution at https://gist.github.com/jaradc/eeddf20932c0347928d0da5a09298147
import numpy as np
from numpy import unique, multiply
from scipy.stats import entropy
def getEntropy(series, base = 2, nBits = None):
"""Obtain Shannon Entropy estimate from a vector values/time series.
Args:
serie... |
540be6f151c62437ec62fc4eefac8ed752e20dffbfd985fabfe4c47bc8bb9554 | Python | 827 | 27 | # Generated by Django 4.1.1 on 2022-11-10 11:26
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('celsus', '0024_alter_differentialsamplecolumn_column_type'),
]
operations = [
migrations.CreateModel(
... |
c6ea751d3bd9d370101771ea08c960870a567f96897b54b22caa307850d7527e | Python | 827 | 25 | import os
import config
def seed_from_name(name):
return int(name.name.split('_')[0][4:])
root = config.datasets / 'BRATS_stylegan_313subjects_noaugmentation_singlesegmentationchannel' / 'out'
all_images = dict()
all_seeds = dict()
for image_folder in ('T1', 'T1CE', 'T2', 'FLAIR', 'SEG'):
modality_folder =... |
babaf8c51c897208fe9fac43f214041401947612bc11dbb4d4894242e4ba360c | Python | 829 | 25 | # 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... |
964157b136de6efc325088a82bf5141bc31c5d28850b0562bb3e1880823a1ba2 | Python | 830 | 35 |
import unittest
import h5py
import os
from deeprank_gnn.Graph import Graph
CWD = os.path.dirname(os.path.realpath(__file__))
class TestGraph(unittest.TestCase):
def setUp(self):
self.graph = Graph()
self.graph.h52nx(f'{CWD}/data/hdf5/1ATN_residue.hdf5', '1ATN_1w')
self.graph.pdb = f'{CW... |
0df7da65e6b3c3a2397d21b1729596c8992aa2bec63ca8d386d2fee992cc0dfd | Python | 832 | 25 | #!/usr/bin/env python3
import unittest
import numpy as np
import odil
class TestIO(unittest.TestCase):
def test_raw_xmf(self):
name = "foo"
xmfpath = "data.xdmf2"
nx, ny, nz = 3, 4, 5
lx, ly, lz = 4, 5, 6
for dtype in [np.float32, np.float64]:
u_src = np.lins... |
3a2b0a74db4f2a74fc91cdfa3c9e03d8cf57f30db7578f85ef9c6335a05a20be | Python | 832 | 25 | """Typed state shared by the SanPy analysis window and interactive plugins."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass(frozen=True)
class WindowState:
"""Describe the recording, sweep, and spikes selected in one window.
Attributes:
file_key: Normalized path r... |
78a8921c59664dbf8c9dced920a10c17edb1b8a72e5602e823337ad4562d91cd | Python | 832 | 24 | import nibabel as nib
from nibabel.processing import resample_to_output
from skimage.measure import marching_cubes
def nifti_to_glb(path, output="prediction.obj"):
# load NIFTI into numpy array
image = nib.load(path)
resampled = resample_to_output(image, [1, 1, 1], order=1)
data = resampled.g... |
f299ee1c24548604d4ae4c33bab532d3e26958c33aa3dcfd9d580d79e503bd6b | Python | 832 | 25 | from dig.threedgraph.evaluation import ThreeDEvaluator
import numpy as np
import torch
import math
def test_ThreeDEvaluator():
input_dict = {'y_true':np.array([1.0, -0.5]), 'y_pred':np.array([0.6, 0.0])}
evaluator = ThreeDEvaluator()
result = evaluator.eval(input_dict)
assert len(result) == 1
asse... |
4392af7867f19abce449d387522bbcdf0a1d394f37e9cef36417a2e2ce25886a | Python | 833 | 25 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from .softadapt import SoftAdapt
from .loss_coeff_scheduler import (
LossCoefficientScheduler,
LinearLossCoefficientScheduler,
)
from .loss_coeff_monitor import LossCoefficientMonitor
from .writ... |
29de7996e1145972b583f804f5eb5f4d75d4ba0f22d6845e1a0047d07dc6aad2 | Python | 834 | 30 | """
ASGI config for celsusdjango project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/
"""
import os
from channels.auth import AuthMiddlewareStack
from channels.routing import Protoc... |
07e2ef24da3ad987f464a4d0ad259ce9a9e0d970483c9f28f0abb329cdb7d33f | Python | 835 | 27 | from numba import jit
from fimpy.roi_extraction.corr_algorithms import _update_labels
from split_dataset import Blocks
import numpy as np
from itertools import product
def merge_rois(blocks: Blocks, block_arrays: np.ndarray):
"""Merges rois extracted from different dataset blocks with padding
:param blocks:
... |
1e3af90e3df6f43bfae0443d83b5bd1e719dbfa3331ee7e209694a6288e47f05 | Python | 835 | 27 | import setuptools
with open(r'README.md', mode=r'r') as readme_handle:
long_description = readme_handle.read()
setuptools.setup(
name=r'hyper-dti',
version=r'1.0.0',
author=r'Emma Svensson',
author_email=r'svensson@ml.jku.at',
url=r'https://github.com/ml-jku/hyper-dti',
description=r'Robus... |
462db7e6644037b0e182c83caf7c22bfa4bf66e748f5a61754b907bcf00d03e5 | Python | 836 | 24 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from ._base_datamodule import NequIPDataModule
from ._ase_datamodule import ASEDataModule
from .sgdml_datamodule import sGDML_CCSD_DataModule
from ._3bpa_datamodule import NequIP3BPADataModule
from .tm2... |
f70c7d1b71b8c1cedae4792114e7c58cbe3af5d6d3ab8dcb3bf246eeb1adfd09 | Python | 837 | 24 | from PymoNNto.NetworkCore.Behavior import *
class STDP(Behavior):
def initialize(self, neurons):
self.stdp_factor = self.parameter('stdp_factor', 0.0015, neurons)
self.syn_type = self.parameter('syn_type', 'GLUTAMATE', neurons)
neurons.spike_old = neurons.vector()
def iteration(self, ... |
312d063df7e1af953db108c152626808e602d0bec8e1bc038582822f1705cc3d | Python | 839 | 29 | from numpy import corrcoef
import dynfc as dyn
def cc(series, size, k = 1):
"""Run cofluctuation analysis for BOLD signal.
Args:
series (double): BOLD signal array for all parcels/voxels in the format [N, Tmax].
size (int): Size of window.
k (int, optional): Standard value k = 1.
... |
8b7d58a2ce69d135b84fb77618315df519982a022905e18a7445c3818cf394d5 | Python | 840 | 42 |
from sleepy.gui.builder import Builder
from PyQt5.QtWidgets import QWidget
from sleepy.processing.parameter import ParameterBase
import pdb
class Processor:
def render(self):
self.buildTree = ParameterBase.getBuildTree(self)
Builder.setAttributesFromJSON(self.buildTree, self, leve... |
6bfe6187df6b53adc33fc2dc1126704f634b743a08cf29248ce165375750974b | Python | 841 | 26 | import numpy as np
from sklearn.decomposition import PCA
if __name__ == '__main__':
data_path = 'PROJECT_DIR/results/restfmri/dnn/sFC/'
fname = data_path + 'sFC_HCP_Session1.npz'
data = np.load(fname)
data_features = data['features']
labels = data['labels']
pca = PCA()
feature_pcs = pca.... |
8ad879b1dc6a6835978598c58fafc4c544a740e736f6f5abb47be557e0d860df | Python | 841 | 28 | from geomstats.geometry.euclidean import Euclidean, EuclideanMetric
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.scalar_product_metric import (
CustomizationTestCase,
InstantiationTestCase,
WrapperTestCase,
)
from .data.scalar_product_metric import (
... |
6dd91dc861e01a34b26825b82947e895af9b3043b332a2e7327be7b12d9cd91b | Python | 842 | 46 | #!/usr/bin/env python
# Takes a snp file and formats it for WASP, splitting it into one file per scaffold/chromosome
# Usage: python3 splitScaffolds.py inFile
import os
import sys
# Read arguments
inFile = sys.argv[1]
# Create output file
outFileSuffix = ".snps.txt"
prevChrom = ""
outFile = "temp.txt"
outFile = ... |
0ed29393a4ab8de49d0fa1ec1b44241989c4601080ce5bfa5eb4ec9381460fdd | Python | 843 | 27 | import sanpy
from sanpy.interface.plugins import basePlotTool
from sanpy.sanpyLogger import get_logger
logger = get_logger(__name__)
class plotTool(basePlotTool):
"""Plot tool for one bAnalysis.
"""
myHumanName = "Plot Tool"
def __init__(self, **kwargs):
super().__init__(**kwargs)
... |
d86d90d3586dc88271f45bf0d7d5ba5485796ff0b0c41dc18db11b5ffc6c0b14 | Python | 843 | 21 | import pytest
import unittest
from reinvent.models import LinkinventTransformerAdapter, SampledSequencesDTO
from tests.models.unit_tests.linkinvent.transformer.fixtures import mocked_linkinvent_model
from tests.test_data import ETHANE, HEXANE, PROPANE, BUTANE
@pytest.mark.usefixtures("device")
class TestLinkInventLi... |
1caaa159b4de65279f8f94ffe266e63e7f94c8524ca20015824c8ce38c140bbc | Python | 844 | 28 | NAV1P1 = "Nav1.1"
KV32 = "Kv3.2"
# labels
CURRENT_LABEL = "Current (nA)"
VOLTAGE_MV_LABEL = "Voltage (mV)"
DISTANCE_LABEL = "Distance from soma (μm)"
INSTA_FR_LABEL = "Instantaneous firing rate (Hz)"
FIRING_RATE_LABEL = "Firing rate (Hz)"
MAX_PROP_LABEL = "Max propagation distance (μm)"
KVMUT_FRAC_LABEL = f"{KV32} mut... |
6b6c60d136dc000c03c886a034fb1b12d1fd6f8df25185026b65d1f5510d7c67 | Python | 844 | 24 | import torch
from dig.sslgraph.method.contrastive.objectives import NCE_loss, JSE_loss
def test_nce_more_view():
zs_1 = torch.randn((32, 32))
zs_n_1 = torch.randn((32, 32))
zs_2 = torch.randn((32, 32))
zs_n_2 = torch.randn((32, 32))
zs_3 = torch.randn((32, 32))
zs_n_3 = torch.randn((32, 32))
... |
ac089731e24337619ac2478f36df738f65dcd328a091da173b8a1524b86a404c | Python | 845 | 25 | # 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... |
af68f702df9846335ca17e6496dbc5a2f28ba05165d9c9f7e529d3e59b460375 | Python | 845 | 37 | import NALSM_GEN_SUPPORT as sup
import numpy as np
import os
SAVE_VER_INP = input('VERSION? [int]: ')
SAVE_VER = int(SAVE_VER_INP)
VER = SAVE_VER
main_Path = os.getcwd()
data_path = main_Path + '/train_data/ver_' + str(SAVE_VER)
net_path = main_Path + '/networks'
for file in os.listdir(data_path):
... |
07f279cb5136b5332183f9e2b9514608fdb3e3c79299eaebeb9ef4136b7425e8 | Python | 847 | 34 | import re
import pytest
from reinvent.scoring.utils import suppress_output, camel_to_snake
def test_suppress_output_does_not_raise():
with suppress_output():
print("stdout suppressed")
import sys
print("stderr suppressed", file=sys.stderr)
def test_suppress_output_yields_streams():
... |
c815da6045c9770d0919616e5671a8fb03fef08f5dd7fd3cd3665537ebced339 | Python | 848 | 29 | import numpy as np
from data.dataset import Brats184, Brats313
brats184 = Brats184(None, None, None)
brats313 = Brats313(None, None, None)
subjects184 = brats184.get_available_subjects()
subjects313 = brats313.get_available_subjects()
if len(set(subjects184).intersection(set(subjects313))) == len(subjects184):
p... |
d8faa8d1853915b1aff3e17bff842261432a665501ff77816e2eae1a89fb839d | Python | 849 | 25 | from PymoNNto.NetworkCore.Behavior import *
import numpy as np
from sympy import symbols
from sympy.physics.units import *
class Clock(Behavior):
def initialize(self, neuron_or_network):
self.add_tag('Clock')
#convert_to(seconds / tau, seconds)
neuron_or_network.clock_step_size = float(... |
982b2de30aec17c2cd7c351242add5c46831a452215f4935ce53467c5979154c | Python | 850 | 35 | # usr/bin/env python
# Filter SNP lists for SNps that we want to keep
# usage python SNPfilter.py infile keep_list outfile
import sys
infile, keep, outfile = sys.argv[1:]
snp_dict = {}
print("Reading SNPs...")
with open(infile, 'r') as infile:
for line in infile:
chrom, pos0, pos1, snp = line.strip().s... |
f1ffa3004bdd358c5023b1e0bc3d96b031ddead079eef2840ad3f30d4a408675 | Python | 852 | 26 | import base64
import os
import numpy as np
from typing import TYPE_CHECKING
DEFAULT_CHROMA_EMBED_URL = "https://embed.trychroma.com"
def get_chroma_embed_url() -> str:
"""Return embed base URL from CHROMA_EMBED_URL or default without trailing slash."""
return (os.environ.get("CHROMA_EMBED_URL") or DEFAULT_CH... |
75d663b555aeed9c58bc109d34b5a7ab2d59a8869e61d8e6aa722a971cd5bfe2 | Python | 853 | 24 | # Generated by Django 4.1.1 on 2022-12-02 11:30
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('celsus', '0036_genenamemap_primary_uniprot_record'),
]
operations = [
migratio... |
960873acaee6d91158e4d18e95d0ff2bcde8fba261900d5eb4f8718581a1f436 | Python | 853 | 36 | #!/usr/bin/env python3
"""
Visualize LSL stream on the network. The signal is visualizd in real time with
spectral filtering, common average filtering option and real-time FFT.
Command-line arguments:
#1 Stream name
Example:
python nd_stream_viewer.py
python nd_stream_viewer.py StreamPlayer
If the scrip... |
7cace5e671992cb1442f8d9f3ec3c5d038624ed47098a77e0932b105b2b697cf | Python | 854 | 47 | from brian2 import *
import time
from globparams import *
set_device('cpp_standalone')
defaultclock.dt = 1*ms
prefs.core.default_float_dtype = float32
prefs.codegen.target = 'cython'
eqs_neurons = '''
dv/dt = (ge + rand() - v*OM_DECAY) / (1*ms) : 1
dge/dt = -ge / (1*ms) : 1
dspiked/dt = -spiked / (1*ms)... |
97b6d609938fd3bc55b0c13910f9c7579ebc34a3aafca38caa0d754c3b0fe807 | Python | 855 | 24 | import config
model_save_folder = config.models
model_save_folder = config.models
for path_object in model_save_folder.rglob('*'):
if path_object.is_file() and path_object.suffix == '.ckpt':
experiment_name = path_object.parts[-5]
if 'fullrandomsplit' in experiment_name or \
('Bra... |
450e71573259496788aaacb8ee934011db6860b7486ec2bcc27398d16fafff07 | Python | 856 | 26 | """The one validation check the nltools estimators share.
`_Glm` and `_Ridge` are independent estimators with different input contracts, so
they share this small private function instead of a common base class.
"""
from __future__ import annotations
from sklearn.exceptions import NotFittedError
def _check_is_fitte... |
735165674c402979fb54cd52d016c49eea8e959f93f7a9ce11479c07feda8d07 | Python | 856 | 28 | """
This code executes preprocessing for all combination of databases,
needed to reproduce results of generalization section.
"""
import logging
import subprocess as sp
datasets_o = ['DrugBank', 'BIOSNAP', 'BindingDB', 'Davis', 'E', 'GPCR', 'IC', 'NR']
datasets = datasets_o
for database1 in datasets_o:
for ... |
8bcd655fce2fccc91c995cf9b6d8b59c015d9b4ea66dae0169f995f1f448e86a | Python | 856 | 17 | """
--------------------------------------------------------------------------------
Date: 05/08/2022
@author: Nikolaos Vardalakis
--------------------------------------------------------------------------------
Implementation Notes
--------------------------------------------------------------------------------
... |
bad0b132599c7ca38fcdd20b27fa55eef50769eafd2d17c21072245ab14203db | Python | 856 | 38 | import numpy as _np
import torch as _torch
atol = 1e-6
rtol = 1e-5
def from_numpy(x):
if _torch.is_tensor(x):
return x
return _torch.from_numpy(x)
def array(val, dtype=None):
if _torch.is_tensor(val):
if dtype is None or val.dtype == dtype:
return val.clone()
return... |
696f49e32048fe9da2e873aae3bb65f0e07a051e1d9c928aa851199c8839910c | Python | 857 | 37 |
import sys
from navec.vocab import Vocab
from ..glove import (
Glove,
parse_glove_vocab,
format_glove_vocab,
trans_glove_vocab
)
from ..quantiles import get_quantiles
def vocab_count(args):
glove = Glove.from_env()
glove.vocab(sys.stdin.buffer, sys.stdout.buffer)
def vocab_quantile(args):... |
100d5f0e333b84fb38368fa440c9601cd6e04ef68ace8ffe504c8f6bcd79d2e4 | Python | 859 | 34 | """Config Validation"""
from typing import Optional
from reinvent.validation import GlobalConfig, Field
class SectionParameters(GlobalConfig):
model_file: str
num_smiles: int
smiles_file: Optional[str] = None
target_smiles_path: str = ""
sample_strategy: Optional[str] = "multinomial" # Mol2Nol
... |
8337be428bb920fb8a54b40a16b9ca560dc1a0515ec5c90352f8276a47f0dc95 | Python | 859 | 26 | """Segmenting small-scale Chinese language corpora"""
import argparse
import jieba
from tqdm import tqdm
def segment_text(input_file, output_file):
with open(input_file, 'r', encoding='utf-8') as f:
text = f.read()
words = jieba.cut(text)
segmented_text = ' '.join(words)
with open(output_fil... |
0021464a2707f55ecfe5dcee85fc4c4a7a422d979086b32b81e8b411ff415631 | Python | 860 | 30 | """
Merge pandas DataFrames based on the values of selected columns.
"""
import argparse
import pandas as pd
from copy import deepcopy
CLI = argparse.ArgumentParser()
CLI.add_argument("--output", nargs='?', type=str)
CLI.add_argument("--data", nargs='+', type=str)
CLI.add_argument("--output_img",nargs='?', ty... |
068e030a5f3e5c76a53343ca209a4aedc2f4953239563aef55e6d1c2b42e17c9 | Python | 861 | 31 | """Global Config Validation"""
from typing import Optional
from pydantic import BaseModel, ConfigDict, Field
class GlobalConfig(BaseModel):
model_config = ConfigDict(extra="forbid", protected_namespaces=())
class ReinventConfig(GlobalConfig):
run_type: str
device: str = "cpu"
tb_logdir: Optional[s... |
1dd7c089dba5962169b5febfea1a6807f4e445f9b21f4940026dafba228429c2 | Python | 861 | 28 | """
This code executes preprocessing for all combination of databases,
needed to reproduce results of generalization section.
"""
import logging
import subprocess as sp
datasets_o = ['DrugBank', 'BIOSNAP', 'BindingDB', 'Davis', 'E', 'GPCR', 'IC', 'NR']
datasets = datasets_o
for database1 in datasets_o:
for ... |
70cd49852ecc2bc00a4d9134d628619f4e084dd4a12993a9dd0ce2fa65d039c5 | Python | 862 | 27 | """Shared Matplotlib helpers for SanPy Qt widgets."""
from matplotlib.backends import backend_qtagg
from qtpy import QtCore, QtWidgets
def _make_navigation_toolbar(
canvas: backend_qtagg.FigureCanvasQTAgg,
parent: QtWidgets.QWidget,
) -> backend_qtagg.NavigationToolbar2QT:
"""Create a compact Matplotlib ... |
b70717a456871c83d0f4ef6459f2e4ff7500b49e7ad493d3f7bb090f62e5a645 | Python | 862 | 30 | # Generated by Django 4.1.1 on 2022-10-02 16:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('celsus', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='ExperimentType',
fields=[
(... |
2dd6f8de4c2724b422df9e2e16aa9301d7485d0a4aa8da74fb93e5828f153b76 | Python | 863 | 24 | from dig.oodgraph import GOODCBAS, GOODCMNIST, GOODCora, GOODHIV, GOODMotif, GOODPCBA, GOODZINC, GOODArxiv
import pytest
import shutil
dataset_domain = {
'GOODHIV': ['scaffold', 'size'],
'GOODPCBA': ['scaffold', 'size'],
'GOODZINC': ['scaffold', 'size'],
'GOODCMNIST': ['color'],
'GOODMotif': ['basi... |
2e27487409e20118384b937c19fa8f22381d76dd9e35bb009f247fbe61c56e0b | Python | 864 | 27 | # Generated by Django 4.1.1 on 2022-10-18 15:07
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('celsus', '0008_remove_differentialsamplecolumn_comparison_name_and_more'),
]
operations = [
migrations.Remo... |
ec78acaf242a8ea268e2e6386231f7ed36b33353d9e3ae50c354ac922b2cb715 | Python | 864 | 32 | import logging
from geomstats.geometry.discrete_curves import ElasticMetric, SRVMetric
from geomstats.geometry.euclidean import EuclideanMetric
from geomstats.metric_geometry.graph_space import GraphSpace
from geomstats.metric_geometry.point_set import PointSetMetric
ELASTIC_METRICS = (SRVMetric, ElasticMetric)
def... |
f4f155244a9bf2330a3dff72db3c5e954f19ede8ba4b4638dd524bd61e4a6df2 | Python | 867 | 28 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from .modules import find_first_of_type
from .dtype import (
dtype_to_name,
dtype_from_name,
torch_default_dtype,
floating_point_tolerance,
)
from .file_utils import download_url, extrac... |
0bb9c912bb8fc21f6aca4a0ef068f3ea250306ddf6a28643649aa0e7bc1e2215 | Python | 868 | 24 | import experiment as exp
import video_system as vid
class SimpleObserver(exp.Experiment):
def setup(self):
# assuming an observer exists with id "hist"
self.obs = vid.image_observers["hist"]
self.obs.start_observing() # start processing image data
def run(self):
# register on... |
3d24cb49b541c99c2f13fb5f2b1a5293d7f9b5576c79f3819fefe9fc1ba71e3a | Python | 868 | 38 | """The documentation functions."""
docdict = dict()
# Define docdicts
docdict["net"] = """
net : Instance of Network object
The Network object.
"""
docdict["fname"] = """
fname : str | Path object
Full path to the output file (.hdf5).
"""
docdict["overwrite"] = """
overwrite : Boolean
True : Overwrite e... |
aa98b5608e749d726c14982dc8e9c86f0d5d4dd07991d0169bdfc00e5553aeaa | Python | 868 | 30 | """
Merge pandas DataFrames based on the values of selected columns.
"""
import argparse
import pandas as pd
from copy import deepcopy
CLI = argparse.ArgumentParser()
CLI.add_argument("--output", nargs='?', type=str)
CLI.add_argument("--data", nargs='+', type=str)
CLI.add_argument("--output_img",nargs='?', ty... |
f3351d703946772b2076f0585f613ea7a34c2f874cd35564da39c25d14f51f7c | Python | 868 | 26 | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
class PositionalEncoding(nn.Module):
"Implement the PE function."
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# ... |
c86f9ec5b770537f42524971c78ee0768459e348a395eb24b9857cfccaed2595 | Python | 871 | 32 | import os
from torch.utils.data import Dataset
import nibabel as nib
import numpy as np
import torch
class ImageDatasets(Dataset):
def __init__(self, path):
self.images = [os.path.join(path, img) for img in os.listdir(path)]
print('"End reading dataset..."')
def apple_LR(self, i... |
dd7f720db3961c379e1db6550b027b903731ee4e471d3f0761864fd01a0793ae | Python | 871 | 37 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="metroem",
version="0.0.9",
author="Seung Lab",
author_email="",
description="Metric learning optimization pyramid for EM alignment",
long_description=long_description,
long_descrip... |
087098c1b0d2d5cd4d64cadac883269ae771159202abecafa2a7d1b776189550 | Python | 873 | 24 | # 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... |
752f6fa3f20182b9fd0abc2bca0a97eabcfd30d7fdf9a31b6d2f915d0b125dc4 | Python | 873 | 33 | """Tests for standalone remote NWB transport configuration."""
from pathlib import Path
from dandi_transport import FsspecNwbTransport, LindiNwbTransport
def test_uses_cache_override(tmp_path: Path) -> None:
"""Retain an explicitly supplied cache directory.
Args:
tmp_path: Temporary test directory.... |
fbac60563e6c56947236ac829b19abedbc34e3cba150082fad0b8ee201eff490 | Python | 874 | 38 | # Copyright (C) 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from copy import deepcopy
import torch
import torchvision\
# Download Test datasets
mnist_test = torchvision.datasets.MNIST(
"./files/",
train=False,
download=True,
transform=torchvision.transforms.Compose(
[... |
552b1cb24b0f1caaa0c558c2c48173f42ed4d6d459efffa4d34d97039278f7aa | Python | 875 | 27 | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
data = pd.read_excel('compare_newTest_tools.xlsx')
indicators = ['Accuracy', 'Precision', 'Recall', 'Specificity', 'F1-Score', 'AUC', 'AUPRC']
data_matrix = data.loc[:, indicators].values
da... |
c50fe801542543d4cf741e4acfb1de500cb7cdb3cce63f62915f3d7190a4217e | Python | 875 | 31 | from PymoNNto import *
from PymoNNto.Exploration.Evolution.Interface_Functions import *
import numpy as np
import matplotlib.pyplot as plt
class Counter(Behavior):
def initialize(self, neurons):
self.inc = self.parameter('inc', 1.0)
neurons.count = np.zeros(neurons.size)#neurons.vector()
pl... |
a6c82b7d9c279de51e8547c44429cd65187b3288e4671f787f0c7b9b83a543c8 | Python | 876 | 38 | """Transform base class
Registers every child class
"""
__all__ = ["Transform", "get_transform"]
from abc import ABC, abstractmethod
registry = {}
def get_transform(name):
clean_name = name.lower().replace("_", "")
return registry[clean_name]
class Transform(ABC):
def __init__(self, params):
... |
22d0c76765b0f9108a0e55c255108ca74b6de5e10e1695666a3228a1de52a160 | Python | 877 | 45 | """isort:skip_file"""
from torch.backends import cudnn
from .device import DEVICE
from .models import (
IdCNN,
IdentifierBase,
IdentifierIdCNN,
IdentifierContrastive,
ResNet18,
load_identifier_model,
)
from .train import (
evaluate,
evaluate_only_acc,
StopTraining,
train_loop,
... |
9aa53e161d0fe5218db7b6b26e68cf812c740927d4c23b88859581c4fb78b5da | Python | 877 | 28 | from PymoNNto.NetworkCore.Behavior import *
class Homeostasis(Behavior):
def initialize(self, neurons):
target_act = self.parameter('target_voltage', 0.05, neurons)
self.max_ta = self.parameter('max_ta', target_act, neurons)
self.min_ta = self.parameter('min_ta', target_act, neurons)
... |
dbeceea2d23c1e2b7912d7d11e1481e77d9c0b03a35cebf2c916062e9deb9841 | Python | 878 | 39 | # Copyright (C) 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from copy import deepcopy
import torch
import torchvision
# Download Test datasets
mnist_test = torchvision.datasets.MNIST(
"./files/",
train=False,
download=True,
transform=torchvision.transforms.Compose(
[
... |
7e239b2d4f89c0db088d61140a3ba9a5f8e7c39a26f6a9b8673d8caad0c0bd3c | Python | 879 | 22 | from PymoNNto.NetworkCore.Neuron_Group import *
import numpy as np
def normalize_synapse_attr_sparse(src_attr, target_attr, target_value, neurons, synapse_type):
neurons.temp_weight_sum = neurons.vector()
for s in neurons.synapses(afferent, synapse_type):
if 'sparse' in s.tags:
s.dst.tem... |
58214946d5a73a49d5ec0212a901cd750c1461e8cab5b38e3003e98829f4af66 | Python | 881 | 35 |
import torch.nn as nn
class Classifier(nn.Module):
"""
General implementation of simple multi-layer perceptron for single prediction.
Author: Emma Svensson
"""
def __init__(self, hidden_layers):
super(Classifier, self).__init__()
inplace = False
self.layers = nn.ModuleLi... |
3244362a704aaa17de2cb0d3011019115c82c843336681cc5d0cb6ed182f040d | Python | 883 | 38 | import nibabel as nib
import numpy as np
def load_ct_to_numpy(data_path):
if type(data_path) != str:
data_path = data_path.name
image = nib.load(data_path)
data = image.get_fdata()
data = np.rot90(data, k=1, axes=(0, 1))
data[data < -1024] = 1024
data[data > 1024] = 10... |
0d35894684abce1a6a1dd2b40e8a65ed39377d911f236f1bb88133b2104b8bfe | Python | 885 | 31 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="pyfizi",
version="0.7.1",
author="Nicholas Mancuso, Megan Roytman",
author_email="nicholas.mancuso@med.usc.edu",
description="Impute GWAS summary statistics using reference genotype data",... |
24ce3e5cf1b5c5871ee7b85f8f3098f7a306994a8f037e2208f4a5b98096d302 | Python | 885 | 26 | from common.trials import run_experiments
from osa.configuration import get_config_dict_from_yaml
def make_config_dict_list():
config_dict_list = list()
for name in [
# "WideResNet",
# "max-SEResNet20-attention-4",
# "BNN-max-SEResNet20-attention-4",
# "uniform-smooth-BNN-max-... |
42f632c007b98edb22d9ef912f4156e4c7a7be4496c8824dfeae983058f10a8c | Python | 886 | 31 | import numpy as np
import NALSM_GEN_SUPPORT as sup
import os
SAVE_VER_INP = input('VERSION? [int]: ')
SAVE_VER = int(SAVE_VER_INP)
LEN = -1
main_Path = os.getcwd()
dataPath_TRAIN = main_Path + '/train_data'
netPath = main_Path + '/networks'
PATH = dataPath_TRAIN+'/ver_' + str(SAVE_VER) + '/'
filen... |
94321b2796b2b89f9fe3b73bfe8fe1f841fd044265452782d87d6aa9b3878363 | Python | 886 | 31 | from dig.xgraph.dataset import BA_LRP
import shutil
def test_BA_LRP():
root = 'datasets'
dataset = BA_LRP(root)
gen_data1 = BA_LRP.gen_class1()
gen_data2 = BA_LRP.gen_class2()
assert len(dataset) == 20000
assert dataset.data.edge_index.size() == (2, 840000)
assert dataset.data... |
d15ad7d0adaba44d483023de1e122f9088e877065dbe16641c49a3201649d07b | Python | 886 | 31 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="GeneSGAN",
version="0.0.1",
author="zhijian.yang",
author_email="zhijianyang@outlook.com",
description="A python implementation of Gene-SGAN for multi-view semi-supervised clustering",
... |
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