sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
62c5af22ece1015e35411728c657d04df298287e7abfd4769e9179d2e1b59400 | Python | 646 | 23 | """
Z-score the signal of each channel.
"""
import argparse
from pathlib import Path
from elephant.signal_processing import zscore
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path, required=True,
help="path to input data in... |
eac4e2e07221383d606f40a9fa41de903994728d370fe24fde3ca4503c2d8899 | Python | 647 | 23 | """
Z-score the signal of each channel.
"""
import argparse
from pathlib import Path
from elephant.signal_processing import zscore
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path, required=True,
help="path to input data in... |
561d0b2804f947ea0cf1dfc15597ae305c115c7cc9d9d887692d614c3cc9a1f5 | Python | 648 | 18 | import os
name_script = "network_perm_equivariant_modular_conv_new_lba.py"
string = "#!/usr/bin/env bash \n\n#SBATCH --partition=batch_default\n#SBATCH --cpus-per-task=2\n#SBATCH --gres=gpu:1\n#SBATCH --time=48:00:00\n\nsource ~/.bashrc\nmodule load anaconda/wml\nbootstrap_conda\nmodule load cuda\n\nconda activate p... |
d084bf1e0bd1e2b817bbc47f9850e6a12de92c75c17c4997322dae16967eb74f | Python | 648 | 16 | import geomstats.backend as gs
from geomstats.test.random import RandomDataGenerator
from geomstats.test.test_case import TestCase
class FiniteDifferenceTestCase(TestCase):
def setup_method(self):
if not hasattr(self, "data_generator"):
self.data_generator = RandomDataGenerator(self.space)
... |
667ea9664a05149db90edda573ba11f930708173ca263df1e8bb4f424f4eff6b | Python | 649 | 22 | class SimpleLossCompute:
"A simple loss compute and train function."
def __init__(self, generator, loss_function, opt):
self.generator = generator
self.loss_function = loss_function
self.opt = opt
def __call__(self, x, y, norm):
x = self.generator(x)
loss = (
... |
af15df7b3dc239026a4147c59a774bd30af8f21fad5c27bcb3c1a39f2e145f2d | Python | 649 | 19 | import unittest
from reinvent.models.reinvent.models.vocabulary import (
Vocabulary,
create_vocabulary,
SMILESTokenizer,
)
from tests.test_data import CELECOXIB, COCAINE, METAMIZOLE, ASPIRIN, SIMPLE_TOKENS
class TestCreateVocabulary(unittest.TestCase):
def setUp(self):
smiles = [ASPIRIN, META... |
3a0c381c0c54942045c98abcf58a76145e05d0a7c2c4a62bcb794f3375639840 | Python | 651 | 27 | import pandas as pd
from GeneSGAN.Gene_SGAN_clustering import cross_validated_clustering
import os
if __name__ == '__main__':
output_dir = './genesgan'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
image_data = pd.read_csv('toy_data_imaging.csv')
gene_data = pd.read_csv('toy_data_gene.csv')
fold_... |
5322d25d4df43763b00e409c556879bca3fb873d0b94e22af74d548eded50df7 | Python | 651 | 21 | try:
# importlib.metadata is present in Python 3.8 and later
import importlib.metadata as importlib_metadata
except ImportError:
# use the shim package importlib-metadata pre-3.8
import importlib_metadata as importlib_metadata
try:
# __package__ allows for the case where __name__ is "__main__"
... |
f89c6d7d78d6cef4c014667ed9e7e8eb6f1094274c3a090983e7dfa6aad72d1e | Python | 652 | 18 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Task package."""
from importlib import util
from openfl.federated.task.runner import TaskRunner # NOQA
if util.find_spec("keras") is not None:
from openfl.federated.task.runner_keras import KerasTaskRunner # NOQA
if util.find_sp... |
64a4ffe0472ed340a494481c4d2da296f816cee4468dd2677fecfc953ca6de7f | Python | 653 | 23 | """
Z-score the signal of each channel.
"""
import argparse
from pathlib import Path
from elephant.signal_processing import zscore
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path, required=True,
help="path to input data... |
de2262425407d62ca6780dee013692f2693b8a1188f63a7805437288b6ec6d95 | Python | 653 | 20 | from torch import nn
import torch
class CrossAttentionModule(nn.Module):
def __init__(self, channel_size):
super().__init__()
self.query = nn.Linear(channel_size, channel_size)
self.key = nn.Linear(channel_size, channel_size)
self.value = nn.Linear(channel_size, channel_size)
d... |
5237c6191cc0b1503ca4d5390257aa2054b79f9df4c09ffbe4029f5a5f9a9d75 | Python | 654 | 24 | from torch import Tensor
from typing import Optional
from . import register_stats_fn
@register_stats_fn(name="top1")
@register_stats_fn(name="top5")
def top_k_accuracy(
output: Tensor, target: Tensor, top_k: Optional[tuple] = (1,)
) -> list:
maximum_k = max(top_k)
batch_size = target.shape[0]
_, pre... |
68c74ac531f7284ddacfccd60fc3dfff7bd2cc1c71994c949f500a4ff15e07c1 | Python | 654 | 25 | import setuptools
with open('README.md', 'r') as rmf:
readme = rmf.read()
with open('VERSION', 'r') as verf:
version = verf.read()
setuptools.setup(
name='live2p',
version=version,
author='Will Hendricks',
author_email='hendricksw@berkeley.edu',
description='Real-time calcium imag... |
0b48b1a49435ce8945b5c866d0e7f71be4ef3840b00ed11c20c1537b5e0727da | Python | 655 | 24 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from typing import Optional, Tuple
from . import register_act_fn
@register_act_fn(name="prelu")
class PReLU(nn.PReLU):
"""
Applies the `Parametric Rectified Linear Unit <https... |
606fe1dc33ed27cdb6604efff0bdd18892cbf9aefc85f5c2440d26263bfdf92c | Python | 655 | 21 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Data package."""
from importlib import util
from openfl.federated.data.loader import DataLoader # NOQA
if util.find_spec("keras") is not None:
from openfl.federated.data.loader_keras import KerasDataLoader # NOQA
if util.find_s... |
0353eb22cb77d96ec0016d2d358519cfa30e4ff0212b253e05d1160f330b1b56 | Python | 656 | 26 | from sklearn.feature_selection import SelectKBest, f_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(f_classif, k=400)
feature_new = model.fit_transform(feature, label)
feature = pd.DataFrame(fea... |
3e6ee8f2779540e806d734b0d1207fb50b5f3aba465e9f3c4f1855df8cb1f1d9 | Python | 656 | 26 | from sklearn.feature_selection import SelectKBest, f_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(f_classif, k=800)
feature_new = model.fit_transform(feature, label)
feature = pd.DataFrame(fea... |
8be6b637eadcf39f5184077614f7899717f3e393804aa2c056701b6231e2b66e | Python | 656 | 17 | from reinvent.chemistry.standardization.filter_types_enum import FilterTypesEnum
from tests.chemistry.fixtures.test_data import INVALID
from tests.chemistry.standardization.base_rdkit_standardizer import BaseRDKitStandardizer
class TestRDKitStandardizerDefaultLongAliphaticOn(BaseRDKitStandardizer):
def setUp(self... |
924686f8bee2da778c670df95e9810903fa71b4275db7fc900e1c0cdfc25e561 | Python | 656 | 26 | from sklearn.feature_selection import SelectKBest, f_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(f_classif, k=600)
feature_new = model.fit_transform(feature, label)
feature = pd.DataFrame(fea... |
e27d62430631370defc8f1fdadf9ec6956af2d6f538192fb99fbd686bc6c8db5 | Python | 656 | 26 | from sklearn.feature_selection import SelectKBest, f_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(f_classif, k=200)
feature_new = model.fit_transform(feature, label)
feature = pd.DataFrame(fea... |
eec303469a7fd266afc2866014cf11f67a5df326739219502283ca17ecba5c17 | Python | 657 | 20 | # 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 math
# Technically, use of this module should probably be guarded by conditional_torchscript_jit
# But its use as a drop-in replacement for functions like torch.nn.functional.silu ... |
4ba5b0fe8f79f0c0463266cf57a394a0bc19de5db0f5564a26f21ab5ed1d09e8 | Python | 658 | 30 | from unittest import TestCase
import unittest
import numpy as np
import filecmp
import os
from smithers.io import STLHandler
stl_file = "tests/test_datasets/cube.stl"
def test_points():
data = STLHandler.read(stl_file)
np.testing.assert_array_almost_equal(data["points"][0], [-0.5] * 3)
def test_number_poi... |
ed8f152cc9ae86295d979927ef2f47d9099ce918ee874ede4358456af01edf55 | Python | 658 | 21 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from .base_datasets import AtomicDataset
from .lmdb_dataset import NequIPLMDBDataset
from .ase_dataset import ASEDataset
from .npz_dataset import NPZDataset
from .hdf5_dataset import HDF5Dataset
from .t... |
13559c302dabd9bccf4bed00672f2a5bdb7deb9ce693609693d3eb73ce13ffdc | Python | 659 | 18 | from nltools.utils import check_brain_data, check_brain_data_is_single
from nltools.mask import create_sphere
from nltools.data import Brain_Data
import numpy as np
def test_check_brain_data(sim_brain_data):
mask = Brain_Data(create_sphere([15, 10, -8], radius=10))
a = check_brain_data(sim_brain_data)
ass... |
653b0734fec3b436be1e9741ba90bf3ff5e8e3d329dde43aa9a68280accb26dc | Python | 659 | 26 | from sklearn.feature_selection import SelectKBest, f_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(f_classif, k=1000)
feature_new = model.fit_transform(feature, label)
feature = pd.DataFrame(fe... |
315870bad38b633bbd1e8c40f56007e1b1973119a83d84891b3d866ed7dcdb21 | Python | 660 | 23 | # Generated by Django 4.1.1 on 2023-01-02 11:51
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('celsus', '0046_extraproperties'),
]
operations = [
migrations.RemoveField(
model_name='socialpl... |
abbb7b41be553608672b2c2fdc81caaae17d86982f09d04869dec26584a6805f | Python | 661 | 26 | import numpy as np
#%%
import e3nn_jax as e3nn
irreps1 = e3nn.Irreps("1o")
irreps2 = e3nn.Irreps("1o")
n_samples = (irreps1.dim * irreps2.dim)
x = np.random.normal(size=[n_samples, irreps1.dim])
y = np.random.normal(size=[n_samples, irreps2.dim])
M = np.einsum("...i,...j->...ij", x, y)
tp = e3nn.tensor_product(e3nn.... |
ad8ef5957932f51e07f8a207a8eb98ef271cb848640d4ffbf63d2d7c715698fb | Python | 662 | 23 | # Generated by Django 4.1.1 on 2022-10-21 10:41
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('celsus', '0013_remove_author_project_project_associated_authors_and_more'),
]
operations = [
migrations.Rem... |
fb0d14aaf14acaad318e06eea8c2574172761e7fc16efb9ef46c7f34f912cd11 | Python | 663 | 26 | """Helper routines
A set of common auxiliary functionality.
"""
from typing import List
import torch
def collate_fn(encoded_seqs: List[torch.Tensor]) -> torch.Tensor:
"""Converts a list of encoded sequences into a padded tensor
:param encoded_seqs: encodes sequences to be padded with zeroes
:return: p... |
9e2716ef0a18800865c1f6b161f7cec34aba55a745347b230c7741a75416bd42 | Python | 665 | 11 |
from sleepy.processing.algorithms.massimi import Massimi
#rom sleepy.processing.algorithms.core import Algorithm
#from sleepy.processing.algorithms.parameter import Parameter
from sleepy.processing.algorithms.walkthrough.walkthrough import Walkthrough
from sleepy.processing.algorithms.relative.relative import Rel... |
c9f0071202e290faf893392fd43235e0d0b1cec0e98fdea3938b2f06633b3bf5 | Python | 665 | 23 | # Generated by Django 4.1.1 on 2022-12-07 12:26
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('celsus', '0041_curtain_enable_curtain_owners_alter_curtain_link_id_and_more'),
]
operations = [
migrations.... |
5e09ea471a5ca82f1869d89d875b0acb084b10ec49faf922cfbe48d0a8064873 | Python | 666 | 29 | import torch
import warnings
def _is_positive_int(x):
return (isinstance(x, int) and x > 0)
def slow_down_warning(base, property, solution):
warnings.warn(
(
"In {}, you have {} enabled, this will slow down training. Set to {} to disable it. ".format(
base, property, solu... |
7b48bdb84abc30471be8877f555593d86f53c2f8767ee369f8bb07b228ce0b7d | Python | 666 | 28 | from mlspm.graph._analysis import GraphStats
from mlspm.graph._molecule_graph import Atom, MoleculeGraph
from mlspm.graph._utils import (
add_rotation_reflection_graph,
crop_graph,
find_bonds,
find_gaussian_peaks,
make_box_borders,
make_position_distribution,
save_graphs_to_xyzs,
shift_m... |
c6d18595cd32387571a6b1df621719e8fb774050519cba4b9c931566cefed97b | Python | 666 | 29 | #!/usr/bin/env python3
from pathlib import Path
from snakebids import bidsapp, plugins
# For Sphinx doc generation, __file__ may not be defined
try:
app_path = Path(__file__).resolve().parent
except NameError:
# Fallback for Sphinx - use current working directory or find snakedwi module
import snakedwi
... |
fd4537659dbf606ef7cc0e560d7a648768fdaabee3c6dcac80366d8eb0bb1b18 | Python | 666 | 18 | import os
import json
from collections import defaultdict
from pathlib import Path
config_path = Path(__file__).parent / "integration_tests_config.json"
if config_path.exists():
with open(config_path, "r") as f:
config = json.load(f)
else:
config = {}
MAIN_TEST_PATH = config.get("MAIN_TEST_PATH", "")
... |
6966c59ffa7215d6fd90bf961c227ad868428cd37a51769e037cf90871ad17e6 | Python | 667 | 26 | import pandas as pd
import deepchem as dc
from sklearn.ensemble import RandomForestClassifier
import random
## Split
KFold = dc.splits.RandomSplitter()
KFold2 = KFold.k_fold_split(dataset, k=10)
## MTRF
for train, test in KFold2:
trainX = pd.DataFrame(train.X)
trainy = pd.DataFrame(train.y)
testX = pd.D... |
25bb860b6e383bda230912160af55606aaa97418615ead35917326c67ceb2fff | Python | 668 | 22 | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'celsusdjango.settings')
try:
from django.core.management import execute_from_command_line
except... |
73a216acb330b5996e00a4292762f3c0ff1401a6d327fab2052449e01bdf9c23 | Python | 668 | 16 | # Copyright (c) 2023 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... |
9d006bf29e033b84e6e1d518c7baea75d3a089a962bb6d7b819b1913c69eda7a | Python | 668 | 16 |
from sleepy.test.data import TestSignal
from sleepy.processing.engine import Engine
from sleepy.processing.algorithms.massimi import Massimi
from sleepy.processing.filters.bandpass.core import BandPassFilter
# Prepare the algorithm and filter instances (we keep the standard values)
algorithm = Massimi().render... |
2f5769ca554aad85bbfb139475afed7711a0de79058ef71f145ad87bf3d10c7c | Python | 669 | 27 | # Generated by Django 2.1 on 2018-08-22 07:07
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='markers',
fields=[
('id', models.IntegerField... |
072552932be9f55e7d4b80f6872783e6233ea487221655f1ebed93d7fda060d9 | Python | 670 | 26 | from .geodesic import LogSolverAgainstMetricTestData, LogSolverTestData
class LogSolverAgainstClosedFormTestData(LogSolverAgainstMetricTestData):
fail_for_not_implemented_errors = False
tolerances = {
"log": {"atol": 1e-3},
"geodesic_bvp": {"atol": 1e-4},
}
class PathStraighteningAgains... |
d4985df0847401ff7d6367a731594e1de94715fb10355908aca127e52d1d1d23 | Python | 670 | 30 |
from sleepy.gui.builder import Builder
from PyQt5.QtWidgets import QWidget
from sleepy.processing.algorithms.parameter import ParameterBase
import pdb
class Algorithm:
def render(self):
self.buildTree = ParameterBase.getBuildTree(self)
Builder.setAttributesFromJSON(self.buildTree, s... |
e228f3ef5b586ed71f86fd0220fe00bb2b5b3c5862e648cc63013f217dd81af8 | Python | 670 | 34 |
SHARES = [
0.5, 0.6, 0.7, 0.8, 0.9,
0.91, 0.92, 0.93, 0.94,
0.95, 0.96, 0.97, 0.98,
0.99, 1.0
]
def pop(items):
return items[0], items[1:]
def get_quantiles(records, shares=SHARES):
if not shares:
return
counts = [count for _, count in records]
total = sum(counts)
acc... |
3b42315ed0c4d04fa4bf7d0015620335851a8ef62f515209307d85b00fc7e530 | Python | 671 | 18 | # 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, Moun... |
65386001daa54691a556e5fd54a76ea0a72a8de83c9604312aca1e6a36ea7c2d | Python | 671 | 30 | import pytest
def _has_ignore_marker(markers):
for marker in markers:
if marker.name == "ignore":
return True
return False
@pytest.hookimpl(trylast=True)
def pytest_collection_modifyitems(session, config, items):
selected = []
deselected = []
for item in items:
if _h... |
df3dbcdd8a01f5c6022552180c904cc9ef1df0ae5342a11d0407d677e9374b5b | Python | 672 | 15 | import os
from shutil import copytree, ignore_patterns
def copy_src(wdir, current_file=None, dst_dir="src", patterns=("*outputs*", "*__pycache__*", "*.pth", "*.pkl", "*.out", "*results*", "*.pdf")):
if current_file is None:
cwd_root = wdir
else:
current_file_fullname = os.path.join(wdir, curre... |
e15272ef61f81065e22e420d4e6de29466628b3cb9f4b62f44681949926bf287 | Python | 672 | 29 | import os
import numpy as np
import crepe
# this data contains a sine sweep
file = os.path.join(os.path.dirname(__file__), 'sweep.wav')
f0_file = os.path.join(os.path.dirname(__file__), 'sweep.f0.csv')
def verify_f0():
result = np.loadtxt(f0_file, delimiter=',', skiprows=1)
# it should be confident enough a... |
f68f363055006223a87aa07f89f919f1f34f78b87f1323ef736632b2d3a8a51a | Python | 672 | 36 |
import sys
from ..glove import (
MB,
Glove,
iter_read,
parse_glove_cooc
)
def cooc_count(args):
glove = Glove.from_env()
glove.cooc(
sys.stdin.buffer,
sys.stdout.buffer,
vocab=args.vocab,
memory=args.memory,
window=args.window
)
def cooc_shuffle(... |
fad5912fbd32ee5c09c062576c75c27f46ded462ec2c67e4bd7b3933dbd68d0b | Python | 672 | 25 | import numpy as np
from typing import Literal
def _format_with_SI_postfix(x, decimals=2):
postfixes = [(1e9, "b"), (1e6, "M"), (1e3, "k")]
postfix = ""
for v,p in postfixes:
if x >= v:
x/= v
postfix = p
break
x = np.round(x, decimals)
x = str(x)
if ".... |
2963314bee78de0fd98afa8e1ea3aad7a1134ad5a48ef8fab068ce99eed5cac3 | Python | 673 | 24 | import jax.numpy as jnp
import jax
from plasticity import inputs
import unittest
class TestInputs(unittest.TestCase):
def test_inputs(self):
mus = jnp.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
sigmas = jnp.array(
[
[[1, 0, 0], [0, 1, 0], [0, 0, 1]],
[[0... |
3cd0c0ea4f33d391da3407c0d008136f8bbfc6cd4e24fded10f73f21b4dc32e8 | Python | 674 | 24 | """Define the state"""
from __future__ import annotations
__all__ = ["ModelState"]
from dataclasses import dataclass
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from reinvent.models import ModelAdapter
from reinvent.runmodes.RL.memories import DiversityFilter
@dataclass
class ModelState:
"""Data... |
940b5e665be4f0c179888092cc7430fb7bac188d4d10d00f08c9bcc6ff836a4c | Python | 675 | 28 | import pytest
from reinvent.runmodes.RL.memories import BucketCounter
@pytest.fixture
def bucket_counter():
# no special setup
yield BucketCounter(max_size=5, A=6, B=5, C=7, D=4)
pass # no tear-down
def test_bucket_full(bucket_counter):
assert bucket_counter.bucket_full("A")
assert bucket_coun... |
3d04738dd7c36bda7dac86b30686525dda79bf931f3bb46d087386d163477575 | Python | 676 | 29 | """Base objects for varifold implementation."""
import abc
import geomstats.backend as gs
class Pairing(abc.ABC):
"""Kernel pairing."""
def _get_kernel_prod_args(self, point_a, point_b):
return [x for pair in zip(point_a[:-1], point_b[:-1]) for x in pair] + [
point_b[-1]
]
... |
9965815172a50c0f8f324341bfa0f72e72dac6f035f92f22b3a3d003af3d2fde | Python | 676 | 23 | # Generated by Django 4.1.5 on 2023-01-14 13:15
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('celsus', '0051_alter_rawdata_value'),
]
operations = [
migrations.CreateModel(
name='KinaseLibraryModel',
fields=[
... |
c63135b8806cb0ef09b6eb3fb3e9c8cf035d41a4d677227466e1cd2c4bfe486f | Python | 677 | 24 | import unittest
import torch.utils.data as tud
from reinvent.models.reinvent.models.dataset import Dataset
from reinvent.models.reinvent.models.vocabulary import SMILESTokenizer
from tests.test_data import (
SIMPLE_TOKENS,
HEXANE,
PROPANE,
ETHANE,
BUTANE,
ASPIRIN,
)
class TestDatasetFunction... |
35de465a0c0f460e74fe37eb05ea5db4d3f5e60d50f8ecf4b9bc426ed5b14242 | Python | 679 | 23 | import geomstats.backend as gs
from geomstats.test.data import TestData
class FullRankMatrices32TestData(TestData):
def belongs_test_data(self):
smoke_data = [
dict(
point=gs.array(
[
[-1.6473486, -1.18240309],
... |
0f886e5e6937ebbb464700fad1049cd5434b397e98555f8f3dafa9e4c2d7dde1 | Python | 680 | 20 | from tkinter import Tk
from tkinter.filedialog import askdirectory, askopenfilename, askopenfilenames
def openfoldergui(rootdir=None, title=None):
return _get_opengui(askdirectory, initialdir=rootdir, title=title)
def openfilegui(rootdir=None, title=None):
return _get_opengui(askopenfilename, initialdir=rootd... |
582187ec47e68367d73d3793792e756ffb5d83d642b02148ec7798d7c4d601ac | Python | 680 | 31 |
import sys
from navec.pq import PQ
from ..pq import (
quantize,
pad
)
from ..glove import (
parse_glove_emb,
trans_glove_emb
)
from ..log import log_info
def pq_fit(args):
records = parse_glove_emb(sys.stdin.buffer)
words, weights = trans_glove_emb(records)
log_info(
'PQ qdim: %... |
74a18c410aa7a456fc4715ab75d05fb692ff90da8b2b644886bdf4877ca7eb27 | Python | 682 | 33 | # type: ignore
__version__ = "0.0.0"
__submodules__ = ["core", "paths"]
from snakebids import _warningformat # noqa: F401
# isort: split
# <AUTOGEN_INIT>
import lazy_loader
__getattr__, __dir__, __all__ = lazy_loader.attach_stub(__name__, __file__)
__all__ = [
"BidsComponent",
"BidsComponentRow",
"Bid... |
c5c310743215fea21ca45c3eb9f8dc2b7882772cd7fa920fcf8bd783c3e11e84 | Python | 682 | 18 | # Set this to the path to Caffe installation on your system
caffe_root = "/path/to/your/caffe/python"
gpu = True
# -------------------------------------
# These settings should work by default
# DNN being visualized
# These two settings are default, and can be overriden in the act_max.py
net_weights = "nets/caffenet/... |
c6f3c14c31cc0e77b207f0967ab45bde05a3d9e0a4c2025f7fef8e4c4d3a64ff | Python | 682 | 27 | from __future__ import annotations
from enum import Enum
class DefaultKeys(Enum):
ENERGY = "REF_energy"
FORCES = "REF_forces"
STRESS = "REF_stress"
VIRIALS = "REF_virials"
DIPOLE = "dipole"
POLARIZABILITY = "polarizability"
HEAD = "head"
CHARGES = "REF_charges"
TOTAL_CHARGE = "tot... |
c9ee9401ca2fd1587166166af2ca376a6beb32ba48387a2f0e146657b77f6e72 | Python | 682 | 25 | """
--------------------------------------------------------------------------------
Date: 19/04/2022
@author: Nikolaos Vardalakis
--------------------------------------------------------------------------------
Implementation Notes
--------------------------------------------------------------------------------
... |
552e45c8f3c885c0f6b57650e90f54ba84aad30d5ec2fd080fbeac0eeea9fc8a | Python | 683 | 12 | def get_read_cut_data(df, read_cut=5, post=False):
# takes a df_data, and returns data that has been filtered for read counts
# either just in the pre selection, or also in the post-selection (post=True)
if post == False:
df_read = df[(df.pre1>read_cut) &(df.pre2 > read_cut)]
else:
df_re... |
b0d9115ea53a96df1671d63a51491ea35fdb7e7d48d7a78cabe9283ce9572934 | Python | 683 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=50)
feature_new = model.fit_transform(feature, label)
feature... |
2facd6253bf57bd13d886aebe4866f1358ab70fa3948f2746b7c9dfe48b8c361 | Python | 684 | 34 | import nidaqmx as ni
def demo():
sys = ni.system.System.local()
# Display the version number of the driver
print('\nNI DAQmx driver version: %s\n' % str(sys.driver_version))
# List all connected device
print('Connected devices:')
n=1
for device in sys.devices:
print('%d. %s' % ... |
e0ae0966a53e194563fb0abcdaa9df9ccde206cd6140ed518a7db38a4c4257fd | Python | 685 | 19 | import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
"Define standard linear + softmax generation step."
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x, temperature=1.0):
... |
5d0546a6cbe63604ac9be76af256cba74fbf1ad719aa1c06229eab3e30ae0cd3 | Python | 686 | 17 | from typing import Optional
from matplotlib import pyplot as plt
from dynaphos.cortex_models import get_cortex_coordinates_default, get_cortex_coordinates_grid
def plot_coordinates(params: dict, n_electrodes_x: int, n_electrodes_y: int,
x_max: Optional[int] = None):
coordinates_cortex = get... |
8bb198f6ad175bd291a56d1ef947d402afb9b04422d8ea1e98d7b3c4ace8db83 | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=200)
feature_new = model.fit_transform(feature, label)
featur... |
919e0949cf70f4dd9462cb00205a54de530e739cdbdb832cbaad7c25320a28db | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=400)
feature_new = model.fit_transform(feature, label)
featur... |
a9a57e78e145a8ddb2c4e47448e516ce6a476c6a0b663b850234143e006a9211 | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=600)
feature_new = model.fit_transform(feature, label)
featur... |
b7d307e512890b7bcb4bf5c6c96c33f27734f898295d9acb868caf0f9d216d43 | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=150)
feature_new = model.fit_transform(feature, label)
featur... |
d1869b1d3cf4313ff489c8f31299830bcc083bb50a2220ec6267e6f0f8dcd50a | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=800)
feature_new = model.fit_transform(feature, label)
featur... |
e07ba0b07e0f03ac9fd34ca5a9e95f3a3226256913265938212bb9c36d6f1962 | Python | 686 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=100)
feature_new = model.fit_transform(feature, label)
featur... |
5809545d91d714606d3e5f10b49ed1f4229ddd378cb5048a234c61464a837533 | Python | 687 | 18 | # 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... |
7216bd8396884cd89d3a0463f57a4d9e3f5a20e2b886bd51f78fd23d0ba3cc0a | Python | 688 | 27 | import sys
from pathlib import Path
from importlib import import_module
from ... import logger
def load_config(cfg_module):
"""
Dynamic loading of a config file module.
Parameters
----------
cfg_module : str
The absolute path to the config file to load.
"""
cfg_module = Path(cfg_... |
a30100882ad9ca54387d201b8e07030281a5bc0e36d9cbb3b24265c89e6841ad | Python | 689 | 26 | from sklearn.feature_selection import SelectKBest, mutual_info_classif
import pandas as pd
import numpy as np
data = pd.read_csv("Feature_3102.csv", sep='\t')
feature = data.iloc[:, 5:-1]
label = data.iloc[:,-1]
model = SelectKBest(mutual_info_classif, k=1000)
feature_new = model.fit_transform(feature, label)
featu... |
4573a750886981aa53998d342e704bfd1a3049c0945d646012606b689db86099 | Python | 690 | 20 | import pytest
from geomstats.geometry.heisenberg import HeisenbergVectors
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.heisenberg import HeisenbergVectorsTestCase
from .data.heisenberg import HeisenbergVectors3TestData, HeisenbergVectorsTestData
class TestHeisenb... |
74c546976ab0f71d5251ba9869a93f0b0c06e5c0ccb5f429cce6bd5b5d916067 | Python | 691 | 27 | # ==============================================================================
# Copyright 2025 Technical University of Denmark
# Author: Nikolas Borrel-Jensen
#
# All Rights Reserved.
#
# Licensed under the MIT License.
# ==============================================================================
import argparse
... |
7c1def19f602698c5a30afc0788cd38a48cc110e0b59ae5354a3d5c6a690c2a6 | Python | 692 | 19 | from assist.experiment_names_lookup import trained_models
import config
from shutil import copyfile
models_to_move = trained_models['ensembles_isbi']
res_path = config.results / 'model_evaluation'
moved_path = config.results / 'tmp_results'
moved_path.mkdir(exist_ok=True)
for model_name in models_to_move:
if 'f... |
7bbf92361a578eeeadf1faa31a52b48874640c94e15876918bbe9e0b0a23c7e3 | Python | 694 | 38 | import utils
"""
The class MyOwnDatasetClass
arguments:
files: files to be read in
functions:
process: main logic of file processing
return_data: returns processed data list
"""
class MyOwnDatasetClass:
def __init__(self, files):
self.files = files
self.data = self.process()
def return... |
981f0084f31bcfb50ffc9c8e2cd2290b6d78b0dd5214ea154157f51eeea21335 | Python | 695 | 21 | import click
from corgie import scheduling
from corgie.log import logger as corgie_logger
from corgie.log import configure_logger
@click.command()
@click.option('--lease_seconds', '-l', nargs=1, type=int, required=True)
@scheduling.scheduler_click_options
@click.option('-v', '--verbose', count=True, help='Turn on de... |
0d42d72b0fa2b8992a0589092c4bd9937c0bb8a3786c37e7f4a2c8e35c17057e | Python | 698 | 19 | # 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... |
39a5afe8d265b9576643f7772b834de2a83f288f3aa951b71a7ac64f155fe126 | Python | 698 | 35 |
import tarfile
from io import BytesIO
from .record import Record
class Tar(Record):
__attributes__ = ['path']
mode = 'r'
def __init__(self, path):
self.path = path
def __enter__(self):
self.tar = tarfile.open(self.path, self.mode)
return self
def __exit__(self, *args)... |
66c56aaad66b32f6676eeb9e3520a937f50e7f25cd5d8200be2c0a60b70e8e56 | Python | 698 | 31 | from geomstats.test.test_case import TestCase
class OptimizerTestCase(TestCase):
def test_minimize(
self,
fun,
x0,
expected,
atol,
fun_jac=None,
fun_hess=None,
hessp=None,
):
res = self.optimizer.minimize(
fun, x0, fun_jac=fun... |
fecf9bb63cf5cf4ab7052245d71caebd8dec9fa27e73added81f0aca610a8e43 | Python | 698 | 19 | """
--------------------------------------------------------------------------------
Date: 16/06/2022
@author: Nikolaos Vardalakis
--------------------------------------------------------------------------------
Implementation Notes
--------------------------------------------------------------------------------
... |
273db317627fd92e05ed76e1d29589d95604b9a17d4d28c059eaf5fc1f959069 | Python | 701 | 20 | import pandas as pd
from Code.utils import plot_heatmap
import matplotlib.pyplot as plt
PATH = 'Baselines/baseline_generalization/'
# save data
df_auc_hmp = pd.read_pickle(PATH + f'auc_mean.pkl')
df_aupr_hmp = pd.read_pickle(PATH + f'aupr_mean.pkl')
df_auc_std_hmp = pd.read_pickle(PATH + f'auc_std.pkl')
df_aupr_std_h... |
35cda004f74cd59caea974b1a8acfdfdddc9f1ed1e92f8db921ada6427d8f4cd | Python | 702 | 27 | import numpy as np
from smithers.io import VTPHandler as Handler
test_file = "tests/test_datasets/cube.vtp"
def test_points():
data = Handler.read(test_file)
np.testing.assert_array_almost_equal(data["points"][0], [-0.5] * 3)
def test_number_points():
data = Handler.read(test_file)
np.testing.asser... |
7eb6e31e54bc2f67c9dadeb5a9c1694911d6a8808748faaa0088513f0de29c2d | Python | 703 | 27 | import numpy as np
from reinvent_plugins.components.RDKit.comp_group_count import (
Parameters,
GroupCount,
)
def test_comp_group_count():
smarts = ["ClccccF"]
smiles = ["c1cc(F)ccc1Cl"]
params = Parameters(smarts)
gc = GroupCount(params)
results = gc(smiles)
assert results.scores[0] ... |
4278eea9b6146d99c1248e747c573ffab38d815d36094d64c7a7ee9f396cf6df | Python | 704 | 20 | import numpy as np
from smithers.io import VTKHandler
poly_file = "tests/test_datasets/cube.vtk"
ugrid_file = "tests/test_datasets/cube_grid.vtk"
def test_polydata():
data = VTKHandler.read(poly_file)
np.testing.assert_array_almost_equal(data["points"][0], [-0.5] * 3)
np.testing.assert_equal(data["point... |
7c61a17e56d31d67b1e6d24882856a4f4b6f50960768ad0f9d9962819a8cca34 | Python | 704 | 30 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from .torch_versions import (
_TORCH_GE_2_4,
_TORCH_GE_2_6,
_TORCH_GE_2_7,
_TORCH_GE_2_8,
_TORCH_GE_2_9,
_TORCH_GE_2_10,
_TORCH_GE_2_13,
_TORCH_IS_2_10_0,
_TORCH_IS_... |
7cbc3a378dde8551c89bab1bcfc03496faae5cf37836a7ba75cc7f3fcd95d356 | Python | 704 | 23 | # Generated by Django 4.1.1 on 2023-01-02 11:33
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('auth', '0012_alter_user_first_name_max_length'),
('celsus', '0045_curtain_curtain_t... |
52b6b54fe1562f294af9fcaa284eb076afa89fdcbf92c933ab8f5837575d5a78 | Python | 712 | 23 | import geomstats.backend as gs
from geomstats.test.data import TestData
from .base import ComplexMatrixVectorSpaceTestData
class HermitianMatricesTestData(ComplexMatrixVectorSpaceTestData):
pass
class HermitianMatrices2TestData(TestData):
def belongs_test_data(self):
data = [
dict(point... |
a82dcf8471e0dbc078ab6c1869770d288e70159e9292a357eff0a93d5044fb7d | Python | 712 | 18 | from geomstats.geometry.positive_reals import PositiveReals
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.base import VectorSpaceOpenSetTestCase
from geomstats.test_cases.geometry.riemannian_metric import RiemannianMetricTestCase
from .data.positive_reals import Posi... |
d1421a177e65f384417912e8be53cd34fa3c6eab9932f663a81843fd0a1714be | Python | 713 | 25 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from typing import Tuple, Optional
from . import register_act_fn
@register_act_fn(name="leaky_relu")
class LeakyReLU(nn.LeakyReLU):
"""
Applies a leaky relu function. See `Rec... |
8aaf6d5b0970f9c7d409d3f393874d2879b1bf4eaf929d381d3e6d92ae008280 | Python | 717 | 26 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from typing import Optional, Tuple
class Softmax(nn.Softmax):
"""
Applies the Softmax function to an input tensor along the specified dimension
Args:
dim (int): Di... |
7bc5afb01715c5df69b9b951f94b63db9ef943627bc90f3ecf355d321b165446 | Python | 718 | 28 | # Generated by Django 2.1 on 2018-08-27 01:38
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='tcgapatient',
fields=[
('id', models.AutoFiel... |
d2eb36158194a01df066dd2919a37de69445caaebe98304640b8149d294a3836 | Python | 718 | 20 | from PymoNNto.NetworkCore.Behavior import *
class Normalization(Behavior):
def initialize(self, neurons):
self.syn_type = self.parameter('syn_type', 'GLUTAMATE', neurons)
self.norm_factor = self.parameter('norm_factor', 1.0, neurons)
neurons.temp_weight_sum = neurons.vector()
def iter... |
214dbdbcd4bde84287bf9faa0b838097cd2599a5b327d1bf5f0bcadd596b5d0a | Python | 720 | 18 | # 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
#
# Un... |
ffd293f6d3e546462e469f340bdb62690063f51e5bba7dcf2ab253abd9b2f3f5 | Python | 721 | 39 |
from StringIO import StringIO
import string
testcases = r"""1 -> 1
1"2" -> 12
1"2 -> 12
1"\"2" -> 1"2
"1" "2" -> 1, 2
1\" -> 1"
1\\" -> 1\
1\\\" -> 1\"
1\\\\" -> 1\\
1" 1 -> 1 1
1\" 1 -> 1", 1
1\1 -> 1\1
1\\1 -> 1\\1
"""
#testcases = r"""1\\\\" -> 1\\
#"""
t = StringIO(testcases)
def quote(s):
result = ... |
b73de9a353c88e8d5e71769a8d38967c275fe3721a85795e61d598ab6b4e99ae | Python | 722 | 23 | # Generated by Django 4.1.1 on 2022-12-19 15:47
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('celsus', '0043_curtain_description'),
]
operatio... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.