sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6a06b35d42fa2a95cf411529b1844d964728274be1b1a37e7b0b0e8c1ead8198 | Python | 239 | 20 | import numpy as np
import scipy.stats
from topaz.stats import gmm_fit, gmm_fit_numpy, norm_fit, normalize
def test_gmm_fit():
pass
def test_gmm_fit_numpy():
pass
def test_norm_fit():
pass
def test_normalize():
pass |
faffd9d0cd4b107e64f958520416eecffa43a779d2678af353d13b888b243c37 | Python | 239 | 12 | import sys
import importlib
def bypass_compiler_fixup(cmd, args):
return cmd
if sys.platform == 'darwin':
compiler_fixup = importlib.import_module('_osx_support').compiler_fixup
else:
compiler_fixup = bypass_compiler_fixup
|
66ba943c8cdb12b7cda3039d470f2d00d76c8538f98893fd65ad8cc0568b71aa | Python | 240 | 4 | """Data and file handling utilities."""
from .data_util import get_new_dataframe, get_value_dict, replace_values, reorder_columns
from .io_util import check_filename, clean_filename, extract_suffix_path, get_file_list, get_path_components
|
7f26bddb707281d3898745ba6d0eafefdb5700dfe1ca00234fe188d97c94f528 | Python | 240 | 8 | import mmpose
from packaging import version
if version.parse(mmpose.__version__).major > 0:
from Pose_multi_mmpose_new import Pose_multi_mmpose_new as Pose_multi_mmpose
else:
from Pose_multi_mmpose_old import Pose_multi_mmpose
|
8655ef21732ee36cbeabc4ee965c4faf9655772327a1f868d6098c8d475cdec6 | Python | 240 | 10 | from .band import *
from .concatenate import *
from .normalize import *
from .pick import *
from .to import *
from .spectrum import *
from .coefficient import *
from .correlation import *
from .band_pyeeg import *
from .downsample import *
|
ee08dab929814117265bcc245ef087fd36a03e980409eabe0a745beca871e44f | Python | 240 | 9 | #!/bin/env python3
'''
Print a table of quasi-particles
'''
from pdlfeynman.particle import PARTICLES
print('symbol', 'name', '2Q', '2P', sep='\t')
for p in PARTICLES:
print(p, p.name, f'{int(2*p.Q):+2}', f'{int(2*p.P):+2}', sep='\t')
|
29e54e033c41cb1dc1f372ba1e39fd0edc42aaa4e2ac2ac64f9c06781ad6b5e2 | Python | 241 | 7 | from .multregconn import *
from .pc_multregconn import *
from .calcconn_parcelwise_noncircular import *
from .calcactivity_parcelwise_noncircular import *
from .corrcoefconn import *
from .partial_corrconn import *
from .combinedFC import *
|
7ac20ac3a88b5d8eb5e084257ba842a026bc7d562147001cbd42f571df26d9de | Python | 241 | 6 | from segm.data.loader import Loader
from segm.data.imagenet import ImagenetDataset
from segm.data.ade20k import ADE20KSegmentation
from segm.data.pascal_context import PascalContextDataset
from segm.data.cityscapes import CityscapesDataset
|
8f65c82e2b4c31d534004b5ec2320d7d350a7c3dd0bf63f6592086062cc899a0 | Python | 241 | 7 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_backends
class Speech2TextFeatureExtractor:
def __init__(self, *args, **kwargs):
requires_backends(self, ["speech"])
|
9bc807244e5397217425611db70d1b59de0de3832b3624a9b128d637639ebbd0 | Python | 241 | 8 | """Loads a trained model checkpoint and makes predictions on a dataset."""
from chemprop.args import PredictArgs
from chemprop.train import make_predictions
if __name__ == "__main__":
make_predictions(args=PredictArgs().parse_args())
|
53381582d99623deb273a6fa596e1e90358a477a0d6fea59249cf561a36e5942 | Python | 242 | 8 | import os.path as osp
class Paths:
rhino_root = '/'
freesurfer_bin = '/usr/global/freesurfer/bin'
matlab_bin = '/usr/global/matlabR2015a/bin/matlab'
ants_root = osp.join(osp.expanduser('~sudas'),'bin','ants')
paths = Paths() |
87ed5c5263b3ea684bb234e33ab27c88f7a3a4674b0b21b89734dfb5f199bcb8 | Python | 242 | 9 | # SPDX-FileCopyrightText: 2015 Eric Larson
#
# SPDX-License-Identifier: Apache-2.0
from .file_cache import FileCache, SeparateBodyFileCache
from .redis_cache import RedisCache
__all__ = ["FileCache", "SeparateBodyFileCache", "RedisCache"]
|
c369b3fb59e34c166071e2179a75f342909b02e8573ee41f6d14102a10ce8116 | Python | 242 | 11 | """Useful utils
"""
from .misc import *
from .logger import *
from .visualize import *
from .eval import *
# progress bar
import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), "progress"))
from progress.bar import Bar as Bar |
51e27c7141d3c1c7345fd5f04ac8c2c3325b4ab26915d360160a97f089187903 | Python | 243 | 10 | """
Package for numerical implementation of differential geometry concepts
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 29.02.16
"""
__author__ = 'Antonio Martinez-Sanchez'
from .curve import SpaceCurve |
6ed975d9b9f03f084b79d5aaa8ff5167071401849cbde950781784ee4512f9d5 | Python | 243 | 4 | from .loss import bce_loss, bce_with_logits_loss, cross_entropy, nll_loss
from .metric import (AUPRC, AUROC, accuracy, c_index, f1, precision, recall,
sensitivity, specificity, youden_j)
from .plot import confusion_matrix
|
a059d55fd88d4f3837afc21c0c4a2f53acfa7a736fbf58808f355f1f9ac5e470 | Python | 243 | 11 | import os
import glob
import gc
from os.path import basename
files = glob.glob('audio_conversion/*.avi')
for file in files:
os.system('ffmpeg -i '+str(file)+' audio/'+str(basename(file))+'.wav')
gc.collect()
print('finished: '+str(file))
|
946b4973118ce38433e026e4e2b6db9ab2b19cdaf5fbded4db94da99e2de859c | Python | 244 | 9 | """
This module exists only to simplify retrieving the version number of chardet
from within setuptools and from chardet subpackages.
:author: Dan Blanchard (dan.blanchard@gmail.com)
"""
__version__ = "5.1.0"
VERSION = __version__.split(".")
|
ee16fe95b6acc8650f16fd2ce95f8a7f26cadd3366915253cc61deaac504aa6b | Python | 244 | 9 | """Test PDB utilities"""
from eudockscore.utils.pdb import get_chain_sequence
def test_sequence():
with open("seq.txt", "r") as f:
correct_seq = f.read()
seq = get_chain_sequence("2j0m.pdb", "A")
assert seq == correct_seq
|
f18a01f3535c837a0f73086ec3c2e1d3f86a13ef9fb795e85a3e148fca37ddd4 | Python | 244 | 9 | """
This file is a configuration file that contains default directories for saving data
"""
import os
HDF_DIR = "./data/hdf/"
FIGURES_DIR = "./data/figures/"
PYPET_LOGGING_CONFIG = os.path.join(os.path.dirname(__file__), "pypet_logging.ini")
|
06a857d979918e14a706679c15c5d378f6b3678da3fb6b78df37607993b118db | Python | 245 | 16 | from __future__ import annotations
from .config import (
SegmentationConfig,
)
from .stage import (
SegmentationResult,
run_segmentation,
)
__all__ = [
"SegmentationConfig",
"SegmentationResult",
"run_segmentation",
]
|
6b0e1abb3cbd76ca714770fa18a840c3561d8c26c0795db1b352d5e071b81828 | Python | 245 | 8 | from .graph_theory import *
from .machine_learning import *
from .information_theory import *
from .visualisations import *
from .data_splitting import *
from .scoring import *
from .deep_learning import *
from .molecular_featurisation import *
|
2e2dd6f691e6c0d3dfbf3ccef01330f70173c8ba6d9c1c3eecc3a185fd823c86 | Python | 247 | 12 | from cellmincer.datasets.datasets import \
build_datamodule, \
build_ws_denoising, \
MovieDataModule, \
wipe_temp_files
__all__ = [
'build_datamodule',
'build_ws_denoising',
'MovieDataModule',
'wipe_temp_files',
]
|
a56e04123ef64847b826b1b0182e44042a3b1541ef4b12ee626e0113ee7928ce | Python | 247 | 11 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup
# https://github.com/pypa/setuptools_scm
use_scm = {"write_to": "napari_bil_data_viewer/_version.py"}
setup(
use_scm_version=use_scm,
include_package_data=True
)
|
5e328be3c6adf76a640656162e888cfe5c82b68e33a4f52a165bba8fe520d739 | Python | 248 | 11 | # -*- coding: utf-8 -*-
#
# Populate namespace with preprocessing frontend
#
from .preprocessing import *
from .resampledata import *
# Populate local __all__ namespace
# with the user-exposed frontend
__all__ = ["preprocessing", "resampledata"]
|
e7653e047becfd0f3b24707b11b58cb8315752cd6023c7c13d2e452ee30bb09d | Python | 248 | 13 | from met_brewer.palettes import (
MET_PALETTES, COLORBLIND_PALETTES_NAMES, COLORBLIND_PALETTES,
met_brew, export, is_colorblind_friendly
)
MET_PALETTES
COLORBLIND_PALETTES_NAMES
COLORBLIND_PALETTES
met_brew
export
is_colorblind_friendly
|
15e2b1be3894330a9ce085113a15effc119542baa51e381a2fd09a8710b5d299 | Python | 249 | 8 | from cuppa.runners.args import RunnerArgParser
from cuppa.runners.prediction_runner import PredictionRunner
if __name__ == "__main__":
kwargs = RunnerArgParser().get_kwargs_predict()
runner = PredictionRunner(**kwargs)
runner.run()
|
2e018ce3bf4e630f175d15a5980db4dbab900cde4ed657365356ca85c967ddc2 | Python | 249 | 5 | from .methods.cluster_factory import cluster_factory
from .methods.reducer_factory import reducer_factory
from .methods.metric_factory import internal_factory, external_factory
from .methods.tester import Tester
from .methods.graph import CKNNGraph
|
90bef9248baebe74dbf518aad38db911b79e58dc78c6d976973ac57ce90435e2 | Python | 249 | 6 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from main import main
main(combinations=[("issue571-base", "issue571-base", "issue571-base-v2"), ("issue571-base", "issue571-base", "issue571-v7"), ("issue571-base", "issue571-base", "issue571-v8")])
|
4c1e555ff3ad750ef12d08037cafacff87e6292712df95e012825d42863c9308 | Python | 250 | 11 | from HPRO import PW2AOkernel
kernel = PW2AOkernel(
lcao_interface='siesta',
lcaodata_root='./aobasis',
hrdata_interface='qe-bgw',
vscdir='../scf/VSC',
upfdir='../pseudos',
ecutwfn=30
)
kernel.run_pw2ao_rs('./aohamiltonian')
|
c98ee4b207f3c8f450708e1354d731106d89c03e286b412d8571cab42e673489 | Python | 250 | 4 | from .regression_evaluator import RegressionEvaluator
from .multilabel_evaluator import MultilabelEvaluator
from .semantic_multilabel_evaluator import SemanticMultilabelEvaluator
from .semantic_regression_evaluator import SemanticRegressionEvaluator
|
11c709b2398d0b9f8f0e5e7fef59f17b259650a9fcd748d6b74bfd1a42dc8894 | Python | 251 | 7 | from .base_dataset import BaseDataset
from .emotion_recognition import *
from .folder_dataset import FolderDataset
from .mne_dataset import MNEDataset
from .numpy_dataset import NumpyDataset
from .personal_identification import *
from .sspev import *
|
203c71c93db86c8e43fbad10b4203d867044d5d585821829de6c7e0f69164980 | Python | 251 | 7 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_backends
class Speech2TextProcessor:
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece", "speech"])
|
4323384e9c338ec25537522f700540a3b701357047d13ab9d3f0a2437cb9c0b7 | Python | 251 | 11 | # -*- coding: utf-8 -*-
#
# Populate namespace with user exposed
# connectivity methods
#
from .connectivity_analysis import connectivityanalysis
# Populate local __all__ namespace
# with the user-exposed frontend
__all__ = ["connectivityanalysis"]
|
6c677f1ba512e3c9dbc1a659334d32a82731bba74751440ebddbcbb3ff9ebc6c | Python | 251 | 11 | from HPRO import PW2AOkernel
kernel = PW2AOkernel(
aodata_interface='siesta',
aodata_root='../aobasis',
hrdata_interface='qe-bgw',
vscdir='../scf/VSC',
upfdir='../pseudos',
ecutwfn=30
)
kernel.run_pw2ao_rs('./aohamiltonian')
|
8d4036be91d2e0ebd8c0b8bf155485d899a7f9e9071ee942dba74d4d24bae198 | Python | 251 | 11 | class ConfigurationError(Exception):
def __init__(self, msg):
self.msg = msg
super().__init__(self.msg)
class InitializationError(Exception):
def __init__(self, msg):
self.msg = msg
super().__init__(self.msg) |
8efd03a9659a8bfe13b256d0352de71f17a91db7d90d41469373eba84691add2 | Python | 251 | 16 |
# db = {
# 'host': '127.0.0.1',
# 'user': 'omnidev',
# 'password': 'we8pizza',
# 'database': 'omniweb',
# 'port': 13306
# }
db = {
'host': '127.0.0.1',
'user': 'root',
'password': 'root',
'database': 'omniweb',
'port': 3306
} |
aca6a441db78a05c2c33f92079ef24f6aa353d360c30a6e0bd0c3c332ab35d17 | Python | 251 | 9 | from cog import BaseRunner, Input
class Runner(BaseRunner):
def setup(self) -> None:
self.prefix = "hello"
def run(self, text: str = Input(description="Text to prefix with 'hello '")) -> str:
return self.prefix + " " + text
|
9a6f7191dcb69da79e46c27b667c0a57320c07ad579cc336358aa4c8957c6760 | Python | 252 | 11 | # -*- coding: utf-8 -*-
#
# Populate namespace with specest routines
#
# Import __all__ routines from local modules
from .freqanalysis import freqanalysis
# Populate local __all__ namespace
# with the user-exposed frontend
__all__ = ["freqanalysis"]
|
071e7fcb6254771884b736a946908e3c598e967d73981c1c523796def81c522a | Python | 253 | 8 | # -*- coding: utf-8 -*-
# @Author: Sadamori Kojaku
# @Date: 2022-10-14 14:27:10
# @Last Modified by: Sadamori Kojaku
# @Last Modified time: 2023-06-15 12:02:59
from embcom.embeddings import *
from embcom.samplers import *
from embcom.utils import *
|
1d17ddefd31a1dd42a4741e1ac1359078c8172c7eea3a31643ceb60efa91079b | Python | 253 | 9 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .scVI_distribution import *
from .loss import *
from .utils import *
from .pairedVAE import *
from .model import *
from .evaluator import * |
747ff4d4fbf5a7aae7f75a9e9869a1f65102ae3eed0ec674c2c7cab7daada583 | Python | 253 | 9 | import numpy as np
hartree2ev = 27.211386024367243
bohr2ang = 0.5291772105638411
TWOCENTER_RGRID_DEN = 100. / 12. # 100 points at 12 bohr
AOFT_QGRID_DEN = 1 / np.sqrt(500) * 2048 # 2048 points at 500 Hartree
hpro_rng = np.random.default_rng(seed=42)
|
bc89f08935ce611fe2a0d818b961352f0622ec8d2b3770bb7146a74eb696c7e8 | Python | 254 | 12 | from memory_profiler import profile
@profile
def test_with_profile(arg1):
"""dummy doc"""
return None
if __name__ == '__main__':
assert test_with_profile.__doc__ == "dummy doc"
assert test_with_profile.__name__ == "test_with_profile"
|
be8c162f43c013f6623eeec42a17d89c07430f20960958afb1af6790c879c834 | Python | 254 | 9 | import pytest
@pytest.fixture
def config(top_level_datadir, validation_config_factory):
file_path = (top_level_datadir / 'validation/file.csv')
test_args = [str(file_path)]
_, config = validation_config_factory(test_args)
return config
|
76415e92b250ed553a84dc963588ea97c30ffbfe3a7ee972e3969b7528037c5e | Python | 255 | 8 | from Pose_mdn_joint import Pose_mdn_joint
class Pose_mdn_joint_fpn(Pose_mdn_joint):
def __init__(self, conf, **kwargs):
conf.mdn_joint_use_fpn = True
conf.mdn_use_unet_loss = False
Pose_mdn_joint.__init__(self,conf,**kwargs)
|
94f5e2b88efc703ed7ddfeb498845ca6c8a1a22fa981234dbd2cd3b1dc5951c1 | Python | 255 | 9 | from .logic import InfidelityOperator
from .overlap import InfidelityOperatorUPsi, InfidelityUPsi
from .infidelity_optimizer import InfidelityOptimizer
# from netket.utils import _hide_submodules
# _hide_submodules(__name__, hide_folder=["overlap",])
|
041b21b1f298416219081f0148740f40746eb1ec2c016ba3bf76c9244b5689cf | Python | 256 | 7 | """Different methods for fitting events."""
from hmp.models.cumulative import CumulativeMethod
from hmp.models.eliminative import EliminativeMethod
from hmp.models.event import EventModel
__all__ = ["EventModel", "EliminativeMethod", "CumulativeMethod"]
|
13fbb76ef55fc8594e6cf0ec9886dd5c7646e1a01ca0cf346bacfb74ab1b98e4 | Python | 256 | 11 | # -*- coding: utf-8 -*-
#
# Populate namespace with plotting routines
#
# Importlocal modules, but only import routines from spy_plotting.py
from .spy_plotting import *
# Populate local __all__ namespace
__all__ = []
__all__.extend(spy_plotting.__all__)
|
a1eb82ceef6206b26a4618f92d0e65627b47234fb2472649a77817adf9cd5fdd | Python | 256 | 13 | """Tests for loading config files.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
import os
class TestConfig:
def test_config_file_exists(self):
from e3fp.config.params import DEF_PARAM_FILE
assert os.path.isfile(DEF_PARAM_FILE)
|
da8cb9ebaf924c2311bd69bb6ca632a6e91b89634f9e6e7ae3db487afcf917d0 | Python | 256 | 12 | from enum import Enum, auto
class SopaResolutionMode(Enum):
'''Enum class for the different ways of resolving multiple SOPA entries of the same atoms.'''
FULL = auto()
MAX = auto()
MIN = auto()
MIN_MAX = auto()
AVERAGE = auto()
|
01919bf715bb2573f1d71e244232536cb24e9e496813a5e923e7c32b3c74e79a | Python | 257 | 9 | #!/bin/env python3
"""
Feynman diagrams for PDL topology
=================================
Python module to create Feynman diagrams for phase defect line (PDL)
topology using NetworkX.
"""
from . import particle, pool, reaction, history, diagram, yamlrepr
|
40f0a21273cb66957f73276c9db76d5ae4ff5e27abbea1ad57469039e0fc0c5d | Python | 257 | 8 | import os
import numpy as np
root_folder = '/media/pjc/expriment/mdd_exam/pjc/EV_GCN-master/data/'
data_folder = os.path.join(root_folder, 'ABIDE_pcp')
subject_IDs = np.genfromtxt(os.path.join(root_folder, 'subject_IDs.txt'), dtype=str)
print(subject_IDs) |
733f5b3a10ffc02237bde9bfaeaab926611969f65ab8020b49f5911518b3f044 | Python | 257 | 13 | from wonambi import Dataset
from .paths import bci2000_file
def test_bci2000_data():
assert bci2000_file.exists()
d = Dataset(bci2000_file)
assert len(d.read_markers()) == 0
data = d.read_data()
assert data.data[0][0, 0] == 179.702
|
f86af4d8f182eb0990fa7d180edfaf62f83e8ee07c74788845bae7369f49380f | Python | 257 | 9 | from plotly.offline import plot
from .paths import PLOTLY_PATH
def save_plotly_fig(fig, name):
div = plot(fig, include_plotlyjs=False, output_type='div', show_link=False)
with (PLOTLY_PATH / (name + '.html')).open('w') as f:
f.write(div)
|
0ecc6f8e1dc480f1996369fea896267f32d7339bceeeecbb20e1637da55dd5a9 | Python | 258 | 10 | """Evaluator scaffold for multi-state predictions."""
class MultiStateEvaluator:
"""
Placeholder for evaluating multi-state predictions.
Input handling and evaluation methods will be defined as multi-state
metrics are implemented.
"""
|
41edc61cf0d994d8f20a3e45fece22414e9485aaaddb90730494c96da2c3a752 | Python | 258 | 9 | """Trains a model on a dataset."""
from chemprop.args import TrainArgs
from chemprop.train import cross_validate
from chemprop.train import run_training
if __name__ == '__main__':
cross_validate(args=TrainArgs().parse_args(), train_func=run_training)
|
c2ef12125733205b6d5c54e513cbdcf00b8dfaaa22117ac6f30f21b817a6dccf | Python | 258 | 16 | import pandas as pd
n_evaluate_targets = 19
past_horizon = 24
forecast_horizon = 3
train_test_split_period = pd.Period("2011-01-01", freq="M")
# Model Training
batch_size = 32
n_epochs = 50
patience = 10
# Model Parameters
n_features_Y = 1
n_models = 25
|
1555adc7385b1fee159cfe9fe1aef0500f3d5193dfdfccdb92f8433d002a0dea | Python | 259 | 6 | """Core algorithms for the tissue-masking tool.
* :mod:`masking` - `compute_tissue_mask`, `process_group`.
* :mod:`io` - folder discovery, channel grouping, atomic file writes.
* :mod:`pipeline` - `MaskingParams` dataclass + `MaskingPipeline.run`.
"""
|
1ffee0c393fd393375d7e71b7f7a824217a5e5c48a57febf0485cb10f1a9c2bf | Python | 259 | 7 | """Validation of HED tags."""
from .hed_validator import HedValidator
from .sidecar_validator import SidecarValidator
from .def_validator import DefValidator
from .onset_validator import OnsetValidator
from .spreadsheet_validator import SpreadsheetValidator
|
2f964bac05fdcecd8d37ef99fc2ed32c9ff2442ed98a0640e5fb13d7634104db | Python | 259 | 8 | from .data import AijData
from .graph import Collater, get_graph, load_orbital_types
from .model import Net
from .utils import LossRecord
from .e3modules import Rotate, e3TensorDecomp
from .kernel import DeepHE3Kernel
from .analyzer import testResultAnalyzer |
e5d61eb2bcb9088b19bdbf31082e14851e3829deb672b93cfdcc5cffce24cf49 | Python | 259 | 10 | """
Set of utilities for to extract topological and geometrical information
from postsynaptic densities
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 09.10.14
"""
__author__ = 'martinez'
__version__ = "$Revision: 002 $"
|
17ee4a77e63a1d2b4ee8674f7dd93c35fb754414d71e5d74713bfd7bf2849f44 | Python | 260 | 10 | from scbs import __version__
with open("pyproject.toml", "r") as toml:
for line in toml:
if line.startswith("version = "):
toml_version = line.strip().split()[-1].strip('"')
def test_version():
assert __version__ == toml_version
|
b81d2fd15e6c1ac4c4b3bbed5169ffb67fc357628367ca7c0f73948fa51a01db | Python | 260 | 13 | """
Directory with scripts for various high-level tasks.
# Author: Vladan Lucic (Max Planck Institute for Biochemistry)
$Id$
"""
from __future__ import unicode_literals
from __future__ import absolute_import
__version__ = "$Revision$"
from . import common
|
b71ed38c30be8fd7957c5ed2283a1f1ef16846e4eff92c0904fc6caa9050f668 | Python | 261 | 5 | import os
default_num_threads = 8 if 'nnUNet_def_n_proc' not in os.environ else int(os.environ['nnUNet_def_n_proc'])
RESAMPLING_SEPARATE_Z_ANISO_THRESHOLD = 3 # determines what threshold to use for resampling the low resolution axis
# separately (with NN) |
4835098ce40d9f196193d8488c90420195771dba6b038b7ca72e5aaf08d4e301 | Python | 262 | 10 | import pathlib
import sys
FILE = pathlib.Path(__file__).resolve()
MODULE_DIR = FILE.parent # repository root
REPO_ROOT_DIR = MODULE_DIR.parent
MODEL_DIR = REPO_ROOT_DIR / 'models'
if str(REPO_ROOT_DIR) not in sys.path:
sys.path.append(str(REPO_ROOT_DIR))
|
b4207df58f1477d8fb91f6671dc3cfd8fd9b35f8ad018dacd0e3e9ad1b0ea15e | Python | 262 | 13 |
try:
from ._version import version as __version__
except ImportError:
__version__ = "unknown"
from .bil_data_loader_widget import napari_experimental_provide_dock_widget
from .reader import napari_get_reader
#from .dataset_info import get_datasets
|
9e0091da4e43a5d595645427a08964c629a69aec001a50eac9eca656b8848cc3 | Python | 263 | 8 | DATA_SNAPSHOT_VERSION_DESCRIPTIONS = {
"783": "Snapshot 783 - Oct 2023 [latest release]",
}
DATA_SNAPSHOT_VERSIONS = sorted(DATA_SNAPSHOT_VERSION_DESCRIPTIONS.keys(), reverse=True)
DEFAULT_DATA_SNAPSHOT_VERSION = "783"
TESTING_DATA_SNAPSHOT_VERSION = "783"
|
c1e9711cd1bfaa80ad1e6fed308c54875a4938f5294bea1b7b5706138fc04d22 | Python | 263 | 12 | __all__ = [
"Implementation",
"VhdlNode",
"Edge",
"FilterParameters",
"edge",
"vhdl_node",
]
from elasticai.creator.ir.helpers import FilterParameters
from elasticai.creator.ir2vhdl import Edge, Implementation, VhdlNode, edge, vhdl_node
|
f83999dda718c578528246aaa0e965f8789a6987e7b76edfecea866067b15b51 | Python | 263 | 4 | from .default_handlers import get_default_shape_inference as get_default_shape_inference
from .shape_inference import IrShapeInference as IrShapeInference
from .shape_inference import Shape as Shape
from .shape_inference import ShapedDatagraph as ShapedDatagraph
|
26267070c4375d6fab43f9e4fda843a60950dbbf6b7fac41904daa8c29f52a76 | Python | 266 | 17 | from __future__ import annotations
from .config import PipelineConfig
from .pipeline import (
PipelineResult,
MorphogliaPipeline,
run_pipeline,
)
__all__ = [
"PipelineConfig",
"PipelineResult",
"MorphogliaPipeline",
"run_pipeline",
]
|
c1e3d0038536d2d2a060047248b102d38eee70d5fe83ca512e9601ba21e52dbf | Python | 266 | 7 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
from .initialise import init, deinit, reinit, colorama_text, just_fix_windows_console
from .ansi import Fore, Back, Style, Cursor
from .ansitowin32 import AnsiToWin32
__version__ = '0.4.6'
|
22ff4f38f1ad09a1d458827bed193b2eb6057b8eeef08fc81c62f1d042b16c46 | Python | 267 | 10 | from setuptools import setup, find_packages
setup(
author="Oliver Contier",
name="dimension_encoding",
version="0.0.1",
packages=find_packages(where="dimension_encoding"),
package_dir={"": "dimension_encoding"},
# package_dir={":": "src"},
)
|
70ee94bd708fd8dc7fc3e415aa5fd7c6bbd2103a0691fffba8faae54e97bbb11 | Python | 267 | 6 | """coglet — high-performance Rust prediction server for Cog ML models."""
from coglet._impl import CancelationException, __build__, __version__, server
from coglet._impl import _sdk as _sdk
__all__ = ["__version__", "__build__", "server", "CancelationException"]
|
b722764a1cfc20da853cf735d573b3bacad2a76a5a8f7011400ffeb4dbcd5955 | Python | 267 | 10 | from collections.abc import Hashable, Iterable, Mapping
from typing import Protocol
class Graph[T: Hashable](Protocol):
@property
def successors(self) -> Mapping[T, Iterable[T]]: ...
@property
def predecessors(self) -> Mapping[T, Iterable[T]]: ...
|
fdc9ab5f1898863ebf8c717de0ce0e6e22212e732e2362c6cb77c042312e038c | Python | 267 | 12 | import typing
import numpy as np
def colors(size: int = 128, seed: int = 0) \
-> typing.List[typing.Tuple[int, ...]]:
np.random.seed(seed)
return [
tuple(map(int, color))
for color in np.random.randint(0, 255, size=(size, 3))
]
|
2e505d8d053f35386fef894680881b5d40b65d5a62ca895a0f2c52eb77c6eecd | Python | 268 | 14 | import torch
def euclidean_dist(x, y):
# x: N x D
# y: M x D
n = x.size(0)
m = y.size(0)
d = x.size(1)
assert d == y.size(1)
x = x.unsqueeze(1).expand(n, m, d)
y = y.unsqueeze(0).expand(n, m, d)
return torch.pow(x - y, 2).sum(2)
|
b4ca5e804b4c14fd3c477fdfc0a15587f848428c91e145e38bcc37b8e58dff7f | Python | 268 | 9 | try:
import binutil # required to import from dreamcoder modules
except ModuleNotFoundError:
import bin.binutil # alt import if called as module
from dreamcoder.domains.logo.makeLogoTasks import demoLogoTasks
if __name__ == "__main__":
demoLogoTasks()
|
de439306d05fb1c9d90eeffe9f03906373be3575803fa02aa5a1134336db1684 | Python | 268 | 12 | """
Set of functions for IO communication with DisPerSe
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 02.04.14
"""
__author__ = 'martinez'
__version__ = "$Revision: 001 $"
from .disperse_io import *
from .handler import DisPerSe |
a2e9af05fdc1d1a7110e2d41c29c8ea0f7d206be67c796882496099490d79f57 | Python | 269 | 7 | __author__ = "Michael Ebner"
__email__ = "michael.ebner@kcl.ac.uk"
__license__ = "BSD-3-Clause"
__version__ = "0.9"
__summary__ = "NiftyMIC is a research-focused toolkit for " \
"motion-correction and volumetric image reconstruction of " \
"2D ultra-fast MRI."
|
0b9fd4e4dc768c77ebab8223d0d7bc3348322f40866a0b4f13b75ec5faba36ea | Python | 270 | 13 | # Add paths for importing modules
from os.path import join
import sys
dir_analysis = join("..", "analysis")
sys.path.append(dir_analysis)
dir_data = join("..", "data")
sys.path.append(dir_data)
dir_model = join("..", "model")
sys.path.append(dir_model)
|
d24e20a867c30c6a7202a37edae56a66169d614df2b9536549d4ba9b64493038 | Python | 270 | 9 | """
Additional FSL wrappers intended to be compatible with FSL python wrappers as far as possible
"""
from .fabber import fabber, mvntool
from .epi_reg import epi_reg
from .fnirt_extra import fnirtfileutils
__all__ = ["fabber", "mvntool", "epi_reg", "fnirtfileutils"]
|
dea7e39b9c41390b8d08daa9471ed760cce58ddcee1b64cc208d1e52fc01f177 | Python | 270 | 19 | import pandas as pd
from topaz.utils.star import (parse, parse_star, parse_star_body,
parse_star_loop, write)
def test_parse():
pass
def test_parse_star_body():
pass
def test_parse_loop():
pass
def test_write():
pass |
a8d5e366bbd27be7564d5e45065e8282187ce71a796ce5af130715d7393cba41 | Python | 271 | 13 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
from __future__ import print_function
from setuptools import setup, find_packages
import sys
setup(
name='gxipy',
version='1.0.1905.9051',
description='',
license='MIT',
packages=['gxipy'],
)
|
3d157548e43e0c4eea538c269b1d6a3871027f9ff89a7b0e31883f26e22b442b | Python | 272 | 11 | """Stdlib-only installer package. Runs on bare system Python BEFORE the
project venv exists - do NOT import regtools or any pip dependency here."""
from dataclasses import dataclass
@dataclass
class Result:
ok: bool
message: str
detail: str = ""
|
f2d07744ace8e33c30111e9880d5d8950c38a037dfc66f1980021d52eb14e3db | Python | 272 | 11 | from Models.InitialCNN import InitialModel
from Models.ViT import ViT
AVAILABLE_MODELS = {
"CNN": InitialModel,
"ViT": ViT
}
def get_model(config:dict, global_config:dict):
model = AVAILABLE_MODELS[config["architecture"]]
return model(**config["params"]) |
80f362dc2fe76ef5ffb035880a3e3b4c3c2c1ab627db396ab5ceef1dcb9f0481 | Python | 274 | 10 | import inspect
def test_registration_app_requests_large_screen_clamped_default():
from regtools.registration.launch import RegistrationApp
src = inspect.getsource(RegistrationApp.__init__)
assert "width=1720" in src
assert "height=1040" in src
|
ac122d8af71a0f92fcef0bc67662e65361a626cbc351cb44d49cb4f21fa1ab94 | Python | 274 | 7 | """Package to detect spindles, ripples, slow waves.
"""
from .spindle import DetectSpindle, merge_close, transform_signal
from .ripple import DetectRipple
from .slowwave import DetectSlowWave
from .arousal import DetectArousal
from .agreement import consensus, match_events
|
5c765dd3bc500a2e209a767c9ee3c2d69d4c8e0163f07d1b656b54a309bee57c | Python | 275 | 14 | from enum import Enum, auto
class EdgeType(Enum):
'''Enum class for the different types of edges.'''
# use bonds as calculated from nbo analysis
NBO_BONDING_ORBITALS = auto()
BOND_ORDER_NON_METAL = auto()
BOND_ORDER_METAL = auto()
SOPA = auto()
|
ce42bf0bf780a97918d48b7d8f1a0a571794e9d1091d7dfb77d7366237030b57 | Python | 275 | 9 | from torch import Tensor
from .math_operations import MathOperations as _FlpOperations
def quantize(x: Tensor, mantissa_bits: int, exponent_bits: int) -> Tensor:
return _FlpOperations(
mantissa_bits=mantissa_bits, exponent_bits=exponent_bits
).quantize(x)
|
00a0bb2082a666bacf64f695da9014105e8621dfc7cd1c505da86c381273f987 | Python | 276 | 9 | """`python -m installer` - launch the wizard at the repo root."""
import os
from installer.wizard import InstallerWizard
if __name__ == "__main__":
root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
InstallerWizard(project_root=root).run()
|
7dcab90f7a86352a00d9fd0b0652954ad07a8399504fe91b6343e2d2173b49e4 | Python | 276 | 9 |
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
with open(str(snakemake.output), 'w') as file:
for name, seq in snakemake.params['seq'].items():
record = SeqRecord(Seq(seq), id = name)
SeqIO.write(record, file, 'fasta')
|
b02dd8f688a1b1853eaeb8414b2e1449e14f4b89f979cb33b0d2db448426c8f8 | Python | 276 | 12 | from wonambi.attr import Surf, Channels
from wonambi.source import Linear
from .paths import (surf_path,
chan_path,
)
def test_source_linear():
surf = Surf(surf_path)
channels = Channels(chan_path)
Linear(surf, channels)
|
13e1c16a480eae2a70df2b5c04ce6fcf168b091158172c77f0890af7ea6a5b3b | Python | 277 | 12 | """ setup python path """
import sys
import os
def add_path(path):
if path not in sys.path:
sys.path.insert(-1, path)
add_path(os.path.join(sys.path[0], '../'))
add_path(os.path.join(sys.path[0], '../../'))
print("add code root path (with `mylib`, 'acsconv').")
|
60a509a65da1741a94ada62a14c125c648845e91dd15ef2f4af1fc186314c664 | Python | 277 | 6 | """Cellpose model-training launcher.
A thin GUI that shows step-by-step instructions for the human-in-the-loop
Cellpose training workflow and launches the Cellpose GUI in a subprocess. No
training logic lives here - Cellpose owns that; this is a guided entry point.
"""
|
eaa2e178e244eb68f391605523ca5d2f25ce04b34a0319d9437e7494ffe9c0a6 | Python | 277 | 13 | import numpy as np
from config import NEW_DATA
DATA = None
if NEW_DATA:
from dataloader_new import DATA, DATA_TEST
else:
from dataloader_old import DATA
print("Dataset {} loaded, containing image preprocessing of size{} ".format(DATA.name, np.shape(DATA.images)))
|
f395aa81936f62086f8d2ff5d7db17cb72945309a22bc255f8fb2f043035e035 | Python | 277 | 11 | #!/usr/bin/python
# -*- coding:utf-8 -*-
# -*-mode:python ; tab-width:4 -*- ex:set tabstop=4 shiftwidth=4 expandtab: -*-
from gxipy.gxiapi import *
from gxipy.gxidef import *
__all__ = ["gxwrapper", "dxwrapper", "gxiapi", "gxidef"]
__version__ = '1.0.1905.9051'
|
9ea4658cf012fe184b1b4a2b0fef62f24bcbec5e414de77a2dd43e0be99d0c7e | Python | 278 | 7 | """Package containing additional functions and classes, such as:
- exceptions
- simulate (functions to create fake data, channels for testing purposes)
"""
from .exceptions import UnrecognizedFormat, MissingDependency
from .simulate import create_data, create_channels
|
f7d9e14934ddfa0867e80d91c5ef143a5d7c32c6a31f5cba2855eecb2d3b5a15 | Python | 278 | 9 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..file_utils import requires_backends
SLOW_TO_FAST_CONVERTERS = None
def convert_slow_tokenizer(*args, **kwargs):
requires_backends(convert_slow_tokenizer, ["sentencepiece", "tokenizers"])
|
0a53f98c22165bd76c8720d4154f2d4db9849395e179327fd61d491fd16034a4 | Python | 279 | 12 | #!/usr/bin/env python
import hashlib
from lab.parser import Parser
def add_hash_value(content, props):
props['translator_output_sas_xz_hash'] = hashlib.sha512(content).hexdigest()
parser = Parser()
parser.add_function(add_hash_value, file="output.sas.xz")
parser.parse()
|
1a31fb37528ac4112b5cbc95eed78ae617ff3c782d64b33922ab208b94cba340 | Python | 280 | 7 | from ._int_arith_protocol import IntArithmetic
from .fxp_arithmetic import FxpArithmetic
from .fxp_params import FxpParams
def int_arithmetic(total_bits: int, signed: bool) -> IntArithmetic:
return FxpArithmetic(FxpParams(total_bits=total_bits, frac_bits=0, signed=signed))
|
8142ca5e85f0a341526471cb58ed9f7c1bb13220da93dfbe608df6fd48cbdde5 | Python | 280 | 9 | from scipy.signal import butter, lfilter
def butter_lowpass(lowcut, fs, order=5):
return butter(order, lowcut, fs=fs, btype='low')
def butter_lowpass_filter(data, lowcut, fs, order):
b, a = butter_lowpass(lowcut, fs, order=order)
y = lfilter(b, a, data)
return y |
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