sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f3a268e0fb744f6bd2a2b4193f173ebf315ce907a0a7e62e30d4b2716920756e | Python | 386 | 24 |
print('\n$1 Net_file (col1: ID; col2: ID)\n$2 Output_net_file \n')
import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
SPLIT=':'
OLD=set()
for line in fi:
seq=line.rstrip().split('\t')
this_net=[seq[0],seq[1]]
this_net.sort()
this_tmp = SPLIT.join(this_net)
if this_tmp not in OLD:
... |
f7637ae8a2a4af271f08b5908dbbec39b1418dec3b655dcc39644ba8a3842128 | Python | 386 | 18 | """
Regression fit over a strip plot
================================
_thumb: .53, .5
"""
import seaborn as sns
sns.set_theme()
mpg = sns.load_dataset("mpg")
sns.catplot(
data=mpg, x="cylinders", y="acceleration", hue="weight",
native_scale=True, zorder=1
)
sns.regplot(
data=mpg, x="cylinders", y="acceler... |
2df2d07456856d750efca69950d5cd440c8547cd1cb6333d410a055613e8d41d | Python | 387 | 13 | from .cosine import CosineDistance, LogitCosineDistance, NegativeScaledDotProduct
from .euclidean import EuclideanDistance, EuclideanMean
from .hyperbolic import HyperbolicDistance, HyperbolicMean
__all__ = [
"CosineDistance",
"EuclideanDistance",
"EuclideanMean",
"HyperbolicDistance",
"HyperbolicM... |
30a38d42915720aa93ae25cd61b651a1e9c3df6ff47ed41199807fefadbdb594 | Python | 387 | 13 | import unittest
from .common import skipif_no_net
@skipif_no_net
class TestWrite(unittest.TestCase):
def test_load_huang(self):
from neurondm import Config
# FIXME placeholder for loading and roundtripping
# neurons with other neurons as asserted equivalent
# or disjoint classes
... |
c74c926537beb07427fa1e769cc85a3277705f365cf2b1d9572afd5822e989e0 | Python | 387 | 17 | """
Timeseries plot with error bands
================================
_thumb: .48, .45
"""
import seaborn as sns
sns.set_theme(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",... |
0f1938528d8d065e1269f91f3d3b06f5e2a70476de3f45ffb0410bc275d44c0e | Python | 388 | 16 | from __future__ import print_function
import csv
import sys
file_name = sys.argv[1]
fold_coverage = int(sys.argv[2])
with open(file_name, 'rd') as fd:
fd_reader = csv.reader(fd)
fd_columns = fd_reader.next()
fd_value = fd_reader.next()
fd_dict = dict(zip(fd_columns, fd_value))
mean_depth = fd_dic... |
f39146982c30e74aa93434cf01b2af5e18b8028e339631de345efd03dcd67996 | Python | 389 | 9 | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++... |
f6a2a8d4b1eeead5e537a00eaa6d65e3f13bec72a2b6a33ebc079ab764549c3a | Python | 389 | 12 | """Tabby time-series foundation model.
The core package contains the pretrained backbone and preprocessing utilities.
Prompt/post-training components live under :mod:`tabby.posttraining`, while
benchmark entry points stay outside the importable package.
"""
from .models.PatchTSTFM import PatchTSTFM, PatchTSTFMConfig
... |
9bd2b82ce8917101558158cd94a6b08bbd2906145216677b91939796b8fac598 | Python | 390 | 14 | """
Linear regression with marginal distributions
=============================================
_thumb: .65, .65
"""
import seaborn as sns
sns.set_theme(style="darkgrid")
tips = sns.load_dataset("tips")
g = sns.jointplot(x="total_bill", y="tip", data=tips,
kind="reg", truncate=False,
... |
b77f1cc72bb21d78525e1cacc9610e087f4a5880e33d0d3cd6851b8e4f2c8a3b | Python | 390 | 18 | """
Grouped boxplots
================
_thumb: .66, .45
"""
import seaborn as sns
sns.set_theme(style="ticks", palette="pastel")
# Load the example tips dataset
tips = sns.load_dataset("tips")
# Draw a nested boxplot to show bills by day and time
sns.boxplot(x="day", y="total_bill",
hue="smoker", palette... |
77a5f315eb36c760bd548b8b1ce964bb301c880a518922bc59442f26789b0738 | Python | 391 | 8 | from futhark_ffi.build import build
build_single_core = build("src/cajal/ugw/c/unbalanced_gw_c", "cajal/ugw/single_core")
build_multicore = build(
"src/cajal/ugw/multicore/unbalanced_gw_multicore", "cajal/ugw/multicore"
)
build_opencl = build("src/cajal/ugw/opencl/unbalanced_gw_opencl", "cajal/ugw/opencl")
build_c... |
1c85b2773430ea0aa7dfff309dc8ca81ab005750eab06a2cc7d770387274e85d | Python | 392 | 15 | """ResNet deep learning classifiers.
This subpackage provides ResNet based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"ResNetClassifier",
"ResNetClassifierTorch",
]
from sktime.classification.deep_learning.resnet._resnet_tf import ResNetClassifier
from sktime.classification.d... |
2b532c3b071798df4993b7ccbf503035a5d9d78f9628b81ffd224cb58be54f1d | Python | 392 | 15 | """TapNet deep learning classifiers.
This subpackage provides TapNet based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"TapNetClassifier",
"TapNetClassifierTorch",
]
from sktime.classification.deep_learning.tapnet._tapnet_tf import TapNetClassifier
from sktime.classification.d... |
bd08c69e5a5593d0d9bf7414a3bc8b63e807d7f747eab03c2eb6536d1480ed98 | Python | 392 | 16 | from abc import ABC, abstractmethod
from typing import Dict, List
class Proposer(ABC):
"""Base model for proposer"""
@abstractmethod
def build_model(self, config: Dict) -> None:
pass
@abstractmethod
def propose(self, input_smiles: List[str],
rxn_types: List[str],
... |
f55a3ab6510d1ea6d2126a4f9b2f551bbf7178d4e27b9d62a3c150781c4cd81a | Python | 392 | 15 | """MCDCNN deep learning classifiers.
This subpackage provides MCDCNN based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"MCDCNNClassifier",
"MCDCNNClassifierTorch",
]
from sktime.classification.deep_learning.mcdcnn._mcdcnn_tf import MCDCNNClassifier
from sktime.classification.d... |
25a5c5913fc4b13a4874015f1a26e8f68cadb620ff662327b92963cfece42f7b | Python | 395 | 15 | """
Facetting histograms by subsets of data
=======================================
_thumb: .42, .57
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, row="sex", col="time", margin_titles=True)
bins = np.linsp... |
3ce7f85e4a77364e893def5da12e94c3342b7d16d42e74482141bafb08e86201 | Python | 398 | 15 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
def compute_binarizer(x, threshold=None):
return ((x > threshold).astype(x.dtype),)
class Binarizer(OpRunAiOnnxMl):
def ... |
9efb18228e8eb08d481a00ee07ce92f24f5c42850c32a7d817c8f4ef42a8722e | Python | 398 | 13 | """Exception types raised by corset-tools."""
class CorsetToolsError(Exception):
"""Base class for all errors raised by corset-tools."""
class DuplicateTranscriptError(CorsetToolsError):
"""Raised when the same transcript name occurs in more than one input set."""
class InputFormatError(CorsetToolsError):... |
a535097a6f65ae1f2118e69880b4348774284373e1c8846ce6fc6257a266ba8b | Python | 398 | 11 | from __future__ import print_function
def getTimeDiffInSeconds(time1, time2):
# print('time1 = ' + str(time1))
# print('time2 = ' + str(time2))
e1 = time1.split(':')
e2 = time2.split(':')
s1 = (((float(e1[0]) * 60) + float(e1[1])) * 60) + float(e1[2])
s2 = (((float(e2[0]) * 60) + float(e2[1])) ... |
12b8cb592b536194b7ce7be5f84ae876d5859e93912920b05887854f7cfb0e13 | Python | 399 | 9 | """Module containing adapters other framework packages covering multiple tasks."""
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__all__ = ["_PytorchDeepAdapter", "_PytsAdapter", "_TslearnAdapter"]
from sktime.base.adapters._pytorch import _PytorchDeepAdapter
from sktime.base.adapters._pyts... |
56fcdea458a51b3fec4cee63ab8d3bc6cbeb6af72e47f9564f2c2aebc1cf13b9 | Python | 402 | 15 | # Copyright 2019-2026 The AmpliGraph Authors. All Rights Reserved.
#
# This file is Licensed under the Apache License, Version 2.0.
# A copy of the Licence is available in LICENCE, or at:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
"""Support for loading and managing pretrained models."""
from .pretrained_util... |
5a7b0c2befe56c6e3e5ea52b1e97419b1fa9fa8f1ed19c783fc1f14652335de6 | Python | 402 | 13 | from pathlib import Path
def posix_path(*args: str) -> str:
return Path().joinpath(*args).as_posix()
TOP_DIR = Path(__file__).parent.parent.parent.resolve()
SOURCE_DIR = posix_path(TOP_DIR.as_posix(), "Historia")
RESOURCES_DIR = posix_path(SOURCE_DIR, "resources")
RESOURCES_HEADERS_DIR = posix_path(RESOURCES_D... |
5dd2da71231319f4a6457b8ed9091b19bf8983f8bee07ddbcc1d6b72aa5ff140 | Python | 404 | 12 | # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Implements base classes for forecasting in sktime."""
__all__ = [
"ForecastingHorizon",
"BaseForecaster",
"BaseProbaForecaster",
]
from sktime.forecasting.base._base import BaseForecaster
from sktime.forecasting.base._base_proba im... |
be552ddc4e2e6e0312fdda6e00efd583b383039e468058732f783484103e11c4 | Python | 405 | 23 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
V={}
for line in fi:
seq=line.rstrip().split('\t')
#print(seq)
this_tag=seq[0]+'\t'+seq[1]+'\t'+seq[2]
if this_tag not in V:
V[this_tag]=[[float(seq[-1]),line]]
else:
V[this_tag].append([float(seq[-1]),line])
fi.close()
for o... |
d4011e190a19aa1152716aa1652845e7d79b4cb70fe04912565b25da2160d3cd | Python | 405 | 7 | #Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#This program is free software;
#you can redistribute it and/or modify
#it under the terms of the MIT License.
#This program is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHA... |
0f831de0db0b97c331caa5ed5439d4ab8eba71ae4d4e2cff14f34f1a88a09a5a | Python | 407 | 10 | # Copyright (c) OpenMMLab. All rights reserved.
from .inference import inference_model, init_model, show_result_pyplot
from .test import multi_gpu_test, single_gpu_test
from .train import init_random_seed, set_random_seed, train_model
__all__ = [
'set_random_seed', 'train_model', 'init_model', 'inference_model',
... |
7ded3bfa3aad5d66e16a2dcc301ad12238c1234df0d90507531feee286107302 | Python | 407 | 11 | # pytest module discovery somehow adds the nested compiled module
# and models module to sys.modules which breaks tests, so we run
# this file as the first test after test collection
# I'm sure there is a non-hacked sanctioned way to actually do this
# but for now let's not worry about that
import sys
def test_fix_sy... |
7f2823d06e235d3cbdab07424c696e67f7a905e9534e80bb11b20fe0bdd4627d | Python | 409 | 16 | import numpy as np
def to_tensor(data):
"""Ensure that data is a numpy 4D array."""
assert isinstance(data, np.ndarray)
if data.ndim == 2:
data = data[np.newaxis,np.newaxis,...]
elif data.ndim == 3:
data = data[np.newaxis,...]
elif data.ndim == 4:
pass
else:
rai... |
15027e1d6f5dba7cb694d7bdfac5e3fe4d2c13586296b93fc10905d41045696d | Python | 412 | 16 | # Copyright 2007 Zachary Pincus
# This file is part of CellTool.
#
# CellTool is free software; you can redistribute it and/or modify
# it under the terms of version 2 of the GNU General Public License as
# published by the Free Software Foundation.
'''
celltool libraries for performing statistical analysis on shape... |
95c72ea60e15ad77d249b6d9c885b9dcdf8dea4a18caa0a19ec4781ed6a95183 | Python | 413 | 17 | """LSTM-FCN deep learning classifiers.
This subpackage provides LSTM-FCN based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"LSTMFCNClassifier",
"LSTMFCNClassifierTorch",
]
from sktime.classification.deep_learning.lstmfcn._lstmfcn_tf import (
LSTMFCNClassifier,
)
from sktim... |
d647ad52322b9eda2e4bfd34bac063f09cb5728ccb0dcf55607c20874c3ca4ba | Python | 413 | 15 | import sys
def rename_contigs():
for l in sys.stdin:
if l.find('.fasta.gz')>0:
continue
if l.startswith('>'):
if l != l.split('|')[0]:
raise Exception("Input FASTA header contains invalid character '|'")
l = l.strip().split('|')[0]
if len(... |
470d239ff07fa993cf87cc3b1219b652f71ca1fa421f0bf48ea27d6139f23bd0 | Python | 416 | 17 | import unittest
from types import SimpleNamespace
from typing import cast
from elftools.dwarf.datatype_cpp import describe_cpp_datatype
from elftools.dwarf.die import DIE
class TestDatatypeCpp(unittest.TestCase):
def test_missing_type(self):
die = cast(DIE, SimpleNamespace(attributes={}))
self.a... |
a0902d9a0048e6130d3f203aae7f27f4c9f2bd82b480c4f81793a54b9c257d12 | Python | 416 | 16 | """Compositions for clusterers."""
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["fkiraly"]
__all__ = [
"ClustererAsTransformer",
"ClustererPipeline",
"SklearnClustererPipeline",
]
from sktime.clustering.compose._as_transform import ClustererAsTransformer
from skti... |
394b5a9a55cc365c0f22c8b46d641cd4a5efde0711e36333ec1c35a67d3e2dd3 | Python | 417 | 23 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
V={}
for line in fi:
seq=line.rstrip().split('\t')
#print(seq)
this_tag=seq[0]+'\t'+seq[1]+'\t'+seq[2]
if this_tag not in V:
V[this_tag]=[[float(seq[-2]),line]]
else:
V[this_tag].append([float(seq[-2]),line])
fi.close()
for o... |
ff595c3c713a5524d4ba2abb38c6eea4c5a944a6d4e9c75e031c1b86cd03ec12 | Python | 417 | 12 | """MLP (Multi-Layer Perceptron) Classifier for Time Series Classification.
This subpackage provides Multi-Layer Perceptron (MLP) based time series
classifier in TensorFlow and PyTorch backends.
"""
__all__ = [
"MLPClassifier",
"MLPClassifierTorch",
]
from sktime.classification.deep_learning.mlp._mlp_tf import... |
121923e369fdd71ca379ad5618ce073da99d032b539951349ace62d94e39a77b | Python | 419 | 14 | import scanpy as sc
import numpy as np
import pandas as pd
import scrublet as scr
import pickle
adata = sc.read("../combined_all_organoids_v2.h5ad")
scrub = scr.Scrublet(adata.raw.X)
doublet_scores, predicted_doublets = scrub.scrub_doublets()
combined = {"doublet_scores": doublet_scores,
"predicted_doubl... |
5557586462b4ea8688ab6815874b85d39dcfa71057a9a88c394a562cee402b9c | Python | 419 | 11 | import tensorflow as tf
with tf.device('/device:GPU:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
tf.compat.v1.disable_eager_execution()
with tf.compat.v1.Session(config=tf.compat.v1.... |
cdf7fa43ef1714cadebfdf2cd8ebd9f50618095886df712530fcf8ac4cb43d67 | Python | 419 | 16 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnary
class Flatten(OpRunUnary):
def _run(self, x, axis=None):
i = axis or self.axis
shape = x.shape
new_shape = ... |
d10ea9c9b031d0601fd864a796308da1397688547f7d2ab770beb725a4cfe9c4 | Python | 420 | 19 | import os
import pathlib
import logging
_logger = logging.getLogger(__name__)
def create_dir(dir_path):
"""
Create directory if it does not exist
"""
try:
if not os.path.exists(dir_path):
pathlib.Path(dir_path).mkdir(parents=True, exist_ok=True)
return 0
except Except... |
da4671cbe30298aa2f3c638a5c69cb998edb880e7088000091f0cb3a87ececec | Python | 421 | 19 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _global_max_pool(x: np.ndarray) -> np.ndarray:
axis = tuple(range(2, np.ndim(x)))
return x.max(axis=axis, keepdims=True)
class ... |
5c09802924b288772c7b4ab74cf77d76b52e98bbe21d1f46c8f00b1cfce8fdd4 | Python | 422 | 18 | """
Anscombe's quartet
==================
_thumb: .4, .4
"""
import seaborn as sns
sns.set_theme(style="ticks")
# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")
# Show the results of a linear regression within each dataset
sns.lmplot(
data=df, x="x", y="y", col="dataset", hue="... |
bfd62d7cf8ab630043d5bc56f4619d0337b398e956a2d09e7e92f093261cf8f6 | Python | 422 | 17 | """
Grouped barplots
================
_thumb: .45, .5
"""
import seaborn as sns
sns.set(style="whitegrid")
# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")
# Draw a nested barplot to show survival for class and sex
g = sns.catplot(x="class", y="survived", hue="sex", data=titanic,
... |
9abb476c7b062dfd0eafa8c24cfa7f8156ab3b704e9b3be253d036fc5605dd67 | Python | 423 | 8 | """Kernel based time series classifiers."""
__all__ = ["RocketClassifier", "Arsenal", "TimeSeriesSVC", "TimeSeriesSVCTslearn"]
from sktime.classification.kernel_based._arsenal import Arsenal
from sktime.classification.kernel_based._rocket_classifier import RocketClassifier
from sktime.classification.kernel_based._svc... |
9e79f9968a8147092e7cc4ff4c3f1161ae059ef73f23f1d27bc0994f1eea6d32 | Python | 423 | 14 | import numpy as np
from scipy.signal import butter, lfilter, freqz
import matplotlib.pyplot as plt
def butter_lowpass(cutoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a
def butter_lowpass_filter(data, cutoff, fs,... |
f700157e654ab7a9c49329dbc3f60fd41fbcb35c2e72e6e20fd46b6b1020cc0e | Python | 423 | 15 | # -*- coding: utf-8 -*-
#
# Copyright © 2018 Charles E. Vejnar
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://www.mozilla.org/MPL/2.0/.
#
import os
__all__ = [f[:-3] for f in os.listdi... |
4b1753e24c0200338df1cc4e0557d2a24de33aea48a2ab0b9ea5cfbfc526368c | Python | 424 | 16 | """
Anscombe's quartet
==================
_thumb: .4, .4
"""
import seaborn as sns
sns.set(style="ticks")
# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")
# Show the results of a linear regression within each dataset
sns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df,
... |
6f9f9dd32e9457c9c34852e9a665dd77946c45ad247815f714cb88b1ed2b174e | Python | 425 | 15 | #!/usr/bin/env python
from distutils.core import setup
setup(name='basilisk',
version='0.1',
description='A generative, probabilistic model of side chains in proteins.',
author='Tim Harder and Jes Frellsen',
author_email='mail@tim-harder.de',
url='http://www.binf.ku.dk/',
license='... |
ff152fa24f7e0c25fb5e859c91eaff694176d207fd5813999f00cb31c2984a1b | Python | 425 | 13 | """Early classification time series classifiers."""
__all__ = [
"BaseEarlyClassifier",
"ProbabilityThresholdEarlyClassifier",
"TEASER",
]
from sktime.classification.early_classification._probability_threshold import (
ProbabilityThresholdEarlyClassifier,
)
from sktime.classification.early_classificati... |
f4213fb39bc58dbd6225b36c6d18899f854a2ac13cf0df4a0f1500159fd5cc5f | Python | 426 | 11 | import os
from pathlib import Path
from tempfile import gettempdir
import pytest
temp_path = Path(gettempdir(), f'.pyontutils-testing-base-{os.getpid()}')
temp_path_ap = temp_path.as_posix()
SKIP_NETWORK = ('SKIP_NETWORK' in os.environ or
'FEATURES' in os.environ and 'network-sandbox' in os.environ['F... |
7b1f129be7ebfdd7c40d9aa862e086e55eaaa31c1a536dbba4e52c2c76195f35 | Python | 427 | 13 | # !/usr/bin/env python3 -u
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Import all time series forecasting functionality available in sktime."""
from sktime.registry import all_estimators
est_tuples = all_estimators(estimator_types="forecaster", return_names=True)
est_names, ests = zip(*... |
90a07ab371f1b81c54337abc1dd33a800beacf15a2ae9aed68ad9fdbb092c5ca | Python | 427 | 15 | import logging
import sys
def get_logger(name):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s %(levelname)s %(process)d: %(message)s')
handle... |
97e18fcb47e5d47f03f09e79eba6ab0bae67239379b89b5b14c61972bff2c75f | Python | 427 | 15 | # author: proloy Das <proloy@umd.edu>
class LazyProperty:
"http://blog.pythonisito.com/2008/08/lazy-descriptors.html"
def __init__(self, func):
self._func = func
self.__name__ = func.__name__
self.__doc__ = func.__doc__
def __get__(self, obj, klass=None):
if obj is None:
... |
532b7afd99f0fe3d6345a173622cde05587235a771213f4a07b9640274ec1219 | Python | 428 | 5 | # data folders (raw clinical notes and SemEHR-processed notes)
MIMIC_3_DIR = './data' # the current folder under 'data'
SemEHR_DIR = './data/mimic-semehr-smp-outputs/outputs' # for MIMIC-III discharge summaries
SemEHR_DIR_rad = './data/mimic-rad-semehr-outputs/outputs' # for MIMIC-III radiology reports
#SemEHR_DIR_tays... |
6b0b0786cc8edb2f01e8ecb419703cd41cd0fb3b718b94c3b570752e39efa4c3 | Python | 428 | 12 | """Version information."""
import importlib.metadata
try:
# Get version from pyproject.toml via package metadata
__version__ = importlib.metadata.version("palantir")
except importlib.metadata.PackageNotFoundError:
# Package is not installed, fall back to hardcoded version
__version__ = "1.4.5" # Shoul... |
48be10350828ce416d072314272f71313a92a657346aa699179e7e1d03009280 | Python | 429 | 12 | import warnings
def simple_stats(image, method='mean', axis=3):
"""Deprecated. Use ``bcblib.imaging.stats.reduce_axis`` instead."""
warnings.warn(
"bcblib.tools.nii_stats.simple_stats is deprecated. "
"Use bcblib.imaging.stats.reduce_axis instead.",
DeprecationWarning, stacklevel=2,
... |
540211554e81e24f5598f448964bfe4275dfc0a34a0a6e361ec5e55830c03822 | Python | 430 | 22 | import json
import logging
logging.basicConfig(level=logging.INFO, format='')
class Logger:
"""
Training process logger
Note:
Used by BaseTrainer to save training history.
"""
def __init__(self):
self.entries = {}
def add_entry(self, entry):
self.entries[len(self.en... |
99f9abcc9a09de3c993896729ade4e5a4294d9d417519a682c0e608db93dfb9c | Python | 430 | 16 | """
Annotated heatmaps
==================
"""
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Load the example flights dataset and conver to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
# Draw a heatmap with the numeric values in e... |
c64deb4b39fdd89c3a073b5b9a9eedd109c3a3bfbc0f37228ec77b8b164e0927 | Python | 430 | 19 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Shrink(OpRun):
def _run(self, x, bias=None, lambd=None):
return (
np.where(
x < -lambd,
... |
19b43afdcdd3ca92b14b05041aa95f230c05930c9a86e5a5ed80da26529f7ca7 | Python | 431 | 20 | """
Grouped barplots
================
_thumb: .36, .5
"""
import seaborn as sns
sns.set_theme(style="whitegrid")
penguins = sns.load_dataset("penguins")
# Draw a nested barplot by species and sex
g = sns.catplot(
data=penguins, kind="bar",
x="species", y="body_mass_g", hue="sex",
errorbar="sd", palette="... |
831f8e5c6ebde5782c623f430ab4ee122941207a514efc2438f96fca41abc9cf | Python | 431 | 20 | import numpy as np
from dnnbrain.brain.algo import convolve_hrf
def test_convolve_hrf():
# prepare
X = np.random.randn(10, 2)
onsets = np.arange(10)
durations = np.ones(10)
n_vol = 5
tr = 2
# do convolution
X_hrfed = convolve_hrf(X, onsets, durations, n_vol, tr)
# assert
as... |
04de939b60465aab20640c40d4708fac39adfffd60476f6a5870973a18b99aa3 | Python | 432 | 17 | from src.cajal.sample_seg import cell_boundaries, compute_icdm_all
import tifffile
import os
def test():
cell_boundaries(
tifffile.imread("CAJAL/data/tiff_images_cleaned/epd210cmdil3_5.tif"),
30,
0,
True,
False,
)
compute_icdm_all(
"CAJAL/data/... |
7a6a3838771dc19180c0ddbe0f65103dfde58c8be2819aa46ff8bbf6e1e4e7dd | Python | 432 | 12 | # ----------------------------------------------------------------------------
# Copyright (c) 2017-2026, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... |
9dc03c4e85e7620bacc3751daf14a0ef2a1ff013ef07a250851dbadf5bda349b | Python | 432 | 16 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class HardSigmoid(OpRunUnaryNum):
def _run(self, x, alpha=None, beta=None):
alpha = alpha or self.alpha
beta = b... |
8aeea4b92fa50d8c1806cf38db165e67342d8c13e3ad93b7dd6f126baa6319df | Python | 433 | 23 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
V={}
for line in fi:
seq=line.rstrip().split('\t')
#print(seq)
this_tag='\t'.join(seq[:9])
if this_tag not in V:
V[this_tag]=[[float(seq[-1]),seq[-2]]]
else:
V[this_tag].append([float(seq[-1]),seq[-2]])
fi.close()
for one in ... |
1a2b85716df5ebd2494c918c061c37f8d0a3d4f5a7d0d295cb2746a8eb4d1326 | Python | 435 | 14 | import seaborn.objects
from seaborn._core.plot import Plot
from seaborn._core.moves import Move
from seaborn._core.scales import Scale
from seaborn._marks.base import Mark
from seaborn._stats.base import Stat
def test_objects_namespace():
for name in dir(seaborn.objects):
if not name.startswith("__"):
... |
1819b5ff895b41710ecd026318edb8334278d0c390dbc118a171c765e9d19299 | Python | 436 | 12 | # ----------------------------------------------------------------------------
# Copyright (c) 2017-2026, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... |
26714e9b4d37ad0b106bf8588968af31dd895ff941328344937db042fe15883d | Python | 437 | 14 | from torch import nn
rnn_layers = {"lstm": (nn.LSTM, 2), "gru": (nn.GRU, 1)}
def create_recurrent_layer(layer_type, initial_size, hidden_size, nlayers, dropout=0, **kwargs):
layer_type = layer_type.lower()
assert layer_type in rnn_layers
module, hidden_count = rnn_layers[layer_type]
if nlayers == 1:... |
f1da4e641474e6631a091deb09007c7b1f54a5844200d05cb5c71320cd376585 | Python | 437 | 18 |
"""
Single start point for system metrics and process metrics script
"""
import logging
import sys
from ts.metrics import system_metrics
from ts.metrics.process_memory_metric import check_process_mem_usage
if __name__ == '__main__':
logging.basicConfig(stream=sys.stdout, format="%(message)s", level=logging.INF... |
675f823d4dec390eec579c3daa6c7d4cfd213100d74ff14a0da897310a7b4f7f | Python | 438 | 23 |
"""
Module to define Unit mappings
"""
class Units(object):
"""
Define a unit of elements
"""
def __init__(self):
self.units = {
'ms': "Milliseconds",
's': 'Seconds',
'percent': 'Percent',
'count': 'Count',
'MB': 'Megabytes',
... |
a6e0f84ddbf8950540989cfcc235656e6d28c73c19b4b0a6be9079d9bc516ff4 | Python | 438 | 18 | from .palette import method_palette, subgraph_palette, dashes
from .scatter import matched_stripplot
from .theme import set_theme
from .utils import (
bound_texts,
draw_colors,
get_text_points,
get_text_width,
get_texts_points,
make_sequential_colormap,
merge_axes,
multicolor_text,
n... |
28d1ebb69fb7d7de8a216fdc909e8429f13566b9e1cd203ce6af809744cd017d | Python | 439 | 18 | # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Anomaly scores for interval evaluation."""
from sktime.detection._anomaly_scores._from_cost import (
LocalAnomalyScore,
Saving,
to_local_anomaly_score,
to_saving,
)
from sktime.detection._anomaly_scores._l2_saving import L2Savin... |
d2df69ff31b9850e3718bd822c50aa9d4745360d49b6bc59ec91484a81e1982d | Python | 440 | 15 | from flair.data import Dictionary
from flair.training_utils import convert_labels_to_one_hot
def test_convert_labels_to_one_hot():
label_dict = Dictionary(add_unk=False)
label_dict.add_item("class-1")
label_dict.add_item("class-2")
label_dict.add_item("class-3")
one_hot = convert_labels_to_one_ho... |
106a88dec2d5a20b46f3d25e93a42dd9d2bf086892fcfbee53398c5fdbbbbd1a | Python | 442 | 13 | from ray.rllib.agents.ppo.ppo_policy import PPOTFPolicy, KLCoeffMixin, ValueNetworkMixin, LearningRateSchedule, EntropyCoeffSchedule
from mprl.rl.common.weights_utils_policy_mixin import WeightsUtilsPolicyMixin
PPOStrategoModelTFPolicy = PPOTFPolicy.with_updates(
name="PPOStrategoModelTFPolicy",
mixins=[
... |
ecc037e21cb7cc5bddcccb588d6f55f292d08bac4244db3568d1b9e42bc5bae2 | Python | 444 | 15 | from torch import nn
class FCNHead(nn.Sequential):
def __init__(self, in_channels, channels):
inter_channels = in_channels // 4
layers = [
nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(inter_channels),
nn.ReLU(),
nn... |
0277780953a5db6b0d84a9de062cd1045df13eec90a3079c75735f9cd291eacc | Python | 445 | 17 | """
Scatterplot with varying point sizes and hues
==============================================
_thumb: .45, .5
"""
import seaborn as sns
sns.set(style="white")
# Load the example mpg dataset
mpg = sns.load_dataset("mpg")
# Plot miles per gallon against horsepower with other semantics
sns.relplot(x="horsepower", y... |
371163ed78f000bdf25c4f9f2fbd18b58290d65af36234131933ceca3fb4e3d0 | Python | 445 | 18 | # SPDX-License-Identifier: LGPL-3.0-or-later
from deepmd.dpmodel.fitting.unimol_pretrain import (
UniMolPretrainFitting as UniMolPretrainFittingDP,
)
from deepmd.pt_expt.common import (
torch_module,
)
from .base_fitting import (
BaseFitting,
)
@BaseFitting.register("unimol_pretrain")
@torch_module
clas... |
c590e9bea214c1c4f8e605d83b761ebfb58fa979dcf2e4428b7f0cf6bed3d2da | Python | 445 | 23 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
V={}
for line in fi:
seq=line.rstrip().split('\t')
#print(seq)
this_tag=seq[0]+'\t'+seq[1]+'\t'+seq[2]
if this_tag not in V:
V[this_tag]=[[float(seq[-1]),seq[-2]]]
else:
V[this_tag].append([float(seq[-1]),seq[-2]])
fi.close()
... |
37619e5fc5961b6e3214dab6dd26d51cc13b9a63f78b00b0e60fc183148aeed6 | Python | 448 | 10 | # !/usr/bin/env python3 -u
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Implements wrappers for estimators from hmmlearn."""
__all__ = ["BaseHMMLearn", "GaussianHMM", "GMMHMM", "PoissonHMM"]
from sktime.detection.hmm_learn.base import BaseHMMLearn
from sktime.detection.hmm_learn.gaussian... |
794d0b29f9696cb449b685a39608a179ce7f1d7e61d56c735361164851e9d48b | Python | 448 | 19 | from extended_ring_model import *
from dict_key_definitions import rmkeys
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
w1 = 0.8
w2 = 0.2
rm = RingModel({rmkeys.n_r:8,
rmkeys.verbose:True,
rmkeys.d_w1:w1,
rmkeys.d_w... |
0ade5e9bb3c4f40760867fae782e6d151c53b9d8209f7468b027d5ad11a13090 | Python | 449 | 16 | #
# # Node properties: size
#
# Netplotbrain allows for specifying different figure properties by only specifyign the column name.
#
# Below we demonstrate how node_size can change based on different columns in the nodes dataframe.
#
# The key lines of code here are when:
#
# `node_size='centrality_measure1'`
#
# and... |
17dad8980e49d8c55d9eb2a507aed1e0ebb379fe43687fd082227e2789ad8d35 | Python | 451 | 15 | # import all gumpy submodules that should be loaded automatically
import gumpy.classification
import gumpy.data
import gumpy.plot
import gumpy.signal
import gumpy.utils
import gumpy.features
import gumpy.split
# fetch into gumpy-scope so that users don't have to specify the entire
# namespace
from gumpy.cla... |
8af8f1e1781c09bf006c786b0dee52e984bb064845c25ac06271a484d4b4f50a | Python | 451 | 17 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class NonZero(OpRun):
def _run(self, x):
if x.ndim == 0:
return (np.empty((0, np.count_nonzero(x)), dtype=np.int64),)... |
908d8f2a2935dc77a911298c81a00e4bd40a3d20016565a0b87b785ae1f61f45 | Python | 451 | 17 | """
Scatterplot with varying point sizes and hues
==============================================
_thumb: .45, .5
"""
import seaborn as sns
sns.set_theme(style="white")
# Load the example mpg dataset
mpg = sns.load_dataset("mpg")
# Plot miles per gallon against horsepower with other semantics
sns.relplot(x="horsepow... |
995615b590128bdc5bb669aa8c597c3a174aef1fed87dae037f9670aae1a60e5 | Python | 452 | 20 | """
Conditional kernel density estimate
===================================
_thumb: .4, .5
"""
import seaborn as sns
sns.set_theme(style="whitegrid")
# Load the diamonds dataset
diamonds = sns.load_dataset("diamonds")
# Plot the distribution of clarity ratings, conditional on carat
sns.displot(
data=diamonds,
... |
69adac7441c06918410c0d052445ede00d194abf795066cb37836f45a6233ee3 | Python | 453 | 18 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def common_reference_implementation(data: np.ndarray, shape: np.ndarray) -> np.ndarray:
ones = np.ones(shape, dtype=data.dtype)
retur... |
9da224c6a85544af6e1b59dbfcf69053bda7a5c8dc865e2f9b79c5a46ad4aba0 | Python | 457 | 13 | # ----------------------------------------------------------------------------
# Copyright (c) 2017-2026, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... |
720575eaf8ec9df1e3116209eeee860e22b023879251a198bb7df4fa007d9ac3 | Python | 458 | 19 | """
Scatterplot with continuous hues and sizes
==========================================
_thumb: .45, .45
"""
import seaborn as sns
sns.set()
# Load the example iris dataset
planets = sns.load_dataset("planets")
cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
ax = sns.scatterplot(x="distance", y="orbital_peri... |
c2d3f850a9b1c1338e3528596c1da3f914b03c853da2d242b874e02b5567ce47 | Python | 458 | 14 | import sciunit
class ProducesSpikeTrains(sciunit.Capability):
"""
Spike trains are to be represented as a list of neo.SpikeTrain objects.
"""
def produce_spiketrains(self, **kwargs):
return [st for st_list in self.produce_grouped_spiketrains(**kwargs)
for st in st_list]... |
659126643820f572381225c529a8e153b80987cc34b4a2e1f9cd03a50c98f523 | Python | 459 | 22 | import random
import numpy as np
import tensorflow as tf
def is_cuda_available():
return tf.test.is_gpu_available(cuda_only=True)
def set_seed(seed):
"""
Referred from:
- https://stackoverflow.com/questions/38469632/tensorflow-non-repeatable-results
"""
# Reproducibility
random.seed(seed... |
8f66b23ec5a5d7db371fae099ea47d291c7b8d8b03431745084580cb778da3a9 | Python | 459 | 17 | from __future__ import print_function
import sys
from itertools import cycle
input_file = sys.argv[1]
number_of_split = int(sys.argv[2])
records = open(input_file).read().strip().split('\n')
output_name = input_file.replace('.bed', '').replace('.txt', '')
output_number = cycle(range(1, number_of_split+1))
for reco... |
b734ee5085f41c7f5805922bc256d273e842e9cad9a1daba24f985cc9e043c0b | Python | 459 | 17 | from vp_suite.base import VPModel
class CopyLastFrame(VPModel):
r"""
A simple, non-trainable baseline model
that simply returns the latest frame as the next predicted frame.
"""
NAME = "CopyLastFrame"
REQUIRED_ARGS = []
TRAINABLE = False
def __init__(self, device=None, **model_kwargs)... |
727b20b96b73c408f190541fbc838689dc632bad51e7fe881ff877764067353e | Python | 460 | 16 | """
Grouped violinplots with split violins
======================================
_thumb: .44, .47
"""
import seaborn as sns
sns.set_theme(style="dark")
# Load the example tips dataset
tips = sns.load_dataset("tips")
# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(data=tips, x="... |
c01317541c9515f58ee9485bf5b955b3c3ef4c46ccfade02e625ba456aedef78 | Python | 464 | 13 | """Compositions for classifiers."""
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__all__ = [
"BaggingClassifier",
"ComposableTimeSeriesForestClassifier",
"WeightedEnsembleClassifier",
]
from sktime.classification.ensemble._bagging import BaggingClassifier
from sktime.classifica... |
12b068fc9ff3dc3ef1dba8d55366afbc9786730c284f24a20dfd41f03002e7e9 | Python | 465 | 19 | """
Lineplot from a wide-form dataset
=================================
_thumb: .52, .5
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style="whitegrid")
rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = ... |
d1741ca170731af73575f9fa4ea4fe5cc46c3b6ae6b6abef96c5bfd8760a7d8f | Python | 465 | 18 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops.op_softmax import Softmax
class LogSoftmax(Softmax):
def _run(self, X):
if X.size == 0:
return (X,)
tmp = X - X.max(axis=self... |
c03f77578d6d58dd416fa555524301bc5b1ff0b5b368c695fd0a05a0c92a5c2f | Python | 466 | 19 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from math import erf
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Erf(OpRunUnaryNum):
def __init__(self, onnx_node, run_params):
OpRunUnaryNum.__init__(self, ... |
1386aace726cfe117e2c934c08a6c1c04ae2d6167374d83a62e964ff2daf599c | Python | 467 | 17 | """
Plotting a three-way ANOVA
==========================
_thumb: .42, .5
"""
import seaborn as sns
sns.set(style="whitegrid")
# Load the example exercise dataset
df = sns.load_dataset("exercise")
# Draw a pointplot to show pulse as a function of three categorical factors
g = sns.catplot(x="time", y="pulse", hue="ki... |
6cba9e432a6515ccc09c9c240ddb0b3a3413d559bc476b34a395e4f7fe4e5415 | Python | 468 | 13 | from __future__ import print_function
import numpy as np
from featureExtractor import FeatureExtractor
class FeatureExtractorRealFourier(FeatureExtractor):
def __init__(self, params):
self.params = params
self.extractorType = 'realFourier'
def getFeatures(self, eegSegment, timeStampSe... |
cbcaa9410f282f2fc78109509096c039593980dcd137e9597f5cd850a8ab6efb | Python | 469 | 20 | """Test functions for loose loaders."""
__author__ = ["fkiraly"]
__all__ = []
import pytest
from skbase.utils.dependencies import _check_soft_dependencies
@pytest.mark.skipif(
not _check_soft_dependencies("statsmodels", severity="none"),
reason="skip test if required soft dependency not available",
)
def ... |
28a34b9e85f6598003dc5544ad54e630886af61deb9c2eddf1eec27f49be2b6a | Python | 470 | 14 | from __future__ import print_function
import numpy as np
from scipy import signal
from featureExtractor import FeatureExtractor
class FeatureExtractorComplexFourier(FeatureExtractor):
def __init__(self, params):
self.params = params
self.extractorType = 'wavelet'
def getFeatures(self, eegSegm... |
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