sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3932dfaf5659eb733aacfc83d035ea4f9e02554faf2e318b46a5b75b951e8d40 | Python | 511 | 21 | import torch
import sys
class AbstractTorchModule(torch.nn.Module):
def __init__(self):
torch.nn.Module.__init__(self)
def save(self, path):
print("Saving to path " + path, file=sys.stderr)
torch.save(self.state_dict(), path)
def load(self, path):
print("Loading from pat... |
a739222af41d1b2a003d60ec954795276fc5389544793bfad1a5c4a7ab711721 | Python | 512 | 15 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.cache import cache_file
if __name__ == "__main__":
file_name = cache_file("auto2.dta", "http://www.stata-press.com/data/r15/auto2.dta")
# Suggested by @adamrossnelson
df = pd.read_stata(file_na... |
4a19d811ed5b6bfb76f6a551f61026f7496cdce90bfea18a8a7fb92f2ec1d34f | Python | 513 | 8 | from agent.data.entities.config import EXTERNAL_ITEM_FILTER, EXTERNAL_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="text"... |
8cd93aef298d45c842e8a0aa9fd0491d8ee31cf52dd8e0b01e99b16d782a93e2 | Python | 513 | 9 | from ..reducers.avg_non_zero_reducer import AvgNonZeroReducer
from ..reducers.base_reducer import BaseReducer
from ..reducers.class_weighted_reducer import ClassWeightedReducer
from ..reducers.divisor_reducer import DivisorReducer
from ..reducers.do_nothing_reducer import DoNothingReducer
from ..reducers.mean_reducer i... |
f264fd7d6766276ca539c293010ad8e92d638d6eb856379f6b4c043e194e2b75 | Python | 514 | 17 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
if __name__ == "__main__":
df = pd.read_csv(
r"https://raw.githubusercontent.com/aliceriot/russian-vocab/master/words.csv",
names=["russian", "english", "part of speech"],
)
profile = ProfileReport(
... |
feef8c2f2691ac51dd88936c6754b719f502b9798b7137eb3cef01b63aa0b5f7 | Python | 514 | 18 | def get_numeric_rating(class_values, text_rating, binary_classifier=False):
if binary_classifier:
if class_values.index(text_rating) == len(class_values) - 1:
return 1
else:
return 0
else:
return class_values.index(text_rating)
def get_textual_rating(class_value... |
1a2193be10e6f608ff0896b96f98ef2ff47908b9c072c04c76adba7a8a49e4c6 | Python | 516 | 20 | from typing import Any, Optional
import pandas as pd
from data_profiling.report.presentation.core.item_renderer import ItemRenderer
class Sample(ItemRenderer):
def __init__(
self, name: str, sample: pd.DataFrame, caption: Optional[str] = None, **kwargs
):
super().__init__(
"sampl... |
5da1b8995be08baa9aece7c172c599c08806bd73cfd4585dd50155923c09302c | Python | 516 | 19 | KEEP_COLS = ["responseN",
"participant",
"blockOrder",
"age",
"sex",
"date",
"frameRate",
"imageFile",
"trials.thisN",
"trials.thisIndex",
"winsize",
"numerosity",
... |
adb482999b5a6ccfd3f6aa8011344280dccea3fced49fe44905127ae67a595c1 | Python | 516 | 23 | import ray
from ray.util.queue import Queue, Empty
ray.init()
# You can pass this object around to different tasks/actors
queue = Queue(maxsize=100)
@ray.remote
def consumer(id, queue):
try:
while True:
next_item = queue.get(block=True, timeout=1)
print(f"consumer {id} got work {n... |
d0984e6b6d3f4dd7e1cec3a538cfd01f7843c3d3380994943ab46a5120709689 | Python | 516 | 17 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
from .adsorbate import Adsorbate
from .adsorbate_slab_config import AdsorbateSlabConfig
from .bulk impo... |
ae07cf29b1f2919ce0b1865325619a6664d607f53e7e291a8af8d6a35a85e927 | Python | 517 | 19 | """Remove facial features from structural MRI data"""
import subprocess
import glob
# Set data path
ROOT = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/openNeuro'
# Set subjects to work on
subs = ['sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09']
# Loop over participants
for sub in subs:
images = so... |
11eeee1e5c4bcc5bc5f61d9ac05afb29f92f95d9673bc4fe3a82538c5d65221d | Python | 518 | 25 | import os
def makedirs(path):
'''
Creates the directories of the given path if they do not exist.
:param path: Directory path.
:return:
'''
if not os.path.isdir(path):
os.makedirs(path)
def makedirs_for_file(filepath):
'''
Creates the directory of the given filepath if it d... |
81e08f30674c4291be6a060ffca59d7c4c3ad2d844a783a8114a421f98ce4296 | Python | 518 | 14 | import logging
from pyro.distributions import constraints
from pyro.distributions.transforms import SoftplusTransform
from rich.console import Console
from rich.logging import RichHandler
from torch.distributions import biject_to, transform_to
from .cell_comm.around_target import compute_weighted_average_around_targe... |
9e7e80dd6ffb2124c874dcc993e100606ea4a0b8fd6bd2d3fcc11d5f9056e174 | Python | 518 | 21 | #!/usr/bin/env python
"""
Useful utilities for IO.
"""
def read_samples_list(path, fail_on_empty=True):
with open(path, 'r') as f:
samples = f.read().splitlines()
if fail_on_empty and len(samples) == 0:
raise ValueError("Samples list empty")
return samples
def read_contig_list(path, fai... |
8cd8187ba19e5f4c1c5e3f77904f93b2684b3b7dd5c099ed44b505ab725fa12a | Python | 520 | 22 | import torch
from sinabs.layers import NeuromorphicReLU
def test_forward():
t_inp = torch.tensor([-5.5, -3.23, 2.3, 0.0])
relu = NeuromorphicReLU()
t_out = relu(t_inp)
assert (t_out == torch.tensor([0.0, 0.0, 2.0, 0.0])).all()
def test_backward():
t_inp = torch.tensor([-5.5, -3.23, 2.3, 0.1], ... |
2be2cb171ac189384404bd20f7adf5f427ca772e116fa9eb1eca27125071f441 | Python | 521 | 8 | from agent.data.entities.config import CTT3HEALTH_ITEM_FILTER, CTT3HEALTH_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="t... |
91d4805498bad0a33e1b60bb3f33abe15e43691d160c7cbe5a68f5ad5ca2eec3 | Python | 521 | 12 | from .vcfcluster import main as vcfcluster
from .bedcluster import main as bedcluster
from .standardize_vcf import main as standardize
from .count_svtypes import main as count_svtypes
from .bincov import main as bincov
from .rdtest2vcf import main as rdtest2vcf
from .resolve import main as resolve
from .annotate import... |
f1e1f7020c73d9cb61ae80f4b3ab8250b4c972e65661e5fdd84e0193482b3173 | Python | 521 | 19 | # flake8: noqa
# __owners_begin__
import ray
import numpy as np
@ray.remote
def large_array():
return np.zeros(int(1e5))
x = ray.put(1) # The driver owns x and also creates the value of x.
y = large_array.remote()
# The driver is the owner of y, even though the value may be stored somewhere else.
# If the nod... |
104d0bbcfec2dd6d571a66f9274ad71f2a6641c5e4bf55ec230c50dcfb4d5668 | Python | 523 | 25 | import numpy as np
import matplotlib.pyplot as plt
from lib5c.util.plotting import plotter
@plotter
def plot_pvalue_histogram(data, xlabel='pvalue', **kwargs):
"""
Plots a p-value or q-value distribution.
Parameters
----------
data : np.ndarray
The p-values or q-values to plot.
kwarg... |
4b567f831bc4243d49412eae18680bb4b2855dafa540105e09f2c326e0a04ed7 | Python | 523 | 14 | #Keep datasets loaded in memory.
import wrapt
from moabb.datasets.base import BaseDataset
for dataset in BaseDataset.__subclasses__():
dataset._preloaded_data = {}
@wrapt.patch_function_wrapper(dataset, "_get_single_subject_data")
def _get_single_subject_data(wrapped, self, args, kwargs):
key = (s... |
81e8ea283310e2f3502b4fa038752b0c40690340b0d85ae7f37711b686d269cd | Python | 523 | 30 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import sys
import itertools
def make_link_combos():
pass
def main():
# parser = argparse.ArgumentParser(
# description=__doc__,
# formatter_class=argparse.RawDescriptionHelpFormatter)
# args = parser.parse_args()
for line in... |
9c3d200bd25d6bc8cf4a69fbce125ef8fe2ed35045cb9865d29dc0b1f1753ba1 | Python | 523 | 15 | from data_profiling.report.presentation.core import FrequencyTableSmall
from data_profiling.report.presentation.flavours.html import templates
class HTMLFrequencyTableSmall(FrequencyTableSmall):
def render(self) -> str:
html = ""
kwargs = self.content.copy()
del kwargs["rows"]
for... |
c945ce835fe7a70c6c8e14bd7b38088bd4716dbf3d975cf921b7d088bb4aef03 | Python | 523 | 16 | import numpy as np
from siapy.entities import SpectralImage
from siapy.utils.plots import pixels_select_click
# Create a mock spectral image with 4 bands
rng = np.random.default_rng(seed=42)
image_array = rng.random((50, 50, 4)) # height, width, bands
image = SpectralImage.from_numpy(image_array)
# Interactive pixe... |
79ba9ffebbdce57eb15d2cfb19a042ac36aa29fc71d0d924b03397fd3d79a4aa | Python | 524 | 22 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import pytest
from ase import build
@pytest.fixture()
def atoms_list():
atoms_list = [
bu... |
fd58b1f477fd79f1ce6c607fd8584021d0b096fe3255f0cd85ae5ae1435c472b | Python | 524 | 20 | """The Neuromorphic Intermediate Representation reference implementation.
Documentation: https://nnir.readthedocs.io
"""
from importlib.metadata import version as metadata_version, PackageNotFoundError
try:
__version__ = version = metadata_version("nir")
del metadata_version
except PackageNotFoundError:
... |
b655f4ae983c733f9673aefc16b5a22137af13b8d6657d786f36a2ef3b93c7c3 | Python | 525 | 24 |
import setuptools
from setuptools import setup
setup(
name='gym_sted',
version='0.0.1',
packages=setuptools.find_packages(where="."),
install_requires=[
"gym>=0.26",
"h5py",
"scikit-learn",
"scikit-image",
"numpy",
"scipy",
"matplotlib",
... |
5c5524fa046c58faa437b1474e75b1889c5d881f35332cf33215b0617e41489b | Python | 526 | 34 | # flake8: noqa
# __env_var_start__
import ray
import os
ray.init()
@ray.remote
def myfunc():
myenv = os.environ.get("FOO")
print(f"myenv is {myenv}")
return 1
ray.get(myfunc.remote())
# this prints: "myenv is None"
# __env_var_end__
ray.shutdown()
# __env_var_fix_start__
ray.init(runtime_env={"env_var... |
5240ed2e75c87b3610ea3e68f6e2065b8c3e29c55baf218ac8f29648b040e2ac | Python | 527 | 17 | # -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""tedana: A Python package for TE-dependent analysis of multi-echo data."""
import warnings
from tedana.__about__ import __copyright__, __credits__, __packagename__, __version__
#... |
5ed8dff7280e84e60515fa55f997f21602fb3fe31e1cb99ab88f52cb6f0c95cd | Python | 527 | 12 | if __name__ == "__main__":
import argparse
from evaluate.evaluate import evaluate
parser = argparse.ArgumentParser(
description="Evaluation code to compare annotations from a seizure detection algorithm to ground truth annotations."
)
parser.add_argument("ref", help="Path to the root folder... |
71c3ddcdd93597e7c9474245ad093e34e129a106f0c3277fbd086fd94f12b1e0 | Python | 528 | 25 | from lop.envs.slippery_ant import SlipperyAntEnv, SlipperyAntEnv3
from gym.envs.registration import (
register,
load_env_plugins as _load_env_plugins,
)
# Hook to load plugins from entry points
_load_env_plugins()
register(
id="SlipperyAnt-v2",
entry_point="lop.envs.slippery_ant:SlipperyAntEnv",
... |
b4a026c271b7d5f8278974d12dc8da2df670f5dd65d38aaf171c026dba0952ea | Python | 528 | 22 | import logging
from pokeformer.config_wrapper import load_class
logger = logging.getLogger(__name__)
class PredictorFactory:
def __init__(self):
self.data = {}
def create(self, config, vocab_dict):
cls = self.data[config.model]
result = cls(config, vocab_dict)
return result
... |
ef52a5511f9675dc6eab1a06afc697e1c7a60699dd86a9dc32643201d65b023f | Python | 528 | 20 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
__all__ = [
"ATOMIC_RADII",
"KHOT_EMBEDDINGS",
"CONTINUOUS_EMBEDDINGS",
"QMOF_KHOT_EMBE... |
04a5aeec86f7716169164a2828882a8120b424c1ad079ebd1f8d2fc899e2a474 | Python | 529 | 30 | import ray
import sys
# Add the RAY_DEBUG_POST_MORTEM=1 environment variable
# if you want to activate post-mortem debugging
ray.init(
runtime_env={
"env_vars": {"RAY_DEBUG_POST_MORTEM": "1"},
}
)
@ray.remote
def my_task(x):
y = x * x
breakpoint() # Add a breakpoint in the Ray task.
retu... |
104b6225482020fd8d1b62a5c4b5d1ba85cd45e1a215720e257487f27317a101 | Python | 529 | 15 | from .batch_data_preprocessor import BatchDataPreprocessor
from .batch_process import stack_batch
from .gt_pixel_preprocessor import (GTPixelPreprocessor,
MultiGTPixelPreprocessor)
from .multi_input_data_preprocessor import MultiInputDataPreprocessor
from .multi_input_multi_batch imp... |
30cf4e4ca1565fdf9abc07a00b168d2dc5923c51670f7bf3911cffa5b19c0ed2 | Python | 529 | 14 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 19 13:31:16 2022
@author: sam
"""
import keras
from analysisScripts.neuralModel_paperMethod.keras_dynamicLayers import trainableInput
from analysisScripts.dualNet.keras_dynamicLayers_RNNcell_dualModel import rnnCell_dualNet_ as rnnCell_
model = ker... |
6372c7c3e6f49290d972a94bbe51aa41372cdf79c4c51c1853baf835169e56bc | Python | 529 | 19 | #!/usr/bin/env python
from unittest import TestCase
from svqc.evaluator import QCEvaluator
import svqc.tests.constants as const
import os
class TestSVQC(TestCase):
def tearDown(self):
os.remove(const.OUT_FILE_PATH)
def integration_test(self):
eval = QCEvaluator(const.METRICS_FILE_PATH, cons... |
0f8b546fb041f4de5d91ae990a54e2b2e0075498527738d5e85dd94b871c2862 | Python | 530 | 27 | """
Test script that attempts to set its own runtime_env, but we should ensure
we ended up using job submission API call's runtime_env instead of scripts
"""
def run():
import os
import ray
ray.init(
runtime_env={
"env_vars": {"TEST_SUBPROCESS_JOB_CONFIG_ENV_VAR": "SHOULD_BE_OVERRIDE... |
9aa7e98d5c492e22b1d6c12cdc0a5af8a064bc24a0d0bff0a8fb4d0e3490850f | Python | 530 | 25 | from distutils.core import setup
from setuptools import find_packages
setup(
name="luminance_analysis",
version="0.1",
author="Luigi Petrucco & Ot Prat",
author_email="lpetrucco@neuro.mpg.de",
packages=find_packages(),
install_requires=[
"ipython",
"numba",
"numpy",
... |
a4dbcc294097e0a7ec6e8a07f767b3d78e9a72bf00b180a725df386714705701 | Python | 530 | 8 | from agent.data.entities.config import TEST_ITEM_FILTER, TEST_LABEL
from agent.classifiers.data.transformer_text_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, val_split=0.8, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _token... |
b4ce510eb587da6673156fd0cff68ef86b268babf365e9c00607446d7f9b10d8 | Python | 530 | 18 | from __future__ import print_function, absolute_import
__all__ = ['accuracy']
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct =... |
35262f996b4e3deec1a08126cd0080be2c27ead0b9238c693f9ef8f0004eb8c7 | Python | 531 | 25 | #usage: conv_uniq_fa.py <fa> <mark>
import sys
fa = sys.argv[1] if 1 < len(sys.argv) else None
mark = sys.argv[2] if 2 < len(sys.argv) else None
mark = "" if not mark else "mark:%s;" % mark
def do(f):
n = 0
while True:
head = f.readline().strip("> \t\n")
seq = f.readline().strip("> \t\n")
... |
98a234328f20b9994933855679569a0a8258d6b618618b06e4b48c1a9a718875 | Python | 531 | 19 | import unittest
import torch
from rising.utils.checktype import check_scalar
class TypeCheckTestCase(unittest.TestCase):
def test_scalar_check(self):
expectations = [True, True, False, False, False, True, False]
inputs = [0.0, 1, None, "123", [1, 1], torch.tensor(1), torch.tensor([1, 2])]
... |
cb26d260104315589e8f10efaaeb179415d3a6eadb1681feb59c40da951abd45 | Python | 531 | 28 | #!/usr/bin/env python3
# vim: set ts=4 sw=4 tw=0 :
from distutils.core import setup
from distutils.extension import Extension
import numpy
try:
from Cython.Build import cythonize
USE_CYTHON = True
EXT = "pyx"
except ImportError:
USE_CYTHON = False
EXT = "c"
cythonize = lambda ext: ext
exte... |
e5956f325781374fbf20236a935c4858d6656396689c2e0213c13e696a75d301 | Python | 531 | 23 | # __anti_pattern_start__
import ray
import numpy as np
ray.init()
@ray.remote
def func(large_arg, i):
return len(large_arg) + i
large_arg = np.zeros(1024 * 1024)
# 10 copies of large_arg are stored in the object store.
outputs = ray.get([func.remote(large_arg, i) for i in range(10)])
# __anti_pattern_end__
#... |
9c9fd1a99251800634a498e8106b78a0fea01912af9774eae1081796a7b896de | Python | 532 | 21 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Base class for an Experiment Runner."""
# pylint: disable=too-few-public-methods
from typing import Any
from aihwkit.experiments.experiments.base import Ex... |
0ac875c0134b00b5adab921dbf0ffb5ac3781b6db753dd558be87ce3b6d83f28 | Python | 533 | 21 | from ray.air.config import (
CheckpointConfig,
FailureConfig,
RunConfig,
ScalingConfig,
)
from ray.air.data_batch_type import DataBatchType
from ray.air.execution.resources.request import AcquiredResources, ResourceRequest
from ray.air.result import Result
import ray.data # noqa: F401 # TODO: This is ... |
45d8abb02a803df3199910747cbc6449798582b47acaef240fcdfd6cfa63ff58 | Python | 533 | 17 | """
Test for issue 200:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/200
"""
import pandas as pd
from data_profiling import ProfileReport
def test_issue200():
df = pd.DataFrame([0, 1, 2], columns=["a"], index=["0", "1", "2"])
assert df.index.dtype == "object", "Index type should be 'ob... |
a1e6a93b03a680f0886145fee525950d79d30195eca879ee354fa9ac060863fa | Python | 534 | 15 | from samri.pipelines.preprocess import generic, legacy
from samri.pipelines import manipulations
BIDS_BASE = '/usr/share/samri_bidsdata/bids'
# Takes too long
#def test_generic():
# generic(BIDS_BASE,
# '/usr/share/mouse-brain-templates/dsurqec_200micron.nii',
# registration_mask='/usr/share/mouse-brain-templates/d... |
45f84a37fb12e5ff1ba77831c7b4397c0bc5057e2be69a5269bdf09cb8be1ed3 | Python | 535 | 17 | import pandas as pd
import numpy as np
import oddt.pandas as opd
import sys
def low_energy_filter(mol_file, out_file):
mol2_df = opd.read_mol2(mol_file, skip_bad_mols=True)
mol2_df['best_energy'] = mol2_df.groupby(['mol_name'])['Total Energy'].transform(min)
mol2_df = mol2_df[mol2_df['Total Energy'] == mol... |
c1dd809200e4fe8f2a13931c6466999ce5a12d4259e7215a69226f6df4f6a09f | Python | 535 | 25 | import pandas as pd
import os
import code
# code.interact(local=dict(globals(), **locals()))
import math
from math import isnan
result_dir = "./tsmr_result_individual"
results = [os.path.join(result_dir, f) for f in os.listdir(result_dir)]
df1 = None
for f in results:
temp = pd.read_csv(f, sep=",", index_col=F... |
428d92a999e55d1a103b873ab679a2fe2ee0274da090e24ac6b083d2a9d4663d | Python | 536 | 23 | import unittest
import numpy as np
import pfrl
class TestLinearInterpolationHook(unittest.TestCase):
def test_call(self):
buf = []
def setter(env, agent, value):
buf.append(value)
hook = pfrl.experiments.LinearInterpolationHook(
total_steps=10, start_value=0.1,... |
713a9f148786c0fdb07d17b7bff5683ceebac5ce0a414aaa89f172ec987d2dee | Python | 536 | 22 | """TxGNN version file
"""
# Based on NiLearn package
# License: simplified BSD
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
# X.Y
# X.Y.Z # For bug fix releases
#
# Admissible pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# ... |
9ce3769058b46339edd48d1423ef4cf8b7ad456ba28cc8d8e7edb25fa002abb0 | Python | 536 | 25 | """Domain Research (alias).
This package is an alias for `omicverse.llm.dr`, provided to expose the
domain research helpers under a clearer module name while maintaining
backward compatibility. It re-exports the primary public API.
"""
from __future__ import annotations
from ..dr import ( # type: ignore F401
Re... |
b6c19a74dd01d6163d1d47321e94c2424c8b3587659078568f8ea05e9749b920 | Python | 536 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
cce2dd9aa6ed99ae676a491d620a04ac8495dbcc9aced9812c8df0ac2d99fdb5 | Python | 537 | 17 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class LMSDiscreteScheduler(metaclass=DummyObject):
_backends = ["torch", "scipy"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "scipy"])
@class... |
338a27e45dac460de5d43c02816552cea890f0bbff6833dc885aa0fb2773ae0b | Python | 538 | 25 | '''numpy-stl: fast STL file handling powered by NumPy.
Read, write, and manipulate STL files with vectorized
array operations.
Quick start::
from stl import mesh
m = mesh.Mesh.from_file('model.stl')
print(len(m), 'triangles')
'''
from .base import Dimension, RemoveDuplicates
from .mesh import Mesh
from .... |
575142fceff08ef1f91e0dd82d4a8d36ca0222d16cfc3837457d876ab1e33fc9 | Python | 538 | 8 | from agent.data.entities.config import CTT3FR_ITEM_FILTER, CTT3FR_LABEL
from agent.classifiers.data.transformer_text_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, val_split=0.8, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _t... |
5f1656a4ee73ab870cd367103a245ea8e29eba7c82e9b3e79e7f7e5a73bfe1da | Python | 539 | 29 | # __start__
from ray import serve
import logging
import requests
logger = logging.getLogger("ray.serve")
@serve.deployment
class Counter:
def __init__(self):
self.count = 0
def __call__(self, request):
self.count += 1
logger.info(f"count: {self.count}")
return {"count": self... |
8f1c52690b954a922f1469cb134bd46e954b04348a04ade57c5b604d39885b2d | Python | 539 | 21 | """
Test for issue 671:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/671
"""
import numpy as np
import pandas as pd
from data_profiling import ProfileReport
def test_issue671():
test = pd.DataFrame([0, 5, 22, 32, 65, np.nan], columns=["a"])
for i in range(0, 10):
... |
0200bb27cd1ac0e3b4fc5751f13bc10cc538d2c3aa667e21864f50da322883e8 | Python | 540 | 8 | from agent.data.entities.config import CTT3_TEST_ITEM_FILTER, CTT3_LABEL
from agent.classifiers.data.transformer_text_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, val_split=0.8, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _... |
62143272a478fd4c9d7f9994645d22a9e0e3c2989ccce4a52ed0ed7190ef360a | Python | 540 | 20 | import threading
class StoppableThread(threading.Thread):
"""Thread with an event object to stop itself.
Args:
stop_event (threading.Event): Event that stops the thread if it is set.
*args, **kwargs: Forwarded to `threading.Thread`.
"""
def __init__(self, stop_event, *args, **kwargs)... |
d0ffedc859d6f0a46913ffd996ce0f86d401f3cde195300841f261b2b0db6c2e | Python | 540 | 20 | import ray
@ray.remote
def echo(a: int, b: int, c: int):
"""This function prints its input values to stdout."""
print(a, b, c)
# Passing the literal values (1, 2, 3) to `echo`.
echo.remote(1, 2, 3)
# -> prints "1 2 3"
# Put the values (1, 2, 3) into Ray's object store.
a, b, c = ray.put(1), ray.put(2), ray... |
3b38f5fe5431688a9a1a60187ec6f74b30d48ff4eeb165c7b6ebbb6b19c065dc | Python | 541 | 15 | def group_in_dict(normalized_ent):
normalized_ents = {}
for k, v in [(key, d[key]) for d in normalized_ent for key in d]:
if k not in normalized_ents:
if isinstance(v, list):
normalized_ents[k] = v
else:
normalized_ents[k] = [v]
else:
... |
738e06734879d4f9f5ece6e152f9111b9e6b02f25a69bbfaad5838ada3c61b54 | Python | 541 | 18 | import pandas as pd
from data_profiling import ProfileReport
def test_decorator(get_data_file):
people_example = get_data_file(
"people_example.csv",
"https://raw.githubusercontent.com/ydataai/coursera-ml/master/week-1/people-example.csv",
)
df = pd.read_csv(people_example)
report = P... |
0418028e5bd1624d469b543015ef5466a44dcd9f1c6539105424b145ca0062e8 | Python | 543 | 9 | from agent.data.entities.config import DEV_ITEM_FILTER, DEV_LABEL
from agent.classifiers.data.transformer_text_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, val_split=0.8, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokeni... |
1aa22dfc2e8b11955952b7cab97d69ac253993cf5798b404590449a53ac902e6 | Python | 543 | 9 | from pfrl.replay_buffers.episodic import EpisodicReplayBuffer # NOQA
from pfrl.replay_buffers.persistent import PersistentEpisodicReplayBuffer # NOQA
from pfrl.replay_buffers.persistent import PersistentReplayBuffer # NOQA
from pfrl.replay_buffers.prioritized import PrioritizedReplayBuffer # NOQA
from pfrl.replay_b... |
aad708c78b6cedb6c7a50b50ad2d1a22b7cb905ad6c238e8873e039e3f526900 | Python | 543 | 15 | #test patches
import os
from src import patches
from src import __file__ as ROOT
import geopandas as gpd
import rasterio
ROOT = os.path.dirname(os.path.dirname(ROOT))
def test_crop(tmpdir):
gdf = gpd.read_file("{}/tests/data/crown.shp".format(ROOT))
rgb_path = "{}/tests/data/2019_D01_HARV_DP3_726000_46... |
d80446683cd4f3f20552204b64970f5a4ac2d23beb969dbfceed955e45dd4e42 | Python | 544 | 22 | class Learner(object):
def log_update(self, o, a, r, op, logpb, dist, done):
self.log(o, a, r, op, logpb, dist, done)
info0 = {'learned': False}
if self.learn_time(done):
info = self.learn()
self.post_learn()
info0.update(info)
info0['learned']... |
e7f6bdcf8e4f9a46897c2482f9f4c3bfbad40f67a5e438fe6695b6b4fff3e42e | Python | 544 | 9 | from case1 import *
import time,os; t0=time.time(); fn="c1_phi2.json"
d=json.load(open(fn)) if os.path.exists(fn) else {"true":[],"full":[],"q":[],"full_noarm_c0":[]}
while len(d["true"])<int(sys.argv[1]) and time.time()-t0<230:
p=TRUE.copy(); p[7]=rng.uniform(-1.5,0.3)
D=design(275,60,80,0); Y=simulate(p,D,"M2... |
f4dd428e20c1b99eab53cee3cc5ba45f579e3ed0cb5ea4e13539c7e0a2a9901a | Python | 544 | 21 | from typing import Any
import pandas as pd
from data_profiling.report.presentation.core.item_renderer import ItemRenderer
class CorrelationTable(ItemRenderer):
def __init__(self, name: str, correlation_matrix: pd.DataFrame, **kwargs):
super().__init__(
"correlation_table",
{"corr... |
68e37c1768db622fb500c5061f9faa083a95062a6d6cc76e55544d60d8f866b0 | Python | 545 | 25 | from dataclasses import dataclass
import numpy as np
from .node import NIRNode
@dataclass(eq=False)
class Threshold(NIRNode):
r"""Threshold node.
This node implements the heaviside step function:
.. math::
z = \begin{cases}
1 & v > v_{thr} \\
0 & else
\end{cases... |
ff098e5c0782868748a77198fa955e9c15f012440c6ad1e748839f05d7e4a720 | Python | 545 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
29ee4465f07d00d60edb96eab6f6faef0f63a8a4dd907cdc46c4640895f4ea7c | Python | 546 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
b08f4d038dccfd412329ee7d98d0ee2924f6b2a97324d6208f4c1bce19f9bcab | Python | 546 | 19 | import logging
from datetime import datetime
from agent.pipeline.review_level_mgmt import update_sentence_review_level, update_item_review_level
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
if __name__ == "__main__":
logging.info("Launching review level updater 9...")
star... |
bcec29d1643fbbf7af7110558e2eba8ec0d0d5362f0c30c176ac0b232a724107 | Python | 546 | 21 | #cand = open('rcnn-bond/model-300-3/test.cbond')
cand = open('att-rcnn-bond/test.cbond')
gold = open('data/test.single')
tot,good = 0,0
for line in cand:
tot += 1
cand_bonds = []
for v in line.split():
x,y = v.split('-')
cand_bonds.append((int(x),int(y)))
line = gold.readline()
... |
cf576d1d7c1435c02a2b6b639fbfc8665f3928f2ea1af9b70cc68567d450b4c9 | Python | 546 | 25 | import numpy as np
def to_array(array, round):
__tracebackhide__ = True
if not isinstance(array, np.ndarray):
array = np.array(array)
if round:
array = array.round(round)
return array
def array_equals(left, right, round=6):
__tracebackhide__ = True
left = to_array(left, ro... |
39c257e5ac2f62c6b04ad310037a0ab7536a2a7c83088cb60627b2f09e9a8ce3 | Python | 547 | 17 | from ipywidgets import widgets
from data_profiling.report.presentation.core import ToggleButton
class WidgetToggleButton(ToggleButton):
def render(self) -> widgets.HBox:
toggle = widgets.ToggleButton(description=self.content["text"])
toggle.layout.width = "fit-content"
toggle_box = widge... |
452d9f0d2140170e2c2c32494f26faa942a8b8700aee480f173cce89c1edb67b | Python | 548 | 15 | import numpy as np
import cebra
neural_data = np.random.normal(0, 1, (1000, 30)) # 1000 samples, 30 features
cebra_model = cebra.CEBRA(model_architecture="offset10-model",
batch_size=512,
learning_rate=1e-4,
max_iterations=10,
... |
783de443fbedcd41592221e84a8304f8ae5dc46db1a208798a42bd1f8bc7e3b5 | Python | 548 | 9 | from pfrl.utils import env_modifiers # NOQA
from pfrl.utils.batch_states import batch_states # NOQA
from pfrl.utils.clip_l2_grad_norm import clip_l2_grad_norm_ # NOQA
from pfrl.utils.conjugate_gradient import conjugate_gradient # NOQA
from pfrl.utils.contexts import evaluating # NOQA
from pfrl.utils.is_return_code... |
a2a36ff241e20bc0bfe16602a7c0c5a54599468a7e000c75a86600dbb1262e1b | Python | 548 | 14 | import numpy as np
def get_ordered_experiment_ids_from_descriptions(experiment_descriptions):
"""
Returns a sorted list of experiment ids according to the order in the experiment descriptions.
"""
if not experiment_descriptions:
return []
experiment_descriptions_values = list(experiment_d... |
a79825aabe6d77402ecbaca79b31b44d0e4fd1763beeb51a0e13d2c4df43067a | Python | 548 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
d807356c09e05c31aab96da2262b4d73eb3d956b82c255dd6042ab37f7839a6e | Python | 548 | 18 | from typing import Any, Dict, List, Union
from data_profiling.config import Style
from data_profiling.model.alerts import Alert
from data_profiling.report.presentation.core.item_renderer import ItemRenderer
class Alerts(ItemRenderer):
def __init__(
self, alerts: Union[List[Alert], Dict[str, List[Alert]]]... |
8dc98249abdd4e00b993ffd1a2f98bce02cc77d031cb49471c3cfa494d8c46bb | Python | 549 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
9cf439f52f0a2bb561cd991be62518acd6e3611fc0fb30194375402d9fc8a092 | Python | 549 | 19 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from .ase_datasets import AseDBDataset, AseReadDataset, AseReadMultiStructureDataset
from .base_dataset i... |
d51c90e2b60aa87c723beb63dad64214e8dde15b58f70966ed6b6d2c72543f0f | Python | 549 | 18 | from transformers.models.auto.tokenization_auto import AutoTokenizer
def convert_to_features(
# example_batch, model_name="prajjwal1/bert-mini", max_length=512
example_batch, model_name="EMBO/BioMegatron345mCased", max_length=512
):
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = list(ex... |
eab91b9d05ba71902fea00134e8a1c14bc4837d900656b0eaf0bee2c4b8f627a | Python | 549 | 19 | import torch
class SurrGradSpike(torch.autograd.Function):
scale = 10.0 # controls steepness of surrogate gradient
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
out = torch.zeros_like(input)
out[input > 0] = 1.0
return out
@staticmethod
def bac... |
8070c57382940e706856473860f8858c34e1b2ee9bccef5df54727c8cf123b12 | Python | 550 | 16 | import argparse
def parse_arguments(default_subjects: list) -> list:
""" Argument parser for parsing from command-line. """
parser = argparse.ArgumentParser(description='Process EEG for later analysis')
parser.add_argument('-s', '--subjects', type=str, help='Subject numbers separated by commas (no spaces... |
849d70004b1991dcf7ce2c1a8d3e7c5c9a05728baa3a38b65734a3e2f0dcf452 | Python | 550 | 19 | import os
import subprocess
def is_return_code_zero(args):
"""Return true iff the given command's return code is zero.
All the messages to stdout or stderr are suppressed.
"""
with open(os.devnull, "wb") as FNULL:
try:
subprocess.check_call(args, stdout=FNULL, stderr=FNULL)
... |
2c7e7b6581e726992a7a3bf7092db4f45496dc845a449e53c99b6fe9c605a050 | Python | 551 | 29 | # -*- coding: utf-8 -*-
import matplotlib
matplotlib.use("Agg")
from model import SelfSustainedPushPull
from experiments import create_experiments
import mozaik
from mozaik.controller import run_workflow
from mpi4py import MPI
import nest
nest.Install("stepcurrentmodule")
from pyNN.nest.cells import UNITS_MAP
UNIT... |
120fd338ebc9259e46708247e605365ec1e2f35ea167dbf01b0d30af0d7cf78d | Python | 552 | 14 | # __init__ file
from ._cot import cot_dense
from ._cot import cot_row_dense
from ._cot import cot_col_dense
from ._cot import cot_blk_dense
from ._cot import cot_combine_sparse
from ._cot import cot_sparse
from ._cot import cot_row_sparse
from ._cot import cot_col_sparse
from ._cot import cot_blk_sparse
from ._usot imp... |
cf1727338b961e03f7f73ee6eef3ef43138e31b545c364ced82b25c05b12736e | Python | 552 | 16 | r"""``ov.epi.single`` — single-cell-specific epigenomics tooling.
Passthrough to :mod:`epione.single` (``epione.single.atac`` /
``epione.single.hic``): scATAC peak calling (``macs3``), pseudobulk
generation, and single-cell Hi-C imputation/embedding. Names resolve
lazily from epione on first access.
>>> ov.epi.single... |
cfaff9ed2a3cf3256dee849ba3ef9cddd1c80aafcb40a5f5316cca5cb58cc753 | Python | 552 | 19 | import ray
@ray.remote
class Greeter:
def __init__(self, value):
self.value = value
def say_hello(self):
return self.value
# Actor `g1` doesn't yet exist, so it is created with the given args.
a = Greeter.options(name="g1", get_if_exists=True).remote("Old Greeting")
assert ray.get(a.say_hel... |
2a44d7e6da6cb8e74b18a3f41b0f6a0de747856c1eec8c5686d27d7ba99461ab | Python | 553 | 23 | # -*- coding: utf-8 -*-
"""
Project: Psychophysics_exps
Creator: Miao
Create time: 2021-01-08 22:31
IDE: PyCharm
Introduction:
"""
from scipy.optimize import curve_fit
from scipy.stats import poisson
import numpy as np
def fit_poisson_cdf(input_np_array) -> float :
"""
:param input_np_array: 2 columns, first... |
c5c27e7133efb8807f3e81e8b99950d04c2318ac8df9f82cb1b979e0e0f2d836 | Python | 553 | 23 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import os
import shutil
CACHE_DIR = os.environ.get(
"FAIRCHEM_CACHE_DIR", os.path.join(os.path.exp... |
b0b3936c07b4193b96789504a2d38f78943a01edc3e7cf3412bb8169e18ebc71 | Python | 554 | 8 | from agent.data.entities.config import CTT3HEALTH_ITEM_FILTER, CTT3HEALTH_LABEL
from agent.classifiers.data.transformer_text_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, val_split=0.8, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=... |
e0cce8fdba94c14981dbcc4008b96e646d70b58d45063cd6f2f78ea17218b5b5 | Python | 554 | 27 | """Compatibility layer for bulk alignment API."""
from ._old import (
Alignment,
AlignmentConfig,
EnhancedAlignmentConfig,
ISeqHandler,
geo_data_preprocess,
fq_data_preprocess,
load_config,
get_example_configs,
check_all_tools,
check_tool_availability,
)
__all__ = [
"Alignm... |
a2bc5d6cbc7f307674566ef1875f05c135067452db57916dbfa6401158231b8b | Python | 555 | 19 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
# pylint: disable=import-error, no-name-in-module, invalid-name
"""AIHWKIT extension"""
from importlib.util import find_spec
EXTENSION_COMPILED = find_spec(".... |
b5dd1d9a3b3cc35ac713ae12fc58dfbc0d5a4d128527f42023aa91e5525c2cd1 | Python | 555 | 16 | # Modified from https://github.com/dbolya/tide
# This work is licensed under MIT license.
# Note:
# 1. We removed several unused codes, improved TIDE's output images,
# and optimized the code format.
# 2. Right now, only COCO dataset is available, others
# (LVIS, PASCAL VOC, and CityScapes) are not fully tested.
from... |
db0393a67806e3313560302dcc49668ff0bc07ef044d89dbab6602a4a55ec96f | Python | 555 | 29 | # -*- coding: utf-8 -*-
"""
@author: wnchang
"""
from torch.utils.data import Dataset
class MyDataset(Dataset):
def __init__(self, data, label, info, transform=None):
self.data = data
self.label = label
self.info = info
self.transform = transform
def __getitem__(self, index)... |
ff84798d7306469accfbb14f1f52747f0df2fc2f9cd5bfa6e00ca00750fb55be | Python | 556 | 13 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
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