sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
27f96387e4558f687d4a969ec2053d7ae1b22ce33e60a8d0de47acd45f3cef82 | Python | 1,609 | 58 | import re
from typing import List, Union, Dict
from ..base_transform import LabelTransform
class StringToInt(LabelTransform):
r'''
Identify numbers in strings and convert strings to numbers. If there is no number in the string, the output corresponding to the string is 0.
.. code-block:: python
... |
37c3a14e8faa6effcf0196f48d6628cc912dd0915b2f3d96a332341e7b191cd4 | Python | 1,610 | 49 | import numpy as np
from ase import Atoms
from sella.internal import Internals
def _two_fragment_internals():
"""Two water molecules far apart -> per-fragment translations + rotations."""
atoms = Atoms(
symbols=['O', 'H', 'H', 'O', 'H', 'H'],
positions=[
[0.0, 0.0, 0.0],
... |
7d412262c98db81af4fd9def15db8101cc5d1c92fa9b04443cb901ac00667c66 | Python | 1,610 | 47 | from google.cloud import pubsub_v1
import json
def create_preprocessing_task(origin, chunk_dims):
args = {}
args['inlayer'] = 'gs://neuroglancer/pinky100_v0/image'
args['outlayer'] = 'gs://neuroglancer/pinky100_v0/image_corrected'
args['contrastlayer'] = 'gs://neuroglancer/pinky100_v0/contrast'
arg... |
9f9e51653012495e9044b4b1db8fe7eb3364d7bc0c733096903d41ba5db15c83 | Python | 1,610 | 42 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
''' Script to submit to cluster the decoding script'''
import os
from warnings import filterwarnings
from stormdb.cluster import ClusterBatch
from stormdb.access import Query
project = 'MINDLAB2020_MEG-AuditoryPatternRecognition'
os.environ['MINDLABPROJ']=project
os.envir... |
bf456d6a91c202f7798fb526aeecc5e4695b7ae91d2909d14f950fc9018ce372 | Python | 1,610 | 37 | # Define here the paths to your OneDrive and other personal paths that you need to redefine for your code to work
# The code will later use the file generated from this code to replace the paths in the code with your own custom ones.
import json
def set_path_mapping(path_mapping_json):
# parse json:
# path_di... |
dcaf49bb4271e873e186a0fc20d937cb0551dcb39910c6fc0fa04027d440444d | Python | 1,610 | 58 | import os
def create_makefile(destination_dir, stop_time="4000ns"):
makefile_content = f"""# automatically generates a Makefile for GHDL
# vhdl files
FILES = $(shell find -type f -name "*.vhd")
VHDLEX = .vhd
# testbench
TESTBENCHFILE = ${{TESTBENCH}}_tb
TESTBENCHPATH = $(shell find -type f -name "${{TESTBENCH}}... |
053a13dd8915c586a0ea33856e57b0847cd380c3769e72ad6d452dfd5b64cc21 | Python | 1,611 | 33 | import sys, os, h5py
from numpy.random import choice
import numpy as np
from optparse import OptionParser
def main():
usage = 'usage: %prog [options] <subsamples> <indir> <outdir>'
parser = OptionParser(usage)
(options,args) = parser.parse_args()
if len(args) != 3:
print(args)
... |
189268cf1c388a7a69b049365b25bb5c54cbe47e97cf8d5b8be95317d16b978f | Python | 1,611 | 57 | #!/usr/bin/env python
import os
import time
import boto
import boto.ec2
# Environment variables you MUST set (either here or by passing them in).
# -----------------------------------------------------------------------
#
# os.environ['AWS_ACCESS_KEY_ID'] = '...'
# os.environ['AWS_SECRET_ACCESS_KEY'] = '...'
# Opt... |
8554992acf2bd9d8510235a8d6eb4cf6f31c6a57bf1f2a9ef805b4850c33cbd0 | Python | 1,611 | 39 | import sys
import argparse
import os
import subprocess
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("cmd",help="cmd to run",nargs='*')
parser.add_argument('-name',dest='name',help='name for the singularity files',default='default')
parser.add_argument('-dir',dest='dir',help='d... |
f7a7e57254cc865db8d250115f29124c970590829b0d31e0636b34403f28f2ca | Python | 1,611 | 55 | """ The cell area click command """
import configparser
import glob
import os
import click
import pandas as pd
def concate_area_dataframes(file_lists, rf_prediction=False):
"""
conctact dataframe locatated in a directory
"""
frames = []
for file_list in file_lists:
for file in file_list... |
614bcc571a60320af30a7b443d40988391169ece44d36bd6aa0b91e2decfd8cf | Python | 1,612 | 51 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.Solubility")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class Solubility(data.ProteinDataset):
"""
Proteins with binary label... |
875d6db22fb4a969d3df96b7df94a8ec0d7dec2e27a10ea1b889b886f33f66e6 | Python | 1,612 | 47 | import numpy as np
import pandas as pd
from scipy import stats, optimize
import warnings
# logarithmic function (https://stats.stackexchange.com/questions/190107/curve-fit-with-logarithmic-regression-in-python)
# exponential equation + offset
def func(x, a, b, offset):
return a*(1.0 - np.exp(-(x)/b)) + off... |
c132e71270211db0ecc969206e7cfdac53c1659d6df96f57fc6e6bc96252bc1d | Python | 1,612 | 42 | """This tests aims at checking the integrity of the circuit built from the snakemake."""
import filecmp
from pathlib import Path
CURRENT_RESULT_DIR = Path(__file__).resolve().parent / "arch_results"
EXPECTED_DIR = Path(__file__).resolve().parent / "expected"
def test_densities():
assert filecmp.cmp(
CUR... |
c3be3d260a8a8bc72504570e6dd71b655aac985e2827f401ca16754866d414dc | Python | 1,612 | 51 | import re
SPLIT_RE = re.compile(r'[\.\[\]]+')
class JsonSchemaException(ValueError):
"""
Base exception of ``fastjsonschema`` library.
"""
class JsonSchemaValueException(JsonSchemaException):
"""
Exception raised by validation function. Available properties:
* ``message`` containing huma... |
39bd5c3f4317593ce3ba5e4348912c1cef32238a6a68676a84d865f879beab84 | Python | 1,613 | 50 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
465a28c166d76da852cc5f7c67398c5b8a0b5720cc827615f8fa4670dffce96b | Python | 1,613 | 35 | import torch
import math
class CosineWarmupDecay(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_step, total_step, multi, print_step, last_epoch=-1, verbose=False):
self.initial_lr = [group['lr'] for group in optimizer.param_groups]
self.min_lr = [lr * 0.0001 for lr in ... |
1d6f386757169a0e1be9a309be5308f68eac1994841ff6963139432acb4bf256 | Python | 1,614 | 55 | """
Backward compatibility for homebrew builds on macOS.
"""
import sys
import os
import functools
import subprocess
import sysconfig
@functools.lru_cache()
def enabled():
"""
Only enabled for Python 3.9 framework homebrew builds
except ensurepip and venv.
"""
PY39 = (3, 9) < sys.version_info < ... |
f019b4d5342af573137b9de9f11b09f466a4e2959fb1957219aeb6ec612e49a2 | Python | 1,614 | 44 | import torch
import numpy as np
from random import randrange
def process_dynamic_fc(timeseries, window_size, window_stride, dynamic_length=None, sampling_init=None, self_loop=True):
# Size of timeseries: batch x time x node
# Size of output: batch x time x node x node
if dynamic_length is None:
dyn... |
f9c261ae4fb5cb4902bd902217ada40807f92ccaef667bae29dc23f3f083860d | Python | 1,614 | 52 | import numpy as np
from pydicom.pixel_data_handlers.util import apply_voi_lut, apply_modality_lut
from skimage.morphology import remove_small_objects
from skimage.measure import label
from skimage.measure import regionprops
def reset_mask_stack(current_labels):
new_labels = {}
for k, v in current_labels.it... |
107a5c4c180c09dd819241b231e85f9a833c1c1bbb1c0fb990dfd340a5b9c8cb | Python | 1,615 | 39 | """Test functions for acquisition parameter."""
from copy import copy
import pytest
from console.interfaces.acquisition_parameter import AcquisitionParameter
from console.interfaces.enums import DDCMethod
def test_save_load(acquisition_parameter: AcquisitionParameter) -> None:
"""Check if saved acquisition para... |
4d496ec27bcb5ff9f0109548695dd6c71c83242fc81d5e8d14512e4c693e78c6 | Python | 1,615 | 68 | import apps
# import reram_register
def userTask(myapp):
userTask_6(myapp)
def userTask_1(myapp):
pulseWidth = 100 # 20 * 100 = 2000us
pulseGap = 46 # 20 * 46 = 920us
voltage_0 = 32 * [3]
voltage_1 = 32 * [3]
voltage_2 = 32 * [3]
voltage_0[1] = voltage_1[1] = voltage_2[1]... |
b9e61cffb46f6f74d0d3f3251a40cb03d21258b7ea2883d64dd10c1e08ffb995 | Python | 1,615 | 60 | import pytest
from elasticai.creator.testing.cocotb_pytest import create_name_for_build_test_subdir
def simple_function():
pass
def test_create_name_for_simple_function():
actual = create_name_for_build_test_subdir(simple_function)
expected = "simple_function"
assert actual == expected
def functi... |
ef3e2554f8d5b58c4b4317289785678700f97744607df352c19bfb446c7ee7a1 | Python | 1,616 | 66 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Before you can run the experiment you need to create duplicates of the
two tasks we want to test:
cd ../benchmarks/trucks-strips
for i in {00..19}; do cp p16.pddl p16-$i.pddl; done
for i in {00..19}; do cp domain_p16.pddl domain_p16-$i.pddl; done
cd ../freecell
for i... |
ae4922407fbe6e8c1c5809b9d5bf9ea8fdb935c8d217ca47ca743ecfa7b0e519 | Python | 1,617 | 49 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
f5486085e6406979496e42d924a5585eb91916b41dbe0b176176409dbf8e4ea7 | Python | 1,617 | 64 | import sys
def seq2kmer(seq, k):
"""
Convert original sequence to kmers
Arguments:
seq -- str, original sequence.
k -- int, kmer of length k specified.
Returns:
kmers -- str, kmers separated by space
"""
kmer = [seq[x:x+k] for x in range(len(seq)+1-k)]
kmers = " ".joi... |
451a44efc9f528de435ccbc01226a3b3b7a90c3825202b27bc8f4fabd104a90a | Python | 1,618 | 60 | # EyeNED OCT-ONH segmentation and feature extraction
# Copyright (C) 2024 Erasmus MC
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your optio... |
717bc2ad716836c3fedff9c726aa8eff55d472a8103a6070bdc6f87941936708 | Python | 1,618 | 47 | import torch
import torch.nn as nn
from typing import List
from ..prixfixe import FirstLayersBlock
from .add_blocks import ConvBlock
import torch.nn.functional as F
class BHIFirstLayersBlock(FirstLayersBlock):
"""
The firstLayersBlock of the BHI model.
Consists of multiple ConvBlocks with different ke... |
9e8eddd03865ca2651a94ff3bc696612e3e9228554717ebc690f867cfb6cee23 | Python | 1,618 | 42 | from __future__ import annotations
import pytest
from ase.build import bulk
from quacc.recipes.emt.elastic import elastic_tensor_flow
def test_elastic_jobs(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_path)
atoms = bulk("Cu")
outputs = elastic_tensor_flow(atoms, run_static=False)
assert outputs["... |
aea8a7d655a9250aeeef2862b8b5ecac5ed7d70320b2c8f43e27a698727bf54a | Python | 1,618 | 51 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleHardTanh(torch.nn.Module):
def __init__(self, total_width: int, frac_width: int, vrange: tuple[float, float]):
super().__init... |
3a876310b03c7496ac14a90dca38639d6245e8a60dbd5451a3a2c04b04bf1a52 | Python | 1,619 | 23 | import argparse
# iGWOS description and version
parser = argparse.ArgumentParser(prog='iGWOS', description="Predict genome-wide CRISPR-Cas9 off-target sites with iGWOS.")
parser.add_argument('-v','--version', action='version', version='%(prog)s 1.0')
# gRNAs file input for off-target prediction
parser.add_argument('-gR... |
8cbbe33e312bd4df6a63f3f3dde662c3896ce274d87bdd2c98da602164121517 | Python | 1,619 | 47 | import torch
from bisect import bisect_right
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer,
milestones,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_epochs=5,
warmup_method="linear",
... |
a8916fa04d1d56a53fa3fd96a504cbea60872311e01ec5aefcab96e692c18a41 | Python | 1,620 | 52 | #!/usr/bin/env python
#================================================================
# Copyright (C) 2023 Yufeng Liu (Braintell, Southeast University). All rights reserved.
#
# Filename : crop.py
# Author : Yufeng Liu
# Date : 2023-01-24
# Description :
#
#==========================... |
490fd06038d5c8cde4c9a5ca68c3197be210d44d9ad22be96f7bc6bd8f96f591 | Python | 1,621 | 51 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
def main(revisions=None):
suite = suites.suite_optimal_with_ipc11()
configs = {
IssueConfig('astar-cegar-original', [... |
ae20ea5e4ab647d21253f007936dd6671b07ff5edc496d74d5cee6fc7239904a | Python | 1,621 | 50 | from .task import Task
from .property_prediction import PropertyPrediction, MultipleBinaryClassification, \
NodePropertyPrediction, InteractionPrediction, Unsupervised
from .pretrain import EdgePrediction, AttributeMasking, ContextPrediction, DistancePrediction, \
AnglePrediction, DihedralPrediction
from .gene... |
e1394f37b077fc736a4c21fd3d01489345863a64c05472570edbcd15ba500d51 | Python | 1,621 | 45 | import pandas as pd
import os
import tempfile
from tests.mock_data import MockCvOutput
from cuppa.performance.performance_stats import PerformanceStatsBuilder, PerformanceStats
from cuppa.performance.confusion_matrix import ConfusionMatrix
class TestPerformanceStatsBuilder:
def test_can_build_from_pred_summ(self... |
36cfbc77c81f8f58767dd679de9da1b0f2bfe8a8853ffd727613346c3d27f2cd | Python | 1,622 | 39 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
d0ddc61c017a482253630301d09fc8cc5c4382c614fb5e901e1b7511fa62d687 | Python | 1,622 | 51 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.Fluorescence")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class Fluorescence(data.ProteinDataset):
"""
The fitness values of ... |
f0c75ea2617706a4fe6391ff2dc615c14cd69bf7e1026e1cb9d758c8b4211322 | Python | 1,622 | 52 | from collections.abc import Iterable, Iterator
import pytest
from elasticai.creator.hw_function_id import HwFunctionIdUpdater
pytestmark = pytest.mark.slow
def test_compute_id_ignores_target_file_contents(tmp_path) -> None:
build_dir = tmp_path / "build"
build_dir.mkdir()
source_file = build_dir / "sou... |
022f9d3b922ac1718981c7f26e4860841aeab83b27c24227c124c2ed0a32891e | Python | 1,623 | 32 | # Copyright (c) OpenMMLab. All rights reserved.
from .aspp import ASPP
from .contextual_attention import ContextualAttentionModule
from .conv import * # noqa: F401, F403
from .downsample import pixel_unshuffle
from .ensemble import SpatialTemporalEnsemble
from .flow_warp import flow_warp
from .gated_conv_module import... |
e89a5eeccde5eed8fd7d246db00ecd1db26d52cfef20bfe0cb31350f822bceb7 | Python | 1,623 | 56 | from typing import Iterable
from elasticai.creator import graph as g
class Graph:
def __init__(self, wrapped: g.Graph[str], data: dict[str, str]):
self.wrapped = wrapped
self.data = data
@property
def nodes(self):
return self.wrapped.nodes
@property
def edges(self):
... |
a8e4341bb3f1c30fda9e1391dee9e64af486909151be8e494be6ffe118cae5e5 | Python | 1,624 | 53 | # -*- coding: utf-8 -*-
"""
Created on Fri Jul 12 16:00:48 2024
@author: mpolonen
"""
import numpy as np
from glob import glob
import os
import shutil
from expyfun.io import read_hdf5, write_hdf5
# %% speech
path_in = 'C:/code/peaky_pitch/stimuli/speech_regress/'
path_out = 'C:/code/peaky_pitch/stim... |
8bc8ece18aaf54da62547d9e47ea99a33707710c9be487879fea1c16ee006230 | Python | 1,625 | 60 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
a3a3e650a469836056f46ed6b2366e1d3983199881b3b4d61202f7ff5fe490ad | Python | 1,625 | 58 | import os
import tiffile
import h5py
import matplotlib.pyplot as plt
import numpy as np
import cv2 as cv
from PIL import Image, ImageOps
from tiffile import imsave
from skimage.measure import label
from skimage.color import label2rgb
def denoise(img: np.ndarray, h: int, templateWindowSize=7, searchWindowSize=21):
... |
8562746c5352f462041a2bfce69355b472937dd2a090aa8b510c89a0dc7eacc0 | Python | 1,626 | 53 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleTanh(torch.nn.Module):
def __init__(
self,
total_width: int,
frac_width: int,
num_steps: int,
... |
a5db40a3b40d368b7fd6fe1f8d4818819e0138884d4c944f23cd67b364822e68 | Python | 1,626 | 40 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
df387027cfded13e35bc8285f530bd66ae5f94f5c6932d9179552c7d6abfb21e | Python | 1,626 | 56 | """I/O utilities for the Vasp calculator."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc.utils.files import load_yaml_calc
if TYPE_CHECKING:
from pathlib import Path
from typing import Any
def load_vasp_yaml_calc(yaml_path: str | Path) -> dict[str, Any]:
"""
Load... |
3d18c9fa186c54e7d1db7ed15174086a3ac58683d1f6ff56165731cf5a472331 | Python | 1,627 | 73 | """Common defect workflows."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
from monty.dev import requires
from quacc import subflow
from quacc.atoms.defects import make_defects_from_bulk
has_pmg_defects = bool(find_spec("pymatgen.analysis.defects"))
has_... |
9c29be4ef7d44839f9e3ba71a6055af65b8069c123a09d9391cf6f5eba5332bb | Python | 1,627 | 43 | from pybioshell.simulations.systems.ising import Ising2D_unsigned_char_unsigned_short_t as Ising2DBB
from pybioshell.simulations.movers.ising import SingleFlip2D_unsigned_char_unsigned_short_t
from pybioshell.simulations.movers.ising import WolffMove2D_unsigned_char_unsigned_short_t
from pybioshell.simulations.movers i... |
9ed1e6ea71bd9da166cdda324d99b84bcdf87faae02a9aeb102ec4bcf594d15a | Python | 1,627 | 47 | import torch
import torch.nn as nn
from utils.params import ParamsPack
param_pack = ParamsPack()
import math
class WingLoss(nn.Module):
def __init__(self, omega=10, epsilon=2):
super(WingLoss, self).__init__()
self.omega = omega
self.epsilon = epsilon
self.log_term = math.log(1 + s... |
867fcebda6d88cf035c0e5a9154e1cfd6ee560bbdfc05ccbb74862a5c78ec701 | Python | 1,628 | 47 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
b181764ae493fe73ce9d0cb0b11933fde5c593b7b7f1c5d2ce447d27dc2e3a6a | Python | 1,628 | 53 | from collections.abc import Iterator
from elasticai.creator.ir2vhdl import DataGraph
from .vhdl_nodes.node_factory import InstanceFactoryForCombinatorial
from .wiring import (
connect_data_signals,
)
def wrap_node(n):
return InstanceFactoryForCombinatorial(n)
def build_declarations_for_combinatorial(impl:... |
be601b03bca00d6ee8faac1f3337b92e451d9527a39f450dff7ce3eaf6f6fd8e | Python | 1,628 | 24 | """posthoc_sff_landscape_2026-09-28.py (post hoc, descriptive). SIGN_FROM_FUNCTION Test 2 ended with 100x lower loss but
fewer correct signs. Is that a search that got stuck (truth fits far better) or a recording that cannot tell (end point
fits as well as the truth)? Losses on the same 20-odour panel, candidate seeds ... |
d86dd95400faba6b43ff45b9bdb917f5ad858f0a33ee22cb78e9aac0fcbc8cd1 | Python | 1,628 | 42 | try:
# Python 2
from StringIO import StringIO
except ImportError:
# Python 3
from io import StringIO
import pddl
from pddl_to_prolog import Rule, PrologProgram
def test_normalization():
prog = PrologProgram()
prog.add_fact(pddl.Atom("at", ["foo", "bar"]))
prog.add_fact(pddl.Atom("truck", [... |
faf23cf90d8258ab9d790a6b6d8f8b737d8f43c6a4d1ba3e87d42639ac50678b | Python | 1,628 | 34 | """
Main run function for finding consensus assemblies
last modified: András Ecker 01.2023
"""
import logging
from assemblyfire.utils import load_assemblies_from_h5, get_nrn_df
from assemblyfire.spikes import SpikeMatrixGroup
from assemblyfire.clustering import cluster_spikes, detect_assemblies
from assemblyfire.asse... |
1d1219d194bebd59c6a2ac064de5f29e5fe0c4e371fcea32b757c7fc00d94966 | Python | 1,629 | 43 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
1fbaa3c43eae0df776b16f5693b129a5c70b6d32fa3d320ca6dec715672cd078 | Python | 1,629 | 49 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
79bde428abf5b5da4fc9b6d6230d3c3499f3fdb099fdf61df4001ade6a5a4dd2 | Python | 1,629 | 42 | #!/usr/bin/env python
# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provide BasicMetric, the basic class of all the metrics.
"""
... |
931dea580ec0d1910f8554adfae471b044785c5f082d98084b79a179c7af44f0 | Python | 1,629 | 50 | '''precompute MUA as spike rate per spatiotemporal bin'''
import os
import h5py
import numpy as np
import mesocircuit.analysis.spike_analysis as sana
from mesocircuit.helpers.io import load_h5_to_sparse_X
def write_mua_file(circuit,
PS, path_lfp_data, networkSim):
'''
Parameters
-------... |
1468c906e3094ea38603f0103d6001d2d271eebb667d096ae53b8dd69d6329a0 | Python | 1,630 | 43 | from elasticai.creator.arithmetic import (
FxpArithmetic,
FxpParams,
)
from elasticai.creator.base_modules.hard_tanh import HardTanh as HardTanhBase
from elasticai.creator.nn.design_creator_module import DesignCreatorModule
from .design import HardTanh as HardTanhDesign
class HardTanh(DesignCreatorModule, Ha... |
62b57ad7567396d54843241f1915abb0e1e5cc5f04afb495602b00734893fa6e | Python | 1,631 | 49 | from itertools import chain
from typing import TypeAlias
from elasticai.creator import ir
from elasticai.creator.ir2vhdl import Code, DataGraph, type_handler
from .language import Port, VHDLEntity
IOPair: TypeAlias = tuple[tuple[int, ...], tuple[int, ...]]
@type_handler()
def lutron(lowered: DataGraph, registry: i... |
78a0787885b5ace7a460be0f1343b96c16b0f5f4bc2659114e37f0bc97ac1bb9 | Python | 1,631 | 59 | import sys
from pybioshell.core.data.io import Pdb
if len(sys.argv) < 2 :
print("""
Extracts PDB clusters from clustering results produced by ap_cluster_ligands output
SEE:
ap_cluster_ligands program to see how to run clustering
USAGE:
python3 pdb_from_clusters.py clustering_output.txt ligand_code
EXAMP... |
fa0a0e080d6b85029094d43f00e49492e48874377a37626aa189bbcc8ba2eac9 | Python | 1,631 | 50 | import numpy as np
from config import MODEL_PATH, DATA_DIR, LABEL_FORMAT
from model import CNN
import csv
from dataloader import DATA
# TODO: adapt such that it works for all formats ('one-per-item', ...), currently only works for 'one'
# restore commit in result.txt to ensure loading model parameters works
def rest... |
744166a498f5b9d89908794768584ea7bf25ee55ecad9db2f03569fe5335faa0 | Python | 1,633 | 76 | import plotly
import plotly.graph_objs as go
import numpy as np
import pandas as pd
import os
#Example shape
# Wall time,Step,Value
# 1698314503.5367975,1,0.6933741569519043
val_loss = pd.read_csv('val_loss.csv')
train_loss = pd.read_csv('train_loss.csv')
val_loss = val_loss.drop(['Wall time'], axis=1)
train_loss = ... |
0c8c6f833e51f7da2779427b0eca0f6bb4a7295a9541bea1759e4e539ec59ac8 | Python | 1,634 | 48 | """
Optuna example that optimizes a classifier configuration for Iris dataset using sklearn.
This example is the same as `sklearn_simple.py` except that it uses a callable class for
implementing the objective function. It takes the Iris dataset by a constructor's argument
instead of loading it in each trial execution.... |
41e2fe9d5a2c2a989d7990c90dc1902aeb945ce46ba911a11f66bcf7c3b867a8 | Python | 1,634 | 55 | import os
from collections.abc import Iterable, Iterator
from pathlib import Path
from elasticai.creator.hw_function_id import HwFunctionIdUpdater as _generalUpdater
class HwFunctionIdUpdater:
def __init__(
self,
build_dir: Path,
):
target_file_name = "skeleton_pkg.vhd"
target... |
7cd0af2f14420a82be9458540de12e3cd4ca523803373467a09f0611aa562b1c | Python | 1,634 | 54 | import csv
from functools import partial
import tkinter as tk
from tkinter import filedialog
from pathlib import Path
import os
import napari
from napari.utils.notifications import show_info
# this is where data will be written
currdir = os.getcwd()
tempdir = filedialog.askdirectory(parent= tk.Tk(), initialdir=currdi... |
de63dc6058397141dc868b5348365f29da9bc4865fbbf841f4a90d5fb620c38a | Python | 1,634 | 47 | from typing import Optional
from .savable import File, Path
from .template import Template, TemplateExpander
class InMemoryFile(File):
def __init__(self, name: str) -> None:
self.text: list[str] = []
self.name = name
def write(self, template: Template) -> None:
expander = TemplateExp... |
28e89c353d7b0860a065d1f4b7a211d027c184de942bc44fbfbc60eec97e3f97 | Python | 1,635 | 93 | """
Provides default parameters used in the analysis.
"""
params = {}
params['mplstyles'] = 'misc/mplstyles/report_plots_master.mplstyle'
params['extension'] = 'png'
params['seed'] = 2106
params['python_sort'] = True
params['rate_histogram'] = {
'binsize': 1., # in ms
't_start': 500., # in ms
't_stop':... |
93fd52222daa61bea5fc35b5130367c12a706d159c06b8f214efe9b9beb171e8 | Python | 1,635 | 52 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
def main(revisions=None):
suite = suites.suite_satisficing_with_ipc11()
configs = {
... |
963ac950743fca7853a715b99717d9da45a66e57cdd78ddb3917a9fb617d8239 | Python | 1,635 | 51 | #!/usr/bin/env python3
import logging
import argparse
# https://stackoverflow.com/questions/2130016/splitting-a-list-into-n-parts-of-approximately-equal-length
def split(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))
def parse():
... |
2a5254f7d119db66218be7d29c0b5729416c611f9d9d3eee4736597597956778 | Python | 1,636 | 43 |
from REACTRL.env_modules.net_framework_curr import *
from REACTRL.env_modules.signal_conv import *
from REACTRL.img_modules.curr_data import train_loader, test_loader
from boss.bo.bo_main import BOMain
from boss.pp.pp_main import PPMain
from datetime import datetime
import pandas as pd
def func(X, model=None, method=... |
557c583b7910910d5b2bca3381bf11a47ecde12c2b7dc4129c201f5c28341054 | Python | 1,636 | 59 | from __future__ import annotations
import pytest
redun = pytest.importorskip("redun")
from ase.build import bulk
@pytest.fixture
def scheduler():
return redun.Scheduler()
from quacc import flow, job
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow # skipcq: ... |
5e0ad0dc9fb0af7701fed4255696f85ee20ef35bac04cdcebc754da5ce5c2f62 | Python | 1,636 | 47 | """Regression: pipeline._git_commit_short must not flash a console window.
Like the hub's updater._git, the pipeline's best-effort commit-stamp shells out
to git; on Windows it must pass CREATE_NO_WINDOW so it doesn't pop a console
window when the caller has no console.
"""
import sys
import subprocess
d... |
8952709c2832972eca8030e36a3ca847caa1da297a13c1ca0c6091c71dbc1255 | Python | 1,637 | 58 | from __future__ import annotations
from enum import StrEnum
from typing import TYPE_CHECKING
import torch_sim as ts
if TYPE_CHECKING:
from collections.abc import Callable
class TSModelType(StrEnum):
"""Enum for model types."""
FAIRCHEMV1 = "FairChemV1Model"
FAIRCHEM = "FairChemModel"
GRAPHPESW... |
ca653a28fada92fd22b30b64e6ecfc83ceb85114f748e2e5974e98ea2a4ea606 | Python | 1,637 | 57 | import unittest
import torch.nn.functional as F
import torch
from torcheeg.models import BGlow, BCGlow
class TestFlow(unittest.TestCase):
def test_bglow(self):
model = BGlow()
# forward to calculate loss function
mock_eeg = torch.randn(2, 4, 32, 32)
nll_loss = model(mock_eeg)
... |
0cb0c3cf525bb77093ae743ad0e685c4d52e4bedc2fb9350dab40e322ba310b7 | Python | 1,638 | 53 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_creator
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleSiLU(torch.nn.Module):
def __init__(
self,
total_width: int,
frac_width: int,
num_steps: int,... |
50a5f8763e4be05d6bb7335a418a5e8bafb4fea86a48b3b96b6c4cd26336ed3c | Python | 1,641 | 53 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
89cd820a018ad911ce48677e4634afb9650a3951c9395c1177de6148f83fac8a | Python | 1,641 | 62 | # Node group "CA Value Float" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerat... |
9c3735032650394d755bc7e009111fb135be6e502ee7e345072532cb8776a9c0 | Python | 1,641 | 57 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.Hetionet")
class Hetionet(data.KnowledgeGraphDataset):
"""
Hetionet for knowledge graph reasoning.
Statistics:
- #Entity: 45,158
- #Relat... |
c4178d90213c5ddd779195365f433dcaf34e6c75c860a1318d7580dcb36e233a | Python | 1,641 | 43 | from pathlib import Path
import argparse
import dolfin as d
from smart import mesh_tools
import cru_args
def main(input_mesh_file, output_mesh_file, num_refinements):
if not Path(input_mesh_file).is_file():
raise FileNotFoundError(f"File {input_mesh_file} does not exists")
cru_mesh = d.Mesh(Path(inpu... |
ccd06adff9377214d3550cb3d27f1f2c5e4cf97f5cc839cc794f418667b23820 | Python | 1,641 | 44 | #----Figure_05_G_preparation----------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure 05 G
# for the connectivity analysis for Reinhard and Fukuda et al. 2024
#---------------... |
03376dc62888beabae1d61f22a0b804af59cbed6e690a99e7493b044458125c7 | Python | 1,642 | 48 | import torch
import torch.nn.functional as F
from diff_frech_mean.frechetmean.frechet import frechet_mean
def frechet_agg(x, adj, man,B=None):
"""
Compute Frechet Graph Aggregation
Args
----
x (tensor): batched tensor of hyperbolic values. Batch size is size of adjacency matrix.
adj (... |
b4b60d96e988527e20921d2c87ee85457083aaf09f3a85c0b8818503d423bf51 | Python | 1,642 | 59 | import prosper_nn.utils.generate_time_series_data as gtsd
import prosper_nn.utils.create_input_ecnn_hcnn as ci
from prosper_nn.models.hcnn_known_u import hcnn_known_u
import torch
# Define network parameters
n_features_U = 10 # setting this to zero reverts to vanilla HCNN with tf
batchsize = 5
past_horizon = 15
forec... |
8532f8cc1762ad9ba60ac6a42d4905a0a46b456e60c25f5d0d6d13486380b21a | Python | 1,643 | 57 | from typing import Any, Protocol
from torch import Tensor
from torch.nn import Conv1d as _Conv1d
from torch.nn.functional import conv1d
from elasticai.creator.base_modules.math_operations import Quantize
class MathOperations(Quantize, Protocol): ...
class Conv1d(_Conv1d):
def __init__(
self,
o... |
be6c9dfd52b74c9009520472f61e4570430c0b223df7707467080c7048959752 | Python | 1,643 | 60 | # -*- coding: utf-8 -*-
#
# Basic checkers to facilitate direct Dask interface
#
import subprocess
from time import sleep
# Syncopy imports
from syncopy.shared.errors import SPYWarning, SPYInfo
from .log import get_logger
def check_slurm_available():
"""
Returns `True` if a SLURM instance could be reached v... |
c3fa2888deff0016f5840f38689943ce5ca0e0a103c6f3e443969a91fc2fb304 | Python | 1,643 | 42 | import numpy as np
from model import CNN
import csv
import time
from dataloader import DATA_TEST
def restore(pathname):
model = CNN('restored', 0)
model.restore_model(pathname)
return model
def ensemble_predict(data, modelpaths,run):
final = np.empty((data.images.shape[0], 10))
for i, modelp... |
e65bd9f1972ee20842a03c1b056654661aab983765981e2f65ff3faa6b417532 | Python | 1,643 | 53 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-1,2,0.001)
binary_threshold = 0.5
f_act = 1 * (x > binary_threshold)
f_surrogate = np.minimum(np.maximum(x, 0),1)
f_prime = np.zeros_like(f_act)
f_prime[np.where(x>binary_threshold)[0][0]]=1
H_of_x = 1*(x... |
f4219d73d9ffec86dd7c14d3615d4dda971d97406294a14672b27ff30e8e37b6 | Python | 1,643 | 33 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
1a11b73a8809c9815f3204b2bbef8099f98a4ecbe1bc082a89fa6a339629e71e | Python | 1,644 | 43 | """Sub-family help fallback: DevCCF developmental-stage families (kim_devccf_<stage>)
inherit the base kim_devccf tooltip without a per-stage OptionHelp entry."""
import pytest
def _hw():
return pytest.importorskip("regtools.registration.gui.help_widgets")
class _Opt:
def __init__(self, key):
... |
2cf2c5389ab95413cd334f64d61c2792b3d55d9f434d672e2785c48fafc5b363 | Python | 1,644 | 55 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import itertools
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment, RelativeScatterPlotReport
DIR = os.path.dirname... |
441a4a6af9cd44d09d2ca35d8b76e88f59fe2eafb0dc2ec025ded20eaf9b0e29 | Python | 1,644 | 55 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import itertools
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment, RelativeScatterPlotReport
DIR = os.path.dirname... |
45919a97066541fa34451332f9f2636ca167cf2916c463f4d74390d82042cce4 | Python | 1,644 | 50 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
8081e76d27957beac51289843c82772d9e2bfd8e40d65f6bbf3b9e4c6e654dfa | Python | 1,644 | 48 | """ANSI-to-segments translation for the embedded log view."""
from regtools.registration.gui.log_view import parse_ansi, ANSI_COLORS, _DEFAULT_FG
def _texts(segs):
return [s[0] for s in segs]
def test_plain_text_is_one_default_segment():
segs, _ = parse_ansi("hello world")
assert segs == [("... |
285d7bfa7f927e4ff5cbc790ab85b7f67f04abe50ab29b5dd51fc716ba2c8a17 | Python | 1,645 | 48 | """Test remove trend."""
import numpy as np
import pyprep.removeTrend as removeTrend
def test_highpass():
"""Test for checking high pass filters."""
srate = 100
t = np.arange(0, 30, 1 / srate)
lowfreq_signal = np.sin(2 * np.pi * 0.1 * t)
highfreq_signal = np.sin(2 * np.pi * 8 * t)
signal = lo... |
3319a39c7e1b99c59941a54d400ca6432c77c0ae29424ee055b10d923fa619b7 | Python | 1,645 | 53 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_fxp
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import Sequential
class SimpleAdaptSiLU(torch.nn.Module):
def __init__(
self,
total_width: int,
frac_width: int,
num_steps: int... |
3a41904299bb78ef9f276d88afa84c2046086803aae84f2d34808337eb5b3644 | Python | 1,645 | 47 | from typing import Dict, Union
import numpy as np
import torch
from ..base_transform import EEGTransform
class ToTensor(EEGTransform):
r'''
Convert a :obj:`numpy.ndarray` to tensor. Different from :obj:`torchvision`, tensors are returned without scaling.
.. code-block:: python
from torcheeg i... |
ebb29d916dce4b03d22b911d242731f2c2f559e6f2f2d4b8eb492bfd9230f68e | Python | 1,645 | 62 | import os, sys
sys.path.append('../../4K_HETU/app')
import arrayexp
import globals.global_func as gf
import numpy as np
import arrayparams
if __name__ == '__main__':
on_chip=True
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
if on_chip:
array = arrayexp.ArrayExp()
else... |
44c77ace987f5d267b3ae451495b779f4852514c32aa4d8949615da2488b586a | Python | 1,646 | 55 | extensions = dict(
extra_params=[('verbose', 'FALSE')],
module="""
#' Determines whether an XGBoost model can be built
#'
#' Ask the H2O server whether a XGBoost model can be built. (Depends on availability of native backend.)
#' Returns True if a XGBoost model can be built, or False otherwise.
#' @export
h2o.x... |
8adac054ffe2ca5d5728f017e723a97c2ec15d043157f8cd7033ebcde4135e57 | Python | 1,646 | 41 | from stable_baselines.common.callbacks import EvalCallback
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import PPO1 as RL
from Env import make_env
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
if __name__ == '__main__':
# SimulationMode:
# True - use memristor... |
0b3a224ad33f9052f43a433f125b68ccc7a7cd8b270a2a9c1d7bbce3284736bf | Python | 1,647 | 53 | # Diana Giraldo
# May, 2023
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.transforms.functional import resize
from tqdm import tqdm
INTERP_MODE = 'bicubic'
ALIGN_CORNERS = False
def apply_model_dataset(
model,
data_set,
device,
batch_size = 5,
scale_f... |
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