sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d9d85e13d2b7dfb09d83d278675caee4f0567a630ef6b076a81483fc78f4b1eb | Python | 2,101 | 46 | import itertools
def wrapper_generate_combination_dict(config_dict):
'''
Given a dictionary where a key is a paramater name. We can pass list of values for a certain parameter.
Now, our goal is to generate a list of dicts from the original dict such that the keys will be the same,
however, each k... |
de000e7261e36a8c5cdec8cc280a37ecb1dcdeecbfc7aad9705aa803b15c3439 | Python | 2,101 | 57 | from __future__ import print_function
import string
import random
import time
import torch
from robustfill import RobustFill
modes = ['single', 'double']
for mode in modes:
print("-"*20, "\nmode:%s"%mode)
print("Making net...")
net = RobustFill(
input_vocabularies=
[string.asci... |
09d438a453bff433aea65b0430b03e84c5c82dc87fbbb45fb8762a28a21ccf23 | Python | 2,102 | 56 | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
import os
import sys
import tensorflow as tf
import matplotlib.pylab as plt
from tensorflow.keras import layers
from tensorflow.keras import models
import numpy as np
import PIL.Image as Image
import glob
... |
4fa3a702ea87496f7ff6e1abec82d696d00ec6c07b82cf5927710c578d8da520 | Python | 2,103 | 67 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from lab.reports import Attribute, arithmetic_mean, geometric_mean
import common_setup
from common_setup import IssueConfig, IssueExperiment
from rel... |
bbb5734b21d0ceb472a0fcae46cf1a6c9eb90901f6fae20249e7eb2ea4e38cfd | Python | 2,103 | 61 | import unittest
import os
import pandas as pd
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../src")
from hornet.input import save_transformed
class TestSaveTransformed(unittest.TestCase):
def setUp(self):
self.tmp = '.tmp'
self.path = '.data_test'
# Create... |
8281863d113be81852b3f86d29a4bcb997811ed36a39eeb0c8e0f5d392c18f1c | Python | 2,104 | 59 | from typing import List, Optional
import torch
from ._functional import soft_tversky_score
from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
from .dice import DiceLoss
__all__ = ["TverskyLoss"]
class TverskyLoss(DiceLoss):
"""Tversky loss for image segmentation task.
Where TP and FP is we... |
07f09992ca55ceb69f939bf418254fec5d3cefc32a21c0fdc4b455af1b10716f | Python | 2,105 | 68 | # 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... |
3eabded6cf8f1516c2c1cb37db327450c76709ac1e56414dabef4a64d45874e6 | Python | 2,105 | 68 | #! /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... |
4240b83b1e78b950715c7976c836598b2d66d6fe2e74a08fe81f5ce91c169e7b | Python | 2,106 | 53 | import argparse
import collections
import logging
from mode import multi_cross_validation_bootstrap, multi_train_bootstrap
from parse_config import ConfigParser
from utils import set_random_seed, override_n_genes
SEED = 1126
set_random_seed(SEED)
def main():
args = argparse.ArgumentParser(description='Main')
... |
5c7e9e48153b784ded945a18b3658ad608479aabb19f1cc24608e76af32f3aa3 | Python | 2,106 | 78 | ###############################################################################
#
#
#
###############################################################################
def define_parameters():
"""Create a Python dictionaruy and return all model's parameters."""
p = {}
### Physical Constants ... |
79da66f4b7391cc66c18a10dcf49fa89c1d90a8df528e480afe3f402becf72cc | Python | 2,106 | 58 | #
# 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,... |
c19f40d8de276d4df29b0cdf5abe05e5e42fbd87869230b1ddaf9c24f9da2c4b | Python | 2,107 | 61 | import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from typing import Tuple, Iterable
from src.analyze.utils import init_mpl
from src.analyze.performance_measures import PerformanceMeasures
from constants import ABSOLUTE_ERROR, ERR_LEVEL_TOTAL_SCORE, ERROR_TO_LABEL
_FIG_SIZE ... |
fdace77ccdbc9e164c6c8c59900bf44dbc0f84b0c6132aac808fc954ade52393 | Python | 2,107 | 65 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: A.Akdogan
"""
import torch
import os
import cv2
from data_visualizer import one_hot_encode
class NailDataset(torch.utils.data.Dataset):
"""
Args:
images_dir (str): path to images folder
masks_dir (str): path to segmentation masks fol... |
e569baff1ee52ab96a5633c8e4c04dfd1bab7111f0558a10ecab2bb3ce1d7bbb | Python | 2,108 | 57 | import functools
import logging
import re
from typing import NewType, Optional, Tuple, cast
from pip._vendor.packaging import specifiers, version
from pip._vendor.packaging.requirements import Requirement
NormalizedExtra = NewType("NormalizedExtra", str)
logger = logging.getLogger(__name__)
def check_requires_pyth... |
49be250f7e58ad4439f9aaf30093f8d5741efe8c083597d1265edf57dab2048a | Python | 2,109 | 59 | from __future__ import annotations
import os
import tempfile
import time
from typing import NoReturn
from optuna.artifacts import FileSystemArtifactStore
from optuna.artifacts import upload_artifact
from optuna_dashboard import register_preference_feedback_component
from optuna_dashboard.preferential import create_st... |
5d3de08e75ac0901408ce662dcce76c600ff7501c267f246b461f51386d9226d | Python | 2,109 | 65 | import pandas as pd
import pickle
import argparse
import os
def parse_nodes(nodes_file):
nodes = {}
with open(nodes_file, 'r') as file:
for line in file:
data = line.strip().split('|')
tax_id, parent_tax_id, rank = map(str.strip, data[:3])
nodes[tax_id] = {'parent': ... |
1229aba7b6d38d56a5b487d5ef8676dc6c14cc0ffb73c4228bef761845f4b104 | Python | 2,110 | 71 | from collections.abc import Callable, Hashable, Iterable, Iterator
from typing import Protocol, Self
class _NodeLT(Hashable, Protocol):
def __lt__(self, other: Self, /) -> bool: ...
class _NodeGT(Hashable, Protocol):
def __gt__(self, other: Self, /) -> bool: ...
type _Node = _NodeGT | _NodeLT
type NodeNe... |
2fe836ef94c64a8ee0119cb42c6c8a0a1c3080c4b86feeb795fe15b4a28a3a84 | Python | 2,110 | 89 | # Node-group asset "Color to LCh" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
... |
49c23e69400df1a6870060dbd45b020c992363cc05c0f1f017c405217017abf4 | Python | 2,110 | 63 | import sys
sys.path.append(r'..\..\..\..\pylib')
import swc_handler
import numpy as np
import os
from pathlib import Path
from multiprocessing.pool import Pool
from math_utils import min_distances_between_two_sets
from morph_topo.morphology import Morphology
def inbox(xyz, shape, center):
xyz = np.abs(np.array(xy... |
8c64b35cb58bc3a7602d8abeb3bf04ac819fd2ce6b4e9739ac2a5ca34aeacc8a | Python | 2,111 | 78 | import torch
import torch.nn as nn
import torch.nn.functional as F
from segmentation_models_pytorch.base import modules
class PSPBlock(nn.Module):
def __init__(self, in_channels, out_channels, pool_size, use_bathcnorm=True):
super().__init__()
if pool_size == 1:
use_bathcnorm = False ... |
01b2e12e0d103ba8933c8ced903f3ac43203e6642f24c7c602b281968f8ffb2a | Python | 2,112 | 60 | import torch
from elasticai.creator.arithmetic import (
FxpArithmetic,
FxpParams,
)
from elasticai.creator.nn.fixed_point.math_operations import MathOperations
from tests.tensor_test_case import TensorTestCase
class FixedPointMathOperationsTest(TensorTestCase):
def setUp(self) -> None:
self.opera... |
0c1336e6ee4017ff8979eaaf995e4d6296f07444e9816a1c61751aa39a78c45b | Python | 2,112 | 58 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_port
from elasticai.creator.vhdl.design.design import Design, Port
from elast... |
798812f2445805f7239e176be3bc2d1e23d871d13553dc339a1f9b13d924735d | Python | 2,113 | 41 | """
Config global options
Options can be changed at run time. See tests/test_backoff.py for usage example
Options:
BACKOFF_MAX_TRIES: maximum number of times to retry failed request to wikidata endpoint.
Default: None (retry indefinitely)
To disable retry, set value to 1
BACKOFF_M... |
89f42408eefb3c8d81d6ee76113eccd865097599e79cb0e34bbafa943f622938 | Python | 2,113 | 62 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
a9365a946b1e24adf4b977c19b8a1d9a4dbfd1ced996c7ec3e731015ccca78b1 | Python | 2,113 | 63 | import numpy as np
from scipy.sparse import *
from skfeature.utility.construct_W import construct_W
def lap_score(X, **kwargs):
"""
This function implements the laplacian score feature selection, steps are as follows:
1. Construct the affinity matrix W if it is not specified
2. For the r-th feature, w... |
ad5be6b1793b591df007ffb601dff105bf1f443bec2f449d23c0be8ef0f96cb9 | Python | 2,113 | 68 | # 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... |
bc3d0164b048ab33eeacb7165fee7104ec2e357ed823a3ea31f240f238ada0fc | Python | 2,113 | 85 | from __future__ import annotations
import pytest
redun = pytest.importorskip("redun")
from ase.build import bulk, molecule
from quacc import flow
from quacc.recipes.emt.core import relax_job, static_job # skipcq: PYL-C0412
from quacc.recipes.emt.slabs import bulk_to_slabs_flow # skipcq: PYL-C0412
@pytest.fixture... |
e3caee115a4066183c70146bf1958729cd67f5fadf9e7f3a153f14cacf05f602 | Python | 2,113 | 71 | """Generates a graphviz DOT file of an evaluation trace.
Usage (need the dot binary, from the graphviz package, www.graphviz.org):
python2 dot_graph.py | dot -Tpdf -o graph.pdf
"""
import autograd.numpy as np
from autograd.tracer import Node, trace
class GraphNode(Node):
# Records the full graph (could having t... |
040d4ad89b61a90c26f36394fc01630d98bb1d47db1f2f737b7f61db0e369563 | Python | 2,114 | 58 | import argparse
import cv2
import glob
import os
def main(args):
txt_file = open(args.meta_info, 'w')
for folder, root in zip(args.input, args.root):
img_paths = sorted(glob.glob(os.path.join(folder, '*')))
for img_path in img_paths:
status = True
if args.check:
... |
243b69d1acf21652afe0606a2cbcceb88240800ef18042b5785655fc8cd4f3cd | Python | 2,115 | 76 | import sys, os
from pandas.io.parsers import read_csv
import numpy as np
from collections import Counter
import pandas as pd
import localInstall
localInstall.require(['pysam'])
import pysam
### Initialize ###
try:
inputBamFile = sys.argv[1]
transcript2geneID = sys.argv[2]
except:
sys.exit('Please, give in... |
3a030fd09477bcf03ccfa0912d29df4943d3260bf33d289e53e2afa77a0bca21 | Python | 2,115 | 72 | # %%
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import numpy as np
import torch
import torch.nn as nn
from torch_geometric.data import DataLoader
import argparse
from metrics import get_cindex, get_rm2
from dataset import *
from model import MGraphDTA
from utils import *
def val(model, criterion, dataloader, ... |
5191026b7c59491b74e51751cc47722603f7770f3566a76d40d8a0c2fd9efec8 | Python | 2,115 | 66 | # 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... |
9847c1d23d38ec45e5e669c532e35b057e55acecd294732b52f61e8a936abbd1 | Python | 2,115 | 58 | """
Created on Fri Aug 13 16:03:48 2021
@author: Haotian Teng
"""
import os,sys
import numpy as np
from matplotlib import pyplot as plt
import argparse
def map_base(string):
base_dict = {'A':1,'C':2,'G':3,'T':4}
return np.asarray([base_dict[b] for b in string])
#bonito_ref = "/home/heavens/twilight_data1/S10... |
bb4f67cb8dc6864b03fdc3b80de1ad5e0b559a812868e8cea7835ea55db3828b | Python | 2,116 | 87 | #!/usr/bin/env python
#******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
#... |
d2d8a5dde0e865ec56ccf9217cb51d75ee4d20eb2d3f79904f40da6a66056d7d | Python | 2,116 | 75 | import click
import torch
import segm.utils.torch as ptu
from segm.utils.logger import MetricLogger
from segm.model.factory import create_vit
from segm.data.factory import create_dataset
from segm.data.utils import STATS
from segm.metrics import accuracy
from segm import config
def compute_labels(model, batch):
... |
8eb6a609ab2ee3e0dcad7ac771a46d5e4ede729690d4703380d4737f297a9f59 | Python | 2,117 | 79 | from collections.abc import Callable
from functools import wraps
import torch
from elasticai.creator_plugins.lutron_filter.nn.binarize import Binarize
from torch.nn import Module
import elasticai.creator.torch2ir as t2i
from elasticai.creator.ir import AttributeConvertable
from elasticai.creator.torch2ir.default_hand... |
c038a011bcd93834122c68f30f6d77a86ea32519a374205f7cfdbcd45717e483 | Python | 2,117 | 65 | import scipy.optimize
import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import grad
from autograd.scipy.special import gammaln
# The code in this example implements a method for finding a stationary point of
# the negative binomial likelihood via Newton's method, described here:
# https://... |
ddfe97c03a00af6d979d4b01776175776a744ca7339ef4b6f0970935b01453e5 | Python | 2,117 | 64 | import torch
class LARSOptimizer(torch.optim.Optimizer):
def __init__(self,
params,
lr,
momentum=0,
weight_decay=0,
eps=1e-9,
thresh=1e-2):
if lr < 0.0:
raise ValueError(f'Invalid learning ra... |
fea8d694eae07b02fb666e75d5509c163432be41282e023acaf918fe71b85e6b | Python | 2,117 | 58 | from typing import List
import matplotlib.pyplot as plt
import mne
import numpy as np
import torch
from .utils import _plot_to_image
_DEFAULT_MONTAGE = mne.channels.make_standard_montage('standard_1020')
def plot_signal(tensor: torch.Tensor,
channel_list: List[str],
sampling_rate: i... |
66bc8272147fc90482b1db0c902a714238cb6d0c4e6c0e460ed2c6d114799867 | Python | 2,118 | 72 | import os
import re
import sys
from typing import List, Optional
from pip._internal.locations import site_packages, user_site
from pip._internal.utils.virtualenv import (
running_under_virtualenv,
virtualenv_no_global,
)
__all__ = [
"egg_link_path_from_sys_path",
"egg_link_path_from_location",
]
def... |
317a2a6a0b05f759931f0b9a638819919a5442579a8d016a42bc0fbe039dd564 | Python | 2,119 | 49 | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
9eab05f8ee185d1f5cdfd0bdb80bad22c77ff59744211998c8faf2986216ebb8 | Python | 2,119 | 67 | import matplotlib.pyplot as mp
import numpy as np
from pyclamp.dsp.fpfunc import geomean, geostd
from pyclamp.gui.iplot import itxtplot, isubplot
class sumstatplot:
def __init__(self, args = None):
self.setplots(args)
self.initialise()
def initialise(self):
self.Label = []
self.Value = []
sel... |
b155491c0fd0570de6035113175b8b401e5077978d40726921600fbd15cf5d9c | Python | 2,119 | 78 | import pytest
import torch
import elasticai.creator.nn.fixed_point as nn_creator
from elasticai.creator.arithmetic import (
FxpArithmetic,
FxpParams,
)
from elasticai.creator.nn import Sequential
from elasticai.creator.nn.fixed_point import MathOperations
from tests.integration_tests.nn.fixed_point.sequential_... |
8d363c8aa243a9ae2898bc59ef8d6c3088bc1a5d2233a63063d641dd34a653f2 | Python | 2,120 | 67 | """Unit tests for per-region NCC (intensity-based, replaces per-region Dice)."""
import os
import sys
PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if PROJECT_ROOT not in sys.path:
sys.path.insert(0, PROJECT_ROOT)
import numpy as np
import pytest
from regtools.registrat... |
c7ded86f27a37c3ff656a7ad0718b90b28c6d07e00a4877fde40bf9eb5577ae9 | Python | 2,120 | 59 | # 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... |
2640742edde2d3d6fff3152953ac7c777d56f3aa12b1f3acb904c5ef1c6e2b84 | Python | 2,121 | 58 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_port
from elasticai.creator.vhdl.design.design import Design, Port
from elast... |
326b67914642fb2b4e2bbe854b44c6893b7657a695965b288343dfdd0bdc128d | Python | 2,121 | 58 | import numpy as np
# Some utility functions for evaluation
class ConfusionMatrix:
def __init__(self, prediction, reference):
self.tp = np.sum(np.logical_and(prediction == 1, reference == 1))
self.tn = np.sum(np.logical_and(prediction == 0, reference == 0))
self.fp = np.sum(np.logical_and(... |
3ad367a1576fd27332bb1091dd0639ce24ed314c113b70120b16c645720ef16c | Python | 2,121 | 54 | from collections import defaultdict
import linecache
import os
_gen_customizations = defaultdict(dict)
def get_customizations_for(language, algo, property=None, default=None):
lang_customizations = _gen_customizations[language]
if algo not in lang_customizations:
custom_file = os.path.join(os.path.di... |
dda962bbc2424981d2ec744cfcef511d6e83a53606fc0f7d2044e535e7d958c8 | Python | 2,121 | 72 | # Node-group asset "Unique Residue ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHEC... |
f9fcf41cac81e1c1028d48c38a6ecbff8c4135f796aea734cc2479833227dbaf | Python | 2,121 | 54 | import os
from typing import Dict, Iterator, Tuple, Union
import pandas as pd
from torcheeg.datasets.module.base_dataset import BaseDataset
from .base_model_selection import BaseModelSelection
from .utils import create_subset, read_indices, write_partitions
class TrainValTestSplitGroupbyFolder(BaseModelSelection):
... |
829eefb5dea8fe405e203fe5bc7b3b854fa0e31fcebe94066a5a49619f7baf03 | Python | 2,122 | 66 | """Loader robustness tests."""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pytest
from scipy.io import savemat
from msfeature.io import load_session
def _write_sample_mat(path: Path, *, n_trials: int = 4, n_samples: int = 50) -> None:
sess = {
"EyeX": np.zeros... |
c7d1e3096947001d8f6796482ac772504fe7c483e36dedea1bbcaa16f4f30fbd | Python | 2,122 | 79 | """Tests for the cog.concurrent decorator."""
import sys
import types
from collections.abc import AsyncIterator
import pytest
if "coglet" not in sys.modules:
coglet = types.ModuleType("coglet")
coglet.CancelationException = type("CancelationException", (BaseException,), {})
sys.modules["coglet"] = coglet... |
2e31f8df672058e9680b4f986dc5253de88f4a80cfdc4d6999b630d3ed8e93bc | Python | 2,123 | 57 | '''
Script that generates the ML table for the ECFP data.
NOTE: It's very important that the ML table that you use lines up with the ECFPs.
So each line number in the ML table should correspond to the line number in the ECFPs .npy file.
This is incorporated in the ML-table generation script. Therefore, generate the ... |
5094898ed7f012e5fc70cf6176edfa7bd00c0001a9304afbf8cb0e5052a0e6aa | Python | 2,123 | 63 | #!/usr/bin/env python
# Purpose: Find avg. FD value for each session and for each participant overall.
#
# Input: Single-column txt file FDRMS for each bold image. These were generated using the CBIG2016 preproc pipeline.
# Output: Avg. FD value for each participant's session an deach participant overall.
#
# Written ... |
e5869fdbce46762d28c8b7e241c0d127b318efdaa0999f0de8ae2ae4febeade8 | Python | 2,123 | 80 | # Node-group asset "Color" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
from bp... |
156c7ce4d27b9c238a13611e5f09722c012bea9244eca65c152e43e69961e1a8 | Python | 2,124 | 57 | """The registration tab's Laplacian correspondence dropdown offers the new
'Exterior (damage-aware)' option -> data 'exterior_damage_aware'."""
import os
import pytest
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
@pytest.fixture(scope="module")
def _qapp():
try:
from PySide6.QtWidge... |
3fbe6eff1f97b65ab15bf817eb0f79ca31f5571b0b7fa10d4eb67846e31e628a | Python | 2,124 | 84 | # 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... |
c586350abae0240ebaccdb5b98dad6ca5343476bd7f775cf1148e867e40f5143 | Python | 2,124 | 73 | import h5py
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
import torch.nn as nn
import torch.optim as optim
class Autoencoder_super(nn.Module):
def __init__(self, latent_dim=10):
super(Autoencoder_super, self).__init__()
self.encoder = nn.Sequential(
... |
288c4368359e818abc2f503a3e3613fcdd6dee2a6aded9ca4fc00ccf380f8e58 | Python | 2,125 | 40 | """EXPERIMENTAL example: the original interface with unit-RMS steering normalization.
Original: direction = centered / max(RMS(centered), 1) (inactive while RMS < 1)
This file: direction = centered / RMS(centered) (always unit RMS)
and no steering at all when RMS(centered) < min_rms (a near... |
cafca2f09ca28987d220038942033138cd49c75eedc41102eecb01ed543f687e | Python | 2,125 | 67 | import math
import meep as mp
from meep import mpb
# A line_defect waveguide in a 2d triangular lattice of dielectric
# rods (c.f. tri_rods.ctl), formed by a row of missing rods along the
# "x" direction. (Here, "x" and "y" refer to the first and second
# basis directions.) This structure supports a single guided b... |
ef8d1fd19a85afdb8ea322d5664ef07d2b4184293043630a296c1a5141f93f1c | Python | 2,125 | 72 | import h5py
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
import torch.nn as nn
import torch.optim as optim
class Autoencoder_charge(nn.Module):
def __init__(self, latent_dim=10):
super(Autoencoder_charge, self).__init__()
self.encoder = nn.Sequential(
... |
230a2b2685e969781be9662933426642f05c9a27e172648e7e73431028b7b68b | Python | 2,126 | 24 | """posthoc_phase_line_split_2026-10-03.py — POST HOC (after LAW_IN_SPIKES S1 put the spiking line at 0.464 with the
standard rule, and showed the two-channel rule does not lower it). PHASE_MAP's spiking runs gave a lower logistic 50% point
(FlyWire 0.412, BANC 0.363; C199). Which conditions pull it down? The same fit... |
a9686730f2fc2bd8ce498d1e46372408c55fe5fc9f6d2d64b2a5337a53a57f4e | Python | 2,126 | 72 | import os, sys, signal, time
from pathlib import Path
class ExitHandler(object):
_instance = None
@staticmethod
def instance():
if ExitHandler._instance == None:
ExitHandler()
return ExitHandler._instance
def __init__(self):
if ExitHandler._instance != None:
... |
bedd0f3c99a5daa1284350708f3f7c548f83c45aee779a11b72ccb1c6bb03978 | Python | 2,126 | 64 | #
# 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,... |
d99a17d51013f1942e6e7f035ab04c0c7ef002e5ce4907aa78b389563fb54e65 | Python | 2,126 | 73 | """Module to merge Data together, along different axis and time points.
It does not merge two instances of Data together (TODO).
"""
from logging import getLogger
from numpy import asarray, empty, expand_dims, unique
from numpy import concatenate as cat
from .. import ChanFreq
lg = getLogger(__name__)
def concate... |
e0005e31060092daf5797ab5f895b9ba61b70d4e05ae18f72d13f1e9b0f4be17 | Python | 2,126 | 86 | import sys, re
from optparse import OptionParser
import time
import os
import getopt
import numpy as np
from scipy import stats
data = []
def save_data(filename, column1, column2):
with open(filename, 'w') as f:
for a,b in zip(column1,column2):
f.write(str(a)+ "\t" + str(b) + "\n")
def ... |
6a57222b341acabc3a8dd8669fa2448135952017573be232d4e33c99e38da0d0 | Python | 2,127 | 44 | """Runs one condition of REAL_ODOURS.py in its own process, odours forward or reversed (same code, same per-odour
seeds 1000+k -> identical counts to a single run). Two processes per condition meet in the middle.
Writes tmp/real_odours_counts_<cond>_<dir>.npz; merged by real_odours_merge.py into 06-OUTPUT/real_odours_c... |
81ddd1cc7841cbd3060d5212765c8381e99d67373c56346983822d0f603b5f12 | Python | 2,127 | 73 | """
for parseing line-based files
"""
key_sep = "||"
class ModRecord:
def __init__(self, fields):
self._chromosome = fields[0]
self._pos = int(fields[1])
self._site_key = key_sep.join([self._chromosome, str(self._pos)])
self._strand = fields[2]
self._pos_in_... |
ef02800e2fe901c535b48b050f26ee0595db6c3db638cbf0c07c4e9cd6ac7c3b | Python | 2,127 | 68 | # 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... |
5c523c0ff482193f08698de327652346c23c5403d341f7f8966d25de5909b4c1 | Python | 2,128 | 71 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup_no_benchmarks import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
def main(revisions=None):
suite = suites.suite_satisficing_with_ipc11()
configs = {
IssueConfig('lazy-g... |
70c3b66d847338753903bc2b764f697dcb148969b647b44eed7bb4b301b63f6d | Python | 2,128 | 62 | #!/usr/bin/env python
import imp
import sys
from setuptools import setup, find_packages
if sys.version_info < (3, 8):
sys.exit("Sorry, Python < 3.8 is not supported")
VERSION = imp.load_source("", "assemblyfire/version.py").__version__
setup(
name="assemblyfire",
authors=["Andras Ecker", "Michael Reiman... |
76865461560079ecbc1bb08ccf9384e98b778e157801d13870cbb3b9ae3b09b9 | Python | 2,128 | 63 | # 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... |
612f1bdb22f8f3be627fbec527d24b27e2ae00f276113fee43a0f24256793fe8 | Python | 2,129 | 73 | # 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 applicable law or agreed to in writing, software
# distributed under th... |
804d32e469262e02b03f21733492b40aba0e847c528c82799afb79b5887b8429 | Python | 2,129 | 67 | from abc import abstractmethod
from typing import Callable, Protocol, runtime_checkable
from torch.nn import Module, Sequential
@runtime_checkable
class _ConvProt(Protocol):
@property
@abstractmethod
def kernel_size(self) -> tuple[int, ...]: ...
@property
@abstractmethod
def stride(self) -> t... |
be03d99dae1badaf08b7f526c9d7c4cba33c5ebe421f47e35a72afd806b56b33 | Python | 2,129 | 86 | # %% Package Imports
import sys, os
sys.path.append(os.path.abspath(".."))
sys.path.append(os.path.abspath("."))
import torch
import torch.nn as nn
import torch.optim as optim
import prosper_nn.models.dhcnn as dhcnn
from prosper_nn.utils import visualize_forecasts
import prosper_nn.utils.generate_time_series_data a... |
ec430a50d867049b1062f2c6a2b383572eb2473d37f99a31b04b97ed1a978f4d | Python | 2,129 | 65 | import pytest
import torch
from torch import nn as nn_torch
from elasticai.creator.arithmetic import FxpParams
from elasticai.creator.nn import fixed_point as nn_creator
@pytest.mark.parametrize(
"total_bits, frac_bits, num_steps",
[(4, 3, 4), (6, 4, 32), (8, 5, 64), (10, 6, 16), (12, 7, 128), (12, 9, 64)],
... |
94cf9342e180f70cf0a90ca8926ecbf0a2427c16241e2762b45ed8c043bef426 | Python | 2,130 | 70 | from __future__ import annotations
import pytest
from custodian import Custodian
from custodian.vasp.handlers import VaspErrorHandler
from quacc.calculators.vasp import vasp_custodian
from quacc.calculators.vasp.vasp_custodian import run_custodian
def mock_custodian_run(*args, **kwargs):
# Instead of running Cu... |
a3e2fb34a630344214211e7172483386e03dbe609e794ee47109ea87a3f8848a | Python | 2,130 | 58 | import pandas as pd
from tests.mock_data import MockCvOutput, MockTrainingData
from cuppa.components.calibration import RollingAvgCalibration
class TestKernel:
def test_edge_weight_default(self):
kernel = RollingAvgCalibration()._gaussian_kernel(window_size=20, edge_weight=0.16)
assert round(ker... |
e5e0fac8f8063018557545ce19c060f5b5e346d8d20c8251aaea4a035ee83c4c | Python | 2,130 | 85 | from __future__ import annotations
import pytest
redun = pytest.importorskip("redun")
from ase.build import bulk, molecule
from quacc import flow
from quacc.recipes.emt.core import relax_job, static_job # skipcq: PYL-C0412
from quacc.recipes.emt.slabs import bulk_to_slabs_flow # skipcq: PYL-C0412
@pytest.fixture... |
480ed47e49ef1c64b6a9abdc7fd1900290690ab2ee53c8c02eba990f3113f2f6 | Python | 2,131 | 64 | def findRuns(lst):
""" yield (start, end) tuples for all runs of ident. numbers in lst
>>> list(findRuns([1,1,1,0,0,1,0,1,1,1]))
[(0, 3), (5, 6), (7, 10)]
"""
start, end = False, False
for i, x in enumerate(lst):
if x and start is False:
start = i
if x == 0 and start... |
891a5a3418d5d0337060fbbfcfa4e21e0469c186a188cef3b48ff8919e14cfd0 | Python | 2,131 | 57 | ######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Universal charset detector code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 2001
# the Initial Developer. All R... |
290250186784697c167d0cf7dc8566409b32adcad6b3e5b277e3f8455a35555d | Python | 2,132 | 66 | #!/usr/bin/python
# encoding: utf-8
import os
import glob
import random
import numpy as np
import torch
from torch.utils.data import Dataset
from PIL import Image
from datasets.clip import *
class UCF_JHMDB_Dataset(Dataset):
# clip duration = 8, i.e, for each time 8 frames are considered together
def __in... |
2c2680201fe62ccc1cd47b7ebcaae38573494192760003a32b6fa8320df62a1f | Python | 2,132 | 57 | import numpy as np
import logging
from typing import Union
from .denoising_model import DenoisingModel
# import model subclasses here...
from .spatial_unet_2d_temporal_denoiser import SpatialUnet2dTemporalDenoiser, PlSpatialUnet2dTemporalDenoiser
# ...and add models to this lookup dictionary
_MODEL_DICT = {
'sp... |
566358239c83c19ff46b972a23a3297669a2730eb6cf94fa97be1fc92bc6a717 | Python | 2,132 | 68 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import itertools
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterP... |
6e9db8f8c1b0ca373dceafcf7a3f657678ad0a79704222e05f7191ca7ab6bb82 | Python | 2,132 | 73 | from __future__ import annotations
import pytest
dask = pytest.importorskip("dask")
pytest.importorskip("distributed")
from ase.build import bulk
from dask.distributed import get_client
from quacc import flow, job
from quacc.recipes.emt.core import relax_job # skipcq: PYL-C0412
from quacc.recipes.emt.slabs import ... |
5c9d8f4c9e1f76770ba90d7c52fe0797341478b0213309c4a3f7f6af1d6c9785 | Python | 2,133 | 56 | # coding=utf-8
# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
#
# 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... |
145fc63b3bd8209ff59ce1cefea284ddde4f070b906094bcec6dee448a9be726 | Python | 2,134 | 65 | """
Filter Median Class
"""
import numpy as np
from .buffer import Buffer
class FilterMedian(Buffer):
def __init__(self, L=62, dtype=float, initB=None):
Buffer.__init__(self, L=L, dtype=dtype, initB=initB)
self._buffer = self._buffer[::-1]
self._history = np.linspace(0, L - 1, num=L, dty... |
3bc1008c37504e5f61092dbc7ceb66ff83a7cb9522478105ccdbb8bc2bc1f078 | Python | 2,135 | 66 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... |
65859bea8f69ca6e8a2f4dedd8fc1ca6a3393456c99b153cdef4e8f25c7a748f | Python | 2,135 | 57 | import torch
import torch.nn as nn
import torch.nn.functional as F
class CALayer(nn.Module):
def __init__(self, channel, reduction=16):
super(CALayer, self).__init__()
self.fc1 = nn.Conv3d(channel, channel // reduction, kernel_size=1)
self.fc2 = nn.Conv3d(channel // reduction, channel, kern... |
255269a7ccc194de91ab7148fc70cc22dfaa9ac40a08dc22b79c267bb826d534 | Python | 2,136 | 53 | import os
from nipype import Workflow, Node, MapNode, IdentityInterface, Function
from nipype.interfaces.io import DataGrabber, DataSink
from nipype.interfaces.fsl import BET, FLIRT, MCFLIRT, FNIRT, ApplyWarp, Smooth
def create_workflow(name, data_dir, output_dir):
# Workflow setup
wf = Workflow(name=name)
... |
1f88165ae69bf6f4b6a6dc27ef9584f21974e4fff0fa6551010b3eae01c0a791 | Python | 2,138 | 79 | # Node-group asset "Residue ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
fr... |
4a0c0c44bc0661ba22c1f9c20313b78a54c7f90f35cd5d47bdf87cd02bea95a3 | Python | 2,138 | 62 | # 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... |
fdf2d379b4fbe6d5c2c1c50883b6b2c3a135ef994a494948f390a8463c9db4af | Python | 2,138 | 64 | import os
import torch
import random
import tqdm
import numpy as np
import scipy.stats as stats
import sklearn.metrics as met
def seed_everything(seed=42):
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch... |
0d759e49e511437271a45b46d7daa63e0754dcbb89d340a281fd66a1e7a88181 | Python | 2,139 | 57 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue659-v1-base", "issue659-v1"]
CONFIGS = [
IssueConfig(h... |
17619fbb838fe6a3c5fc3b127acbb08bcbeca49db7dac736cc0909ded86926b0 | Python | 2,139 | 58 | import os, warnings, time, tempfile, datetime, pathlib, shutil, subprocess
from urllib.request import urlopen
from urllib.parse import urlparse
from pathlib import Path
from tqdm import tqdm
import pytest
def download_url_to_file(url, dst, progress=True):
r"""Download object at the given URL to a local path.
... |
a322c07284d9c0cbd6ed45b978c99822a8e6244b2238aec9550473f9edf2256f | Python | 2,139 | 74 | # 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... |
e01a871135417ecd4d77d89b91c9780414b5707e24419817d039a8eab0a366ed | Python | 2,139 | 23 | """POST HOC: how small a kick ignites the antennal lobe of Shiu's v630 model, and who is inside the ignition?"""
import sys; sys.path.insert(0, 'D:/eucys/03-CODE')
import numpy as np, pandas as pd, scipy.sparse as sp, SHIU783_SIM as S
C = pd.read_parquet('D:/eucys/2023_03_23_connectivity_630_final.parquet', columns=['P... |
751f3353ce92eb5696d9e5ccb780b09aafdbbb94af44ad7ac834da91d3c6acf6 | Python | 2,140 | 83 | # Node-group asset "Boolean Any" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
f... |
1bf3533245980b4b00247a0af6f74f92d403d4f56e1866d3f3cbf743b09f7afb | Python | 2,141 | 78 | """
Algorithms for whitening.
ZCA combines a technique used in recent papers from Andrew Ng's group at
Stanford, with techniques developed by Nicolas Pinto at MIT.
"""
import numpy as np
def flatten_imageset(images):
if images.ndim < 2:
return images.reshape(1, -1)
else:
return images.reshape... |
3f7af81a5c4316ca4a04f2750c7b4611022f966e3b8f5c217a03e105b6517b00 | Python | 2,141 | 68 | # sample 300 points from torus S1 x S1 and 100 points from circle S1 (belonging to the first S1)
# Compute distance among the points
import math
import numpy as np
import h5py
def angle_distance(phi, theta):
s = min(phi, theta)
l = max(phi, theta)
distance = min(l-s, s + 2 * math.pi - l)
return dista... |
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