sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ad44dac82e4830a10016563a5f278fd1d9232ad0229921cf3d5ab947b696d30b | Python | 3,324 | 117 | # -*- coding: utf-8 -*-
# @Author: Guillaume Viejo
# @Date: 2023-07-10 12:26:20
# @Last Modified by: Guillaume Viejo
# @Last Modified time: 2024-07-31 11:17:59
"""Tests of IO misc functions"""
import os
import shutil
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
i... |
d58f7151b53c8fecabea950d9f8101cd76898eaf7fb5166e23afc8010cbc961e | Python | 3,326 | 91 | import os
import sys
import numpy as np
import h5py
import argparse
import json
import pathlib
from os.path import join, dirname
import logging
from encoding_utils import *
from feature_spaces import _FEATURE_CONFIG, get_feature_space
from ridge_utils.ridge import bootstrap_ridge
from config import REPO_DIR, EM_DATA_... |
d2eec3ddb341b3815916edc1520a5e49c5c30e6ddefe273c164114e47f7ce616 | Python | 3,327 | 116 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._activeselection import Activeselection
from ._activeshape import Activeshape
from ._annotation import Annotation
from ._coloraxis import Coloraxis
from ._colorscale import Colorscale
from ._font import Font
from ._geo impo... |
dcf2c289e7355aa8300c2dd95927d28086f50a88d743f0579d99f254a13245ad | Python | 3,327 | 117 | import argparse
from pathlib import Path
import pandas as pd
import wandb
wandb_api = wandb.Api()
import plotly.express as px
from model_functions import construct_model_name
def get_cli_arguments():
"""Get command line arguments."""
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument(
... |
1415301fc8f04f40e73946630c12bbdf8c185e614246ab8d8afd084fc7b8ec50 | Python | 3,330 | 94 | """Layer.annotations: independent per-layer dict + reserved collective mirror.
Regression pins reconciling two requirements that briefly conflicted:
- 1a255943 ("stop Layer.annotations aliasing ops[0]'s user dict"): the M8a
mirror made ``layer.annotations`` ALIAS the first pass op's dict, leaking
op annotations i... |
137d40bec831dd9ebe03ab666518cc223901667f88cab7ecfb1796aeca40d313 | Python | 3,332 | 105 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import copy
import torch
imp... |
fb30f737e5807f9902952fe7dc7d5470472040f52dabbc06b4b77f7840553539 | Python | 3,333 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Selected(_BaseTraceHierarchyType):
_parent_path_str = "barpolar"
_path_st... |
0be06dd8ac13948fb1588ce520e5df846971a63a321d96da9d735bc697aa4a2b | Python | 3,334 | 98 | #==========================================================================
#
# Copyright NumFOCUS
#
# 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/l... |
0f525ae8ac9a881fd4f211736ab591d23469425601b5f220bfe7ede31dad983e | Python | 3,334 | 81 | from timm.scheduler import CosineLRScheduler, PlateauLRScheduler,\
StepLRScheduler, TanhLRScheduler
def create_scheduler(cfg, optimizer):
num_epochs = cfg.train.epochs
if cfg.train.lr_noise is not None:
lr_noise = cfg.train.lr_noise
if isinstance(lr_noise, (list, tup... |
ab3d87363592521e5d3e8ebd235ccb601be52f9bf40a741e05da970cbc15e69b | Python | 3,334 | 117 | # This example requires the following dependencies to be installed:
# pip install lightly[timm]
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import torch
import tor... |
eec056ed8f489d73276478c4c0956cca3a7f906c91480ef8f2f26b096cb336cc | Python | 3,334 | 96 | import os
from math import sqrt
import h5py
import numpy as np
import pandas as pd
import scanpy as sc
import scvi
from scipy.stats import pearsonr,spearmanr
from sklearn.metrics import mean_squared_error
from scbig import setup_seed
for sparity in ['0.8', '0.85', '0.9', '0.95']:
print('----------------sparity l... |
00ee7d3f39180b560e46283ca33922ad23c27ccd0ceed6ec32bc206043b7c9fd | Python | 3,335 | 72 | import glob
import sys
import pandas as pd
from nilearn.glm import compute_fixed_effects
def fixed_effect(sub, task_type, contrasts, input_path, output_path,
save_beta=True, save_var=True, save_tstat=True):
"""
This function takes in a subject, task label, set of computed contrasts using nile... |
0a6a50bfd651581478abe8cda5c94aba6d70df35567a220aabc7ba08202d471c | Python | 3,335 | 145 | import asyncio
import pytest
from flask import Blueprint
from flask import Flask
from flask import request
from flask.views import MethodView
from flask.views import View
pytest.importorskip("asgiref")
class AppError(Exception):
pass
class BlueprintError(Exception):
pass
class AsyncView(View):
meth... |
7a8687649b42ed8679f016048976a1ad5affe1ff2b96dc1602b7a2dddd5f3f53 | Python | 3,336 | 91 | import torch
from typing import Dict
from rbm.custom_fn import one_hot
def compute_gradient(
data: Dict[str, torch.Tensor],
chains: Dict[str, torch.Tensor],
num_colors : int,
centered: bool = True,
pseudocount: bool= False,
) -> Dict[str, torch.Tensor]:
"""Computes the gradient of the log-like... |
c690be733aa6d9611a4df8bf2bf2a77d97879a931321a7a5ec508c173d4ced3f | Python | 3,336 | 99 |
#Hopfield Networks created as described by:
# Wulfram Gerstner, Werner M. Kistler, Richard Naud, and Liam Paninski.
# Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition.
# Cambridge University Press, 2014.
#Run for simulation of CRFS in:
#Identification of Pattern Completion Neurons in... |
fcfa62d5b1c234b8f065d22344de88352002529a724f7d0ef86ab84c79c14b04 | Python | 3,336 | 79 |
import numpy as np
import glob
import pickle
import re
import pandas as pd
'''
CreateEdgeNetwork - Create Edge file
'''
outputDir = 'data'
f = glob.glob(outputDir + '/*.npz')
assert len(f) > 0, 'gimme files - did not find any %g' % len(f)
for fi in f:
s = np.load(fi)
print('=============================... |
2f3236d5f1b304c553bf29df2703105736a0c94483d0fdc157cb1f2c2a7f8283 | Python | 3,337 | 91 | # 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... |
522bef1199ef96410b77fdc0d878f975225ca838c3fc8bb198c6a4aec18d2d8d | Python | 3,337 | 116 | # Test multi model functionality
# TODO save / load
from pathlib import Path
import pandas as pd
import pytest
from polyid import MultiModel, Parameters
from polyid.models import global100
from polyid.preprocessors import PolymerPreprocessor
pwd = Path(__file__).parent
@pytest.fixture(scope="module")
def trained_mo... |
7a0ec87a2669f4ac5aca4fcbc4981860f50d7afbdc388393246e2b15874d2f74 | Python | 3,337 | 91 | """depyf bridge against the real depyf package (no sys.modules fakes).
``tl.bridge.depyf.dump(model, x, path)`` must write the same compiled-graph
sources as ``with depyf.prepare_debug(path): torch.compile(model)(x)``.
"""
from __future__ import annotations
import contextlib
import re
from collections.abc import Ite... |
c8d680414b4c912def2b03af710101a741cad97e8b5e4ad187946bf0ea4c639c | Python | 3,337 | 121 | """tinygrad backend validation tripwire tests."""
from __future__ import annotations
from typing import Any
import pytest
import torchlens as tl
from torchlens.backends import BackendUnsupportedError
from torchlens.backends.tinygrad import TinygradBackend
tinygrad = pytest.importorskip("tinygrad")
Tensor = pytest.... |
e910e1369c147bee04093c1895cbf98973ab8401b172482608f19da6d8bf9fd0 | Python | 3,337 | 83 | import os
import time
import numpy as np
import pandas as pd
import pickle as pkl
import nibabel as nib
from os.path import join as pjoin
from scipy.stats.stats import zscore
from scipy.spatial.distance import cdist
from cxy_visual_dev.lib.predefine import LR_count_32k, proj_dir,\
Atlas, get_rois
anal_dir = pjoin(... |
62daa80e3c0383936c6e522842f01c66fc9d2504ac89303671aab3981fea27bf | Python | 3,339 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "quiver"
_path_st... |
53c9281b15d4aa510b2d2188385fff0d8ee2e6217f6462b1b21560ecaf521aed | Python | 3,340 | 110 | """Test the Pydantic-based configuration."""
import tempfile
from pathlib import Path
import numpy as np
import pytest
import tomlkit as tkit
from neurogym.config.config import Config
rng = np.random.default_rng()
@pytest.fixture
def temp_dir():
"""Create a temporary directory for test files.
Returns:
... |
d3c23ea2031fc4366c76587417c02d0d6eef13aa831f516ca6a8114a0e0edd50 | Python | 3,340 | 101 | __author__ = 'luis'
import logging
logging.basicConfig(level=logging.INFO)
import traceback
from optparse import OptionParser
import subprocess
import json
import os
import glob
from ethoscope.web_utils.helpers import get_machine_id
logging.basicConfig(level=logging.INFO)
#from bottle import Bottle, ServerAdapter, re... |
70b049b265f2d383637cb1da2f8570d8be1284d39f1fe7544c6517ac223da319 | Python | 3,341 | 120 | from permacache import permacache, stable_hash
import tqdm.auto as tqdm
from modular_splicing.data_for_experiments.standardized_sample import (
standardized_sample,
)
from modular_splicing.module_substitution.evaluate_combinations import combine_models
from modular_splicing.models_for_testing.list import FM
from... |
dd7389d61c907e1275534538f6a7fb9c496b89d54a78d1fd1e7dad08f1712cf1 | Python | 3,341 | 98 | import nfp
import keras
import tensorflow as tf
from nfp import masked_mean_absolute_error
from keras import layers
def global100(preprocessor, model_summary=False, prediction_columns=None, params=None):
"this is the global state where the output is the bonds_states"
# for k,v in params.items():
# if... |
7224413602b5c27543436fe490976991ab2b37cb78fb3c3c9b9d44e3a873ff5c | Python | 3,342 | 114 | import numpy as np
import dgl
import torch
import torch.nn.functional as F
import h5py
import sys
sys.path.append('../pkgs/graph-sc/')
import train
import models
device = train.get_device()
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import scanpy as sc
from scbig.utils import setup_seed,l... |
75c42ab44a44728e6dda4e5391639fa88a693af3699f4b3ce4ba752ccb3a2598 | Python | 3,342 | 79 | """
This file was used to get the network-network predictions by loading in the generated imagenet
metamers. Metamers are not released on the github due to file size, but this can be used
for network-network comparisons for new networks.
"""
import numpy as np
import pandas as pd
import os
import glob
import math
im... |
1cbf2871bcda7e89a62cb35fe1001267d0d80362cce61a5a7893d8a85ad53fe6 | Python | 3,343 | 79 | """External counter pins (A07 row gate): closed forms + gpt2@16 + oracles.
Lane A07 (2026-08-27). External oracles rank ABOVE internal
consistency (summary memo 3.1): summary and profile once agreed with each
other while both +31% wrong. The gpt2 pin is the identity-partition figure
3,974,725,648 (costreport item 24; ... |
df755bad995880b4a5e9c0c31c69b211c9b4427658571f07e5ead2aeff06a5a3 | Python | 3,344 | 84 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
sys.path.append("..")
from config import fname,subjects,triggers
from doit import get_var
DOIT_CONFIG = dict(verbosity=2, sort='definition')
sl = get_var("sl", -1)
cum = get_var("cum", -1)
def task_src_bem():
"""Step 02: compute surface-based source space... |
059e6744c23a1b819986c7f85ca0c47ffa68a6bed7bf9a7f8f873466f2cc5679 | Python | 3,345 | 98 | """
Pipeline version stamping for framework_core consumers.
Every cohort feature JSON should be stamped with the result of
`pipeline_version()` at extraction time. The verifier compares the stamp
against the current code; mismatch means the cached features are stale and
must be regenerated before any downstream analys... |
2022fc6d18a86ce435320fbbdee4adb3719af5f68438f3834f881a77f9d72090 | Python | 3,345 | 96 | from __future__ import generators
import logging
import glob, os, functools, gc
import pandas as pd
import numpy as np
import sys
import math
from dateutil import parser
import itertools
sys.path.append('../TM2_segmentation')
def isNaN(num):
if type(num)==str:
return False
if float('-inf') < float(num... |
04773ae7f5b06a88b69cec7bdcd5dda6e0cc21056d787684b2ac2ab0c3e6ea6d | Python | 3,346 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Selected(_BaseTraceHierarchyType):
_parent_path_str = "scattergl"
_path_s... |
1dc224520ca6f3b12d6feac13bd4181b1038ca21ba4de58ecc1eba8dcdbcebed | Python | 3,346 | 123 | """
Tests to make sure the filter visualization interpretation methods work
"""
import torch
from torch.utils.data import DataLoader
import eugene as eu
import numpy as np
import pandas as pd
import pytest
from pathlib import Path
from eugene.interpret._filters import (
_get_first_conv_layer,
_get_activations_... |
3aaef5031f062aa6a8eed486cef6576ce35fad46c7dd078bf4e7cba5ab0fe8ac | Python | 3,346 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Selected(_BaseTraceHierarchyType):
_parent_path_str = "histogram"
_path_s... |
c145631218786e308c191a1b5b118be036b9d2eeca22fe362c336501d5ea6001 | Python | 3,346 | 117 | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... |
fa4ef3857ec376120e2979c5372230cde87978d7b184c2753d90c7d479c2bbf2 | Python | 3,347 | 70 | import glob
import sys
import pandas as pd
from nilearn.glm import compute_fixed_effects
def fixed_effect(sub, task_type, contrasts, input_path, output_path,
save_beta=True, save_var=True, save_tstat=True):
"""
This function takes in a subject, task label, set of computed contrasts using nile... |
797d3318c2c93046a72466c31d43e6292a2ac088e120e19d5cc25fd97a5b0327 | Python | 3,350 | 84 | from __future__ import annotations
from typing import TYPE_CHECKING
from gymnasium import Wrapper
if TYPE_CHECKING:
from neurogym.core import TrialEnv
class ReactionTime(Wrapper): # TODO: Make this a trial wrapper instead?
"""Allow reaction time response.
Modifies a given environment by allowing the ... |
1cbeaf1a4de82f93ece0e87a43131efa08ee1457b03ff54e17ca0fcd0c25edfa | Python | 3,351 | 103 | from cmath import nan
import pickle
from matplotlib import pyplot as plt
from numpy.lib import math
from scipy.sparse.construct import rand
from ssm.plots import gradient_cmap, white_to_color_cmap
import seaborn as sns
import numpy as np
import os
import os
from scipy.io import savemat,loadmat
import os
from os import ... |
5cdab03ccfcf3e97ffc7e6218c43156490f199aceb570f44932de136ff481b3d | Python | 3,351 | 115 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on December 29, 2020
@author: Thomas Bonald <bonald@enst.fr>
"""
import numpy as np
from scipy import sparse
def get_neighbors(input_matrix: sparse.csr_matrix, node: int, transpose: bool = False) -> np.ndarray:
"""Get the neighbors of a node.
If the ... |
381b51ff88d45c37064be8636f512e5d565476abf2b4d1177b9e6fc74c726498 | Python | 3,352 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "scatter"
_path_s... |
ca38a970a6d0a221150aa66e19a4f6c20244bf4df1514ca16f27ea4a958fab1d | Python | 3,352 | 102 | """Predictors for deconvolution"""
# Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from itertools import repeat
import numpy as np
from .._data_obj import NDVar, Case, UTS, asndvar, asarray
def epoch_impulse_predictor(shape, value=1, latency=0, name=None, ds=None):
"""Time series with one impulse for e... |
4d83399a941a03e62d4549da3fc828a8aa38ddc7ce9c90a221f38110d831f3d5 | Python | 3,353 | 102 | import numpy as np
import xarray as xr
from hobj.benchmarks.binary_classification.estimator import (
build_learning_curve_statistics,
)
from hobj.benchmarks.binary_classification.simulation import (
BinaryClassificationSubtaskResult,
)
from hobj.benchmarks.generalization.estimator import build_generalization_s... |
bc83b4f752d825d81c6ec9aa67a5d004ee08d092cea5862fc5db04cc62f87349 | Python | 3,353 | 116 | import torch
from keras.src.backend import config
from keras.src.backend.common import dtypes
from keras.src.backend.common import standardize_dtype
from keras.src.backend.torch.ops.core import cast
from keras.src.backend.torch.ops.core import convert_to_tensor
def cholesky(x, upper=False):
return torch.linalg.c... |
14879b229e244c2e04f3a919e21163a828f0d01b955516e1b7503b1741cb506d | Python | 3,354 | 91 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""
.. _exa-generate-ndvar:
Generate :class:`NDVar` (with artificial data)
==============================================
.. currentmodule:: eelbrain
Shows how to initialize an :class:`NDVar` with the structure of EEG data from
(randomly generate) data arrays.... |
17ddf7bf651bb8873e7169f847593f6027e857cec376673e8560761d5d412362 | Python | 3,354 | 121 | """Ops for tensor initialization"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
import pdb
from dcCustom.models.tensorgraph.model_ops import random_uniform_variable, random_normal_variable
from dcCustom.models... |
bbd94250b00b74fc655aea2ae8d589d95b410b48aa33b5329de9c4deea7baaaf | Python | 3,354 | 81 | """ Padding Helpers
Hacked together by Ross Wightman
"""
import math
from typing import List, Tuple
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
padding = ((stride - 1) + dilation * (kernel_size ... |
fe33cecd325066d5cde608a31e2a07d466eda75a649e155f4061d2388e736dc9 | Python | 3,354 | 127 | import numpy as np
def calc_mean_and_var_sparse(M, N, data, indices, indptr, axis):
i, j = 0, 0
size = 0
value = 0.0
size = N if axis == 0 else M
mean = np.zeros(size, dtype=np.float64)
var = np.zeros(size, dtype=np.float64)
mean_view = mean
var_view = var
for i in range(M):
... |
4e7bb0f9aa5b7670d7effcd56d619adbadeb18f98bc6e676e50368f30c5b3b8d | Python | 3,355 | 89 | """Nothing hands out a shared mutable object for its caller to write into.
Several places used to return, or fill in, an object that outlives the call:
the registry's stored configurations, and the shared default arguments of a few
constructors. Because none of them copied first, one caller's edits silently
became the... |
9d119c36d98ba0feb09731fe1a5adf225ffe115c39353789a9e07fafff5388ef | Python | 3,355 | 83 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
1e6012a6cfdfc4f1b5ae4d37957f92b1b0b3e4bfc8f552d2133b2f11e697b7a7 | Python | 3,356 | 96 | from typing import Dict, List, Optional, Tuple, Union
from lightly.data.dataset import LightlyDataset
class LightlySubset(LightlyDataset): # type: ignore
def __init__(self, base_dataset: LightlyDataset, filenames_subset: List[str]):
"""Creates a subset of a LightlyDataset by filtering samples based on t... |
62fe1d80f42b4dcdc8686a29583ffeab53caf181296dcb92838c83779d2f89ac | Python | 3,356 | 89 | import pytest
import torch
from lightly.utils.dependency import torchvision_transforms_v2_available
if not torchvision_transforms_v2_available():
pytest.skip("torchvision.transforms.v2 not available", allow_module_level=True)
from torchvision.tv_tensors import Image, Mask
from lightly.transforms.detcon_transform... |
e3a45eb4850f26715f7a95c892c79a5c209a6aadecd676e5c218a32537c61111 | Python | 3,356 | 96 | """
Contains methods for determining the distances between motifs and their nearest neighbor.
Useful in analysis for the "motifs conflict with each other and that's why width is needed" theory.
"""
from permacache import permacache
import numpy as np
from modular_splicing.evaluation.predict_motifs import predict_mot... |
4bf73240617bd61de004b547aef9d195ce4074fd415de049c1714f818aeb14d9 | Python | 3,357 | 106 | import tempfile
import numpy as np
import pytest
from nfp.preprocessing.mol_preprocessor import (
SmilesBondIndexPreprocessor,
SmilesPreprocessor,
)
from rdkit import Chem
from rdkit.Chem import AllChem
@pytest.fixture()
def get_2d_smiles():
train = ["CC", "CCC", "C(C)C", "C"]
test = ["CO", "CCO"]
... |
8a3f24e715c68ac94977e52c4f115637be2c8d7ce92680e57e423695eecb7c76 | Python | 3,359 | 121 | from unittest.mock import Mock, patch
import pytest
from celery.app.events import Events
from celery.events.snapshot import Polaroid, evcam
class MockTimer:
installed = []
def call_repeatedly(self, secs, fun, *args, **kwargs):
self.installed.append(fun)
return Mock(name='TRef')
timer = Mo... |
282983332a083fe660ae4e5cd780f04c38e47df88e94780a94ff1fe7e4e4d32f | Python | 3,361 | 91 | from spitfire import ChemicalMechanismSpec, get_ct_solution_array, Library
import numpy as np
from cantera import gas_constant as Ru
from cantera import one_atm
from cantera import stefan_boltzmann as sigma
def tabulate_gas_radiation_model(mechanism, input_library, modelname='Barlow_GreyGas'):
"""Adds gas radiati... |
411bdff512e46ed6237453610418536fe8ca981c7c79aa482a12b54817807144 | Python | 3,361 | 84 | # Copyright (c) 2022 PaddlePaddle Authors. 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 app... |
acebb67dcb2fe4755d087ff21da5734d0be365e99c36c78ed74eefdf18cab317 | Python | 3,361 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Selected(_BaseTraceHierarchyType):
_parent_path_str = "scattergeo"
_path_... |
7154e15aa19737c406b2276efe7b890bf9109e4d85b36d9f66be9d27e373dc6a | Python | 3,362 | 119 | """
Unit tests for DataPoint container class.
Tests the DataPoint OrderedDict-based container for tracking variables.
"""
import unittest
from ethoscope.core.data_point import DataPoint
from ethoscope.core.variables import (
HeightVariable,
WidthVariable,
XPosVariable,
YPosVariable,
)
class TestDat... |
d179f2bc25f2b4239b5488375659cefe4447c992ce7ee3ab0607e546e115608f | Python | 3,363 | 102 | """
===============================================================================
Robust covariance estimation
===============================================================================
Comparison of robustness of different covariance estimators on a corrupted
low-dimensional dataset. See also [1]_.
"""
# Autho... |
82562d32c458c1f0a51878869cb7d5682fed3a3a3b36179a49664a4976d3b385 | Python | 3,364 | 89 | """
Framework signal-processing utilities — single source of truth.
- compute_band_powers(data, fs) -> dict of relative band powers
- compute_I_P(rel_powers) -> normalized spectral entropy on 4 bands
- bandpass_filter(data, fs, low, high) -> FFT-based bandpass
- fft_resample(data, fs_orig, fs_target) -> ... |
8ec0793244fe2f47a2194c13c2ee1ba3dba29551198ad3075eaa287661ddca7c | Python | 3,364 | 90 | """
Extract alternative sequences
Copyright (C) 2024 Tham Cheng Yong
This file is part of NanoVar.
NanoVar is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) an... |
f92e3ff4a8147867dfc471071d4105d1d03960d47c2f789600cc4c204f8ec7c9 | Python | 3,364 | 86 | from model import *
from utils import *
import numpy as np
from sklearn.model_selection import KFold
import torch
import os
import warnings
warnings.filterwarnings("ignore")
dataset = ['toy_reg','BindingDB_reg','JAK','AR','CYP_reg'][int(sys.argv[1])]
TRAIN_BATCH_SIZE =32
TEST_BATCH_SIZE = 256
LR = 0.0001
NUM_EPOCHS =... |
fe445cd81327352383d5f4f239a9c6b3aa627d64efc68dc4a752bf86418c7df2 | Python | 3,364 | 92 | from __future__ import print_function
from __future__ import division
import unittest
import mxnet as mx
import numpy as np
import gluoncv as gcv
def test_viz_bbox():
img = mx.nd.zeros((300, 300, 3), dtype=np.uint8)
bbox = mx.nd.array([[10, 20, 200, 500], [150, 200, 400, 300]])
scores = mx.nd.array([0.8,... |
465b80857b2e4992dae60473385ffaf4b9c01a6f2e6af82ec1a729e2fec472e0 | Python | 3,365 | 121 | from . import utils
from . import extract_signals
import numpy as np
def fit_regressor_model(trial_data, model, input_signal, output_signal, train_indices=None):
"""
Fit a regression model that predicts one signal from another
Parameters
----------
trial_data : pd.DataFrame
data in trial... |
02eaf47532cc854c37e9835d2c8e2afc273c99af43f3ade5223dd6a55a3c2ac8 | Python | 3,367 | 117 | """Create Celery app instances used for testing."""
import weakref
from contextlib import contextmanager
from copy import deepcopy
from kombu.utils.imports import symbol_by_name
from celery import Celery, _state
#: Contains the default configuration values for the test app.
DEFAULT_TEST_CONFIG = {
'worker_hijack... |
1128b330a1185eb280720903ddb13761b987df2e2c2649123d30d16c91d7b3e6 | Python | 3,367 | 101 | # coding=utf-8
# Copyright 2022 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... |
9ae11e4b251fd35bcb0fc69bebcfc7ebd1430b8b97e964f3baf933995781221c | Python | 3,367 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "barpolar"
_path_... |
2b16a8681a71897655424b203d67f3cac7d66f3c68996b84f48a0015113b528e | Python | 3,368 | 102 | import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.cluster import SpectralClustering
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score, homogeneity_score, completeness_score
from clustering ... |
6dea584f2008015bd95c57cc5f6e3754b8c0be90c79381cd81cd10d49bc9be4c | Python | 3,368 | 102 | from permacache import permacache, stable_hash
import numpy as np
import pandas as pd
import tqdm.auto as tqdm
from modular_splicing.legacy.remapping_pickle import permacache_with_remapping_pickle
from modular_splicing.utils.arrays import Sparse
from modular_splicing.utils.run_batched import run_batched
@permacache... |
712d9646b3bb2a7195d88fb2f352669a9fcd98dbfa3abd072a36723ded23a22e | Python | 3,369 | 98 | """
Methods for creating datasets from splice tables.
Puts grab_sequence, create_dataset, and create_datafile together.
"""
from tempfile import NamedTemporaryFile
import h5py
from .create_datafile import create_datafile
from .create_dataset import create_dataset
from .grab_sequence import grab_sequence
def creat... |
b133825f8ddde4d2f03bf678569b0f108a619a312389242e35014a5f86e0e9a3 | Python | 3,369 | 117 | from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext
import sys
import os
import setuptools
__version__ = '0.0.1'
class get_pybind_include(object):
"""Helper class to determine the pybind11 include path
The purpose of this class is to postpone importing pybind11
unt... |
82f01461b784c263e6a78fd049f1d6083377b81af75adc116ec19a25e9497f0a | Python | 3,370 | 110 | # This example requires the following dependencies to be installed:
# pip install "lightly[timm]"
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightn... |
679b5935180fe610c101eb4a8c4ef05267b6dc4c255fdc551f2d9dd282670c09 | Python | 3,371 | 72 | import contextlib
import sys
import typing
from typing import get_args
import pytest
from celery.app import Celery
from celery.app.task import Context, Task
from celery.canvas import Signature
from celery.local import class_property
from celery.result import AsyncResult
from celery.utils.objects import FallbackContex... |
9c2e65f20c243777bbbb1113c9267e26b41f0b50e56dd3073dfc1af55e0fde9d | Python | 3,371 | 94 | import numpy as np
from .run_models import attach_motif_predictions, compute_thresholds, limit
KEY_BY_NAME = dict(
LSSI=lambda x: x["spl"], FM=lambda x: x["FM"]["res"], AM=lambda x: x["AM"]["res"]
)
def ensure_several_correctness_values(exons, models, *ensure_correctness, species):
"""
Ensure several co... |
a38c9dc58b859072ff8c53205a5e223b51ede68de4054a8adc0bd2162f599523 | Python | 3,371 | 103 | from typing import List, Dict
import bokeh.layouts
from bokeh import embed
from bokeh.plotting import figure
from angorapy.utilities.monitor.plotting_base import palette, plot_styling, style_plot
import numpy as np
import pandas as pd
def plot_episode_box_plots(rewards: List[float], lengths: List[float]):
"""Bo... |
aae3e474e5d081cb170e0c18412caa2af356bffddb18f3cdd0f55a181521b592 | Python | 3,371 | 95 | import numpy as np
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class MaxNumBoundingBoxesTest(testing.TestCase):
def test_max_num_bounding_boxes_basics(self):
self.run_layer_test(
layers.MaxNumBoundingBoxes,
... |
be760b8891952cbd8607687bb794e0f00f85fdd8322e0e0b0788bfdc89941662 | Python | 3,371 | 103 | import random
import torch
from torch.utils.data.dataloader import DataLoader
# from torchsummary import summary
from hn_cnn.cnn import MODELS
from hn_cnn.constants import *
from hn_cnn.data_augmentation import get_training_augmentation, TRANSFORM_TO_TENSOR
from hn_cnn.fit import predict
from hn_cnn.parse_data import... |
2cbad3db8206c979153ec16a6fe8527c228d934f44b8922ee8d31fcdf36db6bd | Python | 3,372 | 120 | import numpy as np
from numpy.random import SeedSequence, default_rng
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tqdm import tqdm
import multiprocessing
from multiprocessing import Pool, freeze_support, current_process, RLock
from functools import partial
import pathlib
from causal_... |
5bb9599fd95154a11aafb63a46c60dda5626e7dc00264f8484d6f9fc07ec91d6 | Python | 3,372 | 119 | import os
import random as python_random
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras... |
a5d60918b6f06f4befd7421f92024e016f9eaa2206171b12ffed6f793646c3d1 | Python | 3,372 | 97 | import sys
sys.path.append('../')
import chcochleagram
import torch as ch
import numpy as np
import matplotlib.pylab as plt
signal_sizes = [30000, 2**15, 40000, 50000, 2**16]
env_extractions = [chcochleagram.envelope_extraction.HilbertEnvelopeExtraction,
chcochleagram.envelope_extraction.AbsSubband... |
a9d018b99dcd44bd8d497a06355f35143e0a9f43e0bc0aa6c425dcfb6c536c76 | Python | 3,372 | 100 | import os
import sys
import logging
from typing import Dict, List
import pandas as pd
import numpy as np
import snapatac2 as sa2
import pyprojroot
proj_dir = str(pyprojroot.here())
pack_dir = f"{proj_dir}/package/python"
sys.path.insert(0, pack_dir)
from mylog import StreamToLogger, set_file_logger
from utils import c... |
a5a0acdec8bd2ffd9fd57c665e1d064cdd392f86c07ebe4bc3511cf7aa7ddc14 | Python | 3,373 | 100 | """Tests for the specparam.data.conversions."""
from copy import deepcopy
import numpy as np
from specparam.bands import Bands
from specparam.modutils.dependencies import safe_import
pd = safe_import('pandas')
from specparam.data.conversions import *
################################################################... |
08be6a7e29557b265906b851289386ce7d8be8ad36b383c6915f7302dc3b82d7 | Python | 3,374 | 106 | """
Unit tests for subprocess_run.py retrieve() method.
Covers three core branches of SubprocessExec.retrieve:
- res=list[LarvaDataset]-like
- res=pandas.DataFrame
- res=None (default, with monkeypatched I/O)
Based on GPT-5 concrete implementation with proper I/O mocking.
"""
import types
import pandas a... |
8db2765263835d990967cd6d504f6978606790773f3d399b9ba1710bd9026d06 | Python | 3,374 | 94 | from __future__ import annotations
import panel as pn
import pytest
from larvaworld.lib import reg, util
from larvaworld.lib.reg.generators import ExpConf
from larvaworld.portal.config_widgets import build_trials_widget
from larvaworld.portal.simulation.parameter_resolution import (
resolve_base_experiment_parame... |
aed87864c05616199a9eaff498cb133a5063f8bbf5ef65b17a360f7954b8f658 | Python | 3,374 | 94 | """
============================================
Embedding ERP MEG data in 2D Euclidean space
============================================
Riemannian embeddings via Laplacian Eigenmaps (LE) and Locally Linear
Embedding (LLE) of a set of ERP data. Embedding via Laplacian Eigenmaps is
referred to as Spectral Embedding (... |
11bc1730baf0bff486366fe152cf6330a5da664c1abb9995af76670e4375f9b5 | Python | 3,376 | 91 | import pkg_resources
import numpy as np
from PyQt5 import uic, QtCore, QtWidgets
from .. import units
from .mpl_edelta import MPLEDelta
class TabEdelta(QtWidgets.QWidget):
def __init__(self, *args, **kwargs):
super(TabEdelta, self).__init__(*args, **kwargs)
path_ui = pkg_resources.resource_filen... |
315cf5f77eca697f1eeab2df605f8757de11562ce5f3c7064dc35661dcbf3fd4 | Python | 3,376 | 122 | """Compatibility tests for the lazy :mod:`torchlens.utils` facade."""
from __future__ import annotations
import importlib
import subprocess
import sys
from pathlib import Path
import pytest
import torchlens.utils as utils
_EXPECTED_EXPORTS = (
"AutocastRestore",
"DoctorCheck",
"DoctorReport",
"MAX_... |
c7c0db53cba210d3a7b71c2da1de3643459901136369e49327fb1d5c330d5062 | Python | 3,376 | 102 | """convert_format.py.
Used to convert output to a format that can be used for visualisaton with QuPath.
Note, this is only used for tile segmentation results; not WSI.
"""
import os
import re
import glob
import json
import pathlib
import numpy as np
import shutil
from misc.utils import rm_n_mkdir, mkdir
####
def t... |
c8435e11a7c59e4a2b5e7ad24a666b84532e38d2eca5adc581443b2a597c894c | Python | 3,377 | 113 | from __future__ import annotations
import uuid
import pytest
from kombu import Exchange, Queue
from pytest_celery import RESULT_TIMEOUT, CeleryBrokerCluster, CeleryTestSetup, RabbitMQTestBroker
from tenacity import retry, stop_after_attempt, wait_fixed
from celery import Celery, chord
from t.integration.conftest imp... |
12a4f2bc618e91074955736e369a292dd1a22b7bc23772a4bd906a9118dc4058 | Python | 3,379 | 91 | import numpy as np
# impoer resize 3D image
import tifffile as tiff
import glob
import tqdm
from natsort import natsorted
import os
from skimage.exposure import match_histograms
from skimage.transform import resize
def normalize_01(images):
return (images - images.min()) / (images.max() - images.min())
def fla... |
389d111d7a83c0783a867a7772cc428fdbcf520ed91bca707e88da588e2a036f | Python | 3,379 | 94 | #!/usr/bin/env python3
"""
File: export_replication_stats.py
Created: 2022/06/18
Last Changed:
Author: M.Vochteloo
Copyright (C) 2020 University Medical Center Groningen.
A copy of the BSD 3-Clause "New" or "Revised" License can be found in the
LICENSE file in the root directory of this source tre... |
0383b614d7e4a871ecf7dc23e9cb92bb3ffa6bb951e17b5a95a69251552ac3d7 | Python | 3,380 | 68 | import keras
from tensorflow.keras import layers
import tensorflow as tf
class Resnet:
def __init__(self):
self.model = self
def res_block(self,layer, filter_out, kernel_size, alpha):
layer0 = layers.Conv2D(kernel_regularizer=keras.regularizers.l2(alpha), filters=filter_out,
... |
658b5ba28cf1e29e96f1711d9f8cd891c3dc6061d0f261659f49646f61143b36 | Python | 3,380 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "histogram"
_path... |
e3cbdb4fa9f33e5cdffe37f58334449f8592179274db44a118a854f11c8614bf | Python | 3,380 | 106 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Unselected(_BaseTraceHierarchyType):
_parent_path_str = "scattergl"
_path... |
0ddc15eb756554e3b2156cc2a25409c05c30e468f86587c421b6df68f3eb4c80 | Python | 3,381 | 85 | # coding=utf-8
# Copyright 2025 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... |
972fe9307f05f9bfd2f1b128ce3a4db2859b6f0be4605e1dbe91f56d54aca769 | Python | 3,381 | 118 | import numpy as np
from numpy.random import SeedSequence, default_rng
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tqdm import tqdm
import multiprocessing
from multiprocessing import Pool, freeze_support, current_process, RLock
from functools import partial
import pathlib
from causal_... |
bc5b4e5f4f593b149cd5fd5870c1b1d1f88662d4c98046b8474e158038cc42af | Python | 3,381 | 103 | from __future__ import print_function
from __future__ import division
import mxnet as mx
import numpy as np
import gluoncv as gcv
from gluoncv.data.batchify import *
from gluoncv.data import DetectionDataLoader, RandomTransformDataLoader
class DummyDetectionDataset(mx.gluon.data.Dataset):
def __init__(self, siz... |
2d3392ae3e4a35514405d282fcdc77dd0f61e7473a4ab64a32122b69d3ca855d | Python | 3,382 | 79 | import numpy as np
import pandas as pd
import math
import pdb
# ------------------------------------------------------
# -- 3d distance
def distance3d(a, b):
d = math.sqrt(math.pow(a[0] - b[0], 2) +
math.pow(a[1] - b[1], 2) +
math.pow(a[2] - b[2], 2) * 1.0)
# print("Distan... |
4f08a8f46e086d693f89ac709e030e9fa872e0379eb62856c1fe5e31c12cc7ea | Python | 3,382 | 98 | import re
import datetime
import time
import logging
class DateRangeError(Exception):
pass
class Scheduler(object):
def __init__(self, in_str):
"""
Class to express time constrains.
It parses a formated string to define a list of allowed time range.
Then it can be used to asses... |
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