sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fe249daf8051ed1cb8651df62ae4362e02362787ccee03bc6a260768c529021f | Python | 4,437 | 202 | # Copyright (c) 2021 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... |
b5d235100f48e881af4185a1fb6848cfe4239c3ec847aa2e0456e2b013f12b79 | Python | 4,439 | 118 | #install library
!pip install tensorflow-addons==0.16.1
import tensorflow as tf
import os
import glob
import shutil
import matplotlib.pyplot as plt
import random
import numpy as np
import cv2
import tensorflow_addons as tfa
from keras import regularizers
from google.colab import drive
from sklearn.metrics import ... |
7eeb2215441dfd6e2a9aabe762fb8d814b2b4bb264b06b1751d5f0dc38d33973 | Python | 4,440 | 112 | """ Local functions for SSVEP examples """
import os
from mne import create_info
from mne.datasets import fetch_dataset
from mne.io import RawArray
import numpy as np
DATASET_URL = "https://zenodo.org/record/2392979/files/"
SSVEP_HASHES = {
"subject01_run1_raw.fif": "md5:e33a589abce842bf676dd881d9f4a9b0",
... |
8608a4be52e6722ae7a9a1f44ed921465a995bb8a4fd6d667e70a73bba03b553 | Python | 4,440 | 104 | # coding=utf-8
# Copyright 2024 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... |
ef15ed102bcaa1ea8861542f22edb92279a5824bee3e281a5b96e6589895069b | Python | 4,440 | 120 | """
This script uses Playwright to scrape AMD CPU data from their website and save it as a CSV file.
uv run pip install playwright
uv run python amd_cpu_scrapper.py
"""
import asyncio
from playwright.async_api import Page, async_playwright
async def handle_cookie_banner(page: Page):
"""Attempts to find and cli... |
3b6780432f18b8345d7a4d81105dd896702f5b9663e15416dad87f0eccc66f03 | Python | 4,441 | 142 | """JAX backend replay-validation spine tests."""
from __future__ import annotations
from collections.abc import Mapping
from dataclasses import replace
from typing import Any, cast
import pytest
from torchlens.backends.jax.jaxpr import (
ALL_JAX_EQUATION_KINDS,
JAX_REPLAY_HANDLERS,
JaxEquationCapture,
... |
84621b6d0c0b8e8e6a1b3160ccadaaeff8907c1520448c239f093f65bfa53815 | Python | 4,443 | 133 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""Plot data split for cross-validation"""
from typing import Union
from matplotlib.patches import Rectangle
from matplotlib.ticker import MaxNLocator
import numpy
from .._trf.shared import Splits, split_data
from .._data_obj import Dataset, CategorialArg, NDVa... |
4ab6f95f4579203b59edf5b2fa2ec868aab8330562328e1c86e97d704e09ae26 | Python | 4,444 | 85 | """
Predict next tools in the Galaxy workflows
using deep learning learning (Transformers)
"""
import argparse
import time
import extract_workflow_connections
import prepare_data
import train_transformer
if __name__ == "__main__":
start_time = time.time()
arg_parser = argparse.ArgumentParser()
arg_parser... |
4c840d8f22c3301df9cb29625277dae352893aa89fe3abdc9d306d493f18b024 | Python | 4,444 | 82 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
6fb15f9e1259eece2d8baa98097db5d4e6e0f8ac2d1801289a6c7b0e043d34d4 | Python | 4,444 | 121 | # Copyright 2017 The TensorFlow 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 applica... |
47cb7083ed55fc2ceb279a7675bb2e2200e471cd6bfe447e8f2725d8c3e1ae4d | Python | 4,445 | 86 | import sys
sys.path.insert(1, '../')
import numpy as np
import pandas as pd
import os
import shutil
dataset_dict_paths = {
'ABCD': "/media/sda/Anna/TM2_segmentation/data/t1_mris/registered/z/",
'ABIDE': "/media/sda/Anna/TM2_segmentation/data/t1_mris/abide_ench_reg/z/",
'AOMIC':"/media/sda/Anna/TM2_segment... |
5ed4e01e2b7834eda8a414a3251692e0d0399c131046a3daf3c06607db05e1c8 | Python | 4,445 | 122 | import os
import jax
import jax.numpy as jnp
import numpy as np
import pytest
import keras
from keras.src import backend
from keras.src import testing
from keras.src.backend.config import is_nnx_enabled
from keras.src.backend.jax.ops.core import Variable
if is_nnx_enabled():
from flax import nnx
@pytest.mark.s... |
fe84d633ae18cf8e8abd0bbdaaab059b329bfa33a75b557a53ad44e024491474 | Python | 4,445 | 147 | """
Screen side-panel for pygame-based simulation visualization
"""
from __future__ import annotations
import math
import os
import numpy as np
os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "hide"
from .. import util
__all__: list[str] = [
"SidePanel",
]
class SidePanel:
"""
A class representing a side... |
92831970c748fc6afed7441fea1a730173dac602520f107e195fb438e8f1fcf4 | Python | 4,446 | 105 | import pkg_resources
import numpy as np
from PyQt6 import uic, QtWidgets
class EditDialog(QtWidgets.QDialog):
def __init__(self, ui_name, shape, *args, **kwargs):
super(EditDialog, self).__init__(*args, **kwargs)
path_ui = pkg_resources.resource_filename("impose.gui", ui_name)
uic.loadUi(... |
edc01ca570d4d0559d3a55b9eb2f157b8b38a2cef63fa8b3ecdb91635ce7876f | Python | 4,446 | 87 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
140f847439012545df8b988593e79673eb60ac608b5be9956fc757d30d58137a | Python | 4,447 | 150 | """
This part of the code was originally published by mahmoodlab and adapted for this project.
https://github.com/mahmoodlab/CLAM
Lu, M.Y., Williamson, D.F.K., Chen, T.Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images.
Nat Biomed Eng 5, 555–570 (2021). https://doi.org/10.1038... |
ca8814370f873e886b53faf6a65a65fa5293e9eef6b39f765fc9277f781703ba | Python | 4,447 | 128 | #!/usr/bin/env python
import glob
import os
import tarfile
import wave
import pandas
from deepspeech_training.util.importers import get_importers_parser
COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]
def extract(archive_path, target_dir):
print("Extracting {} into {}...".format(archive_path, tar... |
68954379434f94a938f7231db6aeeff91df25330c4f4c25b0e79d2b707695f37 | Python | 4,448 | 82 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
d313ca5fbeba2709cb36f0d811b01ad2ac281e2571957803ebe18c9e2e5de8cd | Python | 4,448 | 130 | from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
import pickle
from scipy.signal import hilbert, butter, sosfilt, coherence
import os
p = os.environ['PATH']
plt.rc('text', usetex=True)
# preparations
##############
# plot settings
plt.rcParams['figure.constrained_layout.use'] = True
... |
d91735f4bdfe386669ac81c0a20c567a3d561972ff72b8cdcfa68eaf4f9eb056 | Python | 4,448 | 110 | import warnings
import numpy as np
from neurogym.core import TrialWrapper
from neurogym.envs.collections import get_collection
from neurogym.envs.registration import all_envs, make
from neurogym.utils.logging import logger
from neurogym.utils.scheduler import RandomSchedule
from neurogym.wrappers.block import Schedul... |
1f3c1cac9cdd9757cfc1481e1e036ab7f6f04cb73fa6715d141194dd2bed39a9 | Python | 4,449 | 161 | import cv2
import numpy as np
from ethoscope.utils.debug import EthoscopeException
__author__ = 'quentin'
class ROI(object):
def __init__(self, polygon, idx, value=None, orientation = None, regions=None):
"""
Class to define a region of interest(ROI).
Internally, ROIs are single polygons... |
ec9616810f6485ccf20f5cd40f575c45abdcdf5327f037e3f4ccf5f7d2781677 | Python | 4,449 | 128 | import argparse
import pandas as pd
import numpy as np
import os, sys, shutil, importlib, glob
from tqdm.notebook import tqdm
import celloracle as co
from celloracle import motif_analysis as ma
from genomepy import Genome
import scanpy as sc
import pyarrow.parquet as pq
import pyprojroot
proj_dir = pyprojroot.here()
p... |
8b89b3a79f69da412bbb66b7ef004bcabe451af41fc21262e4c80712b7711f7a | Python | 4,450 | 125 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Normalizing flow transforming to and from PCA-whitened space.
"""
# ==========================================... |
b39211834e39baaff43090486d9b34458cd3c967bf68fbcf9254020a1b3ce928 | Python | 4,450 | 114 | """Observe-kit item 4: the pass-level-peak publication gate + its source lint."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.observe import format_pass_peak, pass_peak_facts
TORCHLENS_DIR = Path(tl.__file__).resolve().parent
... |
ef140ddac7feaf31e27a4032dcb4c7dc85c1d7d59f12311f1a0451748b04fd5e | Python | 4,450 | 93 | import torch
from torch import nn
from torch import Tensor
from collections import OrderedDict
import numpy as np
class GetDensity(torch.nn.Module):
def __init__(self,rs,inta,cutoff,nipsin,norbit,ocmod_list):
super(GetDensity,self).__init__()
'''
rs: tensor[ntype,nwave] float
inta:... |
fa8298283c1f2e58cf54554d6718ee528913abf318d6cc2b70652cc0b2e8bc36 | Python | 4,450 | 118 | from typing import (
Any,
TYPE_CHECKING,
)
from sqlalchemy import (
func,
text,
)
from galaxy.exceptions import (
ReferenceDataError,
RequestParameterInvalidException,
)
from galaxy.managers.context import (
ProvidesHistoryContext,
ProvidesUserContext,
)
from galaxy.model import User
f... |
6f2a0f3c7c3108877a2cc23d614d0f0977edffa9f206990c6f87b12da29825ef | Python | 4,451 | 122 | import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.backend.common import standardize_dtype
from keras.src.utils import backend_utils
class BackendUtilsTest(testing.TestCase):
@parameterized.named_parameters(
(... |
94df38ff3970ccf9c6e259c206ab15688b09d221350d72cadc8a05aed8f30d52 | Python | 4,451 | 142 | from six.moves import input
import pandas as pd
import numpy as np
from os.path import dirname, join
from android.os import Environment
import sklearn
# from sklearn.externals import joblib
import _multiprocessing
_multiprocessing.sem_unlink = None
import joblib
import time
# import matplotlib.pyplot as plt
def main()... |
3f484a8deaf4cd923c1a2771a735f43dc4ada6b2daf0697ea0193ffb30d2c8d5 | Python | 4,453 | 127 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
import warnings
class Template(_BaseLayoutHierarchyType):
_parent_path_str = "lay... |
7374108956fcbc3131bea93c4f6a62e3864fc04b6f7a95f9639a3f1d86fd80ed | Python | 4,453 | 75 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from irnlm.utils import check_folder
from fmri_encoder.data import fetch_masker
from fmri_encoder.plotting import plot_colorbar
from irnlm.contrasts import plot_brain_fit
red = plt.cm.get_cmap('black_red')
red = red(np.li... |
79d0fca255a4e2b293284f9510a7924f8a7f4c365375dda87a7d13cb442171e6 | Python | 4,455 | 128 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import json
from multiprocessing.dummy import Pool
import numpy as np
from attrdict import AttrDict
from .flags import FLAGS
from .text import levenshtein
from .io import open_remote
def pmap(fun, iterable... |
d282d22d2189e3d4bb2307f7cf6e8bf827fc8d82a2f716f21cb256fb1bbbc44c | Python | 4,456 | 114 | """Observability helpers for `model.quantize()`.
This module defines :class:`QuantizationReport`, a small structured record of
what happened during a `model.quantize()` call (which layers were quantized,
and which were skipped and why).
"""
from keras.src.api_export import keras_export
@keras_export("keras.quantize... |
f04f0f42c558b0d9d3a0033d196145ad7c1b884b7914ed482884b19b8fa8c672 | Python | 4,456 | 148 | #!/usr/bin/env python
"""
Runs TRF on a sequence file.
For use with trf version 4.04
usage: trf_wrapper.py input_file <required parameters> [options]
Required parameters:
match
mismatch
indels
match_probability (%, 10-100)
indel_probability (%, 10-100)
minimal score (30-150)
maximum p... |
3c5436be5b24fe94843864ed2a2416d0115d22d91fc6c64cbd1707b1bd085c03 | Python | 4,457 | 132 | ##usage: python find_error.py Fish-1_L4.process.site.txt > error.sites
import sys
import collections
from collections import Counter
def Merge(dict1, dict2):
res = dict()
for k,v in dict1.items():
if k in dict2:
v = dict2[k] + v
res[k] = v
else:
v = v
res[k] = v
return res
d... |
6c393cac68d2e6b91e52462bd74b493144f22fba692d189b88fc5a7416e7e644 | Python | 4,457 | 106 | """Encoder module of Monodepth2
Code partially borrowed from
https://github.com/nianticlabs/monodepth2/blob/master/networks/resnet_encoder.py
"""
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import mxnet as mx
from mxnet.gluon import nn
from mxnet.context import cpu
fr... |
2c155159878b08196dfc24a09935357d8530dd550e22cd5acc912bb7be2a5a1f | Python | 4,458 | 120 | """Regression tests for the ``first_input``/``"input"`` facet on the torch backend.
cert9-f5 fix (round-8-prep peripheral MAJOR): ``first_input``/``first_input_spec``
(``torchlens/semantic/recipes/_helpers.py``) read ``ModuleCall.forward_args``, which
finalization ("GC-11", ``torchlens/postprocess/finalization.py``) u... |
34edda2ce0211cb7d3a765654dbab72cb5120b9b1a0bae035b0c84a815ce061c | Python | 4,458 | 123 | #!/home/ubuntu/miniconda2/bin/python
from __future__ import division
import sys
import glob, os, gc
import uuid
import os.path
import csv
import numpy as np
from time import time
from subprocess import (call, Popen, PIPE)
from itertools import product
from PIL import Image
import shutil
import re
import xml.etree.Eleme... |
709506439bd0881ccbda4842583ba64a805ea48a63fd916faaa84d17ad2a9a04 | Python | 4,458 | 132 | import numpy as np
import pandas as pd
import seaborn as sns
from os import PathLike
from ._utils import _save_fig
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from typing import Optional, Union, Sequence, Mapping, List
def positional_gia_plot(
sdata,
vars: list,
id_var: str = "id",
... |
905a79bccbb7ac996bc6c188a89b9c4865664e348174474dec95a49e65ccd7c9 | Python | 4,458 | 130 | """Entry-point phase audits (F35; memo item 11 + composition row 9).
Two obligations the merged test plan names that no shipped suite proves:
- **NO-CODE-ON-ATTRIBUTE-EXISTENCE**: with an installed distribution
declaring ``torchlens.recipes``/``torchlens.appliances`` entry points
whose provider module writes a se... |
a2845f6e12789dc4657afe0e0282d7d9a93b1fb8d76ff6f169caef9ae180d770 | Python | 4,458 | 124 | import datasets
import os
from os.path import join as oj
from tqdm import tqdm
import pandas as pd
import pickle as pkl
import numpy as np
from sklearn.model_selection import train_test_split
def process_data_and_args(dataset):
"""Load dataset + return the relevant dataset key
"""
# load dset
if datas... |
b0656e9ef374eb6eed3ae3a040b2c14844315b580e2a1613a7256c6b5754f291 | Python | 4,458 | 82 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
2d0c5625897e42734dedc04eabd032c186096bbdbac220981103dbbf1daf03ca | Python | 4,459 | 92 | """Four-protocol leakage experiment on the REAL corpora, using the project's own
CSP (fit_csp/apply_csp) so the numbers are comparable to the reported pipeline."""
import os, sys, numpy as np, pandas as pd
ROOT = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.join(ROOT, "src"))
from sklearn.ense... |
30214bdc4eaa4625232023c860b7d3630b7dfb7efb84443e027fe66da2da3c4c | Python | 4,459 | 130 | import os.path
import numpy as np
from typing import Any
from mirp._data_import.generic_file import ImageFile
from mirp._data_import.dicom_file import ImageDicomFile
from mirp._data_import.utilities import get_pydicom_meta_tag
class ImageDicomPlanarImage(ImageDicomFile):
def __init__(self, **kwargs):
su... |
93dfe0d907cb3434296ddca45d9b81144a63d7ae7e9899df7fbdf81d063e0145 | Python | 4,459 | 110 | """ Local functions for SSVEP examples """
import os
import numpy as np
from mne import create_info
from mne.datasets import fetch_dataset
from mne.io import RawArray
SSVEP_HASHES = {
'subject01_run1_raw.fif': 'md5:e33a589abce842bf676dd881d9f4a9b0',
'subject01_run2_raw.fif': 'md5:2e75762abc30b832ad3d3d60f09... |
99fc543265f756fd813badb43f3e37d2d192d6132a94da5cd4193b9dcce08f1c | Python | 4,460 | 83 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
4f5d4c5da039eb5de35e96c00217c558540314b5f89f1e9464f2def125c20ccf | Python | 4,461 | 102 | """P0 probe: fastlog late-buffer behavior, characterized before migration.
Design-of-record section 4.5 predicted an accidental data loss on the fastlog
``to_trace`` path (a pre-0 late-buffer append invisible to materialization
because the capture-end seal cache predates it). The probe shows the loss
does NOT exist at... |
6cd0d4d9b38c5b6362c55845805f862a812cff37e6f76a1bedc72ed4507f75f9 | Python | 4,461 | 183 | # %%
import sys
import os
import logging
import itertools
import numpy as np
import pandas as pd
import pickle
from multiprocessing import Pool
from statsmodels.stats.multitest import multipletests as mt
import os
import sys
sys.path.insert(0, os.path.abspath('../../code'))
import utils
# %%
spatial_fr... |
c1fa3cab1c598676954729145447ab23451e01b67205cded9fec456405e5a149 | Python | 4,461 | 113 | from __future__ import annotations
import os.path
import warnings
from pathlib import Path
from PIL import Image
from tqdm import tqdm
from lightly.data import LightlyDataset
from lightly.utils.bounding_box import BoundingBox
def crop_dataset_by_bounding_boxes_and_save(
dataset: LightlyDataset,
output_dir:... |
0ee056cd6d07746ad0aabe52765323d5ca0436ed376aaa933a7c38b992bc1d07 | Python | 4,462 | 118 | #### Master Script 2a: Prepare study resamples for bootstrapping TIL-based statistics ####
#
# Shubhayu Bhattacharyay
# University of Cambridge
# email address: sb2406@cam.ac.uk
#
### Contents:
# I. Initialisation
# II. Draw resamples for bootstrapping
### I. Initialisation
# Fundamental libraries
import os
import re
... |
593a5e8b3c53abb4a5743e33e27b831ba28c03bf880f2b17adb773b96239f667 | Python | 4,462 | 162 | # Copyright 2020 Google Inc. 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 applicable law or a... |
cb6c3fbfab3347ef34331766670bdae17278eefcd49ed891b3fb67825e83e36a | Python | 4,462 | 81 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
5e41a174d7add5fdfc15e417ab4631dedc014cb91921aba49ed287e86e2dc738 | Python | 4,463 | 116 | import numpy as np
import vtk
import vtk.util.numpy_support as vtk_np
from slicer import vtkMRMLScalarVolumeNode
from slicer import vtkMRMLLabelMapVolumeNode
from SegmentEditorCommon.Helper import Helper
from biomedisa_extension.SegmentEditorBiomedisaTraining.Logic.BiomedisaTrainingParameter import BiomedisaTrainingPar... |
61118d826e053945f80aed6999e5e481dc3965d6174f10e0c4c427fed6eb945d | Python | 4,463 | 94 | # -*- coding: utf-8 -*-
"""
Created on Fri Jun 10 09:56:26 2022
@author: 8
"""
import torch
import math
import random
import numpy as np
import torch.nn as nn
import torch.nn.init as init
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch.optim as optim
class MScnn(nn.Module):
def __init... |
dd3537ac6485c4f3c9d844954df12eae0f38d75600e308668910d7f5b747d505 | Python | 4,463 | 134 | """Utilities to query NVIDIA GPUs.
This module provides utilities to query NVIDIA GPUs using NVIDIA Management
Library (NVML). It is important to run nvmlInit() before any queries are made
and nvmlShutdown() after all queries are finished. For performance, it is
recommended to run nvmlInit() and nvmlShutdown() as few ... |
87fe080768122ae634fe83dc241a7cf69618f1ec0ccf94cb2f1b6e0ee4f00bd0 | Python | 4,464 | 83 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
d7192e9c67e260e98a328b2a88f27076fcc441fdfe889f75961fd79cce8275c3 | Python | 4,464 | 163 | """Script to create (and optionally install) a `.whl` archive for Keras 3.
Usage:
1. Create a `.whl` file in `dist/`:
```
python3 pip_build.py
```
2. Also install the new package immediately after:
```
python3 pip_build.py --install
```
"""
import argparse
import datetime
import glob
import os
import pathlib
impo... |
79cc7fc88d6fc765a23352dc338365d32a86a69e13de2281195ef10009e46524 | Python | 4,465 | 168 | from scipy.stats import spearmanr
import numpy as np
import pandas as pd
import warnings
import networkx as nx
from ...utils import _read_graph
warnings.filterwarnings("ignore", category=RuntimeWarning)
def detect_transition_markers_clades(
adata,
clade,
cutoff_spearman=0.4,
cutoff_pvalue=0.05,
s... |
1906a377a2efeb8d2bc736784535d0e5cdf34657ba956e565d874b2bec90dc27 | Python | 4,466 | 133 | # 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_lightni... |
7599e8ba20a06c0eef9de31a833a48a2ee256baf13af702e650452581a68c724 | Python | 4,467 | 128 | """Multi-graph collection tests: rolled projection + feedback overlay (D10).
Feed-forward captures emit NO rolled graph at all (rolling changes nothing);
recurrent captures append the disclosed rolled DAG projection whose
back-edges leave ``incomingEdges`` (dagre silently reverses cyclic edges)
and ride the named "rec... |
ac448a29a22d50b7ebc089e2c1295e5b2d9533ec346c167973b4f59713df238a | Python | 4,467 | 158 | import pandas as pd
import pytest
from rdkit import Chem
from rdkit.Chem import AllChem
from m2p import PolyMaker
from m2p.stereo_reactions import (
get_vinyl_init_dict,
get_vinyl_prop_dict,
vinyl_prop_stereo,
vinyl_terminate_stereo,
)
pm = PolyMaker()
@pytest.fixture
def vinyl():
return pm.ther... |
73e5465f46e6d8d4937d63862f7677a5ef6d12803e42e0c9204d044e6981b3a7 | Python | 4,468 | 115 | # -*- coding: utf-8 -*-
"""**Lightly Train:** Train a self-supervised model from the command-line.
This module contains the entrypoint for the **lightly-crop**
command-line interface.
"""
# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved
import os.path
from typing import List
import hydra
im... |
ce42da433ea91f697520bdd0b428f6fb5ab0b4b5dee61539f390ec6bc25fae04 | Python | 4,468 | 152 | """Sprint B3a model-profile computed-view regressions."""
from __future__ import annotations
import json
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.constants import MODEL_LOG_FIELD_ORDER
from torchlens.data_classes.trace import ResolvedPostprocessing, ResolvedPr... |
5c0d78f6347e59d3ac5f40ea69fe27017f2d8e1a52856f7055e7fdfd3a89f52d | Python | 4,469 | 119 | import attr
import numpy as np
@attr.s
class SaliencyMap:
codon_address = attr.ib()
saliency = attr.ib()
absolute_saliency = attr.ib()
bar = attr.ib()
salient_locations = attr.ib()
salient_motifs = attr.ib()
possibly_salient_codons = attr.ib()
possibly_non_salient_codons = attr.ib()
... |
a5050e1043f9ebe9a7fc4c953ef81d0dbab286eb1f78e3e084a72f578fb2ecdc | Python | 4,470 | 104 | """Norm-layer attestation/mutation over-trigger relaxation (r29-C4).
Two over-triggers that penalised a VALUE-CORRECT, path-faithful norm-layer replay:
* codex-F3 (false DIVERGE -- the harmful one): ``InstanceNorm``/``BatchNorm`` with
``track_running_stats=True`` updates its running-stat buffers inside the function... |
bcfa77d04087b8e797c1cf9eaf1dca77f73dd8e33a3a3f0661c4dc1dbacd74eb | Python | 4,471 | 115 | import argparse
import numpy as np
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--vocab_file', default='vocab.txt', type=str)
parser.add_argument('--vectors_file', default='vectors.txt', type=str)
args = parser.parse_args()
with open(args.vocab_file, 'r') as f:
words... |
801d7b9878f707f5b00fed2841b1e053086de70015dca0ae71ccd77ac6a7c8a7 | Python | 4,472 | 122 | # 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... |
bb7cc2988370bc4e5664f963c6edc3e0b55c1b8010dc20ae74b1785bd910a463 | Python | 4,472 | 146 | import importlib
import inspect
import torch
def get_class_string_of_object(obj: object) -> str:
"""
Returns the string name of the class of the given object.
"""
return str(type(obj)).split(' ')[-1][1:-2]
def get_class_from_string(string: str):
"""
Returns the class that correstponds to g... |
5bad0ed617a7123b76f3652e1f4134ef3b7f56279efa697b4a33d360fba8aa8f | Python | 4,473 | 140 | from numpy import random
class BaseSchedule(object):
"""Maintains logic for deciding whether to consequate trials.
This base class provides the most basic reinforcent schedule: every
response is consequated.
Methods:
consequate(trial) -- returns a boolean value based on whether the trial
... |
a3bf452d7b37a2a7dde0afad9613d5efdf339e20017c79adf854c4ceadbd720f | Python | 4,473 | 153 | """Lifecycle matrix audits for intervention-owned TorchLens fields."""
from __future__ import annotations
from typing import Any
import torch
from torch import nn
import torchlens as tl
from torchlens._trace_state import TraceState
from torchlens.constants import LAYER_PASS_LOG_FIELD_ORDER, MODEL_LOG_FIELD_ORDER
fr... |
a9381c394c013b2707147467923a15dcec88e3b1d0ac1eb77d9ba2be117d1744 | Python | 4,474 | 88 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
0ebf0c71b0116b8200fc1357e0a4e46d25fbb25a33d2818bece0a5d78113b36d | Python | 4,475 | 98 | from model import *
from utils import *
import numpy as np
from sklearn.model_selection import KFold
import torch
import os,sys
import warnings
warnings.filterwarnings("ignore")
# trained_model protein length capacity should >= new protein length capacity
# trained_model should have same embedding method with new dat... |
20a2fc280197b9007980ab345468b50b5cd0591223b9c1fc16270ff60430a7bf | Python | 4,475 | 142 | import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet2_ops.pointnet2_modules import PointnetSAModule, PointnetSAModuleMSG
from scene_graph_prediction.pointnet2_dir.pointnet2.models.pointnet2_ssg_cls import PointNet2ClassificationSSG
# class PointNet2Classific... |
35a16c5cf73724dbedbcaba030fa16a1a1e816b2b682ea3022282a9414446ff2 | Python | 4,475 | 140 | import numpy as np
from matplotlib.colors import ListedColormap
from irnlm.utils import check_folder
from fmri_encoder.data import fetch_masker
from fmri_encoder.plotting import plot_colorbar
from irnlm.contrasts import plot_brain_fit
from irnlm.peak_regions import plot_peak_regions
green = np.array([[i / 50, 1, 0, 1... |
f4bbd3c2d57704b48e37778bc00d198ff53e02abf75edcf2301fd15bc9dd5504 | Python | 4,475 | 120 | import torch
import torch.nn as nn
from modular_splicing.models.entire_model.modular_predictor import (
ModularSplicePredictor,
)
def compute_setting_as_batch(input_dict):
setting = input_dict["setting"]
setting_batch = setting[:, 0, 0]
assert (setting == setting_batch[:, None, None]).all().item()
... |
8faecd65a276b494abc4a6e28a3ce6df9ed8a4086eb49dedfac48ec7783aaf40 | Python | 4,476 | 126 | """Implementation of a space consisting of finitely many elements."""
from typing import Optional, Union
import numpy as np
from gym.spaces.space import Space
class Discrete(Space[int]):
r"""A space consisting of finitely many elements.
This class represents a finite subset of integers, more specifically a... |
223388efb31fc029caab8999477988aae9bbdb66adf3d174e63611c3dd75e749 | Python | 4,477 | 136 | """Numpy utility functions: concatenate space samples and create empty array."""
from collections import OrderedDict
from functools import singledispatch
from typing import Iterable, Union
import numpy as np
from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Space, Tuple
__all__ = ["concatenate"... |
50afc7e107cb54c14d4ac74e2ab148110ee6b549ab168d7e500192151166cb88 | Python | 4,477 | 86 | from pyabf.abf2.section import Section
from pyabf.names import getDigitizerName
class ProtocolSection(Section):
"""
This section contains information about the recording settings.
This is useful for determining things like sample rate and
channel scaling factors.
"""
def __init__(self, fb):
... |
51b415817b2714f69ccb7bee3edf2cd830b6695ae9e529bca6b0847e8be0e4c7 | Python | 4,477 | 128 | """1. Getting Started with Pre-trained Model on CIFAR10
=======================================================
`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`__ is a
dataset of tiny (32x32) images with labels, collected by Alex Krizhevsky,
Vinod Nair, and Geoffrey Hinton. It is widely used as benchmark in
com... |
0589efeccf49f1fc8c49db363f415962d4c561ceccd777852679b3c7926fde7c | Python | 4,478 | 155 | import os
import re
import subprocess
from keras.src import backend
from keras.src.backend import config
# For torch, use index url to avoid installing nvidia drivers for the test.
BACKEND_REQ = {
"tensorflow": ("tensorflow-cpu", ""),
"torch": (
"torch",
"--extra-index-url https://download.pyt... |
2384831fc3c4cbc89dc279f6dabde95982d2bb84888f81984172aa53ced77ffe | Python | 4,478 | 140 | from pathlib import Path
import argparse
from pymatgen.core.structure import Molecule
from pymatgen.analysis.graphs import MoleculeGraph
from pymatgen.analysis.local_env import OpenBabelNN
from tqdm import tqdm
from instance_mongodb import instance_mongodb_sei
def identify_number_broken_bonds(
molecule_graph1... |
29476a9734f43a4951c09dccef9be5b052547d2d57e0e15e7e5db77d28cfcdd4 | Python | 4,479 | 121 | import pickle
from sklearn.model_selection import train_test_split
from collections import Counter
import numpy as np
import sys
mode = sys.argv[1] #standard or vgg19fullframe
### Load and combine all processed dictionaries
V = {}
if mode == "standard":
with open('./preprocessed_pickles/processed_Mar25_0', 'rb') as ... |
a91b2a6417ffa863dc54d7288c21f68f1d735f8dcf003d28840bbfe1b1a19adc | Python | 4,480 | 115 | __author__ = 'quentin'
import logging
from ethoscope.core.variables import BaseRelativeVariable
from ethoscope.core.data_point import DataPoint
from ethoscope.stimulators.stimulators import DefaultStimulator
class TrackingUnit(object):
def __init__(self, tracking_class, roi, stimulator=None, tracker=None, *args,... |
bc478e58f279911bf34141884f165f6ce59ab269376ad0c396d67f161714fd17 | Python | 4,480 | 101 | import os
from os.path import join as pjoin
from cxy_visual_dev.lib.predefine import proj_dir,\
dataset_name2info, s1200_1096_thickness, s1200_1096_myelin,\
s1200_avg_thickness, s1200_avg_myelin, get_rois
from cxy_visual_dev.lib.algo import ROI_analysis_on_PC,\
calc_map_corr, vtx_corr_col, polyfit, row_corr... |
8c2315f84c44ebba1e0e7eff0799fd4cb026858bd08a67e9ef01d3b772a983a7 | Python | 4,481 | 129 | import importlib
from typing import List, Tuple, Union
from pathlib import Path
import os
from os import PathLike
import torch
import yaml
from .._settings import settings
def list_available_layers(model):
"""List all layers in a model"""
return [name for name, _ in model.named_modules() if len(name) > 0]
d... |
c5b85968b18c3d1a7b50cf6d50cac86e78e0bffeaa5bb1eee4ba7655191b50a8 | Python | 4,481 | 152 | import json
from pathlib import Path
from typing import Sequence, Union
import mongomock
import pytest
from monty.serialization import loadfn
from coeffnet.model.reaction import GateReactionModel
@pytest.fixture
def dataset_options_factory(tmp_path):
"""Returns dataset options common to all tests."""
def _... |
06e2b9d30c65c5e2ece79b6b6990c6432156210a9d6131fdc11ca3b107a22654 | Python | 4,482 | 104 | from keras.src import testing
from keras.src.quantizers.report import QuantizationReport
class QuantizationReportTest(testing.TestCase):
def test_empty_report(self):
report = QuantizationReport(mode="int8")
self.assertEqual(report.mode, "int8")
self.assertEqual(report.num_quantized, 0)
... |
bd99b8b9909085b53fd940b1e9ce8e01289edc5d42602f775d6c3a057a1834c1 | Python | 4,482 | 121 | #!/usr/bin/env python
# Created on: September 30, 2013
__author__ = "Sven Kreiss, Kyle Cranmer"
__version__ = "0.1"
__description__ = """
Creates a counting model with box constraint (RFit) by replacing the constraint
term in the model.
"""
import optparse
parser = optparse.OptionParser(version=__version__, descr... |
0e34d7509601a1e4014175b22adfb822be4328087ca63802089129d98d01d480 | Python | 4,483 | 118 | import numpy as np
from PyQt6 import QtCore
import pyqtgraph as pg
from ..geometry import pg_roi_to_impose_shape
class StructureCompositeROIs(QtCore.QObject):
def __init__(self, image_view):
"""Visualization and modification of the ROIs of a StructureComposite
Parameters
----------
... |
7d1f10eb56a9e1cf0381438a3a01f0d2af972d29fd7b264000f8f773e3ea8d10 | Python | 4,483 | 144 | # coding=utf-8
# Copyright 2023 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... |
92d0b15b9f7ac457777fad27219ebb6040a4904989c2a15653005545297198ca | Python | 4,483 | 139 | import itertools
from slurmpy import Slurm
# slurm params
partition = 'high'
s = Slurm("fit_logistic", {"partition": partition, "time": "1-0"})
# python ../02_fit_logistic.py --dataset financial_phrasebank --checkpoint distilbert-base-uncased --subsample 1000 --ngrams 1 --all all --layer last_hidden_state_mean --seed... |
f610f243a782ccd79319e1fd3a03c96a594d9cd031e327f7ad88dd7ed0ffaaff | Python | 4,483 | 140 | from array_api_compat import array_namespace as get_namespace
import numpy as np
import pytest
from pytest import approx
from conftest import to_backend
from pyriemann.utils._check import (
check_function,
check_init,
check_matrix_pair,
check_metric,
check_weights,
)
@pytest.mark.parametrize("n_m... |
161628b35b03e067aba70c7a5bda20b9aeb3d2ece5e4546e06463ade8ebd8384 | Python | 4,484 | 119 | __author__ = 'cenwan'
#-----The Python implementation of a approach to conduct the CTST on synthetic and real testing protein feature samples.
#-----Please download data via http://bioinfadmin.cs.ucl.ac.uk/downloads/FFPredGAN/TestingData/.
import numpy as np
import math
import glob
import random
import torch
import t... |
6127971a67a790abd5bb08f4f67ade08463519f061f12ab2a9d8bbd47fe7919d | Python | 4,484 | 130 | import os
import h5py
import numpy as np
import torch
import tqdm.auto as tqdm
from permacache import permacache, stable_hash
from modular_splicing.utils.run_batched import run_batched
from modular_splicing.data_pipeline.reprocess_data import create_datapoints
def create_gene_dataset(original_datafile_path, *, crea... |
6d0f5a06bfb96523bef5f1ac1ac0cbcf49a79c5b5e1d46677ed54b921bc70ead | Python | 4,484 | 98 | import distutils.command.build_clib as orig
from distutils.errors import DistutilsSetupError
from distutils import log
from setuptools.dep_util import newer_pairwise_group
class build_clib(orig.build_clib):
"""
Override the default build_clib behaviour to do the following:
1. Implement a rudimentary time... |
702710c626709eb19f199927d67b0234343e237664503792e8d97cec7ee05c4b | Python | 4,485 | 123 | from collections.abc import (
Iterable,
Sequence,
)
from typing import TYPE_CHECKING
from sqlalchemy import (
and_,
false,
func,
or_,
select,
true,
)
from galaxy.model import (
Group,
Role,
User,
UserGroupAssociation,
)
if TYPE_CHECKING:
from sqlalchemy.orm import ... |
369fe9a017e791ea4dab62c99b07d9633bd97352565aa535296a2af92fff0372 | Python | 4,486 | 117 | """
Gregory Way 2017
PanCancer Classifier
scripts/initialize/process_sample_freeze.py
Takes in sample freeze data that was determined by TCGA PanCancer Atlas
consortium along with raw RNAseq and mutation data. The script will process
the datasets and subset each according to the frozen samples. The frozen
samples were... |
6da9d3605b8e88874854b432f6f5c95bff9f4027fcbfcb51aa334a90e55ca02a | Python | 4,486 | 161 | import torch
from torch.utils.data import Dataset
class DummyDataset(Dataset):
"""Generate a dummy dataset.
Example:
>>> from pl_bolts.datasets import DummyDataset
>>> from torch.utils.data import DataLoader
>>> # mnist dims
>>> ds = DummyDataset((1, 28, 28), (1, ))
>>... |
02aaaa9f8e1314cbc8ad8920f2a403587aae4be299af48a4c496af83566c46e6 | Python | 4,488 | 86 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
0dc21c5919c1f2ee410aceef5e02e1402f787052783ccd8bfab895c6da46ee79 | Python | 4,488 | 180 | from typing import List, Union
import numpy as np
import pytest
import seqpro as sp
from pytest_cases import parametrize_with_cases
def length_empty():
return "", 0
def length_single():
return "A", 1
def length_variable():
return ["A", "AA", "AAA"], [1, 2, 3]
@parametrize_with_cases("seq, lengths",... |
28de8ca45563f347b5135b4f88e6bcd43b8325a0c134260c6033b6bf4523a297 | Python | 4,488 | 143 | # --- 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 Y(_BaseTraceHierarchyType):
_parent_path_str = "volume.slices"
_path_str ... |
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