sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
503e2a53164d8e740a806f283c6ded5ac17e0784a94cb185e57b1043a9883fb2 | Python | 4,488 | 141 | # --- 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 Bar(_BaseTraceHierarchyType):
_parent_path_str = "indicator.gauge"
_path_... |
67422d5a6db19c6537f2e459503cbe87fac3ae29c39d24925a5b8874930b8e7b | 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 Z(_BaseTraceHierarchyType):
_parent_path_str = "volume.slices"
_path_str ... |
bc3f87f3549a82586c864ca67d3b613b5245edbe3ac4c7ccb15094848dfc64e1 | Python | 4,488 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "box.unselected"
_pat... |
ef5179b6d08f2070715e4a68b6425230217da119aebc3ebcb3ce54890007e747 | 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 X(_BaseTraceHierarchyType):
_parent_path_str = "volume.slices"
_path_str ... |
88ed98e293bc82a1d043bd54fbb391f9ee1d48c582607f7c4e33b5198b6b6c52 | Python | 4,489 | 129 | """Prepare your dataset in ImageRecord format
============================
Raw images are natural data format for computer vision tasks.
However, when loading data from image files for training, disk IO might be a bottleneck.
For instance, when training a ResNet50 model with ImageNet on an AWS p3.16xlarge instance,
T... |
c1775b57fe851c322ba57eda23fe88eca4301b3cb630bf3d16f865bab5126c48 | Python | 4,490 | 117 | from typing import Optional, Any, List
import numpy as np
import torch
import matplotlib.pyplot as plt
from scipy.signal import find_peaks, peak_widths
from sklearn.preprocessing import MinMaxScaler
from rdkit import Chem
import networkx as nx
def plot_prediction(x_pred: torch.Tensor,
y_pred: torch... |
d5ceab4aceaf1aa3c19f04ac84c4ddb4fe936db9d5fc50295b9c996465817ade | Python | 4,490 | 126 | import os
import pytest
from mirp._images.pet_image import PETImage
from mirp._masks.base_mask import BaseMask
from mirp.extract_features_and_images import extract_features_and_images, extract_images
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
def test_basic_pet_feature_extraction():
data = extrac... |
541f02dbd48fd4eced221ad305f189b41fd1e4f12ceb84ede182618ebbfd58d6 | Python | 4,491 | 94 | from __future__ import division
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
class LossFunction(object):
""" Set the loss function for the :class:`~psychrnn.backend.rnn.RNN` model.
Arguments:
params(dict): Dictionary of parameters including the following keys:
:Diction... |
8af5939d6dd71c45ab5989277fd83e4ee5c55647a634ca34a2a76cfbf8548cea | Python | 4,491 | 155 | """
> :warning: **DEPRECATED**: This will be removed in version 1.0.0. Check [nwbmatic](https://github.com/pynapple-org/nwbmatic) or [neuroconv](https://github.com/catalystneuro/neuroconv) instead.
Class and functions for loading data processed with Phy2
@author: Sara Mahallati, Guillaume Viejo
"""
import importlib
... |
509058ec5e2ea031ae0d524ad1c5b5260f17c11cac402e5d3a60d796caa1cf8b | Python | 4,492 | 177 | #!/usr/bin/env python3
"""
Generate a closed triangulated cuboid surface mesh in OFF format.
Last updated: 09.04.2026
Author: Laura Keto
Description
-----------
This script generates a watertight triangulated surface mesh for a cuboid
aligned with the coordinate axes and writes the result in OFF format.
The cuboid i... |
af97125720cb27cd22af71dcbbd460930f7885ef5855131893b220d8d9cbd38e | Python | 4,492 | 124 | import pandas as pd
import gzip
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Bio import SeqIO
def match_rna2protein_ID(prefix):
mRNA = set()
cds = {}
protein2rna = {}
with gzip.open(prefix+"feature_table.txt.gz",'rt') as inFile:
lines = inFile.readlines()
... |
df33fca6086cca695ae3a96fe649c1a07d4cafadb42759be3a75ab4414084141 | Python | 4,492 | 103 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from eelbrain import Factor, datasets, epoch_impulse_predictor
from eelbrain._trf.shared import DeconvolutionData
def test_deconvolution_data_continuous():
ds = datasets._get_con... |
e27813c14e99f7c3c7e6a3d3e712d87eaa7cfc8b9420fd65ddd6795ddaea4df6 | Python | 4,492 | 121 | from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
from pyvista import DataSet, PolyData, UnstructuredGrid
from sklearn.neighbors import KernelDensity
from spateo.logging import logger_manager as lm
from spateo.tdr.models import add_model_labels
def model_morphology(
model: Union[PolyData, ... |
003d3960ffa854e94f382e8d3d70c0ed48864f38217cf80648f34e74b3d7de22 | Python | 4,493 | 144 | import numpy as np
import tensorflow as tf
import random as python_random
import os
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator.GAModelWrapper import GAModelWrapper
from gradient_accumulator.GAModelWrapperV2 import GAModelWrapperV2
from tensorflow.keras im... |
0ec25da1f9b0f3b1b1c56e535aa264fde674337cacb76dadb2a9a51fa57c024c | Python | 4,493 | 86 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
55e0be2a9f9f797e1e5bc08f69f0049d564550696b2af82a6c254e9c21604d28 | Python | 4,493 | 127 | from ._RL_agent import *
from numba import njit
@njit
def latent_state_session_value(choices, second_steps, outcomes, n_trials, transition_type, params):
# Unpack parameters.
p_r, weight_inf = params[:2]
good_prob = 0.8
V_s = np.zeros((n_trials, 2)) # Second step TD values.
p_o_1 = np.array([[good_prob, ... |
afd370d98d9384738fb7ec7ad7a376d9c03d9d2e293a02a0dcdd19cb6428ebe3 | Python | 4,493 | 115 | """
help function for experiment.
"""
import time
import subprocess
from argparse import ArgumentParser
VIDEO_LENGTH = ['1']
VIDEO_FRAME = ['8']
MAIN_FILE_PATH = '/workspace/Walk_Video_PyTorch/project/main.py'
def get_parameters():
'''
The parameters for the model training, can be called out via the --h me... |
d763e69d5a6e12582c86f5a0ea11538d889528c46ac2d1b7272b07f3c7098c38 | Python | 4,494 | 139 | """
Plot best-fitting model orders for behavioral history bias
"""
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pylab as pl
import seaborn as sns
matplotlib.rcParams["pdf.fonttype"] = 42
sns.set(
style="ticks",
font="Helvetica",
font_scale=1,
rc={
... |
9d9a7c66b3e6abfe7bdb33e46ade2379489582691886c1f05127ce5dbaec0a04 | Python | 4,495 | 106 | import torch
import numpy as np
import os
from lammps_REANN.density import *
from src.MODEL import *
class PES(torch.nn.Module):
def __init__(self,nlinked=1):
super(PES, self).__init__()
#========================set the global variable for using the exec=================
global nblock, nl, ... |
e7d8eb9f5ac66d859cef53298c3a255ae5852d0145057b1a63a5fdd1ea8f2274 | Python | 4,495 | 153 | from __future__ import print_function
from __future__ import division
import os
import mxnet as mx
import numpy as np
import gluoncv as gcv
from gluoncv import data
from .tiny_datasets import COCODetectionTiny, COCOInstanceTiny, VOCDetectionTiny, VOCSegmentationTiny
import os.path as osp
import unittest
def test_pa... |
0fad8592f917a26a67c7d1044e5c9d7aeeefb96bc1b6a06c2ce1c39e85eb72c1 | Python | 4,496 | 141 | from typing import Tuple
import numpy as np
from CEST.src import matlab_style_functions
# Water saturation shift referencing
class WASSR:
# Initialize the class with the maximum shift, ppm range, and initial values for the offset map and mask
def __init__(self, max_offset: float, ppm: float):
self.m... |
190290dcf0e2ac40ef3d722a8a763b1e37d7bc968a08c5b43bb80f7c556fcbca | Python | 4,496 | 116 | import pandas as pd
import numpy as np
import random
from collections import defaultdict
from scipy.stats import spearmanr
from scipy.cluster import hierarchy
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, RobustScaler
fro... |
9617bd94d9a3c39c8a4935f58f0af0c69fbd173994b9366d1481ccd81253fe27 | Python | 4,496 | 113 | """Episode join honesty surfaces (lane F40c): per-artifact claims only.
The F40a claim-off switch flips WITH the measurement behind it, and every
surface keys on THIS artifact's envelope: an absent envelope reads
unmeasured, a measured break renders its break fact, and the build-level
token never upgrades an artifact'... |
a26e0c66725e9cbd829dba53f1635053db3751c5170e038f3cb10d534d9e532b | Python | 4,496 | 129 | # 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... |
bd95d169d822eb483b4850fa83ffd0c8dccd0e05832fd85a98d56a1cbe00f5d4 | Python | 4,496 | 118 | # -------------------
# take an input file of (TCR, HLA) pairs and make prediction
# -------------------
import os
import sys
import pandas as pd
import numpy as np
import tensorflow as tf
import pkg_resources
#from importlib import resources
import argparse
from DePTH import _utils
def predict(test_file, hla_cl... |
bf7b01bb8ca688bf14c0baea6550e8df879e1e4bfc8f67be2e1389c73ada5862 | Python | 4,496 | 138 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: pramod
"""
import sys
import argparse
import torch
from torch import nn
import numpy as np
import pandas as pd
import sklearn.metrics as skm
from sklearn import preprocessing
from torch.utils.data import TensorDataset, DataLoader
from DeepGamiModel import Dee... |
f872a865b5a7f45f86a1888ab630a5a45d905f9a2cafeb612d1f932b7a2d04c9 | Python | 4,496 | 152 | # von Facbook Code übernommen
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import random
from logging import getLogger
import numpy as np
from skimage.util import r... |
43ece34469fb6757d229dad3197e53e567940912adb9daa48794f1137a3d002a | Python | 4,497 | 103 | import os
import sys
import click
from src.utils import load_classes, sample_grid
import src.phenexplain as phenexplain
@click.command()
@click.option('-m', '--method', default='3', show_default=True,
type=click.Choice(['1', '2', '3']),
help="Method for the interpolation. See paper for the... |
5b03e0142da6a802ba0ed630aca5227ef5a8867e73a7956a66b83340574e7feb | Python | 4,497 | 107 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
import torch
import torch.nn as nn
from voxelpose_lib.core.pro... |
88e064b560c1686ce5ff86c4cb41d3b5c1c3bf246fdb23bcc2b0094b836bbacd | Python | 4,497 | 150 | # 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... |
b6c22dd0278760b224ef8c4887f2c0a99570ff6be9d2baf2589943c61d8aee7c | Python | 4,497 | 134 | import os
import numpy as np
import pickle as pkl
import nibabel as nib
from os.path import join as pjoin
from scipy.spatial.distance import cdist
from magicbox.io.io import CiftiReader
from cxy_visual_dev.lib.predefine import proj_dir, Atlas,\
get_rois, LR_count_32k
anal_dir = pjoin(proj_dir, 'analysis')
work_dir... |
154045d224a9bd8be16c01a6c539e514ec4e025681766a8bff89612417a49fef | Python | 4,498 | 94 | # Copyright The PyTorch Lightning 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-2.0
#
# Unless required by applicable law or agreed to i... |
e98b6230cf0320543978481e70f9beb84ef25f6763cc7a80d67f663292a161c7 | Python | 4,498 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "splom.unselected"
_p... |
062e1ac7b69b9f99939db1197f25e7aea8ee8eba746f0d323363539d9c337cdc | Python | 4,499 | 97 | """
The learning rate scheduler.
This implementation is adapted from
https://github.com/chemprop/chemprop/blob/master/chemprop/nn_utils.py
"""
from typing import List, Union
import numpy as np
from torch.optim.lr_scheduler import _LRScheduler
class NoamLR(_LRScheduler):
"""
Noam learning rate scheduler with ... |
2c30ef1633f3af967a34e14a498a97459cf852071d0a3e707b0404dd7eb56c0d | Python | 4,499 | 97 | import sys
sys.path.append('..')
from src.utils import set_random_seed
import argparse
import torch
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.nn import MSELoss, BCEWithLogitsLoss, CrossEntropyLoss
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data.distributed ... |
c7412520921ce7a28b77ce8ec2e84ddd5006b8b8095891ba9a8ca9442c696b43 | Python | 4,499 | 126 | import os
import torch
from rdkit import Chem
from tqdm import tqdm
import seaborn as sns; sns.set(color_codes=True)
import sys
sys.setrecursionlimit(50000)
import pickle
torch.nn.Module.dump_patches = True
import pandas as pd
#then import my own modules
from AttentiveFP import save_smiles_dicts, get_s... |
ea20d69e32183ee3b43e1cb145e2f3b9cac8903bf77d867e0c1c8f0e9285e3eb | Python | 4,500 | 158 | #!/usr/bin/env python
# encoding: utf-8
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import os, sys, re
import logging
import argparse
import collections
import numpy
import time
import TNode
logger = logging.getLogger(__name__)
class TGraph:
... |
29417c02ae416ae78d4b5bdbbd9e84874aacb46a1cec2ff1751f735386934fe5 | Python | 4,501 | 95 | #!/usr/bin/env python
# Takes a .xml formatted blast results file as input and prints the query and hit ids
# for sequences passing the thresholds passed via the command line arguments. For sequences
# with no hits below the thresholds, the program returns "no hits below threshold" rather
# than the hit id.
import ge... |
54709037b9bf66c5b0c7276735837ae4f87b0de1388f5c03613e8b1398140228 | Python | 4,501 | 126 | #!/usr/bin/python
import itertools
import time
import eego_sdk
###############################################################################
def amplifier_to_id(amplifier):
return '{}-{:06d}-{}'.format(amplifier.getType(), amplifier.getFirmwareVersion(), amplifier.getSerialNumber())
##############################... |
79c53ba2a09beba38667320433d30ff1c0171dc4651a912e2cb118c068e888c0 | Python | 4,501 | 121 | import unittest
from numpy import hstack, max, abs, ones, zeros, sum, sqrt
from cantera import Solution, one_atm, gas_constant
import numpy as np
from spitfire import ChemicalMechanismSpec
from os.path import join, abspath
from subprocess import getoutput
test_mech_directory = abspath(join('tests', 'test_mechanisms', ... |
9f0155096dab5cf6311d30c7bcc1af6a3f18f945501482cd1d20359020c37233 | Python | 4,501 | 106 | import torch
import numpy as np
import os
from lammps.density import *
from src.MODEL import *
class PES(torch.nn.Module):
def __init__(self,nlinked=1):
super(PES, self).__init__()
#========================set the global variable for using the exec=================
global nblock, nl, dropou... |
d9d528e20ac9ad456c947f4ce38e4eb281b79c508c8ef85e34218a9a110b1b11 | Python | 4,501 | 172 | """Smoke tests for receptive-field public data types."""
from __future__ import annotations
from dataclasses import FrozenInstanceError
from fractions import Fraction
import pytest
from torchlens.receptive_field import (
GridLayout,
ReceptiveField,
ReceptiveFieldAlignment,
ReceptiveFieldAxis,
Re... |
d9085161edef23a274512af1a538c8d0cfb5896595d519e5c1eafa3fbda93f3d | Python | 4,503 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "violin.unselected"
_... |
2ad138ebe56747c037a909c10efb23bc54dbe94d2c83b9d1180a8d63c2dfda18 | Python | 4,504 | 116 | """r55 corr_1: an UNDRAWN ``random.SystemRandom`` must not ceiling a capture.
``random.SystemRandom`` is a ``random.Random`` subclass whose ``getstate()``
raises ``NotImplementedError`` BY DESIGN (stateless OS-entropy engine). The r53
inventory digested every ``random.Random`` and routed that exception through the
gen... |
bcd18e4a1722d907c73f76229f27c15bddf7adb9e55b7f8875e6f981f681c4fe | Python | 4,504 | 112 | import sys
sys.path.append("../")
import scipy
import numpy as np
import os
from os.path import dirname, join
import random
import h5py
import tensorflow
import matplotlib.pyplot as plt
from matplotlib import cm
import nibabel as nib
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import cv2
import ... |
b1d1dd2f3a73ada7484c90c5ea3b37cf033679d0e17839318ac8ca7989cea0d2 | Python | 4,505 | 104 | """SQLAlchemy models for telemetry data in the CarbonServer API."""
import uuid
from sqlalchemy import JSON, Boolean, Column, DateTime, Float, Integer, String
from sqlalchemy.dialects.postgresql import UUID
from carbonserver.database.database import Base
class Telemetry(Base):
__tablename__ = "telemetry"
... |
989d3c297439fdf66976af520b0121b45f41d8206d9798639236cf6b88abe3c6 | Python | 4,506 | 139 | #!/usr/bin/env python3
import os
f_1 = open("Candidate_probes_annotated.txt", "r", encoding = "utf-8")
f_2 = open("isoform_candidate.txt", "r", encoding = "utf-8")
o_1 = open("tmp.txt", "w", encoding = "utf-8")
i = 0
meth_list = []
meth_index_dict = {}
for file_1 in f_1:
line_1 = file_1.strip().split('\t')
... |
14ecd4e58b068a4c559997de99e7a9f90932ca65599f1d15d9b11bcb2eb6aaad | Python | 4,508 | 124 | # 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... |
157c5ae8802e506722a25bfa457c9ef0b69999239880a94786b2915e2b57743f | Python | 4,508 | 127 | """RDM widening, rdm_compare, and CKA placement oracles (F20 D-5/D-6/D-25).
D-5: the existing ``rdm`` name widens with keyword-only options; the
no-new-keyword call stays bit-identical. D-6: ``rdm_compare`` is the
descriptive rank correlation over aligned upper triangles, diagonal
excluded, with the silent hand-rolled... |
33c2694fcb086cb4c7838223085419812beb953ffe04638fca60257c3377931d | Python | 4,508 | 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 X(_BaseTraceHierarchyType):
_parent_path_str = "isosurface.slices"
_path_... |
444de5fdd616daa3eaf0beafc6e432fa8a6e04952c49ed37d5eb9a9f4ef0fca3 | Python | 4,508 | 81 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
727c19df501b0eab3b7e76e1de1ae8b9d203b4868a35422c0387704a7d3dd3bb | Python | 4,508 | 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 = "isosurface.slices"
_path_... |
cdf02b89d00b8fa0b8d1dd6e947fdecf43a49e5525327ece6776fabd31774374 | Python | 4,508 | 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 Z(_BaseTraceHierarchyType):
_parent_path_str = "isosurface.slices"
_path_... |
fdee0b2d14f6b05c4d15ac1398986854ee84308b01d1fd7ada6125e7622fd345 | Python | 4,508 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "scatter.unselected"
... |
a0c34e7b1f61d8555e883e85f5cf96142887911a4212f596b2d50de47c17f841 | Python | 4,509 | 144 | """
This module the language modeling dataset
"""
import logging
import torch
import numpy as np
from datasets_pre_processing.generic_dataset import GenericDataset
class LanguageModelingDatasetWeightMask(GenericDataset):
"""
class that define language modeling dataset
"""
def __init__(
self,
... |
a9c4c0901c967d76f96c92608031893ed65a8016664af5387b78f747bf3846cc | Python | 4,509 | 123 | from urllib.request import urlopen
import torch
from torch import nn
import numpy as np
from skimage.morphology import label
import os
from Phys_Seg.paths import folder_with_parameter_files
def get_params_fname(sequence, physics_flag):
if physics_flag:
model_name = f"{sequence}_phys_NSS.pth"
else:
... |
2eccc70a7d9ea1e409e5e356ce07ab2f40ec83ea177534c74ee95774be684095 | Python | 4,510 | 116 | from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.metrics import precision_score, recall_score, f1_score
import numpy as np
import torch
import pandas as pd
from tqdm import tqdm
import sys
sys.path.append("models_architectures/")
#from models_architectures.ensembling_model import EnsemblingModel
de... |
7a6e56ef79be0e37b75f5977bca6afa64162d5855e5fa0e58f9ad98b3f246d80 | Python | 4,510 | 136 | import threading
from contextlib import contextmanager
from unittest.mock import patch
import pytest
import celery.contrib.testing.worker as contrib_embed_worker
from celery.app import backends
from celery.backends.cache import CacheBackend
from celery.exceptions import ImproperlyConfigured
from celery.utils.nodename... |
741a3155b09b2c8c77c3fc3bcc81c72059fd53abab8494a81a204f389c09631f | Python | 4,511 | 72 | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), e... |
ec8aac21c0634b2150602b64f99c51bf01b3154ab6c21a4d1f1a2c0c13d513ee | Python | 4,511 | 134 | """r-b4 R27-4: model-OUTPUT container walkers are depth-bounded and cycle-guarded.
A 3000-deep or self-referential forward output used to die mid-capture in a raw
``RecursionError`` (probe: 994 torchlens frames) from the unguarded output
walkers (``_build_container_spec``, ``_walk_tensor_occurrences``,
``_walk_support... |
dd913c630ad164daa968d3c418b6e15069252d1cf4e6e4283e85408aa1c71940 | Python | 4,512 | 109 | import sys
import os
import pandas as pd
import numpy as np
from nilearn import image
from nilearn.masking import apply_mask
# input/output paths
fmri_prep = sys.argv[1]
ROIs_dir = sys.argv[2]
first_input = sys.argv[3] # contrast files location
roi_out = sys.argv[4] # fixed effect output location
# List of Subjects ... |
2f617bd7fef8012fbbbb951004de397a40b0106a2ab3af28de0bc14645b255d1 | Python | 4,513 | 116 | import numpy as np
import math
import pdb
import re
# ------------------------------------------------------
# Neuronstudio check
def neuronstudio_check(swc_matrix):
# for i in commentline_numbers:
# reurn_value = Lines[i].lower().find("neuronstudio")
# if(return_value!=-1):
# bifurc_c... |
d341b4aafbd0d2a28b1f2c1047b4fe9ec29b6a91c9f53f1b0bc6c312cd88634b | Python | 4,513 | 127 | from __future__ import annotations
import logging
import socket
import time
import uuid
from collections.abc import Iterable
from typing import Any, Optional
import bream4
import ont_certifi
import requests
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
from bream4.toolkit.pings.ping_dum... |
8fe2b818386cb38a187024cc7df006833675acb08dcf10621a723bfa1c550fb0 | Python | 4,515 | 122 | import unittest
from numpy import hstack, max, abs, sqrt
from cantera import Solution, gas_constant
import numpy as np
from spitfire import ChemicalMechanismSpec
from os.path import join, abspath
from subprocess import getoutput
test_mech_directory = abspath(join('tests', 'test_mechanisms', 'old_xmls'))
mechs = [x.rep... |
c2ae906a376e5f366201836fd2dc5fc16ae200540efb245d03cd873cc0f20221 | Python | 4,515 | 139 | """Written by @Jinerhal, adapted by @Xiaojieqiu.
"""
from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
from anndata import AnnData
from ..configuration import SKM
from ..logging import logger_manager as lm
from .contour import *
from .utils_old import *
def identify_boundary(
adata:... |
02d99004dc8fb799b466c8957479955c75953bdb9d869b723b243e40f570ad49 | Python | 4,516 | 104 | """
These variables are reused across various plots for the Feather et al. 2023 Metamers paper.
"""
import numpy as np
## Audio model info
audio_models_to_plot = ["kell2018","cochresnet50",
"cochresnet50_l2_1_robust_waveform", "cochresnet50_l2_p5_robust_waveform", "cochresnet50_linf_p002_robust_... |
8e6ca85b1b6052120c0d6267caa823e3c2d3403c1f0e7a027a1a18cb4cd90b9e | Python | 4,516 | 129 | from minknow_api.acquisition_service import MuxScanMetadata
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
COLOURS = {
"single_pore": "00CC00",
"reserved_pore": "EDE797",
"multiple": "F57E20",
"saturated": "333333",
"other": "0084A9",
"zero": "90C6E7",
"unavai... |
13a8e31e8ab2c208a92430824d28c9dbe4bd5ce235dcd89c5a274ee0f98ef1e2 | Python | 4,517 | 116 | import spikeinterface.full as si
import numpy as np
from pathlib import Path
from spikeinterface.preprocessing import highpass_spatial_filter
from spikeinterface.sortingcomponents.motion_interpolation import interpolate_motion
from spikeinterface.preprocessing.motion import load_motion_info
from spikeinterface.preproce... |
149b8cda7fcbbc2afd325ed237373034c77b920c27d85af569f7f5389be9a593 | Python | 4,517 | 135 | from pathlib import Path, PosixPath
import dill
import json
import scipy.sparse
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import FunctionTransformer
from sklearn.pipeline import Pipeline
from omop_learn.sparse.data import OMOPDatasetWindowed
def _sparse_ufunc(... |
665b6b4a21a19670f0468fd3cd93f98d9d9eab6b1e6893d2b791336b2072c2ff | Python | 4,517 | 86 | from pycobi import ODESystem
import numpy as np
import matplotlib.pyplot as plt
from pyrates import CircuitTemplate
# initialize mean-field model
ik_mf = CircuitTemplate.from_yaml("ik/ik_mf")
# define the model parameters
var_values = {"C": 100.0, "k": 0.7, "v_r": -60.0, "v_t": -40.0, "b": -2.0, "g": 15.0, "tau_u": 3... |
bcf93e92fc72ab9e909de5cf46d69a164120f007433d67a6708cb32dd9b1e0b6 | Python | 4,517 | 90 | import argparse
import os
import random
import time
class Option(object):
def __init__(self, d):
self.__dict__ = d
parser = argparse.ArgumentParser()
parser.add_argument('--parser_name', type=str, default="dti")
parser.add_argument('--cuda', type=bool, default=True)
parser.add_argument('--model', type=st... |
cf32b15d80c414ac7738757e73cf84b6c9e2f1d92cf23a8576d7d79f4962cade | Python | 4,517 | 117 | """ Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm import is_fast_norm, fast_group_norm, fast... |
0428161f2f4dc181a788d157169932b1ef5f224383bdf65001cf69c370dc1c1c | Python | 4,518 | 144 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
import torch.nn as nn
import torch.nn.functional as F
class B... |
2d83f3e0058a6df02006546d62af942fd78717a78f98ff95728e61a41bf12f0c | Python | 4,518 | 121 | # pylint: disable=arguments-differ
"""Matchers for target assignment.
Matchers are commonly used in object-detection for anchor-groundtruth matching.
The matching process is a prerequisite to training target assignment.
Matching is usually not required during testing.
"""
from __future__ import absolute_import
from mxn... |
2ee98f24448cccb1831515ed80bbea8676cb789c1e399d2e824894c9f2424288 | Python | 4,518 | 85 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
8555f4fcda1514b1d70372364a97caadcefbba142b9af8216ded25db6c797113 | Python | 4,518 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "scattergl.unselected"
... |
ddcdac0b66ef3be95ab40acb1b986ceb8316b4403ef3f1cd9fbadd44b6639650 | Python | 4,518 | 94 | import os
import pandas as pd
from utils import parse_config, build_EDA_file_list
if __name__ == "__main__":
args, unknown_args = parse_config()
test_file = os.path.join(args.data_dir,args.test_prefix+'.csv')
train_file = os.path.join(args.data_dir,args.train_prefix+'.csv')
val_file = os.path.joi... |
7967131a52a4763dd2f200fbd5a7ac5553bd7d873d2ef8aa74a5f5308963bdc4 | Python | 4,519 | 79 | """Utils for auto YOLO estimator"""
import os
from mxnet import gluon
from ....data import MixupDetection
from ....data.batchify import Tuple, Stack, Pad
from ....data.dataloader import RandomTransformDataLoader
from ....data.transforms.presets.yolo import YOLO3DefaultTrainTransform
from ....data.transforms.presets.y... |
7a5003ea39d639ffae5dc2caf9b53dd6db1ee172cc7a5006a0fc4646411305e5 | Python | 4,519 | 138 | """auditory tone detection task."""
import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
class ToneDetection(TrialEnv):
"""A subject is asked to report whether a pure tone is embeddied within a background noise.
If yes, should indicate the position of the tone. The tone l... |
ddf0c3c6327e2afe486e64687eb6d9cef7eb32a9ca9445354573354016321d3d | Python | 4,519 | 123 | import numpy as np
from numpy import array
import random
import copy
from gym import spaces
class IntervalDiscrimination(object):
'''
Interval discrimination, head-fixed agent. After an initiation cue, the agent pokes to initiate the task.
Then two stimulus are shown sequentially, seperated by a delay per... |
707deef4d0e9710410ca1857f5b331cef9a99d5e7bf96194f35a9781a14fc9b1 | Python | 4,520 | 108 | # Copyright (c) 2025, Johnsonms.
# We recommend locking GPU clocks before running the benchmark to ensure consistent results.
# This can be done using the following commands (2619 MHz is the max clock for B200):
# sudo nvidia-smi -i 0 -pm 1
# sudo nvidia-smi -i 0 --lock-gpu-clocks 2619,2619
# See more here: https://gi... |
3cbdcd27e90f4c2e38c29f7bd867976c0a41015988d4c0afaffba80178cfdfa3 | Python | 4,521 | 85 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
aecca0c4abe5bdbd4c159e5c373ca9666c77229d2ca2e42337db541c517ddcc7 | Python | 4,521 | 116 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
import pytest
from eelbrain import datasets, Celltable, testnd
from eelbrain._stats import t_contrast
from eelbrain._stats.stats import t_1samp
from eelbrain._stats.t_contrast import T... |
18b414720146de28f9e2e5cdb509f4f808bb530bec6866e9347cee36c98aa22e | Python | 4,523 | 143 | import numpy as np
by_strand = {
"+": [
(b"N", b"\x00"),
(b"A", b"\x01"),
(b"C", b"\x02"),
(b"G", b"\x03"),
(b"T", b"\x04"),
],
"-": {
(b"N", b"\x00"),
(b"T", b"\x01"),
(b"G", b"\x02"),
(b"C", b"\x03"),
(b"A", b"\x04"),
},
... |
7181211cddd7f5b82ead38c0cf30d118f24c33aee4b221f2af6c7e68629244d0 | Python | 4,523 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "scattermap.unselected"
... |
c62aaa1a9a5e4b44fa12dc2cd9207e8d32711eeeca7058b79487532b1848bf23 | Python | 4,523 | 142 | # --- 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 Marker(_BaseTraceHierarchyType):
_parent_path_str = "scattergeo.unselected"
... |
e0b34ec163b98129a1c35b38717d5c93fd17ec68b541eee27d9f26ad497cacaa | Python | 4,523 | 93 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
f4be9ba32329036649d4169cb5443cc955faa5444de7149dc9379703836e2e20 | Python | 4,524 | 123 | # Given a list of MsCAVIAR files for a locus, reconcile them so that they
# contain the same set of SNPs
import argparse
import subprocess
import sys
def reconcile_parseargs(): # handle user arguments
parser = argparse.ArgumentParser(description="Given a list of MsCAVIAR " +
" LD files for a locus, take out th... |
68818c2c02cafddf2ab3f972cbc4bbe780587bca16cc1b44aae9173377535098 | Python | 4,526 | 150 | """3. Estimate pose from your webcam
====================================
This article will demonstrate how to estimate people's pose from your webcam video stream.
First, import the necessary modules.
.. code-block:: python
from __future__ import division
import argparse, time, logging, os, math, tqdm, cv... |
81f75fd75da9e87cfdf91c0074c9d0ecd151a814282d7482c487f2ce77f7296e | Python | 4,526 | 141 | import numpy as np
import os
from custom_data_gen import DataGenerator
from openpyxl import Workbook
import json
from matplotlib import pyplot as plt
import tensorflow as tf
from data_processing.genSplits import create_partitions
keras = tf.keras
from keras.layers import *
from keras.callbacks import EarlyStopping, Mod... |
d03b29b9dda0839bab2f4eb6591702b7d6c0b220b1bc2bb31765cf21d2fb2de8 | Python | 4,526 | 97 | import pandas as pd
import os
import numpy as np
import os
import shutil
AGE_FROM = 3
AGE_TO = 35+1
path = "/Volumes/kannlab_sfa$/Anna/TM_segmentation/data/pseudolabels_mris"
def find_file_in_path(name, path):
result = []
result = list(filter(lambda x:name in x, path))
if len(result) != 0:
return ... |
6dfbc924a0717ef47ef4b3bc6e6f9053b626c06d3e32fa967896fdc09b142791 | Python | 4,527 | 250 | #!/usr/bin/env python
'''Generate models for affinity predictions'''
# variables:
# kernel size: 3, 5, 7
# depth: 2, 3, 4
# width: 16, 32, 64, 128
# doubling of width: true/false (e.g, 128->256->512)
modelstart = '''layer {
name: "data"
type: "MolGridData"
top: "data"
top: "label"
top: "affinity"
includ... |
46dfbf1aee6f7b85353b9223b379841be4bbb512c0c638c604aa4acc3ea51b5a | Python | 4,528 | 81 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
d5401fcfac4a78580cce4f5a6e669c42c8c14570767edad1f98a5e70a90aae88 | Python | 4,528 | 93 | import sys
import os
# setting path
sys.path.append('../TM2_segmentation')
import numpy as np
import pandas as pd
import nibabel as nib
from skimage.transform import resize, rescale
from scipy.ndimage import zoom
from settings import target_size_unet, scaling_factor
import matplotlib.pyplot as plt
from scripts.prep... |
75658d710a92ae316885ce030da3719c3abae1ec61b4f570c04d2ba722a0007b | Python | 4,529 | 114 | """Grouped backward attribution floor pins (L9 memo 1.1).
Read-only per-site rollups over BACKWARD records keyed on the merged L1
site_key surface: per-site GradFn/GradFnCall aggregation (fire counts,
per-fire timing once memo 1.3 evidence exists, pass coverage). Accessor-level
only -- no persisted fields, no writes t... |
781d7370f89cabddcd6df83c501e1d0833f3f599eefa939d4ce8a7a85f98a617 | Python | 4,529 | 137 | import csv
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import torch
import os
class RunningMean:
def __init__(self):
self.mean = 0.0
self.n = 0
def __iadd__(self, value):
self.mean = (float(value) + self.mean * self.n)/(self.n + 1)
se... |
27d412cb659df84472f7eeb7828ca44a7ea4f910d097e8dae3d72bd609793f8d | Python | 4,530 | 99 | import glob
import h5py
import numpy as np
import os
import pandas as pd
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import GroupKFold
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
import sys
from scipy.stats import pearsonr
"""
This script will run ridge... |
293171b3e4470009ba6247ca982fef4d0bfd5a0c53a606469373c30966d655b1 | Python | 4,530 | 161 | import numpy as np
import pandas as pd
import networkx as nx
from .global_level import global_level
from .local_level import local_level
from .utils import lambda_dist, resistance_distance
from tqdm import tqdm
def weight_optimizing_global(
adata,
use_label=None,
list_clusters=None,
step=0.01,
k=1... |
7f62d06d8b3e75d5b6a9d570b9e5bd87fe80dd9e938375ddcba7d86baa7a4a71 | Python | 4,530 | 133 | """CodeCarbon CLI - Monitor Command"""
import os
import subprocess
import sys
from typing import Optional
import typer
from rich import print
from typing_extensions import Annotated
def run_and_monitor(
ctx: typer.Context,
log_level: Annotated[
Optional[str],
typer.Option(help="Log level (cr... |
8f5d5a3822758688908348bcbea0bf70ec105d349f4b8ce6e111a273a0ab736f | Python | 4,531 | 122 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Test classes in the ``tfep.app.cartesianmaf.py`` module.
"""
# ===============================================... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.