sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
36945cd16e3564396bd37f48343d949516e4505adc3977267714de744ac775dc | Python | 5,035 | 122 | from treelib import Node, Tree
import csv
import logging
class Hierarchy(Tree):
def __init__(self):
Tree.__init__(self)
@staticmethod
def pair2id(level, tag):
return '%03d:%s' % (level, tag)
@staticmethod
def id2pair(identifier):
return int(identifier[0:3]), ident... |
d0b60397235e80b284209bfa567f444aec38d303e7ef07d12f86704e43abbf2a | Python | 5,035 | 163 | #!/usr/bin/env python
"""
A TCP communication class that acts as the client side for generic communications
between programs in the storm-control project
Jeffrey Moffitt
3/8/14
jeffmoffitt@gmail.com
"""
#
# Import
#
import sys
import time
from PyQt5 import QtCore, QtGui, QtNetwork, QtWidgets
from storm_control.sc_... |
5cb10116ef0b83482b468140c725b7ee84ed4060929e3ab53dac9ac4933106bb | Python | 5,036 | 173 | import numpy as np
import pytest
import torch
from eir.data_load.data_preparation_modules.prepare_array import (
ArrayNormalizationStats,
prepare_array_data,
)
@pytest.mark.parametrize(
"array_data, expected_result",
[
(np.array([1, 2, 3]), torch.tensor([[[1.0, 2.0, 3.0]]], dtype=torch.float3... |
498c62b83526b8eda83e401c1b08556109a60bf5dd5f5ad46e76d9549838dcf4 | Python | 5,037 | 159 | """
models/mlp_model.py
多标签手术指征 MLP（支持残差块 + 可选 Classifier-Chain Head）
• 将所有 ReLU 的 inplace=False，避免 SHAP DeepExplainer 报错
• 主干网络：Linear → BatchNorm → ReLU → Dropout × L + ResidualFC × N
• 输出头：普通线性或 Classifier-Chain Head (--use_cc)
"""
from __future__ import annotations
from typing import Sequence, Tuple, List, ... |
6297832fd1d0c5d24342b5c498233d2490a5b01cbcacc295c14db00a5aee7863 | Python | 5,037 | 142 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This Python script is used to extract the timeseries from functional
images preprocessed by fmriprep.
This pipeline will generate timeseries files, framewise displacement record
and the labels of the used Atlas.
Schaefer 400 Atlas is used by default.
Note!!!!!!!!!... |
3eb8dfb457a368a2cdb55ae9b94af67d24e08c4cf9e4ba3222c22deaad75abad | Python | 5,038 | 142 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This Python script is used to extract the timeseries from functional
images preprocessed by fmriprep.
This pipeline will generate timeseries files, framewise displacement record
and the labels of the used Atlas.
Schaefer 400 Atlas is used by default.
Note!!!!!!!!!... |
ce22d7752be58fefdccd953d4f330822a588427fb5170fe99f852b1b9b113583 | Python | 5,041 | 151 | from typing import List
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.loss import _Loss
import numpy as np
from .functional import soft_dice_score
__all__ = ["DiceLoss"]
BINARY_MODE = "binary"
MULTICLASS_MODE = "multiclass"
MULTILABEL_MODE = "multilabel"
def to_tensor... |
6efa9376d2c33cdfe02ae882cb524eeb909936d1ac0a3a4bd6f370459dc482f1 | Python | 5,047 | 118 | """Per-subject shared-pool preparation for the LAION-fMRI benchmark.
Replaces the earlier cross-subject `concat_shared.py` (which materialized a 5+ GB
NaN-padded matrix in RAM and OOM'd). For the Allen2022-style shared-pool benchmark
we don't need a single cross-subject assembly — `TrainTestNeuralBenchmark` with
`alph... |
769e299e4c8141ea336289f8ec1276e510f08bba684881622fc524a6c02ec8c0 | Python | 5,047 | 136 | # Copyright 2024 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
ba45a9eab16b18c6e39fc32da02bbbbe485f5cce8023e900b2231c9ffb0a3740 | Python | 5,047 | 135 | from typing import Dict, Tuple, Union, List
import torch
import torch.nn.functional as F
from torch import Tensor
@torch.jit.script
def binary_erosion(image: Tensor) -> Tensor:
kernel = _get_binary_kernel3d(3, image.device)
padding = _compute_zero_padding((3, 3, 3))
b, c, h, w, d = image.shape
# map... |
05ffeb8354790bf5b280938abd7da81c42f249bfbb14dda6cf34d824a742875e | Python | 5,048 | 159 | """
models/classification_model.py
多标签手术指征 MLP（支持残差块 + 可选 Classifier-Chain Head）
• 将所有 ReLU 的 inplace=False，避免 SHAP DeepExplainer 报错
• 主干网络：Linear → BatchNorm → ReLU → Dropout × L + ResidualFC × N
• 输出头：普通线性或 Classifier-Chain Head (--use_cc)
"""
from __future__ import annotations
from typing import Sequence, Tu... |
565c085a9950f8e8f0e25072e2f491b2649b5b7d1ac77078698c1168b571fba6 | Python | 5,049 | 160 | import argparse
import os
import os.path as osp
import time
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score
import pandas
import json
import torch_geometric.transforms as T
from torch_geometric.nn import ChebConv, GCNConv # noqa
from tqdm import tqdm
from sklearn.me... |
5bfb160334af07f31d77765aaf8c2013a5a5b0982f4bcb30d948cb2444bc8a20 | Python | 5,049 | 175 | """
This script plots the result of the cross-validation of the switch evidence
direction.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import os
import numpy as np
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
... |
c67cda8e6a25e6acb95d6905f640a82b429ac984d967b8eeede05bf0739bd610 | Python | 5,049 | 140 | """Trainer for model on msi task.
The scalar, image, and figures logged during training can be visualized in
tensorboard by running the following command:
$ python3 tensorboard --log_dir=logs/$run_number/tensorboard
"""
import logging
import numpy as np
import os
from python_utils.logging import scalar_writer as scal... |
ccdfcfc6aa817be937fcaf884f3ceaf7d06b230c026ba9006ef542ddf1e4d3da | Python | 5,049 | 94 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'camera-detached.ui'
#
# Created by: PyQt5 UI code generator 5.10.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.s... |
fe08e8f52173c37fad5c7be15c86e0a904c3247d4bb4ca1e68267fd316793ba3 | Python | 5,049 | 156 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 22 22:36:52 2017
@author: ajaver
"""
import pandas as pd
import numpy as np
import numba
import math
import os
extras_dir = os.path.join(os.path.dirname(__file__), 'extras')
def load_OW_eigen_projections():
eigen_projection_file = os.path.join... |
4ca54e4ba4ae02a88b049c576891763ebf67e3ec63f6b1fc4353fd284dde1dd1 | Python | 5,050 | 117 | import numpy as np
from scipy import stats
from utils import Recorder_select, Q_nak
from mitochondria_PDP1KD import Mito
import matplotlib.pyplot as plt
from figure_properties import *
from default_ca_conc import ca_freq_concs
dt = 0.1
seed = 11
np.random.seed(seed)
def generate_spiketrain(rate, duration): # durat... |
b88b430a6decc2537ee6cf45d16ceec5c86aabd58581ff6b9112013e99d0afde | Python | 5,050 | 93 | #!/usr/local/bin/python3
from pipeline import experiment, reso, meso, fuse, stack, pupil, treadmill, posture
from stimulus import stimulus
from stimline import tune
import time
import logging
import datajoint as dj
## database logging code
logging.basicConfig(level=logging.ERROR)
logging.getLogger('datajoint.conne... |
418ee1f438d083d53b2afd4fca60af7ad3bb79f02b4f635f30efd7f69fb9ab72 | Python | 5,051 | 127 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Utility functions for neural network modules."""
import copy
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
__all__ = "inverse_sigmoid", "multi_scale_deformable_attn_pytorch"
def _get_clones(module, n):
... |
c61d3e9e37ae3d944e77a4dcc26902eb07bc908ca2904a4bb3a69f905aa8e371 | Python | 5,051 | 153 | """Attention-based multiple instance learning implementation.
Modified from https://github.com/AMLab-Amsterdam/AttentionDeepMIL.
References
----------
Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based Deep Multiple
Instance Learning. arXiv preprint arXiv:1802.04712.
"""
from __future__ import annotatio... |
8cf2ae4b68193b9a86de8f203e437f021691eb2336e432e3c7f6b8a04ee591ef | Python | 5,052 | 141 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compute the mean Apparent Fiber Density (AFD) and mean Radial fODF (radfODF)
maps for every connections within a hdf5 (.h5) file.
This is the "real" fixel-based fODF amplitude along every streamline
of each connection, averaged at every voxel.
Please use a hdf5 (.h5)... |
e81ff2a6b258d15f52e0458c0cdfc2f4f531f88f0dc5762411053d8092051bcd | Python | 5,052 | 142 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This Python script is used to extract the timeseries from functional
images preprocessed by fmriprep.
This pipeline will generate timeseries files, framewise displacement record
and the labels of the used Atlas.
Schaefer 400 Atlas is used by default.
Note!!!!!!!!!... |
4d3ba6c5ee9bad80ffa516f28a096a1ff88a0dc36f127430f485482e6100817a | Python | 5,053 | 161 | """Download and prepare all the Therapeutics Data Commons (TDC) ADMET datasets."""
from pathlib import Path
import pandas as pd
from tdc.single_pred import ADME, Tox
from tdc.utils import retrieve_label_name_list
from tqdm import tqdm
from tdc_constants import (
ADMET_ALL_SMILES_COLUMN,
ADMET_GROUP_SMILES_CO... |
6b592ef375e162c8529bec5f5c79a3cdf5b4c5e92d8825918e873141f2f23743 | Python | 5,053 | 159 | import argparse
import os
import os.path as osp
import time
import numpy as np
import torch
import torch.nn.functional as F
import pandas as pd
import json
from torch_geometric.nn import ChebConv, GCNConv, Linear
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, f1_score, pr... |
ff2798543ea573fc2e4fc44027ddc7f31a77dfd24dacdf78f2c663a7e30c304e | Python | 5,053 | 103 | from snakemake.io import expand
from snakemake.logging import logger
import numpy as np
from drop import utils
from .Submodules import Submodule
class AE(Submodule):
def __init__(self, config, sampleAnnotation: "SampleAnnotation", processedDataDir, processedResultsDir, workDir):
super().__init__(config, ... |
38867fd2354dc97e42633331066cba36ee79cec67c34ca5375eaeb3b246beff0 | Python | 5,054 | 136 | # Copyright 2024 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
8e3a517d2da7df362a393af5f933fc028cb041317b0181f2bebe745e40812c61 | Python | 5,054 | 162 | """Layer classes."""
import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from torch_scatter import scatter_add, scatter_mean
from torch_geometric.utils import remove_self_loops, add_self_loops
def uniform(size, tensor):
"""
Uniform weight initialization.
:param size: Si... |
c21509729ff04629f63a2955a39762d37a16c8b71037bcbecb7bc5d8ec2fa147 | Python | 5,054 | 151 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 22 00:24:30 2024
@author: costantino_ai
"""
import os
import logging
from pathlib import Path
import pandas as pd
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_core.supported_data_standar... |
242db39fcc75cd1f3d37275175546a4f4e5f28906ae16a8e50b1aa81d6e67898 | Python | 5,056 | 104 | """Optional LNCC backend backed by the standalone ``fused_lncc`` CUDA kernel.
pip install fused_lncc --no-build-isolation # needs PyTorch + a CUDA toolchain; see its README
reg = GreedyRegistration(..., loss_type='lite_lncc') # falls back to 'cc' if not installed
"""
import warnings
from typing import List... |
76282a87d1158a925fd9580e94748c695e370d560a8aec148bea62914ee5da41 | Python | 5,056 | 105 | from PUMI.engine import FuncPipeline, NestedNode as Node
from nipype.interfaces import fsl, afni
from PUMI.pipelines.anat.segmentation import bet_fsl, bet_hd, bet_deepbet
from PUMI.pipelines.func.compcor import compcor
from PUMI.pipelines.func.concat import concat
from PUMI.pipelines.func.data_censorer import datacens_... |
74f518049af5a50083c9b85012639a44876aac6ae23329b17c47303eb7c2153d | Python | 5,059 | 126 | # Google utils: https://cloud.google.com/storage/docs/reference/libraries
import os
import platform
import subprocess
import time
from pathlib import Path
import requests
import torch
def gsutil_getsize(url=''):
# gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
s = subprocess.... |
9e942258ec074ea3967608c2f1c49fefa42d1eda68fb6ef2532f0baa889fe6b7 | Python | 5,059 | 154 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
def estimate_pv(input_target, input_border, path_output, name_output):
"""Estimate PV.
This function estimates the partial volume contribution in each image voxel
of a target im... |
b8ee445490e068451635abd9da593972f613683e26667609c7781d4e353dad4d | Python | 5,061 | 171 | #!/usr/bin/env python
#
# make_image.py:
# create the image for one frame in cytosim's result file
#
# copyright F. Nedelec, 2008-2010
#
"""
Calls executable to generate an image in each specified directory
Syntax:
make_image.py executable-with-arguments [options] [directories]
The current directory is... |
56ef0aa9bede809d2858fa3f5cb362aa68fee47196c15f723cce47e38c5b5111 | Python | 5,062 | 137 | import glob
import os
import numpy as np
from scipy import stats
from .image_processor import *
from matplotlib import pyplot as plt
class Item:
def __init__(self, image, center, angle, image_name):
self.image_name = image_name
self.image = image # Load image and convert to float32
... |
6d11551f8b790f7630bef50bd54b992c067659ff44177e5bc6041f2c755b0d99 | Python | 5,062 | 104 | ##############################################################
### Definitions of assembly neurons in different paradigms ###
### of overlap between cell assemblies ###
##############################################################
### Copyright 2020-2022 Jannik Luboeinski
### licensed under Apache... |
55015da85cea5f68349361c265bc67564bdda0b621f533d497306efaf46697ca | Python | 5,064 | 119 | from scvi.dataset import CsvDataset
import os
from sys import argv
from pathlib import Path
from scvi.dataset import CsvDataset
import numpy as np
import pandas as pd
from scvi.models import SCANVI
from scvi.inference import SemiSupervisedTrainer
import time as tm
import rpy2.robjects as robjects
def run_s... |
f853390c874047f85f64e8270c384f5cd88c6cb475f98b32faef3edecc682bfb | Python | 5,064 | 185 | """
Wrappers for doing binary IO on file-like objects
"""
import struct
import sys
import numpy
def bytesify(s):
if isinstance(s, bytes):
return s
else:
return s.encode()
# Standard size:
# short is 8 bits
# int and long are 32 bits
# long long is 64 bits
class BadMagicNumber(IOError):
... |
66290e4ad5cca9563f6221b24ce9c0981846e80c14038910217649431a6001f1 | Python | 5,065 | 129 | """``meanap-preflight`` — check a dataset before committing to a run.
Answers, in seconds and without downloading anything: are the recordings my
spreadsheet names actually there, how much will this transfer, and will it fit?
"""
from __future__ import annotations
import argparse
import csv
import io
import sys
impo... |
8d6505b3fec3b350c9fea5cfca21a6e1fb495d4b9689aa33a8e2f10070ffcb58 | Python | 5,065 | 136 | # define multi-offset transformer reconstruction network
import torch
import numpy as np
import torch.nn as nn
from utils.utils import *
from einops import rearrange
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, dim, dim_head=64, heads=8):
super().__init__()
... |
b064fc34fb92e6fa835c72bb6a1ec78fb4fc0f8ac47cf96ae90c5f7e4be8b4e8 | Python | 5,065 | 119 | import numpy as np
import pandas as pd
import os
import copy
def build_network(
rsnn,
datapath,
area_list=["wS1", "wS2", "wM1", "wM2", "ALM", "tjM1"],
with_video=True,
hidden_propability=0.0,
):
"""
Build the network based on the data collected from the dataset and based on the array
r... |
ff91cf5cfe31c0ad4de87e0b36097bb147db3b2eff52010689f25c4c1f975221 | Python | 5,065 | 155 | import numpy as np
import pandas as pd
import os
import torch
import datetime
from torch_geometric.data import Data, HeteroData
import nltk
import pickle
import sys
sys.path.append("..")
from dataset import merge_and_split, get_data_dir, fast_merge, split_user_and_tweet
def preprocess_dataset(dataset, server_id="20... |
c3864d038fae72b003e07c092b6bebd4b49550924f2cd9fe35db2d432bdf397b | Python | 5,066 | 104 | ##############################################################
### Definitions of assembly neurons in different paradigms ###
### of overlap between cell assemblies ###
##############################################################
### Copyright 2020-2022 Jannik Luboeinski
### licensed under Apache... |
775e0828bdb13256ae4567f539e9b81a6418804d33d1d6dddc8172f25dd421f7 | Python | 5,069 | 132 | from __future__ import annotations
import copy
from pathlib import Path
from typing import Final
from typing import cast
from loguru import logger
from .. import _yaml
from .._ai_models import AI_TEXT_MODEL_OPTIONS
from ._migration import migrate_config
from ._shape_color import migrate_shape_color
from ._shape_colo... |
951aa435b3d03c44af2519531c0fab0870b7c6bdc7336e27a951cf2d1e814155 | Python | 5,069 | 126 | #!/usr/bin/env python
"""
HAL module for Coherent laser control.
Hazen 04/17
"""
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.amplitudeModule as amplitudeModule
import storm_control.sc_hardware.baseClasses.hardwareModule as hardwareModule
class CoherentL... |
199ce4a444401ad4aadf78bdc3a6bb6adf922ba5c99735bb7398c9c635885628 | Python | 5,070 | 125 | """
(C) Copyright 2021 IBM Corp.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... |
63259280b3f091916fb2595dce57a71b0a2a9e62f78cb96578316d584a799ec6 | Python | 5,070 | 137 | '''
Auxiliary functions for handling (pseudo-)trials.
'''
import numpy as np
from sklearn.covariance import LedoitWolf
from typing import Any, Union, Callable
def group_by(X: np.ndarray, Y: np.ndarray, conds: Union[np.ndarray, list[str]]) -> np.ndarray:
'''
Group the EEG data by conditions.
INPUTS:
... |
9acdbc8d9494da176d4777b4b1d96ebd86742ca0314bbd36e99a40674e835ead | Python | 5,070 | 161 | import tests.util
def _format_attributes(attrs):
formatted_pairs = list()
for k, v in attrs:
formatted_pairs.append(' '.join([k, tests.util.double_quote(v)]))
return '; '.join(formatted_pairs)
def _group_transcripts_by_gene_id(transcripts):
transcripts_by_gene_id = dict()
for transcript... |
ac8e71e7ec938c636d20478d4b11906b47c9f127fe84613c9ebf0593276159d0 | Python | 5,070 | 151 | import os
import numpy as np
import sys
import yaml
import h5py
import git
import matplotlib.pyplot as plt
filename,pathpath,dstpath,tintfile,exptype = sys.argv[1:]
recid = os.path.basename(filename).split('.')[0]
stylepath = 'config/presentation.mplstyle'
plt.style.use(stylepath)
sr_psth = 2000
k_std = (10/1000)... |
1198a5aa334f5a19bf002c4e7b68e963b5bbc7d190558a514fe940d6e887d81b | Python | 5,072 | 170 | """
This submodule implements a scorer interface for the
various causal metrics implemented.
These scorers can then be incorporated into model selection objects
to select the models (or hyperparameters) that optimizes these scores.
For example, as a `scoring` parameter to a `causallib.model_selection.GridSearchCV
objec... |
23341752b1f228ae46785389651f47e91dd8d0cde4c87f6a39e5ed23286f6e4c | Python | 5,072 | 158 | """This file currently uses opencv to select points for homography and/
or rectification. Ideally, I really want to move away from opencv, and
we can do a lot of the selection just with matplotlib or similar instead.
However, I would have to figure out how to draw those polygons with mpl.
Some of this code was mofifie... |
68c3e8f84d27b61011970a20c0fe569e8fbf60899de2c7fd476a4cddb038ae48 | Python | 5,072 | 133 | """Torch Module for E(n) Equivariant Graph Convolutional Layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch
import torch.nn as nn
import dgl
from dgl import function as fn
class EGNNNormConv(nn.Module):
def __init__(self, in_size, hidden_size, out_size, edge_feat_size=0):
s... |
901f805e3ef0d8b2d90e37da69c30c670bf20ec23abe0b5e9a1a452f5043a87f | Python | 5,072 | 146 | import lmfit
import numpy as np
from impedancefitter import get_equivalent_circuit_model
def calculate_impedance_spectrum_using_fft(
dt: float, voltage: np.ndarray, current: np.ndarray, current_threshold=0.1
):
"""Extract impedance spectrum from time domain data using the FFT.
Parameters
----------
... |
58b7e30c1f05b520dcd0a656f482df7e54bccaac12bc0f0e6d60f814f6d78809 | Python | 5,073 | 157 | from pyro import deterministic, param, plate, sample # type: ignore
from pyro.distributions import ( # type: ignore
HalfCauchy,
Normal,
TransformedDistribution,
)
from pyro.distributions.constraints import positive # type: ignore
from pyro.distributions.transforms import ExpTransform # type: ignore
from... |
e4da2c1fbfb7267ca2c730f062bc90fe6f3b88adb0d1dcdf73f356e8e15c8dd2 | Python | 5,074 | 172 | import argparse
import csv
import datetime
import json
import os
from typing import Any
def find_validation_logs(root_dir: str) -> list[str]:
results = []
for root, _, files in os.walk(root_dir):
if "validation_average_history.log" in files:
results.append(os.path.join(root, "validation_av... |
b9674e1420be7ec6a25f8812c9653da06cc46603e3259cd593f3ebf556fd6bc5 | Python | 5,075 | 120 | """Plot consistency between participants in projection onto tuning shift PCs.
Figure 4C.
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
from matplotlib.patches import Patch
from scipy.stats import sem, ttest_1samp
from utils import (
draw_violinplot,
get_pssd,
get_voxel_to... |
1467e4841bce4e536a2f0d6d63d093429b7d22b81629d00b5cc04e1abfd3b6f1 | Python | 5,076 | 102 | #encoding=utf-8
import sys
sys.path.append("../")
import jieba.posseg as pseg
import jieba
jieba.enable_paddle()
def cuttest(test_sent):
result = pseg.cut(test_sent, use_paddle=True)
for word, flag in result:
print('%s %s' % (word, flag))
if __name__ == "__main__":
cuttest("这是一个伸手不见五指的黑夜。我叫孙悟空，我爱... |
2a66bda8b10ee94248c352eaab8d60a5ae267d860ff33f3f2bb8d038ca7f20cd | Python | 5,076 | 75 | """LAION-fMRI benchmark registration.
Benchmark registry: 20 headline variants matching the authors' re:vision framework.
Headline ridge encoding variants — predict per-voxel responses from model features
using kernel/dual ridge with per-fit CV alpha selection over a 21-value log-spaced
sweep (1e-10 to 1e10).
- Per... |
2b40fa56857a3e6765249366bfd82ce17d691e0fe0f7231d12bd1cb4fc8cd2b6 | Python | 5,076 | 137 | import xgboost as xgb
import numpy as np
import logging
import os
from argparse import ArgumentParser
from sklearn.metrics import f1_score,precision_score,accuracy_score,recall_score,roc_auc_score
from imblearn.combine import SMOTEENN
from collections import Counter
parser= ArgumentParser()
parser.add_argument('--datas... |
71ab0d880b42c964172abf6818545b8367849cd1af8a5dbc9f9b55ff0f7901ae | Python | 5,077 | 116 | import numpy as np
from scipy import stats
from utils import Recorder_select, Q_nak
from mitochondria import Mito
import matplotlib.pyplot as plt
from figure_properties import *
# Version will all the necessary parameters.
dt = 0.1
seed = 11
np.random.seed(seed)
def generate_spiketrain(rate, duration): # duration ... |
eab87c8a830756d91d6e7316293165b73f5a4e8fcb8aab2a79fa40af5a2a56ec | Python | 5,078 | 143 | #!/usr/bin/env python
from optparse import OptionParser
from rpy import r
import bx.align.maf
import bx.bitset
from bx.bitset_builders import binned_bitsets_from_file
def main():
parser = OptionParser(usage="usage: %prog [options] maf_file snp_file neutral_file window_size step_size")
parser.add_option("-o... |
55f2ddae7974fe9fcf81aef5133b49ad04758368610146d7a3aebe8e809dfc1f | Python | 5,079 | 111 | import sys
sys.path.append('./TEMcode/2024-10-22/run0/script')
import pandas as pd
import numpy as np
import torch
import glob
import importlib.util
# Own module imports. Note how model module is not imported, since we'll used the model from the training run
import src.world as world
import src.analyse as analyse
impo... |
73e48523e9c6bbce53b45a92d95ac89e8dbacedb6c4b022bec6c9a36748c82b3 | Python | 5,079 | 129 | # -*- coding: utf-8 -*-
# THIS FILE IS TEMPORARY. THIS FILE WILL BE DELETED AFTER THE PR IN DIPY
from multiprocessing import Pool, cpu_count
import warnings
import numpy as np
from scipy.ndimage import affine_transform
def _affine_transform(kwargs):
with warnings.catch_warnings():
warnings.filterwarning... |
5c18fd71599ceef7a19c24e4d1ac8452ec2635d42df9d8e9da010a1edb21d373 | Python | 5,082 | 108 | import numpy as np
import pandas as pd
from typing import Tuple, Union
from . import ELA_Network_functions
#-----------------------------------------------------------------------------------
# ④calc TP for all combinations
#-----------------------------------------------------------------------------------
#-------... |
db1bb05c3c7eea5f022119ca5ffc42783e778e8fbcef249f557159aeec35d6a1 | Python | 5,084 | 122 | """Electrode geometry for spike sorting: a ``probeinterface.Probe`` per
MEA channel layout.
Spike sorters need every channel's position in micrometres — to decide which
channels a spike can appear on together, and to whiten over neighbours. The
pipeline's own coordinates (:mod:`meanap.pipeline.channel_layout`) are a
u... |
00214ebea4b371c2278fc4d5414222bb92dfbd2119ffdfbc872428c52d6a7e2d | Python | 5,085 | 124 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to compute the mean/median maximum fODF in the ventricles. The
ventricules are estimated from an MD and FA threshold.
This allows to clip the noise of fODF using an absolute thresold.
--------------------------------------------------------------------------
R... |
e114575cf46083d2906d65d4aa67cc85d1b33175d8a6d48561ef3c014d934e90 | Python | 5,085 | 183 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Main CLI entry point"""
from pathlib import Path
import logging
import json
import click
from . import utils, log, profiler
from .connectome_manipulation import connectome_manipulati... |
9044b99b3e2bbc746f34de9f2843b26749a7e64723091a984607a657d347ba74 | Python | 5,086 | 143 | """This script trains a SR model for diffusion MRI (log-tensors) using also structural T1 and T2 as inputs.
If you use this code, please the SynthSR paper in:
https://github.com/BBillot/SynthSR/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot
Licensed under the Apache License, Version 2.0 (the "License"); you ... |
bc7901ce8c964b599bb5095617220854113ca1de34891bc39a4a1afd51d9dc2b | Python | 5,086 | 117 | import h5py
import logging
import numpy as np
import os
from PIL import Image
from result_caching import store_dict
from sklearn.decomposition import PCA
from tqdm import tqdm
from brainscore_vision.model_helpers.activations.core import flatten, change_dict
from brainscore_vision.model_helpers.utils import fullname, s... |
54a883ac5fcd899ccc727b80f60c9326ba7fed564c7b71632cbf7a780335090a | Python | 5,087 | 136 | """ Classes and functions for fitting ivim model """
import numpy as np
from scipy.optimize import curve_fit
from dipy.reconst.base import ReconstModel
from dipy.reconst.multi_voxel import multi_voxel_fit
from dipy.utils.optpkg import optional_package
class IvimModelBiExp(ReconstModel):
def __init__(se... |
14ae550b5bc2ef4a00e46d2e061e9c1df45592ba473434a197b39defb656e76a | Python | 5,089 | 102 | #encoding=utf-8
import sys
sys.path.append("../")
import jieba
def cuttest(test_sent):
result = jieba.cut(test_sent)
print(" / ".join(result))
if __name__ == "__main__":
cuttest("这是一个伸手不见五指的黑夜。我叫孙悟空，我爱北京，我爱Python和C++。")
cuttest("我不喜欢日本和服。")
cuttest("雷猴回归人间。")
cuttest("工信处女干事每月经过下属科室都要亲口交代24口... |
62f98ebff3c4486ee2f1d12bca8d7dc9b83336b943e65f989440b180322d1291 | Python | 5,089 | 109 | from nvidia.dali.pipeline import pipeline_def
import nvidia.dali.types as types
import nvidia.dali.fn as fn
from nvidia.dali.plugin.pytorch import DALIClassificationIterator, LastBatchPolicy
import os
@pipeline_def
def create_dali_pipeline(data_dir,
crop,
size,
... |
9545b026d60fe1c910d4a25f5cbe8b26d5ca34ab58972e4e29c524e111e71693 | Python | 5,089 | 155 | """Completion-safe TIFF output helpers for stitched images."""
from __future__ import annotations
import os
from collections.abc import Iterator
from contextlib import contextmanager
from pathlib import Path
from shutil import disk_usage
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import A... |
058ede200cf0f3a9a3c5bf112898bc5c8a5ef65c19e332d5e4dc1a6d7cfab4fe | Python | 5,090 | 116 | import numpy as np
from scipy import stats
from utils import Recorder_select, Q_nak
from mitochondria_PDP1KD import Mito
import matplotlib.pyplot as plt
from figure_properties import *
# Version will all the necessary parameters.
dt = 0.1
seed = 11
np.random.seed(seed)
def generate_spiketrain(rate, duration): # du... |
68e9ee20226c18b02664478743a6368674a75c32a46fd9d090d6d5d32a9a6e84 | Python | 5,092 | 135 | import time
import argparse
import torch
import numpy as np
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.nn import ModuleList, Linear, BatchNorm1d, Identity
from ogb.lsc import MAG240MDataset, MAG240MEvaluator
from root import ROOT
class MLP(torch.nn.Module):
def __init__(s... |
5c1ed83c9361bca649cbd8a07eec846536f76abed4f4ce1f2b466d415c6197bb | Python | 5,093 | 131 | import sys
import os
import numpy as np
import pandas as pd
import torch
from model.QSAR import QSAR
if __name__ == "__main__":
# check GPU
print('CUDA available:', torch.cuda.is_available()) # Should be True
print('CUDA capability:', torch.cuda.get_arch_list())
print('GPU number:', torch.cuda.device... |
7c973280e8a274c9624c2bc41fce690bbfa24367b145bda012928ec56fefe51b | Python | 5,094 | 115 | """
The following plugin functions by default expect the name and path of the preferences file to be the
main lasagna preferences file. However, this can be over-ridden so that individual plugins can have
their own preferences files and still use these functions.
"""
import os
import yaml
from lasagna.utils.path_uti... |
12a5f0a297e5b54d161c38ac7ffe92425dc82858d10f0e2140228474ad494f68 | Python | 5,096 | 145 | import re
from pathlib import Path
from typing import Optional
import click
import dolfin as df
import numpy as np
import pandas as pd
import panta_rhei as pr
import skimage
import tqdm
from panta_rhei import FenicsStorage, fenicsstorage2xdmf
from simple_mri import load_mri
from gmri2fem.utils import find_timestamps,... |
1863e7e223b02957f21ae7ba0354e86f3cd6b5c8d2ee329fa318d9eb5b9feb1a | Python | 5,097 | 104 | # This is not applied in the NovaScope.
import os
import argparse
import subprocess
def create_layout(outprefix, lane, tile1, tile2, colshift, shifttype):
layout = f"{outprefix}.{shifttype}shift.layout.tsv"
if shifttype == "odd":
with open(layout, 'w') as f:
f.write(f"lane\ttile\trow\tcol\... |
588741ffda1bdc8bd04949afc23761701f6cbe8ce989e7d186df4347c4da248f | Python | 5,097 | 87 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '/media/lg/disk2/PycharmProjects/ISAT_with_segment_anything/ISAT/ui/category_dock.ui'
#
# Created by: PyQt5 UI code generator 5.15.10
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this... |
99f6a78c11fcba04ef284c0878f042231c75dcf8e47e678517b9930ec828d55d | Python | 5,097 | 138 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import json
import os
import torch
from PIL import Image
from sam3.model.box_ops import box_xyxy_to_xywh
from sam3.train.masks_ops import rle_encode
from .helpers.mask_overlap_removal import remove_overlapping_masks
from .viz import visualize
... |
a9a4c56ce8b4a9bc6f9d204f7c52251f045c8f8534ebf8affef1f886e8a5c30d | Python | 5,097 | 118 | """Simulation failures must reach every caller without successful artifacts."""
import json
from unittest.mock import Mock
import numpy as np
import pytest
from ase.build import molecule
from ase.calculators.calculator import Calculator
from ase.calculators.emt import EMT
from ase.io import write
from chemgraph.sche... |
ef2d586a7376ab2a97e802aff835184427769e50ebe793ffd06bb88c407b83ae | Python | 5,097 | 148 | import json
import math
import ijson
from argparse import ArgumentParser
import os
import os.path as osp
from datetime import datetime
import numpy as np
import pandas as pd
from tqdm import tqdm
parser = ArgumentParser()
parser.add_argument('--dataset', type=str)
args = parser.parse_args()
dataset = args.dataset
as... |
0b5d86f36c506ce1a4685264f6acb3ced0d3f725cb6413e7b86fa0132b6e354f | Python | 5,098 | 129 | import numpy as np
import sys
import yaml
import h5py
import os
from glob import glob
import git
varselection = ['M', 'B', 'LvR']
allvarnames = varselection + ['rate']
n_tints_min = 6 #minimum number of tints needed to calculate mean and std
mininput_ifn = 4 #min_nspike_per_trial = mininput_ifn+2#n-spikes = n_interval... |
e885873576717985a3e885698ff5ae224cf51e87e48c1c67d9e358f92bb26ba0 | Python | 5,098 | 146 | """Parity test: reading an Axion .raw directly vs. MATLAB's own AxisFile toolbox.
Ground truth comes from Axion's bundled MATLAB reader driven exactly the way
``Functions/convertRawToMat/rawConvertFunc.m`` drives it::
AllData = AxisFile(raw).RawVoltageData.LoadData(well, [0 2]);
dat = [AllData{wellRow,w... |
2f1be0b15f9bf1f0679b75a064817ae36191965bac9dd675584661526dcec10f | Python | 5,099 | 120 | from qtpy.QtWidgets import QPushButton, QWidget, QVBoxLayout
from napari import Viewer
from napari.qt.threading import thread_worker
from magicgui import magicgui
from magicgui.widgets import FunctionGui
import numpy as np
import cv2
from aicsimageio.readers import CziReader
from napari_dmc_brainmap.utils import get_a... |
85587e9610fe9896e278f4ad727cdecbe4ca584d8785e6147753e76f75a1c364 | Python | 5,099 | 138 | import ttach as tta
import multiprocessing.pool as mpp
import multiprocessing as mp
import time
from train_supervision import *
import argparse
from pathlib import Path
import cv2
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm
def label2rgb(mask):
... |
f6520fa12fb9f51bcb82d0acf8dc08eb41afa2b593de98bc1385d8a03e6f46a0 | Python | 5,099 | 129 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import nibabel as nib
import numpy as np
import pytest
from scilpy.io.dvc import pull_test_case_package
from scilpy.gpuparallel.opencl_utils import have_opencl
# If they already exist, this only takes 5 seconds (check md5sum)
test_data_root = ... |
0f53d04395f90347c8c3c6a5a128aa1f6a0cfa1b79b32223735997abab3ef835 | Python | 5,100 | 121 | from .utils import *
from .rclone import rclone_list_files
def clean_local_files(subject = None,
checksum = True,
dry_run = False,
keep_recent_weeks = 5,
exceptions = ['Session Settings',
'Sessio... |
5d713140b6be500f2636b236ce8dde8408798fd22bfe92ad108a65a9b5ad06d0 | Python | 5,100 | 133 | # Generated by Django 4.2.16 on 2024-10-30 12:03
import django.contrib.auth.models
import django.contrib.auth.validators
import django.utils.timezone
from django.conf import settings
from django.db import migrations, models
BUSINESS_GROUP_NAME = "business"
USER_GROUP_NAME = "user"
def delete_business_group(apps, sch... |
3f0f0c2373c25e6bfe6284a38bf195ea558023cc94e46ca5f33a21dcb7c8334a | Python | 5,102 | 106 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import cv2
import numpy as np
import torch
from PIL import Image
from ultralytics.data.augment import classify_transforms
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results impor... |
15a9e9842e73e63ff8ce1f8fd17b76c169421cd6f6373aae031e671ffda3307c | Python | 5,104 | 158 | #!/usr/bin/env python3
# This script is included in documentation. Adapt line numbers if touched.
import arbor as A
from arbor import units as U
import pandas # You may have to pip install these
import seaborn # You may have to pip install these
from math import sqrt
def make_cable_cell(gid):
# (1) Build a seg... |
85821aac3ba3d80b97a5956a75e6da9a2bd2a3895fc77ba41039ea367b563598 | Python | 5,104 | 134 | from inspect import CO_VARARGS
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from torch.nn.functional import embedding
from torch.nn.modules import conv
from depth.models.builder import HEADS
from .decode_head import DepthBaseDecodeHead
import torch.nn.functional as F
from depth.models.utils impor... |
2cefd2bbe3e2b24a8744bcbabcfdac5507cd360389d0fc0f503cffe8d5ba63a3 | Python | 5,105 | 124 | import argparse
import utils
import keras
from keras.models import Model
import tensorflow as tf
from os.path import join
import numpy as np
import gc
import yaml
from model_zoo import MPRAnn_heteroscedastic
'''
calculate standard deviation of ensemble predictions for train/test/val data
for MPRAnn models trained wit... |
1584851e12b778da421e1e7ab8f2d8369c9570ad9a9ec5b70eb8fbceb79af788 | Python | 5,106 | 173 | """
Copyright 1999 Illinois Institute of Technology
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publis... |
df18dd5980bf56076798ee4acf95663620e867bf7889a896814a1f3b8035a630 | Python | 5,108 | 115 | """
Description:
This script is run after `1_variant_effect_prediction.py`. It computes
the variant effect sequence class scores based on Sei chromatin profile
predictions, and by default, sorts the variants based on the maximum
absolute scores across sequence classes, outputting the results as
TSV ... |
1f863ba34c435e87b8251d12ed07f03cbe2c7ca92a675deaf9ba6cd06b5404c2 | Python | 5,109 | 136 | # python imports
import os
import glob
import sys
import random
from argparse import ArgumentParser
import matplotlib
# third-party imports
import tensorflow as tf
import numpy as np
from keras.backend.tensorflow_backend import set_session
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, ... |
db96ee677c61569651f979be42543169d2c615c58629372a581a4df07da228f0 | Python | 5,109 | 141 | '''
NH3
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for NH3:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
U: diabatic potential energy matrix
UG: gradient of diabatic potential energy matrix
This is listed at the end o... |
c853e5f1866c8fd5c5dcbf2b6ec2123fd0554e76c4e872b057c0e77fdcd5625d | Python | 5,111 | 144 | import torch
import numpy as np
import pandas as pd
from datetime import datetime as dt
from dataset import fast_merge,df_to_mask
from tqdm import tqdm
dataset_names = [
'botometer-feedback-2019', 'botwiki-2019', 'celebrity-2019', 'cresci-2015', 'cresci-2017', 'cresci-rtbust-2019', 'cresci-stock-2018', 'gilani-2017... |
fb6f47e614c6f0060fb349396a296836ee97e2d50301f4783a2f2878d829068b | Python | 5,111 | 178 | # Kai Sandbrink
# 2022-03-09
# Various utility functions
import matplotlib.pyplot as plt
import numpy as np
import yaml, os
from datetime import datetime
# %% GENERAL FUNCTIONS
def combine_tuples(tuples):
'''
Arguments
---------
tuples : list of tuples
Returns
-------
combined ... |
07a39dde3365aeffa42dab9104357ddcfe91f2f1e8f51e7b7af160cf2324b79e | Python | 5,112 | 126 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr, torch_utils
from ultralytics.utils.torch_utils import smart_inference_mode
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS["tensorboard"] is True # v... |
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