sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9e2bf6a119f1b2814540c815226a39b2a49287f29545a55e05ec1b98be0c957e | Python | 6,077 | 167 | import copy
from collections import deque
import numpy as np
from . import swa_utils
class SWADBase:
def update_and_evaluate(self, segment_swa, val_acc, val_loss, prt_fn):
raise NotImplementedError()
def get_final_model(self):
raise NotImplementedError()
class IIDMax(SWADBase):
"""SWAD ... |
c61acd50b1a13d4e509df049f21ebbb8ce757b1cb05f7ffb7257f5bde4e9e5fd | Python | 6,081 | 156 | # -*- coding: utf-8 -*-
"""plot_pd_results.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/11ZJhsuYjfkJ3topN-bRfIwRXmHfc9wc9
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error... |
11dcb3ee1b7b8d8654e693df623bd168cce4f91d905b99444f6946fb877235bf | Python | 6,082 | 206 | #!/usr/bin/env python3
import os
import warnings
import argparse
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from src.drop_width.model import build_lstm_model
from src.drop_width.preprocessing import DropDataProcessor
from src.drop_width.side_to_width import width_e... |
ae0fa6d326ddb86de4302359ee947a92add1b048427ef18f4cd0e5c14320aaa7 | Python | 6,083 | 218 | # -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: py:percent,ipynb
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# -... |
1dc08b72f33b43a3ac5c145f7313309944adec98f257125118addd0d13ff0483 | Python | 6,084 | 166 | # Description: This script resembles ABCD_ROCAUC_Comparison_Fig1.py and compares classification performance of different harmonization
# techniques. We here do no plot the ROC curves, but rather compare the AUC scores for within and across brand comparisons with boxplots.
# This script results in Figure 2 (FA) in the... |
759818b3912da36936f05cd3037c1249f4cfec5babdeee6f031d5cb7b5103ebb | Python | 6,086 | 196 | """Test for Serializable base class."""
import pytest
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import load, loads
from langchain_core.prompts.prompt import PromptTemplate
from langchain_classic.chains.llm import LLMChain
pytest.importorskip("langchain_openai", reason="langchain... |
13f82f71b11b713b45a9b5af2a0144b8517af47b5f58f125db7a7fd1f3e19fb2 | Python | 6,087 | 143 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import os
import numpy as np
import pandas as pd
import logging
import argparse
FORMAT = '%(levelname)s %(asctime)-15s %(name)-20s %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
def replace2na(ratio_d... |
80cca1323181d17307603f0c3274e92d21f7fc86704a6a7e7bf4f4a25cc3e490 | Python | 6,087 | 180 | ########################################################################
# 2D diffusion on an infinite surface from point source.
# AIMS: to verify STEPS spatial-stochastic solver 'Tetexact' supports
# local initial conditions and calculates surface diffusion rates correctly.
# STEPS equivalent model: Stochastic 2... |
e24f877d41c706129ea58022d219f6cb5b5f8a6f54c13c83352d67f2dfd01bc5 | Python | 6,087 | 164 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
304af82dce39f699cc715facc7ca2156d8e0f1775302c5d104e7ecfca8ab7a47 | Python | 6,089 | 130 | import torch
from CEL.networks.models.meta_model import ModelClass
from torch.autograd import Function
from itertools import chain
from copy import deepcopy
from tqdm import tqdm
class InvariantFunc(torch.nn.Module, ModelClass):
def __init__(
self,
y_channels: int,
W_channels: i... |
07a41eed4e7735bc66ee415ca8b27774d45a39c55958787c4fa2ae16f6b82395 | Python | 6,094 | 161 | import pandas as pd
import numpy as np
from numba import jit
import scipy.stats
# @jit(nopython=False)
def get_entropy(x):
entropy_list=[]
for i in sample:
arr=np.array(x[i])
if len(set(arr))==1:
entropy=0
else:
entropy=scipy.stats.entropy(arr[arr>0])
ent... |
aad6533e3ed6579f04b710a0cf116cda71c71953390a3024a116ed6bad1823bd | Python | 6,094 | 178 | """Tests for tools defined in modules with postponed annotations."""
from __future__ import annotations
from functools import update_wrapper
from inspect import Parameter, Signature
from typing import Annotated, Any
from pydantic import BaseModel
from langchain_core.tools import InjectedToolArg, StructuredTool
from... |
4edae7715e53647e9e45521ab783838dff2e592b793da32cdf2c14684ef2efb1 | Python | 6,096 | 225 | import json
import logging
import os
import shutil
from pathlib import Path
from typing import Tuple
import hydra
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from omegaconf import DictConfig, OmegaConf
from tqdm import tqdm
from src import PROJECT_DIR
from src.data.utils import CLASS_I... |
24a24ad8b8402955512a7526900da8e386e0ec4c90690d52420194cfe3215d81 | Python | 6,097 | 196 | import logging
from typing import Dict, Optional, Tuple, Union
import pytorch_lightning as pl
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from torch_geometric.nn import MessagePassing, radius_graph
from torch_scatter import scatter, segment_coo
... |
27f95d30c925846f187bf90b60ccc8f5d39f12570b1edde4bc73b354be3043a3 | Python | 6,101 | 180 | import Bio.SeqIO as sio
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.utils import to_categorical
import random
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tqdm
#strategy = tf.distribute.MirroredStrategy()
#load model
filterm = tf.keras.... |
51c60fbd1e66fdea9da664f14cb32e60b8b9c21058182e93569f8a5e4f09968c | Python | 6,102 | 198 | BaseDir = "R:/Temp/Threads2/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [8] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
rang... |
6f4eb646fc2fc2dc683290e50d4c129021f3c8a21e7144a7214c18954414bff3 | Python | 6,102 | 172 | import argparse
import numpy as np
from oset.generic.lp_filter.lp_filter_zero_phase import lp_filter_zero_phase
from oset.generic.tanh_saturation import tanh_saturation
from scipy.signal import lfilter
def peak_det_modified_pan_tompkins(data, fs, *args):
"""
peak_det_modified_pan_tompkins - R-peak detector b... |
069c64ab24de05e96259201708a05d9458310fcfefdb7c70ad329bf7251d2ac6 | Python | 6,103 | 193 | from functools import partial
import numpy as np
import cv2
import ast
from copy import deepcopy
from utils.geometry import euclidean_distance
def _convert_from_string(data):
for char in '[]':
data = data.replace(char, '')
rows = data.split(';')
newdata = []
count = 0
for row in rows:
... |
ae9a821a13e45382940f67849be85d7433f2c1a860ddfcd940fbcf6f3b853aab | Python | 6,103 | 204 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import numpy as np
import nibabel as nib
import pylab as pl
DESCRIPTION = """
Ultra lightweight 3D data viewer with
- Orthoview
- Slice Mosaic
- Intensity Histogram
"""
def buildArgsParser():
p = argparse.ArgumentParser(description=DESCRIPTION)
... |
fb8f0fd84e0af8464b5d1be77aa723c1806ff1f15faaabb37a718d4eb0dc14de | Python | 6,103 | 198 | BaseDir = "R:/Temp/Threads3/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [30] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
ran... |
33a3a16cc86798491c2c2dd1688382665b270b3c691c962488c1bf8ddbb2b1f4 | Python | 6,104 | 198 | BaseDir = "R:/Temp/Threads1/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [140] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
ra... |
9a1d6c450afdef2cb62abb29802305ea3eaa24aaf409a62a9e41e2c72d096d32 | Python | 6,104 | 198 | BaseDir = "R:/Temp/Threads4/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [8] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [19900] #ms
stopTime = [20000] #ms
rangesL = [10000] #phase calculation crop limit low
ran... |
f572a22a6bdfa9a95bf9d1ca01f43a0484c32ec646cc80a843d48b660d30d7f3 | Python | 6,105 | 198 | BaseDir = "R:/Temp/Threads5/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [30] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [19900] #ms
stopTime = [20000] #ms
rangesL = [10000] #phase calculation crop limit low
ra... |
5c749404b8fa0835633e9f4d7b79c8ff5a1a0115e30d2c7bafa8687131850de5 | Python | 6,106 | 198 | BaseDir = "R:/Temp/Threads6/Thread0"
InnCurr = 150
import sys
file_name = 'Cell'
probeXs = [0] #um
probeYs = [50] #um
frequencies = [140] #Hz
ampltudes = [0.0002, 0] #Hz
DCampltudes = [0.25, 0.35, 0.45, 0.55, 0.65] #Hz
durations = [19900] #ms
stopTime = [20000] #ms
rangesL = [10000] #phase calculation crop limit low
r... |
f6fcefba195ef9ad5b2c6a6d60aa43e60e5b1aad3c374b6eb3931ea934cf139a | Python | 6,109 | 142 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Phase-difference B0 estimation.
.. _sdc_phasediff :
Phase-difference B0 estimation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The field inhomogeneity inside the scanner (fieldmap) is proportional to the
phase dri... |
a0eac380e0e6e6684db5bd94e949602051f6f2573c061f7c6213172a1f341b79 | Python | 6,110 | 227 | import torch
import torch.nn as nn
import torch.nn.functional as F
from util import Conv2d
class MultiResBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
res_channels,
base_kernel_size,
stride=1,
res_scales=1,
drop=0.25,
dila... |
05eb8f768744401757e5f9e610a7cec40198dd1c629d403807a4036d14d229de | Python | 6,111 | 192 | import argparse
import math
import numpy as np
import torch
import torch.nn as nn
# code is adapted from https://github.com/yulunzhang/RCAN/tree/master
class Upsampler(nn.Sequential):
def __init__(self, conv, scale, n_feat, bn=False, act=False, bias=True):
m = []
if (scale & (scale - 1)) == 0: ... |
0aac731321675e1b28e35c4ef6305c96e7b95acc86314daaf45fc20a4381caed | Python | 6,111 | 164 | import os
import numpy as np
import logging
import json
import time
import matplotlib.pyplot as plt
base_setting = {
'train_size': 0.8,
'epochs': 100,
'input_size': 60,
'batch_size': 32,
'pre_len': 1,
'fileName': "",
'model_name': 'lstm',
'checkpoint_root_dir': './che... |
bc925a1f0f042c98cf5d17697b1379cfec74245c12058fbaa8bfbf2f4481b905 | Python | 6,111 | 181 | import copy
import requests
import urllib.parse
import numpy
import pycilium.cilia
expected_annotations_lower_to_limit = {
"base": 1,
"tip": 1,
"exit": 1,
"d_cent": 1,
"vesicles": None
}
def state_from_ngllink(link, request_params={}):
if link is None:
return None
parsed_uri = u... |
a95a2e5f69c1e6969479e68f3cf29b4e7c81be97ae03171eb833b2f7498e82e2 | Python | 6,112 | 119 | """Verify a locally compiled runtime without requiring a full FreeSurfer install."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path, PurePosixPath
import re
import shutil
import subprocess
from postprocessing.core.config import COMMANDS, FS_REVISION, default_prefix, ... |
68c8aba3b5d97aa36a9329cc4049910dd739980ea75fe861c8c7a13ac1b043b4 | Python | 6,113 | 154 | # python program for diffusion simulations
# import packages
import time
import random
import numpy as np
import pandas as pd
import scipy
# import user defined packages
import read_input
import write_output
import write_log
import preprocess
import convert_image
import predict_diffusion_barriers
import KMC
def mai... |
7bee9ebbfed0c84862bfdcea7ae7d9207f13c93f89f2c50455db805ec3ec4b3e | Python | 6,113 | 127 | """Native scalar export preserves values, image geometry and independent outputs."""
import json
from pathlib import Path
import tempfile
import unittest
import nibabel as nib
import numpy as np
import SimpleITK as sitk
from preprocessing.export import SCALARS, export_native
def make_native_cache(root, trace=True):... |
b973cf55180b45eb15091c5ae9f599400774a106a3f33b08f6b370ea0567c333 | Python | 6,116 | 187 | import os
import pickle as pkl
import numpy as np
import pandas as pd
from moabb.paradigms import MotorImagery
from moabb.datasets import Cho2017, PhysionetMI, BNCI2014001
from braindecode.datasets import BaseConcatDataset, create_from_X_y
# ---------------------------------------------
# task 1: sleep stage
# -------... |
1e237e0f699ccc20761a021c5f5830b262291e5481503da22da56028d1ffc389 | Python | 6,118 | 175 | import Bio.SeqIO as sio
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.utils import to_categorical
import random
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tqdm
#load model
filterm = tf.keras.models.load_model(os.path.join(os.path.dirnam... |
4fcb78b9c35798d62994527ff71ba7540dfc92ed276ca48d5f86ec518ee2225b | Python | 6,119 | 195 | import glob
from os.path import dirname, basename, isfile, join
modules = glob.glob(join(dirname(__file__), "*.py"))
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import *
# from .rd import ReactionDiffusion
# from .wave import DampedWaveEquation
# from .pendulum... |
101171aef4cda232d75c8e75ee91018005d0ff6b991cec0c30655e1582bd29f1 | Python | 6,128 | 190 | """
##############################################################################
#
# Calculate motif's activity differences Zscores
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 20-01-2020
# LICE... |
049b13ca2749db34d5c5879ab7ae7e3011ccddc5c1b4fd9e342e1616ccd085ff | Python | 6,129 | 170 | import pandas as pd
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
import numpy as np
def getstat(confusion_matrix):
# 计算真正例（True Positives）
TP = confusion_matrix[1][1]
# 计算真负例（True Negatives）
TN = confusion_matrix[0][0]
# 计算假正... |
cd771d1cb366507d27d3a49889a4ab0ca266867dcf030a42f03adabc74c8a0c1 | Python | 6,131 | 125 | """Compare decoded postprocessing payloads, ignoring path/time text in headers."""
from __future__ import annotations
import argparse
from pathlib import Path
import nibabel as nib
import numpy as np
from utils.files import atomic_json, sha256_file
SURFACES = ('white', 'pial', 'orig', 'smoothwm', 'inflated', 'sphe... |
57cc78f376a9a722b58f4d9651fd02959280cf123a7f645ccb54ec7d94f6d9ca | Python | 6,133 | 201 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
2e1555dc7cf5adb1402d3e98dbfdcc9722bdf43b7c22f892b288ba91569a174d | Python | 6,134 | 201 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
ed8f22d46b8004adf07692e8882365c2b765877f314126d82e95852c262119e2 | Python | 6,134 | 136 | import glob
import os
import pandas as pd
import seaborn as sns
class nebula:
from .MultipleVisualization import barplot
from .MultipleVisualization import circos_plot
from .MultipleVisualization import circular_term
from .MultipleVisualization import diff_reg
from .MultipleVisualization import d... |
9026672d98ef269944875a5f475dca1ac63bdf91885e754a4ac7a4fe5cd1f2b2 | Python | 6,136 | 146 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
28d213ee6d922d7dbfa2ef8d9b5e0f5236a3373e66c61a95af1b3b602609101a | Python | 6,138 | 125 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
a765c1ce502e8ef2e86e4b2f3bf5e58c26ea7b5930647a7287dc7c6b46053531 | Python | 6,138 | 178 | """An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import Base... |
e0fa096974e90818e32ca943ecd908f6b8a41fa3c85afefc91145d13ee3f9572 | Python | 6,139 | 181 | # original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py
#
# modified by Axel Sauer for "Projected GANs Converge Faster"
#
import torch.nn as nn
from pg_modules.blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d)
def normalize_sec... |
2c720e423773c1b48b59f83a6e3c44f0797253f240b858488f3adc97170cab9c | Python | 6,140 | 206 | ### Toy example to test Conv3dConnection (the dataset used is MNIST but with a dimension replicated
### for each image (each sample has size (28, 28, 28))
import argparse
import os
from time import time as t
import torch
from torchvision import transforms
from tqdm import tqdm
from bindsnet.datasets import MNIST
fro... |
a8f2750110d85b5752fb13de20c56d1b2c1a16cc7f37c4e74661294b91842a81 | Python | 6,141 | 143 | import shutil
import tempfile
from pathlib import Path
from MRdataset import import_dataset, save_mr_dataset, load_mr_dataset
from MRdataset.config import MRDS_EXT
from mrQA import check_compliance
from mrQA.parallel_utils import _make_file_folders
from mrQA.run_parallel import process_parallel, split_folders_list
fro... |
6049ba42e91a357ff3804a3ddf6a10e293d104f7854836c6bc5a4a104c3ff369 | Python | 6,143 | 181 | ########################################################################
# 2D diffusion on an infinite surface from point source.
# AIMS: to verify STEPS spatial-stochastic solver 'Tetexact' supports
# local initial conditions and calculates surface diffusion rates correctly.
# STEPS equivalent model: Stochastic 2... |
e67cd81fe3b86cd3d4cd908ee177b9a9924041b31216618b7617cfc40ab3c59c | Python | 6,145 | 189 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
bd36d22090cf928864061fb7fecd42dda35e5294e4a3c0ef49e45406132d5a94 | Python | 6,146 | 185 | ########################################################################
# 2D diffusion on an infinite surface from point source.
# AIMS: to verify STEPS spatial-deterministic solver 'TetODE' supports
# local initial conditions and calculates surface diffusion rates correctly.
# STEPS equivalent model: Determinist... |
3ad953022c8b072393da2f93b190c2605bdb42f83c88ce8b6cd02cccc595100e | Python | 6,147 | 167 | import logging
import os
import numpy as np
import pandas as pd
from helpers.data import normalize_array, assert_normalized
def save_synthetic_dataset(
config_dict: dict, covariance_structure: np.array, dataset_name: str,
synthetic_data_dir: str, white_noise_snr=None, hcp_noise_snr=None
) -> None:
... |
99065d91e2230c79fd2edda4ec37c43633af5f14bcec242f02154622230aa98d | Python | 6,148 | 145 | from typing import List, Union, Tuple
from functools import reduce
import numpy as np
from rdkit import Chem
import torch
import torch.nn as nn
from HMSA.args import TrainArgs
from HMSA.features import BatchMolGraph, get_atom_fdim, get_bond_fdim
from HMSA.nn_utils import index_select_ND, get_activation_function
import ... |
00163f8ff974eb798acb8dc555dc5ff01a43b4dcd57f26d4365cc3130bf54ed9 | Python | 6,150 | 70 | from langchain_core.prompts.prompt import PromptTemplate
EXAMPLES = [
"""Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find... |
1d7e7614ff6fa9a84233ffca44af7e1458c779bf1190db077c02a30b161f3192 | Python | 6,150 | 155 | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import*
from scipy import ndimage as ndi
import napari
from skimage.morphology import*
from skimage.segmentation import*
from skimage.measure import*
import seaborn as sns
from sklearn.linear_model ... |
1390d6e46ce31667cda0ebbc6aae3014935d52b3e91f7d84b7ee0b16791f44f4 | Python | 6,152 | 226 | import numpy as np
from tensorflow.keras import Model
from tensorflow.keras.layers import Input, GlobalMaxPooling2D, \
GlobalMaxPooling3D, MaxPooling2D, \
MaxPooling3D
from explainability.layers import MaxPoolingLRP
def test_maxpool_2d_lrp():
... |
632844b3b55c045b0fd0e9b7275f8ef8d69fb28670ed218488f49db83d3e01de | Python | 6,152 | 191 | import torch
from torch_geometric.loader import DataLoader
from torch_ema import ExponentialMovingAverage
import logging
import numpy as np
from prettytable import PrettyTable
from config import build_arg_parser
from dataset.Pyg3BPA import Pyg3BPA
from dataset.Preprocess_3BPA import Preprocess3BPA
from eval import ev... |
adac2ad4eab242720f74a6800d5546092ed83f2d7e3e1a4a7c92691d621d3134 | Python | 6,155 | 141 | # third party modules
import numpy as np
# pnabind modules
from pnabind.utils import segmentsIntersectTriangles
def getClassSegmentations(edges, labels,
faces=None,
area_faces=None,
merge_nearby_clusters=False,
vertices=None,
merge_distance=10.0,
... |
44b4de8b82d02f5934eeb78b7bb6f6b214f611a6f8edb262f78c3035e3cc8500 | Python | 6,157 | 131 | import numpy as np
from sklearn.linear_model import LogisticRegression, PoissonRegressor
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.exceptions import ConvergenceWarning
import warnings
import csv
def LoadLingFeatures(args,feature_name):
#Load linguistic features
with open(f'data/... |
226b7aa55a93b7aeeab9e33ed19b70af9821b3e0fc1a93678e4ca70e69419b59 | Python | 6,158 | 209 | from langchain_core.utils.html import (
PREFIXES_TO_IGNORE,
SUFFIXES_TO_IGNORE,
extract_sub_links,
find_all_links,
)
def test_find_all_links_none() -> None:
html = "<span>Hello world</span>"
actual = find_all_links(html)
assert actual == []
def test_find_all_links_single() -> None:
h... |
e615111b2fa31ae29f7b1b2954a3b521460d2f148dc413684a1bcf0acb2d3d60 | Python | 6,158 | 141 | from graph_tool.all import *
import sys
import numpy as np
# file_location = "/mnt/e/LabMembers/yt/HB438/20220824_tubemap_newilastik/graph_annotated.gt"
# out_mat_file = "testing.mat"
file_location = sys.argv[1]
out_mat_file = sys.argv[2]
print(file_location)
print(out_mat_file)
grr = graph_tool.load_graph(file_lo... |
1a94d2114fe27e640e1d25a09217366fb347d987d259ec50d219c63e4d790b1d | Python | 6,159 | 149 | import numpy as np
import networkx as nx
import torch as pt
import pytorch_lightning as ptl
import torch_geometric as ptg
import torch
import xgi
from torch.nn import Sequential as Seq, Linear, ReLU
from torch_geometric.nn import MessagePassing
from pytorch_lightning import LightningModule
from torch.optim.adam import ... |
ed408e25ead212df4fab7c9a35ba09be360868bcda493a8b162581a6dafcd4c4 | Python | 6,160 | 119 | """
Simulate the noisy delta rule model and perform the same analyses of the apparent
learning rate as for subjects (analysis around change points, and modulation
by the normative model's cpp and uncertainty).
"""
import data_analysis_utils as dana
import itertools
import learning_rate_around_cp_group
import learning_... |
fa86476f7a58fd7c9268d3f2b41d0c71939da1a0c88c326fb95e7ec579735377 | Python | 6,161 | 189 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
a093bb15d6d7fea1029dd77505b5e30fb788301a50bdc94d6e06381edf3f5d0d | Python | 6,163 | 185 | ########################################################################
# 2D diffusion on an infinite surface from point source.
# AIMS: to verify STEPS spatial-deterministic solver 'TetODE' supports
# local initial conditions and calculates surface diffusion rates correctly.
# STEPS equivalent model: Determinist... |
b37007e6cf8e8d86d2784be7e8b9f7955032596e435949143231aa356bf55981 | Python | 6,167 | 148 | """
Functions for various imaging related processing tasks.
"""
import os
from typing import Union
import numpy as np
from imagingplus.main.core import ImagingTrial
from imagingplus.utils.utils import points_in_circle_np
def normalize_dff(arr: np.ndarray, normalize_pct: int = 20, normalize_val=None):
"""
... |
0181fc5089f4701a5c882081801070691b027193a19649ef165590047dfa7fa7 | Python | 6,169 | 160 | '''
intervalannotation.py - Tasks associated with annotation of genomic intervals
=====================================================================================
'''
import shutil
import os
import sqlite3
import cgatcore.iotools as iotools
import cgat.IndexedGenome as IndexedGenome
import cgat.Bed as Bed
from cg... |
7b6f4a93f17cc42f02ce7e49655a4e9ae4bd861cc5cd47c1ffbf276677c341c0 | Python | 6,171 | 161 | import torch
import numpy as np
import torch.nn as nn
from warnings import warn
from copy import deepcopy
from odl.tomo import fbp_op
from dliplib.reconstructors.base import BaseLearnedReconstructor
from dliplib.utils.models import get_unet_model
class FBPUNetReconstructor(BaseLearnedReconstructor):
HYPER_PARAM... |
f093b1a24c67b55d4492306533bedf04ab31d88c53c3b2e0a7a5a5ff187c6735 | Python | 6,171 | 174 | """
Analyze learning rates locked around change points at the subject level.
"""
import argparse
import data_analysis_utils as dana
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import os.path as op
import plot_utils as plut
import scipy.stats
FNAME_PREFIX = "learning-rate_around_cp"
# ... |
d46b404af2df4d6ad5e737c607d94b02dfcc76ea297180fecdf209148c644fdf | Python | 6,175 | 158 | import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.use('agg')
import pandas as pd
import os, sys
from mantis_ml.config_class import Config
from mantis_ml.modules.unsupervised_learn.dimens_reduction import DimensionalityReduction
import warnings
warnings.filterwarnings("ignore", category=De... |
fdc2ed1354ca90ab403f8d193e25ee34e1b28d263374b2cc0aab53ed9ec0fe95 | Python | 6,177 | 170 | import sys;sys.path.append('../src')
import pylab
from simulate_experiment import get_simulated_data
import spiketools
from GeneralHelper import (
find, nice_figure, ax_label1,
simpleaxis, ax_label_title
)
import scipy.stats as ss
from reaction_times_functions import (
reaction_time_plot, get_reaction_... |
421706f73e47bf5d2267cd2be7df22d6e8e4578ea2948790fc0edc742bcfd0cf | Python | 6,180 | 148 |
from .combinators import *
from .categories import Category
class Grammar:
def __init__(self, max_fwd_B = 2, max_bck_B = 1):
self.max_fwd_B = max_fwd_B
self.max_bck_B = max_bck_B
self.unary_normal = [
TypeRaising( # for topicalization, not used by EasyCCG, but used by C&C an... |
7e3e1d60cdb584964b8cc1c0a2585a23b3e6b5793af301cbc0828db33bf1d4a6 | Python | 6,180 | 180 | """
Test util functions
"""
__author__ = 'John D. Chodera'
import os
from perses.utils.openeye import smiles_to_oemol
from unittest import skipIf
running_on_github_actions = os.environ.get('GITHUB_ACTIONS', None) == 'true'
# functions testing perses.utils.data
@skipIf(running_on_github_actions, "Skip: running on ... |
fbf25cd1e93c5e149c4a527f2383db96133ddf9c3a3cfc9b90e2eac80e294d6b | Python | 6,183 | 187 | #!/usr/bin/env python3
import argparse
import copy
import os
import re
import subprocess
import pathlib
import pandas as pd
import yaml
import collections.abc
def update_dict_recursively(d, u):
for k, v in u.items():
if isinstance(v, collections.abc.Mapping):
d[k] = update_dict_recursively(d.... |
9f68f5246aa35e8829b90fe111d974b4c65496e386833a4e40d367d74dcaac1c | Python | 6,186 | 203 | """
This is a modified version of ns_2d.py from https://github.com/zongyi-li/fourier_neural_operator
"""
import torch
import math
from random_fields import GaussianRF
from timeit import default_timer
import scipy.io
import argparse
import os
from tqdm import tqdm
#w0: initial vorticity
#f: forcing term
#visc: ... |
bbe882900c3ecab97d1c2b6af7e72c4474d2a0374e02de5d2a773052edf79dcd | Python | 6,186 | 176 | import warnings
import numpy as np
import torch
from torch import nn as nn
from torch.nn import Parameter
from torch.nn import functional as F
from .base import Readout
class Pyramid(nn.Module):
_filter_dict = {
"gauss5x5": np.float32(
[
[0.003765, 0.015019, 0.023792, 0.01501... |
988731bda4019f20861edfd82bb9bf2699973fd34de040ce1976621b488b8cc7 | Python | 6,188 | 202 | import torch
import torch.nn.init as init
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from torch.nn.init import xavier_normal
# ---- general RNN core cell
class RNNCore:
"""
RNN Core taken from: https://github.com/sinzlab/Sinz2018_NIPS/blob/master/nips2018/architec... |
e17821f71d7a33164da543ca5cd4dccef0d8e83cf4365eb0efa0d9f6c110e843 | Python | 6,189 | 223 | #!/usr/bin/env python3
"""
Create CSV of node coordinates from Harvard-Oxford atlas regions in 1mm MNI152 (FSL) space
AUTHOR
----
Mike Tyszka, Ph.D.
LICENSE
----
MIT License
Copyright (c) 2019 Mike Tyszka
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated d... |
8e01b1c0bd970471e0fd36aaca7e647360da705120804dd5a0f755101a2d7b35 | Python | 6,193 | 203 | import logging
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F
logger = logging.getLogger(__name__)
def elu1(x, inplace=True, eps=0.0):
return F.elu(x, inplace=inplace) + 1.0 + eps
class Elu1(nn.Module):
"""
Elu activation... |
20eab67726633d9de6d6795df1a73ca2e026fa508b67fb56fc1f8dfc56556f5f | Python | 6,194 | 186 | import os
from net_utils import load_from_file
import plotting
import matplotlib.pyplot as plt
#root = '/Users/danil/Desktop/Habanero/vary_Nh_noMultiRep/'
#root = '/Users/danil/Library/Group Containers/G69SCX94XU.duck/Library/Application Support/duck/Volumes/habaxfer.rcs.columbia.edu – SFTP/vary_Nh_noMultiRep'
... |
ef15689de5762b66045acd04a64a9b68ee8b4d9389d8fea22afda0ab9c905b9c | Python | 6,195 | 200 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... |
dd898fd0ae656e54d09c0fbc7616ced12ce9de4c5f9aa4cbb29faf92af913bca | Python | 6,200 | 251 | import argparse
import os
from time import time as t
import brian2genn
import matplotlib.pyplot as plt
import nengo
import numpy as np
import pandas as pd
import torch
from brian2 import *
from experiments import ROOT_DIR
from experiments.benchmark import plot_benchmark
from nest import *
from bindsnet.encoding impor... |
2e020f92c7c51c766dfc1fea226120bbbe76298b2f7eb5e27c83ec01b76c4a7f | Python | 6,202 | 187 | """
Neural network implementations and utilities.
"""
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
def train(network, train_data, val_data, n_epochs=1, batch_size=1024,
o... |
29574d5187a5ad75814cff5f1b1e110f8890816dfa4d0c53b482f835227db566 | Python | 6,204 | 162 | import numpy as np
import copy
import glob
import pickle
gly_CB_mu = np.array([-0.5311191 , -0.75842446, 1.2198311 ]) #pickle.load(open("pkl/CB_mu.pkl", "rb"))
def get_len(v):
return np.sqrt(np.sum(v ** 2, -1))
def get_unit_normal(ab, bc):
n = np.cross(ab, bc, -1)
length = get_len(n)
if len(n.sha... |
9d7b358cdf6344706b8c324fd7e718038a0a72293929d39e1d02a3b3d2585b3d | Python | 6,204 | 195 | # If your training data has different scaling than your test data, pay attention to _parse_scales below.
import os
import re
import glob
from xml.etree import ElementTree
import logging
import numpy as np
import czifile
from skimage.external import tifffile
#import javabridge
#import bioformats
#javabridge.start_vm(c... |
75b3dabbed20bfdf4636270e262282bfb31afade53ca65ac6487a8805a01e382 | Python | 6,205 | 198 | # Copyright (c) MONAI Consortium
# 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, so... |
9913d230bc4d3cf33f5ce47dcaa48c14c2e8b1d4ec6ecb261ef2fb3c241f5244 | Python | 6,206 | 160 | from functools import partial
from typing import Union
from flax.struct import dataclass
import jax
import jax.numpy as jnp
@dataclass
class EnvParams:
max_steps_in_episode: int = 256
radius: float = 4 # Radius of the cell (μm).
n_receptors: int = 8 # Number of discrete receptors along surface.
spe... |
85a3f9930790efd1fb933a3918ee9c226995d8f8197cdc83fac311188530486c | Python | 6,207 | 168 | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 28 16:22:04 2019
@author: nishe
"""
import numpy as np
from connection import coordination
#%%
def fix_indegree(lattice_src, lattice_trg, degree_in_trg, tau_d, width, src_equal_trg = False, self_cnt = False):
#%%
N_src = len(lattice_src)
N_trg = len... |
b633568770afbb178f8defa0f1f8769811b50dc788c5ed25b76552a2be200c8b | Python | 6,207 | 149 | import numpy as np
from matplotlib import pylab as plt
import argparse
import os, sys, glob
import pandas as pd
import seaborn as sns
if not os.path.exists('results/final_results/'):
os.makedirs('results/final_results/')
dir_final_res = 'results/final_results/'
def errors_ae_don(latent_dim, method, reps, ... |
56826965530e8ff2a0bbdb9394b622a761e65d7d49df3c98cfca299e9636393f | Python | 6,211 | 178 | """
Test storage layer.
"""
__author__ = 'John D. Chodera'
################################################################################
# IMPORTS
################################################################################
import os
import os.path
import tempfile
from functools import partial
from perses.s... |
fb833d3b7931b5ef7015a7fa6db49578966b9d193dbb2ac2963283aafdc7fd1d | Python | 6,212 | 177 | # Copyright (c) MONAI Consortium
# 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, so... |
4609cfe07e31cd4bdacb870e6550b88b292d0d34b2b561c1dc2ea719f5249214 | Python | 6,213 | 206 | import logging
import os
import socket
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from configs.configs import get_config_dict
from helpers.figures import set_size
from helpers.rockland import get_edges_names
def _plot_glm_beta_heatmap(
config_dict: dict,
betas_df: pd.DataFrame... |
96e939f2cfcaebf4f9663795dad65dd0de45ae93323fdd7e9025b92f4d399a0c | Python | 6,215 | 147 | import sys
import os
import os.path
import numpy as np
import scipy.stats as stats
import random
genes={}
e={}
EXTEND=int(sys.argv[1])#######the distance flanking loop anchor to be consider
kind=sys.argv[3]########Protein coding gene or all genes
rootdir='.'
rsemdir='/scratch/users/txie/RNA-seq/3rsem_out/'... |
f4125a576430b8fcd690bb9735c3497433b3d5a0d374a37b7459c2a5d26a2db2 | Python | 6,218 | 217 | ''' Example for the preprocessing functions on a 2D example.
:Version:
1.0
:Date:
06.06.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016 Jan Melchior
This program is free software: you can redistribu... |
8b2d92e1183d34e12ce7e558498dfdebc203b2397335372e632c3a7f0eab00e6 | Python | 6,220 | 169 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
@dataclass
class PDEDataConfig:
n_scalar_components: int
n_vector_components: int
trajlen: int
n_spatial_dim: int
def create_data2D(
n_input... |
fcdf1e273af6ab14940dff0bca7c6c31a49a50cf42ae69fea74327866a34c86b | Python | 6,221 | 159 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
#pvalb
spikrate = pd.read_csv('../PvalbA_140Hz_AllHOF/Results/xxs.csv')
current = pd.read_csv('../PvalbA_140Hz_AllHOF/Results/crnts.csv')
veclengs = pd.read_csv('../PvalbA_140Hz_AllHOF/Re... |
b21dee3908d90ff4f53b0b16a5663703c8cba871725cdcea3bd18b55ebe85c5f | Python | 6,222 | 172 | """Compute conn (PID | II) + stats per condition in the time domain.
This script can :
- Compute connectivity metrics like conn_pid and conn_ii
- Perform RFX-contacts on ii (two-tailed), redu | syn | redu - syn | syn - redu
(one-tailed)
- Computations are performed within conditions (i.e. rew | pun)
- Significance t... |
5f14c62b49a1d8023f8716e21cd7ffc3d7320bdf8db9ca5830c3ffa13fe7f506 | Python | 6,224 | 114 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker
from scipy import stats
# define full list of constructs tested
proteins =['GRA16-HA-MECP2', 'GRA16-HA-TFEB', 'GRA16-HA-SMN1', 'GRA16-HA-ASPA', 'GRA16-HA-ASPAopt', 'GRA16-HA-GALC', 'GRA16-HA-GALCopt','GRA16... |
a5a19f05fe0e973a32f0cd2da18fd1066edf9d55905c2cbdbff157257c10e4a2 | Python | 6,225 | 159 | import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import time
from utils.utils import save_logs
from utils.utils import calculate_metrics
from utils.utils import save_test_duration
class Classifier_INCEPTION:
def __init__(self, output_directory, input_shape, nb_classes, verbose=False, ... |
4ee819d766f6e8ab893ae1bd03c3feba30ba5645281eea7024cdc52a0acfd7bb | Python | 6,231 | 188 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
ed13be34f614ec68a9bb42c987293e8714dfdefde4b3e53c3e20dca135efbd34 | Python | 6,234 | 178 | """
##############################################################################
#
# Filter out values in the exon inclusion table for the subsequent
# MARA model. Also, change the exon coordinates system from GTF to BED.
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institu... |
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