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d847420edded2597987ecc467395eaa5d0b4fd27a6d08d63cfa1ce61ca3acffe
Python
160
6
from snakemake.shell import shell shell( "samtools view -T {snakemake.input.ref} -@ {snakemake.threads} -C -o {snakemake.output} {snakemake.input.bam}" )
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Python
161
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import os VERSION = "3.4.7" if os.path.exists(os.path.join(os.path.dirname(__file__), '_version.py')): from ._version import version VERSION = version
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Python
161
7
lines = open("total_times.txt", "r").readlines() total_time = 0.0 for line in lines: time = line.split()[1] total_time += float(time) print(total_time)
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Python
162
6
from __future__ import absolute_import from __future__ import division from __future__ import print_function from pylib.timer import * from pylib.utils import *
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Python
163
6
#!/usr/bin/env python3 # encoding: utf-8 # @Time : 2018/8/20 下午12:53 # @Author : yuchangqian # @Contact : changqian_yu@163.com # @File : __init__.py.py
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Python
164
7
import unittest loader = unittest.TestLoader() suite = loader.discover(start_dir='../', pattern='*_test.py') runner = unittest.TextTestRunner() runner.run(suite)
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Python
164
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from .gaussian_target import gaussian_radius, gen_gaussian_target from .res_layer import ResLayer __all__ = ['ResLayer', 'gaussian_radius', 'gen_gaussian_target']
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Python
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from .compartment import Compartment, Dendrite, Soma from .ephysproperties import EphysProperties from .equations import library from .neuronmodel import NeuronModel
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Python
167
6
from .cascade_analysis import * from .celltype import * from .process_graph import * from .process_matrix import * from .traverse import * from .generate_adjs import *
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Python
167
6
from . import models from . import training from . import viz from .Dataset import Dataset, calc_dfc, get_network_input_obs from .models import Params, GeneratedData
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Python
168
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# -*- coding: utf-8 -*- """ Created on Mon Oct 28 14:57:35 2019 @author: RJovelin """ # setup and code access debarcer version from this module __version__ = "2.1.4"
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Python
168
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from .dataProcessingUtils import * from .featurize import * from .resnet import * from .model import * from .model2 import * from .utils import * from .dataset import *
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Python
168
8
"""Defines the list of words that should be removed from query""" class QueryStopwords: words = [ "neuron", "cell", # add others here ]
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Python
168
8
ENTITY_KEY = 'Entity' VISCOSITY_KEY = 'Viscosity_at_150' SCHRODINGER_BASE = '/localscratch/software/schrodinger/adv-2021-2' def get_file_path(): return __file__
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Python
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import tensorflow as tf import numpy as np import os def seed_everything(seed): np.random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) tf.random.set_seed(seed)
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Python
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from slideflow.tfrecord.torch import dataset from slideflow.tfrecord.torch.dataset import TFRecordDataset from slideflow.tfrecord.torch.dataset import MultiTFRecordDataset
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Python
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from .augmentation_functions import AllRandomAugmentations # USAGE: import augmentation function and add to this list augmentation_list = [ AllRandomAugmentations ]
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Python
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class Relationship: def __init__(self, subject, relationship, object): self.Subject = subject self.Relationship = relationship self.Object = object
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Python
176
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from M2TR.datasets.CelebDF import CelebDF from M2TR.datasets.dataset import DeepFakeDataset from M2TR.datasets.FFDF import FFDF from M2TR.datasets.ForgeryNet import ForgeryNet
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Python
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#!/usr/bin/env python import sys from openmmlib import pymol_show from openmmlib.polymerutils import load inFile = sys.argv[1] data = load(inFile) pymol_show.show_chain(data)
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Python
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from .decorators import auto_fp16, force_fp32 from .hooks import Fp16OptimizerHook, wrap_fp16_model __all__ = ['auto_fp16', 'force_fp32', 'Fp16OptimizerHook', 'wrap_fp16_model']
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Python
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#!/usr/bin/env python3 # encoding: utf-8 # @Time : 2018/8/3 下午2:59 # @Author : yuchangqian # @Contact : changqian_yu@163.com # @File : version.py __version__ = '0.1.1'
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Python
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from .gcn import GCN_2layer, GCN_3layer from .gin import GIN_2layer, GIN_3layer from .utils import load_data, train, test from .models import SAGE_3layer, GAT_3layer, JKNet_3layer
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Python
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from importlib import metadata try: __version__ = metadata.version(__package__) except metadata.PackageNotFoundError: # package is not installed __version__ = "0.0.0"
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Python
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custom_params = {} custom_params['model_dir'] = 'nn_models/pb_only/model_data/' custom_params['out_dir'] = 'output/pb_only/' custom_params['feat_mask'] = [0.0, 0.0, 0.0, 1.0, 0.0]
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Python
180
14
import os import sys os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" import cenfind def main(): return cenfind.run(sys.argv[1:]) if __name__ == "__main__": sys.exit(main())
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Python
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from . import annotation from . import constants from . import correction from . import image from . import main from . import model from . import multipage from . import selection
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Python
181
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""" This package contains classes that are necessary to encode data for modeling molecules as they are (i.e. without fragmenting). Created by: Martin Sicho On: 26.04.22, 13:12 """
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Python
181
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#!python import sys for l in sys.stdin: sys.stdout.write(l) if l=="#include <Python.h>\n": sys.stdout.write("#undef c_abs\n") #sys.stdout.write("#undef _c_Py_abs\n")
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Python
182
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from enum import Enum class Camera(Enum): LEFT = "left" RIGHT = "right" FRONT_WIDE = "front_wide" class TurnSignal(Enum): LEFT = 2 STRAIGHT = 1 RIGHT = 0
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Python
182
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from .affine_registration import affine_registration from .rigid_registration import rigid_registration from .deformable_registration import gaussian_kernel, deformable_registration
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Python
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# Load subpackages in current namespace from . import data from . import finetuning from . import loss from . import metric from . import model from . import test from . import train
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Python
183
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import ast import numpy as np def string2array(array_string): array_string = ','.join(array_string.replace('[ ', '[').split()) return np.array(ast.literal_eval(array_string))
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Python
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""" Refer to https://github.com/rosinality/stylegan2-pytorch/blob/master/op/__init__.py """ from .fused_act import FusedLeakyReLU, fused_leaky_relu from .upfirdn2d import upfirdn2d
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Python
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from abc import ABC, abstractmethod class ConversionException(Exception): pass class MolConverter(ABC): @abstractmethod def __call__(self, representation): pass
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Python
186
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custom_params = {} custom_params['model_dir'] = 'nn_models/hbond_only/model_data/' custom_params['out_dir'] = 'output/hbond_only/' custom_params['feat_mask'] = [0.0, 0.0, 1.0, 0.0, 0.0]
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Python
186
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from .ObjectCreator import ObjectCreator from .MaterialHandler import MaterialHandler from .NoiseModel import NoiseModel from .Projector import Projector from . import utils, transform
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Python
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''' Plotting tools. TODO: - plotting many curves with the right colors - arrows showing the PIV vector, maybe within a Paramecium shape ''' from .plots import * from .draw_cell import *
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Python
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custom_params = {} custom_params['model_dir'] = 'nn_models/shape_only/model_data/' custom_params['out_dir'] = 'output/shape_only/' custom_params['feat_mask'] = [1.0, 1.0, 0.0, 0.0, 0.0]
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Python
186
4
from PyInstaller.utils.hooks import collect_data_files, collect_submodules datas = collect_data_files("skimage.io._plugins") hiddenimports = collect_submodules('skimage.io._plugins')
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Python
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from setuptools import setup setup(name='BATMAN', version='1.0', description='BATMAN Python package', author='Riverside Research', packages=['batman'], )
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Python
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .build import make_optimizer from .build import make_lr_scheduler from .lr_scheduler import WarmupMultiStepLR
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Python
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import os # please refer to the readme on where to get the parameters. Save them in this folder: folder_with_parameter_files = os.path.join(os.path.expanduser('~'), 'hd_gliomouse_params')
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Python
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import networkx as nx import matplotlib.pyplot as plt BAG = nx.barabasi_albert_graph(n=10, m=1) pos = nx.kamada_kawai_layout(BAG) print(type(pos)) print(pos) nx.draw(BAG, pos) plt.show()
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Python
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from svglib.svglib import svg2rlg from reportlab.graphics import renderPDF def svg_to_pdf(svg_file, pdf_file): drawing = svg2rlg(svg_file) renderPDF.drawToFile(drawing, str(pdf_file))
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from typing import Mapping CURIE = str LABEL = str URI = str PRED_CURIE = CURIE SUBSET_CURIE = CURIE CATEGORY_CURIE = CURIE TAXON_CURIE = CURIE LANGUAGE_TAG = str COUNT_MAP = Mapping[CURIE, int]
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Python
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__all__ = ['__version__'] # major, minor, patch version_info = 1, 3, 0 # suffix suffix = None # version string __version__ = '.'.join(map(str, version_info)) + (f'.{suffix}' if suffix else '')
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Python
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from .dynamics import * from .various.math_phase import rms, get_phase_random, order_parameter, get_mean_phase from .various.my_logging import setup_logging from .various import parallel_with_threads
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from .config import get_parser as translation_parser from .model import TranslationModel from .trainer import TranslationTrainer __all__ = ['trans_parser', 'TranslationModel', 'TranslationTrainer']
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import numpy as np def onehot(data, n): """onehot ecoder""" buf = np.zeros(data.shape + (n,)) nmsk = np.arange(data.size) * n + data.ravel() buf.ravel()[nmsk - 1] = 1 return buf
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Python
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from utils.dataloaders.dataloader import * from utils.dataloaders.folders import * from utils.dataloaders.csv import * from utils.dataloaders.pickle import * from utils.dataloaders.statements import *
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Python
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import logging import sys logger = None if not logger: logger = logging.getLogger('drugex') logger.setLevel(logging.INFO) def setLogger(log): setattr(sys.modules[__name__], 'drugex', log)
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Python
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# Load subpackages in current namespace from . import ccreg from . import fluorescence from . import image from . import metrics from . import multithreading from . import processing from . import script
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#!/usr/bin/env python3 # encoding: utf-8 # @Time : 2018/10/3 下午2:10 # @Author : yuchangqian # @Contact : changqian_yu@163.com # @File : __init__.py from .syncbn import * from .parallel import *
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Python
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lines = open("total_times.txt", "r").readlines() total = 0.0 for line in lines: val = float(line.split(" ")[1]) total += val print(val) print('Total time (cpu minutes): {}'.format(total / 60))
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Python
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lines = open("total_times.txt", "r").readlines() total = 0.0 for line in lines: val = float(line.split(" ")[1]) total += val print(val) print('Total time (cpu seconds): {}'.format(total / 60))
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Python
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from setuptools import setup setup(name='graphxai', version='0.1', description='algorithms and metrics for explaining graph neural networks', packages=['graphxai'], zip_safe=False)
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class NoSiteMeterException(Exception): pass class LabelNormalizationError(Exception): pass class NotSupportingMultiprocessing(Exception): pass class InvalidIteratorArgs(Exception): pass
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Python
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from torch.utils.data import DataLoader from prefetch_generator import BackgroundGenerator class DataLoaderPrefetch(DataLoader): def __iter__(self): return BackgroundGenerator(super().__iter__())
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from .dist_utils import DistOptimizerHook, allreduce_grads from .misc import multi_apply, tensor2imgs, unmap __all__ = [ 'allreduce_grads', 'DistOptimizerHook', 'tensor2imgs', 'multi_apply', 'unmap' ]
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class KeywordCollection(list): def GetKeywordAndSubregionUris(self): result = [] for keyword in self: result.extend(keyword.GetKeywordAndSubregionUris()) return result
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from graphxai.datasets import load_ShapeGGen Owen_root = '/Users/owenqueen/Desktop/HMS_research/graphxai_project/GraphXAI/data/ShapeGGen/unzipped' SG = load_ShapeGGen(name = 'SG_HF_HF=1.pickle', root = Owen_root)
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from .unet import UNet_A,UNet_B # from .atrous_unet import AtrousUNet # from .rdn import RDN # from .split_atrous_unet import SplitAtrousUNet, MinimumUNet, MergeHead # from .lapsrn import LapSRN from .util import *
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Python
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"""carla-agentic-tools: a standalone MCP server exposing the CARLA skill registry.""" # Keep in step with the plugin manifests; tests/test_version.py fails on drift. __version__ = "0.6.0" __all__ = ["__version__"]
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def load_params() -> dict: """Returns the params dictionary""" return params params = { "custom_objects" : None, "learning_rate" : None, "temp_dir" : None, 'best_model_scores' : [] }
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from ._utils import _C # from maskrcnn_benchmark import _C nms = _C.nms # nms.__doc__ = """ # This function performs Non-maximum suppresion"""
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#coding:utf-8 import torch from torchsummary import summary def valsum(model, input_size, device = -1): summary(model, tuple(input_size)) # https://github.com/sksq96/pytorch-summary # pip3 install torchsummary
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from .io import ( read_json, write_json, dcm_info, parse_dcm2niix_fname, parse_bids_fname_keyvals, safe_copy, safe_mkdir, create_file_if_missing, strip_extensions, nii_to_json )
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import torch import torch.nn as nn def weighted_CrossEntropyLoss(output, target, classes_weights, device): cr = nn.CrossEntropyLoss(weight=torch.tensor(classes_weights).to(device)) return cr(output, target)
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from imlib.basic import * from imlib.dtype import * from imlib.encode import * from imlib.transform import *
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import numpy as np import json class NumpyEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.ndarray): return obj.tolist() return json.JSONEncoder.default(self, obj)
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.checkpoint import * from tflib.ops import * from tflib.utils import * from tflib.variable import *
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Python
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import numpy as np from numba import jit # @jit(nopython=True) def sample_rdpg(P, seed): np.random.seed(seed) A = np.random.binomial(np.int64(1), P) A[np.arange(len(A)), np.arange(len(A))] = 0.0 return A
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custom_params = {} custom_params['model_dir'] = 'nn_models/hphob_only/model_data/' custom_params['out_dir'] = 'output/hphob_only/' custom_params['feat_mask'] = [0.0, 0.0, 0.0, 0.0, 1.0] custom_params['n_conv_layers'] = 3
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""" NIRSimple ========= fNIRS signal processing simplified """ from .preprocessing import intensities_to_od_changes from .preprocessing import od_to_od_changes from .preprocessing import mbll from .processing import cbsi
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import torch from graphxai.datasets import Mutagenicity dataset = Mutagenicity(root = '../../explainers/mutag/data', split_sizes = (0.8, 0.1, 0.1), seed = 1234) print(dataset.test_index) print(dataset.test_index.shape)
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""" Optional imports class for RACCOON F. Comitani @2022 """ class OptionalImports: """ Static container to keep track of optional libraries and their imports """ hdbscan = False feather = False
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custom_params = {} custom_params['model_dir'] = 'nn_models/all_feat_1l/model_data/' custom_params['out_dir'] = 'output/all_feat_1l/' custom_params['feat_mask'] = [1.0, 1.0, 1.0, 1.0, 1.0] custom_params['n_conv_layers'] = 1
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""" neuralop.data contains APIs for PDE datasets (.datasets) and APIs to transform raw data inputs into forms more suited to training machine learning models (.transforms) """ from . import datasets from . import transforms
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import tmkit as tmk def test_cath_summary_by_id(): res = tmk.cath.summary_by_id(id="1cukA01") assert ( res["domain"] == "http://www.cathdb.info/version/v4_2_0/api/rest/domain_summary/1cukA01" )
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from .encoder import TextEncoder, SpeakerEncoder from .mel import mel_spectrogram from .tts_decoder import TTSDecoder from .vc_decoder import VCDecoder from .classifier import SpkClassifier from .f0_encoder import F0_Encoder
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# Copyright (c) 2015-present, Facebook, Inc. # All rights reserved. from models import * from cait_models import * from resmlp_models import * #from patchconvnet_models import * dependencies = ["torch", "torchvision", "timm"]
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import errno import os def mkdir(path): try: os.makedirs(path) except OSError as e: if e.errno != errno.EEXIST: raise
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import mesh import mesh.plot.vedo_viewer as mplot folder = 'data/cilia_plane2' num_phases = 20 phases = [0, 5] mv = mplot.FlagellaPlaneViewer(folder, phases=phases, num_phases=num_phases) plotter = mv.show() plotter.close()
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''' We can visualize 3D mesh with one of the supported packages. Best: vedo or trimesh ''' import mesh.plot.vedo_viewer as mplot folder = 'data/sphere_presaved/' m = mplot.MeshViewer(folder) plotter = m.show() plotter.close()
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#! /usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = 'Andalibi, V.' class ParameterNotFoundError(Exception): pass class RelativePathError(Exception): pass class InvalidConfigurationError(Exception): pass
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from Classes.SPARQLDB import SPARQLDB class SearchComponent: """Abstract class from which other search module components inherit""" def __init__(self): # Setup SPARQL db access self.SPARQLDB = SPARQLDB()
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#coding:utf-8 from utils.parm import print_model_parm_flops, print_model_parm_nums def simplesum(model, inputsize, device=-1): print_model_parm_nums(model) print_model_parm_flops(model, inputsize=inputsize, device=device)
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from eelbrain._text import enumeration def test_text(): "Test _report text functions" assert enumeration(['a', 'b', 'c']) == 'a, b and c' print(enumeration({'a', 'b', 'c'})) assert enumeration(['a', 2]) == 'a and 2'
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# Copyright (c) Open-MMLab. All rights reserved. from .hook import HOOKS, Hook @HOOKS.register_module() class DistSamplerSeedHook(Hook): def before_epoch(self, runner): runner.data_loader.sampler.set_epoch(runner.epoch)
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from setuptools import setup from torch.utils.cpp_extension import CppExtension, BuildExtension setup(name='syncbn_cpu', ext_modules=[CppExtension('syncbn_cpu', ['syncbn_cpu.cpp'])], cmdclass={'build_ext': BuildExtension})
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''' We can visualize 3D mesh with one of the supported packages. Best: vedo or trimesh ''' import mesh.plot.vedo_viewer as mplot folder = 'data/sphere_plane_presaved/' m = mplot.MeshViewer(folder) plotter = m.show() plotter.close()
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from .batch_processing import * from .folder_information import * from .units_conversion import * from .load_data import * from .configuration import * from .data_preparation import * from .load_model import * from .file_utils import *
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import mesh import mesh.plot.trimesh_viewer as mplot folder = 'data/cilia_plane2' num_phases = 20 phases = [0, 5] # mv = mplot.MeshViewer(tdir) mv = mplot.FlagellaPlaneViewer(folder, phases=phases, num_phases=num_phases) mv.show()
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""" RACCOON (Resolution-Adaptive for Coarse-to-fine Clustering OptimizatiON) F. Comitani @2018-2022 A. Maheshwari @2019 """ from raccoon._version import __version__ from raccoon.main import * if __name__ == "__main__": pass
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#!/usr/bin/env python from rectool import RecognizerToolbox import sys rt = RecognizerToolbox() if rt.use_existing_state_segmentations(): sys.exit(0) if not rt.use_existing_lnas(): rt.generate_lnas() rt.decode_state_segmentations()
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from iopath.common.file_io import PathManagerFactory _ENV_SETUP_DONE = False pathmgr = PathManagerFactory.get(key="M2TR") def setup_environment(): global _ENV_SETUP_DONE if _ENV_SETUP_DONE: return _ENV_SETUP_DONE = True
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""" abc Created by: Martin Sicho On: 22.04.22, 10:40 """ from abc import ABC, abstractmethod class ConversionException(Exception): pass class MolConverter(ABC): @abstractmethod def __call__(self, representation): pass
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import torch.nn as nn class Distribution(nn.Module): def __init__(self): super(Distribution, self).__init__() def sample(self): raise NotImplementedError def log_prob(self, value): raise NotImplementedError
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import os from os.path import dirname, realpath, join import sys from .herbsgui import main def run_herbs(): current_wd = os.getcwd() script_dir = dirname(realpath(__file__)) os.chdir(script_dir) main() os.chdir(current_wd)