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c372cf3d5179a75c6bf0a6ff03580bb7a3e3fd58f394ff9757412a49ee66301e
Python
247
13
"""Profiler for running Semsimian performance tests""" import pstats import sys def display_stats(filename): p = pstats.Stats(filename) p.sort_stats("tottime").print_stats(20) if __name__ == "__main__": display_stats(sys.argv[1])
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Python
247
13
import os import tmkit as tmk from . import dir_data def test_mapping_pdb2uniprot(): res = tmk.mapping.pdb2uniprot( id="101m.A", ref_fpn=os.path.join(dir_data, "map/pdb_chain_uniprot.csv"), ) assert res == "P02185"
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Python
249
17
import os cmd = 'mkdir Embeddings' os.system(cmd) cmd = 'python3 fasta_to_ds.py' os.system(cmd) cmd = 'python3 predict_embed.py' os.system(cmd) cmd = 'python3 append_embed.py' os.system(cmd) cmd = 'python3 make_predictions.py' os.system(cmd)
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Python
250
14
import os def build_index(): f_index = open("indexing_file") index = f_index.read() index = index.split("\n") dict_index = {} for i in index[:-1]: i = i.split(",") dict_index[i[0]] = i[1] return dict_index
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Python
250
14
""" © 2022, ETH Zurich """ import requests from rescoss_logp_ml.data_handling.download import DATA_URL, SAVED_MODELS_URL def test_islinkup(): for remote in [DATA_URL, SAVED_MODELS_URL]: r = requests.head(remote) assert r.ok
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Python
251
8
from .ade import ADE from .voc import VOC from .cityscapes import Cityscapes from .camvid import CamVid # from .stuff10k import Stuff10K from .pcontext import PascalContext __all__ = ['ADE', 'VOC', 'Cityscapes', 'Stuff10K', 'CamVid', 'PascalContext']
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Python
251
5
custom_params = {} custom_params['cache_dir'] = 'nn_models/sc05/cache/' custom_params['model_dir'] = 'nn_models/sc05/all_feat/model_data/' custom_params['desc_dir'] = 'descriptors/sc05/all_feat/' custom_params['feat_mask'] = [1.0, 1.0, 1.0, 1.0, 1.0]
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Python
252
10
from setuptools import setup, find_packages setup( name = "darela", version = "1.0", description = "DA Release Analysis", author = "Shashaank N", packages = find_packages(), install_requires = ['autograd', 'alive-progress'], )
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Python
252
10
__author__ = "Christopher Preusch" __copyright__ = "Copyright 2017, Christopher Preusch" __email__ = "cpreusch[at]ust.hk" __license__ = "MIT" from snakemake.shell import shell shell("samtools flagstat {snakemake.input[0]} > {snakemake.output[0]}")
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Python
252
13
from .treba_model import TREBA_model model_dict = { 'treba_model' : TREBA_model } def get_model_class(model_name): if model_name in model_dict: return model_dict[model_name] else: raise NotImplementedError
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Python
253
8
import pandas as pd from settings import * def plot(): df = pd.read_table(f'{OUTPUT_FILE}_fit.tsv') ax = df.plot.line(x='epoch', y=['desire_ratio', 'valid_ratio'], ylim=(0,1)) fig = ax.get_figure() fig.savefig(f'{OUTPUT_FILE}_fit.png')
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Python
254
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from collections import defaultdict from typing import Tuple, Any, Dict, List, Iterable def pairs_as_dict(pairs: Iterable[Tuple[Any, Any]]) -> Dict[Any, List[Any]]: d = defaultdict(list) for p in pairs: d[p[0]].append(d[1]) return d
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Python
254
11
from snakemake.shell import shell log = snakemake.log_fmt_shell(stdout=False, stderr=True) shell( "vcfallelicprimitives -t DECOMPOSED --keep-geno {snakemake.input[0]} " "| vcffixup - | vcfstreamsort " " > {snakemake.output[0]} {log}" )
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Python
255
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#%% import numpy as np from graspologic.simulations import er_np p1 = 0.05 p2 = 0.03 p3 = 0.02 p4 = 0.01 ps = [p1, p2, p3, p4] n = 100 As = np.zeros((4, n, n)) for i, p in enumerate(ps): As[i] = er_np(n, p, directed=True, loops=True) As.sum(axis=0)
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Python
255
10
from ppqm.utils import shell def test_steam_timeout() -> None: cmd = "echo 'hello_world' && sleep 1 && sleep 1 && sleep 1 && echo 'too_slow'" timeout = 1 # seconds stdout, _ = shell.execute(cmd, timeout=timeout) assert stdout is None
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Python
255
9
from setuptools import setup setup(name='fdtd_env', version='0.0.1', author='Renjie Li', description='FDTD environment for optimizing photonic crystals by RL', license='MIT', install_requires=['gym>=0.2.3'] )
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Python
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""" This module contains classes and methods for making and analyzing predictions with models that have already been trained. """ from .model_predict import AnalyzeSequences from . import predict_handlers __all__ = ["AnalyzeSequences", "predict_handlers"]
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Python
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from __future__ import absolute_import from __future__ import division from __future__ import print_function """ data module contains data_handler that allows basic preprocessing for input into the sample-wise and feature-wise encoder-decoder. """
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Python
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""" __init__.py Created by: Martin Sicho On: 17.05.22, 9:53 """ import logging import sys logger = None if not logger: logger = logging.getLogger() logger.setLevel(logging.INFO) def setLogger(log): setattr(sys.modules[__name__], 'logger', log)
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Python
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import numpy as np data = np.load('./inputs/sociodemographics_correlation_input.npy') labels = np.load('./inputs/labels.npy', allow_pickle=True) corr = np.corrcoef(data.T) np.save('corr.npy', corr)
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Python
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class RelationshipMap: def __init__(self): pass Forward = { "Located_in": "Located in", "Neurotransmitter": "Releases", } Backward = { "Located_in": "Includes", "Neurotransmitter": "Released by", }
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Python
261
4
from utils.decomposition.Decomposition import Decomposition from utils.decomposition.GloVeDecomposition import GloVeDecomposition from utils.decomposition.ParafacDecomposition import ParafacDecomposition from utils.decomposition.LDA_idf import LDADecomposition
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Python
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11
pcr_prediction_config = dict( amplification_full_cycle_number=40, training_cycle_number=21, interpolation_method='cubic', train_test_split_ratio=0.95, prediction_timestep=5, interpolation_number=10, ) if __name__ == "__main__": pass
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Python
262
9
""" This is the main module for Selene. """ __all__ = ["sequences", "targets", "samplers", "utils", "predict", "interpret", "__version__"] from .version import __version__ from .evaluate_model import EvaluateModel from .train_model import TrainModel
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Python
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from setuptools import setup from torch.utils.cpp_extension import CUDAExtension, BuildExtension setup(name='syncbn_gpu', ext_modules=[CUDAExtension('syncbn_gpu', ['syncbn_cuda.cpp', 'syncbn_cuda_kernel.cu'])], cmdclass={'build_ext': BuildExtension})
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Python
263
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from mb_dual_dan.connectome.loader import Connectome, load_winding, summary from mb_dual_dan.connectome.mb_extract import MBSubgraph, extract_mb, mb_summary __all__ = [ "Connectome", "load_winding", "summary", "MBSubgraph", "extract_mb", "mb_summary", ]
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Python
264
12
__author__ = "Dayne Filer" __copyright__ = "Copyright 2019, Dayne Filer" __email__ = "dayne.filer@gmail.com" __license__ = "MIT" from snakemake.shell import shell shell( "bcftools norm {snakemake.params} {snakemake.input[0]} " "-o {snakemake.output[0]}" )
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Python
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12
mylines = open('bc-100.out').readlines() for line in mylines: # Go through every PDB in the line pdbid_chain = line.split() for pc in pdbid_chain: chain = pc.split('_')[1] if len(chain) == 1: print (pc) break
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Python
264
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from torch.nn.parallel import DataParallel, DistributedDataParallel from mmcv.utils import Registry MODULE_WRAPPERS = Registry('module wrapper') MODULE_WRAPPERS.register_module(module=DataParallel) MODULE_WRAPPERS.register_module(module=DistributedDataParallel)
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Python
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import gurobipy as gp ARCHITECTURES = { 1: [784, 10], 2: [784, 16, 10], 3: [784, 16, 16, 10] } INT = 0 BIN = 1 CONT = 2 EPSILON = 1e-4 LOG = False MULTIOBJ = False TARGET_ERROR = 0.1 # 90% Accuracy MARGIN = 0.5 DO_CUTOFF = True GUROBI_ENV = gp.Env()
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Python
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13
from .constants import SEARCH_CONFIG from .ols_implementation import ( BaseOlsImplementation, OlsImplementation, TIBOlsImplementation, ) __all__ = [ "SEARCH_CONFIG", "BaseOlsImplementation", "OlsImplementation", "TIBOlsImplementation", ]
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Python
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__author__ = "Johannes Köster" __copyright__ = "Copyright 2016, Johannes Köster" __email__ = "koester@jimmy.harvard.edu" __license__ = "MIT" from snakemake.shell import shell shell("samtools index {snakemake.params} {snakemake.input[0]} {snakemake.output[0]}")
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Python
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from .ba_houses import BA_Houses from .gcn import GCN #from .utils import train, test from .testing import train, test from .testing import GCN_1layer, GCN_2layer, GCN_3layer, GCN_3layer_basic from .testing import GIN_1layer, GIN_2layer, GIN_3layer, GIN_3layer_basic
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Python
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from .toy import get_toy_palette, sample_toy_networks from .utils import get_random_seed, set_warnings from .wrangle import ( get_paired_inds, get_paired_subgraphs, get_seeds, remove_group, to_largest_connected_component, to_pandas_edgelist, )
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Python
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10
__author__ = "Michael Chambers" __copyright__ = "Copyright 2019, Michael Chambers" __email__ = "greenkidneybean@gmail.com" __license__ = "MIT" from snakemake.shell import shell shell("samtools faidx {snakemake.params} {snakemake.input[0]} > {snakemake.output[0]}")
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import numpy as np from sklearn.metrics import roc_auc_score posd = np.load('pos_dists.npy') negd = np.load('neg_dists.npy') ytrue = np.concatenate([np.ones_like(posd), np.zeros_like(negd)]) ypred = 1.0/np.concatenate([posd, negd]) print(roc_auc_score(ytrue, ypred))
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# Copyright (c) Open-MMLab. All rights reserved. from .hook import HOOKS, Hook @HOOKS.register_module() class ClosureHook(Hook): def __init__(self, fn_name, fn): assert hasattr(self, fn_name) assert callable(fn) setattr(self, fn_name, fn)
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#! /usr/bin/python import numpy import sys import umap # run with python input_file.txt > output_file.txt dataarray = numpy.loadtxt("data_in.txt",delimiter="\t") embedding = umap.UMAP().fit_transform(dataarray) numpy.savetxt("data_out.txt",embedding,delimiter="\t")
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Python
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import torch from torch import nn class Scale(nn.Module): def __init__(self, init_value=1.0): super(Scale, self).__init__() self.scale = nn.Parameter(torch.FloatTensor([init_value])) def forward(self, input): return input * self.scale
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# Flask settings # FLASK_SERVER_NAME = 'localhost:8888' FLASK_DEBUG = False # Do not use debug mode in production # Flask-Restplus settings RESTPLUS_SWAGGER_UI_DOC_EXPANSION = 'list' RESTPLUS_VALIDATE = True RESTPLUS_MASK_SWAGGER = False RESTPLUS_ERROR_404_HELP = False
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Python
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""" Code is based on https://github.com/MichSchli/GraphMask. """ from .lagrangian_optimization import LagrangianOptimization from .hard_concrete import HardConcrete from .multiple_inputs_layernorm_linear import MultipleInputsLayernormLinear from .squeezer import Squeezer
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""" Dummy conftest.py for gratin. If you don't know what this is for, just leave it empty. Read more about conftest.py under: - https://docs.pytest.org/en/stable/fixture.html - https://docs.pytest.org/en/stable/writing_plugins.html """ # import pytest
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Python
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from .matrix import cascade, tree_cascade, triangular, psvd from .vector import balanced_tree, unbalanced_tree, vector_unit from .forward import ForwardMesh from .coupling import CouplingNode, PhaseStyle from .rectangular import rectangular from .component import Component
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from setuptools import setup setup(name='carpet', version='2021', # description='', url='https://cfaed.tu-dresden.de/friedrich-home', author='Anton Solovev, Benjamin M Friedrich', license='MIT', packages=['carpet'], zip_safe=False)
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from .grid import Grid, YeeGrid from .fdfd import FDFD from .fdtd import FDTD from .mode import ModeLibrary, ModeSolver from .utils import Box, Material, SILICON, POLYSILICON, NITRIDE, OXIDE, TEST_INF, AIR from jax.config import config config.update("jax_enable_x64", True)
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Python
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import numpy as np row_range = np.arange(4, 102) file_name = 'hc' with open(file_name, 'r') as f: content = f.readlines() for row in row_range: if content[row] == 'YES\n': content[row] = 'NO\n' with open(file_name, 'w') as f: f.write(''.join(content))
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#!/usr/bin/env python from rectool import RecognizerToolbox rt = RecognizerToolbox() if not rt.use_existing_lnas(): if not rt.use_existing_adaptation(): rt.generate_lnas() rt.decode_state_segmentations() rt.estimate_adaptation() rt.generate_lnas() rt.decode_batch()
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from .trainer import Trainer from .torch_setup import setup from .training_state import load_training_state, save_training_state from .incremental import IncrementalFNOTrainer from .adamw import AdamW from .tensorgrad import ( TensorGRaD, fno_tensorgrad_param_groups, )
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""" Dummy conftest.py for palm_tools. If you don't know what this is for, just leave it empty. Read more about conftest.py under: - https://docs.pytest.org/en/stable/fixture.html - https://docs.pytest.org/en/stable/writing_plugins.html """ # import pytest
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Python
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''' Initializes for model running mode. See the documentation of Clampy for initialization files for amplifiers. ''' from brian2 import * from clampy.brianmodels import * dt = 0.1*ms board = RC_and_electrode(Ce = 3*pF) board.set_aliases(Ic1='Ic', Ic2='Ic', I_TEVC='I', V1='V')
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# Copyright (c) Open-MMLab. All rights reserved. from .base import BaseFileHandler from .json_handler import JsonHandler from .pickle_handler import PickleHandler from .yaml_handler import YamlHandler __all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler']
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dataset_paths = { 'celeba_train': '', 'celeba_test': '', 'celeba_train_sketch': '', 'celeba_test_sketch': '', 'celeba_train_segmentation': '', 'celeba_test_segmentation': '', 'ffhq': '', } model_paths = { 'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt', }
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"""Main init for phys2bids package.""" from ._version import get_versions __version__ = get_versions()["version"] del get_versions from . import _version __version__ = _version.get_versions()["version"] from . import _version __version__ = _version.get_versions()["version"]
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from eelbrain import * configure(False) ds = datasets.get_mne_sample(-0.1, 0.2, src='ico', sub="modality == 'A'") res = testnd.ttest_1samp('src', ds=ds, tstart=0, tfce=True, samples=100) # ds = datasets.get_uts() # res = testnd.ttest_1samp('uts', ds=ds, tfce=True, samples=1000)
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import os import configparser NEO4J_CONF_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../config/neo4j_config.ini") def get_config(): conf = configparser.ConfigParser() conf.read(NEO4J_CONF_PATH) return conf['NEO4j'] neo4j_config = get_config()
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from .bbox_nms import multiclass_nms from .merge_augs import (merge_aug_bboxes, merge_aug_masks, merge_aug_proposals, merge_aug_scores) __all__ = [ 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', 'merge_aug_scores', 'merge_aug_masks' ]
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''' Some tools for file management ''' import os __all__ = ['up_dir', 'make_subdir'] def up_dir(path): return os.path.split(path)[0] def make_subdir(path, dir): folder = os.path.join(path, dir) if not os.path.exists(folder): os.mkdir(folder) return folder
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from snakemake.shell import shell log = snakemake.log_fmt_shell(stdout=True, stderr=True) shell( "(whamg " "-x {snakemake.threads} " "-a {snakemake.input.fasta} " "-f {snakemake.input.bam} " "-c {snakemake.params.include_chroms} > {snakemake.output}) {log} " )
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import gzip import sys import os fh = gzip.open(sys.argv[1],'rt') fho = gzip.open(sys.argv[2],'wt') print("in: "+sys.argv[1]) print("out: "+sys.argv[2]) for line in fh: line = line.replace('chr','') fho.write(line) fh.close() fho.close() os.rename(sys.argv[2],sys.argv[1])
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Python
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from setuptools import find_packages, setup setup( name='M2TR', version='0.1.1', requires= ['timm', 'fvcore', 'albumentations', 'kornia', 'simplejson', 'tensorboard', ], packages=find_packages(), license="apache 2.0")
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .transforms import Compose from .transforms import Resize from .transforms import RandomHorizontalFlip from .transforms import ToTensor from .transforms import Normalize from .build import build_transforms
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__version__ = "0.0.1" #from ._widget import PalmariWidget from .data_structure.experiment import Experiment from .data_structure.acquisition import Acquisition from .processing.image_pipeline import ImagePipeline # from .processing.image_pipeline_widget import ImagePipelineWidget
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from mmcv.utils import Registry CONV_LAYERS = Registry('conv layer') NORM_LAYERS = Registry('norm layer') ACTIVATION_LAYERS = Registry('activation layer') PADDING_LAYERS = Registry('padding layer') UPSAMPLE_LAYERS = Registry('upsample layer') PLUGIN_LAYERS = Registry('plugin layer')
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from crossmapy.embed import * from crossmapy.utils import * from crossmapy.mi import * from crossmapy.dataset import * from crossmapy.gc import * from crossmapy.te import * from crossmapy.ccm import * from crossmapy.cme import * from crossmapy.cmc import * from crossmapy.plot import *
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import torch from torchsummary import summary from nets.yolo import YoloBody if __name__ == "__main__": device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') m = YoloBody([[3, 4, 5], [1, 2, 3]], 80).to(device) summary(m, input_size=(3, 416, 416))
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from .data_losses import LpLoss, H1Loss from .equation_losses import BurgersEqnLoss, ICLoss from .differentiation import FourierDiff, non_uniform_fd, FiniteDiff from .meta_losses import ( WeightedSumLoss, FieldwiseAggregatorLoss, Aggregator, Relobralo, SoftAdapt, )
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# %% # start demo here import pymaid from pymaid_creds import url, name, password, token # my private CATMAID credentials rm = pymaid.CatmaidInstance(url, token, name, password) skids = [992646, 15599768] pair_morpho = pymaid.get_neurons(skids) pair_morpho.nodes.loc[:, ['x', 'y', 'z']]
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from torch.nn.modules.module import Module from ..functions.roi_crop import RoICropFunction class _RoICrop(Module): def __init__(self, layout = 'BHWD'): super(_RoICrop, self).__init__() def forward(self, input1, input2): return RoICropFunction()(input1, input2)
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# optimizer optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[16, 22]) total_epochs = 5000
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user = 'stephan' exec(open("./AIS_parameters_sak.py").read()) EU_COUNTRIES = ["AUT", "BEL", "BGR", "HRV", "CYP", "CZE", "DNK", "EST", "FIN", "FRA", "DEU", "GRC", "HUN", "IRL", "ITA", "LVA", "LTU", "LUX", "MLT", "NLD", "POL", "PRT", "ROU", "SVK", "SVN", "ESP", "SWE"]
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from dataclasses import dataclass from typing import Any, List, Optional @dataclass class Settings: impl: Any = None autosave: bool = False associations_type: Optional[str] = None preferred_language: Optional[str] = None other_languages: Optional[List[str]] = None
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import numpy as np from matplotlib import pyplot as plt import seaborn as sns from bayesian_bootstrap import linear_regression X = np.linspace(-5, 5, 50) y = 2 * X + np.random.normal(0, 1, 50) results = linear_regression(X.reshape(-1, 1), y, 1000) sns.distplot(results[:, 0]) plt.show()
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import pytest from AFQ.viz.utils import Viz def test_viz_name_errors(): Viz("fury") with pytest.raises( TypeError, match="Visualization backend contain" + " either 'plotly' or 'fury'. " + "It is currently set to plotli"): Viz("plotli")
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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tflib.data.dataset import * from tflib.data.disk_image import * from tflib.data.memory_data import * from tflib.data.tfrecord import * from tflib.data.tfrecord_creator import *
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from snakemake.shell import shell extra = snakemake.params.get("extra", "") log = snakemake.log_fmt_shell(stdout=True, stderr=True) shell( "gatk FilterMutectCalls " "-R {snakemake.input.fasta} -V {snakemake.input.vcf} " "{extra} " "-O {snakemake.output.vcf} " "{log}" )
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""" ``HERBS Code`` ================ Functions present in herbs are listed below. For Image Processing -------------------------- ... ... For Atlas -------------- ... For Others ------------------ ... """ from .run_herbs import run_herbs from .czi_reader import CZIReader
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from .augmentation import AugmentationLayer from .uncertainty_weighting import UncertaintyWeighting, UncertaintyWeightingWithRollingAverage from .upsampling import UpSampling1D, UpSampling2D, UpSampling3D from .depthwise_conv_3d import DepthwiseConv3D from .b_splines import interpolate_spline
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import os with open('../README.md', 'r') as file: content = file.read() content = content.replace( '_images/', 'https://raw.githubusercontent.com/UN-GCPDS/python-gcpds.visualizations/main/docs/source/notebooks/_images/') with open('../README.md', 'w') as file: file.write(content)
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from utils import Config from utils.ArticlesProvider import ArticlesProvider class PostProcessing: def __init__(self, config: Config, articles: ArticlesProvider): self.config = config self.articles = articles def apply(self, tensor): raise NotImplementedError
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from eelbrain import * from eelbrain._wxgui import get_app if __name__ == '__main__': get_app() ds = datasets.get_uts(True, nrm=True) # res = testnd.ttest_rel('utsnd', 'A', match='rm', ds=ds, samples=100) res2 = testnd.anova('utsnd', 'A*B*nrm(A)', ds=ds, samples=100, pmin=0.5)
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"""Statistical tests for multidimensional data in :class:`NDVar` objects""" __test__ = False from ._stats.testnd import ( NDTest, MultiEffectNDTest, t_contrast_rel, corr, ttest_1samp, ttest_ind, ttest_rel, anova, Vector, VectorDifferenceRelated, ) from ._stats.spm import LM, LMGroup
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from deeprank_gnn.GraphGenMP import GraphHDF5 pdb_path = './data/pdb/1ATN/' pssm_path = './data/pssm/1ATN/' ref = './data/ref/1ATN/' GraphHDF5(pdb_path=pdb_path, ref_path=ref, pssm_path=pssm_path, graph_type='residue', outfile='1ATN_residue.hdf5', nproc=1, tmpdir='./tmpdir')
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"""Entrypoint module, in case you use `python -m selene_sdk`. Why does this file exist, and why __main__? For more info, read: - https://www.python.org/dev/peps/pep-0338/ - https://docs.python.org/3/using/cmdline.html#cmdoption-m """ from .cli import main if __name__ == '__main__': main()
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from __future__ import absolute_import import torch import numpy as np from ._ext import nms import pdb def nms_gpu(dets, thresh): keep = dets.new(dets.size(0), 1).zero_().int() num_out = dets.new(1).zero_().int() nms.nms_cuda(keep, dets, num_out, thresh) keep = keep[:num_out[0]] return keep
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# Pablo Gainza Cirauqui 2016 LPDI IBI STI EPFL # This pymol plugin for Masif just enables the load ply functions. from pymol import cmd from loadPLY import * from loadDOTS import * import sys cmd.extend("loadply", load_ply) cmd.extend("loaddots", load_dots) cmd.extend("loadgiface", load_giface)
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from ppqm import chembridge from ppqm.gamess import GamessCalculator from ppqm.gaussian import GaussianCalculator from ppqm.mopac import MopacCalculator from ppqm.orca import OrcaCalculator from ppqm.utils.files import WorkDir from ppqm.version import __version__ from ppqm.xtb import XtbCalculator
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from .anchor import * # noqa: F401, F403 from .bbox import * # noqa: F401, F403 from .evaluation import * # noqa: F401, F403 from .fp16 import * # noqa: F401, F403 from .mask import * # noqa: F401, F403 from .post_processing import * # noqa: F401, F403 from .utils import * # noqa: F401, F403
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"""Hopefully importing everything.""" from . import ( _version, io, regressors, signal, stats, utils, viz, workflows, ) __all__ = [ io, regressors, signal, stats, utils, viz, workflows, ] __version__ = _version.get_versions()['version']
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import sys import pdbfile as p file = sys.argv[1] i = int(sys.argv[2]) with open(file) as f: for line in f: for c in line: c = c.strip() if len(c)>0: print(p.join(p.row([0.0,0.0,i*3.81],p.find_aa_stat("tla","abc",c),i,i))) i = i+1
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from .coco import COCODataset from .voc import PascalVOCDataset from .concat_dataset import ConcatDataset from .abstract import AbstractDataset __all__ = ["COCODataset", "ConcatDataset", "PascalVOCDataset", "AbstractDataset"]
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import pandas as pd import fire def main(input_file, output_file): df = pd.read_csv(input_file, names=['SMILES']) nrows = len(df) df['ID'] = list(range(nrows)) df.to_csv(output_file, header=True, index=False, columns=['ID','SMILES']) if __name__=='__main__': fire.Fire(main)
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import pytest from oaklib.utilities.publication_utils.pubmed_wrapper import PubmedWrapper @pytest.fixture def pubmed_wrapper(): return PubmedWrapper() def test_retracted(pubmed_wrapper): objs = pubmed_wrapper.objects_by_ids(["PMID:19717156"]) [obj] = objs assert obj["retracted"]
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import numpy as np import math def mag2db(y): """Convert magnitude response to decibels for a simple array. Args: y (numpy array): Power spectrum, raw magnitude response. Returns: dby (numpy array): Power spectrum in dB """ dby = 20 * np.log10(y) return dby
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__author__ = "Johannes Köster" __copyright__ = "Copyright 2016, Johannes Köster" __email__ = "koester@jimmy.harvard.edu" __license__ = "MIT" from snakemake.shell import shell ## Extract arguments extra = snakemake.params.get("extra", "") shell("bcftools index" " {extra}" " {snakemake.input[0]}")
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__author__ = "Antonie Vietor" __copyright__ = "Copyright 2020, Antonie Vietor" __email__ = "antonie.v@gmx.de" __license__ = "MIT" from snakemake.shell import shell log = snakemake.log_fmt_shell(stdout=False, stderr=True) shell("samtools idxstats {snakemake.input.bam} > {snakemake.output[0]} {log}")
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from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Ischemia monitoring via out of distribution detection', author='Division of Intelligent Medical Systems at the German Cancer Research Center', license='MIT', )
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from Classes.Relationship import Relationship class GapRelationship(): def __init__(self, end1, relationship1, gap, relationship2, end2): self.Relationship1 = Relationship(end1, relationship1, gap) self.Relationship2 = Relationship(gap, relationship2, end2) self.Gap = gap
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import argparse def get_args(): argparser = argparse.ArgumentParser(description=__doc__) argparser.add_argument( '-c', '--config', dest='config', metavar='C', default='None', help='The Configuration file') args = argparser.parse_args() return args
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"""The Python package of the Oxynet project""" from importlib.metadata import PackageNotFoundError, version try: __version__ = version('pyoxynet') except PackageNotFoundError: __version__ = 'unknown' from . import utilities from .utilities import * from . import testing from .testing import *
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import torch exp_types = ['GES', 'GCF', 'GGF'] exp_methods = [ 'GCAM', 'GRAD', 'RAND', ] def agg_parallel_dicts(exp_method, exp_type): ''' Aggregates dictionaries that result from parallel ''' pass def summarize_data(exp_method): '''Summarizes readings from one file''' pass