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Python
193
10
from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) msp.run_reconstruction(path="model/msp-274.790a3_1", sparsity=0.178e-2, cl=10_000) msp.run()
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Python
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# Author: Christian Brodbeck <christianbrodbeck@nyu.edu> from ._source import source_results, source_time_lm, source_time_results from ._uts import time_results from ._uv import scatter_table
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Python
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# Check if the ray package is available JOBLIB_AVAILABLE = True try: import joblib except ImportError: JOBLIB_AVAILABLE = False def joblib_is_available(): return JOBLIB_AVAILABLE
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Python
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def construct(type_map, spec, **default_kwargs): spec = dict(spec.items()) type_name = spec.pop("type") default_kwargs.update(spec) return type_map[type_name](**default_kwargs)
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Python
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from neuron import h instance = h.pp() assert instance.has_loc() == 0 nseg = 1 s = h.Section() instance = h.pp(s(0.5)) assert instance.has_loc() == 1 assert instance.get_segment() == s(0.5)
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Python
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"""Google Benchmark tooling""" __author__ = 'Eric Fiselier' __email__ = 'eric@efcs.ca' __versioninfo__ = (0, 5, 0) __version__ = '.'.join(str(v) for v in __versioninfo__) + 'dev' __all__ = []
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from __future__ import print_function, division, absolute_import from .resnext101_32x4d_features import resnext101_32x4d_features from .resnext101_64x4d_features import resnext101_64x4d_features
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Python
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import os from os.path import join, dirname REPO_DIR = join(dirname(dirname(os.path.abspath(__file__)))) EM_DATA_DIR = join(REPO_DIR, 'em_data') DATA_DIR = join(REPO_DIR, 'encoding', 'data')
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Python
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from sys import argv from msp import MSP from msp_donorlike import setup_as_adjusted_acceptor msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) setup_as_adjusted_acceptor(msp) msp.run()
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from .fiducialreg import CloudSet, FiducialCloud, GaussFitter3D, RegistrationError __version__ = "0.1.0" __all__ = ("RegistrationError", "CloudSet", "GaussFitter3D", "FiducialCloud", "RegFile")
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Python
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from .utils import * from .autoprompt import AutoPrompt from .ipromptx import iPrompt from .gumbel import GumbelPrefixModel from .hotflip import HotFlip from .prompt_tune import PromptTunedModel
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Python
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# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """This package provide ReadManager to MNE conversion utilities."""
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Python
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"""Config file for pytest for defining additional parser options. """ def pytest_addoption(parser): parser.addoption("--auto_dir", action="store", default="/home/circleci/project/auto-07p")
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import numpy as np _boundary_mode = 'nearest' _dtype_ = np.float32 _phi_ = np.array([1./16, 1./4, 3./8, 1./4, 1./16], _dtype_) __all__=['_boundary_mode', '_dtype_', '_phi_']
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Python
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from distutils.core import setup from Cython.Build import cythonize import numpy as np setup( name = "features", ext_modules = cythonize("features.pyx"), include_dirs = [np.get_include()] )
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Python
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from enum import Enum class DeviceState(Enum): NOT_READY = 1 READY = 2 READY_FOR_TRIAL = 3 ACQUIRING = 4 RECORDING = 5 FINISHED = 6 supported_camera_types = ("opencv",)
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"""GluonCV motion dataset, supports multiple video tasks including video action recognition/detection, object tracking, pose tracking, etc.""" from .dataset import GluonCVMotionDataset, FieldNames
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from angorapy.tasks.world_building.entities import Robot, Stage, External from angorapy.tasks.world_building.base import PhysicalWorld __all__ = [ "Robot", "Stage", "External", "PhysicalWorld" ]
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from .image import OMEZarrImage, OMEZarrLabels, OMEZarrMultiscale from .scene import OMEZarrScene __all__ = [ "OMEZarrImage", "OMEZarrLabels", "OMEZarrMultiscale", "OMEZarrScene", ]
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""" Keras layers that handle the node, edge, and connectivity features returned by the preprocessor classes. """ from .graph_layers import EdgeUpdate, GlobalUpdate, NodeUpdate from .layers import *
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# -*- coding: utf-8 -*- # @Author : Yue Liu # @Email : yueliu19990731@163.com # @Time : 2022/9/21 20:55 from .visualization import similarity_plot, t_sne __all__ = ['similarity_plot', 't_sne']
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"""The default loader used with custom app instances.""" from .base import BaseLoader __all__ = ('AppLoader',) class AppLoader(BaseLoader): """Default loader used when an app is specified."""
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import biomedisa def selected_settings(request): # return the version value as a dictionary # you may add other values here as well return {'APP_VERSION_NUMBER': biomedisa.__version__}
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from .ripser import Rips, ripser, lower_star_img, correlation_to_distance from ._version import __version__ __all__ = ["Rips", "ripser", "lower_star_img", "__version__", "correlation_to_distance"]
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# flake8: noqa: F401 from .base import BaseShape, rotate_around_point from .sh_ellipse import Ellipse from .sh_circle import Circle from .sh_polygon import Polygon from .sh_rectangle import Rectangle
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Python
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from enum import Enum class Safety(Enum): UNSAFE = 2 POTENTIALLY_SENSITVE = 4 SAFE = 6 DEFAULT_SAFETY = Safety.POTENTIALLY_SENSITVE __all__ = ( "DEFAULT_SAFETY", "Safety", )
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from .interpolations import ( deep_intepretation, gp_interpolation, kernel_interpolation, vtk_interpolation, ) from .models import * from .morphometrics import * from .widgets import *
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# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'video_control_ui.ui' # # Created by: PyQt5 UI code generator 5.6 # # WARNING! All changes made in this file will be lost!
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Python
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from gym.envs.box2d.bipedal_walker import BipedalWalker, BipedalWalkerHardcore from gym.envs.box2d.car_racing import CarRacing from gym.envs.box2d.lunar_lander import LunarLander, LunarLanderContinuous
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def normalize(tensor, mean, std): """ Args: tensor (Tensor): Tensor to normalize Returns: Tensor: Normalized tensor """ tensor.sub_(mean).div_(std) return tensor
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from .dataset import PrecomputedCSVDataset, BLOSUMCSVDataset, PrecomputedCSVForCRFDataset, PrecomputedCSVForOverlapCRFDataset from .metrics import compute_metrics, add_dict_to_writer, compute_crf_metrics
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import igraph import numpy as np from itertools import combinations import copy def calc_edge_importance(feat_mask, edge_indices): rows, cols = edge_indices return((feat_mask[rows]+feat_mask[cols])/2)
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from .auc_roc import * from .broadening import * from .cams import * from .graph_preprocessing import * from .ground_truth import * from .rse import * from .visualisation import * from .weight_init import *
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Python
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from bioconfpred.data.split import RandomSplit from bioconfpred.model import ComENetModel data_split = RandomSplit() # default is the split number 0 model = ComENetModel.get_model_for_data_split(data_split)
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import os cmd2 = "cut -f1,2 -d ',' ../../../data/intermediate_data/t5_public_allele_level_tcr_filtered_wrt_vf_and_aa.csv > ../../../data/intermediate_data/public_allele_level_tcr_name.csv" os.system(cmd2)
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Python
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from .bam import BAM from .fasta import FlatFASTA, GenomeFASTA from .table import Table from .vcf import VCF from .wig import BigWig __all__ = ["BAM", "FlatFASTA", "GenomeFASTA", "VCF", "Table", "BigWig"]
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def stars(p): if p < 0.0001: return "****" if p < 0.001: return "***" if p < 0.01: return "**" if p < 0.05: return "*" if p >= 0.05: return "n.s."
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# default matplotlib colors cs_mpl = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] # a nice blue/red color cblue = '#66ccff' cred = '#cc0000'
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"""Module defining models.""" from bioseq2seq.models.model_saver import build_model_saver, ModelSaver from bioseq2seq.models.model import NMTModel __all__ = ["build_model_saver", "ModelSaver", "NMTModel"]
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# pylint: disable=wildcard-import """ SMOT: Single-Shot Multi Object Tracking https://arxiv.org/abs/2010.16031 """ from __future__ import absolute_import from .smot_tracker import * from .tracktors import *
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"""Helper tasks for integration tests.""" from celery import shared_task @shared_task(name='celery.ping') def ping(): # type: () -> str """Simple task that just returns 'pong'.""" return 'pong'
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"""BdPy utility package. This package is a part of BdPy. """ from .utils import create_groupvector, divide_chunks, get_refdata, makedir_ifnot from .info import dump_info from .math import average_elemwise
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# -*- coding: utf-8 -*- from . import roles from . import directives def setup(app): roles.setup(app) directives.setup(app) from . import _version __version__ = _version.get_versions()['version']
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from matplotlib.colors import LinearSegmentedColormap colors = ["#FF807D", "#FFD37D", "#D9FF7D", "#7DFF82"] cm_rating = LinearSegmentedColormap.from_list("gor", colors, N=100) cm_rating.set_under("#DCDCDC")
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''' Module to extract morphometrics and TMD-input from a set of tree-shaped cells.''' from tns.extract_input.input_distributions import distributions from tns.extract_input.input_parameters import parameters
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from .conf_pred_model import ConfPredModel from .atomistic_nn_model import AtomisticNNModel from .schnet_model import SchNetModel from .dimenet_model import DimeNetModel from .comenet_model import ComENetModel
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from larvaworld.gui_v2.tabs.home import build_home_text from larvaworld.gui_v2.tabs.placeholder import build_entry_text, build_group_text __all__ = ["build_entry_text", "build_group_text", "build_home_text"]
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import torch DTYPE = torch.get_default_dtype() DTYPE_INT = torch.int64 DTYPE_BOOL = torch.bool TORCH_FLOATS = (torch.float16, torch.float32, torch.float64) TORCH_INTS = (torch.int16, torch.int32, torch.int64)
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.batch_size = msp.batch_size * 2 // 3 msp.seed = int(argv[1]) msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3 msp.run_binarizer(0.178e-2)
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Python
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__author__ = 'quentin' from ethoscope.hardware_control.arduino_api import SleepDepriverInterface sdi = SleepDepriverInterface() sdi.deprive(0) #sdi.deprive(0) # for i in range(12,32): # sdi.deprive(i)
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from neuron import h def test_internal_function(): s = h.Section() s.nseg = 4 s.insert("internal_function") assert s.internal_function.g() == 42.0 assert s.internal_function.f() == 42.0
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from gym.envs.toy_text.blackjack import BlackjackEnv from gym.envs.toy_text.cliffwalking import CliffWalkingEnv from gym.envs.toy_text.frozen_lake import FrozenLakeEnv from gym.envs.toy_text.taxi import TaxiEnv
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# -*- coding: utf-8 -*- # @Author : Yue Liu # @Email : yueliu19990731@163.com # @Time : 2022/9/21 0:43 from .clustering import k_means from .evaluation import evaluation __all__ = ['k_means', 'evaluation']
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from utilities.files import list_files, splitall from utilities.im import imRescale2uint8, imLevels, customResize, imShowMe, imBackground from utilities.vec import norm, norml, Norml, SSprod, vprod, Vprod, VVprod
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"""Spectral parameterization.""" from .version import __version__ from .bands import Bands from .models import (SpectralModel, SpectralGroupModel, SpectralTimeModel, SpectralTimeEventModel)
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import time class MockSerial: def write(self, str): t = time.time() print(f"{t} : MockSerial > {str}") def close(self): t = time.time() print(f"{t} : MockSerial closed")
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ORBITALS = ["s", "p", "d", "f", "g"] NUM_ORBITALS = { "s": 1, "p": 3, "d": 5, "f": 7, "g": 9, } IRREPS_ORBITALS = { "s": "0e", "p": "1o", "d": "2e", "f": "3o", "g": "4e", }
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from pl_bolts.models.detection.retinanet.backbones import create_retinanet_backbone from pl_bolts.models.detection.retinanet.retinanet_module import RetinaNet __all__ = ["create_retinanet_backbone", "RetinaNet"]
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# pylint: disable=wildcard-import """Video action recognition, something-something-v2 dataset. https://20bn.com/datasets/something-something """ from __future__ import absolute_import from .classification import *
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from importlib.metadata import PackageNotFoundError, version try: __version__ = version("flash_attn_3") except PackageNotFoundError: # not installed (e.g. running from a source tree) __version__ = "3.0.1"
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def pytest_configure(config): try: import sqlalchemy.exc # noqa: F401 except ImportError: pass else: config.addinivalue_line("filterwarnings", "error::sqlalchemy.exc.SAWarning")
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__all__ = ['extraction'] from .extraction import extract, _generate_box_indices, _slice_array from .file import to_file, from_file, from_file_info, load_ds_list, manually_added_neurons_n from .signal import Signal
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from .model import resume_checkpoint from .utils import resolve_data_config, update_cfg from .optimizer import optimizer_kwargs, create_optimizer_v2a from .scheduler import create_scheduler from .metrics import rmse
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import pytest from js_example import app @pytest.fixture(name="app") def fixture_app(): app.testing = True yield app app.testing = False @pytest.fixture def client(app): return app.test_client()
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"""Visualization module.""" from sknetwork.visualization.dendrograms import visualize_dendrogram, svg_dendrogram from sknetwork.visualization.graphs import visualize_graph, visualize_bigraph, svg_graph, svg_bigraph
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# pylint: disable=wildcard-import """Mask RCNN Instance Segmentation""" from __future__ import absolute_import from .mask_rcnn import * from .predefined_models import * from .data_parallel import ForwardBackwardTask
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"""R-like structures for Python. This sub-package provides pure-Python implementation of data structures that, while conceptually across Python and R, have subtle differences in behavior that can lead to issues. """
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from pl_bolts.models.regression.linear_regression import LinearRegression from pl_bolts.models.regression.logistic_regression import LogisticRegression __all__ = [ "LinearRegression", "LogisticRegression", ]
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"""Task utils""" from .classification import train_classification, validation_classification, test_classification from .coot import train_coot, validate_coot from .pose import DirectposePipeline, build_pose_optimizer
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"""Functionality related to definining fit modes.""" from .mode import Mode from .paramdef import ParamDefinition # Link in report function to list available modes from specparam.reports.options import check_modes
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from ._attribute import attribute_sdata from ._filters import generate_pfms_sdata, filters_to_meme_sdata from ._generative import evolve_seqs_sdata from ._gia import positional_gia_sdata, motif_distance_dependence_gia
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import pytest import rpy2.rlike.indexing as rfi def test_order(): seq = (2, 1, 5, 3, 4) expected = (1, 2, 3, 4, 5) res = rfi.order(seq) for va, vb in zip(expected, res): assert seq[vb] == va
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from .agent import AgentConfig, AgentInferenceConfig, AgentTrainingConfig from .env import EnvConfig from .monitor import MonitorConfig, MonitorPlotConfig from .plot import PlotConfig, PlotFontConfig, PlotLegendConfig
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from django import template register = template.Library() @register.inclusion_tag("youtube_thumb.html") def youtube_thumb(video_id, width=640): return { "video_id": video_id, "width": width, }
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import torch from lightly.transforms import RFFT2DTransform def test() -> None: transform = RFFT2DTransform() image = torch.rand(3, 32, 32) output = transform(image) assert output.shape == (3, 32, 17)
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from . import _version from .domain_of_validity import DoV from .parameters import Parameters from .polyid import MultiModel, SingleModel, generate_hash, RenameUnpickler __version__ = _version.get_versions()["version"]
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from PIL import Image from lightly.transforms.jigsaw import Jigsaw class TestJigsaw: def test_on_pil_image(self) -> None: crop = Jigsaw() sample = Image.new("RGB", (255, 255)) crop(sample)
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""" Larvaworld Portal (M1) This package provides a registry-driven landing shell for Panel apps. Core modules must remain UI-agnostic (no Panel imports). """ from __future__ import annotations __all__: list[str] = []
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from neuron import h, gui def test_globals(): s = h.Section() s.insert("globals") h.gbl_globals = 654.0 assert s(0.5).globals.get_gbl() == h.gbl_globals if __name__ == "__main__": test_globals()
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from neuron import h, gui def test_locals(): s = h.Section() s.insert("locals") assert s(0.5).locals.get_a() == 32.0 assert s(0.5).locals.get_b() == 132.0 if __name__ == "__main__": test_locals()
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"""Path module""" from sknetwork.path.dag import get_dag from sknetwork.path.distances import get_distances from sknetwork.path.search import breadth_first_search from sknetwork.path.shortest_path import get_shortest_path
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from setuptools import setup # For compatibility with legacy builds or versions of tools # that don’t support certain packaging standards (e.g. PEP 517 # or PEP 660). The configuration is kept in pyproject.toml setup()
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# pylint: disable=missing-module-docstring from .resnet_encoder import ResnetEncoder from .depth_decoder import DepthDecoder from .pose_decoder import PoseDecoder from .monodepth2 import * from .monodepth2_posenet import *
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from dextr import DEXTR from config import Config model = DEXTR(nb_classes=1, resnet_layers=101, input_shape=(512, 512), weights_path=Config.DEXTR_FILE, num_input_channels=4, classifier='psp', sigmoid=True)
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global fasta_path def _init(): global fasta_path fasta_path = '/base' def set_fasta_path(value): global fasta_path fasta_path = value def get_fasta_path(): global fasta_path return fasta_path
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from .differential_geometry import ( morphofield_acceleration, morphofield_curl, morphofield_curvature, morphofield_divergence, morphofield_jacobian, morphofield_torsion, morphofield_velocity, )
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from pl_bolts.models.detection.faster_rcnn.backbones import create_fasterrcnn_backbone from pl_bolts.models.detection.faster_rcnn.faster_rcnn_module import FasterRCNN __all__ = ["create_fasterrcnn_backbone", "FasterRCNN"]
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from .embedding.pca import run_pca from .embedding.umap import run_umap from .embedding.ica import run_ica # from .embedding.scvi import run_ldvae from .embedding.fa import run_fa from .embedding.diffmap import run_diffmap
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from .lstm_cnn import SequenceTaggingLSTMCNN, SequenceTaggingCNN, SequenceTaggingLSTM, SequenceTaggingLSTMCNNCRF from .linear import SequenceTaggingLinear from .crf_models import LSTMCNNCRF, SimpleLSTMCNNCRF, SelfAttentionCRF
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from tfep.io.log import TFEPLogger, TMBARLogger from tfep.io.dataset import ( DictDataset, MergedDataset, TrajectoryDataset, TrajectorySubset, get_subsampled_indices, ) from tfep.io.sampler import StatefulBatchSampler
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import sys import pytest if_pypy = pytest.mark.skipif(getattr(sys, 'pypy_version_info', None), reason='PyPy not supported.') if_win32 = pytest.mark.skipif(sys.platform.startswith('win32'), reason='Does not work on Windows')
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.batch_size = msp.batch_size * 2 // 3 msp.seed = int(argv[1]) msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3 msp.n_epochs = 1 msp.run_binarizer(0.178e-2)
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.batch_size = msp.batch_size * 2 // 3 msp.seed = int(argv[1]) msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3 msp.n_epochs = 1 msp.run_binarizer(0.752e-2)
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#!/usr/bin/env python3 """Download test data.""" import sys from xcp_d.tests.utils import download_test_data if __name__ == '__main__': data_dir = sys.argv[1] dset = sys.argv[2] download_test_data(dset, data_dir)
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"""Tools for converting derivatives from various pipelines to an fMRIPrep-like format.""" from xcp_d.ingression import abcdbids, hcpya, ukbiobank, utils __all__ = [ 'abcdbids', 'hcpya', 'ukbiobank', 'utils', ]
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from __future__ import absolute_import from . import * from ovseg.networks.UNet import UNet from ovseg.networks.resUNet import UNetResEncoder, UNetResEncoderV2, UNetResDecoder, UResNet, UNetResStemEncoder, UNetResShuffleEncoder
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"""Presets of sophisticated data transforms used in various papers.""" from . import ssd from . import rcnn from . import yolo from . import center_net from . import imagenet from . import simple_pose from . import segmentation
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from .backbone import backbone_scc, construct_backbone, update_backbone from .backbone_methods import ElPiGraph_method, PrinCurve_method, SimplePPT_method from .backbone_utils import map_gene_to_backbone, map_points_to_backbone
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from .CAST_Stack import CAST_STACK_rough, corr_dist, reg_params, region_detect from .main import CAST_MARK, CAST_PROJECT, CAST_STACK from .utils import * from .visualize import dsplot, kmeans_plot_multiple, plot_mid, plot_mid_v2
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from sys import argv from msp import MSP, setup_as_am msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) setup_as_am(msp) msp.acc_thresh = 0 msp.extra_params += " --learned-motif-sparsity-threshold-initial 85" msp.run()