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from .default_runner import DefaultRunner __all__ = ["DefaultRunner"]
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__all__ = ["graph-network", "transformer", "equivariant-transformer"]
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from .admUI import computeQUI from .admUI_numpy import computeQUI_numpy
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from . import self_supervised, xgb __all__ = ['self_supervised', 'xgb']
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from .skew_correction import skew_correct_matlab_one, skew_correct_matlab
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"""Statistical analysis tools for the Fiber Photometry Analysis GUI."""
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from .scorenet import DistanceScoreMatch __all__ = ["DistanceScoreMatch"]
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from . import datasets from . import utils __all__ = ['datasets', 'utils']
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from .audio_mel_dataset import * # NOQA from .scp_dataset import * # NOQA
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Python
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import subprocess subprocess.call("python Codes/Run_program.py", shell=True)
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__all__ = ['MqttCommManager'] from .mqtt_comm_manager import MqttCommManager
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from .conditions import ConditionsExtractor from .labels import LabelExtractor
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# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """
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filter_cfg = dict( type='GaussianFilter', window_size=11, sigma=4.0, )
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filter_cfg = dict( type='OneEuroFilter', min_cutoff=0.004, beta=0.7, )
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from enum import Enum class RunType(Enum): Executed = 1 Imagined = 2
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from .trainer import * from .trainer_seg import * from .trainer_nuclei_seg import *
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import os __version__ = '0.0.1b' ROOT = os.path.abspath(os.path.dirname(__file__))
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from p4.mrp import mrp read('inTrees.phy') a = mrp(var.trees) a.writeNexus('mr.nex')
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read('protein.nex') a=var.alignments[0] a.recodeDayhoff() a.writeNexus('recoded.nex')
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tA = func.readAndPop('m.nex') tB = func.readAndPop('dt.nex') tA.tvTopologyCompare(tB)
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cpr = STMcmcCheckPointReader() cpr.writeProposalAcceptances() cpr.compareSplits(-2, -1)
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filter_cfg = dict( type='SavizkyGolayFilter', window_size=11, polyorder=2, )
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from django.apps import AppConfig class HomeConfig(AppConfig): name = 'exsclaim.ui.home'
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t = func.randomTree(nTax=20) t.eps() t.write() t.draw() #os.system('sleep 1; rm random.eps')
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taxNames = list(string.ascii_uppercase[:4]) t = func.randomTree(taxNames) t.writeNexus('t.nex')
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from django.apps import AppConfig class QueryConfig(AppConfig): name = 'exsclaim.ui.query'
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taxNames = list(string.ascii_uppercase[:5]) t = func.randomTree(taxNames) t.writeNexus('t.nex')
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taxNames = list(string.ascii_uppercase[:7]) t = func.randomTree(taxNames) t.writeNexus('t.nex')
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read('sim.p4_tPickle') t = var.trees[0] read('d.nex') t.data = Data() t.optLogLike() t.model.dump()
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try: from django.test import TestCase except Exception as e: pass # Create your tests here.
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import torch.nn.functional as f def mse(input, target, misc): return f.mse_loss(input, target)
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try: from django.test import TestCase except Exception as e: pass # Create your tests here.
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from django.apps import AppConfig class ResultsConfig(AppConfig): name = 'exsclaim.ui.results'
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from .Environment import * from .Agent import Agent, trajectory_generator, batch_trajectory_generator
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import logging from logging import NullHandler logging.getLogger(__name__).addHandler(NullHandler())
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import logging from logging import NullHandler logging.getLogger(__name__).addHandler(NullHandler())
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from .registration import register_with_ANTs, register_with_ANTs_batch from .utils import load_with_ants
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# Copyright (c) OpenMMLab. All rights reserved. from .dropblock import DropBlock __all__ = ['DropBlock']
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cpr = McmcCheckPointReader(theGlob='*', last=True) m = cpr.mm[0] n = Numbers(m.simTemp_tNums) n.plot()
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# Copyright (c) OpenMMLab. All rights reserved. from mmcv.utils import Registry NODES = Registry('node')
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# Copyright (c) OpenMMLab. All rights reserved. from .utils import ort_validate __all__ = ['ort_validate']
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DEFAULT_AA_GAP_CHARS = ["-", "?", "*", "X", "x"] DEFAULT_NT_GAP_CHARS = ["-", "?", "*", "X", "x", "N", "n"]
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"""Reporting helpers for BrainStat results.""" from ._slm import make_slm_report __all__ = ["make_slm_report"]
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read("mcmc_trees_0.nex") tt = Trees() var.trees = [] read('cons.nex') t = var.trees[0] tt.trackSplitsFromTree(t)
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# -*- coding: utf-8 -*- """ Created on Tue Jun 8 13:33:44 2021 @author: fhu14 """ from .mio_0_1 import ParDict
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tp = TreePartitions('tt.nex') tp.writeSplits() # If you like this sort of thing ... t = tp.consensus() t.draw()
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# -*- coding: utf-8 -*- """ Created on Tue Jun 8 13:31:08 2021 @author: fhu14 """ from .auorg_1_1 import ParDict
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# -*- coding: utf-8 -*- """ Created on Mon Jun 7 17:31:08 2021 @author: fhu14 """ from .elements import ELEMENTS
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from django.urls import path from .views import home_view urlpatterns = [ path('', home_view, name="index"), ]
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"""The code in this module comes from https://github.com/chemprop/chemprop/ and was refactored to suit our needs."""
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''' Author: ---- Date: 2022-04-29 11:15:51 LastEditors: ---a LastEditTime: 2022-09-05 14:45:34 ''' from .loss import *
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read('njTree.nex') t = var.trees[0] t.restoreDupeTaxa() t.draw(addToBrLen=0.0) t.writeNexus(fName='restoredNJTree.nex')
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# Copyright (c) OpenMMLab. All rights reserved. from .webcam_executor import WebcamExecutor __all__ = ['WebcamExecutor']
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from django.urls import path from .views import query_view urlpatterns = [ path('', query_view, name="query_page"), ]
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# Copyright (c) OpenMMLab. All rights reserved. from .utils import _dummy_bbox_sampling __all__ = ['_dummy_bbox_sampling']
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"""clipkit.__main__: executed when clipkit is called as script""" import sys from .clipkit import main main(sys.argv[1:])
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import os os.system('python check_train_hamilton.py --ckpt ../../ckpts/elastic_pend \ --dt 0.1')
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from .vector_quantizer import vector_quantize # NOQA from .vector_quantizer import vector_quantize_straight_through # NOQA
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from osgeo import gdal from osgeo import ogr from osgeo import osr gdal.UseExceptions() ogr.UseExceptions() osr.UseExceptions()
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# Copyright (c) OpenMMLab. All rights reserved. from .nvgesture_dataset import NVGestureDataset __all__ = ['NVGestureDataset']
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from .driver import compute_gammas, train_repulsive_model, repulsive_model_predict from .skf import SKF, SKFSet, SKFBlockCreator
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read('noOpt.p4_tPickle') t = var.trees[0] read('d.nex') t.data = Data() t.optLogLike(verbose=1) t.model.dump() t.tPickle('opt')
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n = Numbers('mcmc_likes_0', col=1) n.plot() if os.path.isfile('mcmc_prams_0'): n = Numbers('mcmc_prams_0', col=1) n.plot()
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# Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors. from .trainer import Trainer __all__ = [Trainer]
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# -*- coding: utf-8 -*- """ Created on Wed Jun 9 18:02:38 2021 @author: fhu14 """ from .precompute_stage import precompute_stage
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read("d3_noDupes.phy") a = var.alignments[0] dm = a.pDistances() t = dm.njUsingPaup() t.draw(addToBrLen=0.0) t.writeNexus('njTree.nex')
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from .poisson_glm import poisson_glm, poisson_glm_constrained, two_param_poisson_glm from .rule_simulator import * from .utils import *
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from pydapsys.file import File, read_file from pydapsys.page import DataPage, PageType, TextPage, WaveformPage from pydapsys.toc import *
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read('d3.nex') a = var.alignments[0] a.checkForDuplicateSequences(removeDupes=True, makeDict=True) a.writePhylip(fName='d3_noDupes.phy')
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read(""" 2 2 one ac two gt """) read('(one,two);') t = var.trees[0] t.data = Data() t.newComp() t.newRMatrix() t.setPInvar() t.calcLogLike()
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read('../noTRuberNoGapsNoAmbiguities.nex') a=var.alignments[0] dm = a.compositionEuclideanDistanceMatrix() dm.writeNexus('compDistMatrix.nex')
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# -*- coding: utf-8 -*- """ Created on Tue Jun 8 12:32:31 2021 @author: fhu14 """ from .geometry import Geometry, to_cart, random_triatomics
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############### Configuration file for Frequentist ############### n_epochs = 200 lr = 0.001 num_workers = 4 valid_size = 0.2 batch_size = 256
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from django.shortcuts import render def home_view(request): template_name = "exsclaim/home.html" return render(request, template_name)
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rNum = 0 sk = 100 n = Numbers('mcmc_likes_%i' % rNum, col=1, skip=sk) n.plot() n = Numbers('mcmc_hypers_%i' % rNum, col=2, skip=sk) n.plot()
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from torch.nn import functional as F def nll_loss(output, target, reduction='mean'): return F.nll_loss(output, target, reduction=reduction)
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from .geometry import * # from .reaction import * from .base import * from .exceptions import * # from .output import * # from .segments import *
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import torch.nn.functional as f def l1(input, target, misc): # print(f"{input.size()}, {target.size()}") return f.l1_loss(input, target)
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from torch.nn import functional as F def bce_loss_with_logits(output, target, misc): return F.binary_cross_entropy_with_logits(output, target)
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read("mcmc_trees_0.nex") tt = Trees() # Use a previously-made cons tree var.trees = [] read("cons.nex") t = var.trees[0] tt.trackSplitsFromTree(t)
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var.doCheckForBlankSequences=False read('noOpt.p4_tPickle') t = var.trees[0] read('d.nex') t.data = Data() t.optLogLike(verbose=1) t.tPickle('opt')
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read('d.nex') d = Data() for i in range(3): read('t%i.p4_tPickle' % (i + 1)) tt = Trees() tt.data = d if 1: tt.consel() else: tt.rell()
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from django.urls import path from .views import SearchResultViews urlpatterns = [ path('', SearchResultViews.as_view(), name="search_results"), ]
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from .transformation import build_4D_tiff, save_4d_tiff, create_planes_tiff, get_unpad_planes, load_unpad_save, calc_stacks_average, get_ordered_tiffs
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from abc import ABC, abstractmethod class Observer(ABC): @abstractmethod def receive_message(self, msg_type, msg_params) -> None: pass
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from .common import MultiLayerPerceptron from .gin import GraphIsomorphismNetwork __all__ = ["MultiLayerPerceptron", "GraphIsomorphismNetwork" ]
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read("../d.nex") d = Data() m = func.unPickleMcmc(0, d) m.run(2000) m = func.unPickleMcmc(1, d) m.run(2000) #n = Numbers('mcmc_likes_0', col=1) #n.plot()
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from torchmdnet.priors.atomref import Atomref from torchmdnet.priors.d2 import D2 from torchmdnet.priors.zbl import ZBL __all__ = ["Atomref", "D2", "ZBL"]
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from oligo_designer_toolsuite.config.pipelines.oligo_seq_probe_designer import OligoSeqProbeDesignerConfig __all__ = [ "OligoSeqProbeDesignerConfig", ]
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class ClipKITException(Exception): pass class InvalidInputFileFormat(ClipKITException): pass class StopCodonValidationError(ValueError): pass
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from .ABCEnvironment import ABCEnvironment from .Rectangle import Rectangle from .OpenField import OpenField from .methods import intersect, projection_rejection
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# This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed # under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
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from .accumulators import GradientAccumulateOptimizer from .accumulators import GradientAccumulateModel from .layers import AccumBatchNormalization from .agc import *
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read('noOpt.p4_tPickle') t = var.trees[0] var.doCheckForBlankSequences = False read('dA.nex') read('dB.nex') t.data = Data() t.optLogLike(verbose=1) t.tPickle('opt')
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# Copyright (c) OpenMMLab. All rights reserved. from .collect_env import collect_env from .logger import get_root_logger __all__ = ['get_root_logger', 'collect_env']
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from torch.nn.functional import mse_loss, l1_loss, huber_loss loss_class_mapping = { "mse_loss": mse_loss, "l1_loss": l1_loss, "huber_loss": huber_loss, }
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appname = 'Multimodal Analysis of Neuronal Gene Expression, Electrophysiology and Morphology (MANGEM)' cell_limit = 1000 # limit to 1000 cells for performance reasons
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# Copyright (c) OpenMMLab. All rights reserved. from .general_data import GeneralData from .instance_data import InstanceData __all__ = ['GeneralData', 'InstanceData']
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read('d.nex') a=var.alignments[0] a.writePhylip() m = func.maskFromNexusCharacterList("1 3-5 7 11", a.length, invert=0) print(m) b = a.subsetUsingMask(m) b.writePhylip()