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2fccd48c81bf62a8c9c4c79e3ce0ff4b79bcb920979eeb02997127edb83a022a
Python
92
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from ema import ( ExponentialMovingAverage ) __all__ = [ ExponentialMovingAverage ]
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Python
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from pycilium.utils.serialize import CiliaAnalysisJsonEncoder # import pycilium.utils.import
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Python
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from langchain_core.output_parsers.xml import XMLOutputParser __all__ = ["XMLOutputParser"]
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Python
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from django.apps import AppConfig class VisualizeConfig(AppConfig): name = 'visualize'
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from langchain_core.runnables.retry import RunnableRetry, U __all__ = ["RunnableRetry", "U"]
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from django.apps import AppConfig class ClusteringConfig(AppConfig): name = 'clustering'
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Python
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"""Implement a GPT-3 driven browser. Heavily influenced from https://github.com/nat/natbot """
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Python
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from langchain_core.callbacks.file import FileCallbackHandler __all__ = ["FileCallbackHandler"]
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from django.apps import AppConfig class Pl2KilosortConfig(AppConfig): name = 'pl2kilosort'
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from langchain_core.load.load import Reviver, load, loads __all__ = ["Reviver", "load", "loads"]
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from django.apps import AppConfig class BrainRenderConfig(AppConfig): name = 'brain_render'
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from ._tokenizer import CIFTokenizer, CinDataset from ._model import ( GPT, GPTConfig, )
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from langchain_core.output_parsers import PydanticOutputParser __all__ = ["PydanticOutputParser"]
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from .coordinates import * from .selection import * from .download import * from .density import *
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Python
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from utils.my_containers import Register atom_loss_register = Register() from .atom_loss import *
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from langchain_core.load.dump import default, dumpd, dumps __all__ = ["default", "dumpd", "dumps"]
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# coding: iso-8859-5 # ±¶ÿàáâãäåæçèéêëìíîï <- Cyrillic characters u = "®âðÄ"
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Python
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from langchain_core.chat_history import BaseChatMessageHistory __all__ = ["BaseChatMessageHistory"]
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#from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA # TODO: Implement this!
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from langchain_core.utils.aiter import NoLock, Tee, py_anext __all__ = ["NoLock", "Tee", "py_anext"]
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# Don't manually change, let poetry-dynamic-versioning handle it. __version__ = "0.0.0" __all__ = []
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import os import sys file_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(file_dir)
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import os from pycromanager import Core def test_connect_to_core(launch_mm_headless): mmc = Core()
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from langchain_core.callbacks.stdout import StdOutCallbackHandler __all__ = ["StdOutCallbackHandler"]
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from .infer_graph_structure import infer_graph_structure from .topological_sort import topological_sort
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#from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as LDA # TODO: Implement this!
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from math import * def myDistFunc(x): y = x/10 + 50*sin(2*pi*x/200) * tanh(x/100) return y
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import morgana.DatasetTools.fluorescence.io import morgana.DatasetTools.fluorescence.computefluorescence
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from langchain_classic.tools.retriever import create_retriever_tool __all__ = ["create_retriever_tool"]
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from langchain_core.tracers.root_listeners import RootListenersTracer __all__ = ["RootListenersTracer"]
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from langchain_core.example_selectors.base import BaseExampleSelector __all__ = ["BaseExampleSelector"]
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from langchain_core.caches import RETURN_VAL_TYPE, BaseCache __all__ = ["RETURN_VAL_TYPE", "BaseCache"]
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from langchain_core.runnables.fallbacks import RunnableWithFallbacks __all__ = ["RunnableWithFallbacks"]
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from .layers import SpatialDescriptor, StructuralDescriptor, MeshConvolution from .MeshNet import MeshNet
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"""Chain that does self ask with search. Heavily borrowed from https://github.com/ofirpress/self-ask """
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from .orbevo.orbevo_wf import EquiformerV2_Eband_Pool from .orbevo.orbevo_dm import EquiformerV2_Eband_DM
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from .train import MolTrain from .predict import MolPredict from .predictor import MOFPredictor, UniMolRepr
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from .matgen import MatGen from .coordgen import CoordGen from .spherenet_light import SphereNetLightDecoder
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from .ema import ExponentialMovingAverage from .utils import create_model from .GVP_diff import GVPTransCond
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from WeightModel.WeightClass import * from WeightModel.STDP import * from WeightModel.SynapticClass import *
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""" This code is copied and modified from scipy rbf RBFInterpolator """ # TODO # no bias # different scaling
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from .movies import H5SequenceSet, MovieFileTreeDataset from .statics import FileTreeDataset, StaticImageSet
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# builtin modules import logging def logOutput(output, logger): for line in output: logger(line)
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from .models import * from .utils import * from .train import * from .predict import * from ._version import *
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from .data_utils import * from .mat_utils import * from .geometric_computing import * from .eval_utils import *
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 11 11:53:40 2020 @author: shni2598 """
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from pgmpy.parameterization.distributions.nominal import NominalDistribution __all__ = ["NominalDistribution"]
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from scana.signal import * from scana.sigartist import * from scana.histfit import * from scana.sigproc import *
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"""Configuration module for BERNN.""" from .training_config import TrainingConfig __all__ = ['TrainingConfig']
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import morgana.ImageTools.fluorescence.computefluorescence import morgana.ImageTools.fluorescence.computeprofiles
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from langchain_core.document_loaders import BaseBlobParser, BaseLoader __all__ = ["BaseBlobParser", "BaseLoader"]
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from HMSA.hyperparameter_optimization import chemprop_hyperopt if __name__ == '__main__': chemprop_hyperopt()
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from .lp_filter_zero_phase import lp_filter_zero_phase from .tikhonov_regularization import tikhonov_regularization
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import numpy as np def uniform(low,up): def f(size): return np.random.uniform(low,up,size) return f
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from langchain_core.runnables.router import RouterInput, RouterRunnable __all__ = ["RouterInput", "RouterRunnable"]
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def tolerable(a, b, tolerance): return a == b == 0 or (a + b != 0 and abs(2 * (a - b) / (a + b)) <= tolerance)
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# from napari_svetlana import threshold, image_arithmetic # add your tests here... def test_something(): pass
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from bindsnet.environment.environment import Environment, GymEnvironment __all__ = ["Environment", "GymEnvironment"]
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import morgana.MLModel.io import morgana.MLModel.overview import morgana.MLModel.predict import morgana.MLModel.train
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# This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
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from torchvision.models import * from torchvision.models.detection import * from .zoo import * __version__ = "0.1.1"
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import pytest import os import importlib # -- Feature Loaders def test_get_features(): assert 4 == 4 pass
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from langchain_core.tracers.run_collector import RunCollectorCallbackHandler __all__ = ["RunCollectorCallbackHandler"]
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import morgana.DatasetTools.straightmorphology.computestraightmorphology import morgana.DatasetTools.straightmorphology.io
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from langchain_classic.agents.openai_assistant.base import OpenAIAssistantRunnable __all__ = ["OpenAIAssistantRunnable"]
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from langchain_core._api.path import as_import_path, get_relative_path __all__ = ["as_import_path", "get_relative_path"]
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from .evaluate import evaluate from .load_data import load_data from .params import set_params from .logreg import LogReg
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from langchain_core.documents import BaseDocumentTransformer, Document __all__ = ["BaseDocumentTransformer", "Document"]
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from pycromanager import ZMQRemoteMMCoreJ core = ZMQRemoteMMCoreJ() core2 = ZMQRemoteMMCoreJ() del core del core2 pass
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__version__ = "0.0.1" from ._reader import napari_get_reader from ._writer import napari_get_writer, napari_write_image
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from langchain_core.utils.env import get_from_dict_or_env, get_from_env __all__ = ["get_from_dict_or_env", "get_from_env"]
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"""Get sdcflows version.""" import sdcflows print(sdcflows.__version__, end="", file=open("/tmp/.docker-version.txt", "w"))
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from .fuse_batchnorm import fuse_batchnorm from .remove_activation import remove_activation, remove_sigmoid, remove_softmax
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"""Launch the SMdRQA UI: ``python -m SMdRQA.ui``.""" from SMdRQA.ui import launch if __name__ == "__main__": launch()
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from abc import ABC, abstractmethod class FoldModel(ABC): @abstractmethod def predict(self, seq): raise NotImplemented
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from langchain_core.prompts import BasePromptTemplate, format_document __all__ = ["BasePromptTemplate", "format_document"]
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import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv('./data.csv') plt.plot(data[:1000]) plt.show()
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import numpy as np import pandas as pd url = "https://dataverse.no/api/info/metrics/filedownloads?parentAlias=ntnu"
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# -*- coding: utf-8 -*- # :noTabs=true: from .multicore import MultiCore_HPC_Driver from .slurm import Slurm_HPC_Driver
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from .base_rnn import * from .conv_rnn import * from .traj_rnn import * from .transformations import * from .common import *
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DEFAULT_WINDOW_WIDTH_MS = 200 DEFAULT_TARGET_IP_ADDR = "tcp://10.42.0.44:5558" DEFAULT_SAVE_PATH = "./waves.csv"
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from langchain_core.vectorstores import VectorStore, VectorStoreRetriever __all__ = ["VectorStore", "VectorStoreRetriever"]
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"""Constitutional AI. The Chain runs self-critique based on the Constitutional AI method proposed by (Bai et al., 2022). """
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# from napari_svetlana import napari_get_writer, napari_write_image # add your tests here... def test_something(): pass
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'''utilities for loading and saving keras models. ''' from .utils import load_model from .utils import save_model_architecture
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import sys from pathlib import Path REPO_ROOT = Path(__file__).parent.parent.parent.resolve() sys.path.append(f"{REPO_ROOT}")
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from cgatReport.Tracker import TrackerSQL class IsoformTracker(TrackerSQL): """Base class for trackers in this report"""
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from langchain_core.prompts.few_shot_with_templates import FewShotPromptWithTemplates __all__ = ["FewShotPromptWithTemplates"]
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# -- coding: utf-8 -- # @Time : 2022/10/15 16:04 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py
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import Treepedia.metadataCollector import Treepedia.Greenview2Shp import Treepedia.GreenViewCalc import Treepedia.createPoints
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# -- coding: utf-8 -- # @Time : 2022/10/18 17:14 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py
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# -- coding: utf-8 -- # @Time : 2022/10/19 20:43 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py
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import mimic3benchmark.mimic3csv import mimic3benchmark.subject import mimic3benchmark.preprocessing import mimic3benchmark.util
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# -- coding: utf-8 -- # @Time : 2022/10/15 12:12 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py
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from langchain_core.chat_history import InMemoryChatMessageHistory as ChatMessageHistory __all__ = [ "ChatMessageHistory", ]
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from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler __all__ = ["StreamingStdOutCallbackHandler"]
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# -- coding: utf-8 -- # @Time : 2022/10/15 16:12 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py.py
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# -- coding: utf-8 -- # @Time : 2022/10/15 17:15 # @Author : Biao Zhang # @Email : littlebiao@outlook.com # @File : __init__.py.py
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# File: __init__.py # The function extracts the spikes and the markers from a .h5 dataset # authors: Stefano Diomedi # date: 10/2022
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from .model import HMSAModel from .dmpnn import DMPNN, DMPNNEncoder __all__ = [ 'HMSAModel', 'DMPNN', 'DMPNNEncoder' ]