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21db77b76bb2581f624bb97bef3f7ce623d6b013ff839d6128c7ea91e1a9704c
Python
179
7
from langchain_classic.schema.cache import __all__ EXPECTED_ALL = ["BaseCache", "RETURN_VAL_TYPE"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
179
9
import numpy as np def identity(x): def f(size): if size[0]!=size[1] : print('Not right') else : return x*np.eye(size[0]) return f
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Python
179
8
from .geometric_edge_features import GeometricEdgeFeatures from .scale_edge_features import ScaleEdgeFeatures __all__ = [ "GeometricEdgeFeatures", "ScaleEdgeFeatures" ]
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Python
180
4
from pgmpy.parameterization.cpds.linear_gaussian import LinearGaussianCPD from pgmpy.parameterization.cpds.tabular import TabularCPD __all__ = ["LinearGaussianCPD", "TabularCPD"]
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Python
180
7
from langchain_classic.schema.chat_history import __all__ EXPECTED_ALL = ["BaseChatMessageHistory"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
181
3
anon_data_dir = '/home/sinhah/datasets/anonymous_data' compl_data_xnat = '/home/sinhah/scan_data/compliant_data' compl_data_bids = '/media/sinhah/extremessd/open_neuro/toy_dataset'
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Python
181
9
""" OpenFF Force fields """ from importlib.metadata import version from openforcefields.openforcefields import get_forcefield_dirs_paths __version__ = version("openforcefields")
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Python
182
5
"""Load question answering with sources chains.""" from langchain_classic.chains.qa_with_sources.loading import load_qa_with_sources_chain __all__ = ["load_qa_with_sources_chain"]
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Python
182
7
from omicscope import Nebula class Test(object): def test_input_nebula(self): df = Nebula('tests//data//MultipleGroups//omics_file') assert len(df.groups) == 4
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Python
183
7
"""Chains for evaluating ReAct style agents.""" from langchain_classic.evaluation.agents.trajectory_eval_chain import ( TrajectoryEvalChain, ) __all__ = ["TrajectoryEvalChain"]
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Python
185
7
from langchain_classic.schema.runnable.fallbacks import __all__ EXPECTED_ALL = ["RunnableWithFallbacks"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
185
7
import omicscope as omics class Test(object): def test_input_nebula(self): df = omics.Nebula('tests//data//MultipleGroups//omics_file') assert len(df.groups) == 4
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Python
188
5
from .arenas_configurations_proto_pb2 import * from .arena_configuration_proto_pb2 import * from .items_to_spawn_proto_pb2 import * from .vector_proto_pb2 import * from .__init__ import *
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Python
188
6
from .batch import Batch from .data import Data from .dataloader import DataLoader, Collater from .dataset import Dataset __all__ = ["Batch", "Data", "Dataset", "DataLoader", "Collater"]
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Python
188
8
from submiter.network_submiter import NetworkSubmiter class Submiter(NetworkSubmiter): def __init__(self, *args, **kwargs): super(Submiter, self).__init__(*args, **kwargs)
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Python
189
7
from langchain_classic.schema.document import __all__ EXPECTED_ALL = ["BaseDocumentTransformer", "Document"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
190
7
from langchain_classic.schema.runnable.router import __all__ EXPECTED_ALL = ["RouterInput", "RouterRunnable"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
190
7
def process_description(app, ctx, lines): if ctx.command.name == "main": del lines[:3] def setup(app): app.connect("sphinx-click-process-description", process_description)
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Python
190
6
from distutils.core import setup from Cython.Build import cythonize import numpy setup(ext_modules=cythonize('random_survival_forest_cython.pyx'), include_dirs=[numpy.get_include()])
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Python
191
7
from langchain_classic import docstore EXPECTED_ALL = ["DocstoreFn", "InMemoryDocstore", "Wikipedia"] def test_all_imports() -> None: assert set(docstore.__all__) == set(EXPECTED_ALL)
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Python
191
6
"""Base interfaces for tracing runs.""" from langchain_core.exceptions import TracerException from langchain_core.tracers.base import BaseTracer __all__ = ["BaseTracer", "TracerException"]
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Python
191
7
from utils.my_containers import Constructor dataset_register = Constructor() from .omics import NewSmilesOmicsDataset, process_data def get_dataset(name): return dataset_register[name]
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Python
191
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"""Schemas for tracers.""" from __future__ import annotations from langsmith import RunTree # Begin V2 API Schemas Run = RunTree # For backwards compatibility __all__ = [ "Run", ]
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Python
192
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from langchain_core.sys_info import print_sys_info def test_print_sys_info() -> None: """Super simple test to that no exceptions are triggered when calling code.""" print_sys_info()
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Python
193
5
# -*- coding: utf-8 -*- # Define self package variable __version__ = "0.4.25" __description__ = 'DNA methylation analysis downstream to Nanopolish for Oxford Nanopore DNA sequencing datasets'
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Python
193
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from langchain_classic.load import __all__ EXPECTED_ALL = [ "dumpd", "dumps", "load", "loads", ] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
194
9
from .bval_bvec_io import ( encode_nrrd_gradient, read_bvals, read_bvecs, write_bvals, write_bvecs, ) from .nhdr_write import write_nhdr from .nifti_write import write_nifti
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Python
194
11
import numpy as np objmask = np.random.randint(0, 1024, size=(1024, 1024)) obc = 0 print(objmask, obc) print((objmask == obc).shape) yx = np.argwhere(objmask == obc) print(yx) print(yx.shape)
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Python
194
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# Consumer ########################## import zmq IP_ADDR = "tcp://10.42.0.44:6667" context = zmq.Context() zmq_socket = context.socket(zmq.PULL) zmq_socket.connect(IP_ADDR) zmq_socket.recv()
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Python
195
11
import os import torch class DiffusionCommon: def get_sample_model(self): if self.ema is None: return self.eps_model else: return self.ema.ema_model
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Python
195
12
import pytest from hbayesdm.models import dd_cs def test_dd_cs(): _ = dd_cs( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
195
12
import pytest from hbayesdm.models import pst_Q def test_pst_Q(): _ = pst_Q( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
196
7
from langchain_classic.prompts.few_shot_with_templates import __all__ EXPECTED_ALL = ["FewShotPromptWithTemplates"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
196
6
from langchain_core.utils.function_calling import ( convert_to_openai_function as format_tool_to_openai_function, ) # For backwards compatibility __all__ = ["format_tool_to_openai_function"]
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Python
197
8
def getResidueID(residue): cid = residue.get_parent().get_id() _, num, ins = residue.get_id() if(len(ins) == 0): ins = ' ' return "{}.{}.{}".format(cid, num, ins)
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Python
197
5
""" from PhiSNet SE(3)-equivariant prediction of molecular wavefunctions and electronic densities <https://arxiv.org/abs/2106.02347> """ from .exponential_bernstein_radial_basis_functions import *
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Python
198
12
import pytest from hbayesdm.models import prl_rp def test_prl_rp(): _ = prl_rp( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import dd_exp def test_dd_exp(): _ = dd_exp( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import gng_m4 def test_gng_m4(): _ = gng_m4( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import gng_m3 def test_gng_m3(): _ = gng_m3( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import gng_m1 def test_gng_m1(): _ = gng_m1( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import gng_m2 def test_gng_m2(): _ = gng_m2( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
12
import pytest from hbayesdm.models import cgt_cm def test_cgt_cm(): _ = cgt_cm( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
198
7
from langchain_classic.schema.prompt_template import __all__ EXPECTED_ALL = ["BasePromptTemplate", "format_document"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
199
6
# third party packages import numpy as np def oneHotEncode(targets, nb_classes): res = np.eye(nb_classes)[np.array(targets).reshape(-1)] return res.reshape(list(targets.shape)+[nb_classes])
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Python
199
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from langchain_classic.schema.vectorstore import __all__ EXPECTED_ALL = ["VectorStore", "VectorStoreRetriever", "VST"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
201
12
import pytest from hbayesdm.models import igt_orl def test_igt_orl(): _ = igt_orl( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import ra_noLA def test_ra_noLA(): _ = ra_noLA( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import ra_noRA def test_ra_noRA(): _ = ra_noRA( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import wcs_sql def test_wcs_sql(): _ = wcs_sql( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import ts_par6 def test_ts_par6(): _ = ts_par6( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import igt_vpp def test_igt_vpp(): _ = igt_vpp( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import ts_par7 def test_ts_par7(): _ = ts_par7( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
13
#!/usr/bin/python import sys import json def run(doc): if 'start' in doc['input']: sums = 1 else: sums = doc['input']['sum'] + 1 output = { "output": { "sum": sums } } return output
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Python
201
12
import pytest from hbayesdm.models import cra_exp def test_cra_exp(): _ = cra_exp( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import ts_par4 def test_ts_par4(): _ = ts_par4( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
201
12
import pytest from hbayesdm.models import prl_ewa def test_prl_ewa(): _ = prl_ewa( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
202
7
from langchain_classic.schema.agent import __all__ EXPECTED_ALL = ["AgentAction", "AgentActionMessageLog", "AgentFinish"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
203
11
# -*- coding: utf-8 -*- """ Created on Thu Aug 27 11:21:35 2020 @author: nishe """ from cfc_analysis import bandpassSig from cfc_analysis import find_Hilbert from cfc_analysis import find_MI_cfc #%%
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Python
203
10
from utils.my_containers import Constructor network_register = Constructor() from .wrapper.miga import MIGA def get_network(cfg): network = network_register.build_with_cfg(cfg) return network
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Python
204
12
import pytest from hbayesdm.models import ug_delta def test_ug_delta(): _ = ug_delta( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
204
12
import pytest from hbayesdm.models import peer_ocu def test_peer_ocu(): _ = peer_ocu( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
204
12
import pytest from hbayesdm.models import ug_bayes def test_ug_bayes(): _ = ug_bayes( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
204
7
from utils.io import preprocess_data import numpy as np PATH = r"H:\learned spectral unmixing\training_processed\ACOUS/ACOUS_train.npz" spectra, oxy = preprocess_data(PATH, 21) print(np.shape(spectra))
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Python
204
12
import pytest from hbayesdm.models import hgf_ibrb def test_hgf_ibrb(): _ = hgf_ibrb( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
204
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from setuptools import setup from setuptools import find_packages setup( name="edNEGmodel_UQSA", packages=find_packages(), package_data={'data': ['initial_values/*.dat']}, version="1.0")
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Python
205
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from langchain_core.runnables.passthrough import ( RunnableAssign, RunnablePassthrough, aidentity, identity, ) __all__ = ["RunnableAssign", "RunnablePassthrough", "aidentity", "identity"]
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Python
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#!/bin/python3 import os zipfiles = os.listdir() zipfiles = list(filter(lambda x: x.endswith(".zip"), zipfiles)) for zipfile in zipfiles: cmd = "unzip %s" % (zipfile) print(cmd) os.system(cmd)
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Python
206
7
from langchain_core.documents import __all__ EXPECTED_ALL = ["Document", "BaseDocumentTransformer", "BaseDocumentCompressor"] def test_all_imports() -> None: assert set(__all__) == set(EXPECTED_ALL)
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Python
207
12
import pytest from hbayesdm.models import pstRT_ddm def test_pstRT_ddm(): _ = pstRT_ddm( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
207
12
import pytest from hbayesdm.models import bart_par4 def test_bart_par4(): _ = bart_par4( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
207
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from langchain_classic import smith EXPECTED_ALL = [ "arun_on_dataset", "run_on_dataset", "RunEvalConfig", ] def test_all_imports() -> None: assert set(smith.__all__) == set(EXPECTED_ALL)
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import pytest from hbayesdm.models import alt_delta def test_alt_delta(): _ = alt_delta( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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"""Serialization and deserialization.""" from langchain_core.load.dump import dumpd, dumps from langchain_core.load.load import load, loads __all__ = [ "dumpd", "dumps", "load", "loads", ]
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import pytest from hbayesdm.models import bart_ewmv def test_bart_ewmv(): _ = bart_ewmv( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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import pytest from hbayesdm.models import alt_gamma def test_alt_gamma(): _ = alt_gamma( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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#!/usr/bin/env python # coding: utf-8 # Copyright (c) venkatesh-sivaraman. # Distributed under the terms of the Modified BSD License. version_info = (0, 10, 5) __version__ = ".".join(map(str, version_info))
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import pytest from hbayesdm.models import cra_linear def test_cra_linear(): _ = cra_linear( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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from langchain_core.utils.input import ( get_bolded_text, get_color_mapping, get_colored_text, print_text, ) __all__ = ["get_bolded_text", "get_color_mapping", "get_colored_text", "print_text"]
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class BaseError(Exception): """ Error class for all local exceptions in this code base """ pass class PredictionResultExistsError(BaseError): pass class FileExistsError(BaseError): pass
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from langchain_classic.chains.structured_output.base import ( create_openai_fn_runnable, create_structured_output_runnable, ) __all__ = ["create_openai_fn_runnable", "create_structured_output_runnable"]
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from langchain_core.utils.json_schema import ( _dereference_refs_helper, _retrieve_ref, dereference_refs, ) __all__ = [ "_dereference_refs_helper", "_retrieve_ref", "dereference_refs", ]
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import pytest from hbayesdm.models import ra_prospect def test_ra_prospect(): _ = ra_prospect( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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## Params for dnn pathway v3 - network12 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 0 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 0 dense1_dropout = 0
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Python
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import HMSA.data import HMSA.features import HMSA.models import HMSA.train import HMSA.args import HMSA.constants import HMSA.hyperparameter_optimization import HMSA.nn_utils import HMSA.utils import HMSA.rdkit
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## Params for dnn pathway v3 - network8 alpha = 0.001 epochs = 0 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 50 dense1_dropout = 0
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Python
214
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## Params for dnn pathway v3 - network10 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 0 dense1_dropout = 0
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## Params for dnn pathway v3 - network11 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 20 dense1_dropout = 0
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Python
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## Params for dnn pathway v6 - network2 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 10 epochs_prime2 = 20 dense1_dropout = 0
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Python
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from setuptools import setup, find_packages setup( name='Clust3D', version='1.1', packages=find_packages(), author_email="orepap@uoi.gr", description='Clust3D - 3D Clustering tool' )
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__version__ = "0.2.4" from ._widget import AutocorrelationTool from . import autocorrelation from . import ClickLabel from . import crosscorrelation from . import functions __all__ = ( "AutocorrelationTool", )
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## Params for dnn pathway v3 - network7 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 0 dense1_dropout = 0.3
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Python
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import pytest from hbayesdm.models import choiceRT_ddm def test_choiceRT_ddm(): _ = choiceRT_ddm( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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import pytest from hbayesdm.models import dd_cs_single def test_dd_cs_single(): _ = dd_cs_single( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
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## Params for dnn pathway v3 - network9 alpha = 0.001 epochs = 50 batch_size = 128 activation = 'elu' epochs_prime = 25 alpha_prime = 0.01 dense1_prime = 0 prepath_neurons = 0 epochs_prime2 = 20 dense1_dropout = 0.3
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import pytest from hbayesdm.models import task2AFC_sdt def test_task2AFC_sdt(): _ = task2AFC_sdt( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
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import pytest from hbayesdm.models import pstRT_rlddm1 def test_pstRT_rlddm1(): _ = pstRT_rlddm1( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
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import pytest from hbayesdm.models import pstRT_rlddm6 def test_pstRT_rlddm6(): _ = pstRT_rlddm6( data="example", niter=10, nwarmup=5, nchain=1, ncore=1) if __name__ == '__main__': pytest.main()
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Python
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from .GCN import GCN, GNN from .discriminator import Discriminator from .attention import Attention, NodeAttention, SemanticAttention, LocalAttention from .fc import FullyConnect, FullyConnect2 from .GCN2 import DGCN
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"""A Tracer implementation that records to LangChain endpoint.""" from langchain_core.tracers.langchain import ( LangChainTracer, wait_for_all_tracers, ) __all__ = ["LangChainTracer", "wait_for_all_tracers"]