sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
48ddef85b2db6707af02875db6adf165e12a6da0d6ad1542a0d6582e01a6540e | Python | 218 | 7 | """Deprecated module for BaseLanguageModel class, kept for backwards compatibility."""
from __future__ import annotations
from langchain_core.language_models import BaseLanguageModel
__all__ = ["BaseLanguageModel"]
|
448399c1aab758673b24b240afd356c7003557547d8b3b440778b58c68371a04 | Python | 219 | 8 | from langchain_core.tracers.langchain import (
LangChainTracer,
get_client,
log_error_once,
wait_for_all_tracers,
)
__all__ = ["LangChainTracer", "get_client", "log_error_once", "wait_for_all_tracers"]
|
45dac2227cb94ef42e1bb58c80a4d6a5e737abdf61414d6e6fa09c1735bf824c | Python | 219 | 12 | import pytest
from hbayesdm.models import rdt_happiness
def test_rdt_happiness():
_ = rdt_happiness(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
4aed0a8f35f4c19513286d3f6c3004c043df70e477e9b2d2e1940ed6e2aa0eb9 | Python | 219 | 6 | from InitFunctions.Gaussian import *
from InitFunctions.Dirac import *
from InitFunctions.Brunel import *
from InitFunctions.Identity import *
from InitFunctions.Uniform import *
from InitFunctions.Dirac_Sparse import * |
52a194e87a259cd01829297075bfaa53b03ed67e3116599a10d8aebaf1c0edc2 | Python | 219 | 12 | import pytest
from hbayesdm.models import igt_pvl_decay
def test_igt_pvl_decay():
_ = igt_pvl_decay(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
9e169206c5b7989a2baf79a4bca43fe272f4a0e72be4e785ac14e2e95ef1463f | Python | 219 | 12 | import pytest
from hbayesdm.models import igt_pvl_delta
def test_igt_pvl_delta():
_ = igt_pvl_delta(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
bd491fd55d67069ca8e215f76cd687ff93a3e7d540737e8f983d72a7180a474d | Python | 219 | 6 | from langchain_classic.agents.agent_types import AgentType
from langchain_classic.agents.types import AGENT_TO_CLASS
def test_confirm_full_coverage() -> None:
assert list(AgentType) == list(AGENT_TO_CLASS.keys())
|
ca70017c37c9dbd3f0b9759a4b0a7a9d61fd52523317a0a26b42d18e5886d7a1 | Python | 219 | 12 | import pytest
from hbayesdm.models import dd_hyperbolic
def test_dd_hyperbolic():
_ = dd_hyperbolic(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
0ccbe33aa47d749e80b62d30d6b35b89edfd3f9186c771a2286dba470944d4c3 | Python | 220 | 6 | from neuralpredictors.layers.readouts import MultiReadoutBase
from neuralpredictors.layers.readouts.gaussian import FullGaussian2d
class MultiReadoutFullGaussian2d(MultiReadoutBase):
_base_readout = FullGaussian2d
|
26265d439bb51b1dfc38537558b2b4cbd91fd711f410bcc164a50ff73c64cdc8 | Python | 220 | 10 | #!/usr/bin/env python
import pstats
p = pstats.Stats('profile.out')
p.strip_dirs().sort_stats(-1).print_stats()
nreport = 100
p.sort_stats('cumulative').print_stats(nreport)
#p.sort_stats('time').print_stats(nreport)
|
47bb160e3eb363e4ed63c89286f5d4fe29512be2e7a4820bc941255c82f4eee8 | Python | 220 | 11 | ## Params for dnn pathway v4 - network3
alpha = 0.001
epochs = 50
batch_size = 128
activation = 'elu'
epochs_prime = 25
alpha_prime = 0.001
dense1_neurons = 1000
dense1_dropout = 0
prepath_neurons = 0
epochs_prime2 = 20
|
2956e22cead21c49a9ebfb1fcab58e86cee0394cdc16f7fa72b702e3e4ca33e3 | Python | 222 | 12 | import os
import sys
import re
import types
import itertools
from cgatReport.Tracker import *
from RnaseqReport import *
class ExonValidationSummary(RnaseqTracker, SingleTableTrackerRows):
table = "exon_validation"
|
60c413b2307e40239a1903190c0cc4fa46fb66a253c6eddb529eb1acec227831 | Python | 222 | 12 | import pytest
from hbayesdm.models import prl_fictitious
def test_prl_fictitious():
_ = prl_fictitious(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b2585b5a7e5278d75fe2e4bc22cedb879d5ca2920ff9e1e3aa11704af10f64b1 | Python | 222 | 12 | import pytest
from hbayesdm.models import pst_gainloss_Q
def test_pst_gainloss_Q():
_ = pst_gainloss_Q(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b4976af048e39f68736f617589fedde1e2993f44465a3cf12ed94a496f4a0691 | Python | 222 | 8 | from qtpy import QtWidgets, QtCore
class ClickLabel(QtWidgets.QLabel):
clicked = QtCore.Signal()
def mousePressEvent(self, event):
self.clicked.emit()
QtWidgets.QLabel.mousePressEvent(self, event) |
447f7fb9b117e8c6fa915fdf3e55ec52a10f0efa061c7806a83c6c3f5e3ddee2 | Python | 223 | 10 | from langchain_core.prompts import PromptTemplate
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
|
dd1673653f10c281753dee33491c0b35946841e4c9295089fd8c252bdc8c7be7 | Python | 223 | 10 | class InconsistentDataException(Exception):
"""
Raised when (parts of) a Dataset is inconsistent
"""
class DoesNotExistException(Exception):
"""
Raised when (parts of) a Dataset does not exist
"""
|
0b81efb88e0af31cf4f84e5a346ef01ecff6994e8a64c8a12e0cac878017c562 | Python | 224 | 6 | """
This module represents a pytorch re-implementation of scipy's
`scipy.optimize._lsq` module. Some of the code is borrowed directly
from the scipy library (all rights reserved).
"""
from .least_squares import least_squares |
6f4d29b774d09bb38fd5dacdd7c9701d1ba6c597d68ce4af6b6b0b2e330dc1b2 | Python | 224 | 6 | NB_NRN = 16
NB_FRAME_PER_BUFFER = 190
DATAWIDTH_BIT_FRAME = 32
DATAWIDTH_BYTE_FRAME = int(DATAWIDTH_BIT_FRAME/8)
SIZE_BYTE_FRAME = (1+NB_NRN)*DATAWIDTH_BYTE_FRAME # +1 for time stamp |
c9352838c12439f9b2d164d9f975569008b2fc11f1c461d97775dbf77a60c9be | Python | 224 | 10 | import numpy as np
import os, os.path
import logging
def test_openmm_platforms():
"""Testing comparison of platforms.
"""
from openmmtools.scripts import test_openmm_platforms
#test_openmm_platforms.main()
|
3b6b3ef271e5c80be2689fc3a691fd1ea206b2fb9266282db0086db3738ad5c3 | Python | 225 | 12 | import pytest
from hbayesdm.models import hgf_ibrb_single
def test_hgf_ibrb_single():
_ = hgf_ibrb_single(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
3e7f9dc6243933d9a70e80aee0afd94fdcf4690fbdadeae4f4b73b6162af76a4 | Python | 225 | 12 | import pytest
from hbayesdm.models import bandit4arm_4par
def test_bandit4arm_4par():
_ = bandit4arm_4par(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
5c3c8d3a78cf16482687d0cd01d88b3880514fd58b7434ee6e1832d0f7289d17 | Python | 225 | 11 | # Accept all incoming requests
c.NotebookApp.ip = "*"
c.NotebookApp.port = 8888
c.NotebookApp.open_browser = False
c.MultiKernelManager.default_kernel_name = "python3"
c.NotebookApp.token = ""
c.NotebookApp.password = ""
|
e97e798190623e4916d7830c9a5587b4f7f3298e4ba410a4714aa47814f44d65 | Python | 225 | 12 | import pytest
from hbayesdm.models import banditNarm_4par
def test_banditNarm_4par():
_ = banditNarm_4par(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
4ce3226d9e8dcd6aaffa1cfc15e2e81a5537e8495ac6b54abc803b8e7fab948b | Python | 226 | 9 | from langchain_core.tracers.log_stream import (
LogEntry,
LogStreamCallbackHandler,
RunLog,
RunLogPatch,
RunState,
)
__all__ = ["LogEntry", "LogStreamCallbackHandler", "RunLog", "RunLogPatch", "RunState"]
|
880e4d2876d883c3a428f73166b7f025b320219da9eca699efde2f3660be0da1 | Python | 226 | 10 | import sys
import numpy as np
import voxcell as vc
vd = vc.VoxelData.load_nrrd(sys.argv[1])
dat = np.loadtxt(sys.argv[2],dtype='int32')
ix = dat.reshape(-1,3)
reg = vd._lookup_by_indices(ix)
np.savetxt(sys.argv[3],reg,"%d")
|
7f5af558b055672676e80dbfb640a13e93164ede423366570633c186e2a6b483 | Python | 227 | 7 | import glob
from os.path import dirname, basename, isfile, join
modules = glob.glob(join(dirname(__file__), "*.py"))
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import * |
8e7f83180ea949efddaa5f4d719650d917b729dac03882888c9551be4ad3e6cc | Python | 227 | 8 | __version__ = '1.0.5'
# from pathlib import Path
# import subprocess
#
# __version__ = subprocess.check_output(
# ["git", "describe", "--tags"],
# cwd=Path(__file__).parent.parent.absolute()
# ).strip().decode('UTF-8')
|
0268fe9f319595193648104a2e56578a46585a0a3d29ba18122de42b99cc0e0b | Python | 228 | 12 | import pytest
from hbayesdm.models import bandit2arm_delta
def test_bandit2arm_delta():
_ = bandit2arm_delta(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
0f73c3b86b954032a7a0607fa0894de7ee75dddb241db9d4626a9a7847ea5308 | Python | 228 | 7 | from langchain_classic.schema.runnable.passthrough import __all__
EXPECTED_ALL = ["RunnableAssign", "RunnablePassthrough", "aidentity", "identity"]
def test_all_imports() -> None:
assert set(__all__) == set(EXPECTED_ALL)
|
3929bea50991b176a59b5e0b7629d47e352f86cc04f8ee2451ff61a665cd45ae | Python | 228 | 12 | import pytest
from hbayesdm.models import banditNarm_delta
def test_banditNarm_delta():
_ = banditNarm_delta(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
4861376660dfbdff5105b9db5f9315dd48a9709e8089497d9d0a9e14bb08efd7 | Python | 228 | 12 | import pytest
from hbayesdm.models import banditNarm_lapse
def test_banditNarm_lapse():
_ = banditNarm_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b1592641bb40634d94d73a33979a25d2fdfe71546525f129024195757afacd20 | Python | 228 | 12 | import pytest
from hbayesdm.models import bandit4arm_lapse
def test_bandit4arm_lapse():
_ = bandit4arm_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b44b5b66f6d509f0be75487ca4ea4cbac451592dbd0764fbf8702740cd920d9f | Python | 228 | 12 | import pytest
from hbayesdm.models import prl_rp_multipleB
def test_prl_rp_multipleB():
_ = prl_rp_multipleB(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b8d3884dd6d6c15cf8bd704aebd62b415dded3757de59d2e62609e445f9eab53 | Python | 228 | 12 | import pytest
from hbayesdm.models import dbdm_prob_weight
def test_dbdm_prob_weight():
_ = dbdm_prob_weight(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
ea0f043531d10502ed685737f5afa69077b3caa1329a738916ad3af96395ae60 | Python | 229 | 7 | from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
JsonOutputToolsParser,
PydanticToolsParser,
)
__all__ = ["JsonOutputKeyToolsParser", "JsonOutputToolsParser", "PydanticToolsParser"]
|
30c06409aebd3b5513f1ca9599acc4805092444b36f4c8529916a37e65dfe69d | Python | 230 | 11 | import numpy as np
from paths import TRAINING_DATA_PATH
PATH = TRAINING_DATA_PATH + "/ALL/ALL.npz"
spectra = np.load(PATH)["spectra"]
oxy = np.load(PATH)["oxy"]
np.savez(PATH,
spectra=spectra,
oxygenation=oxy) |
693f8ad41cb441723bae6786cca0e3052d939b21224a230fbb4f7c299dc0aee6 | Python | 230 | 11 | import numpy as np
from ZScore import z_score
x = np.array([1,2,3,4,5,6,7,8,9,10])
y = np.array([2,4,6,8,10,12,14,16,18,20])
print(np.corrcoef(x,y))
print(np.std(x))
print(np.std(y))
print(z_score(x))
print(z_score(y)) |
df93258e1dcdad64fc198cdca5f0e36a596c3b80c3050561bd1e007b9d9cd35f | Python | 230 | 8 | prompt_template = """Given the following question and context, return YES if the context is relevant to the question and NO if it isn't.
> Question: {question}
> Context:
>>>
{context}
>>>
> Relevant (YES / NO):""" # noqa: E501
|
11d5dcc28087b311e0a5668d18fc97c0b3f9a95817136c2f0f8fe4fb6c7440b8 | Python | 231 | 12 | import pytest
from hbayesdm.models import prl_fictitious_rp
def test_prl_fictitious_rp():
_ = prl_fictitious_rp(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
49fe9c6286cdffc712bba5f5247948c1838d69c0e5b9cacd1da21938d6a8d305 | Python | 231 | 10 |
import glob
from os.path import dirname, basename, isfile, join
modules = glob.glob(join(dirname(__file__), "*.py"))
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import *
|
500a042f4a7fffa679cb1e4004dc28fc82ff81ee10096101c466796c3a808c87 | Python | 231 | 12 | import pandas as pd
import numpy as np
a = np.zeros(40) + 0.6
df = pd.DataFrame(a,columns = ['Mean'])
df['StD'] = 0.1
df['SM'] = 0.1
df['SS'] = 0.1
df['PM'] = 0
df['PS'] = 0
df.to_csv('./required_files/Values.csv', index = False) |
6ea7a4cf15692040531ec1ebd4cd7665ec482b3639429b03c38605f11e777046 | Python | 231 | 14 | from langchain_core.load import __all__
EXPECTED_ALL = [
"InitValidator",
"Serializable",
"dumpd",
"dumps",
"load",
"loads",
]
def test_all_imports() -> None:
assert set(__all__) == set(EXPECTED_ALL)
|
ca2a45905026b72d313bc813fa32b50127cf9c5b34b8dd89e3a78c1357a20ed4 | Python | 232 | 9 | from setuptools import setup
setup(name='SSCD',
#version='0.1',
#url='https://github.com/zzh8829/yolov3-tf2',
#-author='Zihao Zhang',
#author_email='zzh8829@gmail.com',
#packages=['yolov3_tf2']
) |
fa29c12cca18a2442c372648f436680e208f15970294659e1ec837fd7d3da794 | Python | 232 | 8 | from typing import Any, Dict, List
class LRPStrategy:
def __init__(self, layers: List[Dict[str, Any]] = None,
pooling: List[Dict[str, Any]] = None):
self.layers = layers
self.pooling = pooling
|
bc50adf4e1c9cb41dd4ab1eadb5cab4320447dfc2074c1f8a52dae35318eb990 | Python | 233 | 12 | import pandas as pd
import numpy as np
a = np.zeros(40) + 0.5
df = pd.DataFrame(a,columns = ['Mean'])
df['StD'] = 0.06
df['SM'] = 0.1
df['SS'] = 0.01
df['PM'] = 0
df['PS'] = 0
df.to_csv('./required_files/Values.csv', index = False) |
0244a53c89cda1e1acfcd6902a1ad0fb7282fa3583ab13bd309c8c6d387f3587 | Python | 234 | 8 | """A tracer that runs evaluators over completed runs."""
from langchain_core.tracers.evaluation import (
EvaluatorCallbackHandler,
wait_for_all_evaluators,
)
__all__ = ["EvaluatorCallbackHandler", "wait_for_all_evaluators"]
|
10f061b91598576c03d17f41a8c4f9703fb6217769919f4c1157c0a0b8cdf8d3 | Python | 234 | 12 | import pytest
from hbayesdm.models import prl_fictitious_woa
def test_prl_fictitious_woa():
_ = prl_fictitious_woa(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
b0c847f7de47f3140ccd151750aa2a841589f427ba801863e13b9108355c1c83 | Python | 235 | 8 | #
import os
def get_datadir():
"""Returns the data directory of this package"""
return os.path.join(os.path.dirname(__file__), 'data')
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
|
114ee2b7fda939132da7f023a596cc011c640abc872120228f89bf9b71af5497 | Python | 237 | 13 | #!/usr/bin/python
import sys
import json
doc = json.loads(sys.stdin.read())
if 'start' in doc['input']:
sums = 1
else:
sums = doc['input']['sum'] + 1
output = { "output": { "sum": sums } }
sys.stdout.write(json.dumps(output))
|
ed99abf37b2be89fc415225a34e7df774106cd4c6fd1a6ea8815497dcb6d57f0 | Python | 237 | 12 | import pytest
from hbayesdm.models import choiceRT_ddm_single
def test_choiceRT_ddm_single():
_ = choiceRT_ddm_single(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
2dc72e1c13d947398be1dd44320650358d8f8d937f36cabb3feed0041d2cdedb | Python | 238 | 12 | ## Params for dnn pathway v5 - network2
alpha = 0.001
epochs = 50
batch_size = 128
activation = 'elu'
epochs_prime = 25
alpha_prime = 0.001
dense1_neurons = 1000
dense1_prime = 0
prepath_neurons = 10
epochs_prime2 = 20
dense1_dropout = 0
|
04ca5417a4ef59e78f192b2a874d5312068c52640f4a2c3ffa7647eea083cd5a | Python | 240 | 5 | import morgana.ImageTools.morphology.anchorpoints
import morgana.ImageTools.morphology.computemorphology
import morgana.ImageTools.morphology.meshgrid
import morgana.ImageTools.morphology.midline
import morgana.ImageTools.morphology.spline
|
3edd5d7585a8a543c81d9f1f9edf5398bbccfd04a9e636a67f3775f568e4b127 | Python | 240 | 8 | import morgana.GUIs.fluo
import morgana.GUIs.inspection
import morgana.GUIs.mainwindow
import morgana.GUIs.manualmask
# import orgseg.GUIs.spot
import morgana.GUIs.visualize0d
import morgana.GUIs.visualize1d
import morgana.GUIs.visualize2d
|
6f18dcd37bf9bc8f4ac7a2e99dfc39527f88e24fc20efd77a904caaa874204c7 | Python | 240 | 12 | import pytest
from hbayesdm.models import dd_hyperbolic_single
def test_dd_hyperbolic_single():
_ = dd_hyperbolic_single(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
86d30bc7b981c74bae6c7c4de3dac5b29fe546a93f988b9444af6974bbd58a7d | Python | 241 | 11 | import numpy as np
from sklearn.base import BaseEstimator
class DummyModel(BaseEstimator):
def predict_proba(self, X):
return X
def fit(self, X, y):
self.classes_ = np.array([0, 1])
return self
|
26b3b6e11de6e84e23738b1a98d08967dee0d57afabf52d116b99c13cecf2727 | Python | 242 | 5 | from CEL.utils.register import register
def load_model(dataset, ds_config, model_config):
model_name = model_config.name
model = register.models[model_name](ds_config.name, dataset=dataset, **model_config[model_name])
return model |
920bb3eac7d1379d45b3f04f03350a7a05f022e206eb967724957ff7fba10a21 | Python | 242 | 8 | """Test the cohere reranker."""
from langchain_classic.retrievers.document_compressors.cohere_rerank import CohereRerank
def test_cohere_reranker_init() -> None:
"""Test the cohere reranker initializes correctly."""
CohereRerank()
|
3b678e984f6c299699fa52b695875859baa3c5eec7c11f7934c8592fb4447b89 | Python | 243 | 12 | import pytest
from hbayesdm.models import banditNarm_2par_lapse
def test_banditNarm_2par_lapse():
_ = banditNarm_2par_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
695abb59ebfcd947e3e364306da9cb245da341d53024e2cead59958bfcb3c420 | Python | 243 | 12 | import pytest
from hbayesdm.models import prl_fictitious_rp_woa
def test_prl_fictitious_rp_woa():
_ = prl_fictitious_rp_woa(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
8ad5977966c9835adb2adb47c4e9c527aaae8a525fc4faa4968dff1fa28fba9b | Python | 243 | 12 | import pytest
from hbayesdm.models import bandit4arm_2par_lapse
def test_bandit4arm_2par_lapse():
_ = bandit4arm_2par_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
f76fa4c99fc5bb777161934dfffbd7a68ca47f602ae759eb6741235bfa1a6f44 | Python | 243 | 5 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
from ._contract import Contracter
from ._channels import MakeWeightedChannels
__all__ = [Contracter, MakeWeightedChannels]
|
291db7c8f882bd8354aa543104c83d90692affb29c5c574d184bcd1f2ec5874a | Python | 245 | 11 | from setuptools import setup
setup(
name='synister',
version='0.1',
description='Neurotransmitter classification',
url='https://github.com/funkelab/synister',
author='Funkelab',
packages=[
'synister',
])
|
8bab79be87efd4bd70fc56822b6c7c05ecfea5fa86553702551ab99445d5f775 | Python | 245 | 6 | from createdir import runBB, parse_bellybuttonseg_args
if __name__ == "__main__":
args = parse_bellybuttonseg_args('Run BellyButton training on specified file/directory')
runBB(train=True, predict=False, file_path=args['file_path'])
|
c1e54343817c14e584779b19d56f4a63f0b56083d8e8cf8e36dfb7290c3736bb | Python | 245 | 11 | from voxcell import VoxelData
import sys
input_nrrd = sys.argv[1]
z_index = int(sys.argv[2])
output_nrrd = sys.argv[3]
vd = VoxelData.load_nrrd(input_nrrd)
res = vd.raw[z_index,:,:]
res_vd = VoxelData(res, (1,1))
res_vd.save_nrrd(output_nrrd)
|
da7207a52071a518c615560bdda0cad328f94e52adcc5dd11979be9a32206999 | Python | 245 | 13 | import torch
import torchviz
from minimal import Synapse
pre = torch.arange(3., requires_grad=True)
post = torch.arange(5., requires_grad=True)
syn = Synapse()
delta = syn(pre,post)
print delta
torchviz.make_dot(syn(pre,post))
#%% |
0a9ae025c786cde6cf596088d5dace2869835e5d67ee51c88f2eaa3cf388ddf1 | Python | 246 | 12 | import pytest
from hbayesdm.models import bandit4arm_lapse_decay
def test_bandit4arm_lapse_decay():
_ = bandit4arm_lapse_decay(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
324840942a7273db5ed93470b73142e9f0b86ee6d77d00b182bdc9b988ed7a32 | Python | 246 | 12 | import pytest
from hbayesdm.models import banditNarm_lapse_decay
def test_banditNarm_lapse_decay():
_ = banditNarm_lapse_decay(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
a17799b221657f254c25bba88f1f9913d5af8c7f06fb051a7a0741591cfae4fc | Python | 246 | 12 | #!/usr/bin/python
import sys,os
from PDBpy import *
for fname in os.listdir("."):
res = fname.split(".")
if(len(res) > 1):
if(res[1]=="pdb"):
deb=fname.split(".")[0]
x = PDB("%s.pdb" % deb, hetSkip = 1)
x.out("%s-noHet.pdb" % deb)
|
bcf6b4c1608adce10da6dbd98db3da94d35e979663a447e2b965dd699b2c1542 | Python | 246 | 14 | from config import conf
from runner import Runner
import os
binding_site_range = 15.0
out_path = 'trained_model'
if not os.path.isdir(out_path):
os.mkdir(out_path)
runner = Runner(conf, out_path=out_path)
runner.train(binding_site_range) |
c31d667f948644404795b50e34bf417b0965d114a25aa30febaf0766e8b23b6e | Python | 246 | 13 | from pdearena.pl_models.cnnmodel import (
CNNModel,
NeuralSolver,
NeuralCFL,
ShockCast
)
MODELS = {
"cnn": CNNModel,
"neural_solver": NeuralSolver,
"neural_cfl": NeuralCFL,
"shock_cast": ShockCast,
} |
d15ac15ef52affb5c0d4266bb27f2e07cdbe0cf256c9362f45fa0fd752ce9701 | Python | 246 | 11 | from langchain_core.tools import (
create_retriever_tool,
render_text_description,
render_text_description_and_args,
)
__all__ = [
"create_retriever_tool",
"render_text_description",
"render_text_description_and_args",
]
|
d2658f620949aadbe46baec6b2349b126314550ff973f7a244bb70e3eb84b99b | Python | 247 | 6 | from createdir import runBB, parse_bellybuttonseg_args
if __name__ == "__main__":
args = parse_bellybuttonseg_args('Run BellyButton prediction on specified file/directory')
runBB(train=False, predict=True, file_path=args['file_path'])
|
35b8df8496e482ba69da7f63bb51bcdf0a781e39074e4b5d2f1e3919b1aace4d | Python | 248 | 5 | import NeuronModel
from NeuronModel import Hodgkin_Huxley
from NeuronModel.Hodgkin_Huxley import hodgkin_huxley_parameters
from NeuronModel.Hodgkin_Huxley import mainer_parameters
from NeuronModel.Hodgkin_Huxley import neuronal_dynamics_parameters
|
6883047327635cd6b37ae80a8c95c6f2ddad667553c3ca20a0235a759987f0fe | Python | 248 | 9 | # from pycromanager import Dataset
#
# dataset = Dataset('/Users/henrypinkard/Desktop/ndtiffv3.0_stitched_test_1')
from PIL import Image
# Open the tiff image
pil_img = Image.open('/Users/henrypinkard/Desktop/ndtiffv3.0_test_NDTiffStack(1).tif') |
7a02ce13ebc1e5e60ec6c71eba25f75df941e4cf55a96e215842d8170fdd4850 | Python | 248 | 6 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
from ._version import __version__
from . import _compile
from . import _extern
__all__ = ["__version__", "_compile", "_extern"]
|
775cdc5fe2c2e81f98de22470bb915432e9bfd4f3aff1fedd975c4cd0fe35343 | Python | 249 | 5 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
from .allegro_models import AllegroModel
from .param_groups import MuonParamGroups
__all__ = ["AllegroModel", "MuonParamGroups"]
|
cbfe9fb9d09ebd1f6805c4618a5e049805765b1d5d4fcc0c66900466697ead3c | Python | 250 | 15 | from .artemis_clip_vinvl_dataset import ArtEmisAttCLIPVinVLDataset
__all__ = [
"ArtEmisAttCLIPVinVLDataset",
"",
]
DatasetMapTrain = {
"WESD": ArtEmisAttCLIPVinVLDataset,
}
DatasetMapEval = {
"WESD": ArtEmisAttCLIPVinVLDataset,
}
|
efed27008a03965d735331af8a493c8241e4129135fd146b85ecd687dc1e3e0f | Python | 250 | 9 | import os
import sys
# Appends the root keras-explainability directory to the path
def pytest_configure(config):
testpath = os.path.dirname(os.path.abspath(__file__))
libpath = os.path.join(testpath, os.pardir)
sys.path.append(libpath)
|
bb2b5cb4b1d5321bd2ff22da21ad8f908477f83702a24a27d5a7fcc807f5f86f | Python | 251 | 12 | #!/usr/bin/env python
# coding: utf-8
# Copyright (c) venkatesh-sivaraman.
# Distributed under the terms of the Modified BSD License.
"""
Information about the frontend package of the widgets.
"""
module_name = "emblaze"
module_version = "^0.10.5"
|
37e890174cbe5a88d201c6b9c4a0dad50d119c77c61fdd71ec15f6c9c181bb23 | Python | 252 | 12 | import pytest
from hbayesdm.models import banditNarm_kalman_filter
def test_banditNarm_kalman_filter():
_ = banditNarm_kalman_filter(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
87b59aeeb1295aec9b259b3195f96b235cc1ce63abb6c0a3c6f7304ea07fcb44 | Python | 252 | 12 | import pytest
from hbayesdm.models import prl_fictitious_multipleB
def test_prl_fictitious_multipleB():
_ = prl_fictitious_multipleB(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
ab3652cb51ee4054c9f7403a98e9763da70b1010b47324b48624ac1f377d9867 | Python | 252 | 6 | """**Chat Loaders** load chat messages from common communications platforms.
Load chat messages from various
communications platforms such as Facebook Messenger, Telegram, and
WhatsApp. The loaded chat messages can be used for fine-tuning models.
"""
|
bcff0c407ef7b0b7a240422e5cd85b0de1174d7247c7563bd368ce912dd4225c | Python | 252 | 12 | import pytest
from hbayesdm.models import banditNarm_singleA_lapse
def test_banditNarm_singleA_lapse():
_ = banditNarm_singleA_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
e8c85c95dac6b50d86b435760ae4575adf6ad880e179466a7a08b61147cb24af | Python | 252 | 9 | import pickle
import os
import numpy as np
def pre_normalize(eeg_in_volts):
root = os.path.dirname(__file__)
fname = f'{root}/norm.pkl'
with open(fname, 'rb') as f:
r = pickle.load(f)
return (eeg_in_volts - r['sub']) / r['den']
|
ed0f3d5d37d663fcf986a8f757722dca89256324d1b0834050486b380eebed80 | Python | 252 | 12 | import pytest
from hbayesdm.models import bandit4arm_singleA_lapse
def test_bandit4arm_singleA_lapse():
_ = bandit4arm_singleA_lapse(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
f7784e710e60371c2682357486a7181c94355a413cb5cf09ac042ec0a7f65fcd | Python | 252 | 5 | from langchain_core.indexing.api import _abatch, _batch, _HashedDocument
# Please do not use these in your application. These are private APIs.
# Here to avoid changing unit tests during a migration.
__all__ = ["_HashedDocument", "_abatch", "_batch"]
|
0b5605dbcf8c6d260cfadb4f49c18136642cdbe16f2f5691d0493f7736b89512 | Python | 254 | 11 | import torch
def reduce_edge(inputs, edge_index, output_shape):
outputs = torch.zeros(
*output_shape,
device=inputs.device,
dtype=inputs.dtype,
)
outputs.index_add_(0, edge_index, inputs)
return outputs |
aae3128a17513d090b1505e8fd0c0a03fc71a2b58cee8ea2b6e065503bb80923 | Python | 255 | 12 | import pytest
from hbayesdm.models import bandit4arm2_kalman_filter
def test_bandit4arm2_kalman_filter():
_ = bandit4arm2_kalman_filter(
data="example", niter=10, nwarmup=5, nchain=1, ncore=1)
if __name__ == '__main__':
pytest.main()
|
892e1d976681c74fab592f7509090c4d1da9ccb2c0a53b78ef08de21313a505a | Python | 257 | 13 | from langchain_core.tracers.stdout import (
ConsoleCallbackHandler,
FunctionCallbackHandler,
elapsed,
try_json_stringify,
)
__all__ = [
"ConsoleCallbackHandler",
"FunctionCallbackHandler",
"elapsed",
"try_json_stringify",
]
|
718d7301d0f8144cf13b7f631164801e784efddd27a9110af3b9a325881f929e | Python | 258 | 11 | from langchain_classic.prompts.few_shot import __all__
EXPECTED_ALL = [
"FewShotChatMessagePromptTemplate",
"FewShotPromptTemplate",
"_FewShotPromptTemplateMixin",
]
def test_all_imports() -> None:
assert set(__all__) == set(EXPECTED_ALL)
|
9754faa3402f9c652d910bf21a395d571e01f26c2bb879a84ae644e7a43ef732 | Python | 258 | 9 | from imagingplus.processing.microscopes import PrairieViewMetadata
def test_PrairieViewMetadata():
testdir = '/home/pshah/mnt/qnap/Data/2021-01-28/2021-01-28_PS14_t-002'
pvdata = PrairieViewMetadata(pv_xml_dir=testdir)
test_PrairieViewMetadata()
|
eb4a2850dfc17d350f39a7c4d8a7e3d0ac1ef931aa30d5a56f135e1702c24b5e | Python | 258 | 13 | """
openff-arsenic
Report results for free energy simualtions
"""
# Handle versioneer
from ._version import get_versions
versions = get_versions()
__version__ = versions["version"]
__git_revision__ = versions["full-revisionid"]
del get_versions, versions
|
056fa3812795720d49c51c7d16219a4766153d59f638a285df713b70fad15155 | Python | 259 | 6 | from createdir import runBB, parse_bellybuttonseg_args
if __name__ == "__main__":
args = parse_bellybuttonseg_args('Run BellyButton training and prediction on specified file/directory')
runBB(train=True, predict=True, file_path=args['file_path'])
|
50e0e894a135fdc6a891e17178823b9d9dc122e8634061e4a2bcc518d9c222b0 | Python | 260 | 4 | TRAINING_DATA_PATH = r"H:\learned spectral unmixing\training_processed/"
TEST_DATA_PATH = r"H:\learned spectral unmixing\test_final_mp/"
MODEL_PATH = r"H:/learned spectral unmixing/models_LSTM_mp/"
EXAMPLE_PATH = r"H:\learned spectral unmixing\sample_images/"
|
98615987d5aad6ec7b9a1af873bd0180a73e330d01d367492cdccbf41432eac7 | Python | 260 | 11 | import time
def log(func):
def wrap(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
end = time.time()
print('Generated', args[0], round(end-start, 2), 's')
return result
return wrap |
cfc265d3ded8c6c7c38f98878b49d94260beedc25df9e0f030438d7103e24aa6 | Python | 260 | 11 | from langchain_core.runnables.history import (
GetSessionHistoryCallable,
MessagesOrDictWithMessages,
RunnableWithMessageHistory,
)
__all__ = [
"GetSessionHistoryCallable",
"MessagesOrDictWithMessages",
"RunnableWithMessageHistory",
]
|
63998b5cfae55a923ab75f8136c0b9028c199f1a9a04dbb19aaaba516ba0ade5 | Python | 261 | 10 | from voxcell import VoxelData
import numpy as np
import nrrd
import sys
input_nrrd = sys.argv[1]
output_base = sys.argv[2]
vd = VoxelData.load_nrrd(input_nrrd)
nrrd.write('{}.nhdr'.format(output_base), vd.raw, detached_header='{}.bin.gz'.format(output_base))
|
7c834d7884d0481d366519f32ae5684824b375c4b2e652dc38946daf3d828a0c | Python | 261 | 10 | import cv2
import numpy as np
from src.data.utils import CLASS_COLOR
if __name__ == '__main__':
for class_name in CLASS_COLOR:
img = np.zeros((224, 224, 3))
img[:, :] = CLASS_COLOR[class_name]
cv2.imwrite(f'{class_name}.png', img)
|
07a6bd5b4017f0b24b11f5db0573d4296deab7c789ad737c6d17f6bdb6bd74df | Python | 262 | 8 |
from .aphynity_exp import APHYNITYExp
from .soft_intervention_exp import *
from CEL.utils.register import register
def load_experiment(dataloader, model, exp_config):
exp = register.experiments[exp_config.name](dataloader, model, exp_config)
return exp |
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