sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d76b351e25da1a5c18f76b200710e66207d04ac31f35691d0caaf3d0ea544939 | Python | 472 | 16 | import sys
import wget
from tqdm import tqdm
import os
not_found = open("404.txt","w")
for line in tqdm([l.strip() for l in open(sys.argv[1],'r').readlines()]):
line = line.strip()
url = "https://alphafold.ebi.ac.uk/files/AF-" + line + "-F1-model_v2.pdb"
try:
filename = wget.download(url)
... |
db25120ae42b869273ae07eb8af0912ed55c9699e5d75d1d6bd9ac733ee5c012 | Python | 472 | 19 | from langchain_core._api import __all__
EXPECTED_ALL = [
"beta",
"deprecated",
"LangChainBetaWarning",
"LangChainDeprecationWarning",
"suppress_langchain_beta_warning",
"surface_langchain_beta_warnings",
"suppress_langchain_deprecation_warning",
"surface_langchain_deprecation_warnings",... |
fbbec56854e99b0f466d882355438509367e5ad410c89f0ddbafb14cc6c2e32d | Python | 472 | 19 | from langchain_classic.schema.runnable.config import __all__
EXPECTED_ALL = [
"EmptyDict",
"RunnableConfig",
"acall_func_with_variable_args",
"call_func_with_variable_args",
"ensure_config",
"get_async_callback_manager_for_config",
"get_callback_manager_for_config",
"get_config_list",
... |
970dc4aae8a6121b5714ff78630a643febc565643911bc45dcf59512ade35e2a | Python | 474 | 20 | import numpy
import shapely
import shapely.wkb
def numpy_to_ewkt(arr):
return shapely.wkb.dumps(shapely.geometry.point.Point(*arr)).hex()
def ewkt_to_numpy(ewkb):
return numpy.array(
shapely.wkb.loads(
(ewkb.hex() if isinstance(ewkb, bytes) else ewkb),
hex=True))
def linese... |
95dfc4ba64a6e3c5bce8118db1b6ebcd72f8833491f936e48175fdd9b1b55bca | Python | 475 | 18 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/9/27 17:59
# @Author : zhangchao
# @File : __init__.py.py
# @Software: PyCharm
# @Email : zhangchao5@genomics.cn
from .dnn import DNNModel
from .spatial import SpatialModel
from .loss import KDLoss, MultiCEFocalLoss
from .loader import DNNDataset
fro... |
e373faa47a3028889d80a14ce1910da96e7ac97d131abe5e4b9d2947db3be6fa | Python | 475 | 26 | import voxcell as vc
import numpy as np
pixelSize = 10
vd = vc.VoxelData.load_nrrd('flatmap_centered_200um.nrrd')
x = vd.raw[:,:,:,0]
y = vd.raw[:,:,:,1]
xnan = np.isnan(x)
ynan = np.isnan(y)
xmin = np.min(x[~xnan])
ymin = np.min(y[~ynan])
x = np.floor((x - xmin) / pixelSize)
y = np.floor((y - ymin) / pixelSize)
... |
1a588817a2d2dce48712a3093bf828e663da8f44e9e216fd6497a60093885a93 | Python | 476 | 16 | """
A TensorFlow-powered python toolbox to train deep neural networks to perform motor tasks.
.. note::
The API referenced in the documentation is outdated and corresponds to version 0.1.5 of MotorNet, which relies on
TensorFlow. However, it is still referenced here for users who may wish to consult it.
"""
_... |
bc25dbfc4f32ea67450e1ad1b964a90b5dbfc5052ea5d31fc7d34a70a57449fd | Python | 476 | 14 | from langchain_core.utils.function_calling import FunctionDescription, ToolDescription
from langchain_core.utils.function_calling import (
convert_to_openai_function as convert_pydantic_to_openai_function,
)
from langchain_core.utils.function_calling import (
convert_to_openai_tool as convert_pydantic_to_openai... |
89cdad6b02bf171f132bf19f4a0728925c6d76237568b2dc947674a3d8abc8f5 | Python | 481 | 13 | from .base_options import BaseOptions
class TestOptions(BaseOptions):
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
parser.add_argument('--model_path')
parser.add_argument('--no_resize', action='store_true')
parser.add_argument('--no_crop', action='sto... |
0e5829501c8119dc4c6ba2821a505c16ab8b1fea553bb815f485ef02c78ef4d8 | Python | 482 | 22 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 3 10:57:50 2019
@author: ngritti
"""
import sys
import PyQt5.QtWidgets
from morgana.GUIs.mainwindow import morganaApp
# filepath = os.path.dirname(os.path.abspath(__file__))
# sys.path.insert(0,os.path.join(filepath))
if __name__ == '__main__':
... |
6b9d2ece38775385feab037482860e26399792100d6cec0a05470a5de1ef13fb | Python | 482 | 21 | #!/usr/bin/python
import sys
import json
doc = json.loads(sys.stdin.read())
if 'start' in doc['input']:
sums = doc['input']['start']
finish = sums + 1
doc['cache']['finish'] = finish
else:
sums = doc['input']['sum'] + 1
finish = doc['cache']['finish']
if sums > doc['cache']['finish']:
success... |
636fdeabe66eca2fd3c26c83eb6294bff6d4d692a78ba8b7fbaa317112aa0038 | Python | 483 | 16 | MODEL_CONFIG = {
"weight":{
"protein": "pocket_pre_220816.pt",
"molecule_no_h": "mol_pre_no_h_220816.pt",
"molecule_all_h": "mol_pre_all_h_220816.pt",
"crystal": "mp_all_h_230313.pt",
"mof": "mof_pre_no_h_CORE_MAP_20230505.pt",
},
"dict":{
"protein": "poc.dict... |
a45f1fc11707d914083d4691f1588ec08399f50d4052c86cd30ad1495a923c01 | Python | 483 | 10 | def __getattr__(_: str = "") -> None:
"""Raise an error on import since is deprecated."""
msg = (
"This module has been moved to langchain-experimental. "
"For more details: https://github.com/langchain-ai/langchain/discussions/11352."
"To access this code, install it with `pip install l... |
9a57dd093bdd09cb58704fefb50866da0ec516af98d540b550f2b3b8081dcb5d | Python | 484 | 22 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 3 10:57:50 2019
@author: ngritti
"""
import sys, os
filepath = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0,os.path.join(filepath,'..'))
import PyQt5.QtWidgets
import GUIs.mainwindow
if __name__ == '__main__':
def run():
... |
9d8aa86793daacfc04dff10b0a3cd8c0c93ccc59a4edf9864332c7724d285926 | Python | 485 | 16 | """Test the run collector."""
import uuid
from langchain_core.language_models import FakeListLLM
from langchain_core.tracers.context import collect_runs
def test_collect_runs() -> None:
model = FakeListLLM(responses=["hello"])
with collect_runs() as cb:
model.invoke("hi")
assert cb.traced_ru... |
15b88514cf72b9d6e57e67c4a1465e6db24361945a229c7674ebb0e7e2f42a08 | Python | 486 | 13 | import sys
sys.path.append('../source')
import tinaui
folder = '../test/sampledata/ocn'
t1 = tinaui.load_status(folder, load_imagedata=True)
# use the name of image data, the extent is given in voxels check the GUI
# of mayavi pipline and for possible ways to change apearence, or
# mlab.pipeline.surface?
dataset, surf... |
fbc801ce5035ae175f07a7ebfc17043f64bac713263ee442aaf8034f30800296 | Python | 487 | 22 | #%%
import os
import tensorflow as tf
from shutil import copyfile
for ch in (1,2,4,8):
root0 = f'C:/MyFiles/MyCodes/research/DLAG_Clear/brainagemodel/pretrained'
root = f'{root0}/channel{ch}'
models = []
for sn in range(100):
fname = f'{root}/model117_sn{sn}_fs64_fr30_ch{ch}/model.h5'
p... |
9f27b68bf45f9b31cf88411a08654bb93eb01bcfb72a5a35ec21ffa43651ade6 | Python | 488 | 16 | from langchain_core.tracers import schemas
from langchain_core.tracers.schemas import __all__ as schemas_all
def test_public_api() -> None:
"""Test for changes in the public API."""
expected_all = [
"Run",
]
assert sorted(schemas_all) == expected_all
# Assert that the object is actually ... |
af9b4a2bc90ab0fad7c4e9e81a3762a25e08a2065999e6953b66373fb78d17aa | Python | 488 | 14 | import numpy as np
# make_napari_viewer is a pytest fixture that returns a napari viewer object
# capsys is a pytest fixture that captures stdout and stderr output streams
def test_example_q_widget(make_napari_viewer, capsys):
# make viewer and add an image layer using our fixture
viewer = make_napari_viewer... |
6287e043b99f6915ed7600ae43254bc1a66a6b6d9619c820816f9a98afc91280 | Python | 491 | 15 | import numpy as np
import omicscope as omics
np.seterr(divide='ignore', invalid='ignore')
class TestEnrichmentScope(object):
def test_user(self):
data = omics.OmicScope(Table='tests/data/proteins/progenesis.xls',
Method='Progenesis',
... |
a94e8fecd2787aba4f2c8a663537f0b60aa8f1406c03099929d174f7f33e46c3 | Python | 491 | 10 | """
This file contains the three architectures used for the model ensemble in scaden
The architectures are stored in the dict 'architectures', each element of which is a tuple that consists
of the hidden_unit sizes (element 0) and the respective dropout rates (element 1)
"""
architectures = {'m256': ([256, 128, 64,... |
2984e519866b8458a23daeabbe4953f3799c652aa2cb594bd35aa8daf72282be | Python | 492 | 15 | # third party modules
import numpy as np
def getConvexHullDistance(mesh):
try:
import igl
except ModuleNotFoundError:
raise ModuleNotFoundError("The dependency 'igl' is required for this functionality!")
hull = mesh.convex_hull
distances, indices, closest_pts = igl.point_mesh_squar... |
2e4cd53809f03f6690da240242643741810d1273cdaf445a343c408afb7cafdb | Python | 492 | 19 | from .edge_pooling import EdgePooling
from .continuous_crf import ContinuousCRF
from .mesh_pooling import MeshPooling
from .hist_conv import PPHConv
from .local_attn_pooling import LocalAttentionPooling
from .edge_attention_conv import EdgeAttentionConv
from .sa_module import SAModule
from .fp_module import FPModule
_... |
852bd4327b63975bf9a76ad874bad4474495825d23b43de49ee35f1dbe48e9b3 | Python | 492 | 17 | import platform
from functools import lru_cache
@lru_cache(maxsize=1)
def get_runtime_environment() -> dict:
"""Get information about the LangChain runtime environment."""
# Lazy import to avoid circular imports
from langchain_classic import __version__
return {
"library_version": __version__... |
97059ec20dfb7ab81e3488414d7e32f7a32f7ba603dcb9c64927d617fde7ad6a | Python | 492 | 21 | import os
import torch
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def unnormalize(tens, mean=[0.485, 0.456, 0.406], s... |
569d30259e8cc68c1752d59c07692df5ce2863bb0b1d91d0a5411e3ca1925fad | Python | 494 | 18 | import tensorflow as tf
from tensorflow.keras import layers
class GaussianExpansion(layers.Layer):
def __init__(
self,
dmin,
dmax,
num_gaussian=50,
var=None
):
super().__init__(name="gaussian")
self.filter = tf.linspace(dmin, dmax, num_gaussian)
... |
30584a657c0a76dcdbc79356a007bd8746a7ebaac5ff13ebd2622dbef1279492 | Python | 496 | 22 | from .coordinates import \
extract_coordinates, \
extract_coordinates_combined, \
align_coordinates, \
sort_coordinates, \
merge_coordinates, \
merge_and_sort_coordinates
from .selection import \
range_to_string, \
load_selection
from .download import \
download_from_gpcrmd, \
... |
892731cd9921efe1dd8631d328201ace9c4754c9d74a8d9cce4a97fad4d8285d | Python | 496 | 24 | from langchain_classic.schema.runnable.base import __all__
EXPECTED_ALL = [
"Runnable",
"RunnableBinding",
"RunnableBindingBase",
"RunnableEach",
"RunnableEachBase",
"RunnableGenerator",
"RunnableLambda",
"RunnableMap",
"RunnableParallel",
"RunnableSequence",
"RunnableSerial... |
53f88ed67641246184731c49bf605687a8cc61c718797354e6bd555cd20189d9 | Python | 498 | 16 | from pycromanager import JavaBackendAcquisition, multi_d_acquisition_events
with JavaBackendAcquisition(directory=r"/Users/henrypinkard/tmp", name="tcz_acq", debug=False) as acq:
# Generate the events for a single z-stack
events = multi_d_acquisition_events(
num_time_points=8,
time_interval_s=... |
1e6720797d167bca88b26d161a5663d29f269895ef334e5d35dcb7234a3ebacc | Python | 500 | 23 | from setuptools import setup, find_packages
import numpy
from Cython.Build import cythonize
from setuptools import Extension
requirements = (
'cython==0.29.35',
'numpy==1.23.5',
'scipy==1.10.1',
'matplotlib==3.7.1',
'scikit-learn==1.2.2',
'networkx==3.1',
'mediapipe>=0.10.10',
'scikit-video>=1... |
fd77fd7768bf4917fc6b9bb75432479cbd9e87a00d37164f402614bf8a75f1a4 | Python | 500 | 20 | from setuptools import setup
setup(
name='genie',
version='0.0.1',
description='de novo protein design through equivariantly diffusing oriented residue clouds',
packages=['genie'],
install_requires=[
'urllib3==1.26.14',
'charset-normalizer==2.1.1',
'tqd... |
6b307c463f66cbcb6652c29f061dd736a328f680566c1df49af47ae756072fb3 | Python | 501 | 13 | """SDCflows - :abbr:`SDC (susceptibility distortion correction)` by DUMMIES, for dummies."""
__packagename__ = "sdcflows"
__copyright__ = "2020, The NiPreps developers"
try:
from ._version import __version__
except ModuleNotFoundError:
from pkg_resources import get_distribution, DistributionNotFound
try:
... |
f97ce72f739a7649f871e19630ca4b3a829b0c1925c998f997ba034738a6deb7 | Python | 501 | 17 | """
Methods for state-specific information.
The methods here are based on the following paper:
| Neil J. Thomson, Owen N. Vickery, Callum M. Ives, Ulrich Zachariae:
| Ion-water coupling controls class A GPCR signal transduction pathways.
| https://doi.org/10.1101/2020.08.28.271510
"""
from .disc... |
b13758e26277da368e6d8ba8260b6541d66f55941e7ef19578c9de5d04b32364 | Python | 503 | 34 | from pathlib import Path
from bindsnet import (
analysis,
conversion,
datasets,
encoding,
environment,
evaluation,
learning,
models,
network,
pipeline,
preprocessing,
utils,
)
ROOT_DIR = Path(__file__).parents[0].parents[0]
__all__ = [
"utils",
"network",
"... |
3271bf85c3371e15c540d8685f1f16c351936d8383f5c7e726603928b17f2e8f | Python | 504 | 15 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# -*- coding: utf-8 -*-
from .checkpoint import load_checkpoint
from .layer_decay_optimizer_constructor import Learning... |
497269f94dc9fd146fafd77432e7615da57e04ff9d0ab83ce0a27ed013026964 | Python | 505 | 21 | from pycromanager import ZMQRemoteMMCoreJ, JavaClass
from threading import Thread
### Pass object to a different thread
core = ZMQRemoteMMCoreJ(debug=False)
def other_thread(core):
cache = core.get_system_state_cache()
print(cache)
Thread(target=other_thread, args=(core,) ).start()
core = None
### Create an ... |
ce3d4dc188223d93f2a5d498062612564e4787fd413bea4b2bca398aaf08d310 | Python | 505 | 19 | def RGB(R,G,B):
return (R/255.,G/255.,B/255.)
colors = {
'red' : RGB(217,25,32),
'blue' : RGB(32,123,193),
'green' : RGB(0,159,76),
'yellow' : RGB(251,200,52),
'black' : RGB(0,0,0),
'brown' : RGB(149,89,28),
'white' : RGB(255,255,255),
'grey': RGB(14... |
df1cc4419496df8632a978f58b78b1f2e071cb0bdb9ebe1e02c8f4342f041b55 | Python | 505 | 15 | from importlib import import_module
from typing import Callable
from typing import Tuple
def split_module_name(abs_class_name: str) -> Tuple[str, str]:
abs_module_path = ".".join(abs_class_name.split(".")[:-1])
class_name = abs_class_name.split(".")[-1]
return abs_module_path, class_name
def dynamic_imp... |
a05db5181d29249b26bca3ac602473b051e51838c5299607eb97c03ff09abf7b | Python | 506 | 17 | from skimage.io import imread
import numpy as np
def load_images_ch0(fnames, downshape):
print('Using multiple cores')
N_files = len(fnames)
img = [[] for i in fnames]
for i in range(N_files):
fname = fnames[i]
img1 = imread(fname).astype(float)
if img1.ndim == 2:
im... |
10151633f745a31ffaeea77a3659d12f42985af316b458657339268ff06f1e06 | Python | 507 | 21 | from langchain_core.prompts.prompt import PromptTemplate
NAIVE_FIX = """Instructions:
--------------
{instructions}
--------------
Completion:
--------------
{completion}
--------------
Above, the Completion did not satisfy the constraints given in the Instructions.
Error:
--------------
{error}
--------------
Pleas... |
4385a2231cb6fae2c418b7ea321e5beb8d63557a9c8def79ed0784a479878ae4 | Python | 509 | 18 |
def getAtomDepth(atoms, vertices, feature_name="depth"):
try:
from Bio.PDB.ResidueDepth import min_dist
except ModuleNotFoundError:
raise ModuleNotFoundError("The module 'Bio.PDB.ResidueDepth' is required for this functionality!")
if not isinstance(atoms, list):
atoms = atoms.g... |
7dba7fd21cb163411d599434e2e8d8246098fc2fdeffd7325263229eb9e56446 | Python | 509 | 22 | #!/usr/bin/env python
import setuptools
import os
path = os.path.abspath(os.path.dirname(__file__))
# User README.md as long description
with open("README.md", encoding="utf-8") as f:
README = f.read()
setuptools.setup(
name='chreyesees',
version='0.0.0',
description='ChreyeSees',
long_descript... |
9717fd274f3dd279fc26a49a96764a3b73f1eb0452d90aa7160ab4cbd51065d0 | Python | 509 | 22 | import numpy as np
import pandas as pd
from tabulate import tabulate
np.set_printoptions(suppress=True)
np.set_printoptions(threshold=np.inf)
loc = "./build/results_neural-network-runtime.csv"
df = pd.read_csv(loc, sep=";", header=0)
nb_iters = 11
IEs = ["OpenVINO CPU", "OpenVINO GPU", "TensorRT"]
ret = []
for i i... |
5f93317f473be25ca311632fd55eda798b7c2d6022bb0a2d052c25b239c23075 | Python | 511 | 23 | from langchain_core.callbacks.base import (
AsyncCallbackHandler,
BaseCallbackHandler,
BaseCallbackManager,
CallbackManagerMixin,
ChainManagerMixin,
LLMManagerMixin,
RetrieverManagerMixin,
RunManagerMixin,
ToolManagerMixin,
)
__all__ = [
"AsyncCallbackHandler",
"BaseCallback... |
e182b6c8c5f854d047f5e7601c4293bea87a3cdb4597c477646abe0eece70f02 | Python | 511 | 20 |
base_setting = {
'train_size': 0.8,
'epochs': 100,
'input_size': 60,
'batch_size': 32,
'pre_len': 1,
'fileName': "",
'model_name': 'rnn',
'checkpoint_root_dir': './checkpoint-0507/dir',
'true_epochs': 100,
'data_root_dir': './data',
'train_data_mode': 'pre',
... |
7182b75d338c7f21d6baf6eecbe6ca479eb6eb1b557e4c62f091b77f186ef8e6 | Python | 513 | 24 | """For backwards compatibility."""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.sql_database.prompt import QUERY_CHECKER
_importer = create_importer(
__package__,
deprecated_lookups={
"QUERY_CHECKER": "... |
760f4d6c48b606035479fc13b6d9710e93dc66a0de0035198367c9a76e15daca | Python | 513 | 16 | def load_dataset(name=None):
if name==None:
name = 'EBMData'
import pandas as pd
import requests
import io
if name == 'EBMData':
urlname = 'https://raw.githubusercontent.com/88vikram/pyebm/master/resources/Data_7.csv'
elif name == 'CoinitDEBMData':
urlname = 'ht... |
7ee5fd2d56e1f3fbb0eb129f06ce11b0c57d0e82ac826be4681522d30366e2bd | Python | 513 | 20 | from paths import EXAMPLE_PATH
import simpa as sp
import matplotlib.pyplot as plt
import numpy as np
images = [
"Baseline_10000.hdf5",
"Skin_10000.hdf5",
"Water_4cm_10000.hdf5"
]
for image in images:
p0 = sp.load_data_field(EXAMPLE_PATH + image,
sp.Tags.DATA_FIELD_INITIAL_P... |
f9a521f4f55aedc967b6e696103bc19345d577c5b044c104ae1866fb16812478 | Python | 513 | 17 | """
Specify the parameters regarding the data (e.g. number of residues of your protein) and the designed experiments
(how many Markov states to be estimated, how many models to estimate per dataset etc.)
"""
NUM_MODELS_PER_DATASET = 20
NUM_MARKOV_STATES = 3
NUM_INTERRESIDUE_DISTANCES = 780
NUM_RESIDUES = 42
SKIPPED_DI... |
2d6151e649f8ce5afb296381656107fa8223790d2942a31519dd2c19e01828f1 | Python | 515 | 19 | from util import mkdir
# directory to store the results
results_dir = './results/'
mkdir(results_dir)
# root to the testsets
dataroot = './dataset/test/'
# list of synthesis algorithms
vals = ['progan', 'stylegan', 'biggan', 'cyclegan', 'stargan', 'gaugan',
'crn', 'imle', 'seeingdark', 'san', 'deepfake', 's... |
30771f53f5b3a02af09c63e731f8adbc9391b038206330dda49bbc42d94e2ad8 | Python | 515 | 20 | #!/usr/bin/python3
from z3 import *
def same8(v):
return Or(v == 0, v == 0xff)
magic = BitVec('magic', 64)
x = BitVec('x', 64)
y = x * magic
s = Solver()
solve_using(s, ForAll([x],
Or(
Not(And([same8((x >> (i * 8)) & 0xff) for i in range(8)])), # x has bytes that aren't equal to 0xff or 0x00
And([(x >>... |
58f0e7b0e267c91a5765ab4be1bd76616f0b17193b08264bc10a1db06d127566 | Python | 517 | 18 | import decoupler as dc
import pathlib
import sys
def main():
species = sys.argv[1].strip()
net = dc.get_dorothea(organism=species, levels=["A", "B", "C", "D"])
net.source = net.source.astype("category")
net.target = net.target.astype("category")
net.confidence = net.confidence.astype("category")
... |
85b7a7746cea85e1961a5421c767e9026bc34e68cd1c900bc602cbd6ee41fe89 | Python | 518 | 15 | import numpy as np
import sys
# Load mapping between hi-res and lo-res voxels
mapping = np.load(sys.argv[1])
# Setup a dict
assoc = { tuple(np.int32(x[range(0,3)])) : tuple(np.int32(x[range(3,6)])) for x in mapping }
sfmap = np.loadtxt(sys.argv[2]) # Load text sorted flatmap at 25um resolution
fvox = np.int32(sfmap[... |
9eec2cca8475de5b4493b2dc5d0b16a4e7d214df275e1f832c15f59fb2328984 | Python | 518 | 30 | from pycromanager.mm_java_classes import ZMQRemoteMMCoreJ, JavaObject, JavaClass
from pycromanager import _Bridge
import gc
import threading
def new_b():
core = ZMQRemoteMMCoreJ()
for i in range(100):
# core = Dummy()
core = ZMQRemoteMMCoreJ()
core2 = ZMQRemoteMMCoreJ()
threading.Thread(target=ne... |
04e16840d9acdfab3909b9e524f6bb34b53edc2278f1ad14e61714ebf2e465b3 | Python | 519 | 19 | """
Test classes and function in module openff.toolkit.typing.engines.smirnoff.io.
"""
import pytest
from openff.toolkit.typing.engines.smirnoff.io import XMLParameterIOHandler
class TestXMLParameterIOHandler:
def test_from_string(self):
pass
def test_raise_file_not_found(self):
"""Raise F... |
415875d3e3abf84f814940f52340321ad810af73613c759fe147622d25d60163 | Python | 520 | 27 | """
Objects useful for dealing with files saved by Micro-Manager. https://micro-manager.org/
Classes
----------
.. autosummary::
:toctree: generated/
Image
Position1d
Position2d
PositionList
Property
PropertyMap
MultiStagePosition
"""
__all__ = [
"PositionList",
"Position2d",
"Pos... |
6ca707d12d84b4abd60f57b592a34d323db51dd86473da30595222af1309617e | Python | 520 | 13 |
import os
import sys
from loguru import logger as log
from tqdm import tqdm
log.add('train_10_fold.log')
os.system(f"pyenv local yolov7 ")
for i in tqdm([1,2,3,4,5,6,7]):#8, 9, 10
log.info(f"train GPU in fold {i}...")
os.system(f" python train.py --workers 8 --device 0,1,2,3 --batch-size 32 --data fold_{i}/... |
3966799c542e796e565ac95e02a25e1612f484371319fbbd7937d5cde3ba8faf | Python | 521 | 12 | from imagingplus import TwoPhotonImaging
from imagingplus.processing.paq import PaqData
def test_import_paqdata(paqpath = '/home/pshah/mnt/qnap/Data/2021-01-25/2021-01-25_PS12_009.paq'):
paq_data_obj, paqdata = PaqData.import_paqdata(file_path=paqpath)
def test_cropData(existing_trialobj_twophotonimaging_fixtur... |
dd4a0eabc3f61ad48a11bad7b1375377a2ac2d3e4fe230892cd51b18d95d8900 | Python | 522 | 19 | #!/usr/bin/python
import sys
import json
import os
import hashlib
import shutil
import random
doc = json.loads(sys.stdin.read())
if doc["state"]["iteration"] == 1:
size = int(doc["input"]["size"]) // 100
for x in range(100):
fileData = bytearray(random.getrandbits(8) for _ in range(size))
binF... |
fec408f711cc9b89bd3e6764f911dc18f389ef4fb7845fc9b730759cf480a494 | Python | 522 | 20 | import numpy as np
import plotext as plt
import sys
NB_NEURONS = 1024
TIME_ROW = 0
SPK_ROW = 1
data = np.genfromtxt(sys.argv[1], delimiter=';', skip_header=1)
# data = np.genfromtxt("raster.csv", delimiter=';', skip_header=1)
max_id = max(data[:,SPK_ROW])
plt.theme("clear")
plt.scatter(data[:, TIME_ROW], dat... |
8fa4f625309b1f97a79ed877d89023abb1c9a21b5162d32227eee4bfb9708827 | Python | 525 | 18 | from voxcell import VoxelData
import numpy as np
import sys
nrrd_file = sys.argv[1]
output_file = sys.argv[2]
vd = VoxelData.load_nrrd(nrrd_file)
n_notnan = np.sum(~np.isnan(vd.raw))
shape_str = " ".join([str(x) for x in vd.raw.shape])
voxdim_str = " ".join(["{:.8f}".format(x) for x in vd.voxel_dimensions])
origin_st... |
04cd2f0a984d7e22201dfc1e9a74d8d5b1fce8ead2ae0c13012362882e298741 | Python | 529 | 30 | from pathlib import Path
import os
REPO_ROOT = Path(__file__).parent.parent.resolve()
STRIDE_EXE = os.environ.get('STRIDE_EXE', str(REPO_ROOT / "stride" / "stride"))
PYMOL_EXE = "/Applications/PyMOL.app/Contents/MacOS/PyMOL" # only required if you want to generate 3D images
_3to1 = {
'ALA': 'A',
'CYS': 'C',
'ASP': '... |
506be2337ce1719d1aa6da4c3a361dcece857508f65073613d776d6afe578a25 | Python | 530 | 17 | import sys
sys.path.append('../source')
from tina_main import dataset
import tinaui
t1 = dataset(folder='../test/sampledata/ocn') # initialize the dataset
# load images with smaller extent, using the information from .xml-files
# for spacing of the image data
tinaui.load_initial_data(t1, ftype='tif', nfilter=[
... |
8b5839dd4c9648b374413737afa3433a02c6648948a5ae3b3b5396e932b7dbab | Python | 530 | 13 | from typing import Any
def __getattr__(_: str = "") -> Any:
msg = (
"This tool has been moved to langchain_experimental. "
"This tool has access to a python REPL. "
"For best practices make sure to sandbox this tool. "
"Read https://github.com/langchain-ai/langchain/blob/master/SEC... |
1a6fbfe036625501cf8e34e93ee71afb2dd767acd35ccf1ccf28fbf2af579719 | Python | 531 | 21 | import setuptools
requires = "tqdm"
def get_requirements():
ret = [x for x in requires.split("\n") if len(x) > 0]
return ret
setuptools.setup(
name="NLPreprocessing",
version="1.0.1",
description="NLP package for text preprocessing",
url="https://github.com/uf-hobi-informatics-lab/NLPreproc... |
d4c1fc2db8347f61df8530b8ce86ca32fa55a0745fa6ccaf587abd69ee3110da | Python | 532 | 15 | """Utilities for image processing."""
from typing import Any
def __getattr__(name: str) -> Any:
if name in {"encode_image", "image_to_data_url"}:
msg = (
f"'{name}' has been removed for security reasons.\n\n"
f"Usage of this utility in environments with user-input paths is a "
... |
bf31368db72180cd157b1881a9e0b9773b3c95e309255eb29264e954c9f76773 | Python | 533 | 21 | import torch
import torch.nn as nn
class LogReg(nn.Module):
def __init__(self, ft_in, nb_classes):
super(LogReg, self).__init__()
self.fc = nn.Linear(ft_in, nb_classes)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Li... |
55768716da1414fe3233da2662b18541af77b45c6d6e52afecdf07796d9f1034 | Python | 536 | 23 | from setuptools import find_packages, setup
setup(
name='hienet',
version='1.0.1',
description='HIENet',
author='Keqiang Yan, Montgomery Bohde, Andrii Kryvenko, Ziyu Xiang',
python_requires='>=3.8',
packages=find_packages(include=['hienet', 'hienet*']),
install_requires=[
'ase',
... |
70829afa2ae9eddf96c7fb20dcad8b808c37e2c67f3780fdbd3ece24a992bd11 | Python | 536 | 20 | from langchain_classic import document_transformers
EXPECTED_ALL = [
"BeautifulSoupTransformer",
"DoctranQATransformer",
"DoctranTextTranslator",
"DoctranPropertyExtractor",
"EmbeddingsClusteringFilter",
"EmbeddingsRedundantFilter",
"GoogleTranslateTransformer",
"get_stateful_documents"... |
d129b065c51935a418da74715f675cbe922df4935770c0095d3a8703562b0f8e | Python | 536 | 26 | # -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from .visualization import (
compute_saliency_map,
display_saliency_overlay,
display_pca,
display_tsne,
display_confusion_matrix,
display_record,
display_receptive_fields
)
__all__ = [
'util... |
79220f7e82699811e33450bde2f63a269380d311eeabcba017c9b5c6f49f83de | Python | 540 | 17 | import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def save_results_table(table, table_key):
"""
Saves results (hyper param selection) to a file
:param table: ResultTable
:param table_key: Key that i... |
fcb0af0dbeec805e560ce1cce42847749a6104d5a0327787da9bf3d57b886b3f | Python | 540 | 27 | import starwrap as sw
import numpy as np
from operator import itemgetter
arg = sw.args()
arg.trainFile = './tagged_post.txt'
arg.trainMode = 0
sp = sw.starSpace(arg)
sp.init()
sp.train()
sp.nearestNeighbor('barack', 10)
sp.saveModel('tagged_model')
sp.saveModelTsv('tagged_model.tsv')
sp.initFromSavedModel('tag... |
2cd9d8a45ea81f415c2413676d6194be171d4e7cae0538c48367973cc65413a0 | Python | 541 | 18 | from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class FakeParrotRetriever(BaseRetriever):
"""Test util that parrots the query back as documents."""
def _get_relevant_documents( # type: ignore[override]
self,
query: str,
) -> list[Document... |
44b1d95cc9c623225c0cb139389a3702ad93e74f88aa660f9e7db913d2310c6c | Python | 542 | 16 | from sam.diffusion.diffusers_dm import Diffusers
def get_diffusion_model(model_cfg, network, ema=None):
model_type = model_cfg["latent_generative_model"]["type"]
if model_type == "diffusers_dm":
diffusion = Diffusers(
eps_model=network,
sched_params=model_cfg["latent_generative... |
e1c7a795030da544c84ab094b344127cd9181216b551d9c87430b47902b2e4c5 | Python | 542 | 26 | import os
import subprocess
from pathlib import Path
import pytest
import chroma
from chroma.data import Protein
@pytest.fixture(scope="module")
def XCS():
input_file = str(
Path(Path(chroma.__file__).parent.parent, "tests", "resources", "1n8z.cif")
)
protein = Protein(input_file)
length = ... |
24c0ee9b5903ae5b7f2cf60d1e68c2702a91a98cef28cda8669af9546af67d3b | Python | 543 | 14 | import numpy as np
from pycromanager import JavaBackendAcquisition, multi_d_acquisition_events
with JavaBackendAcquisition("/Users/henrypinkard/megllandump", "l_axis") as acq:
# create one event for the image at each z-slice
for time in range(5):
z_stack = []
for index, z_um in enumerate(np.ar... |
65f189c4f622a0d1991357ddc1d9b69f84d35dd46da35f3bdf1f22dca05ed862 | Python | 543 | 19 | from voxcell import VoxelData
import numpy as np
import sys
import flatmap_util as fmutil
flatmap_nrrd = sys.argv[1]
pixel_res = int(sys.argv[2])
output_nrrd = sys.argv[3]
fmap_vd, fmask = fmutil.load_flatmap(flatmap_nrrd)
fx_d, fy_d = fmutil.get_discrete_flat_coordinates(fmap_vd.raw, pixel_res, fmask)
pix_uniq, cou... |
0d27c06ac771524b6bdc29c98ff977c02fd561f59c2fe9a9fd65a43f4d1e6fc3 | Python | 544 | 22 | import os
import pandas as pd
import pickle as pkl
from types import SimpleNamespace
import yaml
from yaml.loader import SafeLoader
import numpy as np
from scipy.ndimage import gaussian_filter
from omegaconf import OmegaConf
from utils import plot
name = '/two_envs_lazy'
folder = 'outputs' + name
i=0
#... |
8070a63f5e840ab854295cb3b85a40f39111957445521130dfca490164965389 | Python | 544 | 22 | from pathlib import Path
from src.models.results import PredictionResult
def test_results_without_chain_id():
mock_pdb_path = Path("/path/to/test_chain_id.pdb")
mock_chain_id = "test_chain_id"
mock_md5 = "1234567890abcdef"
mock_chopping = "12-34_56-78"
mock_confidence = 0.0123
res = Predictio... |
aee647dd3ecbf08d6cbec6849a11cea52cecc01fedc0d6eab1ebc44dd68a8156 | Python | 545 | 14 | from algorithm import *
if __name__ == '__main__':
x = zeta(np.array([[0.0, 0.0]]),
np.array([[1.0, 0.0], [0.0, 1.0]]), eps=1e-12, param=2.0, R=4, verbose=True)
print(x)
x = exp(np.array([[0.0, 0.0, 0.0]]), np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]), eps=1e-12,
... |
0e0360d95a03fbaf008468183de81b48a525bce92e7f8eeb9a4d4ddfdb07289f | Python | 547 | 23 | import pandas as pd
import yaml
import pathlib
def read_files(file):
return len(pd.read_csv(file, index_col=0))
def main():
with open("params.yaml", "r") as file:
params = yaml.safe_load(file)["rnaseq"]["deg_count"]
files = pd.Series(params)
counts = files.map(read_files).to_dict()
outp... |
e56a426c7794e686145d4fd195c205fe1b1cf45a3996a56e2491b4df3dddb205 | Python | 547 | 18 | __all__ = (
"ALLOWED_AROMATICITY_MODELS",
"ALLOWED_CHARGE_MODELS",
"ALLOWED_FRACTIONAL_BOND_ORDER_MODELS",
"DEFAULT_AROMATICITY_MODEL",
"DEFAULT_CHARGE_MODEL",
"DEFAULT_FRACTIONAL_BOND_ORDER_MODEL",
)
ALLOWED_AROMATICITY_MODELS = ["OEAroModel_MDL"]
DEFAULT_AROMATICITY_MODEL = ALLOWED_AROMATICI... |
80e90a30c1bddc288e94d8ed9f65d16b07daf9056b203e33474e49e31662a5f5 | Python | 549 | 8 | TEMPLATE_TOOL_RESPONSE = """TOOL RESPONSE:
---------------------
{observation}
USER'S INPUT
--------------------
Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Rememb... |
e14b68a1f3358f0a7e4e686bfc7ee2d11c73e07ad083315ccb1401a5962d68e4 | Python | 549 | 21 | from langchain_core.prompts import PromptTemplate
template = """You are a teacher coming up with questions to ask on a quiz.
Given the following document, please generate a question and answer based on that document.
Example Format:
<Begin Document>
...
<End Document>
QUESTION: question here
ANSWER: answer here
Thes... |
4ef58158d8c8140e8c179d79e9018feb16e5f62da5c2a7e8115713524a600f3a | Python | 550 | 29 | import os
import pickle as pkl
def make_dir(mdir):
if not os.path.isdir(mdir):
os.mkdir(mdir)
def pkl_dump(data, file):
with open(file, "wb") as f:
pkl.dump(data, f)
def pkl_load(file):
with open(file, "rb") as f:
data = pkl.load(f)
return data
def read_file(file, encodin... |
251b2c68b8878a0242a79e177f75ad57d23eb427f259d6307b05847d741ec24a | Python | 552 | 19 | import numpy as np
def getFaceArea(V, F):
area_faces = np.linalg.norm(np.cross(V[F[:,0]]-V[F[:,1]], V[F[:,0]]-V[F[:,2]]), axis=-1)/2
return area_faces
def getVertexArea(vertices, faces):
# compute face areas
area_faces = getFaceArea(vertices, faces)
# compute vertex areas
area_v... |
110dc4f02f5a403585d6353b292a8a59fccf97a5779639c136ac57cc5f4cae2e | Python | 553 | 28 | from voxcell import VoxelData
import numpy as np
import sys
geom_nrrd = sys.argv[1]
data_txt = sys.argv[2]
output_nrrd = sys.argv[3]
dtype = np.float32
if len(sys.argv) > 4:
dtype = np.dtype(sys.argv[4])
bkgval = -1.0
if len(sys.argv) > 5:
bkgval = float(sys.argv[5])
vd = VoxelData.load_nrrd(geom_nrrd)
dat ... |
3ce30962f3c178bbae584ad28f0e129d765e88931b7f9ac1aac2f0a9dcb4acea | Python | 553 | 14 | import scipy.io as sio
class dataloader_AP():
def __init__(self,path,num_neuron=0) :
self.path = path
self.data = sio.loadmat(self.path)
self.__header__ = self.data['__header__']
self.__version__ = self.data['__version__']
self.__globals__ = self.data['__globals__']
... |
987dd554789f6110c5e5cab27e596e4afc1327a3188d23d2104a732bfec598b8 | Python | 553 | 20 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 16:28:30 2023
@author: biener
"""
import numpy as np
from NN_Rslt_Anal import NN_Stat as stat
import mAECell_Utils as utils
NN_Stat_O = stat()
WeiRes = NN_Stat_O.Multi_Res(Weighted_Res = True)
Res_FName = utils.fSaveAs(Title='Save weighted residual fil... |
621c38bee73fddcb1034586c72607992d06a725dfc2214b27d3d18379762f472 | Python | 555 | 23 | import pandas as pd
import yaml
import pathlib
def read_files(file):
return len(pd.read_csv(file, index_col=0))
def main():
with open("params.yaml", "r") as file:
params = yaml.safe_load(file)["proteomics"]["deg_count"]
files = pd.Series(params)
counts = files.map(read_files).to_dict()
... |
9f8b03e99d54a978279db09ecc7e66f120f8cd4d54e720e29c3e6127ca12e3a8 | Python | 555 | 18 | #! /usr/bin/env python3
import logging.config
from predmoter.utilities.utils import get_log_dict
from predmoter.core.parser import ConverterParser
from predmoter.utilities.converter import Converter
def main():
pp = ConverterParser()
args = pp.get_args()
logging.config.dictConfig(get_log_dict(args.output... |
bd19aeca82ea32c42744d9a14555ef5089ede5e3c4d52d95037210e02a090ad1 | Python | 555 | 21 | """uniPort: a unified single-cell data integration framework with optimal transport."""
__version__ = '1.2.0'
__author__ = 'Kai Cao'
__email__ = 'caokai@amss.ac.cn'
from .function import Run, get_prior, label_reweight, load_file, filter_data, batch_scale, TFIDF_LSI
from .metrics import batch_entropy_mixing_score, sil... |
10050357d65d9e314ad9efbdc04a51c0a16733a086e6201ee446eaa509ed5755 | Python | 556 | 19 | from collections.abc import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
... |
eaaba4b34b0272945721abe823e6b85f61e5c3f8611cac8734058842edb399cd | Python | 556 | 25 | __version__ = "2.4.3"
__version_vector__ = (2, 4, 3)
from . import config
logger = config.get_logger(__name__)
# Flatten namespace by importing contents of all modules of pymaid
from .fetch import *
from .client import *
from .upload import *
from .cluster import *
from .user_stats import *
from .core import *
from ... |
07b5a0ebd651ea16899088f74d3799b3604d7e89bc94f89319c30971c2d89652 | Python | 557 | 19 | """
Combine parquet files into one. This uses with to reduce memory
by only needing to load one files at a time.
"""
import pandas as pd
from pathlib import Path
def main():
data_dir = Path(".")
with open("BrainSEQ_TOPMed.interaction.txt", "a+") as tgz_handle:
for i, parquet_file in enumerate(data_dir.... |
80f2297cde0df3688ea42a8e2736545f995c443bf9253b45d9fd74d78bbbb1c5 | Python | 557 | 8 | import pandas as pd
data=pd.read_csv('/apps/home/lulab_liuxiaofan/qhsky/project/multi_omics_paper/data_TCGA/1.raw_data/BRCA.CNV_masked_seg.csv',sep=',')
data=data[['Sample','Chromosome','Start', 'End','Num_Probes','Segment_Mean']]
data['type']=data['Sample'].map(lambda x:x.split('-')[-1])
data_=data.loc[data['type']==... |
631f35b9d2fcce7382961ec20938d78d3642753b094ec6cd4eb5abc37587a2c9 | Python | 558 | 14 | from pytorch_lightning.utilities import rank_zero_info
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
class EarlyStoppingVerbose(EarlyStopping):
"""
The default EarlyStopping callback's verbose mode is too verbose.
This class outputs a message only when it's getting ready to s... |
b2ea3cd818d1506d83124c69d28f14f6b905c1ac3d380533ea54a1035da2bfe7 | Python | 558 | 17 | import os
def clear_folder(_dir):
'''Clear a folder recursively
https://stackoverflow.com/questions/13118029/deleting-folders-in-python-recursively
'''
if os.path.exists(_dir):
for the_file in os.listdir(_dir):
file_path = os.path.join(_dir, the_file)
try:
... |
188dff2c4288a5026896191320ef5a2280c57423e9530f7e2a6b2acbeeac821e | Python | 559 | 22 | import collections
from typing import TypeVar
T = TypeVar("T", bound=collections.abc.Mapping)
def deep_update(d: T, u: collections.abc.Mapping) -> T:
"""
Recursively update a dictionary with the values from another dictionary
Args:
d: dictionary to be updated
u: dictionary that is used to... |
08aeb0ea80fe782db6304e6cdc10c91fb37ff0f0924f4f359a8d00f8d339c04a | Python | 560 | 16 | """t5chem - A Unified Deep Learning Model for Multi-task Reaction Predictions"""
from t5chem.__version__ import __version__
from t5chem.data_utils import LineByLineTextDataset, TaskPrefixDataset, data_collator
from t5chem.model import T5ForProperty
from t5chem.mol_tokenizers import AtomTokenizer, SelfiesTokenizer, Simp... |
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