sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6f2a78d3d60dd3ccf42e0174335c225e17023d57d778537fc8237fe2a173fcf3 | Python | 11,477 | 266 | # -*- coding: utf-8 -*-
"""
################################################
MIT License
Copyright (c) 2021 L. C. Lee
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, inc... |
19ab6a0b7f66e974e7d31ca53ef4ba18543d733aff9bae6c1a4a128e7d8a49ea | Python | 11,480 | 252 | """
In this file the functions to evaluate the state, reward are defined and also the action is implemented
"""
import numpy as np
from DSS_Initialize import*
# To get the details of switches and their status
def switchInfo(DSSCktobj):
#Input: DSS Circuit Object
#Returns: A list of dictionaries wh... |
30185be20f114583b670f6682b9463b2a2cbfbd9021de396ca031a54423c9f3e | Python | 11,480 | 383 | #!/usr/bin/env python3
"""
Analyse mutual volume overlap of lesion labels with probabilistic atlas labels
- Outputs relative and absolute volume overlaps
- Overlap volumes relative to both lesion and atlas labels
Usage
----
atlas_lesion_analysis.py
-l <3D lesion label image>
-a <4D prob atlas image>
[-lk <... |
29798c689b07ee79dd535f038e87e0119149ad04b4707579a1937b9804f08886 | Python | 11,485 | 317 | # This file uses sklearn library to setup ML model comparison experiment with cross validation
# It will write out files to an 'out' directory that it will try to create
import time
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures, F... |
f77065fb06778cb2e14640001b70d8569aabcd092d54f2c540560d15031fc703 | Python | 11,485 | 293 | import numpy as np
import MDAnalysis as mda
import matplotlib.pyplot as plt
def residue_visualization(names, data, ref_filename, pdf_filename, pdb_filename,
selection='max', y_label='max. JS dist. of BB torsions',
offset=0):
"""
Visualizes features per resid... |
03b2033d683cca99e1b3c6efa5c2659177d3628f9add7ec11d62e94f188ee2f6 | Python | 11,489 | 329 | """Contains tests for the Dense-layer LRP implementations"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.initializers import Constant
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Input, LayerNormalization
from explainability.layers import DenseLRP
from explainabi... |
54865b9e8f13e24f252f6a06b279ddd0bd9e26ca9fd4e6d064ce9744ea84f048 | Python | 11,494 | 324 | # Copyright 2022 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... |
c81d7939f2c7016e4747394abfa5e4074f999ae982df40871a31f909753a6a2b | Python | 11,494 | 243 | import os
from time import time
from copy import deepcopy
import gc
import json
import re
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from utils.read_mda import readmda
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelwei... |
9eee5fd962d5d9fc725c025c30475b855508be29e112d662b668054aee543262 | Python | 11,498 | 303 | import sys
sys.path.append(".")
import pickle as pk
import gzip
import tarfile
from collections import Counter
from pymatgen.symmetry.groups import SpaceGroup
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from pymatgen.analysis.structure_matcher import StructureMatcher
import n... |
4aa28de81446a4e3367d08bfd348b5ed6e65ec4823ae525bcbb46d2b06850691 | Python | 11,499 | 287 | import numpy as np
import networkx as nx
from tqdm import tqdm
from copy import deepcopy
from scipy.sparse.linalg import eigsh
import matplotlib.pyplot as plt
from numpy.random import randn
import os
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import ReLU
from torch_sc... |
e971abbb8f3313e9f6d6baa911095037766be56591f7c9c87c8512979ae4c35f | Python | 11,500 | 302 | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
2e20bf7c4d729290ba70194e714a7107b40f5820f05240a19f6d03f11c2247e2 | Python | 11,511 | 304 | import logging
import time
# Instantiate logger
_logger = logging.getLogger("parallelism")
_logger.setLevel(logging.INFO)
class Parallelism(object):
"""
This class maintains a running parallelism (with support for the parallelism libraries and schedulers
specified in `supported libraries`).
The class... |
46a1b5f329cb79d71acb19dbc74926438212a562ebe366fcbe724f39a5c2b093 | Python | 11,521 | 315 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ast
import collections
import logging
import os
import re
import shutil
import traceback
from typing imp... |
4c5051107fe4c2efe92cf11eaa8d4c19d1f6eb3e941e04aa3b2019fd34887806 | Python | 11,521 | 302 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.transforms as transforms
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
import zipfile, os
# Reconstructs an image given the tracks of female... |
fc104d02ff7b1ed251382d84e0b9f11ba9b82418768b457033f418aa361da689 | Python | 11,522 | 275 | import math
import torch
from torch.optim.optimizer import Optimizer
version_higher = torch.__version__ >= "1.5.0"
class AdaBelief(Optimizer):
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
... |
d3f496ec793b6c2bd82649a98f47f3ee2bb10234d1471e5ce532f67d23253a19 | Python | 11,526 | 280 | import numpy as numx
import pydeep.base.numpyextension as numxExt
from pydeep.base.activationfunction import Sigmoid
import pydeep.base.costfunction as Cost
import pydeep.misc.measuring as MEASURE
import pydeep.misc.visualization as VIS
import pydeep.misc.io as IO
class SoftMax(object):
''' Soft Max function.
... |
98f86340043e42a0efd7fd9993d3d2b23de13a5cfbe89e59da2d9901f1742b05 | Python | 11,527 | 252 | """
In this file the functions to evaluate the state, reward are defined and also the action is implemented
"""
import numpy as np
from Environments.DSSdirect_123bus_loadandswitching.DSS_Initialize import*
# To get the details of switches and their status
def switchInfo(DSSCktobj):
#Input: DSS Circuit ... |
9e2fb96e9462b6fd90a80f8c997b459ad77a5412448a3185a684821f52bc6b1c | Python | 11,531 | 295 | import albumentations as A
from albumentations.pytorch import ToTensorV2
from joblib import Parallel, delayed
from torch.utils.data import DataLoader
from CNN2D import CNN2D
import os
import torch
from torch import nn
import numpy as np
from skimage.io import imread, imsave
from CustomDataset import CustomDataset
fro... |
ce885978652bb535177ef77397c289f2f2d529e12243ea2a9970845eb3ad928e | Python | 11,533 | 320 | import sys
import torch
import torch.nn as nn
from typing import Optional, Tuple
from copy import deepcopy
from h3xsemble.model.primitives import Linear, ipa_point_weights_init_
from h3xsemble.utils import residue_constants as rc
from h3xsemble.utils.residue_constants import (
restype_rigid_group_default_frame,
... |
80441181c2c4a72cba0acb914ef7a5ef3c1825ac0ec61523dbd14a828e5b4053 | Python | 11,536 | 278 | # -*- coding: utf-8 -*-
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~IMPORTS~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# Standard library imports
from collections import OrderedDict, namedtuple, Counter
import gzip
# Third party imports
from tqdm import tqdm
import numpy as np
# Local imports
from pycoMeth.common import *
from py... |
7aaad3bd261440691709ee67252a159af85f31b1bc72931fa7859eb3a2efae94 | Python | 11,537 | 380 | # Copyright 2023 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... |
865a321aa0d842319d73f18d3e3736eaeaf6de2411788bb513ab8d247ddef65c | Python | 11,537 | 370 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Wrapper around various loggers and progress bars (e.g., tqdm).
"""
import atexit
import json
import loggin... |
2292f4fd32df58dcf5e494e2602c16b0d991aa84f9130e9c9c027743122aa3c1 | Python | 11,542 | 291 | """Chain that combines documents by stuffing into context."""
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import Callbacks
from langchain_core.documents import Document
from langchain_core.language_models import LanguageModelLike
from langchain_core.output_parsers i... |
cc4d1a907e0c9f2c0a97c3b9b8379d46756ace7d1fd71de43b4504a4942e7be3 | Python | 11,548 | 408 | #!/usr/bin/env python3
'''
Interpolate label between sparse sections
- Speeds up manual labeling for larger label volumes
Usage
----
interp_labels.py <label image> <index list>
interp_labels.py -h
Example
----
>>> interp_labels.py -i labels.nii.gz -l 1,3,4
Authors
----
Mike Tyszka, Caltech Brain Imaging Center
Date... |
f88ae69b838b338630231666e0e25bfefeba826a10a2f0a985136b51d6052dfa | Python | 11,548 | 276 | from __future__ import print_function
import tensorflow as tf
from .nn import linearND, linear
from .models import *
from .ioutils_direct import *
import math, sys, random
from collections import Counter
from optparse import OptionParser
from functools import partial
import threading
from multiprocessing import Queue
i... |
f51b385fabe49d9ad9b4fa8103b037a66dd7b954e3513d5727f13bc41e7c7251 | Python | 11,555 | 342 | ########################################################################
# 1D diffusion in a finite tube with fully-reflective boundary.
# Circular "point source" from one face.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetexact' in the context of the Bounded Diffus... |
323c9cb82bfe04e5143a9772438c8bbae484b9390db6cdaee26586c1bcca9685 | Python | 11,556 | 295 | from tqdm import trange
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
import os
import copy
from sklearn.metrics import (
mean_absolute_error,
mean_squared_error,
r2_score,
roc_auc_score,
accuracy_score,
log_loss,
f1_score,
matthews_corrcoef,
precision_sc... |
d03104ad58336b0207e544277745e07b94012f16ea252e32de54e113deb4e091 | Python | 11,564 | 222 | # Sconda conda activate plot_gc; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/cpg_composition_differences; python
import pybedtools
import pyfaidx
import matplotlib.pyplot as plt
import pandas as pd
import os
# Function to TPM normalize the gene counts
def tpm_normalize(d... |
64ec960b8683e7989a247f881aff3ced51335f95bc4cd252b415546177660060 | Python | 11,565 | 320 | import itertools
from typing import Callable, Dict, Optional, Tuple
import torch
from tqdm import tqdm
from bindsnet.analysis.pipeline_analysis import MatplotlibAnalyzer
from bindsnet.environment import Environment
from bindsnet.network import Network
from bindsnet.network.monitors import Monitor
from bindsnet.networ... |
0e49dbd91c20cc40a529ebaf3e5f0b8d4a57db33fa3029456a6da332ad8fa2c7 | Python | 11,574 | 282 | import sys
import os
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import itertools
sys.path.insert(1, os.path.join(sys.path[0], '../../../'))
from Simulator.SimulatorDMS import simulatorPatternDMS
from Articles.DMS_DLS.Code.FiguresDMS import *
Apostpre_list = np.array([-1., 0.0, 1.])
def f_S... |
72c372370e54b650c3170e93a295ff3de0a9485f38e4baaa970299b475eca9cd | Python | 11,574 | 338 | import yaml
import numpy as np
import mne
import textgrid
from pathlib import Path
from scipy.io import wavfile
class SpeechWaveExtractor:
""" Class to load audio data from a wav and TextGrid.
My wavs are stereo wav files, with the first channel containing the speech and the second channel containing
an ... |
3cc23c177b045a7311459ed761b99cfd5708ba6a993cd81495961cb179b287c0 | Python | 11,588 | 289 | """
Authors:
Samir Akre, Archana Raja
Purpose:
The goal of this script is to aggregate QC metrics for the motrpac RRBS initial
processing pipeline. Read the usage() function for details on usage.
Output:
writes csv file: '${SID}_qcmetrics.csv' where ${SID} is based on the sample ID sent
as input wit... |
1400222be2d0dbce8c63175479cd733a5a06dbe2730aa5b096dd045570c330eb | Python | 11,590 | 320 | import numpy as np
import matplotlib.pylab as plt
from matplotlib.patches import Rectangle
import seaborn as sns
from pandas import DataFrame
import PIL
from io import BytesIO
from MuTILs_Panoptic.configs.panoptic_model_configs import (
RegionCellCombination as rcc, VisConfigs
)
def get_visualization_ready_combi... |
a39a70941a0adc12227ad58d3ee1b45e097bfc2f05027e1ab49f335ee6401cad | Python | 11,590 | 289 | # Copyright Generate Biomedicines, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... |
f2d7cd448ac9f207c36293f69691f72f15c86414fa1185f239db81926a3b2058 | Python | 11,590 | 315 | import json
from os.path import join
import os
from random import sample
import torch
import h5py
from tqdm import tqdm
import numpy as np
def get_seq_mapping():
"""convert mutation type to integer (index)"""
#TODO add support for indel and make it more convenient to modify this function
mut_mapping = {'C... |
339a2a4a7b21014699e5d883a286612846e2a9ee9abdd0fa6353f205c8e4a85f | Python | 11,596 | 282 | import logging
import os
import socket
import sys
import numpy as np
import pandas as pd
from sklearn.metrics import explained_variance_score
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, KFold
from configs.configs import get_config_dict
fr... |
2f78c91918346e4ced5022759920fb3f2ca88092d322b8ff8593dd0cafb62fa0 | Python | 11,597 | 357 | """
This script parses a number of files from Reactome to match pathways to their
corresponding titles and genes
"""
import argparse
from dataclasses import dataclass, field
from typing import List, Dict, Tuple, Set
import pandas as pd
import numpy as np
from utils import get_ensembl_mappings
# Top level pathways f... |
8f2472ffab4d5c28d535f86bb20864b35275614461497d92c0439e6bb4e53ca3 | Python | 11,597 | 271 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import B... |
ff7207dc97ae256bc50ae39b56d84af2cfac8ffe9442b7da55efc4cdd835c314 | Python | 11,603 | 359 | ###########################
# Latent ODEs for Irregularly-Sampled Time Series
# Authors: Yulia Rubanova and Ricky Chen
###########################
import os
import matplotlib
if os.path.exists("/Users/yulia"):
matplotlib.use('TkAgg')
else:
matplotlib.use('Agg')
import matplotlib.pyplot
import matplotlib.pyplot as pl... |
96d7320523baca53788af916a92a56fb16f510395b57f6ad772da02975b69611 | Python | 11,617 | 267 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import B... |
dad09b33cceb3508449613dda67b1a1c0375690145d12fdbb510d0088c70e862 | Python | 11,617 | 367 | """Unit tests for agents."""
from typing import Any
from langchain_core.agents import AgentAction, AgentStep
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.runnabl... |
20923d6d226d88dfb525245e2794437f2806fa6dfde942c64ec539c69bef858b | Python | 11,621 | 343 | import re
from collections.abc import Sequence
from typing import (
TYPE_CHECKING,
Any,
Literal,
TypedDict,
TypeVar,
)
if TYPE_CHECKING:
from langchain_core.messages import BaseMessage
from langchain_core.messages.content import (
ContentBlock,
)
def _filter_invocation_params_for_tracing(... |
de007675d6fb983e9c24bc61ab966798ef7fc3611bc3d7bf602e00399343bb0c | Python | 11,626 | 343 | ########################################################################
# 1D diffusion in a finite tube with fully-reflective boundary.
# Circular "point source" from one face.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetexact' in the context of the Bounded Diffus... |
142453cd029d15fe91f5ea506afa67b7ea705f80e54018ee4d102440a401fb2c | Python | 11,628 | 414 | # -*- coding: utf-8 -*-
"""
Created on Mon Nov 11 10:54:28 2019
@author: dalan
list of unit and default parameters:
time: ms
voltage: mV
current: uA/cm^2
capacitance: uf/cm^2
conducrance: mS/cm^2
Cm = 1 uF/cm^2
Vna = 115mV
Vk = -12mV
Vl = 10.6mV
gna_bar = 120mS/cm^2
gk_bar... |
72b9d75c97cdb4e59d6574d59baf9ed817fa6d2fb45a82bf13e1e555e985760e | Python | 11,633 | 325 | #!/usr/bin/env python
"""
NBtrain.py -
Model generator for langid.py
Marco Lui, January 2013
Based on research by Marco Lui and Tim Baldwin.
Copyright 2013 Marco Lui <saffsd@gmail.com>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted p... |
9afe24ac4880a0a72da3f0eb940c36b2d275900aaa5c3073ab9fabff725c7b98 | Python | 11,634 | 288 | import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import os, sys
import pandas as pd
import numpy as np
import pickle
from palettable.colorbrewer.sequential import Greens_9
from palettable.colorbrewer.qualitative import Paired_12
from matplotlib.patches import Patch
from scipy.stats.stat... |
932303008784b26218b2ad91fa6a6c4fcc7b142bec7a2d8b83b0dcd60da8e50b | Python | 11,638 | 361 | """Base parser for language model outputs."""
from __future__ import annotations
import builtins
import contextlib
from abc import ABC, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Generic,
TypeVar,
cast,
)
from typing_extensions import override
from langchain_core._api import depreca... |
d95443f21abc866f02014b55e2ce6739d8af8d4e94a875688cc9087e7d48eacd | Python | 11,638 | 161 | """Results figures. Reads the 5-seed final files by default; pass --v0 for the preliminary files.
fig04_inflation : MAPE by protocol x model (log y), mean +- 95% CI over seeds/folds
fig05_p4_heatmap : P4 MAPE per held-out dataset x model
fig07_coverage : PICP (target 0.9) and MPIW per conformal method, P3 v... |
9034eba0b368c7d6bb2108a6615d47553536fd4e7cabc3cdc797fe7bc8c09073 | Python | 11,655 | 253 | import numpy as np
import argparse
import os
import imp
import re
from mimic3models.length_of_stay import utils
from mimic3benchmark.readers import LengthOfStayReader
from mimic3models.preprocessing import Discretizer, Normalizer
from mimic3models import metrics
from mimic3models import keras_utils
from mimic3models ... |
4ad5f94f9f1be79857f00433fd224c9793eca0d8c3593f80ce6c6cb1be407e41 | Python | 11,662 | 231 | ''' Test module for WE models.
:Version:
1.0
:Date:
08.02.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016
This program is free software: you can redistribute it and/or modify
it under the terms of... |
8df5f1c5e4b76001dbb8c8a300de85ada88d1bfc8681ebb56dc690e265c4c594 | Python | 11,667 | 353 | """
Approach: "StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation"
Reimplemented and modified by Axel Sauer for "StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets"
"""
import os
import re
import click
import legacy
from typing import List, Optional
import PIL.Image
import imageio
from... |
df8eb377fb71f6796b6ab39ce0667ba7f90eb9c2fb0be61485b43850d918f5b3 | Python | 11,675 | 327 | """Model retry middleware for agents."""
from __future__ import annotations
import asyncio
import time
from typing import TYPE_CHECKING
from langchain_core.messages import AIMessage
from langgraph.errors import GraphBubbleUp
from langchain.agents.middleware._retry import (
OnFailure,
RetryOn,
calculate_... |
6ba85208d170078034fdf567cf4f7db265fdddf7b046d2bb39ac4b3f189a93df | Python | 11,682 | 278 | # the test scripts currently run over the molecule one by one
# causing the evaluation needs a long time
#!/usr/bin/env python3
import os
import time
import torch
import hydra
import pyscf
import logging
import numpy as np
from tqdm import tqdm
from models import QHNet
from torch_scatter import scatter_sum
from datase... |
460e47dbe073db8c640fe1306323a2e130ae82f0bc823ee2439b4fcdf0a66530 | Python | 11,684 | 236 | import sys
sys.path.append('..')
from src.utils import set_random_seed
import argparse
import torch
from torch import nn
from torch.utils.data import DataLoader
import transformers
from torch.nn import MSELoss, BCEWithLogitsLoss, SmoothL1Loss
import numpy as np
import random
from src.data.featurizer import Vocab, N_AT... |
e1a99b2d022cb14922ff1bbc0fdbfc0564f35c6bbeae05d1d17f688dd39667be | Python | 11,689 | 255 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import pstRT_preprocess_func
__all__ = ['pstRT_rlddm1']
class PstrtRlddm1(TaskModel):
def __init__(self, **kwargs):
... |
d459cb4e06557c19f2e89b84f3c4c04575b022c32491e9b003d82a2b957d3217 | Python | 11,696 | 341 | import numpy as np
import scipy as sp
import scipy.stats
import mdshare
import pyemma
from pyemma.util.contexts import settings
import MDAnalysis as mda
import matplotlib.pyplot as plt
# --- METHODS FOR CLUSTERING ---
def obtain_clusters(data, algorithm='kmeans',
num_clusters=2, min_di... |
7019a1675509e05a5f7f0933e85f5446e9b7191209f8a8272e907374b53976df | Python | 11,699 | 353 | #!/usr/bin/env python3
import os
import sys
from os.path import dirname
import re
import math
import argparse
import subprocess as sp
from io import StringIO
from snakemake import io
from snakemake.io import Wildcards
from snakemake.utils import SequenceFormatter
from snakemake.utils import AlwaysQuotedFormatter
from ... |
6885eec50990ce1bad12062313d0025ec6699dfe496234d897e8dd7f55a15d98 | Python | 11,702 | 303 | """Derivations of standard content blocks from LangChain v0 multimodal content."""
from typing import Any, cast
from langchain_core.messages import content as types
def _convert_v0_multimodal_input_to_v1(
content: list[types.ContentBlock],
) -> list[types.ContentBlock]:
"""Convert v0 multimodal blocks to v1... |
fa8cdeb80d9f6e22c581a68d01828c36bb74c8b29498b7c72fe3ae8e0983383f | Python | 11,703 | 264 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
20a8016f9690cdd36a3fe8afde071314f43b9061947f41ecb2bbdba5d08cf2f8 | Python | 11,704 | 294 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""SDC workflows coordination."""
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype import logging
from niworkflows.engine.workflows import LiterateWorkflow ... |
231cef670f58a999d5c3424f69893c38d9ebb2b8071c27fd1a739e17fff90053 | Python | 11,704 | 291 | import numpy as np
import re
from pandas import DataFrame, Series
from mimic3benchmark.util import dataframe_from_csv
###############################
# Non-time series preprocessing
###############################
g_map = {'F': 1, 'M': 2, 'OTHER': 3, '': 0}
def transform_gender(gender_series):
global g_map
... |
4aaed1f96a888f4b2678c1f21b72be474978cdbe74088e14d83466935d50d304 | Python | 11,707 | 294 | """
Downloaded from:
https://github.com/broadinstitute/gtex-pipeline/blob/master/gene_model/collapse_annotation.py
(20.06.2020)
Modifications: Maciej Bak
"""
# Author: Francois Aguet
import numpy as np
import pandas as pd
from collections import defaultdict
from bx.intervals.intersection import IntervalTree
import arg... |
089ba53ba051f9e8fe35eaabfe56647f0c14bed7d8da45eadacf9af08dee02cd | Python | 11,710 | 268 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@Description: :
@Date : 2022/06/15 13:17:49
@Author : Zilan Yu
@version : 1.0
'''
import os, sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import keras_metrics as km
import numpy as np
import pandas as pd
import keras
from ke... |
a7ecb306d4f080eeef15bda54ccf8f2c07de4e84499093d76585cd20f6dd689d | Python | 11,713 | 307 | """Surface nested named agents as typed `run.subagents` handles.
Detects subagents via the `lc_agent_name` transition that langgraph's base
`_TasksLifecycleBase` now computes. `create_agent(name=...)` binds
`lc_agent_name` into the run config; the base transformer records, per
namespace, the `lc_agent_name` seen on ea... |
e635567fa482c7a1fdc2f17387dc403dec040172557f0e84379288a71dc33e68 | Python | 11,714 | 290 | import os
import copy
import argparse
import pandas as pd
import torch
import torch.nn as nn
import torch.utils.tensorboard
from torch.utils.data import DataLoader, Dataset
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB import Selection
from Bio.PDB.MMCIFParser import MMCIFParser
from Bio.PDB.Polypeptide import i... |
1d138b58005b8645caf24879b4175f5ec540573818aba7bb3629cf2eacbef1aa | Python | 11,715 | 281 | from matplotlib.backends.backend_pdf import PdfPages
import h5py
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
gg = PdfPages('./0_Traces.pdf')
#PVALBA
nwb_file = '../PvalbA/1_Supplemental/569998751_ephys.nwb'
with h5py.File(nwb_file, "r") as f: #Read spike train f... |
a299b42fd479116e33c5ca4357859ea933e36f3b14ce345999d179827c44a154 | Python | 11,715 | 327 | """
##############################################################################
#
# Generate table with motifs' scores for the final HTML report.
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 03-0... |
ab699d645f5e14664cebf0800c7e4c9bd40f820be7caed5807f3c8797ebbcb26 | Python | 11,716 | 236 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 21:22:12 2022
@author: Shencong Ni
"""
'''
Calculate the phase locking value (PLV) between the MUA in area 1 and area 2.
Run the onoff_detection.py first before running this script.
'''
import matplotlib as mpl
mpl.use('Agg')
import scipy.stats... |
eb810edb839c8f9a806abfef749129354bb6659f80724cd6b5d294d84484b4cf | Python | 11,720 | 290 | import sys
import re
import pandas as pd
from mantis_ml.modules.pre_processing.data_compilation import (process_generic_features,
process_features_filtered_by_disease,
process_ckd_specific_features,
process_cardiov_specific_features)
from mantis_ml.config_class impo... |
8cc2cf7313e64546224ed2a67745166a6e3d62599182370917df7fac0accdcb0 | Python | 11,726 | 290 | import os
os.nice(19)
import numpy as numx
import pydeep.rbm.model as MODEL
import pydeep.rbm.trainer as TRAINER
import pydeep.rbm.estimator as ESTIMATOR
import pydeep.misc.visualization as VISUALIZATION
import pydeep.misc.io as IO
import pydeep.misc.numpyextension as npExt
import mkl
import scipy.io as sio
mkl.set_nu... |
bc3cd302b8cb37a00279d17c76d7525cf597e4f5fc16853cb077bb0df3e4b6b1 | Python | 11,727 | 338 | from itertools import chain
import numpy as np
import openpyxl
import pandas as pd
import xlrd
import tensorflow as tf
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Lasso
from sklearn.manifold import TSNE
from sklearn.multiclass import OneVsRestClassifier
all_healt... |
74f4ddb94c466673c91267cb8a0ea6789044e674cd08de3669bb37502b200bea | Python | 11,729 | 350 | from abc import ABC, abstractmethod
from functools import cache
from typing import Optional, Union, List, Tuple
import re
import networkx as nx
import pymaid
from .utils import _eval_remote_instance
from .client import CatmaidInstance
from .core import CatmaidNeuron
from .config import get_logger
from .fetch.annotati... |
4f34060266fc5b6f412633d1ed4607f28607b58596410db758dea806d99d226e | Python | 11,732 | 296 | import math
from functools import partial
import torch
from torch import nn
from e3nn.nn import BatchNorm, Gate, Dropout
from e3nn.o3 import Irreps, Linear
import numpy as np
from nnunet.network_architecture.neural_network import SegmentationNetwork
from .voxel_convolution import Convolution
from .pooling import Dynami... |
6078b0d0421aea5312f7cda0fe341e1b030c168b95f790f0a6188b45daf1d33e | Python | 11,735 | 327 | from collections.abc import Callable
import numpy as np
import pandas as pd
from scipy.special import psi
from scipy.stats import differential_entropy
from skbase.base import BaseObject
from skbase.lookup import all_objects
from pgmpy.ci_tests import BaseCITest, get_ci_test
def _spacing_entropy(values: np.typing.Ar... |
cd3ce0d72d3467f904aa0fd177ec80f5720098f2ba75a8e18748db07c6ee9288 | Python | 11,735 | 275 | import importlib
import os
import re
from abc import ABC, abstractmethod
from collections import Counter
from typing import Dict, List, Optional, Union, Tuple
import torch
from tqdm import tqdm
from transformers import PreTrainedTokenizer
is_selfies_available: bool = False
if importlib.util.find_spec("selfies"):
... |
f8b0bbbcf2478f49877169fa778ac19af6bb9bce2d46500ddb410dce87ff409b | Python | 11,737 | 296 | # -*- coding: utf-8 -*-
"""
################################################
MIT License
Copyright (c) 2021 L. C. Lee
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, inc... |
8b98119f3518d42140c4c22dff0fb0b65907fd9040ad525de7dd020606653e4b | Python | 11,741 | 302 | import pytest
from openff.toolkit import Molecule
from openff.toolkit._tests.utils import requires_openeye
from openff.toolkit.utils.toolkits import OpenEyeToolkitWrapper, RDKitToolkitWrapper
class TestTautomerEnumeration:
tautomer_data = [
{"molecule": "Oc1c(cccc3)c3nc2ccncc12", "tautomers": 2},
... |
42e3fc1b44c18edeae6911f32a7957977ee5f4e04f56c6f89de649ec6fe4b2a2 | Python | 11,746 | 301 | from tqdm import tqdm
import random
from rdkit import Chem, RDLogger
from rdkit.Chem.rdMolTransforms import GetBondLength, GetAngleDeg, GetDihedralDeg
from .mmd import compute_mmd
# from mmd import compute_mmd
import torch
import time
import numpy as np
import os
import pickle
# Bond distance
def get_bond_symbol(bond... |
f2e97e6b1e9fbbc6ca927bbb46962b09902b23f205e449ac5e5cf1d0f561e816 | Python | 11,750 | 304 | """
extract_traces.py: Extract fluorescent activity traces.
Using keypoint coordinates in annotations.h5, both manual annotations and Zephir
results, extracts fluorescent activity traces by analyzing pixel intensities.
This uses build arguments and container attributes from checkpoint.pt.
A modified version of the Zep... |
f556398970248de8d4ccba9b89db0499c4bbac4ba372950a6b462800e12b5b1e | Python | 11,751 | 272 | import copy
from torch.optim import SGD, Adam
from model import *
import numpy as np
from config import Scenario
import pickle as pkl
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter, gaussian_filter1d
from scipy.interpolate import interp1d
def make_data(cfg: Scenario):
n_sa... |
e153435965bc359804e771b10426d29a5a446be7b06a424d9319a574761b30d9 | Python | 11,755 | 259 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://w... |
a66d408fdc44755abd5338c47ad096f7cab837211b4de94fb582eb9f99f98453 | Python | 11,757 | 343 | import logging
import glob
import os
import sys
import gc
import pandas as pd
import anndata as ad
import numpy as np
from rich.progress import BarColumn, Progress
logger = logging.getLogger(__name__)
def create_fractions(no_celltypes):
"""
Create random fractions
:param no_celltypes: number of fractio... |
82a376f5fa5a5c5a6c692f0a1aa5477171a8395af485c78b4d3d8ab1057b4de3 | Python | 11,758 | 303 | # -*- coding: utf-8 -*-
# @title Generate parameters related to synapses
# @file Synapses.py
# @author Romain Beaubois
# @date 05 Dec 2022
# @copyright
# SPDX-FileCopyrightText: © 2022 Romain Beaubois <refbeaubois@yahoo.com>
# SPDX-License-Identifier: GPL-3.0-or-later
#
# @brief Generate p... |
260dd76211146423bbc15a68076925cdb841153ecb8ce78fd8f822f0259064bb | Python | 11,764 | 298 | import math
from e3nn import o3
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import Tensor
from torch_sparse import SparseTensor
import torch.nn as nn
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.typing import Adj, OptTensor, PairTensor
f... |
82d410e8d2767f734993143f0b35312911f9241bd03d52f68e32bc7da7bb7982 | Python | 11,767 | 310 | # This program is used to calculate the green view index based on the collecte metadata. The
# Object based images classification algorithm is used to classify the greenery from the GSV imgs
# in this code, the meanshift algorithm implemented by pymeanshift was used to segment image
# first, based on the segmented imag... |
f4e4af36fe8a8c6e2588ffa760b329a7abeba614450c91a5e9fd8ab369eebbc0 | Python | 11,770 | 298 | """tests for results_bundle.py's filename shortening and the two delivery
paths (zip bytes vs writing straight to a folder) sharing the same file set.
"""
import json
from pathlib import Path
import numpy as np
import pytest
import trimesh
from api.results_bundle import (
build_analysis_bundle,
build_meshes_... |
ccd5792f67ac54b92e8ddaaa5b947f2fb6805d36ac3c164d6f2231eaf8d81762 | Python | 11,777 | 394 | '''Code for collecting and preprocessing training data.
NOTE as a consequence of how this project evolved, the preprocessing
here is decoupled from the preprocessing in the prediction (see
retchat.tasks.run_segmentation). In the long term, one might consider
harmonizing them.
'''
import os
import re
from glob import... |
d2e4090e98e1731f53c0aa40ff6ed70d4f672630b3382101b4e66b83e0295166 | Python | 11,779 | 385 | ########################################################################
# Stochastic degradation-diffusion process.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetexact' in the context of the Degradation-Diffusion model
# (see validation/kisilevich.py)
###########... |
3eecfb536dda8f91b17711a134fe7506513eda566d873848991e681e796ef0e5 | Python | 11,781 | 294 | import os
os.nice(19)
import numpy as numx
import model as MODEL
import pydeep.rbm.trainer as TRAINER
import pydeep.rbm.estimator as ESTIMATOR
import pydeep.misc.visualization as VISUALIZATION
import pydeep.misc.io as IO
import pydeep.misc.numpyextension as npExt
import mkl
import scipy.io as sio
mkl.set_num_threads(4... |
d7cdff992c329a36050e5f198d52d53208a07f7a94a0388a08536eb91d6a6efe | Python | 11,783 | 318 | """Mass spectrometry file type input/output operations."""
import collections
import csv
import logging
import operator
import os
import re
from collections.abc import Iterator
from pathlib import Path
import natsort
from .. import __version__
from ..config import Config
from .psm import PepSpecMatch
logger = loggi... |
a3891e00d42748588081ef6107ea281d6bbd2704373dd3ab7fd4c3cd9a89c6f4 | Python | 11,785 | 388 | from torch import nn
from torch.nn.functional import cross_entropy
import torch as tr
from tqdm import tqdm
import pandas as pd
import math
from sincfold.metrics import contact_f1
from sincfold.utils import mat2bp, postprocessing
from sincfold._version import __version__
SINCFOLD_WEIGHTS = f'https://github.com/sinc-l... |
28c6a42501b15f60732304995514c6c4771cc2af5d77857c9b9d0f8dad50ab18 | Python | 11,790 | 292 | from pathlib import Path
import pandas as pd
import torch
import numpy as np
import pyBigWig
import tabix
import os
from selene_sdk.targets import Target
from tqdm import tqdm
import random
from src.dataloaders.utils.selene_utils import MemmapGenome
from src.tasks.utils import index_mapping
class GenomicSignalFeatur... |
cb2b9222210b0ab9301e3dab8022785127aba923971a1be9e74ea55ecb780512 | Python | 11,794 | 233 | import argparse
from models import twostage_RCAN3D, twostage_Unet3D
from tensorflow.keras import optimizers
import glob
import numpy as np
from utils.utils import prctile_norm
from utils.loss import read_mrc
import tifffile as tiff
import os
import tensorflow as tf
import math
os.environ["TF_ENABLE_AUTO_MIXED_PRECISIO... |
616711e565d42c6e394358791ac1db6ab6ff74543c1736ce3c8cac5f62bb5bc0 | Python | 11,796 | 385 | ########################################################################
# Stochastic degradation-diffusion process.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetexact' in the context of the Degradation-Diffusion model
# (see validation/kisilevich.py)
###########... |
ef6c39c22d05c2c58103f635df0c6381eb2b56e16afd5badc046a2ccac291c1e | Python | 11,796 | 368 | """Chain for question-answering against a vector database."""
from __future__ import annotations
import inspect
from abc import abstractmethod
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
... |
8dedc6bacf5d4be1628b02b43fdeeda57dfbd250f133b7dddd1cd4ecbe0e8aea | Python | 11,804 | 382 | import inspect
import warnings
from typing import Any
import pytest
from pydantic import BaseModel
from langchain_core._api.beta_decorator import beta, warn_beta
@pytest.mark.parametrize(
("kwargs", "expected_message"),
[
(
{
"name": "OldClass",
"obj_type"... |
d93ccba93d6dd2e178172305d0307f651d1dd250a1de05060084a4849759b085 | Python | 11,817 | 185 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
import numpy as np
from torch_scatter import scatter
from tqdm import tqdm
from .spherenet_light import SphereNetLightDecoder
from .modules import build_mlp
import sys
sys.path.append("..")
from utils import get_pbc_cu... |
c9d699a8ee23af41efa0e93b62e9af7087d1d1240f90b8e13813ad204efaa935 | Python | 11,820 | 304 | """
Perform a multiple linear regression of model variables (cpp, uncertainty)
on subject's learning rate.
"""
import argparse
import data_analysis_utils as dana
import itertools
import matplotlib.pyplot as plt
import numpy as np
import os.path as op
import sklearn.linear_model
import pandas as pd
import plot_utils as... |
591fddc3646822f8c045e71ff930c3f1f080cce63dc7121ae34dc9b6fad9b8b6 | Python | 11,831 | 266 | # *=========================================================================
# *
# * Copyright Erasmus MC Rotterdam and contributors
# *
# * Licensed under the GNU GENERAL PUBLIC LICENSE Version 3;
# * you may not use this file except in compliance with the License.
# *
# * Unless required by applicable law ... |
f7bbf723d8f4130d142dbd9246cd2120312c9e014917cc0e4c353baefccd6466 | Python | 11,831 | 194 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://w... |
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