sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f1549537edb6d345a7d4e15cff032b6fa09895400609d0a57730d95f093cbc0d | Python | 5,266 | 173 | """
2023.08.30
author: femke.hurtak@epfl.ch
File containing parameters for the plots.
"""
import os
import params
# --- Global plotting parameters --- #
DARKRED = "#9c0d0b"
DARKGREEN = "#0e6117"
DARKCYAN = "#0bbfb3"
DARKBLUE = "#0f0b87"
DARKORANGE = "#d17c04"
DARKPURPLE = "#730d71"
LIGHTGREY = "#c3c3c3"
DARKPINK = "... |
5b5bc1b3cc0b7ca102ec44a6d036c0aa840b6d892755d383001aed1e56a86e44 | Python | 5,267 | 97 | # this file tries an approach of building twophoton imaging trial on top of a general Trial class
import os
import time
# grabbing functions from .utils_funcs that are used in this script - Prajay's edits (review based on need)
from imagingplus.main.core import ImagingTrial
from imagingplus.main.subcore import CellAn... |
d521fd6d7558ea3b7198dbcf8c0b39d507818470cb8c8bd73c6ca1be0f0a0932 | Python | 5,267 | 148 | import numpy as np
import pandas as pd
import os
from skimage.io import imread
from scipy.ndimage import label
from skimage import measure, img_as_bool
if __name__ == '__main__':
import sys
sys.path.append(os.path.join('..','..'))
from morgana.ImageTools.morphology import anchorpoints, spline, midline, meshgr... |
77b86c0fb1eed1428369482cef9ae01b811ebeab9658f6dafcb29af058b99e4f | Python | 5,269 | 125 | # from torch import optim
# import os, sys, argparse
#
# from CEL.experiments.experiments import APHYNITYExp
# from CEL.networks.networks import ReactionDiffusionParamPDE, ConvNetEstimator, DampedWaveParamPDE, DampedPendulumParamPDE, MLP
# from CEL.networks.models.forecasters import Forecaster
# from CEL.utils.utils im... |
758f8657bff48f17996d65581366ad34e536f2cdbb5148a20e3756644be2f5aa | Python | 5,276 | 176 | '''cgat_logfiles2tsv.py - create summary from logfiles
===================================================
Purpose
-------
This script takes a list of logfiles and collates summary information
about execution times. This can be useful for post-mortem
benchmark analysis.
This script uses the ``# job finished`` tag t... |
3ba8c93cabf4d89511bbe5a6254dcff839bb254b0d17ba01291ae95c90428e64 | Python | 5,280 | 176 | import torch
from warnings import warn
from math import ceil
from copy import deepcopy
from odl.contrib.torch import OperatorModule
from odl.tomo import fbp_op
from odl.operator.operator import OperatorRightScalarMult
from odl.operator.default_ops import ZeroOperator
from dliplib.reconstructors.base import BaseLearn... |
e56ede89f347c0e8b28803154d5238a51366a40408567636f0674e61b346aee0 | Python | 5,280 | 141 | import logging
import os
import socket
import sys
from fcest.models.sliding_windows import SlidingWindows
import pandas as pd
from configs.configs import get_config_dict
from helpers.data import load_data
from helpers.evaluation import leave_every_other_out_split
if __name__ == "__main__":
hostname = socket.ge... |
f489edaf7ecca778063b71521a8be1d8df5a5dbd2894f9720922f37385d00f52 | Python | 5,284 | 123 | import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.patches as patches
from figure_saver import save_figure
import numpy as np
import torch
import random
try:
from torchdiffeq.__init__ import odeint_adjoint as odeint
except ImportError:
from torchdiffeq import odeint_adjoint ... |
31bf611f60a8d6db4ee271c830cb947477722245d34ec247106a8d852645a162 | Python | 5,288 | 148 | import numpy as np
import matplotlib.pyplot as plt
from pensa.clusters import \
obtain_clusters, obtain_combined_clusters
def wss_over_number_of_clusters(data, algorithm='kmeans',
max_iter=100, num_repeats=5,
max_num_clusters=12,
... |
1414c9985b28b84486f4c6eb9680214b404087dbdc3f645d503dc522b5f9955b | Python | 5,289 | 174 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... |
c80ecf32bea0b4e7d5a86ae816614622e63f082e45bf2dc665118ddcb8400d87 | Python | 5,291 | 159 | import sys
from inspect import isclass
from typing import Any
import pytest
from pydantic import BaseModel
from pydantic.v1 import BaseModel as BaseModelV1
# pydantic.v1 models cannot be exercised under the compatibility shim on
# Python 3.14+, so v1-specific tests are skipped there.
skip_if_no_pydantic_v1 = pytest.m... |
eed9e6db331bcd4bb92acf9d003583213e3c6c2ecd1ba7163b2721336588e1a4 | Python | 5,293 | 137 | import warnings
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import numpy as np
import torch
from neuralpredictors.measures import corr
from ..models.model import Model
from .predictions import compute_model_predic... |
3952be616db87b49c731ff46bdb113d16d4002396df3240aacef7b2fa4a0ddcf | Python | 5,294 | 118 | import numpy as np
import scipy.stats
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from Articles.Sequential_Learning_Striatum.Figures.cfg_color import colors
latex = '$'
def stats_text(p_value):
text = ''
p = 0.05
while p_value < p and p > 0.00005:
text += '*'
p... |
465c4865e9bec7facb8ddf474f14a53acb36e5d76d9b11058118d704bf3b6e1c | Python | 5,295 | 175 | import itertools
from typing import List
import networkx as nx
import numpy as np
from networkx import average_shortest_path_length, floyd_warshall_numpy
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
def tsne(data: np.ndarray, nc_com: int) -> np.ndarray:
tsne = TSNE(n_components=nc_com,... |
259f43b45a8c1f0e7b2bcb9132add546ddcfa2607c70f4d9200f143c93748b4f | Python | 5,298 | 127 | """Tests for langchain_core.utils.formatting."""
import pytest
from langchain_core.utils.formatting import StrictFormatter, formatter
class TestStrictFormatter:
"""Tests for the `StrictFormatter` class."""
def test_vformat_with_keyword_args(self) -> None:
"""Test that `vformat` works with keyword a... |
70971376e29e2f875baf9cbecbcc764925b2289f5f65585b652dcae293d2f697 | Python | 5,298 | 108 | import tensorflow as tf
import numpy as np
import pandas as pd
import scanpy as sc
import anndata as ad
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
from model import MinMaxNorm
def FeatureList(paths: list) -> list:
features = None
for path in paths:
mydata = pd.read_csv(... |
73bb2092436c7df5e5a262fdd3edf1ba84c0a061aa74364e02b472a732c108fc | Python | 5,299 | 145 | import jax
import jax.numpy as jnp
import jax.random as jrng
import flax.linen as nn
from flax.linen.initializers import Initializer
from typing import Callable, Optional, Any, Tuple
PRNGKey = Any
Shape = Tuple[int, ...]
Dtype = Any # this could be a real type?
Array = jax.Array
InitFn = Callable[[PRNGKey, Shape, Dty... |
79e9adec0388b008156617d0a6b8b134ae3b532401aa6909a7c4b2126ade4b7d | Python | 5,301 | 95 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
# Example values for three lines, the last one without CI
cut_lengths = range(1, 13)
pwn = np.array([0.724619606040199,0.724619606040199,0.724609452704102,0.724729978242851,0.731469239967536,0.745321657229588,0.763269716149029,... |
87f5cfcd375a063cc4a6d73b77b8587d88ef6fada7ce9e8a7c2532e878f3d660 | Python | 5,301 | 152 | from gunpowder import *
from synister.synister_db import SynisterDb
import random
import numpy as np
class SynapseSourceMongo(CsvPointsSource):
def __init__(self, db_credentials,
db_name,
split_name,
synapse_type,
point... |
f81ff14bd63d180e2b992e7fe6e76874a64910b0726983bb85d77c2b0e845746 | Python | 5,304 | 179 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... |
98a48b05069ebdc3a79e6163af195605b1c206cd6cf8edf0e6ad52adf82b4383 | Python | 5,307 | 127 | #-*- encoding:utf8 -*-
import pickle
import numpy
import torch
import numpy as np
from torch.utils import data
#my lib
from config_PPI import DefaultConfig
class dataSet(data.Dataset):
def __init__(self,window_size,sequences_file=None, label_file=None, protein_list_file=None):
super(dataSet,self).__init_... |
73889507eece86ef9bbb7f2454cef7e3cebb7ba6f6e9602daf4131f76da19e82 | Python | 5,310 | 130 | import numpy as np
import mdtraj as md
import os
import glob
class NonequilibriumSwitchingAnalysis(object):
"""
This is a helper class for analyzing nonequilibrium switching data
"""
def __init__(self, trajectory_directory, trajectory_prefix):
"""
Create a NonequilibriumSwitchingAnaly... |
a7b6d3d9b73b577866819e7b7eec28db21fc518a9ba75fa0a1e186876e34515c | Python | 5,310 | 146 | import numpy as np
import os
import random
import sys
import uuid
# For preprocessing
from sklearn.preprocessing import StandardScaler
import torch # For building the networks
torch.backends.cudnn.deterministic = True
import torchtuples as tt # Some useful functions
from pycox.evaluation import EvalSurv
from pycox.... |
8e024b6256508973a993aecd43f8dbe496eadbe4d5abebf6385c6a455a68f745 | Python | 5,312 | 122 | '''
divide a long continuous recording session (10's of hours) into several segments (e.g. 4h)
each segment is stored into a .mda file
then in downstream processing these mda files will be spike-sorted separately and then the tracking of clusters will be needed
'''
import os
import gc
import warnings
from copy import... |
498f405b981977ad2490963ea1ae474177a795ffcfbf6c7297e2fdc77648ab0c | Python | 5,313 | 150 | import nibabel as nib
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import os
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from utils import plot_dendrogram, findVoxel
from sklearn.cluster import AgglomerativeClustering
############... |
f421529948d8cf6236663e30a245d7b5b807e5f3bef6e4b26b9ce6407a3dc7ba | Python | 5,313 | 167 | import re
import networkx as nx
import pandas as pd
from os.path import join
# from config_path import REACTOM_PATHWAY_PATH
from data.gmt_reader import GMT
# reactome_base_dir = REACTOM_PATHWAY_PATH
reactome_base_dir = '/apps/home/lulab_liuxiaofan/qhsky/project/multi_omics_paper/comparison_methods/P_net/data/pathway/'... |
e25be2abdbba6cb11b77203d1db8dd5db6160da20be614e6ba9f37a21d8f82b0 | Python | 5,315 | 162 | import torch
from torch.nn import Sequential, Conv2d, ReLU, MaxPool2d, AdaptiveAvgPool2d, Linear, Module, BatchNorm2d
from torch import nn
"""class CNN2D(Module):
def __init__(self, labels_number, channels_nb, depth, kersize):
super(CNN2D, self).__init__()
depth = 1
kersize = 15
n... |
180a8c6d0c2ea0690b0163685f7adf178395867fa879f7b01a8acebe708596e4 | Python | 5,316 | 114 | #!/usr/bin/env python
# Imports
import unittest
import os.path
import sys
import subprocess
import tempfile
import filecmp
from scipy import stats
path_to_script = os.path.join(os.path.dirname(os.path.realpath(__file__)),'..','bin','enrichm')
path_to_data = os.path.join(os.path.dirname(os.path.realpath(__file__)),'da... |
d9ec528d950bbad8ccba6b9d108c73bc11247fce693f8b54fca48fd4aad625c0 | Python | 5,317 | 152 | import numpy as np
import pandas as pd
from scipy import stats
from sklearn.cross_decomposition import CCA
from pgmpy.utils import preprocess_data
from ._base import BaseCITest, _CITestResult, _ResidualMixin
class WilksLambda(_ResidualMixin, BaseCITest):
r"""
Wilks' Lambda CI test for mixed data [1].
T... |
f8f3757829bde36bc72e2f365f61cbc695c2718caa5f0434ba0c8e9f71c0fcfc | Python | 5,317 | 154 | """ Example for sparse Autoencoder (SAE) on natural image patches.
:Version:
1.0.0
:Date:
25.01.2018
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2018 Jan Melchior
This file is part of the Python library PyDeep.
... |
2b90839801d5a180981ed9e1388343d38444656855adbd7fe4f2b65165dd926d | Python | 5,320 | 173 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... |
4d37b034f6ee88e95d5b28740bd384d586063c1c27663c9ec35d5adc07e8d4b9 | Python | 5,320 | 151 | """Drive the graph factory through a real `langgraph dev` server with auth.
This is the end-to-end check the example describes but cannot show inline: two
users hit one deployment, each authenticates with their own `x-api-key`, and
each run's agent talks to the MCP fleet as that user. It:
1. mints one shared keypai... |
a8c5e8766f8becd262f4246dd964054cc81202e164ce14a9e0ddb7c77dc14508 | Python | 5,322 | 146 | import warnings, logging, os, sys
warnings.filterwarnings('ignore', category=FutureWarning)
logging.disable(logging.WARNING)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import json
import tensorflow as tf
from utils import *
def predict(args):
if not os.path.exists(args.NPZDIR):
os.makedirs(args.NPZDIR)
... |
cae795244143f15a3fc9c7f1b5c51ccffd84771a22c53a9503ee71c090f040ac | Python | 5,322 | 183 | import torch
import torch.nn as nn
def init_weights(
module: nn.Module,
xavier_init: bool = True,
gain: float = 1.0,
nonlinearity: str = "leaky_relu",
):
for m in module.modules():
if isinstance(m, (nn.Conv2d, nn.Conv1d, nn.Linear)):
if xavier_init:
nn.init.xavi... |
022010a23760c2854e835b71b2e5ab86b25ea1de14ede928c38f269c92257767 | Python | 5,323 | 140 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
ea32d703f76f82f490d131e66557adc5debcbc74216106b6969f0fd90895feb8 | Python | 5,326 | 134 | """Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core.retriever... |
0ab861597c570d661540291f58f0b3b23946f7ee133e9f9b60c58dfe249c9495 | Python | 5,328 | 153 | from torchvision.models import densenet121
from example_code.example_dataset import ShapesDataset,Randomize
from torchvision import transforms
from engine.sup_classifier import ExperimentRunner
from engine import utils
import torch.nn as nn
import torch
from torch.utils.tensorboard import SummaryWriter
from torch.util... |
5b7ee77cc8da0fbb24fc34c369631a7a77903ebf6fa3419749245a9982615c20 | Python | 5,329 | 158 | import tempfile
from collections.abc import Generator
from pathlib import Path
import pytest
from langchain_core.stores import InvalidKeyException
from langchain_classic.storage.file_system import LocalFileStore
@pytest.fixture
def file_store() -> Generator[LocalFileStore, None, None]:
# Create a temporary dire... |
12fccd3405fa9c93398b395ffa4095f21846ba60c38ca48d384226b8268ef18c | Python | 5,330 | 150 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
26a32506426e370d8ed087b26174b2b97953fc28d1a924a08d0eb67a282c6b47 | Python | 5,336 | 176 | import torch
from torch_geometric.loader import DataLoader
from torch_ema import ExponentialMovingAverage
import logging
import numpy as np
from prettytable import PrettyTable
from config import build_arg_parser
from dataset.PygrMD17 import rMD17
from dataset.Preprocess_rMD17 import rMD17Preprocess
from eval import e... |
2739144d837d71fba1d920af569eb480b42fcc15b443d4c6a33313c93d78d1c9 | Python | 5,338 | 159 | """Test functionality related to combining documents."""
import re
from typing import Any
import pytest
from langchain_core.documents import Document
from langchain_core.prompts import PromptTemplate, aformat_document, format_document
from langchain_classic.chains.combine_documents.reduce import (
collapse_docs,... |
97ab2ee57b0354be1b55576b542266a5be2b9b3356f7f954442a67d2d2966095 | Python | 5,338 | 141 | """Resample a NIfTI volume into a target image space."""
from __future__ import annotations
import argparse
import logging
from pathlib import Path
from typing import Iterable, Sequence
import SimpleITK as sitk
LOGGER = logging.getLogger(__name__)
def _parse_direction(direction: Sequence[float]) -> tuple[float, .... |
c7ccdb4ec62616e99a90e2ea4a0e334c8b340747a8068fc51c82f4be61fbdab0 | Python | 5,338 | 148 | import itertools
import networkx as nx
import numpy as np
from openff.arsenic import stats
from openff.arsenic.stats import bootstrap_statistic
from openff.arsenic.wrangle import FEMap
def test_mle_easy(input_absolutes: list = [-14.0, -13.0, -9.0]):
"""
Test that the MLE for a graph with an absolute
esti... |
85ac88d35aafe77d70f4741072c0dfc271a54ecffc493147b7a7eb8e80ff4e30 | Python | 5,339 | 130 | # Copyright (c) <2024> <University of Paderborn>
# Signal and System Theory Group, Univ. of Paderborn, https://sst-group.org/
# https://github.com/SSTGroup/relationship_structure_identification
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and as... |
e01e025f59c6346538c1066e9a5ed4f85fdd4b9a4df8b3e50208042f7dbbaddd | Python | 5,340 | 105 | import argparse
import os
import json
import sys
import h5py
import numpy as np
def main(input_json, h5_file, overwrite, text_file):
if text_file is None:
print(f"Starting adding mask/blacklist regions to {h5_file}. Please make sure the {input_json} "
f"file/your genome assembly is from eith... |
2a6e7c1e5971625cef29e9a7ba7fdb3469b283229bfc82277ece323c32aed484 | Python | 5,341 | 140 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
a5439f43bd5c227c39cb9181844b66c78da901712b4d474e671469d55b612fee | Python | 5,341 | 116 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#~~~~~~~~~~~~~~IMPORTS~~~~~~~~~~~~~~#
# Standard library imports
import argparse
import sys
# Local imports
import pycoMeth as pkg
from pycoMeth.common import *
from pycoMeth.CpG_Aggregate import CpG_Aggregate
from pycoMeth.Interval_Aggregate import Interval_Aggregate
fr... |
078611393f5d25468dd938f9501b13e34f593a069e154ab609e5362dd7764114 | Python | 5,343 | 143 | import argparse
from openff.interchange.drivers.openmm import get_openmm_energies
from rdkit.Chem import rdMolAlign
from openff.toolkit import ForceField, Molecule
from openff.toolkit.utils import get_data_file_path
def compute_conformer_energies_from_file(filename):
# First, load conformers from an SDF file.
... |
abb8bc39e94e2d37393981c79888ce751fc42f5243b73f25f5eff7bf83f6155b | Python | 5,344 | 146 | import numpy as np
import pandas as pd
from scipy import stats
from sklearn.cross_decomposition import CCA
from pgmpy.utils import preprocess_data
from ._base import BaseCITest, _CITestResult, _ResidualMixin
class PillaiTrace(_ResidualMixin, BaseCITest):
r"""
Pillai's trace test for conditional independence... |
01983a2fdd84c84ac8971f55b4657b18b7fb6a2043decf6d9cb4d06caefbe171 | Python | 5,348 | 173 | import torch
from torch_geometric.loader import DataLoader
from torch_ema import ExponentialMovingAverage
import logging
import numpy as np
from prettytable import PrettyTable
from config import build_arg_parser
from dataset.PygAcAc import PygAcAc
from dataset.Preprocess_AcAc import PreprocessAcAc
from eval import ev... |
28fe79db9f6161b91812cf9534025e8914b3b014b20b682f80f7d8381eeafd65 | Python | 5,351 | 132 | import os
# this stops jax from stealing all the memory
os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"]="false"
import jax
import jax.numpy as jnp
import jax.random as jrng
import tensorflow as tf
import optax
import hydra
from omegaconf import DictConfig
from orbax.checkpoint import (CheckpointManager,
... |
f4f4d0c6ad3dfc245d0911d7b60dc5864981b2cb82831707fa57933ce32d7478 | Python | 5,351 | 104 | import gym
import random
from gym import spaces
import numpy as np
import logging
from random import sample, choices, uniform, randint
from math import ceil
from Environments.DSSdirect_34bus_loadandswitching.DSS_Initialize import *
from Environments.DSSdirect_34bus_loadandswitching.state_action_reward import *
from gym... |
3a605dc3038bd63a5f56b50c2f841f5b13a859372d7e954aeba40c64f1554bf4 | Python | 5,353 | 109 | from torch.utils.data import DataLoader
from qm9.data.args import init_argparse
from qm9.data.collate import PreprocessQM9
from qm9.data.utils import initialize_datasets
import os
def retrieve_dataloaders(cfg):
if 'qm9' in cfg.dataset:
batch_size = cfg.batch_size
num_workers = cfg.num_workers
... |
692aedf3900806753fd83cc72d3d75edd4c11a45f235930e5b2af505fa144a3b | Python | 5,355 | 139 | import cv2
import numpy as np
import pandas as pd
from joblib import delayed,Parallel
import torch
from torchvision import transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader,Dataset
from engine import utils
from constants import BASE_DATA_DIR
from dataloaders import data_tfms
... |
c235a2dcfb1f25925c2911f7ed94cd0de0095e1d9229e84bf83ad64c5063ad1b | Python | 5,360 | 111 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# :noTabs=true:
# (c) Copyright Rosetta Commons Member Institutions.
# (c) This file is part of the Rosetta software suite and is made available under license.
# (c) The Rosetta software is developed by the contributing members of the Rosetta Commons.
# (c) For more inform... |
d59ebb9e85bbe8d2b2d9b5e8816661803d7b628ceb090c501f754be4fd0dc343 | Python | 5,360 | 163 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 29 15:28:47 2021
@author: nicol
"""
from skimage.io import imread
from sklearn.metrics import jaccard_score, recall_score, precision_score, accuracy_score
import os, glob, tqdm
import numpy as np
from skimage import color
import pandas as pd
import seaborn as sns
import ... |
f08856b74bfd0469a850ffcffdd3d60481a827899088965d71cb1a4b04564c5c | Python | 5,360 | 162 | import os
import sys
import re
import types
import itertools
from cgatReport.Tracker import *
from cgatReport.odict import OrderedDict as odict
from RnaseqReport import *
import numpy
class TrackerGeneCounts(RnaseqTracker):
'''tracker with gene counts.'''
pattern = "(\S+)_gene_counts$"
class TrackerReal... |
ca8c38346ba9b4e1d8689c774b0a6f01053e31a8d1442228951c1e1e9b26521f | Python | 5,361 | 113 | import argparse
import os
import util
import torch
#import models
#import data
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--mode', default='binary')
parser.add_argument('--arch', type=str, default='res50', help=... |
1b747c01487565579f6ab75303214b101c0b41679605eddbc62217ed57bc40c3 | Python | 5,362 | 130 |
import numpy
import scipy.signal
__all__ = ['SignalProcessor',
'LowPassBesselFilter', 'LowPassButterFilter',
'HighPassBesselFilter', 'HighPassBesselFilter',
'BandStopBesselFilter', 'BandStopButterFilter']
class SignalProcessor:
def apply(self, data):
raise NotImplemente... |
25004bde1caf54dc42c513223d49bb864a2748d066cff64fe1ef6e67e427d399 | Python | 5,363 | 150 | from __future__ import annotations
from collections.abc import Sequence
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.to... |
0d4cbabc0b8e2634b20a8bb96ec19dfcf859e3064f96cae954f21680252a56dc | Python | 5,365 | 154 | import numpy as np
import dedalus.public as d3
import logging
import h5py, os
logger = logging.getLogger(__name__)
from matplotlib import pylab as plt
# Load input data
file = np.load('./inputs_KLE_lx_5_ly_8_v_4.npz') # original
#file2 = np.load('./inputs_KLE_lx_0.25_ly_0.3_v_0.15.npz') # smaller length scale (for Nat... |
aa35107f7daea5a9b4918aa18539bd481aed156fa3f9bc527e462fb5f6ff8766 | Python | 5,365 | 161 | """
Class that represents a file with the prediction results
* allows prediction results to be appended to the file in chunks
* allows results to be skipped if they already exist in the file
"""
import csv
from typing import List, OrderedDict
from pathlib import Path
import logging
from .models.results import Predic... |
34daa539bb7a96a18ae3da27ffd1c2c0b2aaeb94d31ba47860f0cbdd85b1fdba | Python | 5,368 | 131 | # Define Data Generating class
import numpy
import torch
import math
import matplotlib.pyplot as plt
import mAECell_Utils as utils
class FakeData:
nTPoints: int
tPoints : float
nVoxels : int
nInterms: int
nTSeries: int
Conc : float
Interms : float
DataSet = []
de... |
332d31d9903e1978f0a454449d3daeeb7e98cd7600d2a6237d671a00bd892430 | Python | 5,372 | 143 | import torch
import torch_geometric
import pickle
import numpy as np
import pandas as pd
import os
from src import utils
class GraphDataset(torch_geometric.data.InMemoryDataset):
def __init__(self, root, name, transform=None, pre_transform=None):
self.file_name = name
super().__init__(root, transf... |
863f61c3b8448f0e7e9ac03268a4b6b052413c9727e118d4194d84b64820ee7d | Python | 5,372 | 169 | import numpy as np
from scipy.optimize import brentq
from scipy import special as sp
import sympy as sym
import tensorflow as tf
# This code is extracted from dimenet (https://github.com/gasteigerjo/dimenet).
def Jn(r, n):
"""
numerical spherical bessel functions of order n
"""
return np.sqrt(np.pi/(2... |
70e7b9ff0cfe7747067fe1a306a3e68e52f0df3b4b5aea7d76c77dd7fad1b058 | Python | 5,382 | 141 | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import utils
import argparse
from protein.models import get_model
import os
from equivariant_diffusion.utils import assert_mean_zero_with_mask, remove_mean_with_mask,\
assert_correctly_masked
import t... |
30d3d4e0d7bbcc8cbbe0bde6a101af835118fb61e03202ed4ef6298f57b844c7 | Python | 5,383 | 175 | """
##############################################################################
#
# Select top N distinct motifs with highest (statistically significant)
# activity Z-score (for every site separately)
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics... |
75480ebce43e37e3a8b66f6ff38dd5af1ddeb18d46f2c1265710789fbfbde69a | Python | 5,384 | 123 | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from mimic3benchmark.readers import DecompensationReader
from mimic3models import common_utils
from mimic3models.metrics import print_metrics_binary
from mimic3models.decompensat... |
b0aa2910f0995d798d38bc741ae881cc5c5231d01d3257b592105701e964973e | Python | 5,384 | 148 | """ Test module for corruptor.
:Version:
1.1.0
:Date:
13.03.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Python library PyDeep.
PyDeep is free softwar... |
bfc5f73a4844e87fa1cfbbb506714f0411fce6b4b5a90eb449247dc203dca5a6 | Python | 5,384 | 160 | """
##############################################################################
#
# Normalize pas expression for per-sample library size (result in TPMs)
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREAT... |
ca3870316e5cfcb26d4bd452afb246abcff0f3d5c226c3fad532f6012e6edeee | Python | 5,385 | 121 | """ VQ-VAE model [1]
References:
[1] - Neural Discrete Representation Learning (https://arxiv.org/pdf/1711.00937.pdf)
"""
import torch
import torch.nn.functional as F
from torch import nn
from torch.cuda.amp import autocast
from layers.quantizer import Quantizer
class ResidualLayer(nn.Sequential):
def __in... |
43161bac6e0a734a5bbe03f326550317db80b1df18b9fe6776d8da6b2b17346a | Python | 5,386 | 133 | import pytest
import os
import filecmp
def compare_all_but_volume(fname1, fname2,equal=True):
n=0
nEqual=0
with open(fname1) as f1:
with open(fname2) as f2:
for line1, line2 in zip(f1, f2):
if("olume" not in line1):
if line1==line2:
... |
3bdf3ad8541b8d3e048b8e1d2eab6c4bebbe50141c9137d5127c4461665bb575 | Python | 5,388 | 154 | from torch.utils.data import Dataset, IterableDataset, ConcatDataset, Sampler
import random
from sys import stderr
import ccg
from ccg.combinators import Punc
from .any2int import Any2Int
class TreeTrainingBatchSampler(Sampler):
def __init__(self, data_lengths, batch_size_in_words, out_of_order_noise: float = 1... |
9fa78b8d3e5d9c4a28b09d70fa7f0ba9e1479fca86522bd20df5892e7f57b9e1 | Python | 5,390 | 107 | from torch.optim import SGD, Adam
from model import *
import numpy as np
from config import Scenario
import pickle as pkl
import copy
from train import Trainer
from tqdm import tqdm
import matplotlib.pyplot as plt
from utils import calculate_hessian_eigs
from time import time
def analyzer_decorator_funct... |
ad61a36cc77b63d7e2e5784e52d5aa61a3f644da17e6d493d965ab83b13e097e | Python | 5,390 | 131 | # -*- coding: utf-8 -*-
"""
Created on Sat Apr 25 16:59:16 2020
@author: gritti
"""
import os, glob, tqdm
from skimage.io import imread, imsave
import numpy as np
from morgana.DatasetTools.morphology import overview
from morgana.DatasetTools.morphology import computemorphology
from morgana.DatasetTools.morphology im... |
0d598dfefdba43552222397c0ec95b3a59a5cc3e02d018ab7674564a629a9633 | Python | 5,391 | 153 | """
track_frame.py: track keypoints in a single frame.
Annotator GUI macro for ZephIR to track keypoints in a single frame (and its
child frames, if specified). Necessary components are loaded from checkpoint.pt
to expedite the process.
"""
import shutil
from docopt import docopt
from . import *
from ..models.contai... |
e183806478200b9f0f7b4f8d96b73a4e6da0b66019454afd28fc986c945799e7 | Python | 5,392 | 148 | import os
import sys
import re
import types
import itertools
import matplotlib.pyplot as plt
import numpy
import numpy.ma
import Stats
import Histogram
import cpgReport
from cgatReport.Tracker import *
from cgatReport.odict import OrderedDict as odict
##################################################################... |
475c23966c4463e15daa760a37ff8189f20b3198803d16e76d6cab0b87592abb | Python | 5,393 | 201 | import argparse
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
import SimpleITK as sitk
from p_tqdm import p_map
from surface_distance import (
compute_average_surface_distance,
compute_surface_dice_at_tolerance,... |
e91cd6ecb540a906b25d5fe9681f7c3cafede28d4ea2564079922621469a5575 | Python | 5,394 | 155 | """
SSIM
https://github.com/jinh0park/pytorch-ssim-3D
"""
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window... |
676d6f0f7ec103932d5da2766a78802c0ed8e607c2bf5ecd5ab44b1ea8348ada | Python | 5,396 | 139 | import math
import torch
import torch.nn as nn
import traceback
from einops import rearrange, repeat, reduce
#################################
# Define custome autograd function for masked connection.
class CustomizedLinearFunction(torch.autograd.Function):
"""
autograd function which masks it's weights by 'm... |
6aea8b750b550d487b2f6bed509f5a7fbb6c84073ce4c40dfb834fbe895178d3 | Python | 5,397 | 150 | import os
import sys
from shutil import copyfile, rmtree
import configargparse
import configparser
import click
import numpy as np
p = configargparse.ArgParser()
p.add('-d', '--base_dir', required=True,
help='base directory for storing synister experiments')
p.add('-e', required=True, help='name of the experime... |
b56f5081224db628dd89fce336aa3a19eebafd7c6a95006529f80bd0e578d479 | Python | 5,399 | 173 | import os
import pandas as pd
import numpy as np
if __name__ == '__main__':
import sys
sys.path.append(os.path.join('..'))
from morgana.DatasetTools.fluorescence import computefluorescence, io
def collect_fluo_data(groups, channel, distType, isTimelapse=False):
try:
to_unicode = unicode
exce... |
71ca339b06870cf049cdd9a623281d1be6078258bc667f40b9bbf309a807e9a0 | Python | 5,400 | 157 | """This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.... |
5861fe05c77d07e4622f701c98932859263a1fb862001f7bb298874c0d4d8b3d | Python | 5,401 | 164 | """
This is the base class for generating a biasing potential
for expanded ensemble simulation
"""
import openmoltools
import simtk.openmm as openmm
import simtk.openmm.app as app
import simtk.unit as units
import logging
_logger = logging.getLogger()
_logger.setLevel(logging.INFO)
class BiasEngine(object):
"""
... |
6d6fa91d8e591a7cacfff7276f5bc4cd3bd3b4aa618016c4a96ad9841e8d07cc | Python | 5,401 | 172 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
7873341f85cf6a359736ca4ceac7157af32bfa7ee4832cb6521744baf902362c | Python | 5,402 | 172 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
8ab5c7eca6c0b4ec8a5819201778c6fbaa9e44c5d01da4756471d5358484bc10 | Python | 5,403 | 118 | # Picks all Infinium 2 probes that cover mouse when picking the maximum number of species for each probe. Selects
# all probes that cover mouse, then up to the desired number of probes ranked in order of conservation.
from __future__ import print_function
import sys
import gzip
from collections import defaultdict
impor... |
5cc619b11297d4cee5eb10838699c2404b4d5aaa56545f50fe48c8ffad7f2ecd | Python | 5,404 | 171 | from scipy import sparse, linalg
import umap
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import os
import networkx as nx
import plot_params
from common import select_subset_matrix
from loadda... |
11d09384235ad46abd473c162e40af6169dc44cee11def0783559e5f919a70c7 | Python | 5,406 | 186 | """ Different kind of cost functions and their derivatives.
:Implemented:
- Squared error
- Absolute error
- Cross entropy
- Negative Log-likelihood
:Version:
1.1.0
:Date:
13.03.2017
:Author:
Jan Melchior
:Contact:
JanMel... |
f99d13664c066d57a390b34462b03757c32d411a31f96da532ddfee045601671 | Python | 5,408 | 165 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
eb240a66fb8d8598de693f18ab58e1acf738ea2ab46e9b5ddda1a1c8d89842a5 | Python | 5,409 | 95 | import torch
import torch.nn.functional as F
from torch_geometric.nn.acts import swish
from torch_scatter import scatter
from .modules import emb, init, update_v, update_e, update_u
import sys
sys.path.append("..")
from utils import xyz_to_dat
from utils import get_pbc_distances
class SphereNetEncoder(torch.nn.Module... |
dd191fa0b79139d8c44526f99788128abbf93c20ad73ad9cbd25d26c482cac01 | Python | 5,410 | 153 | """Chain that carries on a conversation and calls an LLM."""
from langchain_core._api import deprecated
from langchain_core.prompts import BasePromptTemplate
from pydantic import ConfigDict, Field, model_validator
from typing_extensions import Self, override
from langchain_classic.base_memory import BaseMemory
from l... |
8e57b6aeeb70630baef3566b039987158a3076fdeb1ead21efa246084d782740 | Python | 5,412 | 182 | from gunpowder import *
from gunpowder.ext import torch
from gunpowder.torch import *
import json
import logging
import math
import numpy as np
import os
import sys
from funlib.learn.torch.models import Vgg3D
from synister.gp import SynapseSourceMongo, SynapseTypeSource, InspectLabels, AddChannelDim
from synister.read_... |
c6fa375935e6782f6708a9ba5c010abc41a627ce071ffa9e49b5a0f7456554ec | Python | 5,414 | 156 | from langchain_core.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from typing_extensions import override
from langchain_classic.retrievers.ensemble import Ensemb... |
b25af1f18f71ff2f608273c66a3dcc67bd8122f74e4e53143a7978ad3b34a4b6 | Python | 5,416 | 165 | from rdkit import Chem
from rdkit.Chem import AllChem, QED, Descriptors
import copy
import numpy as np
def mol2smiles(mol):
try:
Chem.SanitizeMol(mol)
except ValueError:
return None
return Chem.MolToSmiles(mol)
class BasicMolecularMetrics(object):
def __init__(self, dataset_info, dat... |
d1b91f482f318cea50ffd228d082217e253c60d62df4e0b827e292944b5f137b | Python | 5,422 | 143 | from itertools import combinations
import pandas as pd
from sklearn.metrics import f1_score
from pgmpy.base import DAG
from pgmpy.ci_tests import get_ci_test
from pgmpy.metrics import BaseUnsupervisedMetric
class CorrelationScore(BaseUnsupervisedMetric):
"""
Score to compute how well the model structure rep... |
7ebb34618b5abe122f1393d6066650106d80bc940cd5e45ebfe740981242ba86 | Python | 5,424 | 197 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import argparse
from time import time
# import pylab as pl
import nibabel as nib
import numpy as np
from odf_utils import extract_patches
DESCRIPTION = """
Pick the best odf ratio for each voxel based on smoothed AIC... |
c742cdb962f3cec4ac26cd589ef63c7782f3d83de9267f4b7db7ada27896bac5 | Python | 5,424 | 125 | import argparse
import os
import h5py
import numpy as np
from predmoter.core.constants import EPS
def pear_coeff(replicate, target, is_log=False):
"""Calculate the pearson correlation coefficient (numpy version).
This function only accepts tensors with a maximum of 2 dimensions. It excludes NaNs
... |
81eb4f702bbd9f225910c0ec81ee9c68299e36e2bd4bd34fcda565d613d4ce4a | Python | 5,425 | 96 | """**Chains** are easily reusable components linked together.
Chains encode a sequence of calls to components like models, document retrievers,
other Chains, etc., and provide a simple interface to this sequence.
The Chain interface makes it easy to create apps that are:
- **Stateful:** add Memory to any Chain t... |
1fac1f4368b38805215822523d9d5fe9e62b148c87cb123db03d8f3d85c4640b | Python | 5,427 | 125 | import torch
import torch.nn as nn
from odl.contrib.torch import OperatorModule
from odl.operator.default_ops import ZeroOperator
class IterativeBlock(nn.Module):
def __init__(self, n_in=3, n_out=1, n_memory=5, n_layer=3, internal_ch=32,
kernel_size=3, batch_norm=True, prelu=False, lrelu_coeff=0... |
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