sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
54001559126618150949924983032c0708d7b1c8a7e7172899d65a9a78045b82 | Python | 3,692 | 104 | # Copyright 2021 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
7e8bcde8b462ff0674ffec812ef127a8b078203bae2c4a603e9e0ea68c1b45bf | Python | 3,692 | 120 | # -*- coding: utf-8 -*-
"""ESMFold2_Embeddings.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1IuZeaW8-8F0mMpLPWhkvuAn6XOeupMQs
"""
!pip install git+https://github.com/facebookresearch/esm.git
!curl -O https://dl.fbaipublicfiles.com/fair-esm/examples/P... |
5a60f6526d1de8264622fea08c5ebbe8dc743060dcbb61c88b6b8acb1a9f32a2 | Python | 3,694 | 107 | #!/usr/bin/env python3
"""
LLPhyScore出力からsum scoreだけ抽出
入力: llphyscore_*.txt (種ごと)
出力: llphyscore_summary.tsv (1ファイルに統合)
使い方:
python3 extract_llphyscore.py /Users/kyotayasuda/rbp_pfam/output/
"""
import sys
import os
import re
def extract_scores(filepath, species):
"""LLPhyScore出力ファイルからprotein IDとsum scoreを抽出"... |
e04f65efc6638076bd39b7ecab7b96f3fc903a3e27eedf0a229e422b80d85e63 | Python | 3,697 | 85 | """
src/methods — Clinical Methods Layer
=========================================
21 clinical methods for missingness-robust EHR classification.
Methods
-------
Baselines (2):
- mean_lr : Mean Imputation + Logistic Regression (naive benchmark)
- mean_xgb : Mean Imputation + XGBoost (imputation ablation anchor)
... |
f2296fc9c051f82cea97c3b3fa631d2a2e34c856aa2724e5dae1d2e5e06fea62 | Python | 3,699 | 138 | import pytest
from pytest import WarningsRecorder
from shapely.decorators import deprecate_positional
@deprecate_positional(["b", "c"])
def func_two(a, b=2, c=3):
return a, b, c
@deprecate_positional(["b", "c", "d"])
def func_three(a, b=1, c=2, d=3):
return a, b, c, d
@deprecate_positional(["b", "d"])
de... |
01917af9a83a6069ee932cf33ddad8bff7890580675f21366fd6ce1fa3dfcabc | Python | 3,702 | 97 | #!/usr/bin/env python
"""
Example script to register two volumes with VoxelMorph models.
Please make sure to use trained models appropriately. Let's say we have a model trained to register
a scan (moving) to an atlas (fixed). To register a scan to the atlas and save the warp field, run:
register.py --moving mov... |
91ca8e1e3d183dd1b2043d3babe411e3672126d486648fddcdc553163febdad2 | Python | 3,703 | 87 | from multiqc import report
from multiqc.modules.fgumi.tests.conftest import general_stats
from multiqc.modules.fgumi.umis import summarize_observations
UMI_COUNTS = (
"umi\traw_observations\traw_observations_with_errors\tunique_observations\tfraction_raw_observations"
"\tfraction_unique_observations\n"
"AA... |
c2fb73a3f91bc2e39276b84d72886b2674e1e6c49320dc4d36d24a43a5d38c85 | Python | 3,706 | 100 | """MultiQC submodule to parse output from deepTools plotEnrichment"""
import logging
from multiqc.plots import linegraph
# Initialise the logger
log = logging.getLogger(__name__)
class PlotEnrichmentMixin:
def parse_plot_enrichment(self):
"""Find plotEnrichment output."""
self.deeptools_plotEnr... |
0d6a8f39bd2dc02f13d981c6a246961d59365b54ae544365e54f0662d5ea8d45 | Python | 3,709 | 115 | """
engine/model_cache.py — Hash-Based Model Checkpoint Cache
==========================================================
Avoids re-training identical model+config combinations by caching the
best model state_dict on disk. Lookup is based on a hash of the full
``ExperimentConfig`` dict.
Design
------
- One ``.pt`` fil... |
fc7ffae352b2e672ca2e65c9529d7bad8ba41ca6412d373029188078aff0b38b | Python | 3,709 | 102 | """Pipeline stage 1: convert raw ADNI PET (DICOM or ECAT) to NIfTI.
This is the very first preprocessing step. Source data was pulled from
ADNI with the following search criteria:
* Modality = PET
* Radiopharmaceutical = "18F-AV45" (amyloid PET)
The downloaded archive is organised as::
PET_ADNI/
<subject_... |
14ea04e65c5c9e85bb71c3a60388dbe7dbe4423b04eb50fd2df715047aa0248d | Python | 3,712 | 97 | import argparse
import glob
import os
import re
import shutil
import subprocess as sp
from contextlib import contextmanager
from tempfile import TemporaryDirectory
# YAML imports
try:
import yaml # PyYAML
loader = yaml.load
except ImportError:
try:
import ruamel_yaml as yaml # Ruamel YAML
ex... |
2987a260fec5ebce0afecb00cc957f43c85e1a7c30bc016a3d26056381c39fdd | Python | 3,716 | 87 | """
# File : Generation.py
# Description:
"""
from Generation.pred_spec import Generator
from TemplateSearch.QueryDB import QueryTemplates
from Model.Configs.config import Config_databse
import numpy as np
import pandas as pd
import faiss
def get_meta(meta_dict, key):
for k, v in meta_dict.items():
... |
aa7efdd47f35607f227f1e92de1b4c7516048aebaa1a86b56e31915cf3173b56 | Python | 3,716 | 114 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
1074c8e0a625dd1025cccd8ef41b997d12efda4c739eeb49b4658cb9b25897c8 | Python | 3,723 | 108 | import unittest
import numpy as np
import pytest
from shapely.geometry import MultiPolygon, Point, box
@pytest.mark.filterwarnings("ignore:The 'shapely.vectorized:")
class VectorizedContainsTestCase(unittest.TestCase):
def assertContainsResults(self, geom, x, y):
from shapely.vectorized import contains
... |
7e80e2917d0a05871f05a99fd06e5459c26cb400db36b441eeafd363b4e845d7 | Python | 3,724 | 104 | """
Evaluation module for the GO Annotation agent.
This module implements evaluations for the GO Annotation agent using the pydantic-ai-evals framework.
"""
import asyncio
import sys
from typing import Optional, Any, Dict, Callable, Awaitable
from aurelian.evaluators.model import MetadataDict, metadata
from aurelian.... |
ee2706e93cc2af5bcd8243078eeebad8871e4503ac12de572e48a99f26f11e5c | Python | 3,726 | 95 | """
Agent for working with bibliographies and citation data.
"""
from pydantic_ai import Agent, RunContext
from .biblio_config import BiblioDependencies
from .biblio_tools import search_bibliography, lookup_pmid, search_web, retrieve_web_page
biblio_agent = Agent(
model="openai:gpt-4o",
deps_type=BiblioDepen... |
0e721cb5ff7c2374871e323ecf5daaecaa9e60fdf489bfeb599c6ecc6ae161ba | Python | 3,731 | 123 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import MDAnalysis as mda
import numpy as np
import pytest
from openfe.protocols.restraint_utils.geometry.boresch.guest import (
_bonded_angles_from_pool,
_get_guest_atom_pool,
_so... |
28dfe18da3d6b9c172ddf13311f47ee8822e906438e4210c3d51b1eb9298ea77 | Python | 3,731 | 101 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2021 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
a52015895fdef6062d34af1da4eeb77c7a6d5bbe153fc191f85cd6b6a3c93e8e | Python | 3,733 | 113 | """Tests for the upsampling layers"""
import numpy as np
from tensorflow.keras import backend as K
from keras import keras_parameterized
from deepcell import layers
@keras_parameterized.run_all_keras_modes
class TestUpsampleLike(keras_parameterized.TestCase):
def test_simple(self):
# channels_last
... |
aecfcc629bc5f76991365c0afb7b5886e7dbf576eba7631ab095fb894f32e7d7 | Python | 3,733 | 141 | # Ensure QCPortal is imported before any OpenEye modules, see
# https://github.com/conda-forge/qcfractal-feedstock/issues/43
try:
import qcportal
except ImportError:
qcportal = None
import multiprocessing
import os
import shutil
import pytest
from openff.interchange._tests import MoleculeWithConformer
from op... |
e4b532865c62d52fa8bd6645dea6499a86b1311039840dc1dc96020413f6bbaa | Python | 3,736 | 107 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import pytest
from rdkit import Chem
from rdkit.Chem import AllChem
import openfe
from gufe import SmallMoleculeComponent
from openfe.setup.atom_mapping import LomapAtomMapper
from .conftes... |
0332fbfcf7b635569c9eb27741346b5135077bb438d862ba52fbd6ec263ad166 | Python | 3,738 | 96 | import argparse
import itertools
import numpy as np
import os
import pickle
from analyses.decoding.ridge_regression_decoding import RIDGE_DECODER_OUT_DIR, TESTING_MODE
from data import get_fmri_voxel_data
from eval import calc_rsa, calc_rsa_images, calc_rsa_captions, create_dissimilarity_matrix, rsa_from_matrices
fr... |
b157f037d743498418b1cad3a831f0e88315552ea079c5d3d8c27f2638cba470 | Python | 3,738 | 112 | import json
import logging
from pathlib import Path
from multiqc.base_module import BaseMultiqcModule, ModuleNoSamplesFound
from multiqc.plots import bargraph
log = logging.getLogger(__name__)
class MultiqcModule(BaseMultiqcModule):
"""
The module recognizes files with the `*_gopeaks.json` suffix (which is ... |
316ca2ca89fee845677c8e15c600c6ada00f3eccf0a48f07069104679bc5add6 | Python | 3,740 | 136 | import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal
from openff.nagl.features.atoms import (
AtomAverageFormalCharge,
AtomConnectivity,
AtomFormalCharge,
AtomHybridization,
AtomicElement,
AtomInRingOfSize,
AtomIsAromatic,
AtomIsInRing,
)
from openff.... |
38f30d54922ad835e085639d35b49b14b2ad905f32477f162b61407d485b386a | Python | 3,748 | 122 | #!/usr/bin/env python
__author__ = 'Pavel Polishchuk'
import rdkit
from rdkit import Chem
import argparse
import sys
def read_pdbqt(fname, sanitize, removeHs):
mols = []
with open(fname) as f:
pdb_block = f.read().split('MODEL ')
for j, block in enumerate(pdb_block[1:]):
m = Che... |
e3be8fd7460e964e12147ed2e206b255d8283e14c6e2d83eef9bdf3b77307ed6 | Python | 3,750 | 96 | import logging
import re
from multiqc import config
from multiqc.base_module import BaseMultiqcModule, ModuleNoSamplesFound
log = logging.getLogger(__name__)
class MultiqcModule(BaseMultiqcModule):
def __init__(self):
super().__init__(
name="leeHom",
anchor="leehom",
... |
2f72b2d00bdfc6750e6f3885cbccfedd0a7200c855b8a708e0d804d7015b449a | Python | 3,752 | 104 | ###################### Libraries ######################
import numpy as np
# Deep Learning
import tensorflow as tf
from keras import backend as K
import warnings
warnings.filterwarnings('ignore')
from tensorflow.keras.layers import *
import tensorflow.keras.backend as K
###################### Loss Functions #####... |
a494c1f6f42ca3cb0bdbec7207815d3dc7eaca27c0836a1a287983493ecbdec7 | Python | 3,754 | 109 | import os
import argparse
import numpy as np
import nibabel as nib
def compute_skewness(x):
x = x[np.isfinite(x)] # Optional: remove NaNs or infs
mean = np.mean(x)
std = np.std(x)
skew = np.mean(((x - mean) / std)**3)
return skew
def compute_kurtosis(x):
x = x[np.isfinite(x)]
mean = np.me... |
c20ef3806b019ba44dfae08db640ba71560a2a692ce66a291425aef49599c578 | Python | 3,754 | 94 | import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
def cellRecordsPreprocessing(file_path, experiments_start_triggers):
# Open the file in binary mode
with open('./'+file_path, 'rb') as file:
loaded_data = pickle.load(file)
print("Loaded Data Dictionary Keys:", loaded_dat... |
86ccfefd950a72a3e11a24898c87b023cc3f4f4f9b07d800ae1cef064596195a | Python | 3,761 | 117 | from __future__ import division
from __future__ import print_function
from future import standard_library
standard_library.install_aliases()
from builtins import range
from past.utils import old_div
import os, sys, glob, subprocess
import numpy as np
from scipy import loadtxt
from matplotlib import pyplot
def get_mm... |
5be387aef4efa8c59dcebee1d8dc986d871327420ae726257eb082de1612381f | Python | 3,771 | 85 | from openmmtools import states
from openmmtools.states import GlobalParameterState
class SepTopParameterState(GlobalParameterState):
"""
Composable state to control lambda parameters for two ligands.
See :class:`openmmtools.states.GlobalParameterState` for more details.
Parameters
----------
p... |
8c12bdec14b06e1499db92dbf7db07fd45654859e42a699748333943ff5ffa5c | Python | 3,771 | 108 | import os
import pickle
import pandas as pd
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from utils import COCO_IMAGES_DIR, STIM_INFO_PATH, LATENT_FEATURES_DIR, \
model_features_file_path
class CoCoDataset(Dataset):
r"""
Pytorch dataset that loads the pres... |
ef2fcf874c8d4aeb29f0e4d644d320d14c88e6365f34520a7cc132bb854b9bcc | Python | 3,785 | 116 | import pytest
from copy import copy
import uvicorn
from alchemiscale.settings import get_base_api_settings
from alchemiscale.base.api import get_s3os_depends
from alchemiscale.compute import api, client
from alchemiscale.tests.integration.compute.utils import get_compute_settings_override
from alchemiscale.tests.int... |
8d19907e85f9169953f86574ad4a427de69316b0f27d912b65b840d0bfe5af67 | Python | 3,789 | 130 | #!/usr/bin/env python3
"""
Batch evaluation of spatial correlations between subject-specific and
normative MPmoments.
Automatically detects all subject MPmoments files in a directory and
computes spatial Pearson correlations for matching moment rows.
Expected filename pattern:
sub-XXX_space-fsaverage5_desc-MPmom... |
8ec99d4cc9f4b30e98d28de9dadb928fbc1ceffa5904f38325796a635e21dcaa | Python | 3,789 | 106 | import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import scanpy as sc
from sklearn.preprocessing import MinMaxScaler
from scipy.sparse import csr_matrix
import numpy as np
def calc_marker_gene_score(adata, target_cluster, n_genes, p_val_threshold, cluster_column, use_raw=True, n_hvg=2000):
... |
061e3277a12ae1b2a961036ae32b7d1df4137cd9e13724f500502a516f2a5e2e | Python | 3,791 | 94 | # -*- coding: utf-8 -*-
import sys
import gzip
import string
import os.path
import glob
count = 0
class FastaWriter(object):
def __init__(self, sourceFastaFilename, qUseOnlySecondPart=False, qGlob=False, qFirstWord=False, qWholeLine=False):
self.SeqLists = dict()
qFirst = True
accession = ... |
548dbf8a4d48bdac840f11c17265656d085056b597db236d2d7af4cb7fd4998c | Python | 3,791 | 105 | """MultiQC submodule to parse output from Picard OxoGMetrics"""
import logging
from collections import defaultdict
from typing import Dict
from multiqc.modules.picard import util
# Initialise the logger
log = logging.getLogger(__name__)
def parse_reports(module):
"""Find Picard OxoGMetrics reports and parse th... |
65974003bf5a3bf7e931c7155ada55637e75ff3f7af65c394375090ecb12a44e | Python | 3,791 | 88 | # Copyright 2021 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
8200732aa7c164f9b70462932451b9450c68f13db0f6761b8bcd2a66d66f23c3 | Python | 3,791 | 117 | from dataclasses import dataclass, field
from typing import Callable
import numpy as np
from pathlib import Path
from loguru import logger
from sl.datasets.nums_dataset import PromptGenerator
from sl.datasets.data_models import DatasetRow
from sl.llm.data_models import SampleCfg
from sl.llm import services as llm_servi... |
21d0a67ac246bdcf9c2942bd3d9eb898a44f98be39c107787b0679dbfecae1d0 | Python | 3,794 | 115 | import h5py
import numpy as np
import pickle
import zlib
import os
from typing import *
def _obj2uint(obj: object, compression: int=9, protocol: int=2) -> np.ndarray:
"""Transform a python object in a numpy array of uint8
Arguments
---------
obj: object
The object to encode
compressio... |
9191331bebde6fcef9eadefc696228da2605b099ad9875bc86a5a43710975bd7 | Python | 3,794 | 98 | import pytest
from shapely import Point, Polygon, geos_version
def test_format_invalid():
# check invalid spec formats
pt = Point(1, 2)
test_list = [
("5G", ValueError, "invalid format specifier"),
(".f", ValueError, "invalid format specifier"),
("0.2e", ValueError, "invalid forma... |
50e250a21fd6c77371302dd5549d58deb276349ac440643d78017d9b04b17a4d | Python | 3,795 | 97 | from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNClassifier
import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transf... |
0e824a0423fb5c1f0750102273fc322aa5ff81644ae25c53ee9eb1ecaa399ef5 | Python | 3,796 | 126 | """Plugin base classes and basic instances thereof.
Also discovers and loads KIMMDY plugins.
"""
from __future__ import annotations
import logging
import sys
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from kimmdy.config import Config
from kimmdy.recipe i... |
276d46396072676046aca6a3a0a5094d706fcdff2cd61acf1feb7085381ca041 | Python | 3,800 | 88 | # This script plots the decoding performance when randomly dropping X channels.
import argparse
import os
import numpy as np
import pickle as pkl
from datetime import datetime
import matplotlib.pyplot as plt
'''
Example cmd (when run from this directory; provide python script path appropriately if run from different ... |
b9885dcdb7943604093773047745c1e6de85b074efd7cb3497e4b71eb378a535 | Python | 3,805 | 103 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os, re
from os import path
import argparse, textwrap
from mripy import utils, io
def parse_fname(fname, pattern='hemi', trailing_exts=['.gz'], compound_exts=['.gz']):
'''
{dir}/{hemi}.{stem}{ext}
{dir}/{stem}{view}{ext}
'''
res = dict()
res[... |
c0d45c20b13f9677527503743d316c08327a51c32ecd5edff0070aad06fe66d9 | Python | 3,809 | 113 | import pandas as pd
import numpy as np
import nrrd
import pickle
from nemsi.visual import PlotterWindow
from nemsi.spatial import Mesh
def align_bounding_boxes(source, target):
source_barycenter = (np.min(source, axis=0) + np.max(source, axis=0)) / 2
target_barycenter = (np.min(target, axis=0) + np.max(target... |
db11a6e97b41011a1f115bcbb47025d53b9f6ab889b0bd683e727421375414b2 | Python | 3,810 | 116 | import logging
from typing import Dict, List
import requests
from utils.translator_utils import (
ENDPOINT_URL,
PREFIXES,
build_sparql_query,
get_normalized_curies,
run_query,
uri_to_curie,
)
logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)
logger.setLevel(loggin... |
53fc95ca7d18ee02bbf47e20554811d18383f55f2c695d862e6d39c151944e50 | Python | 3,811 | 98 | # code to create a version of the Juelich atlas for each subject in their own native T1w space. By using NN interpolation we can retain the original label values.
# file to run from within the nipype environment with ANTS enabled; from the MPI cbs system this is a mess - getserver -sL / ANTSENV /
# conda activate nipy... |
2dc4aa0d090cf6fa16fa14fcc3872cbb01d208b111c44e05d1d581ec4b3bf1a1 | Python | 3,813 | 80 | #!/usr/bin/env python3
__author__ = 'Pavel Polishchuk'
import argparse
from functools import partial
import sys
from read_input import read_input
from rdkit import Chem
from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers, StereoEnumerationOptions
from multiprocessing import Pool, cpu_count
def enum... |
63a1db138432864176b5e9bec5b2665db3aa3d0dc0f9ba5f09a72bc31470efa0 | Python | 3,813 | 85 | import sys, os
def make_log_useful(log_path, status, config, config_definitions):
# Snakemake v9 passes log as a list
if isinstance(log_path, list):
log_path = log_path[0]
logs_processed_dir = "/".join(log_path.split("/")[:-1]) + "/processed_logs_for_mail/"
os.makedirs(logs_processed_dir, exis... |
117fe877a2a6189b36f01c696e07e71ebdc5ab358ab2ab5539568b34cb0a4367 | Python | 3,815 | 128 | from gufe.protocols import ProtocolDAGResult
from gufe.tokenization import (
GufeTokenizable,
TOKENIZABLE_REGISTRY,
get_all_gufe_objs,
is_gufe_obj,
modify_dependencies,
)
from itertools import chain
import networkx as nx
import zstandard as zstd
from .compression import decompress_gufe_zstd, json_t... |
6a4bc81375efdbe75c076a600e19ec469617e648cd8e9f2abf4fbfa5eb71e9f2 | Python | 3,815 | 128 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 22 11:16:24 2023
@author: ashwin.bhandiwad
"""
import os, re
import numpy as np
import pandas as pd
from anytree import Node,RenderTree
import SimpleITK as sitk
def db_to_tree(swc_db):
names=swc_db[:,0].astype(int)
assert swc_db[0,-1]==-1
soma_coords = [swc... |
0e620e76225827fdd76c3d310b4d7208db498f4858d57c8c4d06b379becc7401 | Python | 3,816 | 108 | """
Copyright (C) 2025, 2026 Sotiris Lamprinidis
This program is free software and all terms of the GNU General Public License
version 3 as published by the Free Software Foundation apply. See the LICENSE
file in the root directory of the project or <https://www.gnu.org/licenses/>
for more details.
"""
import os
impo... |
c50aaf15cc230b49ccc26be69902aa4649098dae62a636616ad545dc47c0357a | Python | 3,819 | 86 | #!/usr/bin/env python3
__author__ = 'Pavel Polishchuk'
import argparse
from rdkit import Chem
from rdkit.Chem import inchi
from read_input import read_input
def get_inchi_key(mol, stereo):
inchi_key = inchi.MolToInchiKey(mol)
if not stereo:
q = inchi_key.split('-')
inchi_key = q[0] + '-' + q... |
27b011a1fcc90fcaf049996829ec5fc25076eea61e9984f554ab2da44e14a980 | Python | 3,826 | 84 |
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import gensim
import numpy as np
from TemplateSearch.Embedding import GenerateSpec2vec
from MS2Tools.util import CalSpecVec
import faiss
from rdkit import Chem
class QueryTemplates(object):
def __init__(self, Embed_dict:dict=None, index_dict:dict=None, d_model=5... |
1eb8da63691f27f6efeb53246f695b467092a0ea4c7b5f5d996c4ce2e8fbdb1d | Python | 3,829 | 140 | #!/usr/bin/env python3
"""
ATAC-seq preprocessing command for Hi-Compass.
"""
import logging
from pathlib import Path
import json
from ..preprocess import ATACPreprocessor
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
def ... |
9c7bf99fd4208ca3b19eba1d99090c08763fd387277eb91b6b6eddcf9c22c149 | Python | 3,829 | 100 | from __future__ import print_function
import argparse
import itertools
import regex
import re
import gzip
import sys
import collections
from findCleavageSites import regexFromSequence, alignSequences, reverseComplement, extendedPattern, realignedSequences
"""
FASTQ generator function from umi package
"""
def fq(file)... |
6f22cc0e0ba3fe8b155794389b5169fc8d667a0666dbc4f3577615a22e6a6b54 | Python | 3,831 | 152 | """
MCP tools for interacting with GO KnowledgeBase via AmiGO solr endpoint.
"""
import os
from typing import Dict, List
from mcp.server.fastmcp import FastMCP
from aurelian.agents.amigo.amigo_agent import SYSTEM
import aurelian.agents.amigo.amigo_tools as at
from aurelian.agents.amigo.amigo_config import AmiGODepend... |
5515ef6997af5691a08ab86fbcc1e1620b59a6816976b76d924168ff2d86f6f0 | Python | 3,836 | 120 | import os
import numpy as np
import helper as hp
import multiprocessing as mp
from configparser import ConfigParser
from evostrat.init_mlp import MLP
from evostrat.evolution_strategy import EvolutionStrategy
from kinetics.jacobian_solver import check_jacobian
# declare reward functions
def reward_func(weights):
... |
fcf6f0dea113708c0c5aa93437d884b148e17f8d72c4468d9c00cbd79ed4ad7e | Python | 3,836 | 92 | '''
Script for evaluating image quality metrics on the challenge test submissions
'''
import numpy as np
import subprocess
import os
from Utils import RegisterNifti, Comp_Metrics
# define directories etc:
subm_dir = 'Submissions' # path to the folder containing subfolders for each
# pa... |
e3adbf7d4d3946eb78bbc3681972cae39d7ee6523571d10e1bb2de712e3687b7 | Python | 3,837 | 123 | #! python
# -*- coding: utf-8 -*-
from typing import Dict, List, Optional, Union
import pandas as pd
from gseapy.base import GSEAbase
from gseapy.gse import gsva_rs
from gseapy.utils import mkdirs
class GSVA(GSEAbase):
"""GSVA"""
def __init__(
self,
data: Union[pd.DataFrame, pd.Series, str... |
fc1b13fa14c98eff74257a6b84f0237fd887a12a1f88f88a5450c28da869bb6f | Python | 3,837 | 114 | """Tests for the Logger class in ethopy.core.logger module.
These tests verify the functionality of the Logger class while properly
handling thread cleanup to avoid test hangs.
"""
import time
from queue import PriorityQueue
import pytest
@pytest.mark.usefixtures("patch_imports")
class TestLogger:
"""Test Logge... |
1992d17873fa151467e3786f48ea060b161a984acacf2a7a460390c55782de48 | Python | 3,839 | 107 | ######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Communicator client code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 1998
# the Initial Developer. All Rights R... |
f045c98285f8432bbfcb86ff9e850df07b1ec5ccdaa274e73748c0e756d9d71d | Python | 3,839 | 110 | import os
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
from nibabel import GiftiImage
from nibabel.gifti import GiftiDataArray
from nibabel.nifti1 import intent_codes, data_type_codes
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.expanduser("~/data/multimod... |
2edd778414aa76cf86422aa28b3120b671c893d73b9f60f135f39cbe018415e4 | Python | 3,840 | 69 | import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * (level)
print('{}{}/'.format(indent, os.path.basename(root)))
subindent = ' ' * 4 * (level + 1)
for f in files:
... |
e423c8015210918c44b25b6e8c82ffad45e0be5d3419a6656e2c4c33adc9959a | Python | 3,842 | 85 | """
Offline Brain Area Analysis Script
This script loads offline decoding predictions for different cortical area groupings,
computes success rates, and compares them to online simulations.
Usage (via CLI):
python run_offline_brain_area_analysis.py --monkeys Monkey1 Monkey3 --experiments "Center-out" "Continuous ... |
65923e70b062275d338d01bc1ac6dd3d9c1063b127a463080ec8e3e4fe171470 | Python | 3,844 | 112 | import os
import numpy as np
import nrrd
import math
from sklearn.decomposition import PCA
from scipy.spatial.transform import Rotation as R
from scipy.spatial import distance
from trimesh.convex import convex_hull
# pylint:disable="import-error"
from nemsi import Subtree
from nemsi.visual import PlotterWindow
from ne... |
68208d241148a30bdeb4d569492306be677738f599708f4e56a9c2962e5f97be | Python | 3,844 | 106 | # !/usr/bin/env python
# -*-coding:utf-8 -*-
import torch
class Vocabulary(object):
def __init__(self,
*,
pad="<pad>",
unk="<unk>",
sep="<sep>",
mask="<mask>",
special_tokens=None,
add_special_toke... |
f7ca3408bfce3547cb2dbf4c3a99cac1c3e11e684ed3fff64c1ce64064788724 | Python | 3,844 | 129 | # This code is part of cinnabar and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/cinnabar
import math
from typing import Iterable
import numpy as np
from numpy.typing import NDArray
def _create_2d_histogram(y_true: Iterable[float], y_pred: Iterable[float]) -> tuple[NDArray... |
c2c9d3595a63f78203235944558fd220d0518998dc58036963d99a354e96957e | Python | 3,848 | 103 | from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNClassifier
import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transf... |
05f086e8821ef31e5a0fea3769289c9462bcdcc20dfebbd3b0ba0319d4b544c1 | Python | 3,852 | 93 | """" Theo Gauvrit
05/06/2023
First test of Cebra library"""
import os
import cebra
import json
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import matplotlib
import matplotlib.pyplot as plt
from multiprocessing import Pool, cpu_count, pool
plt.rcParams['font.size'] = 10
plt.r... |
72279793a7ceb22fa905a9151b6ce4dd1513ed139de66e219aa20cd25b78ae62 | Python | 3,853 | 86 | # !/usr/bin/env python
# -*-coding:utf-8 -*-
# @Time : 2023/04/26 11:37
# @Author : Liangdi.Ma
import torch
from transformers import BertConfig, BertModel, BertLMHeadModel
path_dict = {
"bert": "/GPUFS/gyfyy_jxhe_1/User/maliangdi/models/bert",
"chinesebert": "/home/gaozebin/project/ddpm_clip/pre... |
4ded904ff3b7545bddb6ef9dbdda06a55f201a5b20d342677e02e0286870156d | Python | 3,856 | 92 | from __future__ import division
from __future__ import absolute_import
from builtins import str
from builtins import range
import os
import forcebalance
import numpy
from .__init__ import ForceBalanceTestCase
class TargetTests(ForceBalanceTestCase):
def setup_method(self, method):
super(TargetTests, self)... |
edff1dfede72dc28f244c1a018264fa35b975490198bc51f7cea3e9209805208 | Python | 3,859 | 133 | """Models used within YAMMBS."""
from typing import Any, TypeVar
import qcelemental
from openff.toolkit import Molecule
from pydantic import ConfigDict, Field
from yammbs._base.array import Array
from yammbs._base.base import ImmutableModel
hartree2kcalmol = qcelemental.constants.hartree2kcalmol
bohr2angstroms = qc... |
b2e2aa552abf3847e9fb185ce3a9972fdca73028237bbb1ec78a73ff6b405dc6 | Python | 3,860 | 95 | import argparse
import os
import re
import glob
import shutil
import subprocess as sp
from tempfile import TemporaryDirectory
from contextlib import contextmanager
# YAML imports
try:
import yaml # PyYAML
loader = yaml.load
except ImportError:
try:
import ruamel_yaml as yaml # Ruamel YAML
exce... |
c3d86122abcb5d6a6d993a838e6428c4a1acc34a690554502cb3b825d67038a8 | Python | 3,862 | 105 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import numpy as np
import pytest
from numpy.testing import assert_equal
from openff.interchange.components._packmol import UNIT_CUBE
from openff.units import unit
from pontibus.utils.settin... |
150dc6a990b7e224d6252f999e1686d56c381fe713e5d8e466d0f4f148b3c3fd | Python | 3,865 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
21cfa3658df1a4c2231b4e36d42cf3d6a8f70cc1bfedf1a684b0591815fcf4ad | Python | 3,865 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
5d7485697a51b6d21fadbcf42e061f1ebdb475bd7bd011649d6d05cd64ef6100 | Python | 3,866 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
b8a01c2f310d90aedc7bc23cce0e3cf93edac674972a196a0f6a47a1e1fdf452 | Python | 3,866 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
22fd39523ec4409a8a5bc842fd6371ec01483660ec20794b663f58d15504700b | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
3f2b9a00e4778869072d7e501d768e59ca3940dfc43e32e6cbd7126fc1fd6d37 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
5b8ecbbe62ba481ba1769b3063f842136aa17daf132c1dee972b2952a50030b3 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
8410a027f44ec113024329dde782bc97395ad57a3987c43d3b0cebfa47083563 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
8f64097558d057cea3bf066d7482430a390cdd13f7f29465f7704242db9a42c3 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
e08e25ab09511dff472e4af82bebdf02730ed196da0cc48979515d8cb7ff97b4 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
e704ae78c247ba689c5debbd6150694d54bb021b067f4d7fe169e4cefb3a7a5f | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
fa3ca9d6f084b97b9cfcfe77f31e3ea790f248954763004c877c9b323ba0be89 | Python | 3,867 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
802764f74189e6dbe15f8ad7c949b5229f824f623a66ca5871ae755358b323f8 | Python | 3,868 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
99f738ba2c23ced6bbc90bc307fcbed9862ea51d302d7ce186ed2088583030c8 | Python | 3,868 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
fa41e7aa37619a48a6cd68a0f39bc9bcebf31e808ca06e1a1e15005b825ba04f | Python | 3,868 | 126 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
70a8ee0600174f33298d94e2a5928223e8aefc3b3a2e62f9667b87351a85c071 | Python | 3,871 | 109 | import functools
from itertools import chain
from wikidataintegrator.wdi_core import WDItemEngine
from data_tools.df_processing import char_combine_iter, expand_col_on_char, add_curi
def parse_result_uris(result):
"""Parse the result URI to get just the WikiData identifier"""
for c in result:
if 'Labe... |
7b103cf7a98d4199e79550500fe451f6376be47d92814a68db47e54d3b2b4435 | Python | 3,874 | 102 | """
components/datasets/mock_dataset.py — Mock Dataset for Framework Smoke Testing
==============================================================================
Generates synthetic data in the correct Batch format.
Use this to verify that the framework pipeline runs end-to-end without
requiring real patient data.
Reg... |
350b2fd85d05ee532cb8ad566b149be1bd3800211693c66ca506fff9761ad90d | Python | 3,877 | 97 | from bio_embeddings.embed import ProtTransBertBFDEmbedder
import pandas as pd
import pickle
import time
import os
import lmdb
import hashlib
import numpy as np
from tqdm import tqdm
# start_time = time.time()
# Application Execution
df_human_prot_fasta = pd.read_csv("pro_id_seq_human.csv")
print(df_human_prot_fasta.co... |
b1bfd7d6197adaddc0c270d67345629ce955da842d88e05361c26652c1b46cd6 | Python | 3,877 | 98 | """MOP molecular process recognition evaluation cases from CRAFT corpus."""
from typing import Any, Dict
from aurelian.evaluators.model import MetadataDict, metadata
from aurelian.evaluators.knowledge_agent_evaluator import SimpleEntityEvaluator
from pydantic_evals import Case, Dataset
class MopMetadata(Dict[str, An... |
d110ac05cc8ebfb8f19025895f4728d1159723a99afc8aa69b751e1c5ffcac0c | Python | 3,881 | 97 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import, unicode_literals
import subprocess, os
import numpy as np
from collections import OrderedDict
from . import six, afni, io, utils
def afni_costs(base_file, in_file, mask=None):
'''
In general, the al... |
7c251508512c20b095e8fa9052064084d2911e755eec1ca9c10334158b920846 | Python | 3,884 | 106 | #!/usr/bin/env python
#
# MIT License
#
# Copyright (c) 2018 Volker Hovestadt
#
# 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, including without limitation the rights
# ... |
14aff13d1247cc38d5662fcea2e7b444840f45988e130b9e5cdc07029be436f9 | Python | 3,885 | 129 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
2e85695449b7bc03306212802957a8f0ae1f5f6c59ef95dd541cc8b809511ff3 | Python | 3,885 | 129 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
7956e7e69f9538402c7676477b4c1c5e414396f32bf991d3455de249e69fbf7d | Python | 3,885 | 129 | import pathlib
import os
import pandas as pd
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.... |
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