sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a6b1a7eb70a81f8f6d440c39a41bad5a17b7be31f8b608356794f1db1896cbd2 | Python | 15,574 | 470 | from __future__ import annotations
import contextlib
import logging
import shutil
from datetime import datetime
from datetime import timedelta
from datetime import timezone
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from zipfile import ZipFile
import pytest
from packaging.metad... |
949b136edab521d797e28bb065650a3f7cd5fd7e594a8ba8ebabfe590d04673d | Python | 15,577 | 304 | #三个头0414
import logging
import argparse
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
import dataload.meta_datasets as data
import utils.gpu as gpu
from utils import cosine_lr_scheduler
from utils.log import Logger
#from modelR.meta_lodet_hbb import... |
5252cbf94f1335ead21ba74d1e196f56614434e4ee8f1ea9a5e86e94f9eea7f7 | Python | 15,579 | 384 | import itertools
import json
import re
import shutil
import typing
import webbrowser
from pathlib import Path
from typing import Literal, Union
import networkx
from pyvis.network import Network
from rnalysis import __version__
from rnalysis.exceptions import InvalidValueError
from rnalysis.gui import gui_windows
from... |
28e7defb67ee37ccf4a7acf888510f0c6be46144851f0a5c76f13666c0e1bc21 | Python | 15,584 | 333 | from __future__ import annotations
from time import time
from typing import Union, List, Tuple, Type
import numpy as np
import torch
from acvl_utils.cropping_and_padding.bounding_boxes import bounding_box_to_slice, insert_crop_into_image
from batchgenerators.utilities.file_and_folder_operations import join
import nnu... |
b6806bb81913116ef915887d9e0edccd77dc17fb806ede17256bbca0a844910f | Python | 15,584 | 476 | import random
import numpy as np
from deap import base, creator, tools, algorithms
from scipy.optimize import newton
from scipy.optimize import minimize
import pandas as pd
from numba import jit
import datetime
import time
import os
import multiprocessing as mp
from functools import partial
Lr = 0.013
gr = 1.
# =====... |
4deb69ddad3cb222d8ac2263580c0a2795681a251cea2a15977a502f811cb6cd | Python | 15,589 | 497 | #!/usr/bin/env python3
"""
Allen NWB Data Loader for NEMO Benchmark
Provides utilities to load spike times, waveforms, and metadata from
Allen Brain Observatory NWB files. Supports both local NWB files and
remote S3 access (when S3_BUCKET / S3_PREFIX / S3_ENDPOINT are set).
Usage:
# Local NWB file (default workfl... |
27af7b3513e422748bfafc8b44c5019e77231ce376e81143ac3ceb1240010f4d | Python | 15,593 | 529 | import unittest
import numpy as np
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy import config
from pgmpy.factors.discrete import DiscreteFactor, TabularCPD
from pgmpy.models import DiscreteBayesianNetwork, DiscreteMarkovNetwork
from pgmpy.readwrite import UAIReader, UAIWriter
class Test... |
4f950a343e6c73cb780e6c6643ba289fe5c9c04ce70186b8890c533dfebb0a94 | Python | 15,596 | 406 | #!/usr/bin/env python3
"""Copy and simplify code form original repo."""
import numpy as np
import numpy.typing as npt
from tqdm import tqdm, trange
####################################
# FROM simulator.py
####################################
def correlated_poisson_spike_train(
num_neurons: int,
firing_rate... |
afe81faab1fa88556b8665953f27598e6ab17c5b8dabd1e25fb16b79b0786649 | Python | 15,619 | 355 | from typing import Dict, Tuple, Optional
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgr.models.phc_net import (
ModalityEncoder,
CrossModalFuse,
B0SpatialAttention,
)
class ResidualBlock(nn.Module):
def __init__(self, ch: int) -> None:
super().__init_... |
34a6946f3c9aa9109f66d6fe737a41049689328e56cc4aae57d12069367c8471 | Python | 15,626 | 478 | import torch
import torch.nn as nn
import timm
import numpy as np
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
sel... |
0718bbc74474932a3fca2b3669b71548cc94ffbd78f5392ca774e34a85523179 | Python | 15,629 | 350 | import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import os
from skimage.segmentation import mark_boundaries
from matplotlib.patches import Wedge
import matplotlib.collections as collections
import decord
import click
import matplotlib
import zarr
from ..utils.decorators impor... |
196816b5c825e88cd9b82127880d631ea0ae63aee73e9acc4f188afb44e0e1c9 | Python | 15,640 | 434 | import collections
from os.path import basename, dirname
from os.path import join
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import gridspec
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from sklearn import metrics
from config_path import PROST... |
239b35097ac93dea27dfd4be4af364474c86b3f8229d2a05ed3a8da761e3a1b2 | Python | 15,640 | 397 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
# TODO:
# - make this "exchangeable" by shuffling all columns but the last (requires handling packed_sequence separately)
class RNN(nn.Module):
def __init__(self, input_size, output_size, num_layers=2, dropout=0.0):
"""
... |
0d995c2bbc2bda017fb449130d1a6b099ad6ee94aa1364489d870209e8b4ff66 | Python | 15,648 | 434 | import collections
from os.path import basename, dirname
from os.path import join
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import gridspec
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from sklearn import metrics
from config_path import PROST... |
83f5d3293b23baad2ea9ba7b07c2cc0a5c75d2629c883e77f15215a79925cff1 | Python | 15,655 | 423 | """Creation and loading of models."""
import abc
import importlib
import itertools
import os
import pathlib
import tempfile
import pysam
import requests
import torch
import medaka.common
import medaka.datastore
import medaka.options
logger = medaka.common.get_named_logger('ModelLoad')
class DownloadError(ValueEr... |
f32ad24e0651fcc265bea950890e3da6666164ce53b4fae5937e6d5bf8adf379 | Python | 15,657 | 464 | from typing import Optional, Dict, Any, List, Tuple
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from scipy.interpolate import splprep
from matplotlib.patches import Wedge
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.collections import LineCol... |
457f636ad85d3b5c0979f6b8d7bddfbb4bf0b6ae502a09cbc781f746cba5524f | Python | 15,658 | 452 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
from dataclasses import dataclass,... |
e9ebdd79ae56bcab01d3f6844d6e5a43f8d599fed21fcab1f4ab7fded722bdc6 | Python | 15,660 | 411 | #!/usr/bin/env python
import argparse
import logging
import os
import pathlib
import warnings
from collections.abc import Mapping
from typing import cast
import h5py
from d3text import (
checkpoint,
data,
encodings_store,
factory,
linking_corpora,
runtime,
token_labels,
tracking,
)
fr... |
4b871cc675bd45424a7561292443f6f20a6f2c4d194cbb44a9d27931b675f6ee | Python | 15,681 | 532 | """
This is a modified version of the code present in:
https://github.com/nanoporetech/pipeline-umi-amplicon/blob/master/lib/umi_amplicon_tools/extract_umis.py
"""
import argparse
import logging
import os
import edlib
import pysam
import sys
def parse_args(argv):
"""
Commandline parser
:param argv: Com... |
161791690e02b68b74bef6c8b4b87503120c058650ce3ac43dfe3888d9dc142d | Python | 15,685 | 443 | # 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 math
import numpy as np
import torch
from . import FairseqDataset, data_utils
def collate(
samples,
pad_idx,
eos_idx,
... |
45553ea1c66b02dbc7b54854325ef94dd1eb740f1e37b51ba418d0c2841456e0 | Python | 15,692 | 361 | import matplotlib.pyplot as plt
import numpy as np
import igraph as ig
import networkx as nx
import os
import sys
import seaborn as sns
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
os.chdir('..\\src\\')
sys.path.append(os.getcwd())
from read_graph import read_graph
# datapath = ... |
82e99edaf1548da0acf5ae58f7e23f4c5ffc31819f75b069e03bd5bdf4a6bb87 | Python | 15,710 | 481 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA import ANOVAModel
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
... |
8da3b1004683e02f305aa49e404bf4a1fb4d7f93e68b6aea72d501fe0bcf85c3 | Python | 15,718 | 448 | # 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 logging
import torch
from torch import nn
from fairseq import utils
from fairseq.data.data_utils import lengths_to_padding_mask
from ... |
1873a85fc801a4318ddc12984d55b3605191ed9fbcd84ee6d057191fb11404c2 | Python | 15,720 | 413 | """
Visualization script for weight parameter (ω) ablation study results.
This script generates publication-quality figures showing:
- Performance vs weight value
- Spatial coherence vs weight value
- Graph connectivity statistics vs weight value
- Trade-offs between spatial and expression contributions
"""
import os... |
f25f4b2f329db51508ddf0dafd5abed5fc52a89bd1673470cace3abed9665fd5 | Python | 15,732 | 332 | import unittest
from pathlib import Path
import copy
import re
import tempfile
from GMXMMPBSA.input_parser import input_file
ROOT = Path(__file__).resolve().parents[1]
class LoggingDocumentationTest(unittest.TestCase):
def test_logging_guide_documents_supported_modes_and_cluster_monitoring(self):
guide... |
750fda6f531801239391aaadd866d51bf817d6b4b4b3b641deb8a8c534074466 | Python | 15,746 | 482 | import numpy as np
import pandas as pd
from matplotlib.lines import Line2D
import statsmodels.formula.api as smf
from .paper_ANOVA import ANOVAModel
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", ... |
568d0c761483b4f2b41fad3d7a9305c30da151ac41fe911c7d136d9585eda1a8 | Python | 15,757 | 473 | import numpy as np
import pandas as pd
import pytest
from joblib.externals.loky import get_reusable_executor
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy import config
from pgmpy.base import DAG
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import DiscreteBayesianNetwork
... |
15379accfe4618707c76ea5752ed00dc15e2da4f5b5253a77dc1edf03dc9170e | Python | 15,762 | 450 | """
Utility functions for matching
"""
import pandas as pd
import numpy as np
import pynndescent
from scipy.optimize import linear_sum_assignment
from . import _utils as utils
# modify from Maxfuse: https://github.com/shuxiaoc/maxfuse/blob/main/maxfuse/match_utils.py#L273
def match_cells(arr1, arr2, base_d... |
1606ebd24bb63c63ae4a81c6e6caf79041e45b98868b0388449fabb73100121f | Python | 15,768 | 403 | import re
import os
import sys
import argparse
import numpy as np
import pandas as pd
import pprint
from pprint import pprint
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score
sys.path.append('/scratch/l.lexi/WAPIAW2024/Source_Code/utils_py')
import utils_py
... |
fd845db1fca2a81d942b7f72340eeaf21ee9e7fe6ab4109f87232bd1b5e8de59 | Python | 15,781 | 389 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import argparse
import yaml
import logging
from Simulator_calibration import (ForcefieldBuilder, SimulationBuilder, ReporterAdder,
SimulationRunner, AmpConfigurator, SystemBuilder,
... |
946db64eb1305557a49e2f3ac11ab28fd9ff857bfb16909675dce238ef81c2e7 | Python | 15,805 | 417 | #!/usr/bin/env python3 -u
# 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.
"""
Translate pre-processed data with a trained model.
"""
import ast
import logging
import math
import os
import sy... |
7e290933acf164f6e20f27f13493c68f188c8a8f3f28a3c8e8f3240191b0a5c2 | Python | 15,807 | 373 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from dataclasses import dataclass
from functools import cached_property, lru_cache
from typing import Literal
import numpy as np
import numpy.typing
from pandas import DataFrame
from pymatgen.analysis.phase_diagram import PhaseDiagram
from pymat... |
e69ddc3a4da2ad9d9570a485ef335b2d6b15e8a870c6ac025fbd2a72ac3a7fea | Python | 15,815 | 399 | import os
import torch
import copy
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from ProteinMPNN.vanilla_proteinmpnn import protein_mpnn_utils as utils
from dotenv import load_dotenv
load_dotenv()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DEFAULT_PROTEIN_MPNN_C... |
acba98377ad088545ec62366d39143bc025558cc416d14577b6f58138700405e | Python | 15,824 | 476 | import matplotlib
from rnalysis.utils.ontology import *
matplotlib.use('Agg')
def _stub_graphviz_binary(monkeypatch):
"""Stub out the real GraphViz binary probe so these unit tests stay hermetic.
``render_graphviz_plot`` calls ``graphviz.version()`` - a real subprocess probe of the ``dot``
executable -... |
fd0d690f4cad50b12514f96cacea4d031aa894dc4f98f7aff95bbb33dcaa6f20 | Python | 15,837 | 557 | # -*- coding: utf-8 -*-
"""
Created on Sun Mar 2 21:29:32 2025
@author: hanna
"""
import os
import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sb
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
# from scipy.optimize import cur... |
4014cb69170b366049e7a390e080ec1afcb14907d98f3ef819be651a8fd22577 | Python | 15,841 | 363 | '''Unit tests for core.py.'''
from __future__ import division
from datetime import datetime
import random
import os, os.path
import shutil
import unittest
import numpy as np
from .base_test import BaseTestCase
from .. import core
from .. import datahandler
class TestExperimentA(BaseTestCase):
'''Test Experime... |
ad45c08beec823377f0584a2fef8ff413bb730653986de785e7d2afce0e50273 | Python | 15,845 | 488 | # 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 random
import unittest
import pytest
import torch
from fairseq.modules.multihead_attention import MultiheadAttention, _mask_for_xform... |
40b9c62cc642e524f663d99677805c1dca397035615cc09653106cf9e122c7f8 | Python | 15,867 | 406 | import pandas as pd
import pdb
# sys.path.append("../../corecode/")
from build import *
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib.colors as colors
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from pathlib imp... |
1ebbe9eccbf7d3174e42a86ff42de80a9593891fc30ea5f6ff90507d3d1f2014 | Python | 15,873 | 390 | # code adapted from
# https://nipype.readthedocs.io/en/latest/users/examples/fmri_fsl.html
#
# This is supposed to simulate a FEAT run, but it doesn't actually use
# FEAT, it uses direct calls to all the constituent functions FEAT otherwise
# calls. Constructing this requires careful comparison with feat output
# logs ... |
c909ab036d4fb6e8215d239501682fd77bd6c13eba76026f2e1ffd25bb3cb678 | Python | 15,877 | 352 | #!/usr/bin/env python3
"""A script to generate importance scores in the style of the original BPNet.
BNF
---
.. highlight:: none
.. literalinclude:: ../../doc/bnf/interpretFlat.bnf
Parameter Notes
---------------
genome, bed-file
If you specify these two parameters in the configuration, then this program
... |
9d7573c456dc99bec157baf3f8119adb8efb5cf21aa507ee5e2a6233b66bd7b1 | Python | 15,881 | 436 | import logging
import numpy as np
import pandas as pd
from config_path import *
data_path = DATA_PATH
processed_path = join(PROSTATE_DATA_PATH, 'processed')
# use this one
gene_final_no_silent_no_intron = 'P1000_final_analysis_set_cross__no_silent_no_introns_not_from_the_paper.csv'
cnv_filename = 'P1000_data_CNA_pa... |
f52e1f3342fb80310500e2c656802989b03881ae581a1f2a857f01875cfb2a8c | Python | 15,907 | 429 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Counts cells"""
import os
import traceback
from pathlib import Path
from datetime import datetime
from typing import Literal, Optional
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from tqdm import tqdm
from apply_prev_rdf_mo... |
32829dfd1ef8d94e76416373e6c644c36c300e7942dca1d936cb391f67112424 | Python | 15,908 | 506 | from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib.figure import Figure
from .bokeh_html_plots import render_html
from .df_common import (
analysis_option_from_frame,
read_analysis_output,
set_axis_labels,
)
# ------------------------------... |
0191a84232ad310ec29032931140cd049454bf02a8d45821c7929957a5dea08e | Python | 15,921 | 413 | from typing import Tuple, List
import string
import itertools
from Bio import SeqIO
import numpy as np
import os
import random
import pickle
import pandas as pd
class DataReader(object):
def __init__(self, theme, data_type="seq", use_cache=0, cache_dir=None, pss_type=3):
"""
:param theme: "protein"... |
caca05af2223d39f1b20b47b764587a6f441dd22a9a8c3a5d6b914757a4d71fa | Python | 15,929 | 395 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
import numpy as np
from ...cores.analog_core import AnalogCore
from ...backend import ComputeBacke... |
b6220dc986682ecff0d89bdeabe488feec2b8ced090ce1a737a388f797fc2689 | Python | 15,937 | 375 | """
This is a module that contains functions responsible for mutating
the trajectory file for alanine scanning in gmx_MMPBSA. It must be
included with gmx_MMPBSA to insure proper functioning of alanine
scanning.
"""
# ##############################################################################
# ... |
fe2b961e31a79c5889c19d500a8e8c85beeba15facd2237779a873aff943f3e4 | Python | 15,939 | 404 | """Inference program and ancilliary functions."""
import os
import queue
import threading
from timeit import default_timer as now
from medaka.architectures.base_classes import ReadLevelFeaturesModel
import medaka.common
import medaka.datastore
import medaka.features
import medaka.models
import medaka.torch_ext
def r... |
5e7c4d7dcbea2228869d92ab6e95344cde0dabf00feef8aa11acecf9fee969c1 | Python | 15,951 | 411 | import copy
from typing import Optional, Any
from collections import defaultdict
from tqdm import tqdm
import torch
from torch import nn
from torch.utils.data import DataLoader
from simulation_encoder.logger import Logger
from simulation_encoder.loaders.loader import Loader
from simulation_encoder.models.rbm import R... |
efcc80a7b00f0e44341742650b379c6df8ead57947aab03a2aa0a5fbf9e4919f | Python | 15,959 | 452 | from __future__ import annotations
import io
import re
from dataclasses import dataclass
from typing import Any
import numpy as np
import pandas as pd
from skbase.base import BaseObject
from skbase.lookup import all_objects
from pgmpy.base import ADMG, DAG, MAG, PDAG
from pgmpy.causal_discovery import ExpertKnowledg... |
abd1c3a86941e37c187d2abb0034b7b9723b8a0523b827fec0cd9cdc64b00c01 | Python | 15,972 | 342 | #!/usr/bin/env python3
# 13_perstudy_scRNA_validation_py — generated from notebook spec
# ============================================================
# # 13 — Per-study scRNA validation (tissue + cell-type aware)
#
# Re-runs the inverse-concordant + CO7 panel **inside each study's own
# proper context**:
#
# - *... |
724424dd53d9f3d76aa695f93ddcf13d4598e21f097925f10ef73935ea82d623 | Python | 15,981 | 354 | """Between-participant neural pattern similarity (R4, R4b, R5, R5b).
Faithful port of `pattern_similarity/utils.py` and the four
`pattern_sim_between_subjects*_loop*.py` entry scripts. Dead variants in the
original utils (within-subject, group-level, off-diagonal null, `old_*`) are not
carried over — the reported resu... |
32221dd0c7336105a3bc02750f5651caf0f0fb41552996ed972c2061333afc0b | Python | 15,998 | 394 | # 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.
"""
BART: Denoising Sequence-to-Sequence Pre-training for
Natural Language Generation, Translation, and Comprehension
"""
import logging
from t... |
fd41216d15c3aeeebd440f1af54a463a73c252ff96f7f307fb6e1568d3cbfeba | Python | 16,024 | 435 | import numpy as np
import theano
import theano.tensor as T
def floatX(arr):
"""Converts data to a numpy array of dtype ``theano.config.floatX``.
Parameters
----------
arr : array_like
The data to be converted.
Returns
-------
numpy ndarray
The input array in the ``floatX``... |
5e2835752b5ceeffad77ebbfe9be86bd4d6395c94172ce31d174352429c3a59b | Python | 16,046 | 487 | # 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.
from typing import List, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from fairseq.models import FairseqE... |
8a60602ea27fe2c4056f80de226b7231640db00a8f6aea17be27d366556b3604 | Python | 16,078 | 348 | from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from torch import nn
from torch.nn.functional import linear, one_hot
from scvi import REGISTRY_KEYS
from scvi.distributions import (
NegativeBinomial,
Normal,
Poisson,
ZeroInflatedNegativeBinomial,
)
from scvi.external.d... |
8db55e9557863b6d586e8f7dfa7a5abf989a89e520ca0b61016a48ea91aa16d3 | Python | 16,090 | 421 | """ Utiltiy functions to manipulate video data. """
import os
from pathlib import Path
import subprocess
from typing import Optional, Dict, Tuple
import click
import decord
import matplotlib.pyplot as plt
from matplotlib.widgets import RectangleSelector
import numpy as np
from tqdm import tqdm
from PIL import Image
d... |
a9411540b57bd6f231153b69322b1f9b699bd3990fde8021570f2aa983af135a | Python | 16,092 | 427 | import time
import argparse
import torch
import numpy as np
import os
from tqdm import tqdm
from pathlib import Path
from omegaconf import OmegaConf
from types import SimpleNamespace
from torch_geometric.data import Batch
from ..common.eval_utils import load_model, load_control, load_classifier
from ..common.model_ut... |
fdf00ab726a36abbccb9fc84a03911f31358fd76540ce1ee742f32cece12eaf0 | Python | 16,093 | 464 | # 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 os
from typing import Sequence, Tuple, List, Union
import pickle
import re
import shutil
import torch
from pathlib import Path
from .co... |
6ed8bb0cd48c83fbbda70fecdd2fb65d2d9a18b4f2a7faa8884abe585a303955 | Python | 16,096 | 414 | #!/usr/bin/env python3
"""
Combined LRS transcript calling comparison figure (5 panels).
Row 1 — Venn diagrams comparing Bambu and IsoSeq transcript models by intron chain.
A: Novel transcripts B: Known transcripts
Row 2 — Multi-omic validation of expressed long-read transcript models.
C: SR splice junction supp... |
19c31cf0c8c380605d29fe723a19e3d264c80457f05191b647a780898e84da78 | Python | 16,103 | 469 | import copy
import logging
import shlex
import sys
from pathlib import Path
from unittest.mock import patch
import pytest
from gsMap.config import RunAllModeConfig
from gsMap.main import main
def parse_bash_command(command: str) -> list[str]:
"""Convert multi-line bash command to argument list for sys.argv"""
... |
53ea1d700c1d7c88660c6b34fd73a4227cf2ca9ff111e3f31413aeb088056f8f | Python | 16,117 | 372 | import json
import os
import pandas as pd
import gzip
from Bio import SeqIO
from Bio import pairwise2
from binnd.core.utils.logger import setup_logger
logger = setup_logger(__name__)
class SequenceFilterStats:
""" Class to store statistics about the sequence filtering process. It includes methods to increment va... |
9fde8b8d658b9f974b89f3cba87358e7056cc69793f16e4cfd055292ebeb8c7c | Python | 16,122 | 388 | import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
from scipy.stats import f_oneway
from collections import defaultdict
import pickle
import pingouin as pg
import pandas as pd
def get_plot_group_order(n_components):
if n_components == 2:
plot_group_order = [2, 1]
elif n_... |
64569cbb9ac8c18a4447a8bb7ccb9bcf69e0c44675b0b1069b3581d34d725e78 | Python | 16,125 | 374 | from typing import Sequence
import torch
import torch.nn as nn
import copy
from ..modules import PCoder
class PNetSameHP(nn.Module):
r"""
Implements the base class for adding Predicitive Coding Dynamics to an existing network with shared hyperparameters for all PCoders.
Assume that there are :math:`n` PCod... |
d0545ab2717b2cc7f8fa447c221bd5ba4332616c517e0c8e024faeb5ed5964b4 | Python | 16,126 | 470 | """This module is adapted from https://github.com/Open-Catalyst-Project/ocp/tree/master/ocpmodels/models
"""
import torch
import torch.nn as nn
from torch_scatter import scatter
# # from torch_geometric.nn.acts import swish
# from torch.nn.functional import silu as swish
# from torch_geometric.nn.inits import glorot_o... |
1f164a49d91064f552c076a9207482592c36dbc1451450d080ee2668002f1243 | Python | 16,141 | 446 | from os.path import join
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
from adjustText import adjust_text
from matplotlib import pyplot as plt, gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from upsetplot import from_memberships
from upsetplot import plot
from confi... |
279cbce08b95ec03ff5980c925c9af7b75132fb7d456dfc2120295e828d25d23 | Python | 16,152 | 400 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import io
import os
import random
from dataclasses import dataclass
from pathlib import Path
from zipfile import ZipFile
import ase.io
import hydra
import numpy as np
import torch
from hydra.utils import instantiate
from omegaconf import DictCon... |
1ecc28d7b025e1a94146210c10b19c49c9843232f9b9a700913e6edb816bbb09 | Python | 16,154 | 501 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA import ANOVAModel
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
... |
9947d4b1ce74a5f653c289f5e61b78f992fc9d28ee090e50b791494eb1e57231 | Python | 16,154 | 541 | #!/usr/bin/env python
"""
This is a modified version of the code present in:
https://github.com/nanoporetech/pipeline-umi-amplicon/blob/master/lib/umi_amplicon_tools/parse_clusters.py
In this version, we check that reads within each input cluster are “similar” by
clustering them based on their pairwise edit distances.... |
6bc977ccf2171acecc3bb3ead493c7445342e9986d3b023b8cd1c3b9cf16148d | Python | 16,155 | 483 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
6b3a615016b08a0dc11dad4e33c43a73240a20f32ac098533c9b61a822953c41 | Python | 16,164 | 499 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os, sys, h5py
import numpy as np
from copy import deepcopy
def make_directory(path, foldername, verbose=1):
"""make a directory"""
if not os.path.isdir(path):
os.mkdir(path)
p... |
a81d73ebd8d7c71d03da87f138b53ad576f993925f41ea37de6e85962a77b2c4 | Python | 16,171 | 354 | import torch
import torch.nn as nn
import numpy as np
import random
import os
import copy
import pandas as pd
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import sys
import warnings
warnings.filterwarnings('ignore')
import yaml
import wandb
from s... |
0acff46ca571dbaa423e95907874753a6c77df6997a5eeeb02c2cc89e25d62b5 | Python | 16,178 | 502 | """Plotting functionality based on VTK."""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import os
import warnings
from collections import defaultdict
import numpy as np
from numpy.lib.stride_tricks import as_strided
from vtk import vtkCommand
import vtk.qt as vtk_qt
from enigmatoo... |
67163b8ed0e884c84d9b780068e8ebfb5050c054e08ced957449bf11eb791798 | Python | 16,185 | 370 | from itertools import chain, combinations, permutations
from sklearn.base import BaseEstimator
from pgmpy.base import PDAG
from pgmpy.ci_tests import get_ci_test
from pgmpy.utils._warnings import _warn_external
class ExpertKnowledge(BaseEstimator):
"""
Class to specify expert knowledge for causal discovery ... |
8bb2cf300b9f80ea2ccb7e2709aa69f9c31224ac50c5f88f916e9435348fa4de | Python | 16,188 | 441 | import numpy as np
from ClusterWrap.decorator import cluster
import bigstream.utility as ut
from bigstream.align import affine_align
from bigstream.transform import apply_transform
from scipy.ndimage import zoom
import zarr
from zarr import blosc
from aicsimageio.readers import CziReader
from xml.etree import ElementTr... |
6b1bdfe61cff898a1e50f6a79477c4238ace4d12eb8621c7c93e2f64b56d7bea | Python | 16,189 | 461 | """
Visualization script for Garfield ablation study results.
This script generates publication-quality figures showing:
- Performance comparison: SVD vs Dropout augmentation
- Impact of GNN iteration steps on performance and runtime
- Effect of SVD rank (svd_q) on denoising quality
- Trade-offs between performance an... |
3313f9dd60345ff85c5dec3d345009b46b7f9cc0b9f0343501382c16878c9a99 | Python | 16,192 | 504 | #!/usr/bin/env python3
import pyro
import torch
import numpy as np
import muon as mu
from muon import MuData
from sklearn.preprocessing import LabelEncoder
from anndata import AnnData
from ..models.SOFA import SOFA
import pandas as pd
import scanpy as sc
from typing import Union
import numpy as np
from sklearn.preproce... |
eec1eec2b5140df2dc07f062d8ffd0b75e4f831350457e240f2055f626bbdcc8 | Python | 16,201 | 423 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 6 09:54:13 2025
@author: vbp
Reward-magnitude analysis for Figure 4.
Fig 4A, 4B - example session raster + average traces, per region x
reward size (0.3, 1, 2.5, 5, 10 uL)
Fig 4C - GRAB-DA amplitude vs. reward size, p... |
737aa6958efe5178be3564f722279a7132951cf42063dca3fc07d59a2f9275ad | Python | 16,203 | 296 | # 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... |
d7fd613b6aa483a82a8ce9adedc8a13f83020a7e90d4d0c47e32ac0c2479a62e | Python | 16,204 | 316 | import multiprocessing
import queue
from torch.multiprocessing import Event, Process, Queue, Manager
from time import sleep
from typing import Union, List
import numpy as np
import torch
from batchgenerators.dataloading.data_loader import DataLoader
from nnunetv2.preprocessing.preprocessors.default_preprocessor impo... |
40ae4f3725c1a43a865eaa8855a7337bc7fe6d232d6319b599ccec633bb6ab13 | Python | 16,212 | 401 |
import collections
import os
import tempfile
from datetime import datetime
from functools import partial
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.distributed as torch_dist
import torch.nn as nn
import torch_geometric.transforms as T
import torchmetrics
from beartype im... |
dfb1e6c4dcb635b04d2fad3cd444a7314f917ac8e8022dcc08764fc6eb8e2c6c | Python | 16,214 | 455 | # -*- coding: utf-8 -*-
"""
@Time:Created on 2019/9/17 8:36
@author: LiFan Chen
@Filename: model_v2.py
@Software: PyCharm
"""
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import math
import numpy as np
from sklearn.metrics import roc_auc_score, precision_score, recall_... |
d092c9f5ada999bca8a87d53e12e2ca7cd43a6d12fc117063ea67d46962fb189 | Python | 16,222 | 369 | from collections.abc import Callable, Hashable
from itertools import combinations
import pandas as pd
from sklearn.base import clone
from pgmpy.base import PDAG
from pgmpy.causal_discovery import ExpertKnowledge
from pgmpy.causal_discovery._base import BaseCausalDiscovery, _ConstraintMixin
from pgmpy.ci_tests import ... |
484e9be2b79d42ba2bee20a030ed7a65cffe87417396f17d80579db43cc1316c | Python | 16,224 | 453 | import argparse
__all__ = ["add_density_args", "add_observable_args", "add_transferable_args"]
def _add_only_density_and_observable_and_transferable_args(parser):
parser.add_argument(
"--ansatz",
"-a",
choices=[
"psiformer",
"psiformer-new",
"envnet",
... |
efce8e108bd764c59f8ae78a7256fd79a9e2ebb7dcff900d92c1da560dd4e311 | Python | 16,226 | 371 | #!/usr/bin/env python3
r"""Calculates quantile values for seqlets and called motif instances.
This little helper program calculates quantile values for seqlets and called
motif instances. For each pattern (patterns in different metaclusters are
distinct), it looks at the seqlets and determines where that seqlet's
impo... |
46aa97aa931827c2b0422e45d7ea338962589120fc96e2beabc393614ccc5e24 | Python | 16,229 | 427 | import logging
import os
import warnings
from collections.abc import Iterable as IterableClass
from collections.abc import Sequence
from typing import Literal
import numpy as np
import scipy.sparse as sp_sparse
import torch
from lightning.pytorch.strategies import DDPStrategy, Strategy
from lightning.pytorch.trainer.c... |
de64646bfefe907824286a361a90dcd11e04f7e80b808bb740f870dce3fe1bb6 | Python | 16,248 | 460 | from __future__ import annotations
from typing import List, Optional, Sequence, Tuple, Type, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
def _infer_ndim(kernel_sizes: Sequence[Sequence[int]], strides: Sequence[Sequence[int]]) -> int:
if kernel_sizes:
return len(kernel_sizes[... |
27bf26364bc3a7857f5b6fabff11fa8803a5265e6f493fd6f0c6ceed796cfda0 | Python | 16,269 | 315 | import multiprocessing
import queue
from torch.multiprocessing import Event, Queue, Manager
from time import sleep
from typing import Union, List
import numpy as np
import torch
from batchgenerators.dataloading.data_loader import DataLoader
from nnunetv2.preprocessing.preprocessors.default_preprocessor import Defaul... |
5162b40060185d545c1005a895265b25b8234a8a2488291d70da891a720dd086 | Python | 16,272 | 473 | """Summary sheet helpers for telemetry workbook exports."""
from __future__ import annotations
from datetime import datetime
from pathlib import Path
from src.features.telemetry_alignment.exporters.export_frames import (
get_standardized_native_window_bounds,
)
def populate_summary_sheet(exporter, writer, sort... |
6cc196956f97967e61e604582ad59d0479c22a8cbff9049089c111f025d89006 | Python | 16,275 | 408 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
from matplotlib.patches import Wedge
from collections import defaultdict
from matplotlib.image import imread
from scipy.spatial import ConvexHull
from s... |
7a922d4e7e5cf424036e08bd2216c6ce443a9d54f1955176f2da24715a5342fc | Python | 16,281 | 614 | # -*- coding: utf-8 -*-
"""
Created on Mon Feb 10 12:28:19 2025
@author: hanna
"""
"""
Figure 6: relationships between behavior and neural activity
"""
#%%
import os
import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sb
import matplotlib as mpl
import matplotlib.p... |
20347128dd5042597d823ecef18f542dc72f654687cfe150f551c16248e0a6c1 | Python | 16,295 | 372 | import logging
import operator
import os
import time
from functools import partial
from typing import Callable, Iterable, Optional, Sequence, Union
import jax
import jax.numpy as jnp
import optax
from jax import tree_util
from tqdm.auto import tqdm, trange
from uncertainties import ufloat
from .data import DataLoader... |
4b0129968c09bdf0c9c77ff46337a86e365620d21dbd7059037b0cb9c202e67f | Python | 16,299 | 427 | # 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 numpy as np
import torch
from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
class SpeechGenerator(object):
def ... |
a0cefd0cc497b3f27c49b472318ff8cf9ece88817b0f555782c898e4dfbed8b2 | Python | 16,314 | 427 | from neuron import h, gui
import math
import time
import random
import numpy
class cell() :
def __init__(self, verbose=True):
#random.seed(1) #use the same seed to get same number in every run
random.seed(time.time())
cellspec = dict()
cellspec["soma_diam"] = 17
c... |
e96d3ec10eec3cd9f7136104a393f560bd58223de52e4deef53f41fd912e9440 | Python | 16,314 | 378 | # This script is used to build the index for similarity search using faiss. It supports loading embeddings and meta labels from directories and add to faiss index. The index is saved to disk for later use.
# Options can be set to custom the building process, including:
# - embedding_dir: the directory to the embedd... |
f683595891a1f2afdfd8e4e66f9a4045465734a36aaa3c9d22f148de1e76f371 | Python | 16,319 | 345 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from datetime import datetime
import os
from scipy.stats import gaussian_kde
from sklearn.neighbors import KernelDensity
import matplotlib.colors as mcolors
from scipy.ndimage import gaussian_filter
import matplotlib.pyplot as ... |
ec59b810d35117490cafff4486c2790bf6174d11eab194eeaa9cebe99c2b7bf8 | Python | 16,330 | 359 | """Shared SegCT/MRI ZIP protocol for the desktop application and Colab."""
from __future__ import annotations
import hashlib
import json
import os
import sys
import uuid
import zipfile
from pathlib import Path, PurePosixPath
import nibabel as nib
import numpy as np
BRIDGE_SCHEMA = "segref3d-segct-mri-bridge"
LEGAC... |
b775b39d7e3f6b43bbf6c58c0c1015e799e50810d2c51d7898e0efe4eb02f1fd | Python | 16,332 | 411 | import itertools
import math
import pathlib
import random
import warnings
from collections.abc import Collection, Iterator
from typing import Annotated, Any, Literal
import tomlkit
import torch
from pydantic import (
BaseModel,
ConfigDict,
Field,
NonNegativeFloat,
NonNegativeInt,
PositiveFloat,... |
31faaa07403b16723c0be22394d4054ead31392f42bd7e8bf3a3db90ecec1438 | Python | 16,337 | 443 | # 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 logging
import math
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
... |
c9ae4db66240a498e005fa2688868e8bfc4d297d89ef6713a56a6d0f9c2dbb73 | Python | 16,348 | 420 | """`pool_token_dim`: the memory-lean pooling of the token dimension.
`_pool_logits` used to open with `logits.float()` — a float32 copy of the
largest tensor in a training step, held by autograd until backward had run.
These pin the two things the replacement must get right: it must not save a
float32 copy, and it mus... |
d7b87d3f8a2eff7663633cf0a55576d46f6d8de637cac7f70aefd8022c61d52d | Python | 16,366 | 251 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import yaml
import argparse
import json
from typing import Dict, List
def read_molecules_file(path:str)->List[str]:
with open(path, "r") as file:
all_lines = file.readlines()
molecule_definitions ... |
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