sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b382d080f74124e0b3ca9a168b9c03039c0f23f247b79453dd609e6abc4e7566 | Python | 13,409 | 336 | import sys
import numpy as np
import matplotlib.pyplot as plt
import threading
import csv
import mappy as mp
base_dict_op = {0:'A', 1:'C', 2:'G', 3:'T'}
base_dict = {'A': 0, 'C': 1, 'G': 2, 'T': 3, 'a': 0, 'c': 1, 'g': 2, 't': 3}
def str2bool(v):
if v.lower() in ('yes', 'True','true', 't', 'y', '1'):
return True
... |
cd0bb64c98dd70046609a5b17f257fc1f4f625549a9c4cb38817419518d6499f | Python | 13,411 | 306 | import pysam
import os
import gzip
import copy
import shutil
import pytest
from TestUtils import make_data_files, BAM_DATADIR
def setUpModule():
make_data_files(BAM_DATADIR)
class TestFastaFile:
sequences = {
'chr1':
"CACTAGTGGCTCATTGTAAATGTGTGGTTTAACTCGTCCATGGCCCAGCATTAGGGAGCTGTGGACCCTGC... |
39a2e214243dfd128b5839e06a4707c1cded3af31f51177f251a59849c7a709b | Python | 13,414 | 431 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ErrorY(_BaseTraceHierarchyType):
_parent_path_str = "histogram"
_path_str... |
87f3e336b2648aed37c6dbc7c2d56f549859fda4ae0e3ad2603c6e968a605dda | Python | 13,414 | 431 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ErrorZ(_BaseTraceHierarchyType):
_parent_path_str = "scatter3d"
_path_str... |
e42082f0bb938f0d781179a812e0307d8f19a60e28e682a1ba14fff6715a8f37 | Python | 13,414 | 431 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ErrorY(_BaseTraceHierarchyType):
_parent_path_str = "scattergl"
_path_str... |
7aa1a7e99cbfc726180ceb08024d01205db28b7e7a3d48568a71562d883d5b56 | Python | 13,416 | 364 | import random
from pyoperant.utils import rand_from_log_shape_dist
import pickle
import numpy as np
def random_queue(conditions,tr_max=100,weights=None):
""" generator which randomly samples conditions
Args:
conditions (list): The conditions to sample from.
weights (list of ints): Weights of e... |
6e5bbab700fc4e0824a8e17223de78011b1c5368abda228bf094c879851fe089 | Python | 13,418 | 299 | import numpy as np
import torch
from torch.autograd import Variable
from torch import autograd, optim, nn
import torch.nn.functional as F
from torch.distributions import Categorical
from collections import namedtuple
use_cuda = True
if torch.cuda.is_available() and use_cuda:
device = torch.device('cuda')
else:
... |
6e6e8726d5b9fd7c00f93b9d915db041aed748e44ad13e2760d30acf3d9c0f6a | Python | 13,422 | 319 | '''
Load K-folding models from groups and evaluate performance
author : obanmarcos
'''
import os
import os, sys
from config import *
sys.path.append(where_am_i())
import lightning as pl
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utilities import dataloading_utilities... |
86cb828b39d310e524032d1ff48140bd44cad05fcace07b04146e245d4bc6591 | Python | 13,423 | 317 | #!/usr/bin/env python3
"""
Dx AL — single-channel ablation sensitivity (figshare cohort).
Question for clinical deployment: which of the 19 standard 10-20
channels carries the most diagnostic weight, and how much accuracy do
we lose if a particular electrode fails?
Method:
Re-extract per-epoch raw input data for th... |
4bfbb35a9edc998206e89b346a19733d5e86617c3df76ce3f10bb75223e1e927 | Python | 13,427 | 320 | """
Error analysis agent for tool error diagnosis.
"""
import logging
import re
from functools import partial
from pathlib import Path
from typing import (
Any,
)
import anyio
from pydantic import BaseModel
from pydantic_ai import Agent
from galaxy.schema.agents import ConfidenceLevel
from .base import (
Act... |
90d19558d8f68f8d0f13e5b8b8bcd434f3b324aceda87a1f35352c32e2bd9359 | Python | 13,428 | 322 | """
Transforming Power Spectra
==========================
Apply transformations to power spectra.
One of the goals of parameterizing neural power spectra is to be able to understand
if and how power spectra may be different and/or be changing across time or context.
In order to explore how power spectra can change, ... |
48381bc8d42f14d0b62d4f2e995210a9024f743a9dac92a11b620807154c652c | Python | 13,429 | 368 | # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 app... |
56307fe7711e7195078659148ad8506d4d69e1ee4ac512140ac26c306e69243f | Python | 13,430 | 360 | from typing import Dict, List
from abc import ABC, abstractmethod
import logging
import math
import random
import imodels
import numpy as np
from scipy.special import softmax
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
import torch.cuda
import tqdm
import imodelsx.llm... |
d9b0a1ad6ab982331d172ea9cc49faad943623b13a78fde05318e613c387fd5d | Python | 13,431 | 397 | """Pool presets: axis-explicit, mask-aware, refuse ambiguity (extract D10).
Presets (spellings DOCUMENTED-UNSTABLE): ``flatten``, ``spatial_mean`` /
``spatial_max`` (4D feature maps), ``token_mean`` / ``token_max`` /
``token_sum`` (3D token sequences, MASK-AWARE), ``cls`` (first token), and
``last_token`` — the last V... |
d5ea66416e8e9aa30196b545c97fa66d4f413b298ac75b9c0f49bc32d090af47 | Python | 13,433 | 310 | import json
from pathlib import Path
import numpy as np
import torch, random
import open3d as o3d
from PIL import Image
from helpers.configurations import OR_4D_DATA_ROOT_PATH
def zero_mean(point):
mean = torch.mean(point, dim=0)
point -= mean.unsqueeze(0)
# Norm to unit sphere
furthest_distance = p... |
581b3a07d546ca2dcb9409dffc7558419eb05f72a94eb616ad3553ebf5966f8a | Python | 13,435 | 390 | import json
import random
from typing import List, Tuple, Dict
import numpy as np
import pandas as pd
from tensorflow.keras.utils import Sequence
class TrainDataset(Sequence):
def __init__(self,
pos: Tuple[List[Tuple[str, str]], List[int]],
neg: Tuple[List[Tuple[str, str]], List[int]],
fe... |
3e5ee13effdf80d6120338f1bbc9c76274921894239f72fbee4262e3b55fce9a | Python | 13,440 | 379 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Optimizer."""
import slowfast.utils.lr_policy as lr_policy
import torch
def construct_optimizer(model, cfg):
"""
Construct a stochastic gradient descent or ADAM optimizer with momentum.
Details can be found... |
a7a48e1c8a3654a9f0548dcaba1720fce28afe34d2fa72e9bc4bd8a5c9bc46a4 | Python | 13,441 | 370 | #!/usr/bin/env python3
"""
File: create_regplot_per_dataset.py
Created: 2022/03/21
Last Changed:
Author: M.Vochteloo
Copyright (C) 2020 University Medical Center Groningen.
A copy of the BSD 3-Clause "New" or "Revised" License can be found in the
LICENSE file in the root directory of this source t... |
f66e82d375061b6e0c3beeb545fd9f7da3d8c95ea4a19babc5652b308811c49b | Python | 13,443 | 416 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
5fe0c0f5f6b86080fb440f3d6f451e87e377db4ab95df79a476262d6dd3a096b | Python | 13,450 | 300 | from functools import partial
import numpy as np
import pandas
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.utils import compute_class_weight
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow_addons.l... |
c318036e4f7bdddadc12ba95eff29c97bfc2da296b52e933174c72a8934de95b | Python | 13,450 | 362 | from __future__ import annotations
import contextlib
import io
import os
import shutil
import tempfile
from fractions import Fraction
from typing import Any, Generator
from unittest import mock
import PIL
import pytest
import torch
import torchvision
from lightly.data import LightlyDataset, NonIncreasingTimestampErr... |
386cd0e3e38fddef5d434fbcfd58264bedfa37295534e9a2b7174a3436d4db62 | Python | 13,454 | 301 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import sys
import time
import torch
from ultralytics.utils import LOGGER
from ultralytics.utils.metrics import batch_probiou, box_iou
from ultralytics.utils.ops import xywh2xyxy
def non_max_suppression(
prediction,
conf_thres: float = 0.25... |
9ed6c0073b72ca4103ccdabe2539ee756515faee659a6903b1b1a555a8c60b31 | Python | 13,455 | 339 | """LeJEPA loss functions and regularizers."""
import torch
from torch import Tensor, nn
from torch import distributed as torch_dist
from torch.distributed import nn as torch_dist_nn
from lightly.utils import dist as lightly_dist
def lejepa_invariance_loss(*, all_proj: Tensor, global_proj: Tensor) -> Tensor:
"""... |
fa068bfb55c24ab69af6d14ac1b4a2f547c1404814f54c5703107ab3682257ef | Python | 13,455 | 262 | from typing import Any
from dataclasses import dataclass
from mirp.settings.utilities import setting_def
@dataclass
class ImagePerturbationSettingsClass:
"""
Parameters related to image and mask perturbation / augmentation. By default images and masks are not perturbed or
augmented.
Parameters
--... |
fb5f77ca96ecec1b45a0b1b6798d6bcb238622c4b532c0ae739660ac2d8a810c | Python | 13,456 | 419 | """
Indexing of the datasets stored in a workspace.
Lists the imported and simulated datasets a workspace holds, reading only the
stored metadata rather than the datasets themselves.
"""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from larvaworld.lib.util.dictsN... |
c14c798edbe26bff094fb350fc7d42e3453cbe6145069002e754e1867fc93c76 | Python | 13,460 | 416 | #!/usr/bin/python
import numpy as np
import matplotlib as pl
import struct
import glob
from scipy.sparse import *
from scipy.io import mmread, mmwrite
current_version = (0,8,3)
class AurynBinaryFile:
'''
This class is the abstract base class to access binary Auryn files.
'''
def unpack(self, data):... |
88d873962c1d97367ce20adfcc76f742e696dd9887fa581b61f96df6e0524b8f | Python | 13,461 | 382 | """CTM, HRM, and AKOrN: frontier neural dynamics models.
CTM (Continuous Thought Machine):
Sakana AI, "Continuous Thought Machines." arXiv:2505.05522 (2025).
Source: https://github.com/SakanaAI/continual-learning-baselines
(Official: https://github.com/SakanaAI/continuous-thought-machines)
HRM (Hierarchical Rea... |
22bb810c9d1aede62e736d2457ee49f7ac1b72180596e25e8685928dae87dd4e | Python | 13,467 | 321 | import math
import random
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
import triton
from flash_attn.layers.rotary import apply_rotary_emb, apply_rotary_emb_torch
from flash_attn.layers.rotary import apply_rotary_emb_qkv_, apply_rotary_emb_kv_
from flash_attn.bert_padding i... |
9091f68972379a64fa3f99bcee74c480f3fb1ef733dfce0392844c37faefae6e | Python | 13,468 | 287 | import os
import logging
import contextlib
import numpy as np
from tqdm.auto import tqdm
import concurrent.futures as cf
from .XCorrCpu import XCorrCpu
# Setting the logger
from .TqdmLoggingHandler import TqdmLoggingHandler
logger = logging.getLogger(__name__)
logger.setLevel(logging.WARN)
logger.addHandler(TqdmLoggi... |
0ae42a576045e2c5fadc5fbe9398ebcbc554d16763cca81b9f0e78fc9c359961 | Python | 13,469 | 348 | import json
import logging
import urllib.parse
import urllib.request
from pathlib import Path
import geopandas as gpd
import pandas as pd
import pytest
from shapely.geometry import box
import pyrosm
import pyrosm.data.extract_index as ei
import pyrosm.data.geocoding as gc
# The cache folder function itself; each tes... |
c64d6b8627f9a2e676b47b4256f74d7e81365bd7fdf6484f5b4ac56d9f2d35fd | Python | 13,471 | 323 | '''
Load K-folding models from groups and evaluate performance
author : obanmarcos
'''
import os
import os, sys
from config import *
sys.path.append(where_am_i())
import pytorch_lightning as pl
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utilities import dataloading_u... |
4926c92c3f1bb729d80dc7a099ba8df4567174324df2e229bc2e7916fcb7bd4d | Python | 13,474 | 336 | """Lane F44 stage 2: injected-op persistence, degrade-to-unattested, replay.
foldA MEMO s5 item 12: the persistence codec rides the trusted-callable
resolver and the C07 ``Op.injection_provenance`` slot (no schema bump);
loads degrade to ``unattested`` -- never refusing on an unresolvable
callable; :func:`torchlens.in... |
d7c4857e4ce4a6c2fdb298a86d0a4e4e5d44a9f35f9f9ea8fb7a49e130c80f1a | Python | 13,474 | 304 | #
# MIT License
#
# Copyright (c) 2022.
#
# 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
# to use, copy, modify, merge, publish, ... |
31ba1783902695b61fcb0a239b33d4d72d729c84bfca7d2d806203da8f66ad73 | Python | 13,476 | 337 | import os
import pandas as pd
from typing import List, Dict
from rdkit import Chem
from rdkit.Chem.rdchem import Mol
from tqdm import tqdm
from bioconfpred.conf_ensemble import ConfEnsemble
from bioconfpred.utils import MolConfViewer
from bioconfpred.params import (BIO_CONF_DIRNAME,
GEN_CONF_DIRNAM... |
c4ef6a79db905e6dcc4cdaa2058587b604763f2439ba2ca3451fc2701e68100f | Python | 13,476 | 360 | """
glm_plots.py
This script contains functions to plot results of GLM analyses (glm.py) from
dictionaries.
Authors: Colleen Gillon
Date: October, 2019
Note: this code uses python 3.7.
"""
import copy
import warnings
from pathlib import Path
import numpy as np
from util import file_util, gen_util, logger_util,... |
a65d3c85156cd0492fb1ff07600124c396d502298467c4964ac841c82bdfce49 | Python | 13,478 | 327 | """ResNet model definition, adapted from https://pytorch.org/docs/stable/torchvision/models.html,
to take single channel instead of 3-channel images.
"""
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d... |
2a84b234ec3197bd4a0aed4b4c6542642b891d4a725693c98fb3d0899f830bea | Python | 13,479 | 347 | # Copyright 2022 Deepfold Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
4369f0fe76e5c4899f29c1280070c9893d806cc9d7633b5a3f6279eccd4697ff | Python | 13,486 | 317 | # Dependencies:
# - dcm2niix
# - pydicom
# - nibabel
# - pandas
# - fsl
import os, re, pydicom, threading
import nibabel as nib
import pandas as pd
# variables - feel free to change
report = "ct-report.csv" # name of the report csv
to_scan = "/Users/gravo/Google Drive/MEIC-A/2° ANO/tese/data/TAC_gravo" # absolu... |
429b003beedf48de46739c011e7cd37b7b55eac772471f165c0172263a17e25e | Python | 13,493 | 354 | from typing import Any
import numpy as np
import copy
import pandas as pd
from mirp._images.generic_image import GenericImage
from mirp._imagefilters.generic import GenericFilter
from mirp._imagefilters.utilities import pool_voxel_grids, FilterSet2D
from mirp._images.transformed_image import TransformedImage
from mi... |
a602199c0cab583689c647254282f4691887ac9ebdbfef6ccc4801c3ec0c3085 | Python | 13,493 | 441 | """
The simulated larva, driven by a brain and a body model.
Couples the behavioural modules to the segmented body, translating the
locomotor's linear and angular velocity into motion of the body segments.
"""
from __future__ import annotations
from typing import Any, Tuple
import math
import numpy as np
import par... |
3cdec2d120cddb0f3ef28984c60f58126006f8bb16c9caa9e3d7e5ad74957d9b | Python | 13,494 | 392 | """Phase 4 capture target tests."""
from __future__ import annotations
import weakref
from pathlib import Path
from types import SimpleNamespace
from typing import Any
import pytest
import torch
import torchlens as tl
from torchlens.backends.torch.ops import _save_activation_fields
from torchlens.data_classes impor... |
c8210337c96eceb34ac2b36968750d8116840ff340482b68f5fb5f1603a197a7 | Python | 13,495 | 348 | """Dtype-aware gradient-validation tolerances (tripwire strengthening).
The legacy module constants ``PARAM_GRAD_VALIDATION_*`` /
``LAYER_GRAD_VALIDATION_*`` are the fp32 rows of an error model that was
silently applied to every dtype: fp64 gradients were checked ~4.5e11 of their
own ULPs loose (masking corruption far... |
1c6f1e4017f47d4c7ff7a5a615b9518ab4b1aa6c68ba9a0eaceaf39f2817939d | Python | 13,504 | 340 | import copy
import unittest
import os
import json
import glob
import deepdiff
import copy
class TestSum(unittest.TestCase):
def test_lts(self):
pass
def test_chin(self):
pass
def test_fs(self):
model_dir = os.path.join("..", "data", "neurons", "striatum", "fs")
models... |
efad62e539b875cada366010658c415a5ea70d056f3ea3b010cd7ac8e71a1c45 | Python | 13,510 | 368 | """
Manager and Serializer for HDCAs.
HistoryDatasetCollectionAssociations (HDCAs) are datasets contained or created in a
history.
"""
import logging
from galaxy import model
from galaxy.exceptions import RequestParameterInvalidException
from galaxy.managers import (
annotatable,
base,
deletable,
hda... |
12b03205b7a9d1d7a4f13a0dafc2f3f280234611a339ba7a5c6a4548c1f967d9 | Python | 13,515 | 348 | import re
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Muon"])
class Muon(optimizer.Optimizer):
"""Optimizer that implements the Muon algorithm.
Note that this optimizer should not be used in the follo... |
3d6abbc5678c08f942419ef62bf21b1ab57df18c00074cf7ccad9cce64956cb0 | Python | 13,516 | 341 | import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
class CVLearning(TrialEnv):
"""Implements shaping for the delay-response task.
Agents have to integrate two stimuli and report which one is larger on average after a delay.
Args:
stim_scale: Controls the dif... |
74f76aa570963a3434d1ef620d91858f3021d5e4fec94c91a931c64229270968 | Python | 13,524 | 468 | import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as f
import torchvision
from PIL import Image
from scipy import stats
IMG_EXTENSIONS = ['jpg', 'jpeg', 'png', 'ppm', 'bmp', 'pgm']
def _tensor_size(t):
return t.size()[1] * t.size()[2] * t.size()[3]
... |
b87ebf48fbdea6a55edf099a9bb28dfd9df44d0eb71a6c1c10f5d15b66c54b0e | Python | 13,525 | 324 | #!/usr/bin/env python3
"""
Dx AX — Complete normalization-choice picture
================================================
Two follow-up tests after Dx AW's surprise finding (z-score outperforms
rank-norm on held-out figshare under proper per-fold):
1. Z-score under STRICT mode (pooled training HCs as anchor) on
... |
0557a06a96f7e70aa87a4b5c764e6e3b6f1ddc5669aa4c0888cae93c45a85202 | Python | 13,529 | 384 | import threading
import unittest
from time import sleep
from unittest import mock
from codecarbon.core.units import Power
from codecarbon.emissions_tracker import OfflineEmissionsTracker
from codecarbon.external.hardware import (
CPU,
MODE_CPU_LOAD,
AppleSiliconChip,
SystemCPULoadMeter,
)
@mock.patch... |
89b36d331f8917c68553fc8a016661c7e86ed21bd5e33c0edb7860072c6e5013 | Python | 13,530 | 477 | from torch.utils.data import Dataset
import numpy as np
import mdtraj as md
import torch
from torch import nn
from .utils import pbc, get_rc_edges, get_mol_edges
from .data_augmentation import transform_traj_to_knn_list, transform_traj_to_ndist_list
class NNDataset(Dataset):
"""Unified dataset for non-graph (NN) ... |
86461b35a0a726377d2f6cdbc9f03aee4ab347f5cb849be82bb1495efd4f9628 | Python | 13,538 | 412 | """Embedding SPD matrices via manifold learning techniques."""
import numpy as np
from scipy.linalg import solve, eigh
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.manifold import spectral_embedding
from .utils.kernel import kernel
from .utils.distance imp... |
a15d8a8abc534dd983fb7c82605a311165fd52c1f47247d0c8e0112d1c2b4b68 | Python | 13,538 | 305 | import itertools
import unittest
import numpy as np
from permacache import stable_hash
from modular_splicing.dataset.basic_dataset import basic_dataset
from modular_splicing.dataset.h5_dataset import H5Dataset
class RegressionTestData(unittest.TestCase):
def test_hashes(self):
expected = {
"b... |
bc342ae52802f5d1366c7f9c648cfc442357eceeec3e395f39d7bb52ccf2e1f4 | Python | 13,543 | 250 | from os.path import join
import numpy as np
import matplotlib.pyplot as plt
import os
from library.comput_utils import get_tspdiff
from library.script_wrappers import best_worst_analysis, exin_analysis, find_nidx_along_traj
from library.shared_vars import sim_results_dir, plots_dir
from library.utils import load_pickle... |
2f371973b74b4b3fbbdd65c7f5dcf91d3a8773fdd9ed26a086bccb0e824842fb | Python | 13,547 | 366 | """
train.py: Functions for training neural networks on single tasks and two tasks.
"""
__author__ = "Louis Kang"
__date__ = "2023/03/07"
__license__ = "GPLv3"
__reference__ = "To be determined"
import os, sys
import gc
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoade... |
9d27726e98794838ddcdb122664d4c5a8489e2c225aa18d75ec3bcd7328bba9b | Python | 13,548 | 417 | """
Reproduce univariate data curves: observed vs DEBtool vs Larvaworld (closed/stepped).
This module generates comparison plots for the four univariate datasets from
Drosophila_melanogaster:
1. tWw_f: time vs wet weight (larval development)
2. tR_C: time vs reproductive output (standard diet)
3. tR_DR: time vs repro... |
3b1aebf546cde3f781c5723facd21d0e11386671981644a44064a68c10547560 | Python | 13,550 | 339 | # Converting bam to read information
# Cell barcode, UMI, gene alignment, and counts
import os
import sys
from multiprocessing import Process
import pandas as pd
from collections import defaultdict
import gzip
#from pylab import *
import numpy as np
#import HTSeq
import pysam
PATH = os.path.dirname(__file__)
bases... |
f2aee7d0e91705131c57785a2b0a6dcf88d68a0f4c2d5453368c65e870614b7b | Python | 13,552 | 362 | from math import pi as PI
from typing import Optional
import torch
import torch.nn.functional as F
from torch.nn import Embedding, Linear, ModuleList, Sequential
from torch_scatter import scatter
from torch_geometric.nn import MessagePassing, radius_graph
from torch_geometric.utils import to_dense_batch
import torch... |
4db3a68b24cd622e0063f4a509df15206e83db3579b4860216321a00467c96e4 | Python | 13,556 | 359 | # flake8: noqa: E402 (matplotlib.use has to be right after the import)
import os.path as os_path
import pathlib
import importlib.resources
import signal
import sys
import traceback
import webbrowser
import matplotlib
matplotlib.use('QT5Agg')
from PyQt6 import uic, QtCore, QtWidgets
from PyQt6.QtCore import QStandardP... |
38e8a0c47a1ee423b82a78125c72e06332f15c6e5b2b72fc73870b293b31ca01 | Python | 13,557 | 443 | """Asteroid source-separation architectures in compact pure PyTorch form.
Sources: Asteroid model docs for DCUNet/DCCRNet, DPRNN-TasNet, DPTNet, and
SuDORMRF variants.
These reconstructions keep the architectural primitives that make the original
models distinct: encoder-masker-decoder separation, complex-style U-Net... |
d34385e300d341357457ebd29e2cdc78850bfc3c3f851e9d2abd5e4ef98d2490 | Python | 13,560 | 365 | # Force import of standard library logging module
import builtins
import functools
import sys
import time
from contextlib import contextmanager
_original_import = builtins.__import__
def _import_stdlib_logging():
"""Force import of stdlib logging, not spateo.logging"""
# Temporarily remove current directory ... |
fa25d744ff87b5cea5a144d05caf982b37fc18773dfed775a98453e13a1dbc95 | Python | 13,560 | 330 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from dataclasses import replace
from itertools import product
from math import floor
import os
from warnings import catch_warnings, filterwarnings
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose
import pickle
import pytest
from p... |
fafd0c09bc6de56ba29f22507c7caf8aabb7a5a5b28a3f0ca3868324869fe8c8 | Python | 13,564 | 361 | import tensorflow as tf
from tensorflow import keras
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import random
#%% select a portion of data from database
from gemmi import cif
number = []
I = []
for i in range(0, 1000000):#1667834
try:
doc = cif.read_file(".../... |
af2a40fe2cc6ea9bb830bd945fb24fde8f980f1df374aad6e6ec00ac360bcf1d | Python | 13,566 | 256 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
fa35dbab91aca0bb4a23cf265b1ab557652822712ab3a67918e56a155071aeff | Python | 13,568 | 334 | """Round-59 hon_1 immunizer -- registered-module ``__slots__`` RNG generators are inventoried.
r58 hon_1: a REGISTERED ``nn.Module`` (the TOP model or a registered submodule) holding a live
RNG generator in a ``__slots__`` slot instead of ``__dict__`` escaped the host-nondeterminism
inventory belt. The seed loop read ... |
40434ab6a9503e295aeb19bd0b028bfe469bcb89c84fe4855fcde61ed36610e5 | Python | 13,571 | 281 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from typing import Any
import torch
import torch.nn.functional as F
from torch import nn
from ultralytics.utils.metrics import bbox_iou
from ultralytics.utils.ops import linear_sum_assignment, xywh2xyxy, xyxy2xywh... |
b5d7a9c427b0b175cd14a4886064b188bbf38b5c42bfb515153f634420cef740 | Python | 13,578 | 353 | """Dependency Resolution
The dependency resolution in pip is performed as follows:
for top-level requirements:
a. only one spec allowed per project, regardless of conflicts or not.
otherwise a "double requirement" exception is raised
b. they override sub-dependency requirements.
for sub-dependencies
... |
b556813df004b55990fa5294eeb74832a26c19a286108f09bf9ecb36b31ef366 | Python | 13,584 | 325 | #Create a stitched well from the original files and save as zarr
# imports
import argparse
import configparser
import re
import zarr
import numpy as np
import xml.etree.ElementTree as ET
from numcodecs import Blosc
from pathlib import Path
from skimage import io, morphology, filters, measure
import pandas as pd
import ... |
e6045b5a6bae9b8359afff9d66f0b5904b45c41f69ab01ec2701a7eb2c04308d | Python | 13,585 | 289 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
4fa1b15d3e082726e59bd216c84bbc82ea37a66639b687867034300cd36c2e5e | Python | 13,588 | 432 | """Regression tests for round-14 postprocess metadata hardening."""
from __future__ import annotations
import importlib.util
import textwrap
from pathlib import Path
from types import ModuleType, SimpleNamespace
import pytest
import torch
import torch.nn as nn
import torchlens as tl
from torchlens.data_classes.trac... |
de76185a291d46b2c9c1eba41626c3637936ed3dfe54196470ba5ad4b4f26128 | Python | 13,588 | 384 | """P2 tests for input-side container capture."""
from __future__ import annotations
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.ir.container import DictKey, TupleIndex
from torchlens.ir.container_registry import Phase, Role
class ScalarConfigModel(nn.Module):
"""Use sc... |
24233f773f22e6e3e81c5ce37713f701b6e408a348a0e489a97672095bf293ca | Python | 13,590 | 410 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import warnings
import numpy as np
import pandas as pd
import scanpy as sc
from sklearn.metrics import r2_score
from scipy.sparse import issparse
from scipy.stats import wasserstein_distance
import torch
warnings.filterwarnings("ignore")
import ... |
8ec64a5751697c5f1423b992252f266c32a8a4f257b5b3b77f871af3e68a9533 | Python | 13,592 | 295 | """Basic class for TreeGrower Algorithms"""
import copy
import logging
import numpy as np
from tmd.Topology.analysis import get_lengths
from tns.generate.algorithms.abstractgrower import AbstractAlgo
from tns.generate.algorithms.common import bif_methods
from tns.generate.algorithms.common import TMDStop
from tns.gen... |
9bf289dad6d085645a9088afa900e08fdf33a4eef77cb2709543987b38435fdb | Python | 13,596 | 320 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import math
import numpy as np
# ROS libraries
import rospy
# ROS messages
from std_msgs.msg import String
from geometry_msgs.msg import Twist
from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint
from nav_msgs.msg import Odometry
from sensor_msgs.msg im... |
0ce36ddfc3bba43719652b1c4ea9586fec5ca110a4a7bd239f31748689a400fb | Python | 13,597 | 359 | """Tests for portable pickle scrubbing and rehydration."""
from __future__ import annotations
import pickle
import pytest
import torch
from safetensors.torch import save_file
from torch import nn
import torchlens as tl
from torchlens import Trace, trace as trace_fn
from torchlens._io import TLSPEC_VERSION, TorchLen... |
d971a8b67d79f3085f51bbf80c1b68ef98872ab95b6048fed5b803b92d54010b | Python | 13,597 | 346 | import torch
import numpy as np
from ovseg.data.utils import crop_and_pad_image
from ovseg.utils.torch_np_utils import maybe_add_channel_dim
import os
from time import sleep
try:
from tqdm import tqdm
except ModuleNotFoundError:
print('No tqdm found, using no pretty progressing bars')
tqdm = lambda x: x
c... |
533f9c5dd27542570b6ea0922eecd09d0ff1f23481855f8913a553cb8b6a9e77 | Python | 13,600 | 350 | """The universe registry: the closure object of the composition harness.
Compo memo D1/3.2: closure lives on a COUNTED, OWNED, code-derived universe
registry, not on any taxonomy of bugs. Every universe carries a descriptor
(id, owner, derivation, public boundary, filters, independent census,
adequacy status, review t... |
d860610b2ed25748480796ab157415dafdc994bc8b5a256c7a14abc7120ee1e0 | Python | 13,604 | 315 | import os,re
from tqdm import tqdm
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
from utils import parse_config, setup_fonts, build_output_dir
from Bio.SeqUtils import seq3
from scipy.stats import pearsonr
import numpy as np
import pandas as pd
import seaborn as sns
def getLongestORF(mRNA)... |
df5ba6c3740c66bd15c3ffc0db66be2fc4fe91ad43ade8e4f07ff7ddb8845e3f | Python | 13,604 | 344 | # objectives of choice
import numpy as np
from numpy import prod
import torch
import torch.nn.functional as F
from src.utils import log_mean_exp, is_multidata, kl_divergence
from src.datasets import convert_label_to_int
# helper to vectorise computation
def compute_microbatch_split(x, K):
""" Checks if batch nee... |
041dc1fc47ce3b879f903edd56a24838f8b38df09c515d9ca081e668b76d9ef2 | Python | 13,605 | 252 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import os
import sys
import argparse
import math
import time
from model import BIML_S, describe_model
from eval import evaluate_ll
import datasets as dat
from train_lib import seed_all, timeSince
DEVICE = torch.device... |
acadcc2cd8f0de77cba75eb2687e9e8e19c2940b00c591e5fe54f0f0294b14ec | Python | 13,613 | 350 | # -*- coding: utf-8 -*-
import brainpy as bp
import brainpy.math as bm
import numpy as np
bp.math.set_platform('cpu')
class LIF(bp.NeuGroup):
def __init__(self, size, tau_neu=10., tau_syn=0.5, tau_ref=2.,
V_reset=-65., V_th=-50., Cm=0.25, ):
super(LIF, self).__init__(size=size)
# parameter... |
140bd89b2a1b53cb40e38b074c12c46d87d447ef25b9838028e9d4a586fd4710 | Python | 13,615 | 384 | import pandas as pd
import pytest
from types import SimpleNamespace
from larvaworld.lib import reg
import larvaworld.lib.process.importing as importing
@pytest.fixture
def tracker():
return SimpleNamespace(Npoints=3, dt=0.25)
@pytest.fixture
def filesystem():
return SimpleNamespace(read_sequence=["t", "x",... |
311d3469c9146f37489224731f9a4d77d50af7101be931fd0f4c96b8ec2e3d84 | Python | 13,616 | 354 | from typing import Optional, Tuple, Union
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
# import tensorflow as tf
from dynamo.tools.sampling import sample
from .backbone_utils import... |
8e20a31c4d462463e14a2a1521f96acac07057bad53e711ce080cf744ce6865b | Python | 13,616 | 267 | import pickle
import torch
import torch.nn.functional as F
from models import Encoder,Decoder,GaussianDecoder,VAE,CellStateEncoder,\
CellStateDecoder, CellStateVAE,\
SimpleEncoder,LocalDiscriminator,PriorDiscriminator,\
EmbInfomax,MultiEncInfomax,Classifier,\
... |
28f4c52b08d9931ea1cf8beb9e6de278c5236ba29f00245db011bbd0d7b444a1 | Python | 13,617 | 322 | #!/usr/bin/env python3
"""
Dx BP — Data-quality audit on tool-missed AD subjects
======================================================
Are the AD subjects the tool misses (Dx BO inventory) recordings of
unusually marginal data quality? If so, data-quality screening could
be expected to improve sensitivity. If not, th... |
e3f372b7291fd94a436afa60d1c01755f543af5fa10e7be24c95f4a720217bf6 | Python | 13,620 | 344 | <<<<<<< HEAD
import numpy as np
import torch
from sklearn.metrics import roc_auc_score
from torch import nn
from tqdm import tqdm
from models.Estimator import EarlyStopping
=======
import time
import torch
from torch import nn
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
>>>>>>>... |
74f43bc6bf15b79e6b57b3026d149bca7304bed31718d7873f37cff946557c80 | Python | 13,623 | 312 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import contextlib
from functools import partial
from pathlib import Path
import numpy as np
import torch
from ultralytics.nn.modules import Detect, Pose, Pose26
from ultralytics.utils import ARM64, AUTOINSTALL, LI... |
fd63be29b8aedb893158a6e5ed5900a3eca10e58e9fa4a55d0853c190839381d | Python | 13,624 | 358 | """Unit tests for the creation of RDMs."""
import numpy as np
import pytest
from mne_rsa import (
compute_rdm,
compute_rdm_cv,
pick_rdm,
rdm_array,
searchlight,
create_folds,
)
from mne_rsa.rdm import _ensure_condensed, _n_items_from_rdm
from numpy.testing import assert_allclose, assert_equal
... |
5f793e3c4877bb12cced0ee64fa242fc98adc2174c27e40a5e573c9d09b416ee | Python | 13,625 | 381 | """Interfaces/mixins for transaction-like objects.
These objects describe the context around a unit of work. This unit of work
is very broad and can be anything from the response to a web request, the
scheduling of a workflow, the reloading the toolbox, etc.. Traditionally,
Galaxy has simply passed around a GalaxyWebT... |
533139c29413d18a2cb8c02faa767c8bb26b8f36b7d727bea6488377bc4e9525 | Python | 13,629 | 352 | # performance.py
# Authors: Alex Tseng <amtseng@stanford.edu>
# Jacob Schreiber <jmschreiber91@gmail.com>
"""
This module contains performance measures that are used to evaluate the
model but are not explicitly used as losses for optimization.
IMPORTANT: MANY OF THESE FUNCTIONS ASSUME THE INPUTS TO BE PREDIC... |
fdb21182298b97354da1f7313d7d909ef74d7bf3e7f621c9cda2e0a38f6131f3 | Python | 13,632 | 405 | import numpy as np
import numpy.typing as npt
import pandas as pd
import scipy as sc
import scipy.spatial as spatial
from anndata import AnnData
from numba import njit, prange
from numba.typed import List
from .het import create_grids
def calc_distance(adata: AnnData, distance: float | None) -> float:
"""Automat... |
9cccaea9eceb1dda1c380d718b09424808a5e6ef9060c4b5afce7a992bb763e3 | Python | 13,634 | 348 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Three-line (booktabs) tables for `manuscript/manuscript_en.docx`: apply / check.
WHY THIS SCRIPT EXISTS
----------------------
On 2026-09-12 the author converted the manuscript's tables to the three-line
convention by hand, inside Word/WPS. That created a trap: `expor... |
b2193b12301ee9af175cc0aa87e7be9125a1c07025abc49c5baf07f3e40458a8 | Python | 13,636 | 318 | import ipdb
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import statsmodels.api as sm
import torch
from sklearn.preprocessing import MinMaxScaler
import random
import tqdm
def file_name(file_dir):
for root, dirs, files in os.walk(file_dir):
# print(root) # 当前目录路径
... |
62e9c68e7ddfff6b36173686d3da70a605477129f589f9141fc293aa018bf6d9 | Python | 13,638 | 384 | # import scipy
import copy
from scipy import signal
from scipy.signal import lfilter_zi
import numpy as np
from scipy.stats.stats import pearsonr
import pdb
def make_mr():
return MR(trsamples=9750, N_thr=5, corr_thr = 0.995, forget=5, highpass=[3, 1.0, 5000])
class RtFilter():
def __init__(self, *args, **kw... |
da9292837f869d3a51eb65473fa9027bbf002a13f1d6f6e755c2c0230bb8b27f | Python | 13,639 | 377 | '''
From Niklas Wilming et al. Nature Communications 2020
https://github.com/DonnerLab/2020_Large-scale-Dynamics-of-Perceptual-Decision-Information-across-Human-Cortex
Preprocess an MEG data set.
The idea for preprocessing MEG data is modelled around a few aspects of the
confidence data set:
1. Each MEG dataset i... |
911973db15bc49868d189d8137d4ec7315562996801577abd5bee4b433f25541 | Python | 13,647 | 316 | #!/usr/bin/env python3
"""
File: prepare_picalo_files.py
Created: 2021/12/06
Last Changed: 2023/01/31
Author: M.Vochteloo
Copyright (C) 2020 University Medical Center Groningen.
A copy of the BSD 3-Clause "New" or "Revised" License can be found in the
LICENSE file in the root directory of this sou... |
466d9fdc9ea07daceff8a432e953d08b32ca5e69d00ef5db9f03cdd6e9c36eae | Python | 13,655 | 308 | import csv
import os
import argparse
from operator import add
from functools import reduce
from copy import deepcopy
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
from read_deep.rt import rt_deep
from importlib import import_module
import yaml
def get_parameter_nu... |
e4745ebff471442b047c30eb060fc86a88992d5400d611e77c46d97e89d26d83 | Python | 13,655 | 425 | from __future__ import annotations
import tensorflow as tf
from tensorflow.python.data.ops.dataset_ops import Dataset
import numpy as np
from kgcnn.graph.adj import (
coordinates_to_distancematrix,
define_adjacency_from_distance,
)
from kgcnn.graph.adj import sort_edge_indices
from ase.io import read
from pathl... |
156f3076d73b0ef370242946aff4ef19996a7283b8f361987b4518f672a6a6ca | Python | 13,657 | 421 | from __future__ import absolute_import, division
import contextlib
import itertools
import logging
import sys
import time
from signal import SIGINT, default_int_handler, signal
from pip._vendor import six
from pip._vendor.progress.bar import (
Bar, ChargingBar, FillingCirclesBar, FillingSquaresBar, IncrementalBar... |
3ac0fc937dbf63b26152f7f3eb9c2808f70040910e02a2a32c08be96b9bf0f72 | Python | 13,658 | 454 | """
Firmware management module for Arduino-based ethoscope modules.
Provides functionality to check firmware versions, compile, and upload
firmware to connected Arduino modules using arduino-cli.
Requires arduino-cli to be pre-installed on the system (SD image build-time).
All functions gracefully handle missing ardu... |
84ffd6ad072bcccb4e49870b71d742e5c44e4dcb204f3c78894b40ae13df51e0 | Python | 13,658 | 333 | from typing import Dict, Any, Optional
from src.neuron_morphology import NeuronMorphology
from src.cpd_registration import RigidRegistration
from src.synthetic_morphology import MorphologyInterpolator
from legacy import rpc_interface
from src.utils import *
class SBA_interface:
def __init__(self, sbaHostAlpha: O... |
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