sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
6039fad83ddc3e5b4f0be9687378302a30b58cd39f0dfe127ebee3e41ca74e20 | Python | 19,888 | 481 | # Source code:
# https://github.com/zbmed-semtec/doc2vec-doc-relevance-training/blob/main/code/train_model/utilities.py
# This file includes the modifications to the source codes according to this project!
import tqdm
import numpy as np
import pandas as pd
import gensim
import logging
from scipy.spatial.distance impo... |
9cd5a93f9a86273e7657011fb9e09f01c8e5b4d77cac29b24c1fc2adfd56855f | Python | 19,896 | 528 | #!/usr/bin/env python3
from collections import defaultdict
from collections.abc import Hashable
import numpy as np
from pandas import DataFrame
from scipy.linalg import eig
from pgmpy.factors.discrete import State
from pgmpy.utils import sample_discrete
from pgmpy.utils._warnings import _warn_external
class MarkovC... |
b70f28441bcb23080f1d529b993feff2c77a052c030d4eeab0071ccdf2b62aa3 | Python | 19,923 | 522 | from itertools import combinations
import numpy as np
try:
from pyparsing import Combine, Literal, Optional, Regex, Word, alphas, nums
except ImportError as e:
raise ImportError(
f"{e}. pyparsing is required for using read/write methods. Please install using: pip install pyparsing."
) from None
f... |
cf3299d93143b30cea0b0e0dcbb75af486f474c2a90c253947f2dba5781776c9 | Python | 19,925 | 555 | # -*- coding: utf-8 -*-
"""
Created on Sun Feb 9 23:49:03 2025
@author: hanna
"""
"""
FACTOR ANALYSIS || Trial-to-Trial (TTT) Models || single model per session (baseline (BL) block data only)
Spike counts summed over a fixed window of 200ms to 600ms for each neuron on each trial.
[1] data formatting
... |
8d99bb3f8586ee9c427a2237a7f546f4c0ad7aad62a1d8030fa8d25e8b7b38d8 | Python | 19,938 | 412 | #!/usr/bin/env python3
"""
═══════════════════════════════════════════════════════════════════════════════
EIF2S1 R3 REVISION — PIPELINE 2: DISEASE DATASET REPLICATION (PD BA9 RNA-seq)
═══════════════════════════════════════════════════════════════════════════════
Author: Drake H. Harbert (D.H.H.) | ORCID: 0009-0007-7... |
844b66e519d270a256d8e06570a2424deb660b127986c3d30c31cf35ac69b5b9 | Python | 19,941 | 486 | #!/usr/bin/env python
# %%
from einops.layers.torch import Rearrange
from einops import rearrange
import os
import sys
from pathlib import Path
from logging import getLogger, basicConfig
from typing import Literal, Optional
from argparse import ArgumentParser
import torch
from torch.utils.data import DataLoader
impor... |
b77385d9c7660e5254fb80eee5b8008c849bbc1f8a6114841c9d3ba09b5d1fa8 | Python | 19,959 | 453 | import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Conv2D, LayerNormalization, GlobalAveragePooling1D
CFGS = {
'swin_tiny_224': dict(input_size=(224, 224), window_size=7, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24]),
'swin_small_224': dict(input_size=... |
427029db3c146d963ddbdff294d8c62653f8f7218c06cf0d97a714bcab317f44 | Python | 19,964 | 566 | """
BAG Index Calculator (Bias-Adjusted Generalization)
Calculates the BAG (Bias-Adjusted Generalization) index and BAD (Bias-Aware
Degradation) values for evaluating model generalizability across datasets with
different bias characteristics.
The BAG index quantifies how well a model generalizes to datasets with dif... |
49749447889c3dde38e9004595bb00bb483850c4f245d556d8539e591b6eb69d | Python | 19,970 | 476 | # app_state.py
import logging
import os
import threading
import json
import re
class Var:
"""Drop-in replacement for tk.StringVar with .get()/.set() interface."""
def __init__(self, value=""):
self._value = str(value)
def get(self):
return self._value
def set(self, va... |
5362e1f9b904651de86099c0153fbf1df504ae0eead25678ee8c2626673f09a3 | Python | 19,980 | 568 | #!/usr/bin/env python
# ENCODE DCC common functions
# Author: Jin Lee (leepc12@gmail.com)
import os
import gzip
import re
import subprocess
from encode_lib_common import (
get_num_lines, get_peak_type, human_readable_number,
rm_f, run_shell_cmd, strip_ext, strip_ext_bam,
strip_ext_peak, strip_ext_ta)
d... |
6d35cb02f84f07a28b0a9482489b610140eb9c7baae6e00cd84cacea99c170d2 | Python | 19,987 | 500 | # 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
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn ... |
8935f83cb9c1a76c6e62b6f4ea7d8be4c2bf3c5177a219660335d80059a08d66 | Python | 20,055 | 602 | from __future__ import annotations
import os
import re
import subprocess
import sys
from importlib import metadata
from pathlib import Path
from threading import Thread
from typing import TYPE_CHECKING
import packaging.tags
import pytest
from installer.utils import SCHEME_NAMES
from poetry.factory import Factory
f... |
1e4ddf7d0d2a5abee1c47704b131418cc224ed7ed387e6410e97147e3c8f8c39 | Python | 20,081 | 117 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
from tractseg.libs.system_config import SystemConfig as C
# HCP_105
# (bad subjects removed: 994273, 937160, 885975, 788876, 713239)
# (no CA: 885975, 788876, 713239)
all_subjects_F... |
7879997f12ea7a2ca0973426987d8f5fed066d8b9c14b170001fab4ea05ef376 | Python | 20,092 | 410 | from typing import List, Union, Tuple
import numpy as np
import torch
from batchgenerators.dataloading.single_threaded_augmenter import SingleThreadedAugmenter
from batchgenerators.transforms.abstract_transforms import AbstractTransform, Compose
from batchgenerators.transforms.color_transforms import BrightnessTransfo... |
a633066a61470d3c07adf514c1eb33ef4695d2add7f5629bbcc666924225b216 | Python | 20,098 | 465 | #!/usr/bin/env python
# ENCODE DCC ATAQC wrapper
# Author: Daniel Kim, Jin Lee (leepc12@gmail.com)
import sys
import os
import re
import argparse
import multiprocessing
from encode_common_genomic import *
from run_ataqc import *
from encode_common_log_parser import parse_dup_qc, parse_flagstat_qc
import warnings
warn... |
713bc83a452ba849f9aa093e718481ba0faa141fd2f739bc31c516fe01a7f18e | Python | 20,113 | 520 | # Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... |
863c47c568bcaf1a9bfdcf01e4eb185c62934c4610f0e1c9988262aaf411d033 | Python | 20,116 | 581 | from __future__ import annotations
import os
from importlib import metadata
from typing import TYPE_CHECKING
import keyring
import pytest
import requests
from poetry.core.packages.package import Package
from poetry.core.packages.utils.link import Link
from poetry.repositories.exceptions import PackageNotFoundError... |
15bdca7289f34b5a0815243de579c643a1e832ed485ba11b7487788dda53f562 | Python | 20,131 | 510 | # 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 torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.... |
dff0aade64866cf9776d970148d4b054d62320ffef5fdad6198c329f8737d4e3 | Python | 20,137 | 509 | from collections.abc import Hashable, Iterable
from itertools import combinations
from typing import Any
import networkx as nx
import numpy as np
import pandas as pd
from pgmpy.base import PDAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery, _ScoreMixin
from pgmpy.structure_score import BaseStructureSco... |
4b60ed8c68864cab153b0867d56dfc0a17f690bf0adba8c6bf61adb733d5e75f | Python | 20,142 | 468 | ## run ROI-based mTE
#
# written by S-C. Baek
# update: 16.12.2024
#
'''
The following code is to run multivariate transfer entropy (mTE) analysis a subset of ROIs.
Only the ROIs that shows significant representations for phonemes or prosody were considered in the mTE analysis
to investigate the transfer of the corres... |
7ca9f91383ae4c7f13c42be9ddcd1abfacf1475b0ae3b585d13be4a785345e19 | Python | 20,161 | 555 | # -*- coding: utf-8 -*-
"""Physiological feature extraction utilities."""
import mne
import numpy as np
import scipy.signal as sg
import matplotlib.pyplot as plt
import random
import pickle as pkl
from pathlib import Path
from .preprocessing import auto_identify_bads
#%% ################## TIME-FREQUENCY ANALYSIS ##... |
5a0b712499ef939dea7ec65efa7239341d17f56875b524b75a05f2e26cfb3aab | Python | 20,169 | 552 | import numpy as np
import pandas as pd
from st_risk.eval.layer_eval import (
calibrate_proxy_threshold,
evaluate_proxy_threshold,
bootstrap_metric_summary,
paired_sample_metric_summary,
boundary_enrichment_summary,
boundary_spot_mask,
celltype_risk_association_summary,
cross_sample_laye... |
01960097a171d53a9d861b9092cc372a14fcc97f08e17f92db5948db272b5efe | Python | 20,190 | 439 | """
run_analysis.py
===============
Full parameter-sweep analysis pipeline.
For every simulation result file found under RESPATH, this script:
1. Loads excitatory spike times and the corresponding input signal.
2. Computes analytic input characterisation (H, τ_input).
3. Computes dynamical metrics (σ, χ, τ_net, ... |
d66c0d6e37a5559c3be039566199c3d5f4f0610b898356b99e352389577095f3 | Python | 20,191 | 576 | # 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
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import... |
829f8d999e7cd342fe8317bc0f9cc1558f83a69292a6d9b14a2b9e0755bc1836 | Python | 20,203 | 483 | #!/usr/bin/env python3
r"""Calculates useful metrics of model performance.
This little helper program reads in two bigwig files and a bed of regions. For
each of the regions, it calculates several metrics, and then displays the
quintiles of the values of those metrics over the regions. For each region,
four metrics ar... |
55565dc790111092217e80c382d5f6b38522e2b8e39c80813c1da0da208bfaa8 | Python | 20,226 | 469 | #!/usr/bin/env python
# ENCODE DCC ATAQC wrapper
# Author: Daniel Kim, Jin Lee (leepc12@gmail.com)
import sys
import os
import re
import argparse
import multiprocessing
from encode_common_genomic import *
from run_ataqc import *
from encode_common_log_parser import parse_dup_qc, parse_flagstat_qc
import warnings
warn... |
e24ae77793d7bfc660a294f010165e37ff000e3c931b8b91dd0c523f51a1d19d | Python | 20,243 | 514 | # 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
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn ... |
228f9db815d620f80781cc4c9c9e544fd68879c70146c4ae6006ed510e798abe | Python | 20,245 | 551 | import multiprocessing.process
import sys
k_fold = sys.argv
PROJ_NAME = "DualDataset"
def process_gan_training(k_fold: int, EPOCH_RESUME_FROM = 0, MAX_RAM_GB = 4, max_epochs: int = 300):
global PROJ_NAME
import TSA
import tools
# import analysis as A
import importlib
import numpy as np
imp... |
bda8ae994dc87d8b341e2ecda23a915526a74c9a0b53fd65e02d1cb76a8bc77e | Python | 20,260 | 546 | from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
import pytest
import scvi
from scvi.data import synthetic_iid
from scvi.data._constants import _ADATA_MINIFY_TYPE_UNS_KEY, ADATA_MINIFY_TYPE
from scvi.data._utils import _is_minified
from scvi.model import SCANVI, SCVI
from scvi.m... |
bab0800fd20037ce48bc22b5b7843412e1d1a8075d08151374970d1f743e02f8 | Python | 20,269 | 567 | #!/usr/bin/env python3
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# ========== USER CONFIGURATION ==========
main_csv = "Data/input.csv"
output_csv = "Data/input_with_splits.csv"
summary_dir = "summaries_joint"
outdir = "plots_iso_deam_splits"
window_min = 1... |
5e995cd38a1c2718547aec65082cb542820af03d4a129d38e3699270b486025f | Python | 20,271 | 410 | #! /mnt/projects/ETLAS2/data/code/diffusion_prepared_pcasl/.venv/bin/python
"""Diffusion-prepared pCASL processing utilities for ETLAS2.
This module provides:
- motion correction of diffusion-prepared pCASL series to M0 space
- robust delta-M estimation from label/control dynamics
- parameter estimation of CBF, arter... |
c9c1ebf2b0dd2a9c901395cdb8f1fac038b54a4dd18453869b15141436c99f85 | Python | 20,280 | 511 | """Implementation of MultiheadAttention.
This code has been modified from the original implementation
by Facebook Research, describing its ESM-1b paper."""
import math
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
fro... |
e90ea0448f59f1f9dad9c79ce4927ff02f2839d120d79eb68434d805fa58e48b | Python | 20,289 | 568 | import os
import pandas as pd
import numpy as np
import argparse
def kmer2seq(kmers):
"""
Convert kmers to original sequence
Arguments:
kmers -- str, kmers separated by space.
Returns:
seq -- str, original sequence.
"""
kmers_list = kmers.split(" ")
print(kmers_list)
... |
d048cb02dd1cb3dbd4b3c0cef3f1cc62428bd4d1043a8eed0227efbb325d746e | Python | 20,317 | 491 | """
BDP Pipeline — Bridge-Domain Pipeline (Multi-Source)
Step 1: CI-based partition into Bridge set B and Far set F
Step 2: Proxy tuning: train on F_proxy, validate on B_proxy (DA as part of phi)
Step 3: Final training: B_final -> target T with best phi
Partition modes:
normal — F_raw non-empt... |
cea4fe2a678a80f6866f4144263ef3f85e0716acff965c17c2e947b1f3ec7177 | Python | 20,318 | 620 | import numpy as np
import os, tempfile
import SimpleITK as sitk
from bigstream.configure_irm import configure_irm
import bigstream.utility as ut
from itertools import product
from ClusterWrap.decorator import cluster
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
from scipy.optimize import m... |
a07394fce5d03ded9b4ec699073d964b1c522f317b03f4d1087c60110f9c7d3b | Python | 20,347 | 525 | import scipy
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import pandas as pd
from tqdm import tqdm
from nistats.hemodynamic_models import spm_hrf
from scipy.ndimage import gaussian_filter
from sklearn.preprocessing import StandardScaler
from sklearn.model_selec... |
97d01fdab6c40f85aeb7ab480292fe1e992da18c987fd2b64bd8e610044fe289 | Python | 20,356 | 574 | from __future__ import annotations
import re
from collections.abc import Mapping
from contextlib import suppress
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar
from cleo.helpers import option
from packaging.utils import canonicalize_name
from tomlkit impo... |
db55498f8b81178a666cf07ac98ee9789cc6fa12b28b6233110faac64c83c820 | Python | 20,378 | 491 | import os
import copy
import torch
import random
import numpy as np
import pytorch_lightning as pl
import torch.nn.functional as F
from torch import amp
from pathlib import Path
from einops import rearrange
from torch.utils.data import DataLoader
from torch.optim.optimizer import Optimizer
from torch.optim.lr_schedule... |
e1ec242b61687bb4cbc156102a6b948ee27bc378bb96f33f75d4b7ebb46900fa | Python | 20,396 | 610 | """
Module for reading and processing PLINK genotype data and calculating LD scores.
Note:
This code is adapted and modified from:
https://github.com/bulik/ldsc/blob/master/ldsc/ldscore.py
"""
import logging
import bitarray as ba
import numba
import numpy as np
import pandas as pd
import pyranges as pr
import torch
... |
813ef52444c18e2f10080ba7d9a9a1c2669af7b832f870f580ea8c0e143f9599 | Python | 20,398 | 526 | #!/usr/bin/env python
# %%
from einops.layers.torch import Rearrange
from einops import rearrange
import os
import sys
from pathlib import Path
from logging import getLogger, basicConfig
from typing import Literal, Optional
from argparse import ArgumentParser
import torch
from torch.utils.data import Dat... |
cb99b872b69fe4779f705a835213f38a46acff55ec8ef6023b5fcf4617e5c323 | Python | 20,415 | 485 | # 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 os
from collections import OrderedDict
from fairseq import utils
from fairseq.data import (
BacktranslationDataset,... |
30db75452d8ddbe676fa4f89b5233aaa05bb5cc22ec993b57b65bb8505059c9d | Python | 20,422 | 548 | import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from utils.get_grad import get_grad
from utils.EarlyStopping import EarlyStopping
from utils.Integral import montecarlo,trapz2D,simps2D
from utils.NodesGenerater import genMeshNodes2D,genHeteroTip2D
from utils.NN import weight_init
import matplot... |
bba4816dabd40ecd75c0f88fa90d842caa63a0c726affedc71544b7de93cfe2a | Python | 20,427 | 429 | import qt, vtk, slicer
from qt import QToolBar
import os
import json
from slicer.util import VTKObservationMixin
import glob
import re
import WarpDrive
import ImportAtlas
from ..Helpers import LeadDBSCall
from ..Widgets import ToolWidget
class reducedToolbar(QToolBar, VTKObservationMixin):
def __init__(self):
... |
752a052ccca782a705a1de47fac4cbc7fe86535904100bc07e0b253e653034e7 | Python | 20,445 | 519 | ## ts_simulators outputs tree sequence. This cannot be used as a simulator for simulate_for_sbi!
import tskit
import msprime
import demes
import torch
import numpy as np
import stdpopsim
from sbi.utils import BoxUniform
class BaseSimulator:
def __init__(self, config: dict, default: dict):
for key in conf... |
b975390a0e28f887d9061c5cd1056bcb823e3f5b9804a42d1f798704bf854f27 | Python | 20,453 | 470 | import torch
import torch.nn as nn
import numpy as np
from math import sqrt
import torch.nn.functional as F
from utils.masking import TriangularCausalMask, ProbMask
from reformer_pytorch import LSHSelfAttention
from einops import rearrange
import os
import random
from utils.tools import create_sub_diagonal_matrix, plo... |
da08f891e59433f5162e4ee677f65b3e3e0f558e298e8d621c25bfeb39093d37 | Python | 20,473 | 526 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import itertools
from collections import Counter
from copy import deepcopy
from dataclasses import dataclass
from functools import cached_property
from typing import Literal, Sequence
import cachetools
import numpy as np
import numpy.typing
impo... |
354169d8e6aafe88eb48753b0ceeb0591c57dc00db895e94ca039c664cec70c4 | Python | 20,503 | 595 | """The residency benchmark's arms, equivalence check and source regime record.
Written out rather than borrowed from the live method, an arm can drift from
it, and a release the shipped method makes and an arm skips can be worth a
whole `[chunks, WINDOW_LENGTH, embedding]` block of the peak the script
reports. The reg... |
993805f67d8d8d7c109d1168201a85c5677e230942e0d9f6fb63d81e02efcd64 | Python | 20,506 | 590 | # 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 contextlib
import json
import logging
import os
import tempfile
import unittest
from io import StringIO
import torch
from fairseq imp... |
050ccf6edeca78c2b4d8bd8a8daa022a9971775bd7b2a1f84a8acbc0ceecc68f | Python | 20,507 | 588 | # Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... |
f25e9d4150395e2c0b1fefde67e9dff8d136aaa9d5fbd1ae3d9075dd8d87c219 | Python | 20,533 | 556 | import logging
import os
import pickle
import sys
from collections import namedtuple
from datetime import datetime
from pathlib import Path
from typing import Sequence
import h5py
import jax
import jax.numpy as jnp
import numpy as np
import tensorboard.summary
from jax.tree_util import tree_map
from uncertainties impo... |
96893111082e78a5d400eb10996bd8c694d22142599ca79bfca098f8e237444e | Python | 20,540 | 564 |
import os
import clip
import zarr
import torch
import sparse
import pickle
import shutil
import random
import urllib
import argparse
import itertools
import contextlib
import numpy as np
import pandas as pd
import torchvision.transforms.functional as F
from torch import nn
from tqdm import tqdm
from pathlib import Pa... |
1f6785f1f5988f4e20fd03f3ac644dff21da163044877916497920fb498d37de | Python | 20,549 | 548 | import os
import random
import anndata
import numpy as np
import pandas as pd
import pytest
import torch
from scipy.sparse import csr_matrix
import scvi
from scvi import REGISTRY_KEYS
from scvi.data import AnnTorchDataset, _constants, synthetic_iid
from scvi.data.fields import ObsmField, ProteinObsmField
from scvi.ut... |
1f0b79ee2f2c6f4aede2e71b3a750381e9e6cf40185ef6a2a84acf845ce0fc31 | Python | 20,567 | 617 | #!/usr/bin/env python3
"""
Compare posterior distributions from multiple trained models.
This script runs the same tree sequence through different trained models
and creates comparison plots showing how different architectures perform
on the same data.
"""
import argparse
import numpy as np
import tskit
import sys
im... |
882f1bcd8360d273b5bdfc5272b3f3cb87605cc09c036c5b179e377bf74dddc2 | Python | 20,582 | 539 | """
MMP Pipeline — Minimum-distance Multi-source Pipeline
1) Distance-to-target with bootstrap CI; CI-aware source gating
2) Optional CORAL harmonization to medoid
3) Two combiners: merge_then_adapt / moe (weighted voting)
4) Near/nearest proxy tuning within the target-defined near set
- if |N(t)| >= 2: u... |
5519cc724e02dede9398766c554b4a0b1b9257cb096c8afa52fe067cb13b75d5 | Python | 20,585 | 566 | import csv
import pathlib
import string
import pytest
import torch
import transformers
from d3text import utils
from d3text.utils import (
Token,
load_fast_tokenizer,
merge_off_tokens,
merge_predictions,
repr_sequence,
token_merge,
)
from d3text.models.base import load_base_model
from d3text.ru... |
3d482631610b5be35474d43b778099e8b515437a0fcde0e3f69d1eef90e05160 | Python | 20,592 | 642 | import functools
import json
import logging
import os
from pathlib import Path
import random
import re
import subprocess
from typing import Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import pandas as pd
from anndata import AnnData
from matplotlib import pyplot as plt
from matplotlib im... |
0a2b303024fd92490c185e1f27570f6d78c3d1771d536044ecbdf9f664c01510 | Python | 20,629 | 361 | import argparse
import multiprocessing
import shutil
from typing import Union, Tuple, List, Callable
import numpy as np
from acvl_utils.morphology.morphology_helper import remove_all_but_largest_component
from batchgenerators.utilities.file_and_folder_operations import load_json, subfiles, maybe_mkdir_p, join, isfile,... |
20476d73d2835f4cd6b984d03f407ef1d45afdedbc78bc0c6aba4afd606561fb | Python | 20,648 | 552 | # 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 pathlib import Path
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from to... |
07472ae26b43d8279f90feff59d51e67c23a88876b25738c680d0826d47d360e | Python | 20,657 | 505 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import sys, os, math, random
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
# ====== 工程依赖（与你给的工程保持一致）======
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.a... |
45880ce302dbc15c020e49c9e6768369965bde3c78e5282f24f4c8488906c95e | Python | 20,662 | 579 | # 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.
"""
Base classes for various fairseq models.
"""
import logging
from argparse import Namespace
from typing import Dict, List, Optional, Tuple
... |
7b7be39bb0593fd0d8542cbef0b808a43abc712573a799d6e8f0950706608e5d | Python | 20,672 | 585 | # 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 torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
Fairs... |
b85f1181970505d9cd8a9f5d538561e496db41a8dbf3a1686b1952eb65e879cd | Python | 20,687 | 608 | """Reading the corpus: what text actually reaches the tokenizer.
Both precompute commands turn a row into one string and each used to do it its
own way, neither disagreement visible from the output. A missing abstract is
`nan` or `None`, never `""`, and `str(nan)` is the *truthy* `"nan"`, so `str(
row.abstract) or ""`... |
0bc666bbaabbedafd547bf2b389bbd647efecc771dbeea0cd852fc81eb39a4e4 | Python | 20,695 | 362 | import argparse
import multiprocessing
import shutil
from multiprocessing import Pool
from typing import Union, Tuple, List, Callable
import numpy as np
from acvl_utils.morphology.morphology_helper import remove_all_but_largest_component
from batchgenerators.utilities.file_and_folder_operations import load_json, subfi... |
de3a85e3493e2cd410a7dd65b4b064aaa155fb304bb6efc53caebf0a545c092a | Python | 20,723 | 581 | #!/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.
"""
Train a new model on one or across multiple GPUs.
"""
import argparse
import logging
import math
import os
impor... |
1a602c97047fc74050c0aa5e2ca39f1c58abde5ec8d4855e39944e9e52b1dfdb | Python | 20,729 | 632 | '''
Reference:
https://github.com/bio-ontology-research-group/deepgo2/blob/main/deepgo/metrics.py
https://github.com/bio-ontology-research-group/deepgo2/blob/main/deepgo/utils.py
'''
import numpy as np
import pandas as pd
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_si... |
29e9ad72164bc849fe78724c199b954e28b24863790dc09ea58ec537e7186cde | Python | 20,741 | 514 | from collections import deque
from collections.abc import Hashable, Iterable
from itertools import pairwise
import networkx as nx
from pgmpy.utils.types import Self
class _GraphAlgorithms:
"""Graph-algorithm methods for ``_CoreGraph``-based classes (inherited, not instantiated alone)."""
def get_ancestral_... |
07a988cc8d0d9000c41254a9ceb61e838a1c7b05e6b0a5c3e6d39b499efc7ed2 | Python | 20,747 | 514 | import cv2
import copy
import torch
import random
import pyvips
import argparse
import warnings
warnings.simplefilter(action='ignore')
import numpy as np
import pandas as pd
import multiprocessing as mp
from pathlib import Path
from einops import reduce
from PIL import Image, ImageEnhance
from utils import MOUSE, MAL... |
55d68cad6c601c5d6b815a388f18875a90712010a1062483ec2339d801794111 | Python | 20,773 | 600 | import os
import statistics
import networkx as nx
import numpy as np
import pandas as pd
import tensorboardX
import torch
from matplotlib import pyplot as plt
from utils import gengraph, featgen
# dataset: stad, coad, ucec, syn1, syn2, syn3, syn4, syn5, citeseer, cora
def obtain_dataset(args):
if args.dataset ==... |
3f94ac1cc9e9547cf6665de4659a42e33b31133753bc77d531ccd101246bbdf6 | Python | 20,774 | 636 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 21 03:02:24 2025
@author: hanna
"""
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 9 23:49:03 2025
@author: hanna
"""
"""
FACTOR ANALYSIS || Trial-to-Trial (TTT) Models || single model per block (baseline (BL), perturbation (PE))
One model is fit per block (2 m... |
2271abcdb512d4624808832add8ffbeb0870702c9b3f72036b489c04309218b9 | Python | 20,784 | 576 | from collections.abc import Callable, Iterable
from typing import Literal
import numpy as np
import torch
import torch.nn.functional as F
from torch import logsumexp, nn
from torch.distributions import Beta, Categorical, Independent, MixtureSameFamily, Normal
from torch.distributions import kl_divergence as kl
from s... |
6d31307a6fc4baaea2d6698a1ea1dff0431ac20783505abc73f5fe8d2b0f9054 | Python | 20,798 | 642 | #!/usr/bin/env python3
import json
import os
import shutil
from datetime import datetime
from typing import Any
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np # noqa
import torch
import torch.nn as nn
import torch.optim as optim
from torch import Tensor
from torch.utils.d... |
bbcb3997bf5bbd4ac890e34eeefff78896aa4e5c128f3c2bd617e0fb1870953f | Python | 20,825 | 498 | """Some utilities for dealing with bed files."""
from typing import Literal, Any
import multiprocessing
from collections import deque
import os
import pybedtools
import pysam
import numpy as np
import pyBigWig
from bpreveal import logUtils
from bpreveal.logUtils import wrapTqdm
from bpreveal.internal import constants
f... |
fd69c6a093930321d63e36190f5fe295570360e6100ebb297f9533d663a5d125 | Python | 20,825 | 482 | """Generate the four main-text figures for the JCTC submission from the
results CSV/JSON cache and from the existing PNG outputs of the perovskite
active-learning pipeline.
Outputs (all under figures/):
fig1_method.png — CT2F construction diagram (3 panels)
fig2_perov.png — AL curve + parity plot (c... |
453abbc7d4df5d20ff01cd530205e4176251ca465d73583017435be750b80343 | Python | 20,870 | 482 | from copy import deepcopy
import unittest
import numpy as np
import os
from medaka.common import Region, Relationship, Sample, OverlapException
import medaka.labels
root_dir = os.path.abspath(os.path.dirname(__file__))
test_file = os.path.join(root_dir, 'data/test_probs.hdf')
def get_test_samples(num_train=1000, num... |
322027a8b4aad27b54eac2fcb1a58dc9bd9f1d9ee44563c80e1cfb32d8c26768 | Python | 20,891 | 486 | # 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 ... |
1f11050b131eb896c27c7e8dd38fea4538480a5c67c144ebd471955f4ee2d7fa | Python | 20,896 | 571 | import sys
import numpy as np
from keras import backend as K
from keras.engine import InputLayer
from keras.layers import Dropout, BatchNormalization
from keras.models import Sequential
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from model.model_utils import get_lay... |
93964e88c70406cde4b6420fbce3be2dd6f634ee8ebab92bfedf4fa270436903 | Python | 20,902 | 538 | """Shared machinery for isolated calculation validation.
The runners in this package deliberately execute copied examples under an
external results directory. They do not modify the checkout or reuse output
files from a previous run.
"""
from __future__ import annotations
import csv
import hashlib
import json
impor... |
cb8700c8b750990ea4fa14441e742515529d697c0a93a818c5c0b7e79d3255e8 | Python | 20,909 | 581 | #!/usr/bin/env python
import xml.etree.ElementTree as etree
from io import BytesIO
from itertools import chain
import numpy as np
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.utils import compat_fns
from pgmpy.utils._warnings import _warn_external
try:
... |
c8e6659410e4a7d0fc40468386731a189134eaeb9546c150cee8376d9b37f32c | Python | 20,913 | 373 | #04 3
"olddetion"
import logging
import argparse
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
import dataload.fs_datasets as data
import utils.gpu as gpu
from utils import cosine_lr_scheduler
from utils.log import Logger
from modelR.fs_2_lodet_hbb... |
dcf8ef1a82634b424f4b989184dda8ed2af3283cea463642612e6cc126ba0a3c | Python | 20,916 | 517 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 26 16:21:46 2025
@author: vbp
K-fold cross-validation of the R/S regression model
Approach: for each session, split trials into k folds (stratified by
region, so each fold has enough trials per region for the downstream
per-region fit). For each f... |
d8f8b654ced06cfe871469b27cc722e71a0be4a2b727f6f466ecf9ff823406f4 | Python | 20,928 | 630 | # 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 dataclasses import dataclass, field
import logging
import math
from typing import Optional, Tuple
from omegaconf import II
import sys
im... |
bf725994be4a573511bb9663049557cfd091c04f292c441026be2b3ff98e457f | Python | 20,931 | 627 | from __future__ import annotations
import shutil
import sys
from pathlib import Path
from typing import TYPE_CHECKING
from typing import ClassVar
from typing import Protocol
import pytest
from cleo.io.buffered_io import BufferedIO
from cleo.io.outputs.output import Verbosity
from poetry.core.constraints.version imp... |
c5cdd84205a1084f95495505baaea47d401c5335f36bbc0a71c1cd51647b6448 | Python | 20,936 | 486 | """Function that generates new inferences from trained models in the frame classification family."""
from __future__ import annotations
import json
import logging
import os
import pathlib
import crowsetta
import joblib
import lightning
import numpy as np
import pandas as pd
import torch.utils.data
from attrs import ... |
efd8fdb7f6d2a0b3af6bba9ef3f261442e633ab846924851aa359aed44725987 | Python | 20,939 | 416 | from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import numpy as np
import pandas as pd
from st_risk.reporting.anchored_validation import PROXY_SHORT_NAMES
from st_risk.reporting.gallery import dominant_celltype_table, repo_relative_link
ABSTAIN_REVIEW = "review needed"... |
f049e7e38f0da3580ccc6e98cea97a9107f1da165b68a450da932ddd9fb5bb36 | Python | 20,940 | 530 | #!/usr/bin/env python3
"""Implements a small interpreter.
Syntax:
This interpreter interprets a subset of the Python programming language,
with an extension to the behavior of default arguments in lambdas. Since it
uses the Python parser, it obeys Python's operator precedence. If it
encounters a name in the expressio... |
0e1c640d0ac464ca0110b54653ef13d4c35a875174dc5d09d90fc1dde74d742b | Python | 20,941 | 433 | """
Code to load data and to create batches of 2D slices from 3D images.
Info:
Dimensions order for DeepLearningBatchGenerator: (batch_size, channels, x, y, [z])
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
import random
im... |
5406f874ace84c4fbd8babef704a145911174579837a1cbfe811fbaec47bdf21 | Python | 20,977 | 551 | import gradio as gr
import torch
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
import re
import yaml
import requests
import json
import time
# from spellchecker import SpellChecker
from easydict import EasyDict
from scipy.stats import norm
# from .init_model import model, all_index, ... |
c5a1f8c807e521e18e48d742c5a33b0279b702ad9be36738f99608bb1ae53a92 | Python | 20,977 | 612 | # 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
import torch.multiprocessing as mp
import numpy as np
import json
import torch
from torch.distributions.categorical import Categori... |
5e1503945b89173c30dc7f43dad307d7de389ceb9099c71cd38b0fa120e7c780 | Python | 20,987 | 672 | """
kmeans_effect_clustering
========================
This module provides utilities for performing K-means clustering on gene expression
effect matrices and generating visual outputs for analysis. It integrates with
AnnData and Scanpy for preprocessing, clustering, and visualization, and supports
end-to-end workflows... |
ce6bb45fc31a6a868a1742c71706242a5b54cc7eede0b296e708bd6f0a652812 | Python | 20,990 | 501 | '''
By K. Butenko
Runs OSS-DBS to compute stimulating fields for sEEG
'''
import pandas as pd
import numpy as np
import sys
import os
import json
import subprocess
import re
from ossdbs.electrodes.defaults import default_electrode_parameters
def check_electrode_availability(reco_electrode):
"""
... |
c0aea4c0c5f19fbabf2242ae5764ec1ffcd066b45afb5203d40b392ccf993c60 | Python | 20,997 | 559 | # Copyright 2015 Google Inc. 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 applicable law or a... |
e416b6190b05674563146a3d215cf62fe6b1811f30636f41d7e47fc0143f739a | Python | 21,006 | 505 | import copy
import numbers
import os
import signal
from textwrap import dedent
import matplotlib
matplotlib.use('Qt5Agg')
import yaml
from PyQt5.QtCore import QTimer
from PyQt5.QtCore import pyqtSlot
from PyQt5.QtWidgets import QDialog
from PyQt5.QtWidgets import QDialogButtonBox
from PyQt5.QtWidgets import QFileDial... |
f0c775580f89dcabd3233f0de70798e3e39dc3f88bd8b71cd2d6d7b7b4cad919 | Python | 21,037 | 523 | """What every tracked metric's y-axis actually is.
MLflow charts a metric under its key and offers no field for a unit, a
direction, or the denominator an average was taken over, so the keys are
written to say what they are and this module renders the glossary
`tracking.run` posts as the run's description. A leaf: no ... |
c6dfa524a9a1ab0916d4aeae45b25e7976d1699069db64a2566a7b0d696d6d40 | Python | 21,039 | 471 | #!/usr/bin/env python3
"""A little program that displays progress information as a model trains."""
import curses
import sys
import argparse
import re
from typing import Any
from bpreveal import logUtils
# These will be initialized after we have a window.
_COLOR_GREENBG = _COLOR_REDBG = _COLOR_ALARM = _COLOR_HIGHLIGHT ... |
484a4710a1780444a6f205d9e5b28bf2cec3101df66dc5fb036b8742a1ed3d74 | Python | 21,041 | 534 | """Metrics for segmentation adapted from information retrieval."""
from __future__ import annotations
from collections import defaultdict
from typing import List, Literal, Mapping, Tuple
import attr
import torch
from vak.common import validators
def find_hits(
preds: torch.FloatTensor,
target: torch.FloatT... |
9a766c6b4f578a3ce8658d0fddc31172eec91b649ad0e81c8302827dab425a8b | Python | 21,082 | 507 | # utils/label_utils.py
import os
import shutil
import pandas as pd
import pickle
import threading
import torch
import torch.nn.functional as F
import evfuncs
import re
from scipy.signal import spectrogram
from PyQt6.QtWidgets import QApplication
from moove.qt_helpers import invoke_in_main_thread, show_info
from moove... |
e8134f7e4245505cdd3718c2f095f2fe0663a8c9ed92f880d247f045504d5f62 | Python | 21,084 | 554 | import itertools
from os.path import join
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
from lifelines import KaplanMeierFitter
from lifelines.statistics import logrank_test
from matplotlib import pyplot as plt, gridspec
from matplotlib.ticker import NullFormatter, FormatStrFormatter
f... |
10652aa75c5401346cf4d6eab2e85fc5359afe1825973504ca1e1e062a6c4fd1 | Python | 21,103 | 470 | #!/usr/bin/env python3
"""
B0 variant generation using either 2D polynomial or 3D spherical harmonics fitting.
Supports two fitting methods:
1. poly2d: 2D polynomial fitting per slice (like the original B0 fitting process)
2. sh3d: 3D spherical harmonics fitting across the entire volume
Both methods:
- Use only mask ... |
99f310693f1603b8e8905c8312a808fe4e5b1394e0c55f181f19863d0ccecb0d | Python | 21,113 | 455 | #!/usr/bin/env python
"""
# Author: XU Kui
# Created Time : 09 Nov 2020 11:14:31 PM CST
# Description:
decription: x
"""
import os,sys
import numpy as np
import xgboost as xgb
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import argparse
from sklearn.metrics import r2_score
# feature_list=... |
ba02ce4706a5da903a3b1060080e901a74de626e19f5e2fb9ccd883322973782 | Python | 21,126 | 631 | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 22 10:20:43 2025
@author: mayc06
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pynumdiff
import utils
from utils import unwrap_angle,wrapToPi
import warnings
from scipy import special
def get_trajlist_from_behd... |
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