sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f9cdb5335d630d8a8ef6015bee330ccf5e90014bfa9f9341cada67095ce8c10f | Python | 8,432 | 210 | import pandas as pd
from pgmpy.base import DAG
from pgmpy.estimators import ParameterEstimator
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.parameter_estimator import DiscreteBayesianEstimator, DiscreteEM
from pgmpy.utils._warnings import _warn_external
cl... |
bee8110d1510137578d0da9a856eb5aff174c165daadafba11472e9e94dfa643 | Python | 8,433 | 232 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def get_init(myelin_data, gradient_data, highest_order, init_para):... |
8966b2febd0f1b52f4a39cca976b759331ec2c459ce60ef4ed2f6d71f94ae888 | Python | 8,437 | 236 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
The required parameters include model_path, data_path, save_path, save_type
Given the input sequences, output and save specific predictions
"""
import argparse
import os
import sys
from os import mkdir
import torch
from torch.backends impor... |
96302fe74db1b671f7942e550310feefd725c7b7a91088bbefe39f480801e349 | Python | 8,439 | 242 | # 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 pathlib import Path
from typing import Dict, List, Optional
from dataclasses import dataclass
import torch
from fairseq.d... |
e8649fe21de30fd8e5c3cd930c9ed191da49748a6f188abaa07b41e47041780a | Python | 8,440 | 240 | """Configuration for Garfield"""
import os
import seaborn as sns
import matplotlib as mpl
class GarfieldConfig:
"""configuration class for Garfield"""
def __init__(self, workdir="./result_Garfield", n_jobs=1):
self.workdir = workdir
self.n_jobs = n_jobs
self.set_gf_params(... |
878f6aa1ac89d8793c53525bbaeaf14b92817306f06b453d44221dc6cec88cf2 | Python | 8,445 | 242 | ## functions for mTE analysis
#
# written by S-C. Baek
# update: 16.12.2024
#
"""
Collection of functions to run multivariate transfer entropy (mTE) analysis.
The code here were mainly created by tranlating the original MATLAB code in the following Github repository:
https://github.com/ide2704/Kernel_Renyi_Transfer_Ent... |
268ff79eff50f00db34baeca65e23bac1ba7baf3599709be9d6baf66c70a6c9a | Python | 8,448 | 252 | # extract_timeseries_c3.py
# Extract timeseries for cluster C3 (ds003721) from *preprocessed* SPM outputs,
# using Cambridge/BASC multiscale atlas at a fixed scale (e.g., scale064).
import os, glob, json
import numpy as np
try:
from nilearn.maskers import NiftiLabelsMasker
except Exception:
from nil... |
c830f176e267c351116bde85b03ddf5d793e397a57bde70c3cf5ff733e086cd8 | Python | 8,449 | 233 | from collections.abc import Iterable as IterableClass
import pandas as pd
from rich import print
from scvi import REGISTRY_KEYS
from scvi.external.scviva import SCVIVA_REGISTRY_KEYS
from scvi.model.base._de_core import _fdr_de_prediction, _prepare_obs
from scvi.model.base._differential import DifferentialComputation
... |
de01a5ac521e27d3758f207240dda98eb7fbd9023508c3cb7e2392ac97eee59a | Python | 8,450 | 241 | import os
import pandas as pd
import gzip
import GEOparse
import numpy as np
import mygene
dest_dir = "__MS_GEO_ROOT__/Expression_Data"
def get_ensg_to_symbol_mapping(ensg_list):
mg = mygene.MyGeneInfo()
print("Querying mygene for symbols...")
results = mg.querymany(ensg_list, scopes='ensembl.gene', field... |
03447cd78cf43bb969fd847a950d22ef8f2c5b50e5b62161cb59e6db33c85bf1 | Python | 8,451 | 224 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(myelin_data, highest_order, init_para):
'''
This function is i... |
67b55171e39334f7f4f8670ec4b6009e45c55879022aafb08f19721eae06e1a6 | Python | 8,455 | 222 | #!/usr/bin/env python3
"""Select regions from a bed file to match the GC distribution of a reference bed.
For training bias models, the ChromBPNet method requires that the bias regions
match the peaks regions in GC content. This little script arranges for that.
You feed it two bed files. One represents your training ... |
568f23c1890d96e63f4a559876de81683cf2efd7ab9f4f541469e9162052ac3f | Python | 8,460 | 225 | # -*- coding: utf-8 -*-
"""
Created on Mon Jul 15 11:52:25 2019
@author: 俊男
"""
# In[] Import Area
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
# In[] sample_model(): Draw the model as line, and sample data as scatter
# USAGE: model_drawer.sample... |
b14835e8e973d41bd23b45ec8a3e7365683a8e6c320ba3e279669ec9c41d8ff5 | Python | 8,463 | 270 | from lifelines import CoxPHFitter
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import warnings
basedir = "..\\Data\\"
outbasedir = "..\\Out\\"
alg = 'COXPH'
ctype = 'BLCA'
dataseed = 100
algnpseed = 13333300
survindex =... |
f9bab035f5b5c4f5536ef892b3d6732ae5ce1216b23131590636be91fc34e8b5 | Python | 8,463 | 175 | import os
import pandas as pd
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader, TensorDataset
from transformers import BertTokenizerFast, PreTrainedTokenizerFast, AutoTokenizer,BertTokenizer, DistilBertTokenizer, BertForMaskedLM, RobertaTokenizer, XLNetTokenizer, AlbertTokenizer, Electra... |
07f3e756296dbd92f0b6a59c1c32b0c3e32509423d09a786b8bab6f6bc5f9978 | Python | 8,471 | 270 | import copy
import glob
import os
import random
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.stats import binned_statistic
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
class AddGaussianNoise:... |
2ca1fb5c629f08b6afd3e2a68d5eb50259e5bfa3f625d524b3d9d263c0804922 | Python | 8,473 | 186 | import numpy as np
from phantoms.phantom import phantom
import func.geometry_func as gf
import func.BphP_func as bf
import logging
#logging.basicConfig(level=logging.DEBUG)
class BphP_cylindrical_phantom(phantom):
def gen_regions(self, cfg : dict, rng : np.random._generator.Generator,
... |
fdf5e6a6a71965c0427cf54c871bde4fe8bcf61b997bff50579913f4cc91dda4 | Python | 8,474 | 212 | """Phase 17 — drive DFT/AIREBO ratio spread below 1% via more realizations.
Variance decomposition (phase 12+15+16) showed σ_within = 0.0716 dominates
σ_between = 0.0313; the 2.4% cross-lattice spread is statistically consistent
with a single common ratio. Need n=30 per lattice → ratio SEM ~1%.
Adds 24 more clusters... |
4a5e9f092e7f08daadd12b8bc954f0d6a6fb8fbf3ab16f9b86e24d1cdd964696 | Python | 8,478 | 211 | import numpy
import pandas
import logging
from .JointAnalysis import Context, ContextMixin
from .. import MatrixManager
from .. import Exceptions
from .. import PredictionModel
from ..misc import GWASAndModels
from ..misc import DataFrameStreamer
from ..misc import KeyedDataSource
from ..genotype import GeneExpression... |
a5d8643acf14499158abce570fe933e21ac43e8ff7ba14b738e526210a22a443 | Python | 8,479 | 245 | """
Acr sampling pipeline using Evo.
Usage: python pipelines/acr_sample.py --config <config_file_path>
"""
import argparse
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict
PACKAGE_ROOT = Path(__file__).resolve().parents[1]
if str(PACKAGE_ROOT) not in sys.path:... |
03327199697929c71462f3d503cc238ca67117bc191d4c21c8ccbe95279195cb | Python | 8,480 | 189 | import tqdm
import multiprocessing
import pandas as pd
import numpy as np
import scipy.stats
from sklearn import linear_model
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import... |
0296ad1a87cc9d4fa4f49df16cf79f3f88a84351e43d1cb906d00f4536125684 | Python | 8,487 | 218 | import pyBigWig
from Bio import SeqIO
import polars as pl
from src.utils import gtf_to_SJ, read_gtf, read_SJ
import os
import numpy as np
import polars.selectors as cs
from pathlib import Path
bw = "data/hg38.phyloP100way.bw"
pbw = pyBigWig.open(bw)
genome = list(SeqIO.parse(os.getenv("GENOMIC_DATA_DIR") + "/GENCODE/... |
7b1180faea1750033fd2256e6897a42b26a89ee6a43fc649dcda5c86870c710a | Python | 8,488 | 203 | ##
# Doxygen filter for Google Protocol Buffers .proto files.
# This script converts .proto files into C++ style ones
# and prints the output to standard output.
#
# version 0.6-beta
#
# How to enable this filter in Doxygen:
# 1. Generate Doxygen configuration file with command 'doxygen -g <filename>'
# ... |
337f9dd75edb897e6e456f5b03dd58164f7b60f149df053075b662b311bd7060 | Python | 8,489 | 243 | from pathlib import Path
import mne
import mne_bids
import numpy as np
import pytest
from scipy import signal
import pylossless as ll
def test_empty_repr(tmp_path):
"""Test the __repr__ method for a pipeline that hasn't run."""
config = ll.config.Config()
config.load_default()
fpath = tmp_path / "tes... |
6b8cc77d78fba382ae4e770a4e14db28d2b5bfc85ffbf4adbfbe53ae93665aab | Python | 8,494 | 230 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def get_init(gradient_data, highest_order, init_para):
'''
... |
ca77f8272b23532d229af9aaefdaf681ec56915286f2bafaf4bd049271605595 | Python | 8,494 | 344 | import os
import torch
import numpy as np
from torch_geometric.loader import DataLoader
from sklearn.model_selection import KFold
from scipy.stats import pearsonr
from tqdm import tqdm
from model import GeometryAwareGNN
from model_ablation import GNN_NoRBF, GNN_WithBN
ABLATION_MODES = [
"NoRBF",
"WithBN",
... |
07e0dab3615decab1a33b02c52fec67e274048da84c94320606f05eeab24e9c1 | Python | 8,497 | 217 | import numpy as np
import numpy.matlib as matlib
import numpy.linalg as linalg
import sys # to implement args later
import copy
import warnings
def LyE_W(x, Fs, tau, dim, evolve):
"""
inputs - x, time series
- Fs, sampling frequency
- tau, time lag
- dim, embedding dimension
... |
02a06b44fcd2b4821264bd9a59fa10a8ebb371d783119bf25dd89839aceb573f | Python | 8,499 | 224 | import os
import glob
from typing import List, Tuple
import numpy as np
import matplotlib.pyplot as plt
try:
import pydicom
except ImportError as e:
raise SystemExit("The 'pydicom' package is required. Install with: pip install pydicom") from e
def _find_dicom_files(folder_path: str) -> List[str]:
"""Re... |
c9fd9f7b20beb3aef911576885978400bdcf460ed110f76e6dc23167c64ce340 | Python | 8,499 | 220 | """
Split a sub-dataset from the training/test set into high-expression and low-expression subsets based on the expression level of a specified gene or protein.
Examples of usage:
# 1. Median Gating:
python scripts/split_by_expression.py \
--input path/to/data/MERFISH_mouse_cortex_test.csv \
--config configs/e... |
0e47800acc7cec9bce88aab587d66b6743cf76135c06816728a844ceda3f8add | Python | 8,504 | 210 | """
This module runs rna rna interaction prediction.
Author: wangning(wangning.roci@gmail.com)
Date : 2022/12/7 7:41 PM
"""
import os.path as osp
import argparse
from functools import partial
import paddle
from paddlenlp.utils.log import logger
from paddlenlp.transformers import ErnieModel
from paddlenlp.datasets i... |
84bbd141de74e8be2ce84180efe7e7f92d1ddc111d3977101b712e4b4fd564e6 | Python | 8,505 | 320 | #!/usr/bin/env python3
"""
Human fetal anatomical segmentation pipeline
Adapted in Nipype from an original pipeline of Alexandre Pron by David
Meunier.
parser, params are derived from macapype pipeline
Description
--------------
Base pipeline for running the dhcp pipeline (segmentation and
surface extraction) from a... |
c56e9e34c1af8b81a85c1ee82c999868197cca8a05c9a8ea0733b0e1e502d9a9 | Python | 8,507 | 238 | """
Shared Utility Functions for Medical Image Processing
This module provides helper functions for medical image preprocessing including:
- Format-specific loading (NIfTI, DICOM)
- Anatomical reorientation
- Physical spacing computation
- Standardized preprocessing pipeline
These utilities support the main image loa... |
41bd77278c8bb62764d0a7a913be9706910b775dd112dcb090afdb91d0ca23b4 | Python | 8,514 | 238 | import torch
from torch import nn, Tensor, FloatTensor
import torch.nn.functional as F
from typing import Dict, Iterable, Callable
from PIL import Image
import numpy as np
from math import exp
from scipy import optimize
from lucent.optvis import param, transform
from lucent.optvis.objectives import wrap_objective
d... |
c6cf6111c6d42509a9756148c127790a817dd400bb55dd601899d5c0311ff881 | Python | 8,514 | 170 | #!/usr/bin/env python3
# MIT License
#
# Copyright 2024 Broad Institute
#
# 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 u... |
321f866345f2cbc12d83c63389ba53ca6072993ed3f93a14f7cd386d75b55676 | Python | 8,515 | 215 | #!/usr/bin/env python3
"""Minimal HIPPIE-WF+3DACG trainer for the CVAE-only experiment harness.
Trains HippieWF3DACGCVAE on one or more C4 H5 files and saves a checkpoint
for the G1–G7 experiments. Multi-dataset training is required for source/
super_region/technology embeddings to be informative — single-dataset =
co... |
302c4571da7afc4935b604b90be81c06e418bb06fd35f27a4f77585e0a759d38 | Python | 8,519 | 182 | # coding=utf-8
import os
import sys
sys.path.append("..")
sys.path.append("../utils")
import numpy as np
import cv2
import random
import torch
from torch.utils.data import Dataset
import config.cfg_lodet as cfg
import dataload.augmentations as DataAug
import utils.utils_basic as tools
class Construct_Dataset(Datase... |
1a9691eb28b082e57a4c35d42accf428709e68b0e4c056505326df658950a1f9 | Python | 8,520 | 225 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'''
This function is... |
2ed79555cec01e5cbd542db828761b7ebf34c0ab2be712b802c5387966b2a1b9 | Python | 8,523 | 202 | """This module contains some routines for sorting volumes and lists voxel-wise.
For example, in some applications it can be desired to sort volume fractions voxel-wise over an entire volume. This
module contains functions for creating sort index matrices (determining the sort order), sorting volumes and lists
and anti... |
2b1433abe19a4821346f4715271dacfbea1435e95c6f2e335812e5af41087180 | Python | 8,527 | 239 | # 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 torch
import torch.nn as nn
class RowSelfAttention(nn.Module):
"""Compute self-attention over rows of a 2D input."""
... |
e9050bcbe794084857e0abcdc925b78d6f321b2bc47e54edd46852fb963fa8af | Python | 8,537 | 200 | import os
import polars as pl
import sys
import glob
pl.enable_string_cache()
def read_gtf(file, attributes=["transcript_id"], keep_attributes=True):
if keep_attributes:
return pl.read_csv(file, separator="\t", comment_prefix="#", schema_overrides = {"seqname": pl.String}, has_header = False, new_columns=... |
2e7d5d95a206248063aac0477c035eb06cc6d381bcf6f7326b4b979ef1b59009 | Python | 8,539 | 244 | # Copyright 2021 RangiLyu.
#
# 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 in wri... |
767baa13735e02dec9f8eb290718ddba91e154c04fcd35897ef6aa0905c385c1 | Python | 8,540 | 264 | """Tests for RAG-GNN model."""
import numpy as np
import pytest
from rag_gnn import RAGGNN, GNNEncoder, KnowledgeRetriever, FusionModule
from rag_gnn.utils import normalize_adjacency, compute_silhouette
class TestGNNEncoder:
"""Tests for GNN encoder."""
def test_init(self):
encoder = GNNEncoder(n_la... |
7693095e968f6b5bf0b96e03f0e912f6d7856d32223cae349e212bff30eef51e | Python | 8,540 | 237 | """
Functions for removal of neuropil from calcium signals.
Authors:
- Sander W Keemink (swkeemink@scimail.eu)
- Scott C Lowe
Created:
2015-05-15
"""
import numpy as np
import numpy.random as rand
from sklearn.decomposition import FastICA, NMF, PCA
def separate(
S, sep_method='nmf', n=None, maxi... |
c370ae6d83c3feb24c30cac0ad0d20f1280b92238e28ce6bb0312ffb79272bc9 | Python | 8,540 | 263 | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.layer_norm import Lay... |
6355d1f08c19cd3a36c49b0bc0406fb70b50ba2bf4667a245518760443062a19 | Python | 8,543 | 221 | #!/usr/bin/env python3
"""
Author: Ken Chen
Email: chenkenbio@gmail.com
"""
import argparse
import os
import sys
from tqdm import tqdm
import numpy as np
import h5py
import pickle
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from collections import OrderedDict
from torch.cuda.am... |
e7a27dac3b53e134cacbb5f81f9bddb16c4f683286500536f395f6c7189604df | Python | 8,546 | 223 | #!/usr/bin/env python3
import numpy as np
from scipy.special import logsumexp
from tqdm.auto import tqdm
from pgmpy.estimators.base import MarginalEstimator
from pgmpy.factors import FactorDict
from pgmpy.utils import compat_fns
class MirrorDescentEstimator(MarginalEstimator):
"""
Class for estimation of a ... |
79991db06b1392a4bed9d602302c5ca32d5d15dcccbf42919bf1bc2164edec5f | Python | 8,548 | 235 | import logging
import os
import pickle
import re
from functools import partial
from typing import Tuple
import haiku as hk
import yaml
from .log import H5MetricLogStream, MultiStreamMetricLogger, TensorboardMetricLogStream
from .types import ModelDimensions, TrainState
from .wf.envnet import EnvNet
from .wf.orbformer... |
e0957b333869136fa4c099f2b264dbd68f954ab63650a998251c27ed7bd12215 | Python | 8,548 | 266 | import torch.nn as nn
import torch
from torch.nn import functional as F
from utils.Geometry import LineSegement,LocalAxis,Geometry1D
import numpy as np
class Embedding:
def __init__(self,
HeavisideZero = 0):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.devic... |
cc14b0c0b2b2162e031b0773f827c5ff8c3799a390e11be024ea6db5f9a139f4 | Python | 8,550 | 222 | from pathlib import Path
import anndata as ad
import numpy as np
import pandas as pd
from st_risk.models.base import BaseSpatialModelOutput
from st_risk.models.cell2location_model import Cell2LocationRunner
from st_risk.models.destvi_model import (
DestVIRunner,
_destvi_sampled_proportion_summary,
_normal... |
03e122bbf7a6577159a16d45daac8012104e72dd0415ac0f33fed60c1bcb006a | Python | 8,553 | 243 | import logging
import os
import hydra
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.layers.torch import Rearrange
from torch.utils.data import DataLoader, Dataset
from .utils import Accuracy
logger = logging.getLogger(__name__)
def save_ckpt(model, path, model_class):
ckpt = {
... |
3581b45661b8e10ae40995fef5f0a4db9cc19089a04d2870eba79f5c47bce417 | Python | 8,553 | 234 | import torch
import torch.nn as nn
import math
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model).float()
pe.require_gr... |
c2a27c1bd78aaf379c70a3771baccb6683ba2c883272a7b14f979b3f5a698303 | Python | 8,553 | 230 | """
Routines to read in association file between genes and GO terms.
"""
__copyright__ = "Copyright (C) 2010-present, H Tang et al. All rights reserved."
__author__ = "various"
import gzip
import os
import sys
from collections import defaultdict
from .anno.factory import get_anno_desc, get_objanno, get_objanno_g_kw... |
cbc692c733bec24e4c482f13f979166347b3c33e818ca9f8af9e2ea36e0c832f | Python | 8,555 | 266 | import torch.nn as nn
import torch
from torch.nn import functional as F
from utils.Geometry import LineSegement,LocalAxis,Geometry1D
import numpy as np
class Embedding:
def __init__(self,
HeavisideZero = 0):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.devic... |
91b518745b006fb826b79ea439b0c6fe3ce8384888c1f6a147176eb0b37d59d2 | Python | 8,557 | 235 | 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... |
1374828938eac04f42a7c9e65c78314be3b67a8a5c32889e7b9bf9a69071e3ac | Python | 8,562 | 179 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
from utils import *
from self_calibration import *
from torch.fft import fftshift, ifftshift, ifft2, fft2
from fft_conv_pytorch import fft_conv, FFTConv2d
dtype = torch.float32
class forward_model_wf_variant(nn.Module):
def __in... |
38f5ee87089fc2cdee0d598ee75b22a4375be091014a72e0945049268b087b35 | Python | 8,563 | 167 | import json
import tempfile
import matplotlib.pyplot as plt
from torch.autograd import Variable
from torch.utils.data import DataLoader
from pycocotools.cocoeval import COCOeval
import time
from tqdm import tqdm
from dataload.cocodataset import *
from eval.evaluator import Evaluator
from utils.utils_coco import *
from ... |
50145670185c4a5e3fc3bb047b3bc60aeb7ba9157b80d656eef445d286c3faeb | Python | 8,565 | 167 | import json
import tempfile
import matplotlib.pyplot as plt
from torch.autograd import Variable
from torch.utils.data import DataLoader
from pycocotools.cocoeval import COCOeval
import time
from tqdm import tqdm
from dataloadR.cocodataset import *
from evalR.evaluator import Evaluator
from utils.utils_coco import *
fro... |
9c8720fdd4819c044635c7bf23677c925ad551767fb5d2218aa481afef812195 | Python | 8,566 | 150 | import warnings
from typing import List, Type, Optional, Tuple, Union
from batchgenerators.utilities.file_and_folder_operations import join, maybe_mkdir_p, load_json
import nnunetv2
from nnunetv2.configuration import default_num_processes
from nnunetv2.experiment_planning.dataset_fingerprint.fingerprint_extractor imp... |
9f0712f3ab7ddfa025d66542f1ed5c6cca1702a435ae8d4f6e9a070ed6d453f5 | Python | 8,566 | 197 |
from analysis.compute_modelFeatures import computePreTrainingFeatures, computePostTrainingFeatures ## wandb has to be setup
from analysis.paperFigures import *
# Plotting arguments
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['axes.spines.right... |
0417588c0b5ef4d4ca31a28fa5d2bd3a2595ce978d335606b795d2b220e6af06 | Python | 8,568 | 176 | """A simple set of functions that train with a curses display."""
import json
from typing import Any
import h5py
from bpreveal.internal import disableTensorflowLogging # pylint: disable=unused-import # noqa
import tensorflow as tf
import keras
import numpy as np
from bpreveal.callbacks import getCallbacks
from keras.c... |
a929ae03a47a8308a986d168d0f61b5f29f07e2d6c21e861937bca283033b606 | Python | 8,568 | 206 | # Ranger deep learning optimizer - RAdam + Lookahead + calibrated adaptive LR combined.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# Ranger has now been used to capture 12 records on the FastAI leaderboard.
#This version = 9.13.19A
#Credits:
#RAdam --> https://github.com/LiyuanLucasLiu/... |
15b1350aa67f2f9daadc9ff3698802d3b99087fd855709fb1fc272b846c9ea7a | Python | 8,569 | 235 | # %%
"""Generate noise-robustness data for Fig. 3.
The trained model weight files are assumed to already exist as model_<id>.
For each model and noise level, Gaussian input noise is sampled several times;
the stored value is the mean R2 over those repeated noise trials.
"""
from pathlib import Path
import numpy as np... |
baabd26e38a353cb9a863a074082d1c4f182edc1029588ba0875a8e7a2464212 | Python | 8,569 | 208 | """Per-food KAN interpretability (Paper #2), single quantification target = authentic fraction %.
Mirrors kanfood.interpret_mango: compact (3,) B-spline KAN read off the SAME pipeline as the benchmark
(preprocessing + group hold-out from <food>_meta.json), giving a closed-form symbolic equation, an
accuracy-vs-complexi... |
f46723e906e974201182019a1890dbdd5ec69450034042cadfc7f40a9e29142e | Python | 8,573 | 242 | # 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 torch
import torch.nn as nn
class RowSelfAttention(nn.Module):
"""Compute self-attention over rows of a 2D input."""
... |
9969c151ecfcfa4eb814b46e5f63a91870fef2a283d59b7bd25ad3fb4173e62a | Python | 8,574 | 228 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
9c2c43d9fab3cd55dfa94dd16926aedebb5efec74ddbabb03527d7cbd589b8ba | Python | 8,577 | 300 | """
Decorators for wrapping/unwrapping vtk objects passed/returned by a function.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import inspect
import functools
from .wrappers.base import (wrap_vtk, _wrap_input_data, _wrap_output_data,
_unwrap_input_dat... |
3daf8a4975eccbf9289686f7d335729134e888ceda2c06fd022f39d2f86a2582 | Python | 8,579 | 226 | import json
import os
import pandas as pd
from mudata import MuData
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import mean_absolute_error as mae
from sklearn.metrics import r2_score
from scvi._types import AnnOrMuData
from scvi.model._utils import REGISTRY_KEYS
from scvi.model.base import BaseMo... |
740982ca452ac3fb7375802b74af66ad9eba3debcd998ad6390589da6e6e9a08 | Python | 8,583 | 218 | import itertools
import networkx as nx
from pgmpy.base import ADMG, DAG, MAG, PDAG
from pgmpy.identification import BaseGraphicalIdentification
from pgmpy.utils.sets import _powerset
class Adjustment(BaseGraphicalIdentification):
"""
Given a causal graph, finds the adjustment set.
This class implements... |
e77f99ae2237d0b71a776e25adc4112c5f0739cd00c3e9aea88a266a377dceb4 | Python | 8,583 | 229 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions_example as fc
import warnings
def get_init(myelin_data, gradient_data, highest_order, ini... |
13d91cbd281f667db38a3744e66142029d5b0f3e494dda4b11a6a008f9ca1262 | Python | 8,585 | 259 | """Predictions in the write shape annotation-hub accepts on `POST /save/`.
Maps one record `infer` wrote onto one annotation object, carrying what only
the predictions know. What the hub also requires and they cannot — the
posting account, the project, the reference's own key and its bibliographic
record — belongs to ... |
8ea5c38587be3ae5a111e3d66cda0e6501628673a42c79a92a1dba6caa2bff15 | Python | 8,585 | 218 | """Pure unit tests for `d3text.models.ner.NERClassificationModel`.
Every test here runs on CPU with tiny synthetic tensors and no data, network,
or GPU. Methods are exercised through the `stub` fixture (see
`tests/conftest.py`), which supplies only the attributes each method reads.
"""
import torch
from torch.utils.d... |
83ed98b63dc5c96481ce604291d8b636939c5a3086ed414009ea786b4407d4e1 | Python | 8,587 | 278 | from scipy.spatial import KDTree
import numpy as np
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from .figure_Tools import point_value_PMF_1darray
def nn_PMFs(ax, group, df, x0, x1, n_bins, n_boots):
"""Nearest neighbour PMF plot"""
# get data arrays
a = df.loc[df.Subtype == g... |
8da2030a0fbf8029b8ed68a3c3e4ceb34c4fed277d06b4194c001baf2a0426b2 | Python | 8,588 | 291 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import bisect
import os
import pickle
from abc import abstractmethod
from collections.abc import Iterable, Iterator, Sequence
from pathlib import Path
from typing import Any, Generic, TypeVar
import lmdb # type: ignore [import]
from tqdm import... |
c979d3e0a2f397750fb9a5ee3c621d4045cc942046008fb512498f8bcb5e4dbf | Python | 8,589 | 212 | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. 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://ww... |
e87f2e5f057f952405994ff8d9cf6c6920a8030f5f852c0f65ba74debfb541aa | Python | 8,590 | 268 | from __future__ import annotations
import shutil
from pathlib import Path
from typing import TYPE_CHECKING
from typing import NoReturn
from unittest.mock import PropertyMock
import pytest
import requests
import responses
from poetry.factory import Factory
if TYPE_CHECKING:
from cleo.testers.application_tester... |
523e86fcde32106c36bf59df346b3309dd8201b5854f88ae49e18a90881eaa22 | Python | 8,591 | 214 | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. 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://ww... |
2d15b5c040232ddfc6df04f171cbd8e734c913c97ac35a824f1160cda2a0acd3 | Python | 8,592 | 195 | # 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 fairseq.tasks import register_task
from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
from fairseq.utils impo... |
137dadb37b781fd5141580ec3df04c6736836112d94570adb74199b11f74e43b | Python | 8,594 | 213 | from collections.abc import Callable
import networkx as nx
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, clone
from pgmpy.base import DAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery
from pgmpy.causal_discovery.bivariate_scores import BaseBivariateScore, get_bivariate_... |
7f3b0b687ae5022168c171baa3978a41e3ddb03d148814cbcc264536c1c6392c | Python | 8,597 | 233 | from tape.models.modeling_utils import PairwiseContactPredictionHead
from torch import nn, pdist, Tensor
from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss
from torch.nn.modules.loss import _Loss
from torch.nn.utils.weight_norm import weight_norm
from torch.utils.data import Dataset
from transformers imp... |
14065adb12dc94f85a8eb9edd33c8dfd6243e7e035ca39dd2eb6c5d13dd889a9 | Python | 8,603 | 233 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def get_init(myelin_data, gradient_data, highest_order, init_para):... |
4b49295eabbc484b98f132dc97ce08cee08facf989e1b5d00c9210339bac33a3 | Python | 8,610 | 210 | import torch
import torch.nn.functional as F
import numpy as np
dtype = torch.cuda.FloatTensor
def apply_transform(img, theta, device):
if img.ndim == 2:
img = torch.unsqueeze(torch.unsqueeze(img, 0), 0) # (Batch, Channel, H, W)
if theta.ndim == 2: # (2, 3)
theta = theta.unsque... |
93088e88e2cd208e9a78e314c8069b747a677687faee268f4b90b989addd045f | Python | 8,620 | 194 | # Do this here to suppress warnings before we import vak
import logging
import shutil
import warnings
from numba.core.errors import NumbaDeprecationWarning
warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
import pandas as pd
import tomlkit
import vak
from . import constants
logger = logging.getLo... |
a602653f7a4796d33ff2b54aa006a1317a350190f041a4f4a51405490af9d7cc | Python | 8,622 | 207 | import itertools
import json
import random
import time
from ast import literal_eval as make_tuple
from multiprocessing import Process, Queue
import numpy as np
import psutil
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.gaussian_process i... |
a7b7810b07d2d3fb454e50e87ff679ed24858569149aca98480304d78933bf0a | Python | 8,624 | 226 | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... |
e2ef64b75aece5f448b34f8bfd80bcfb83d2c1793a2ff5dcc6256bfaa427d61a | Python | 8,627 | 267 | # 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 re
import typing as tp
from collections import Counter, deque
from dataclasses import dataclass
from bitarray import bitarray, util
fr... |
df762147b7c682f6ecf149d7f6949173e668b0463c8d2f7caed8e66838c3a5d6 | Python | 8,628 | 223 | from anndata import AnnData
from mudata import MuData
# read data
from ..data.datareaders import concat_data
# preprocessing
from ..preprocessing.preprocess_utils import preprocessing
def DataProcess(
adata_list,
profile,
data_type=None,
sub_data_type=None,
sample_col="batch",
genome=None,
... |
31a18ad47d5cb95e743cc0728f24fc12feccab9cb8544132429ce26d8176ce13 | Python | 8,631 | 204 | # Copyright 2021 HIP Applied Computer Vision Lab, 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... |
168a8ef4835c2886a769a28e15ff9aacf52f4baebcfd3c4eaf2d836c378862e1 | Python | 8,632 | 232 | from collections.abc import Callable
from itertools import islice, permutations
from math import factorial
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from pgmpy import config
from pgmpy.base import DAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery
from pgmpy.ci_tests import get_c... |
7626ec4cb635c335b1f77b9a3b549f4b3fd78334f2064fd59bf6ab51fc23d06e | Python | 8,634 | 269 | # -*- coding: utf-8 -*-
#
# scikit-fmm documentation build configuration file, created by
# sphinx-quickstart on Wed Feb 8 06:45:28 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# ... |
48adc9e767990db88803ca0b72c3bd6fc5648301d0060dba2eeae58e7d9ed0e1 | Python | 8,638 | 188 | # -*- coding: UTF-8 -*-
"""
@Project: iDCF
@File : train.py
@IDE : PyCharm
@Author : hjguo
@Date : 2025/7/9 11:37
@Doc : Simulate pseudo-bulk data from single-cell data
"""
import anndata
import numpy as np
import pandas as pd
from tqdm import tqdm
from numpy.random import choice
from typing import Union, Opt... |
65473fc03f78005342dcd35f7476375d78d6d8988debc1824d2bd10941d27cd4 | Python | 8,638 | 226 | """Figure 3 panel C — L4/L5 misclassification analysis on S1 (Yu).
Three groups of excitatory cells are compared:
1. L4 correct (true=E_L4, HIPPIE pred=E_L4)
2. L5 correct (true=E_L5, HIPPIE pred=E_L5)
3. L5→L4 misclassified (true=E_L5, HIPPIE pred=E_L4)
Panels:
Left — mean... |
218926ccf5f476e5f3a6eff3994661207901dfb73564161893b2ea0f7f5bf4a8 | Python | 8,641 | 200 | import argparse
import numpy as np
import bigstream.io_utility as io_utility
from dask.distributed import (Client, LocalCluster)
from bigstream.configure_bigstream import (configure_logging)
from bigstream.distributed_transform import (distributed_apply_transform_to_coordinates)
from bigstream.configure_dask import (C... |
1d9655824a0b356b3c3e02731a3180871d90ccd378a5a282da32b945a91033d1 | Python | 8,643 | 225 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import time
import torch
import os
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_pc1, gradient_pc2, highest_order, init_para):
'''
Thi... |
1bd0c050fbc1b56c8b134e443bb8629c3b90f3cc661fceea04a89c2622456595 | Python | 8,645 | 199 | import os
from textwrap import dedent
from mdt.lib.nifti import load_nifti
from PyQt5.QtCore import pyqtSlot, QObject, pyqtSignal
from PyQt5.QtWidgets import QFileDialog
from mdt.visualization.maps.base import SimpleDataInfo, MapPlotConfig
from mdt.gui.maps_visualizer.main import start_gui
from mdt.gui.model_fit.desi... |
aefacf9d7a8f11aca59529f4969c602576907e7f7a870c76ac04ae93d4468b84 | Python | 8,647 | 228 | # 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
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
def _s... |
716ca5893433bd97423c8dffff510d8795449622050efd51451a46b859928ddc | Python | 8,649 | 196 | import shutil
import time
from tqdm import tqdm
from dataloadR.augmentations import *
from evalR import voc_eval
from utils.utils_basic import *
from utils.visualize import *
from utils.heatmap import Show_Heatmap
import config.cfg_lodet as cfg
current_milli_time = lambda: int(round(time.time() * 1000))
... |
a41e33556ce42681ac796183406ead98cd4115279fe6747837d061798464f957 | Python | 8,652 | 232 | """
download_singlecell_datasets.py
--------------------------
Download the MS single-cell h5ad files from CELLxGENE Discover into data/.
Two modes:
(A) If metadata/ms_datasets_cellxgene.csv exists (produced by 01_query_...py),
iterate its rows and download each h5ad via the Discover datasets endpoint.
(B) F... |
19460493dc8822fb1e2f05bcb33e36cc12c59cc2b00873a9bd6a898b9878d73b | Python | 8,654 | 155 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'generate_protocol_update_dialog.ui'
#
# Created by: PyQt5 UI code generator 5.10.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_UpdateColumnDialog(object):
def setupUi(sel... |
42f00c330cdf33928140581e2619bf438d1b54252ff55d123caf1dcaf711d4ef | Python | 8,654 | 217 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.init as init
from torch.utils.data import DataLoader, TensorDataset, random_split
class Sequential(nn.Module):
def __init__(self):
super(Sequential, self).__init__()
self.layers = nn.ModuleList()
def add(self, ... |
33dd042c6532394e3e4e4caa6099f6979af3e9f515a0f5bd3bca25af2cc1f650 | Python | 8,655 | 259 | # 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 gensim
import logging
import numpy as np
import pandas as pd
from gensim.models.word2vec impo... |
d3dc5d2678579f74ce4bbe7a2dc443fe1f56c050b996f2dd529d6e9bc3cdde68 | Python | 8,657 | 210 | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... |
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