sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
838b8eb108fc49c44ce2fea64eb6123b47aad0d6972024ebd7b815df79e924da | Python | 2,450 | 60 | import sys
import os
import gzip
from optparse import OptionParser
import numpy as np
import pandas as pd
from inmoose.pycombat import pycombat_seq
def main(argv):
if len(argv) == 1:
argv.append("--help")
usage = "usage: %prog -i counts.tsv -b batch.tsv -o batch_corrected.tsv"
desc = "Batch corre... |
8ed9f883287cf54440a57277a686f3490ee8de3727e2bce85c7293387634a8c9 | Python | 2,450 | 80 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
92e4bec79d69cc768d0c6b1448806b2b9bc2d8ca6277ea3cdaee9bf32387e504 | Python | 2,450 | 88 | #!/usr/bin/env python
#================================================================
# Copyright (C) 2023 Yufeng Liu (Braintell, Southeast University). All rights reserved.
#
# Filename : anatomy_vis.py
# Author : Yufeng Liu
# Date : 2023-02-22
# Description :
#
#===================... |
6188d000441139ffec3cfc09992afc12c82569b6b7f348a429b257b242826512 | Python | 2,451 | 74 | import pytest
import numpy as np
@pytest.fixture(scope="session", autouse=True)
def set_seed():
np.random.seed(42) # noqa: NPY002
import logging
import torch
from ase.io import read
from pathlib import Path
from quacc import get_settings
from ts_workflow_examples.geodesic_ts_with_hessian.using_newtonnet import... |
b89ecef281353e73297a70f02a3b25eef534db036b7a8b6483c4685a77328268 | Python | 2,451 | 66 | import os
import numpy as np
import pytest
from regtools.atlas.registry import ANNOTATION_IMAGE
from regtools.geometry_registration import ccf_bridge
def test_edges_from_label_slice_marks_region_boundary():
# Two regions: background (0) and a 6x6 square of label 5.
labels = np.zeros((20, 20), np.... |
beded04386daf6d341b428e4a88cb9c4954585e24da7c58779ced7aa647a7b2a | Python | 2,451 | 81 | """
The skeleton id should be computed and set after all other code is generated.
"""
import logging
from collections.abc import Iterable
from pathlib import Path
from warnings import warn
from elasticai.creator.hw_function_id import _HwFunctionIdHash
from elasticai.creator.vhdl.hw_function_id import _replace_id_in_v... |
ca94b1f64c01bf25133282cffc00e61349c98457639bbca8bb0f040dbe535386 | Python | 2,451 | 86 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Author:
Maximilia F. S. Degenhardt <frazaodesouzam2@nih.gov>
Hermann F. Degenhardt <degenhardthf@nih.gov>
"""
import os, sys
import pandas as pd
sys.path.append("../src")
from hornet.input import prepare_inputs
def help():
return """
prepare_input... |
e83903d2ee1a5d4bb029f727ad7fdf23204c058fc5a3a5319c4255ac311c0b12 | Python | 2,451 | 63 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue717-base"]
CONFIGS = [
IssueConfig(
"lama-firs... |
075f9b87fc4122fa2ee80a4a4f6074d7c1606e565612d2b47683646b84f64861 | Python | 2,453 | 49 | import numpy as np
class TransferFunction:
def __init__(self, cond_vec, radius_vec, max_l=300):
self.cond_vec = cond_vec
self.radius_vec = radius_vec*10**(-2) ## cm to m
self.max_l = max_l
self.eps = 0.0001*10**(-2)
def create_frequencies(self, max_nx, max_ny):
... |
3221b713170ab6157cbb7ceadfbd6e59fa048d3b4568e5ed6f2937b08a309190 | Python | 2,453 | 66 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/simul_study_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["split"]= float... |
a2b34eafd61852590a098364579407ee304928a480b901a398617709ef3ce4b8 | Python | 2,453 | 71 | # Publication: "Protein Docking Model Evaluation by Graph Neural Networks", Xiao Wang, Sean T Flannery and Daisuke Kihara, (2020)
#GNN-Dove is a computational tool using graph neural network that can evaluate the quality of docking protein-complexes.
#Copyright (C) 2020 Xiao Wang, Sean T Flannery, Daisuke Kihara, and... |
ae7b7ed02ff5bffc77a5c2a444414ed50999d87c30dcbf276fd994e965708a1e | Python | 2,453 | 66 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/MNIST_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["cuda"]= str(config["cud... |
d945621d412c81dc5418d543a72bf4da8e89d08b705bdbbc028ca5ea1c6cfd6b | Python | 2,453 | 74 | import os
from six.moves import urllib
import pandas as pd
import magic
import matplotlib.pyplot as plt
import datetime
def create_new_folder(path):
"""Create a folder in 'path' if it does not exist yet."""
try:
os.makedirs(path)
except OSError as e:
if e.errno == 17:
pass
... |
dc3acfcf518b344190929339d3f240394dbc6edf4a9bcc6e8a699f2219962062 | Python | 2,453 | 73 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
e5251ad95339c16ae6be02969541f2e359ab27259fdabb293aafa80c08acaf32 | Python | 2,453 | 49 | # Publication: "Protein Docking Model Evaluation by Graph Neural Networks", Xiao Wang, Sean T Flannery and Daisuke Kihara, (2020)
#GNN-Dove is a computational tool using graph neural network that can evaluate the quality of docking protein-complexes.
#Copyright (C) 2020 Xiao Wang, Sean T Flannery, Daisuke Kihara, and... |
8bc4ba21bd4e4237082c0fa5e2093392d3197b5f1369e50d238f4f2d1a7a3815 | Python | 2,454 | 90 | import functools
import warnings
import re
import textwrap
import email.message
from ._text import FoldedCase
from ._compat import pypy_partial
# Do not remove prior to 2024-01-01 or Python 3.14
_warn = functools.partial(
warnings.warn,
"Implicit None on return values is deprecated and will raise KeyErrors."... |
a75955a0ff4d1dfc00269d3253e8819ae8099b8264000a4be2ef13a9943819d0 | Python | 2,454 | 83 | import os
import pandas as pd
from calvados.cfg import Config, Job, Components
import subprocess
import numpy as np
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--name_1',nargs='?',required=True,type=str)
parser.add_argument('--name_2',nargs='?',required=True,type=str)
args = pars... |
e5652aa31ae3a2f11ef4f7297042046847109bb90b190228bab17bde68824025 | Python | 2,454 | 43 |
import torch
import torch.nn as nn
import numpy as np
from .utils import categorical_to_one_hot
def cal_dice_per_case(pred_logit, target, smooth = 1e-8): # target is one hot
pred_classes = pred_logit.max(dim=1)[1]
batch_size, n_classes = pred_logit.shape[:2]
pred_one_hot = categorical_to_one_hot(pred_cl... |
05bdcd342427512895a91e932b39aac68e2fa24e47a3a817016e1d031d7d9007 | Python | 2,455 | 93 | """
Helpers for distributed training.
"""
import io
import os
import socket
import blobfile as bf
from mpi4py import MPI
import torch as th
import torch.distributed as dist
# Change this to reflect your cluster layout.
# The GPU for a given rank is (rank % GPUS_PER_NODE).
GPUS_PER_NODE = 2
SETUP_RETRY_COUNT = 3
d... |
3259592f583aa3979e6c291d3afe1a2a65ab9af7420fb960c01cfec1ec494290 | Python | 2,455 | 70 | import torch
import torch.nn as nn
import numpy as np
from evaluate import *
def train(model, criterion, optimizer, data_loader, device, transform=None, shuffle_feat=None, class_map=None):
""" Train a neural network for one epoch.
"""
model.train()
epoch_loss = 0.
epoch_count = 0.
epoch_total ... |
58f2adf852afa336c94d4711a347993c623593e39d686a877277c289e05410d8 | Python | 2,455 | 89 | from typing import Tuple
import numpy as np
def euler_to_homo(Xe: np.array) -> np.array:
"""
convert euler to homogenous coordinates
"""
num_points = np.shape(Xe)[0]
Xh = np.column_stack((Xe, np.ones(num_points)))
return Xh
def homo_to_euler(Xh: np.array) -> np.array:
"""
convert homo... |
79874e00e21823a2babe324b66893a5acc35900e6edb26a63bdbdc52bd4ad088 | Python | 2,455 | 43 | """AL_SUBNET.py 2026-09-25. The antennal lobe cut out of Shiu's LIF (v783 >= 5 network) so that one odour runs in ~1 s
instead of ~40 s. Kept: olfactory receptor neurons, AL projection, local, input and output neurons (~3,400 cells).
Everything outside is removed, so feedback from the rest of the brain is lost; `valida... |
7ee6739c290d42626128c0f0835cf22b459310b166d7c4149da380438bebee6a | Python | 2,455 | 81 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
ce39e712ac41ec422d09cc20ab7eb5868d841d9a3179b043e430f6a1b3fd6ea3 | Python | 2,455 | 80 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
270e1b8d754c96638392ba0820302f807d44606f1e292bf9766f011d9b4700e9 | Python | 2,456 | 64 | """Transform paths containing commas (or brackets) must be staged to a safe
temp path before ANTs point transformation.
ANTsPy formats an inverted transform as the bracket argument ``[<path>,1]`` and
ITK splits that on commas with no escape, so a comma anywhere in the path (e.g.
a microscope output folder named a... |
3222a48290e82372c2cddbbe03c8d2c52713e71542ec6de82c9747f6ad612cd9 | Python | 2,456 | 81 | #!/usr/bin/env python3
"""
Author: David Kappel
Copyright (C) 2024, by the author.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at you... |
827bdb2110ba1df193a2e9e81fe4562e6d645bc7567e1132fea2cef3605a06bc | Python | 2,456 | 76 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
##this function is adopted from Sample et al. (2019)##
######################################################
def one_hot_encode(df, col='utr', seq_len=50):
nuc_d = {'a':[1... |
9affb144806ebc6542bd0a73622670f48256c2ab7310531e58b447d127e5e843 | Python | 2,456 | 64 | import os
import torch
import pandas as pd
from run import *
from data import *
from utils import read_params
def main():
# # get patient list
# df = pd.read_csv('./RecurrentGBM/biopsies69_3marker_5_5_threshold0.5.csv',sep=',')
# patients = df["Patient"]
# p = patients.drop_duplicates().to_list()
# dire... |
b7f2f7db60df5ba6815483f966952564b82be267310b65b605fab5c0e48cf579 | Python | 2,457 | 63 | """computeAllMetrics overlap= skip + mask_scale plumbing (PR: metric flags).
- overlap=False must skip Dice/Hausdorff AND the (expensive) GrabCut mask, so the
intermediate delta-only call costs nothing on masking.
- mask_scale must thread to _get_brain_mask and be part of the cache identity.
"""
import numpy... |
e9f291f505a0be7ef2a0576a6b3af6e05690556cbe4a9092c1ba851f678e6f10 | Python | 2,458 | 65 | #!/usr/bin/env python
import sys
import os
import argparse
import requests
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Download genomic data')
parser.add_argument("genome",
help="genome to download: hg19, hg38, mm10, hg18",
type=str)... |
c18c5fe429fcd10fb84dba1a79a9f635363e1d0efdfcdc7e91a882bd2bbd0716 | Python | 2,459 | 70 | import os
import torch
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.SecondaryStructure")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class SecondaryStructure(data.ProteinDataset):
"""
... |
59f4cb6b3643d7de03251f85c98838b1bd4479f78ccaabd755838dcaa69902f5 | Python | 2,460 | 55 | from rdflib import Graph, plugin
from rdflib.parser import Parser
from rdflib import URIRef, Literal, Graph, RDF, Namespace
# Set the paths to your CSV file and the ontology file
csv_file = "Seri.csv" # Replace with the data source
ontology_file = "foo.ttl" # Replace with the ontology
# Create an RDF gr... |
a07f900cdcb2ba1f00aaef4a0b09d618a119aba7c0d2f59e47508b2d9d136bc2 | Python | 2,460 | 79 | #!/usr/bin/env python3
## this script creates dataframes from the distance matrices
## (upper triangle) for each movie and parcel (HCP 7T version)
import pandas as pd
from pathlib import Path
# ------------------------------------------------------------------
# settings
# -------------------------------------------... |
d90e5e99fb01a816da2be5e5f69ee822cc60737c0d365fe48b7218ace7288b91 | Python | 2,460 | 64 | """
Optuna example that demonstrates a pruner for XGBoost.cv.
In this example, we optimize the validation auc of cancer detection using XGBoost.
We optimize both the choice of booster model and their hyperparameters. Throughout
training of models, a pruner observes intermediate results and stop unpromising trials.
Yo... |
4f1dfeb3ffe3b0a4a55683f0c116ab1525667662410e6473142564013c924651 | Python | 2,461 | 59 | TEST_ID = '00001' # used when saving some files
MODE = 'II' # 'I' or 'II'
# 'I': run VMD every timestep, need labelled anomalies
# 'II': run VMD once at the beginning, normal data only
L = 200
TRUNCATE_MODES_STAR = False
OFFLINE_DATA_DIR = 'data_offline'
ONLINE_DATA_DIR = 'data_online'
PP_OFFL_DATA_DIR = 'datapp_... |
98b66135402589fa6634fec03cbb58113481cc28747ab0d8c887859d4886660b | Python | 2,462 | 100 | from math import floor
import numpy as np
from scipy import stats
from scipy.signal import butter, filtfilt
def smooth(data, W=10):
return filtfilt(np.ones(W) / W, 1, data)
def getBinsX(minbin, maxbin, nbins=101, step=None):
if step:
nbins = int((maxbin - minbin) / step) + 1
bins = np.linsp... |
d910c247bc899a2dce405c99e3f0855643fdd6c58b420c5e96dea623d222aa3f | Python | 2,462 | 66 | import os
import tempfile
import unittest
from torcheeg.model_selection import train_test_split, train_test_split_cross_trial, train_test_split_groupby_trial
from .test_utils import _make_dataset
class TestTrainTestSplit(unittest.TestCase):
def setUp(self):
self._directory = tempfile.TemporaryDirectory... |
dac0fde19e58a53e2355995fa99af0b6ec7aa91a1c373173bf85f6a2102420c7 | Python | 2,464 | 62 | import numpy as np
import pandas as pd
import sklearn
from . import types
def standardized_mutual_information(y_true: types.Array, p_label: types.Array) -> float:
"""MI(ground truth, predicted binary labels) / MI(ground truth, ground truth)."""
return sklearn.metrics.mutual_info_score(y_true, p_label)/sklearn... |
dae4e8348440edd1bdf79a5ff28c7c6b8afb9c9b4feb6fa1a4b873b118ab9f1d | Python | 2,464 | 58 | """setuptools.errors
Provides exceptions used by setuptools modules.
"""
from distutils import errors as _distutils_errors
# Re-export errors from distutils to facilitate the migration to PEP632
ByteCompileError = _distutils_errors.DistutilsByteCompileError
CCompilerError = _distutils_errors.CCompilerError
ClassEr... |
f9317e49644cacfbad16df72278d069f95c106a89ce06305b2392557e1caf286 | Python | 2,464 | 69 | #! /usr/bin/env python3
import os
import subprocess
import logging
import APT_interface
class cd :
"""Context manager for changing the current working directory, and automagically changing back when done"""
def __init__(self, path):
self.path = path
def __enter__(self):
self.old_path = ... |
57b3adb01a27bc8e765bb095c16657951da308828f2970bc4704242bf6834ed9 | Python | 2,465 | 58 | """Report actual hardware rather than assuming the target machine is present."""
from __future__ import annotations
import importlib.metadata
import os
import platform
import sys
import shutil
import subprocess
from pathlib import Path
import torch
def _ram_bytes() -> int | None:
try:
return os.sysconf("S... |
ddd08a29e4a824aef60fa61660b2f4ecc352b788e61f282ed037b6e3d556b792 | Python | 2,466 | 69 | # coding=utf-8
# Copyright 2021 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://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
5e7de131d994347c3ca1ad58d7fabc1f8d46222fd06c96a21af6022b2c3bb7fe | Python | 2,467 | 73 | ### Parsing the result!
import tensorflow as tf
import os
import numpy as np
import pickle
def get_test_acc(event_file):
val_auc_list = np.zeros(100)
test_auc_list = np.zeros(100)
for e in list(tf.train.summary_iterator(event_file)):
if len(e.summary.value) == 0:
continue
if e.s... |
6681c89c1814e5ebb9f3cac79abc208b89f6c18fc1123c2f55e6ed8250c84424 | Python | 2,467 | 78 | #!/usr/bin/env python3
## this script creates dataframes from the full distance matrices
## for each movie and parcel (HCP 7T version)
import pandas as pd
from pathlib import Path
# ------------------------------------------------------------------
# settings
# --------------------------------------------------------... |
78851940c334d50aa99ae05ec5434f37891206f6c240f47e46b974e66bedf1a0 | Python | 2,467 | 60 | import glob
def get_files(datasets: list, directory: str, file_format: str = '*', num_levels: int = 1):
"""
Updates the list "datasets" with the list of files in the "directory" with the appropriate "format".
Takes into account the number of directory layers supplied by the user.
:param datasets: A li... |
8ab97763395b155f89125ec6a1336840f1f960492356b827aa6af1c836fe7dea | Python | 2,467 | 95 | from __future__ import annotations
from logging import WARNING, getLogger
from types import MappingProxyType
import pytest
from quacc import Remove
from quacc.utils.dicts import recursive_dict_merge, remove_dict_entries
LOGGER = getLogger(__name__)
LOGGER.propagate = True
def test_remove_dict_entries():
d = {... |
2f3e1c80416a667d9e7fa3ffd56e15f66bfe671996047d3a2477c048c86ab02b | Python | 2,468 | 82 | # Compute principle component regression functional connectivity
import numpy as np
import os
os.environ['OMP_NUM_THREADS'] = str(1)
from sklearn.decomposition import PCA
def pcaFC(stim,resp,n_components=500,nproc=10):
"""
stim - time x feature/region matrix of regressors
resp - time x feature/region matrix of ta... |
8bde71f6a5c3a5d15b7fa0fddd8c03472c9133758f1996124ab6ddce5cafb84c | Python | 2,468 | 102 | import pandas as pd
import numpy as np
from cuppa.components.preprocessing import Log1pTransformer, MaxScaler, NaRowFilter
class TestLog1pTransformer:
def test(self):
X = pd.DataFrame([[2.718-1]])
X_trans = Log1pTransformer().transform(X)
assert round(X_trans[0][0], 3) == 1
class TestM... |
2ee1eaf6f0f19b30fa2d79222378f6f3bd861ddb41b12d39ccee2c2f7c390c38 | Python | 2,469 | 75 | import platform
import nox
NIGHTLY_INDEX_URL = "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"
UV_NIGHTLY_ENV_VARS = {
"UV_INDEX_URL": NIGHTLY_INDEX_URL,
"UV_PRERELEASE": "allow",
"UV_INDEX_STRATEGY": "first-index",
"UV_NO_CONFIG": "1",
}
nox.needs_version = ">=2024.4.15"
nox.opti... |
b40b964f386bc7e386e586c7d28e4e802352924f120b24949bc232a0d04bae0f | Python | 2,469 | 78 |
"""
Nerfstudio Depth Pipeline
"""
import typing
from dataclasses import dataclass, field
from typing import Literal, Optional, Type
import torch.distributed as dist
from torch.cuda.amp.grad_scaler import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from edge_nerf.datamanager import DepthD... |
e7062fc2156811ed77ee4519e95c46bf21f70a87e0cbb2962d8b1b12366907d4 | Python | 2,469 | 72 | from __future__ import print_function
from . import axioms
from . import predicates
class Task(object):
def __init__(self, domain_name, task_name, requirements,
types, objects, predicates, functions, init, goal,
actions, axioms, use_metric):
self.domain_name = domain_nam... |
2d9d1f1bbfaab2478dc91eac062c4bc4723e2d0028ff80fd5c3a7f4fd0d9c499 | Python | 2,470 | 73 | import os
import argparse
import nibabel as nib
import numpy as np
import glob
def separate_masks(combined_img):
"""
Separates a combined lesion mask into binary masks for each label.
Assumes labels: SV=1, V3=2, V4=3.
"""
combined_data = combined_img.get_fdata()
# Create binary masks
sv_m... |
c92a79659bc7a929866acf7325068760a3a65083b2eeccde9b7da4f1498c8bdd | Python | 2,470 | 79 | import click
from tqdm import tqdm
from pathlib import Path
from PIL import Image
import numpy as np
import torchvision.transforms.functional as F
import segm.utils.torch as ptu
from segm.data.utils import STATS
from segm.data.ade20k import ADE20K_CATS_PATH
from segm.data.cityscapes import CITYSCAPES_CATS_PATH
from ... |
cdb55a85bbd260a4d2b5933bc35ae9c7c62a94ca8a095b50c778f8acf2455756 | Python | 2,470 | 77 | #!/usr/bin/env python3
## this script creates dataframes from the similarity matrices
## (upper triangle) for each movie and parcel
## and saves them to a new CSV file
import pandas as pd
from pathlib import Path
# ------------------------------------------------------------------
# settings
# ----------------------... |
03390fc721b5d1f7fcd159b3958cc9e8c44a234426c3e2b743b878af054ed34c | Python | 2,471 | 90 | import os
import sys
import math
import pickle
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
from torchdrug import utils
module = sys.modules[__name__]
path = os.path.dirname(__file__)
# Calculate synthetic accessibility of molecules
# Code adapted from RDKit
# https://github.com/rdkit/rdkit/blob/m... |
b19547f0d7b21c28f08af8b36cd91a08023bf984c5ec3b261394227a6a0f5d2e | Python | 2,471 | 72 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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,... |
87ffdc497d3f4a7efe706a96d7aa89c7d7c731e424256aa5df2f1a02d88d7db4 | Python | 2,472 | 69 | #! /usr/bin/env python
import argparse
import os
def str2bool(v):
# susendberg's function
return v.lower() in ("yes", "true", "t", "1")
def filter_one_signal_feature_file(sf_fp, wfp, label):
wf = open(wfp, 'w')
with open(sf_fp, 'r') as rf:
for line in rf:
words = li... |
1417f61d2a5bbd78ca829136fcac9734131e621ca961c81e8a4598e734ff292b | Python | 2,473 | 50 | from collections import defaultdict
from ckg.graphdb_builder import builder_utils
import re
def parser(ontology, files):
"""
Multiple ontology database parser.
This function parses and extracts relevant data from: Disease Ontology, Tissues, Human Phenotype Ontology, \
HUPO-PSI and Gene Ontology databa... |
9e1a94d2da9bd5c7d0051b3fc41beabe46c0952758f2a59588ed284361d8f56b | Python | 2,473 | 61 | import unittest
import numpy as np
import torch
from torcheeg.datasets.constants.emotion_recognition.deap import (DEAP_ADJACENCY_MATRIX, DEAP_CHANNEL_LIST,
DEAP_CHANNEL_LOCATION_DICT, DEAP_LOCATION_LIST)
from torcheeg.datasets.constants.emotion_recognit... |
7c20d790d6082fb97d4fed2b5b492d4b7d1823eccaf36b01d0de0a29a695485d | Python | 2,474 | 67 | #!/usr/bin/env python
#
# Copyright 2008, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... |
fa7ce249ae54cdba55b7bc2deaefdc64247b583b797c4b47bb75d81df54fd092 | Python | 2,474 | 71 | """"""
"""
Prosper_nn provides implementations for specialized time series forecasting
neural networks and related utility functions.
Copyright (C) 2022 Nico Beck, Julia Schemm, Henning Frechen, Jacob Fidorra,
Denni Schmidt, Sai Kiran Srivatsav Gollapalli
This file is part of Propser_nn.
Propser_nn is free softw... |
42f96960cfa4f8df713eb0f6baa5cc33ff2528a01be04472c1f922fe7bc0d5c4 | Python | 2,475 | 81 | import math
import numpy as np
import pandas as pd
from scipy.spatial.distance import cdist
from scipy.interpolate import RBFInterpolator
from visualize import plot_pts
def filter_outliers(fpath_src: str, fpath_dst: str):
src = np.loadtxt(fpath_src, delimiter=' ')
dst = np.loadtxt(fpath_dst, delimiter=' ')
... |
bf5a05f9c85cbd1bb63b582ea126cac40bbdfd5c7bfb80e6d7076d6f3a5778d5 | Python | 2,475 | 63 | #!/usr/bin/env python
#
# Copyright 2018 Google LLC. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of... |
3ab3778f099f5d0e15385d32071416ddc8aec16f12dc62d7593b79466aab557e | Python | 2,476 | 83 | """
Saving model output.
"""
import json
import pickle
from copy import deepcopy
import os
import numpy as np
import xarray as xr
def save_to_pickle(datafield, filename):
"""
Save datafield to pickle file. Keep in mind that restoring a pickle
requires that the internal structure of the types for the pi... |
837249d863baff440b265897699a9bcb0d8cbeeb86020cbafe9f144df3e5a069 | Python | 2,476 | 94 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
129a99ef602ab186e9deb40f6e3320a7d9113685a2a6e9bc4443c6bf1a714b68 | Python | 2,477 | 71 | import os
from cirrocumulus.util import get_fs
def get_path(dataset, dataset_path):
path = dataset["url"]
if path[len(path) - 1] == "/": # remove trailing slash
path = path[0 : len(path) - 1]
if path.endswith(".json"):
path = os.path.dirname(path)
path = path + "/" + dataset_path
... |
2ce89ff890147b94b0ee9f5198eefe1b3e68dab89883c647dd8c2c6b5b08fe42 | Python | 2,477 | 60 | # coding=utf-8
# Copyright 2020 The Allen Institute for AI team 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://www.apache.org/licenses/LICENSE-2... |
6d260d300d9bfc7b69286d76a4714c796c44f256651bf07567bf56d8294166f4 | Python | 2,477 | 76 | import matplotlib.pyplot as plt
import matplotlib.pylab as pl
from Functions.f_AnalysisFunctions import *
from Functions.f_SignalProcFuncLibs import *
from Functions.f_ECGAnalysis import *
from Functions.f_AnalysisGraphs import *
from Functions.f_AnalysisStatistics import *
import os
import pandas as pd
import... |
f6e20fde9715c75039131b0c47c0a73fb6d87a2442475312021d80ade4ec02d0 | Python | 2,477 | 70 |
import os
import dllogger as logger
from pytorch_lightning.utilities import rank_zero_only
from dllogger import JSONStreamBackend, StdOutBackend, Verbosity
# inspired by the NVIDIA nnU-Net GitHub repository available at:
# https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/nnUNet
# logg... |
b9847233228f28c1550d30ccd5cbd68b15c74b683a58f4321f2bd41b6d9876ce | Python | 2,478 | 77 | import torch
import torch.nn as nn
from torch import Generator
from typing import Any
from abc import ABCMeta, abstractmethod
class FirstLayersBlock(nn.Module, metaclass=ABCMeta):
"""
Network first layers performing low-resolution feature generation
"""
@abstractmethod
def __init__(self,
... |
dad7e1e829858dfec550ba74ef072daa12a4aa1dc80ca59ec84e749f519c3b61 | Python | 2,478 | 83 | from collections.abc import Callable, Collection, Iterable, Iterator, Mapping
from typing import Self, overload, override
from .datagraph import DataGraph, Edge, Node
_registry_types: list[type] = []
def is_registry(obj: type) -> bool:
return obj in _registry_types
def mark_as_registry[T](object: type[T]) -> ... |
f105d6be91b12f4fdfca441ed4e70308b7b5200e8bba29206cf7b068fd8efa95 | Python | 2,478 | 64 | from __future__ import print_function
import sys
import os
import argparse
sys.path.extend([
'ss-pybullet',
])
from perception_tools.common import get_models_path
from pybullet_tools.utils import ensure_dir, connect, create_obj, approximate_as_cylinder, wait_for_user, remove_body
from retired.mesh_tools.create_o... |
c48f571ce11c77b0cd08a27ed08514dbfd36a49ad10bd8911e9adcc4d6de7d75 | Python | 2,479 | 75 | from .base import AtomsDataset
from typing import List, Optional
from ase import Atoms
from ase.io import read
from ase.db import connect
class ASEData(AtomsDataset):
def __init__(self,
frames : Optional[List[Atoms]]=None,
indices : Optional[List[int]]=None,
... |
4009df76587406ee303b2f5e1cfd8793e8f0bfbcc1dcacdc88fdae0b3b1c37ca | Python | 2,480 | 79 | """ Yu Group's implementation of sklearn_NMF """
""" Source: https://github.com/Yu-Group/staNMF/blob/master/staNMF/nmf_models/sklearn_nmf.py """
from sklearn.decomposition import NMF
#from sklearn.decomposition.nmf import _initialize_nmf
import numpy as np
class sklearn_nmf(NMF):
'''
Train non-negative matri... |
d19f0502bd0e1258a555d9d7901a788429f654fcfb5974cfcebf1e9e23029c9a | Python | 2,480 | 82 | #!/usr/bin/env python
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import warnings
from scipy import stats
import numpy as np
import seaborn as sns
# def FittedMixturePlot(gm, outdir,comb, NoisedIDs):
# with warnings.catch_warnings():
# warnings.simplefilter('ignore')
# #Mixture Plo... |
037c993ba9108a931c7f81bddef14014ce8dc8b6058070b02ebcb926ba4d52ac | Python | 2,481 | 31 | """SEROTONIN_MATCHED_CONTROL.py 2026-09-24. Regenerates an orphan number of Act 4 (from an 11 Sept briefing
with no surviving script): re-signing the 1,021 SER-called neurons excitatory, from the Pospisil convention,
moves rho(W_EE) by ~1.49x; re-signing 1,021 GABA/glutamate neurons matched on synapse budget moves it ... |
70e6ab1548007c1654082602bd41f3cf5e1c9b254027950765af1968787d8dd8 | Python | 2,481 | 62 | import numpy as np
import pytest
from SurvivalEVAL import QuantileRegEvaluator
@pytest.fixture
def quantile_evaluator() -> QuantileRegEvaluator:
"""Build two piecewise linear survival curves with medians two and four."""
return QuantileRegEvaluator(
pred_regs=[[1.0, 2.0, 3.0], [2.0, 4.0, 6.0]],
... |
9e640c0ff9da8c4274ff1436d33b289883ab864db4254e68c194cefb9fd0dcb8 | Python | 2,481 | 60 | """_peek_shape_and_spacing reads a header without materializing the volume.
registration_2d used to `load_volume(...).data` the fixed image just to test
2D-vs-3D and read spacing - a ~616 MB float64 materialization of the 25um CCF,
once PER SECTION in registration_batch_2d. For a 3D NIfTI atlas the peek now
reads... |
ad120767faca7d9f38240f9579e78d21bf1a336e73b7f8e2847c008e82d1c2ae | Python | 2,481 | 81 | # dataset settings
dataset_type = "ADE20KDataset"
data_root = "data/ade/ADEChallengeData2016"
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
crop_size = (512, 512)
max_ratio = 4
train_pipeline = [
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", r... |
59d15329a40beef91a4b3285ea3cc59a35407753d35c8baf7915f749a4a7da59 | Python | 2,482 | 91 | # Node-group asset "Color Backbone" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKIN... |
65a6871468336bd8dcce36e76fac33c079d363ab5a77805df4a2ac6554dfb0ad | Python | 2,482 | 32 | """posthoc_sign_worlds_smell_2026-10-08.py (POST HOC, descriptive). In the U and V worlds of SIGN_WORLDS (H6, FlyWire v783),
which sign did each v2LN30 draw, and how much of s do the 150 uncalled local neurons carry? Same draws as
SIGN_WORLDS_ANALYSE.smell(). Output: 06-OUTPUT/posthoc_sign_worlds_smell.txt"""
import sy... |
d7470a5e11447ca284c57f4e3ae98eaccf9bf26d8620701b1dbf2136bc3ea0cd | Python | 2,483 | 63 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.nn.integer.ram.design import Ram
from elasticai.creator.nn.sequential.design import Sequential as _SequentialDesign
from elasticai.cr... |
dc6d975ab99e0e48b16e5fa48ec9fa9dedd1f5f69e9e6708554f814cf38d5ae6 | Python | 2,483 | 63 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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,... |
fc286429cb6a9bd3eb809e8b0343597162372f7592be4debfb43f3a1a97faa63 | Python | 2,483 | 66 | from typing import Any, cast
from elasticai.creator.arithmetic import (
FxpArithmetic,
FxpParams,
)
from elasticai.creator.base_modules.conv1d import Conv1d as Conv1dBase
from elasticai.creator.nn.design_creator_module import DesignCreatorModule
from elasticai.creator.nn.fixed_point.conv1d.design import Conv1d... |
42765b9d2f9bc9c1afd02fa15ff05693eb56b51ddeaf91bf7f7ba37171c9d80f | Python | 2,484 | 72 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
652049f1f387815c8016a0d7a9481f60822f7dc72bd00209b72f4fe8675844a3 | Python | 2,484 | 76 | import cocotb
import pytest
from cocotb.triggers import Timer
from elasticai.creator.arithmetic import FxpArithmetic, FxpParams
from elasticai.creator.testing import CocotbTestFixture, eai_testbench
from elasticai.creator_plugins.act_func.utils import load_and_plugin
def act_heaviside(xin: list[float], config: FxpPa... |
8058e999484bfe5515c92cbe17d025d3f30d586c8a6568bef86243bcb7b1f8cb | Python | 2,484 | 54 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
3c4cc186537f0d3503f147b58f7997115b94cba5a01c982a76eb8955037fd838 | Python | 2,485 | 99 | # Node-group asset "Color OKLab Mix" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKI... |
756707eb5e0efc922b7623964f055feb47a45aa9dbd75945872a1721e2a36f41 | Python | 2,486 | 72 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... |
7749b595ba3f71ce36c7f039b817c1683e84e97ffab483f2c0bb97c099948b8a | Python | 2,486 | 47 | from sklearn.preprocessing import StandardScaler
import numpy as np
# Definition of CustomScaler that scales all columns except the ones in columns_to_exclude (mole fraction here)
class CustomScaler:
def __init__(self, Embedding_BERT):
self.scaler = StandardScaler()
self.columns_to_exclude = [1] # ... |
f6dc6aa31b71dbbdcc78e3eff84070a8b51c94c1cf5ba0c5f3e50af21f7c5592 | Python | 2,486 | 71 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
151b2b581cf84e6e1d021eb3184853a79bf404078047f4b9517bd306b217eee6 | Python | 2,487 | 66 | """Selectable affine-stage methods for the 01_affine registration stage.
Each non-'default' method maps to an ANTs linear `type_of_transform`. 'affine' is
tuned to reproduce the affine sub-stage baked into the fused `SyN` preset (verified
against antspyx registration.py: shrink 4x2x2x1, conv 2100x1200x1200x0, smoo... |
261b645b9be28cb1467398e2b5b5747c09aaf0b5d95d3b897eeaf50c5893ecb0 | Python | 2,487 | 66 | import unittest
from torcheeg.transforms import Select, Binary, BinariesToCategory, StringToInt, BinaryOneVSRest, FixCategory, Mapping, Multilabel, Normalize
class TestLabelTransforms(unittest.TestCase):
def test_normalize(self):
t = Normalize(min=0.0, max=1.0)
self.assertEqual(t(y=0.5)['y'], 0.5... |
e06bf7d911e1b2289e6e48a4bd51676ceee8df7d6a420e99ce9c2a495a309141 | Python | 2,487 | 69 | from typing import Callable
import torch
from torch import Tensor, optim
from torch.nn import Module
from elasticai.creator_plugins.quantized_grads.base_modules import parametrized_modules
class _QOptim(optim.Optimizer):
def __init__(
self,
model,
*args,
buffer_quantizations: dic... |
2c8e29779e38a0bdd6aec3e979029e2c3279d07641b1a21be0532afb1617e80e | Python | 2,488 | 81 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils import data
from .autoencoder import autoencoder_loss
# Import utilities for Cluster distributed runs
# from mle_toolbox.utils import set_random_seeds
# Minimal VAE implementation
# Adopted from https://github.com/Atcold/pytorch-Deep-Lear... |
8720191f659e20f9025a6da58f5550cb4e04e935dbe2ba1f391a2bd4d1a5a89a | Python | 2,488 | 94 | #!/usr/bin/env python3
import os
import pandas as pd
# ------------------------------------------------------------------
# paths
# ------------------------------------------------------------------
proj_path = '/project/3011157.03/Simon/proj_2022_CABB_movie'
# location of R output files
r_path = os.path.join(
pr... |
3bd10740c5b12124aefec56575e7f7759451267ef2b60f747ee227e41ac99653 | Python | 2,489 | 80 | from typing import cast
from elasticai.creator.file_generation.in_memory_path import InMemoryFile, InMemoryPath
from elasticai.creator.nn.fixed_point.relu.layer import ReLU
def test_vhdl_code_matches_expected() -> None:
expected = """-- This is the ReLU implementation for fixed-point data
-- it only checks the h... |
612b1a3a869ed7a997c0416f1f607261be93e26e0c96a7031a74d1c8a7ecd1d5 | Python | 2,489 | 76 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
b8529538f75d055257dba22bdb95b40e50a561a1b6a7186afbb8e87860a6e494 | Python | 2,489 | 84 | ##
# \file siena.py
# \brief
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date Nov 2016
#
# Import libraries
import SimpleITK as sitk
import numpy as np
import sys
import os
import re
from skimage.measure import compare_ssim as ssim
# Import modules
import pysitk.simple_itk_helper as sitkh
imp... |
d8511b765adb891c8b48c53d416ec2428dac6ef3671591de5170807c4260c8b4 | Python | 2,490 | 61 | from optparse import OptionParser
### help and usage ###
usage = "usage: %prog [options] args"
description = '''Function: calculate biallelic minor allele frequency'''
version = '%prog 08.15.2021'
parser = OptionParser(usage=usage,version=version, description = description)
parser.add_option("--in_snp",
... |
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