sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a8a2f2bfd6f8f92653b6cb2ed480baf37b667a51c3b217e3dc7fecd0a899b670 | Python | 1,713 | 52 | import numpy as np
def hard_sigmoid(x: np.ndarray, k: float) -> np.ndarray:
return (k * x > 0).astype(np.float32)
def stable_sigmoid(x: np.ndarray, k: float, base_10: bool = True) -> np.ndarray:
h = k * x
if base_10:
h = h * np.log(10)
hp_idx = h >= 0
y = np.zeros_like(x)
y[hp_idx] =... |
275499a692a7b8d8b713722bd466ea614b6e7f08004725ea834fc68937b2ac10 | Python | 1,714 | 41 | """`ruff` over the tree, so `uv run pytest -q` predicts CI.
CI's `check` job runs `ruff check .` and the test suite did not, so a lint error was discoverable
only from a pull request: the `harmonise_vocabulary` import went in unsorted and an `E731` lambda
with it, both green locally across 1,149 tests and both red on ... |
646572a48103875f827431ea14592ceafb61a3d88faa68eea5c959c037fae152 | Python | 1,714 | 73 | """Column declarations. Everything is a projection of :mod:`vdjdb.schema.fields`."""
from .fields import (
AIRR_MAP,
ALL_COLUMNS,
CHAIN_COLUMNS,
CHUNK_COLUMNS,
CHUNK_DEDUP_KEY,
CLUSTER_MEMBERS_COLUMNS,
COMPLEX_COLUMNS,
EPITOPE_ASSESSMENT_COLUMNS,
EPITOPE_COLUMNS,
EVIDENCE_COLUMNS... |
aba758d0178efb915d98db42b26d2fa7756b18afc54f06335b45292c4ac8a239 | Python | 1,714 | 44 | import os
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
from activation_models.generators import counterexample_models
from custom_model_tools.eigenspectrum import EigenspectrumImageNet
from utils import timed
from scripts.fit_encoding_models import fit_encoder, get_... |
dbeace8ab04017d4d8e407f125070dda4f455e5a682540ca8a0841ea2228e3ad | Python | 1,714 | 55 | """Input check functions."""
# Authors: Nick Tolley <nicholas_tolley@brown.edu>
from .params import _long_name
from .externals.mne import _validate_type, _check_option
def _check_gids(gids, gid_ranges, valid_cells, arg_name, same_type=True):
"""Format different gid specifications into list of gids"""
_valid... |
f8bbb2f9e80855ece7cf999aa4126fa8515de8cfaeb9b0fa8bed714d78944c1b | Python | 1,715 | 54 | """Module containing viz utils"""
import numpy as np
import matplotlib.pyplot as plt
def make_mosaic(data):
"""Reshape data into a mosaic plot
Parameters
---------
data : array of shape (dim1, dim2, dim3)
input volume
Returns
------
mosaic : array
mosaic matrix that c... |
8a4d6909d7fc485d265bb5a84092b8429d9e9abc89d1cc80adeb9c097aab4563 | Python | 1,716 | 48 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
28cbbca23d1b4d06d4e60d2ec84c6c180f6a55d7971d222ed80cea23b510bc46 | Python | 1,718 | 48 | import os
import copy
import torch.distributed as dist
from urllib import request
from common import TMP_CACHE_LOC
from utils.ddp_utils import is_start_rank_node
try:
from utils_internal.blobby_utils import get_local_path_blobby
def get_local_path(opts, path, recursive=False, *args, **kwargs):
"""
... |
b94c2567c623e57a339452601127389beeaa15779a3d5bcf59fbb10f2d2cfdb2 | Python | 1,718 | 66 | """Evaluate functions for TB"""
from __future__ import annotations
__all__ = [
"mutual_similarities",
"internal_diversity",
"compute_similarity_from_sample",
]
from typing import List, Iterable
from rdkit import DataStructs
import numpy as np
def mutual_similarities(fps: Iterable[float]) -> np.ndarray:... |
91acd51ec7eb43d9bf92c6727afcf217c6188184818896a0acc53e6e00040023 | Python | 1,719 | 54 | from __future__ import annotations
import os
import logging
from reinvent.models import ModelAdapter
from reinvent.runmodes.RL import memories
from reinvent.runmodes.RL.validation import SectionInception
from reinvent.utils import get_tokens_from_vocabulary, config_parse
logger = logging.getLogger(__name__)
def se... |
939b0a93c6b408c5a853aee0e931157ef6a232a7aa2f2f7f701a061739b6c7af | Python | 1,721 | 53 | import geomstats.backend as gs
from geomstats.test.data import TestData
from .dirichlet import DirichletDistributionsTestData, DirichletMetricTestData
class BetaDistributionsTestData(DirichletDistributionsTestData):
def point_to_pdf_against_scipy_test_data(self):
return self.generate_random_data_with_sam... |
27c63e09ac3b9370a23374494bcc23fbab14809d2115ee8282e6f67d4fcdd9e5 | Python | 1,724 | 48 | import unittest
import pytest
import torch
import torch.utils.data as tud
from reinvent.models import TransformerAdapter, Mol2MolModel
from reinvent.models.reinvent.models.vocabulary import SMILESTokenizer
from reinvent.models.transformer.core.dataset.paired_dataset import PairedDataset
from reinvent.runmodes.utils.h... |
ffeb404ca158a5aea7f5ac4772f6aa8f80c85fd250ee9a2281143d3767f121d7 | Python | 1,724 | 68 | """Group count
Allow groups of atoms defined by SMARTS only a certain number of times in a
molecules.
"""
from __future__ import annotations
__all__ = ["GroupCount"]
from typing import List
from rdkit import Chem
import numpy as np
from pydantic.dataclasses import dataclass
from ..component_results import Compone... |
8b2ef8a14d6ec046ec2a0fbbebe70234738e977cad1f7dcae510f427ed0ed2e1 | Python | 1,725 | 58 | import numpy as np
from . import descriptive as descr
class AggCluster1D(object):
"""
Performs a simple agglomerative clustering of angular data.
:param numclust: number of clusters desired, default: 2
"""
def __init__(self, numclust=2):
self.numclust = numclust
def train(self, alp... |
2e5cc9e95b21bbced9caec96f9c14a64cb15d125b28dc0e42768a974c5a27495 | Python | 1,727 | 52 | """
Calculate the time from the first to the last trigger in each wave.
"""
import numpy as np
import argparse
from pathlib import Path
import pandas as pd
from utils.io_utils import load_neo, save_plot
from utils.parse import none_or_path
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Pat... |
a5c3dccdc0519f7b20e882f2a25191c302678804b27bb9a10c286f54a6b3204a | Python | 1,727 | 71 | # -*- coding: utf-8 -*-
import os
import argparse
import logging
import h5py
import pickle
import numpy as np
import random
# use this format to tell the parsers
# where to insert certain parts of the script
# ${imports}
def parse_args():
# Argument Parser
parser = argparse.ArgumentParser()
# my args
... |
4d6b9821e6ce6d4bf6593f6bc7c6a21d1d9551efffc96ae6041cc29923657850 | Python | 1,730 | 61 | from pathlib import Path
import click
import SimpleITK as sitk
from tqdm import tqdm
@click.command()
@click.option(
"-i",
"--input",
"input_path",
required=True,
type=click.Path(exists=True, file_okay=False, readable=True, path_type=Path),
)
@click.option(
"-o",
"--output",
"output_p... |
8c43dbb23bf3eac1805478072cc15c9b1e642f78e965c6d41e1a5b6ea6c08ec8 | Python | 1,730 | 48 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
import packaging.version
from .version_utils import get_version_safe
_TORCH_VERSION = packaging.version.parse(get_version_safe(torch.__name__))
_TORCH_GE_2_4 = packaging.version.parse(
... |
39a873193487f482a92835edc7cbfe78be8e52d2e8c9363668e64483eaa08458 | Python | 1,731 | 58 | import unittest
import numpy as np
from deeprank_gnn.tools.pssm_3dcons_to_deeprank import pssm_3dcons_to_deeprank
from deeprank_gnn.tools.hdf5_to_csv import hdf5_to_csv
from deeprank_gnn.tools.CustomizeGraph import add_target
from deeprank_gnn.tools.embedding import manifold_embedding
import os
CWD = os.path.dirname(o... |
644feb68399b1e0e0bfee85fee1f4d79e51a36277cfb7db29413dac6e451a774 | Python | 1,731 | 40 | import shutil
from dig.sslgraph.evaluation import NodeUnsupervised
from dig.sslgraph.dataset import get_node_dataset
from dig.sslgraph.utils import Encoder
from dig.sslgraph.method import GRACE, GraphCL, NodeMVGRL
def test_NodeUnsupervised():
root = './dataset'
dataset = get_node_dataset('cora', root... |
037e4377cdae3973ee92510e0e8bf671a659a53ea547e028022b68ae96e29654 | Python | 1,732 | 55 | import os
import re
import openpyxl
import csv
import codecs
import sys
import argparse
import subprocess
from ontoutils.core import *
from ontoutils import RobotTemplateWrapper
from ontoutils.lucid_chart import *
from logging_conf import setup_logging
setup_logging()
## PROGRAM EXECUTION --- required arguments: in... |
3a056dc706c28a5e3c31b60476268601d38aa25c0e80d47b4b6551f6322ec657 | Python | 1,732 | 54 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from typing import Optional, Union, List
from torch.utils.data import DataLoader
from ..sampler.base_sampler import BaseSamplerDP, BaseSamplerDDP
from ..datasets.dataset_base import BaseImageDataset
... |
0b94df8c93b6b0ef25a740baaacc9751e57c0f149cd52d220d28896be0df3d91 | Python | 1,733 | 55 |
class Generator():
r"""
The method base class for graph generation. To write a new graph generation method, create a new class
inheriting from this class and implement the functions.
"""
def train_rand_gen(self, loader, *args, **kwargs):
r"""
Running training for random generation... |
31acc350c3a66b7a0ecc64c7d8d7cee63e32b7dd87c0341336dc3e23af74eee9 | Python | 1,733 | 54 | # Author: Youzhi Luo (yzluo@tamu.edu)
# Updated by: Anmol Anand (aanand@tamu.edu)
import copy
from dig.auggraph.method.GraphAug.constants import *
base_generator_conf = {
BATCH_SIZE: 32,
INITIAL_LR: 1e-4,
GENERATOR_STEPS: 1,
TEST_INTERVAL: 1,
MAX_NUM_EPOCHS: 200,
REWARD_GEN_STATE_PATH: None,
... |
82c3517837c7ce5778c6296d2f8889b72a60aee8fb89f6e3d47832a50b101234 | Python | 1,733 | 57 | from numpy import zeros, arange
from .get_LEiDA import get_LEiDA
from .phDiff import phDiff
def dPL(N, Tmax, phases):
"""Dynamic Phase-Locking.
This fuction returns the dynamic Phase-Locking for all parcels/voxels
of the input.
Args:
N (int): Number of parcels/voxels of the input array.
... |
bf748b081637d8869374e8719acd8c70ca39cf68a2caadd447cf24d02ae329c8 | Python | 1,733 | 56 | """Tests for nltools.plotting.prediction — the drawing internals behind Roc.plot
and BrainData.predict(plot=True)."""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pytest
from nltools.plotting.prediction import (
_plot_class_probability,
_plot_decision_mar... |
fea51a669a58957814dc387057c8f34fb7e72b14634fc5cf8f8af6ac4c13e41a | Python | 1,733 | 56 | import numpy as np
import pytest
from ase.build import fcc111
from mace.calculators import mace_mp
pytestmark = [pytest.mark.network]
@pytest.fixture(name="setup_calculator_")
def setup_calculator():
calc = mace_mp(
model="medium", dispersion=False, default_dtype="float64", device="cpu"
)
return... |
10398f82ca6b1c0088dcc3c8ab9b5b89bc7a7931f9d5a7a6e43ec843b41b4ed2 | Python | 1,735 | 57 | import unittest
from tests.common import get_svcall
class TestGaps(unittest.TestCase):
def test_Gaps(self):
from sniffles.util import UnresolvedReferenceRegions
unresolved = UnresolvedReferenceRegions('chr1', [(0, 10_000), (20_000, 30_000)])
with self.subTest('Outside gap region/normal ... |
90b4fbc3df4a3235b1a66ce8264194d6e76267991442b4f4ee63c460d29f7e4b | Python | 1,736 | 60 | """NIBR substructure filter
Simple implementation using a cutoff to delineate between "good" and "bad"
molecules. Could be implemented as a normal scoring component using
a transform.
This is a demonstration on how one could build a filter catalog with the help of RDKit.
The actual code was taken from RDKit commit 4... |
109ffa7a8bf2c3d8a9e52b05663642a5f288a56ad554a8b3f7dcf10ef058e209 | Python | 1,737 | 44 | import numpy as np
import xarray as xr
from numpy.testing import assert_raises
from gemmr.plot import *
from gemmr.plot import hv
hv.extension('matplotlib')
def test_mean_metric_curve():
rs = np.asarray((.1, .2))
n_per_ftrs=np.asarray((1,2,3))
n_req = xr.DataArray(np.arange(6).reshape(2, 3), dims=('r',... |
8d6b0b2af8801f5cffd6cb03ae299fdb224570323b9da80ea0e9dd2f92cdcb77 | Python | 1,739 | 50 | import pandas as pd
from typing import List, Dict
def combined_datasets_dataframes(
queries: pd.DataFrame,
corpus: pd.DataFrame,
qrels: pd.DataFrame
) -> pd.DataFrame:
qrels = qrels.merge(queries, left_on="query-id", right_on="_id", how="left")
qrels.rename(columns={"text": "query-text"}, inplace... |
de6de12d313a70e82b8788482b9c8e1c0439af03ff5eb79b522b090af11b2471 | Python | 1,742 | 56 | from sklearn.metrics import auc, precision_recall_curve, roc_auc_score, mean_absolute_error, mean_squared_error
import numpy as np
def prc_auc(targets, preds):
precision, recall, _ = precision_recall_curve(targets, preds)
return auc(recall, precision)
def compute_cla_metric(targets, preds, num_tasks):
... |
3cff34d0198b60473c5458fdddf1ff712411ee7bf9e2faa5de040767418b4876 | Python | 1,744 | 74 | import json
from scipy.spatial import distance
import sys
from Bio import SeqIO
def calc_tetra(seqs_record):
tetramers = {}
for a in ['A', 'C', 'G', 'T']:
for b in ['A', 'C', 'G', 'T']:
for c in ['A', 'C', 'G', 'T']:
for d in ['A', 'C', 'G', 'T']:
tetramers[a+b+c+d] = 0
for seqs in seqs_record:
st... |
10e36ac12d220b0bf64742e7adfb4dbe8b43a93cf9992d7167d8a775cfce21cf | Python | 1,745 | 50 | import warnings
from typing import Tuple
import numpy as np
import torch
from . import MoleculeGraph
def collate_graph(
batch: Tuple[np.ndarray, list[MoleculeGraph], list[np.ndarray]]
) -> Tuple[torch.Tensor, list[torch.Tensor], list[torch.Tensor], list[torch.Tensor], list[MoleculeGraph], list[np.ndarray]]:
... |
f28ceac71279194717a28ff2ab14d1fd3e9a5927903ee61c51b640ddd0d25a60 | Python | 1,745 | 55 | """Visualization for Geometric Statistics."""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # NOQA
import geomstats.backend as gs
AX_SCALE = 1.2
class PoincarePolyDisk:
"""Class used to plot points in the Poincare polydisk."""
def __init__(self, points=None, coords_type="ball", ... |
581db139b08e22d6a9c61b7029a8a59ef04b725d42b885cc958692ed77c9115e | Python | 1,746 | 57 | """Compute scores with RDKit's QED"""
__all__ = ["CustomAlerts"]
from typing import List
import numpy as np
from rdkit import Chem
from pydantic.dataclasses import dataclass
import logging
from .component_results import ComponentResults
from reinvent_plugins.mol_cache import molcache
from .add_tag import add_tag
lo... |
73070df7be55bbbb61191eb0a2022cd51dbf424e837959becf382674a67bbe95 | Python | 1,746 | 53 | import torch
from molx.dataset import Molecule3DProps
from molx.model import SchNet2D
from molx.mol3d import TransformGT3D
from molx.proppred import RegTrainer, eval_reg
conf = {}
conf['epochs'] = 100
conf['early_stopping'] = 200
conf['lr'] = 0.0001
conf['lr_decay_factor'] = 0.8
conf['lr_decay_step_size'] = 10
conf['... |
dc99f0cf4f462f3e29382fc99ccc4bc8bb05a555a05c461230d626da6623ff04 | Python | 1,749 | 50 | import pytest
import numpy as np
from reinvent_plugins.components.RDKit.comp_rdkit_descriptors import Parameters, RDKitDescriptors
def test_comp_rdkit_descriptors():
input_smiles = ["c1ccccc1", "CCC#CCC"]
component_result = {
"qed": np.array([0.44262837, 0.39083325]),
"MolWt": np.array([78.11... |
242cc1c05dca4dc1f6105cc2f6a360bd915828d8bd6837574d9ec52a95bc8dac | Python | 1,750 | 68 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
82e18c7b701e6c909df86cb8489554dac250eb48c7703912cc231509f8c688e0 | Python | 1,751 | 53 | import sys
import time
import numpy as np
from matplotlib.backends.qt_compat import QtCore, QtWidgets, is_pyqt5
if is_pyqt5():
from matplotlib.backends.backend_qt5agg import (
FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
else:
from matplotlib.backends.backend_qt4agg import (
Figure... |
ee65e8d2e317eb6f1a4b526118f0a27623064d2b719fcb39789a68f67a577068 | Python | 1,752 | 61 | """
Dynamic loading of python modules and classes
Author: Tal Eisenberg, 2021
"""
import importlib
from pathlib import Path
import inspect
def instantiate_class(class_name, mod, *args, **kwargs):
module_name, class_name = class_name.rsplit(".", 1)
mod = importlib.import_module(module_name)
ClassObject = ... |
79a4df23f6e1bee86b02da26d3a2404528bbf1611ca94dc9fc902cfb1131aaa7 | Python | 1,754 | 72 | """
Holds a reference to the application config module.
Author: Tal Eisenberg, 2022
"""
import importlib
import sys
import traceback
from types import ModuleType
_default_config_name = "config"
_config = None
def add_defaults(config, defaults):
for (k, v) in defaults.items():
if k not in config.__dict... |
3379ff4959247843e98a506b5780174d69609c98c44eb20b51299e892122df2e | Python | 1,755 | 57 | from setuptools import setup, find_packages
# read the contents of your README file
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name="deepd3",
long_description=long_de... |
44197a3946755809c0401dd4721ef082f73ce86c1e4ccd92e16996b37f8f73f0 | Python | 1,755 | 43 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch import nn, Tensor
from typing import Optional, Tuple, Union
class UpSample(nn.Upsample):
"""
This layer upsamples a given input tensor.
Args:
size (Optional[Union[int, ... |
78d97f10036033cde1526b3909915ca145092a1b4a7b68803a318e68e1d139ae | Python | 1,755 | 58 | from sklearn.manifold import TSNE
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
import matplotlib.pyplot as plt
data_141 = pd.read_csv('Feature_random_forest.csv', sep='\t')
data_150 = pd.read_csv('Feature_mutual_info_classif_SelectKBest_150.csv', sep='\t')
data_40 = pd.read_csv('Feature_Seq... |
441af7e0b1ac655b0c9251862e9130c3ff68c7721425e66f7264bd235a7e2e91 | Python | 1,756 | 59 | """
Baseline: pre-trained Densenet 161 with adapted head and custom final layer
"""
import torch
import torch.nn as nn
class Dense161_model(nn.Module):
def __init__(self):
super().__init__()
back_bone = torch.hub.load("pytorch/vision:v0.10.0", "densenet161", pretrained=True)
input_layer ... |
decd2f000dea1de1c6e10f5d8574fda5b5f4b1a50e4f24670f66f055720bffd3 | Python | 1,756 | 63 | from __future__ import absolute_import, division
import numpy as np
from numpy.testing import assert_allclose
from nose.tools import assert_equal, assert_true
from pycircstat.regression import CL1stOrderRegression, CCTrigonometricPolynomialRegression
def test_circlinregression():
alpha = np.random.rand(200)*np.... |
02d09c8d8ad21d59805bbb00c6229a0c71f6f4827a17cc9d6bd647478f6e18b7 | Python | 1,760 | 69 | """
Script to make the inputs for alphafold to run on sherlock
Copy fasta files and rename them to a3m files (this skips MSA)
"""
import os
import shutil
import logging
import argparse
from pathlib import Path
import numpy as np
def build_parser():
"""
Build a CLI parser
"""
parser = argparse.Argume... |
ae39d1228b5662c17faa7e49ef85c9fa817310031aa8b01743cc5fa9d1e54fe0 | Python | 1,760 | 48 | from numpy.random import default_rng
from safe_msmt.annotations import make_annotation_text_files
from safe_msmt.data_helper import identify_classes_to_use
from safe_msmt.feature_partition import make_tfrecords
# seed_value = 68103
seed_value = 0
clip_length = 3
# threshold: to find species that appeared in at least ... |
90e11d8b21551bd276376d4a6dd2d8b02342ec8e32442918db96f200fa63d5ee | Python | 1,763 | 49 | from e3nn.util.test import assert_auto_jitable
from nequip.utils.test import assert_AtomicData_equivariant
from nequip.nn import SequentialGraphNetwork, GraphModel
from nequip.nn.embedding import (
PolynomialCutoff,
SphericalHarmonicEdgeAttrs,
EdgeLengthNormalizer,
BesselEdgeLengthEncoding,
)
from nequ... |
6a1b1022563a60837217cba73ef89322fa738b3c228a0ae40e05527ac3b2e930 | Python | 1,767 | 64 | #!/usr/bin/env python
"""
ModelAngelo: Automated Cryo-EM model building toolkit
"""
def main():
import argparse
import warnings
import model_angelo
parser = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument(
"-... |
91e7dc6d93929960ed6b256f84ec92cd6188333d6ef983516458fd844f5317a8 | Python | 1,769 | 56 | import glob
import matplotlib
from geomstats.test.parametrizers import ExamplesParametrizer
from geomstats.test.test_case import TestCase
ALL_BACKENDS = ["numpy", "autograd", "pytorch"]
AUTODIFF_BACKENDS = ALL_BACKENDS[1:]
NP_LIKE_BACKENDS = ALL_BACKENDS[:2]
EXAMPLES_DIR = "examples"
matplotlib.use("Agg")
class... |
edeef24b1832703c0bde3880d9726988ae314a446357e218d261ba8579e8121f | Python | 1,770 | 56 | import numpy as np
from datetime import timedelta as t
def get_firing_power(prediction, times, SOP, window_size):
firing_power = []
for i in range(len(prediction)):
begin_time = times[i] - t(minutes = SOP)
end_time = times[i]
window_values = prediction[(times>beg... |
1d1030fda8b92c8ee0351387a0b5d0e0c86e913791c38fb26b50db3cf62b1ef2 | Python | 1,772 | 58 | """Config Validation"""
from typing import Optional
from pydantic import Field
from reinvent.validation import GlobalConfig
class SectionParameters(GlobalConfig):
num_epochs: int = Field(ge=1)
batch_size: int = Field(ge=1)
input_model_file: str
output_model_file: str # FIXME: consider for removal
... |
7f8c3c3d4d404dbbfe00a37a9a511b747a60690c2d17c094029da4b51b94e6c4 | Python | 1,773 | 50 | from chromadb.api.client import Client
from chromadb.config import System
from chromadb.test.property import invariants
def test_log_purge(sqlite_persistent: System) -> None:
client = Client.from_system(sqlite_persistent)
first_collection = client.create_collection(
"first_collection", metadata={"hns... |
fee8fd75ecaaaeb871d9445a00e00fe2853e1a5040bb1a926f93aa9e0e08db49 | Python | 1,775 | 47 | from setuptools import setup
def readme():
with open('README.md') as f:
return f.read()
setup(name='neuroHarmonize',
version='2.5.2',
description='Harmonization tools for multi-center neuroimaging studies.',
long_description=readme(),
long_description_content_type='text/markdown',
... |
b1ca9befa59145f48dfa87e47a6de42291b2a962b34bed334fe352452dbff5aa | Python | 1,778 | 40 | # Figure 5 of https://www.biorxiv.org/content/10.1101/2021.03.21.436284v1.full.pdf
# Original code found in https://github.com/bsorsch/geometry_fewshot_learning/blob/master/ResNet_macaque_stimuli.ipynb
#
# Parameters:
# - Dataset: majaj_2015 (brainscore IT object dataset)
# - Number of objects: 64
# - Numbe... |
b6fd69412b865fc14c1fd338a1a3178b327936846edf10090d5d250d422841cd | Python | 1,778 | 57 | import numpy as np
def simple_bootstrap(data, iterations):
"""
Generator to perform iterations bootstrap iterations along the first axis.
:param data: data
:param iterations: iterations
"""
m = data.shape[0]
for _ in range(iterations):
yield data[np.random.randint(0,m,m)]
def inde... |
2263a0ae17de60a94d1743fbb6566d80fcce5726a2790d034b6fc513a838a594 | Python | 1,781 | 49 | import random
import pytest
import geomstats.backend as gs
from geomstats.geometry.poincare_ball import PoincareBall
from geomstats.numerics.path import UniformlySampledPathEnergy
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test.random import RandomDataGenerator
from geomstats.test.t... |
7f5cc88ff695b77f2acb915e3a47da3e93ed277c9a896480872ba2e948d6f274 | Python | 1,781 | 60 | """Perform tangent PCA at the mean on SO(3)."""
import logging
import matplotlib.pyplot as plt
import numpy as np
import geomstats.visualization as visualization
from geomstats.geometry.special_orthogonal import SpecialOrthogonal
from geomstats.learning.frechet_mean import FrechetMean
from geomstats.learning.pca imp... |
a6ca30e220f9935098a20792505ad467690c1afe3efd754bd8c95223b7781385 | Python | 1,781 | 56 | """Mesh-related data structures."""
import geomstats.backend as gs
class Surface:
"""A surface mesh.
Mesh info (face centroids, normals and areas) is cached
for greater performance.
Parameters
----------
vertices : array-like, shape=[n_vertices, 3]
Mesh vertices.
faces : array-l... |
a743eb62e9046dacff2f52d1353fd958f65aade31cbd986eb47c7534a046b54a | Python | 1,781 | 75 | from .arg_parser import build_default_arg_parser, build_preprocess_arg_parser
from .arg_parser_tools import check_args
from .cg import U_matrix_real
from .checkpoint import CheckpointHandler, CheckpointIO, CheckpointState
from .default_keys import DefaultKeys
from .finetuning_utils import load_foundations, load_foundat... |
d71bcd8b0c641039d22ff337ebd6161c055e43932d394cde41fee43e3a145090 | Python | 1,781 | 47 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... |
3e49e081c176799747f6f88b0e2e9604db18b571e7f349988033d37f162ece51 | Python | 1,782 | 50 | import random
import pytest
from geomstats.geometry.general_linear import GeneralLinear
from geomstats.geometry.group_action import SpecialOrthogonalComposeAction
from geomstats.geometry.matrices import MatricesMetric
from geomstats.geometry.quotient_metric import QuotientMetric
from geomstats.geometry.spd_matrices i... |
582a2c38f0f1e23a4343427e53f5f8ed681fe622e6f0d078e4738be240729cf3 | Python | 1,782 | 50 | import pandas as pd
import numpy as np
import pickle
import yaml
if __name__ == "__main__":
meta_fullname = "/home/hoang/workspace/OAPOP/adni/Metadata/cv_split_10folds_DXTARGET_fdg_12345.pkl"
config_fullname = "/home/hoang/workspace/OAPOP/adni/configs/data/img:1_fdg_meta:all.yaml"
with open(meta_fullname,... |
69968bd084ea82c9f7d8a827e4901be71a77c1a83fe554da6f6c8ff9247bf6b8 | Python | 1,782 | 68 | # -*- coding: utf-8 -*-
import os
import argparse
import logging
import itertools
# use this format to tell the parsers
# where to insert certain parts of the script
# ${imports}
def parse_args():
# Argument Parser
parser = argparse.ArgumentParser()
# my args
parser.add_argument("--verbose", action =... |
aeb92be8a1bde95fc11346acc8be875c55352f4feb8db33bf69ef1f5695b0921 | Python | 1,783 | 33 | """
This script combines all the necessary files and organizes them as needed by preprocess_features.py
"""
import pandas as pd
ss_prot = './step3/prot_seq_ss.txt'
ss_peptide = './step3/peptide_seq_ss.txt'
# Format the peptide-protein data like
# seq, pep, label, pep_ss, seq_ss
prot_df = pd.read_csv(ss_prot, sep='\t'... |
e6de096a2c9080481e99d5abfa4cc8d7fb9cb0e534233fa1287f186d21ddd05b | Python | 1,783 | 37 | import sys
import argparse
import os
def path(string):
if os.path.exists(string):
return string
else:
sys.exit(f'File not found: {string}')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', metavar='input', type=path, help='Path to the input image, should be .... |
b027f943b75f09e4be7081d9b920ec0c5058aa5e9cfd0644456024c8ef2e5246 | Python | 1,784 | 56 | """isort:skip_file"""
import multiprocessing
import os
import sys
from contextlib import suppress
from importlib import metadata
if "forkserver" in multiprocessing.get_all_start_methods() and sys.platform != "darwin":
# In macOS (sys.platform == "darwin"), "forkserver" is broken in Python 3.13.8
# and 3.13.9,... |
e6b8077407b3c537ec4ee49c282991c76794ef8443a37c91270878d0a2e0fe11 | Python | 1,784 | 47 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
32b45023cca9654f541b850dc69e91d7d33787ae32e21b2da33bfede48d427fd | Python | 1,785 | 40 | import json
import argparse
from rdkit import RDLogger
from torch_geometric.loader import DenseDataLoader
from dig.ggraph.method import GraphAF
from dig.ggraph.evaluation import ConstPropOptEvaluator
from dig.ggraph.dataset import ZINC800
RDLogger.DisableLog('rdApp.*')
parser = argparse.ArgumentParser()
parser.add_... |
55408f6aefdfd01955d9c7342bccb27bc79667a86b0014ba23d91db2a50c71ff | Python | 1,786 | 49 | from torch_geometric.utils import degree
import torch
class NormalizedDegree(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, data):
deg = degree(data.edge_index[0], dtype=torch.float)
deg = (deg - self.mean) / self.std
data.x =... |
589e3b5dbc4a7338f7f33c757b4c4f511e251eb7f916ccf96f6e14c32f79c139 | Python | 1,786 | 77 | from numba import jit
import numpy as np
import math
import cv2
import json
def stack_from_polygon_file(path, pol_i=0, imshape=(200, 610)):
"""Make a binary array from polygon masks
:param path:
:param pol_i:
:param imshape:
:return:
"""
rawpolygons = json.load(open(path, "r"))
cb_pol... |
a3e86230c25095b6461b098c6ae9d37028842c3fd4426e41d8d4718594612ee3 | Python | 1,786 | 88 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 8 22:48:08 2023
@author: rob
"""
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import pandas
from scipy.optimize import curve_fit
mat = scipy.io.loadmat('psfData.mat')
def sigmoid(x, a, b, c, d):
y = a + (b - a) / (1 ... |
b554e2dcbc70997a8c9860fec1bc194ae9fabde39875541fe38c1ca1c7c0d918 | Python | 1,788 | 55 | import argparse
from torch.optim import SGD
from . import register_optimizer
from .base_optim import BaseOptim
@register_optimizer("sgd")
class SGDOptimizer(BaseOptim, SGD):
"""
`SGD <http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf>`_ optimizer
"""
def __init__(self, opts, model_params) -> No... |
fc9bcfa4e8b4ec147c41f45fd19a58f6c516c968657bbab241a737babf2de13b | Python | 1,788 | 59 | from .dipole import (
simulate_dipole,
read_dipole,
average_dipoles,
Dipole,
_read_dipole_txt,
)
from .params import Params, read_params, convert_to_json
from .network import Network, pick_connection
from .network_models import (
neymotin_2020_model,
jones_2009_model,
law_2021_model,
... |
2f54bc9cc91d7eacb85c1c29508c8acd4360a9c7672f6b9be37be83fdbc1ab64 | Python | 1,789 | 56 | import os
import numpy as np
def readDadiResultsFromDir(dadiResDir, minRuns):
res = []
for resFileName in os.listdir(dadiResDir):
currRes = {}
mode = 0
with open(dadiResDir + "/" + resFileName, 'rt') as f:
for line in f:
if mode == 0:
if ... |
99227e10d4effc2b14f706577a9589d2ba6be71a09905a0585a61560fdc35325 | Python | 1,790 | 43 | import matplotlib.pyplot as plt
import cortex
import numpy as np
from glob import glob
# Run this code to download fsaverage surface for pycortex, labeled by Mark Lescroart
cortex.utils.download_subject(subject_id='fsaverage', download_again=False)
# We are going to compute median tSNR across all runs.
# We already p... |
d4fdf75422c2bb33c2d2ee39c6d5416b1ce85b20a0261bb70c67b29de96a4198 | Python | 1,790 | 61 | """Compute a desired list of RDKit descriptors up to a total of 210"""
__all__ = ["RDKitDescriptors"]
from typing import List
import logging
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator
import numpy as np
from pydantic.dat... |
e3c7b480359ae4c5af0f52c98230580843e0b85b300accabd119daa16a19ec3e | Python | 1,790 | 54 | # ==============================================================================
# Copyright 2025 Technical University of Denmark
# Author: Nikolas Borrel-Jensen
#
# All Rights Reserved.
#
# Licensed under the MIT License.
# ==============================================================================
import jax.nump... |
0822182aaa164abf4ae066ba5648e7fe2b3d90e2f6ab089c8adf653055648c93 | Python | 1,791 | 44 | import pytest
import geomstats.backend as gs
from geomstats.test_cases.geometry.base import VectorSpaceOpenSetTestCase
from geomstats.test_cases.geometry.riemannian_metric import RiemannianMetricTestCase
from geomstats.test_cases.information_geometry.base import (
InformationManifoldMixinTestCase,
)
class Dirich... |
3e647ceff98ed4609e4e4dff035fc006e928272c1489758b9fb94b33c7f7beac | Python | 1,796 | 68 | # === Utility functions adapted from torch geometric (https://pytorch-geometric.readthedocs.io/en/latest/) ===
import ssl
import os
import os.path as osp
import urllib
import zipfile
import tarfile
from pathlib import Path
from typing import Optional
import logging
logger = logging.getLogger(__name__)
def download... |
545664d73526a2f284a7c26b9ce8a6863e45dfa2856b38b34d15cd736a773c8d | Python | 1,797 | 66 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Task module."""
from dataclasses import dataclass, field
@dataclass
class Task:
"""Task base dataclass.
Args:
name (str): Name of the task.
function_name (str): Name of the function to be executed for the task... |
2dfef1e5577dd6acfaa4443860d95fc54c0af2a82e4e67409195f7c5ed678d66 | Python | 1,798 | 73 |
from .tar import Tar, DumpTar
from .meta import Meta
from .vocab import Vocab
from .pq import PQ
from .record import Record
META = 'meta.json'
VOCAB = 'vocab.bin'
PQ_ = 'pq.bin'
class Navec(Record):
__attributes__ = ['meta', 'vocab', 'pq']
def __init__(self, meta, vocab, pq):
self.meta = meta
... |
9b16386627830ca487b6172ba0c4d445b202a43ffc6c73376c496d264f8ffd7e | Python | 1,803 | 54 | """LibInvent transfer learning
Train a given model with new data. The data comes from a file with SMILES
strings. The file is assumed to be in multi-column format separated by commas
(CSV) or spaces. The SMILES strings are taken from the first two columns.
The two SMILES in each row correspond to a single SMILES (... |
e1b8de81cc8cfec41625a0a73a08de1a1834f915d1d8778e291f76d3ad657238 | Python | 1,804 | 33 | import json
import argparse
from torch.utils.data import DataLoader
from dig.ggraph3D.method import G_SphereNet
from dig.ggraph3D.evaluation import PropOptEvaluator
from dig.ggraph3D.dataset import QM93DGEN, collate_fn
parser = argparse.ArgumentParser()
parser.add_argument('--prop', type=str, default='gap', choices=['... |
ef24ea292804e1a6bc5a07ac17c2ab0af818ad16eb6e68bde692e1e1741e0e37 | Python | 1,804 | 57 | import numpy as np
def find_event_channel(inst=None, ch_names=None):
"""
Find the event channel using heuristics.
Disclaimer: Not 100% guaranteed to find it.
If raw is None, ch_names must be given.
Parameters
----------
inst : mne.io.Raw | numpy.ndarray (n_channels x n_samples)
T... |
e0038cfaa6898d5ea9ee323b0b5546a7559ee426bcd7172a90c0ffcec880e568 | Python | 1,805 | 58 | import os
import time
import warnings
import numpy as np
from pydfc import MultiAnalysis, data_loader
warnings.simplefilter("ignore")
os.environ["MKL_NUM_THREADS"] = "16"
os.environ["NUMEXPR_NUM_THREADS"] = "16"
os.environ["OMP_NUM_THREADS"] = "16"
################################# Parameters #####################... |
0cfe363c863d4f82adfeaf5427bb17f6bca5c3556c9513155d560d1439bbe7d8 | Python | 1,808 | 28 | import matplotlib.pyplot as plt
name_list = ['Asthma', 'Systemic lupus erythematosus', 'Rheumatoid arthritis', 'Crohn', 'Multiple sclerosis', 'Type 1 diabetes', \
'Psoriasis', 'Ulcerative colitis', 'Ankylosing spondylitis', 'Osteoarthritis', 'Immunoglobulin', \
'Primary sclerosing cholangitis'... |
aebf5b718cafd125d5be977c4fb896dc858736ac4acd778fd782d74976650a5f | Python | 1,810 | 59 | import torch
def test_conv_block():
from mlspm.modules import Conv2dBlock, Conv3dBlock
torch.manual_seed(0)
x = torch.rand(10, 8, 12, 12)
with torch.no_grad():
a = Conv2dBlock(8, 4, kernel_size=3, depth=1, res_connection=False, activation=torch.nn.ReLU(), last_activation=True)(x)
as... |
dac055bb694149a2d635151ffc09d182f8176bfe2a296c52161f99bb86e53e18 | Python | 1,810 | 64 | import pytest
from torch.utils.data import SubsetRandomSampler, DataLoader, Subset
from nequip.data import AtomicDataDict
class TestDataset:
def test_subset(self, dataset):
Subset(dataset, [1, 3])
class TestDataLoader:
def test_whole(self, dataset):
dloader = DataLoader(
datase... |
d305ce2ab689311ffb1d7a40516bc9d71fd9f0a1fb82fdea6bd0eb49f3282863 | Python | 1,812 | 61 | from PymoNNto import *
#https://brian2.readthedocs.io/en/stable/examples/frompapers.Wang_Buszaki_1996.html
def exp(x):
return np.e**x
def exprel(x):
return (exp(x) - 1)/x
class Wang_Buzaki_main(Behavior):
def initialize(self, n):
self.set_parameters_as_variables(self)
self.dt = 0.01# * ... |
464a6595c878612474a919b2974a4bba5401c90ba80032cb644ca0fc26a91609 | Python | 1,814 | 56 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from typing import Optional, Tuple
class Dropout(nn.Dropout):
"""
This layer, during training, randomly zeroes some of the elements of the input tensor with probability `p`
... |
46d41e787ce1be22555ff7e6c6d0618586614d4df0f37f4e0968de903fc6fad0 | Python | 1,814 | 55 | import torch.nn as nn
from reinvent.models.model_mode_enum import ModelModeEnum
from reinvent.models.model_parameter_enum import ModelParametersEnum
from reinvent.models.libinvent.models.decorator import Decorator
from reinvent.models.libinvent.models.model import DecoratorModel
from reinvent.models.libinvent.models.v... |
2f655577cb1f3a7e6812380b6a05f5ceab336fea9d2d26ad5604a736e726c68f | Python | 1,816 | 68 | import random
import pytest
from geomstats.geometry.siegel import Siegel, SiegelMetric
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.base import ComplexVectorSpaceOpenSetTestCase
from geomstats.test_cases.geometry.siegel import SiegelMetricTestCase
from .data.base ... |
aefb5a56666e2a20f6fa68d237a42b646c1700810188e6e0e9238467bf11bf96 | Python | 1,816 | 92 | from rich import print
import neurodesign
from neurodesign import generate
# define experimental setup
exp = neurodesign.Experiment(
TR=2,
n_trials=20,
P=[0.3, 0.3, 0.4],
C=[[1, -1, 0], [0, 1, -1]],
n_stimuli=3,
rho=0.3,
stim_duration=1,
t_pre=0.5,
t_post=2,
ITImodel="exponent... |
00bb916ef53ff09a1213ba0f4c7cc066e76443bd23f71c1b5dfa6ac2e323a837 | Python | 1,817 | 47 | import os
# Define a list of file names
file_names = [
"CD16p_Mono_conditional_eQTL_FDR0.05.txt",
"CL_Mono_conditional_eQTL_FDR0.05.txt",
"CM_CD8_conditional_eQTL_FDR0.05.txt",
"DN_B_conditional_eQTL_FDR0.05.txt",
"EM_CD8_conditional_eQTL_FDR0.05.txt",
"Fr_I_nTreg_conditional_eQTL_FDR0.05.txt",... |
2b3f8fe2910ff79b5bb848e58237eb725544b2fa67a0aeb4f82b24d681c498b0 | Python | 1,817 | 62 | """Script to generate a Snakebids project."""
import argparse
from pathlib import Path
from cookiecutter.main import cookiecutter
import snakebids
from snakebids.app import SnakeBidsApp
from snakebids.cli import add_dynamic_args
def create_app(args):
cookiecutter(
str(Path(snakebids.__path__[0]) / "pro... |
45d4b1baaf0696bf4c2a62ca6781df3be26c3c9f6ac5d9bac55a0fc4c20a6e5c | Python | 1,817 | 71 | from abc import abstractmethod
from enum import Enum
from typing import Dict, Any, Optional
from uuid import UUID
from chromadb.api.types import (
Embeddings,
Metadatas,
Documents,
URIs,
IDs,
CollectionMetadata,
Where,
WhereDocument,
)
from chromadb.config import Component, System
cla... |
6d306a520f95e76a545a324164c00046050ad46df15af06cbf551b114711acd4 | Python | 1,817 | 59 | import pysam
import argparse
from tqdm import tqdm
from collections import defaultdict
def pipesplit(col):
return lambda input: input.split("|")[col]
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("snps", help="SNP BED file")
parser.add_argument("reads", help="Mapped reads ... |
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