sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
733325a0b479550dcfc388267882772be456f54a44a37d80e8e773753dc05959 | Python | 2,497 | 91 | import GeoJax as gj
import numpy as np
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
scale : tuple of 3 fl... |
83d0bfad5a500059034355fdf729df39fa91d2089d1c835ee719ac87461ed2a3 | Python | 2,498 | 80 | # Copyright 2023 BioMap (Beijing) Intelligence Technology Limited
import torch
import torch.nn as nn
from .performer import Gene2VecPositionalEmbedding, RandomPositionalEmbedding
from torch.nn import functional as F
class pytorchTransformerLM_mse(nn.Module):
def __init__(self,
num_tokens,
... |
fbfb25ae067e8e86cdfdad150f3c7ecfd94c9f3c7c2b2a93ce62b3524bfe5077 | Python | 2,500 | 69 | """test loss functions"""
import torch
from torch.autograd import gradcheck
from torch.testing import assert_close
import vak.nn.loss
def tensor_to_gradcheck_var(tensor, dtype=torch.float64, requires_grad=True):
"""Converts the input tensor to a valid variable to check the gradient.
`gradcheck` needs 64-bit ... |
07cd0d6f460fcee1857d7011eb5024458ece1742828b4cf8f27f6c9541465b57 | Python | 2,501 | 69 | # 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... |
83460e1cafbf4b783aadbbd970687efb3c6e47f7ef80f80b299ec08093fbf570 | Python | 2,501 | 54 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'UI/AddWindow.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow: QtWidgets.QM... |
ff425a3c2bd076bb7f64c8496839748fa24f0ed8b4afc3e73af31d5a9a5bdf4a | Python | 2,501 | 81 | """tests for vak.cli.train module"""
from unittest import mock
import pytest
import vak.config
import vak.common.constants
import vak.common.paths
import vak.cli.train
from . import cli_asserts
@pytest.mark.parametrize(
"model_name, audio_format, spect_format, annot_format",
[
("TweetyNet", "cbin",... |
b37740e9f0c0f96d70661975287b4c3da07def3b4268bfbd4bf0b76d50692534 | Python | 2,502 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
9cd6fe334f297c735b75ba31a5629e82a1b97f6f3459570c89bd2135fcba816c | Python | 2,505 | 65 | # coding=utf-8
# Copyright 2018 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... |
eafcf1233029875f4bdce813e904d1644b4a576a749fc1059a0da6bafb419d05 | Python | 2,505 | 83 | import torch
import torch.utils.data as Data
import torchvision
from lib.network import Network
from torch import nn
import time
# def calc_acc(x, y):
# x = torch.max(x, dim=-1)[1]
# accuracy = sum(x == y) / x.size(0)
# return accuracy
train_data = torchvision.datasets.MNIST(root='./mnist', train=True,
... |
3492038eaa161aefb9137c8fffc9312538701c7c35193e7d399aa728cc965f12 | Python | 2,507 | 79 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Objects to classify nuclei"""
from typing import Optional, Tuple
import numpy as np
import pandas as pd
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
class PolygonClassifier(object):
"""Classifies data"""
def __init__(self)... |
91de7d47e39ccbb594eca23eef193f7bb732c3ed14df7c4369c4d60e0133c512 | Python | 2,507 | 67 | # coding=utf-8
# Copyright 2018 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... |
30bb75353547a990c14de27d470643205d730e585771fdaf87f271702b214363 | Python | 2,508 | 74 | '''
Provides a general testing class which inherits from unittest.TestCase
and also provides the numpy testing functions.
'''
import contextlib
import unittest
import os.path
from inspect import getsourcefile
import numpy as np
from numpy.testing import (assert_almost_equal,
assert_array_eq... |
89b74df9c3cd67a553753b0332307add345e82b69b5a939a1a72f9e3dbddb170 | Python | 2,509 | 72 | #!/usr/bin/env python
# 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 argparse
import fileinput
from tqdm import tqdm
def main():
parser = argparse.ArgumentParser(
desc... |
b8edd9b82e3316b5c4ff9e17f71cfd486d37a6de8f3a0d901b7a16952893a15f | Python | 2,511 | 80 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
"""basic scatter_sum operations from torch_scatter from
https://github.com/mir-group/pytorch_runstats/blob/main/torch_runstats/scatter_sum.py
Using code from https://github.com/rusty1s/pytorch_scatter, but cut down to avoid a dependency.
Py... |
b0fde5882929907086fb237b60219e09f3e46f7c715a1def2500dcb51833b497 | Python | 2,512 | 89 | import os
import csv
import random
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
CSV_FILE = os.path.join(BASE_DIR, "tm_score_results.csv")
OUTPUT_DIR = os.path.join(BASE_DIR, "processed_data", "splits", "similarity_experiments")
TOTAL_SAMPLES = 2600
def create_similarity_splits():
if not os.path.exists(... |
df4d717da4a6866bf8e1e14bea6a520e39ebb0dc6b78d5fa4e1dc81fd29fb2f9 | Python | 2,513 | 87 | #!/usr/bin/env python3
# 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.
"""
Data pre-processing: build vocabularies and binarize training data.
"""
import argparse
import glob
import os
impor... |
ec5564b1e3209f66889b83aeec45c7a0a79c0c973b79cd39b68adec78299a13a | Python | 2,513 | 64 | import torch
from nnunetv2.training.nnUNetTrainer.variants.benchmarking.nnUNetTrainerBenchmark_5epochs import (
nnUNetTrainerBenchmark_5epochs,
)
from nnunetv2.utilities.label_handling.label_handling import determine_num_input_channels
class nnUNetTrainerBenchmark_5epochs_noDataLoading(nnUNetTrainerBenchmark_5ep... |
ac18cb14c8a92765f4e915119978186f683045701ab5c97e34eefb892ad785ba | Python | 2,514 | 79 | """
Text Processing for NeuroVFM-LLM
This module provides utilities for text processing including:
- Text processing for visual instruction tuning
- Task classes for visual instruction tuning objectives
"""
from typing import Dict, List
import torch
import random
import json
import logging
def process_text(
con... |
2515bf687882c7501dc94e57f36c9727a43e827a2511543c5345ede2d4d639d5 | Python | 2,515 | 84 | import os
import subprocess
from datetime import datetime
from threading import Thread, Event
from configs import SYNC_INTERVAL, LOGGER, LOG_PATH, ROOT_DIR, SYNC_TIMEOUT, RESULT_PATH, SYNC_SCRIPT_PATH
def now_int():
epoch = datetime.utcfromtimestamp(0)
return int((datetime.now() - epoch).total_seconds())
c... |
703553f47f2d3126e94b6ddbde58907e861742e7cea6f419c4c1e2e6ca1129f1 | Python | 2,515 | 73 | import json
import numpy as np
epsval = np.finfo(float).eps
minval = np.finfo(float).min
maxval = np.finfo(float).max
def _log_bounded(x): # transforms, roughtly speaking, [0.00001, 100000] to [-10, 10]
if not np.isfinite(x): return x
if x<epsval: x=epsval
if x>maxval: x=maxval
y = np.log(x)
retur... |
cc2c65851fa9cde1cdea0b7280a3007ff9f5d2e0a96bb8ded300dccf5e5540d9 | Python | 2,515 | 87 | import os
import tempfile
import re
import librosa
import torch
import json
import numpy as np
import argparse
from tqdm import tqdm
import math
from transformers import Wav2Vec2ForCTC, AutoProcessor
from lib import falign_ext
parser = argparse.ArgumentParser()
parser.add_argument("--uroman_txt", type=str)
parser.ad... |
dedff9465469c50f3f3e045c35fa54db38df28b955560777f0c8ca875fbd4910 | Python | 2,515 | 78 | """Simple test/demonstration program of pileup counting."""
import argparse
import logging
from timeit import default_timer as now
import numpy as np
import medaka.common
import medaka.features
def main():
"""Entry point for pileup counting program."""
logging.basicConfig(
format='[%(asctime)s - %(n... |
9ad231f36e253599c2a52da3541c52ead4b49f75b027890e60c67ca08317cd91 | Python | 2,517 | 69 | from argparse import ArgumentParser
from pathlib import Path
import nibabel as nib
import numpy as np
import pandas as pd
def parse_args():
parser = ArgumentParser()
parser.add_argument("metadata", type=Path)
parser.add_argument("mask", type=Path)
parser.add_argument("image_dir", type=Path)
parse... |
728de7596ee8bb3a5c038b7394b29eb6b8c7bd6226b80733efdf238d2d8c2204 | Python | 2,518 | 77 | from ... import options as opts
from ... import types
from ...charts.basic_charts.geo import GeoChartBase
from ...commons.utils import OrderedSet, JsCode
from ...exceptions import NonexistentCoordinatesException
from ...globals import ChartType
GMAP_API = "https://maps.googleapis.com/maps/api/js?key={}"
class GMap(G... |
fbf2753d78521f702943c389d547804d0013a804d6ee008b2188cf77a0bf81ed | Python | 2,518 | 93 | import GeoJax as gj
import numpy as np
import pandas as pd
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
... |
1028403eb4437871407d260a901efd884666c5d0004a4b730669a50c0ad54b79 | Python | 2,519 | 82 | import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... |
f0940a9bc1d43ab885136255fd34d99549238e446e969987f038eae2ff49bc4b | Python | 2,519 | 68 | import torch.nn as nn
import torch
class SKConv(nn.Module):
def __init__(self, features, WH, M, G, r, stride=1, L=32):
""" Constructor
Args:
features: input channel dimensionality.
WH: input spatial dimensionality, used for GAP kernel size.
M: the number of branc... |
4d193615578d1c425e5b7d2356d304942e12771afb0fcdefde3d431c9202d2fd | Python | 2,521 | 65 | """tests for vak.config.spect_params module"""
import pytest
import vak.config.spect_params
from .attr_helpers import simple_attr
# approaching to testing validators adopted from
# https://github.com/python-attrs/attrs/blob/a025629e36440dcc27aee0ee5b04d6523bcc9931/tests/test_validators.py#L338
def test_freq_cutoffs... |
7cbc505db554fbf9c710752f794789de078c7faa44d73966bfdb17f14efbf2ee | Python | 2,522 | 114 | # -*- coding: utf-8 -*-
import os
import json
import shlex
import shutil
import subprocess
from jinja2 import Environment, FileSystemLoader
PKG_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
TPL_PATH = os.path.join(PKG_PATH, "templates")
def load_json(filename):
with open(filename, 'r') as... |
accab0e5b02e07a7663ce5385e9bed5ceb036778964ccf330554f83c10079870 | Python | 2,522 | 74 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import torch.nn as nn
# Different encodings for the atom distance embeddings
class GaussianSmearing(torch.nn.Module):
def ... |
f40768f8d9d1e6ffc070a72c871a62e9016bf4f9af2018284a62fd8efb04b254 | Python | 2,522 | 75 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Compares automated vs. manual cell counts"""
import os
from glob import glob
from typing import Generator
import pandas as pd
import matplotlib
matplotlib.use("Qt5Agg")
from tqdm import tqdm # noqa E402
from utils import Image # noqa E402
from utils.data_pickling ... |
047ea6a6dbe91e3906997a0163e46b4e966e91e4984754ea359e5a48172d8b9b | Python | 2,525 | 59 | """Comparison of pattern-similarity z-maps to Neurosynth meta-analytic maps (R6, Fig 5).
Ports `paper_revision_analyses/pattern_sim_to_neurosynth_smooth25mm.py`. The
per-parcel neurosynth averaging, Pearson correlation, negative-zeroing, and
within-map BH-FDR are unchanged. The pattern-similarity z-values now come fro... |
5f0778055012ef659d36a289dadd0391e6b4cdedbb2b7ac35c35486a233cd3ae | Python | 2,525 | 71 | import pytest
import torch
import scvi.module._vae as vae_module
from scvi import REGISTRY_KEYS
from scvi.module import VAE
@pytest.mark.parametrize("n_samples", [1, 2, 3])
@pytest.mark.parametrize("gene_likelihood", ["zinb", "nb", "poisson"])
@pytest.mark.parametrize("n_input", [100])
@pytest.mark.parametrize("batc... |
6b15427d2c2f3bc2ee2919f3765cb58a23ffe3a68c42be715811a45edb1e9107 | Python | 2,525 | 85 | #!/usr/bin/env python
"""Extract protein PDB files required for the US-align similarity analysis.
Raw PDBbind structures are not included in this repository. By default, the
script reads the published split lists and a local PDBbind v2020.R1 directory.
"""
from __future__ import annotations
import argparse
import shu... |
75e05526e3781a380fbd89411357fe455fde8bca76d9707463fee2a1b6f9632d | Python | 2,526 | 83 | import pytest
from pgmpy.base import DAG
@pytest.fixture
def cg():
edges = [("U", "X"), ("X", "M"), ("M", "Y"), ("U", "Y")]
roles = {"exposures": "X", "outcomes": "Y"}
return DAG(ebunch=edges, roles=roles)
@pytest.fixture
def cg2():
cg2 = DAG(
ebunch=[("U", "X"), ("X", "M"), ("M", "Y"), ("U... |
962184667f28eb669031422a4aa47a061ef8a040a2da6bc58b1716b6b1b8ab50 | Python | 2,526 | 75 | import pandas as pd
import pytest
from sklearn.metrics import accuracy_score, f1_score
from pgmpy.base import DAG
from pgmpy.example_models import load_model
from pgmpy.metrics import CorrelationScore
@pytest.fixture
def model_and_data():
alarm_model = load_model("bnlearn/alarm")
alarm_data = alarm_model.sim... |
f015e6b313a90de0bc714e24b309cc6e59465af0e1943bcc528b2a34d389db5b | Python | 2,528 | 71 | import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Sankey
class TestSankeyChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_sankey_base(self, fake_writer):
nodes = [{"name": "category1"}, {"name": ... |
4bd5f1af75f642c97f5cb4b4721609c83d69ee9c5417003f9f8fb698e821b0ba | Python | 2,530 | 81 | #!/usr/bin/env python
# ENCODE DCC Count signal track generation
# Author: Jin Lee (leepc12@gmail.com)
import sys
import os
import argparse
from encode_common import *
def parse_arguments():
parser = argparse.ArgumentParser(prog='ENCODE DCC Count signal track generation',
... |
f1dec248378c61d06d49645254416d89b270a478cfcdbcf33854123932240318 | Python | 2,531 | 83 | import torch
import torch.utils.data as Data
import torchvision
from lib.network import Network
from torch.autograd import Variable
from torch import nn
import time
def calc_acc(x, y):
x = torch.max(x, dim=-1)[1]
accuracy = sum(x == y) / x.size(0)
return accuracy
train_data = torchvision.datasets.MNIST(... |
94d22d58aad248c9526a4c9ff1d1bbad02f7336b47a926ee11a009765168d3fd | Python | 2,532 | 59 | '''
Logic to generate inflammation profiles for prescribed min/max induction days
'''
import numpy as np
import pickle
np.random.seed(0)
def induction_linear(max_years, year_s, upper_prop, min_length_induction, max_length_induction):
# Make linear propensity function
prop_induction = np.zeros(max_years * 12)
... |
6a722d94d092e90a1cfcabc1517f31c1ee37072528f0fc9a02868be279baa000 | Python | 2,534 | 73 | """Resolving a tagged mention to the BRENDA entities it could name.
Deliberately not part of the model: it holds no learned parameters, so it can
be swapped without touching a checkpoint. The answer is a *set*, since a
surface form is not owned by one entity, and the empty set is an answer — a NIL
mention — rather tha... |
ee08476d4ed28abe43c7f2f2323ebaedc6f002c868d57ad671947ca8d4ff52f3 | Python | 2,534 | 59 | from typing import Tuple, List
import loompy
import numpy as np
from sklearn.decomposition import IncrementalPCA
import logging
from harmony import harmonize
import pandas as pd
class IncrementalResidualsPCA():
"""
Project a dataset into a reduced feature space using PCA on Pearson residuals.
See https://www.biorx... |
a470dc16c6169fbcf390a9bde4f73d4ebc12907df4699e7d5a7921cae8b139d3 | Python | 2,537 | 93 | import GeoJax as gj
import numpy as np
import pandas as pd
df_point = pd.read_
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Numb... |
f1a7767103694ccc4195c4bc798fadc4f72732f63414feabefb8e43e384b2ca9 | Python | 2,537 | 84 | import h5py as h5
import numpy as np
class Epoch():
"""
Represents a data epoch with a start time (in seconds), an end time (in seconds), and a name
Optionally includes a start_index and an end_index
"""
def __init__(self, name, start_time, end_time):
"""
name : str
... |
fea958e181d775d2ff6d32fd5221e06ac46f010fa1f30dad11fe6e02cca36c22 | Python | 2,539 | 80 | from src.utils import read_gtf, read_SJ, gtf_to_SJ
import polars as pl
from pathlib import Path
import os
SR_SJ = (
pl.concat([read_SJ(file) for file in Path('export/STAR_results').rglob('*_SJ.out.tab')], how='vertical')
.unique(['chrom', 'start', 'end', 'strand', 'motif'])
.select(['chrom', 'start', 'end'... |
a7f434989dba76f926660e99c3c6e321cec11b5351cafb1213c905b4d7bfac01 | Python | 2,540 | 76 | import shlex
import os
import sys
from argparse import ArgumentParser, SUPPRESS
from collections import defaultdict
from sys import exit, stderr
from subprocess import check_output, PIPE, Popen
from subprocess import PIPE
def subprocess_popen(args, stdin=None, stdout=PIPE, stderr=stderr, bufsize=8388608):
... |
e78ab2577abd8b60e1e2a6851b2cf3b69c7578257593ae0df0ec973dc6780491 | Python | 2,543 | 116 | """Definitions of the protocol parameters.
The type of these parameters signifies that the data for this parameter should come from the protocol file or from
the protocol maps. These parameters are never optimized and are always set to the given input data.
"""
from mdt import ProtocolParameterTemplate
__author__ = '... |
8be7bf37c500c9e318bb881339ca65eed49df26d3b718714be40604328dc4d3e | Python | 2,544 | 54 | import itertools
aa_set = {"A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y"}
aa_list = ["A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y"]
foldseek_seq_vocab = "ACDEFGHIKLMNPQRSTVWY#"
foldseek_struc_vocab = "pynwrqhgdl... |
6bb0edf69ed1286dd65626fbab8646a37e3eb4c1023b941b39cfce6693e8c8ee | Python | 2,545 | 93 | import GeoJax as gj
import numpy as np
import pandas as pd
df_point = pd.read_pickle()
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
... |
c744672f0f3bf108e42909e3f6880525419a0d6cef396c8b9f15410075889439 | Python | 2,546 | 74 | from __future__ import annotations
import functools
from dataclasses import dataclass
from html.parser import HTMLParser
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Callable
# The following code was originally written for PDM project
# https://github.com/pdm-project/pdm/blob... |
bfcb5269154fcb2b65b02d36b718c89f99c720b42ee7ee93ed99b41f1f05589d | Python | 2,547 | 71 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
class PQLinear(nn.Module):
"""
Quantized counterpart of nn.Linear... |
72b0cbb4cfd39cc818482d93882e91c1c6e17533fab9d80f43ece74fd28708d3 | Python | 2,548 | 77 | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 6 20:52:03 2025
@author: hanna
"""
import numpy as np
import pandas as pd
def get_trial_inds(df, blockType, mode, n_sets):
"""
Finds the putative trials (and their lengths) within a block for use in analysis.
Ensures that the number of sets of 8 tria... |
84900a63f2ac6f2abda388248a2752e06eb90924af991c3656b9b1f1dcf31021 | Python | 2,548 | 75 | import polars as pl
from src.utils import read_gtf
# Define functions
def remove_peptide_with_conflicted_mapping(peptide_mapping: pl.DataFrame) -> pl.DataFrame:
"""Remove peptides that map to transcripts that include known and novel transcripts."""
peptide_to_remove = peptide_mapping\
.group_by("peptid... |
13e0908992eec03c62b32d1b882a97985f70993cce809173ce04486216d5a339 | Python | 2,549 | 73 | from typing import Protocol
import jax
import jax.numpy as jnp
import jax_dataclasses as jdc
from ..data import Batch
from ..types import MolecularConfiguration, RandomKey
class Augmentation(Protocol):
def __call__(self, rng: RandomKey, pytree: Batch) -> Batch:
raise NotImplementedError
class Rotation... |
b87a59171e26120ccb17fb194c38c654cf91246bb4634e02c936c4483eb96f95 | Python | 2,549 | 74 | import shlex
import os
import sys
from argparse import ArgumentParser, SUPPRESS
from collections import defaultdict
from sys import exit, stderr
from subprocess import check_output, PIPE, Popen
from subprocess import PIPE
# 为phase筛选snp
def subprocess_popen(args, stdin=None, stdout=PIPE, stderr=stderr, bufs... |
184d86fa9e1a65ab4692aa9e970c0e96daa0d6421c65003e222b7548d0579573 | Python | 2,551 | 86 | #!/usr/bin/env python3
# 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.
"""
Split a large file into a train and valid set while respecting document
boundaries. Documents should be separated by... |
62ea0cb728eabd66d721e6e42e083157ab1c7ab5cde50a950138de9a7c83cf08 | Python | 2,552 | 78 | import torch
import torch.nn as nn
#import torch.nn.functional as F
#from .activate import *
from .deformable_conv import DeformableConv2d
norm_name = {"bn": nn.BatchNorm2d}
activate_name = {
"relu": nn.ReLU,
"leaky": nn.LeakyReLU}
# "mish": Mish}
class Convolutional(nn.Module):
def __in... |
3ef9096cd3de12d22c1d5e96b4d01020889a403e46568dc18cfa1a1185e2b39c | Python | 2,553 | 47 | import time
import numpy as np
from kanfood.split import stratified_group_kfold
from kanfood.preprocess import Preprocessor
from kanfood.features import PLSFeatures
from kanfood.models import build_model, front_end_components
from kanfood.metrics import regression_metrics, classification_from_tahini, classification_met... |
5690b6b8169eba968d29c1e2cc9ee4fcef74fced4a1ca3bcc2fb9e5dd1720327 | Python | 2,553 | 90 | """Compatibility facade for behaviour parsing and signal extraction.
New code may import the focused input-parsing and window-extraction modules
directly. Existing imports from this historical module remain supported.
"""
from __future__ import annotations
import re
from pathlib import Path
import numpy as np
impo... |
709bf4a20882140979beb87f8c73643df995b349ceab7f703b0d0f0c4acbd3e3 | Python | 2,554 | 63 | import gzip
class LiftRsNumbers:
def _read_rs_history(self, histFile):
RS_HISTORY = set() # store rs
print("Reading '{}' file...".format(histFile))
for ln in gzip.open(histFile, mode='rt'):
fd = ln.strip().split('\t')
# Some very few entries in SNPHistory file are a... |
727d5cc3e26a249b15246ab9bedf6152fc4e26dbea0063c8085b487b11bcc13e | Python | 2,555 | 67 | """Diagnose which isomer group fails for the best selection methods."""
from __future__ import annotations
from collections import defaultdict
from pathlib import Path
import numpy as np
from oce.atomic_table import load_table
from oce.correlations import correlations_for_molecule
from oce.dataset import entry_to_at... |
d3ea35fae9598387474110ed424906d60c1dfd5b14c5f336e8f3cebe20055942 | Python | 2,556 | 66 | import pandas as pd
import numpy as np
import sys
import argparse
"""
Contains a logic for making unique binary annotation categories from overlapping
binary annotations produced by annot2annomat.py.
All annotation categories:
100kDown 10kDown 1kDown 100kUp 10kUp 1kUp 3UTR 5UTR Exon
Intron ProteinCoding Non... |
50127c6cf42573799ea6d21d765b4e3dab5acf28b838c00e913408256b33d1c6 | Python | 2,557 | 93 | import GeoJax as gj
import numpy as np
import pandas as pd
df_point = pd.read_pickle()
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
... |
6a4425136b03defed7e0d4a8498050c4a041b9988314ee1cdb4a13a67212627a | Python | 2,557 | 73 | from itertools import product
import numpy as np
from pgmpy.factors.discrete import TabularCPD
class NoisyORCPD(TabularCPD):
"""
Initializes the NoisyORCPD class.
The NoisyOR CPD is a special case of TabularCPD for binary variables
where a given variable can be activated by each of the parent varia... |
971a1c37d00ece702ac827a47d7340561d99a22a1bd10544fd60bc503c67dbc5 | Python | 2,557 | 59 | #!/usr/bin/env python
# MIT License
#
# Copyright 2019 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 us... |
04d32f0cf434cc8eb595a3fb241da94311f1f864c9ef03989c92fd5e9360bff9 | Python | 2,558 | 94 | from __future__ import annotations
from pathlib import Path
def project_root() -> Path:
return Path(__file__).resolve().parents[2]
RESULTS_LAYOUT = {
"tables": "tables",
"figures": "figures",
"metadata": "metadata",
"artifacts": "artifacts",
}
def results_dir(path: str | Path) -> Path:
re... |
e5fac1124f0310d3be3936bff67c8b30d53473b32223161bda2eca2e7cec2214 | Python | 2,559 | 76 | import pathlib
from unittest import mock
import pandas as pd
import pytest
import vak
@pytest.mark.parametrize(
"config_type, audio_format, spect_format, annot_format, dataset_prep_func_to_mock",
[
("eval", "cbin", None, "notmat", "vak.prep.prep_.prep_frame_classification_dataset"),
("learnc... |
44202eadb88b998b9e90d135e3e5e749edd953185438005ab6cfacce803ef381 | Python | 2,560 | 56 | from SparseCoding import fixed_s_mask
import torch
import torch.nn as nn
class omics_net(nn.Module):
''' Two hidden layers
One clinical layer (age)
'''
def __init__(self, in_nodes, gene_nodes, pathway_nodes, hidden_nodes):
super(omics_net, self).__init__()
# activation fun... |
7219b2226583630c8ec8406cd1530ac447d6c6c8f1bd50ca3a430b8b82a45cfe | Python | 2,560 | 74 | # /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 torch
import CBIG_pMFM_basic_functions as fc
import warnings
def CBIG_mfm_test_desikan_main(gpu_index=0):
torch.cuda.set_device(gpu_inde... |
ca1d0215af17db69f38af932428277829098b0ed8bc0b2ffb98f80c815a27ff7 | Python | 2,560 | 82 | import pytest
from skbase.utils.dependencies import _check_soft_dependencies, _safe_import
from pgmpy import config
torch = _safe_import("torch")
@pytest.fixture(autouse=True)
def reset_config():
"""Reset pgmpy config to defaults after each test."""
yield
config.set_backend("numpy")
config.set_show_... |
e1b3f5c8344ed5732edb718cdc10cff31526e520fe3a9fa3ce8a62c44923e4ec | Python | 2,560 | 93 | import GeoJax as gj
import numpy as np
import pandas as pd
df_point = pd.read_pickle()
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
... |
3e23b44e1768f5096b9427af9449fcbc0674df82e5dd8c760871eec91b77a434 | Python | 2,561 | 85 | #### ::: DNABERT-viz SNP analysis ::: ####
import os
import sys
sys.path.append('../motif')
import pandas as pd
import numpy as np
import argparse
import motif_utils as utils
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--orig_seq_file",
default='../examples/sample_dat... |
84ea461a3b8c25716463129d305e379869e7a49ba9160abe11f4ad12d844bf64 | Python | 2,562 | 84 | # Authors: Christian O'Reilly <christian.oreilly@sc.edu>
# Scott Huberty <seh33@uw.edu>
# License: MIT
"""Tests for topo_viz.py."""
import pytest
import mne
from dash import html
from pylossless.dash.topo_viz import TopoPlot, GridTopoPlot, TopoData, TopoViz
def get_raw_ica():
"""Get raw and ICA objec... |
8b162f5f7af4b765cbfe5e717be036050510ebf7c0c6e20b7ed6cbc7d32671c5 | Python | 2,562 | 76 | from collections import OrderedDict
import theano
import theano.tensor as T
from lasagne import utils
from .base import Layer
__all__ = [
"InputLayer",
]
class InputLayer(Layer):
"""
This layer holds a symbolic variable that represents a network input. A
variable can be specified when the layer i... |
24a425cac6714e1eb79b44a911b7f39e32ad146beb1c7089d0e2efaafdeb1463 | Python | 2,564 | 75 | import logging
from typing import List
import numpy as np
import loompy
class GSEA:
"""
Gene set enrichment analysis according to http://software.broadinstitute.org/gsea/doc/subramanian_tamayo_gsea_pnas.pdf
Note: we rank genes based on the enrichment score from MarkerSelection, not correlation as in the paper.... |
4104b1438e2f262483f5a3700ce000e71f838d72bbc1f2c6d360fe7fe1dacd25 | Python | 2,565 | 76 | import numpy as np
import altair as alt
from currentscape_visualization.utils import *
def plot_currentscape(part_pos: pd.DataFrame, part_neg: pd.DataFrame, vm: np.array, taxis: np.array, tmin: int, tmax: int,
return_segs: bool=False, segments_preselected: bool=True,
vmin: ... |
7d4719066211b293ccb13dca696f4c8fe2332cc3f87eec0cd6b3db4f654bd277 | Python | 2,565 | 71 | import os
import pandas as pd
from BLRun.runner import Runner
class PPCORRunner(Runner):
"""Concrete runner for the PPCOR GRN inference algorithm."""
def generateInputs(self):
'''
Function to generate desired inputs for PPCOR.
If the folder/files under self.input_dir exist,
t... |
854c628e2039db47d3f140e6dc88ba73789ecc8780fa49fc8e71fb102d1a8d11 | Python | 2,565 | 81 | # 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 typing import List
import torch
from torch import nn
from fairseq.modules.quant_noise import quant_noise
class AdaptiveInput(nn.Modu... |
bbfaa8436440731ee696bfc35251500781131c42a602c7967686692fcb5ad9df | Python | 2,567 | 84 | import numpy as np
import scanpy as sc
import ot
import os
import json
import pandas as pd
from scipy.spatial.distance import cdist
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
def mclust(adata, arg, refine=False, key='z', pca_dim=32):
f... |
e6f6b95380be8e96b45d026eb6f4a24f380b8da7d35d91607e583cdfca044ec9 | Python | 2,568 | 107 | # Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
from tqdm import tqdm
class Stat:
def __init__(self, keep_raw=False):
self.x = 0.0
self.x2 = 0.0
... |
1c60cfe940e3bcda64ac1c121f24df4c62c18b9df8146d254b67964eef921383 | Python | 2,569 | 91 | # -*- coding: utf-8 -*-
# Copyright (C) 2018 Phillip Alday <me@phillipalday.com>
# License: BSD (3-clause)
"""Utilities for (testing) MNE-based functionality."""
import warnings
from tempfile import mkdtemp
import mne
import numpy as np
def _mktmpdir():
"""Create a temporary directory for testing writers."""
... |
a7093b53287f2a4f98c472e971858f7bd54c68c9e3bf3a97306bca7ec945505d | Python | 2,569 | 98 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import sys
from copy import deepcopy
from scipy.signal import lfilter
import numpy as np
from tqdm... |
6e5b5701cfb7a070f070fbee73cb9da9c7684c01faaf637f2132f8173b77fd36 | Python | 2,570 | 75 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a network across multiple GPUs.
"""
from fairseq.dataclass.configs import FairseqConfig
from fairseq.distributed import utils as di... |
e2019bf86e5470e35b9cc2edff349fa85c2408ce23363aaa89698019485fb847 | Python | 2,571 | 41 | import pytest
class TestAICCondGauss:
def test_score_bnlearn_no_parents(self, aic_cond_gauss_score):
assert aic_cond_gauss_score.local_score(variable="B_cat", parents=()) == pytest.approx(-83.6952, abs=1e-3)
assert aic_cond_gauss_score.local_score(variable="B", parents=()) == pytest.approx(-259.00... |
2d6c3bd59ec560eafffd03be3017731bef81f878002c3ef0b4dbeb7bf9a95448 | Python | 2,572 | 75 | __author__ = "alvaro barbeira"
import gzip
import os
import logging
import numpy
from .Genotype import GF
from .. import Utilities
from . import Helpers
def model_training_file_geno_lines(file, snp_annotation, snps=None):
logging.log(9, "Processing Dosage geno %s", file)
with gzip.open(file) as file:
... |
e2e56a43b2b76c34459441e455b2299d9bea98102457c583f463ba82aaf30d62 | Python | 2,572 | 85 | import pytest
import torchvision
import torch
import cv2
from pytorch_grad_cam import GradCAM, \
ScoreCAM, \
GradCAMPlusPlus, \
AblationCAM, \
XGradCAM, \
EigenCAM, \
EigenGradCAM, \
LayerCAM, \
FullGrad, \
KPCA_CAM, \
SegEigenCAM
from pytorch_grad_cam.utils.image import show_cam... |
fcc7f05319be0c824ad3b61a8f0a28c4799b99af1ddc7c3148eaac4f3b3a8fb6 | Python | 2,572 | 73 | import argparse
import numpy as np
import os
import pickle
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(
"Plot bivariate posteriors output by `compare-posteriors.py`"
)
parser.add_argument(
"--inpaths",
help="Directories containing likelihood surfaces, etc output by `compare-posteriors... |
0e3349e56b78e2626b2c434c074ddbcf925833fa29e7094465b650b9da5e5dcf | Python | 2,574 | 83 | import argparse
from pathlib import Path
import soundfile
def get_insl_frame(parse):
out = []
def is_ont_token(tok):
return tok[0] in ["[", "]"];
res = []
x = []
for tok in parse.split():
if is_ont_token(tok):
res.extend('_'.join(x))
x = []
res.a... |
5d3ba2be3b5366c1f96743860c39d64dc1d4a3c3c34d0d966b3cb78f2f0f6015 | Python | 2,574 | 41 | import pytest
class TestBICCondGauss:
def test_score_bnlearn_no_parents(self, bic_cond_gauss_score):
assert bic_cond_gauss_score.local_score(variable="B_cat", parents=()) == pytest.approx(-86.3004, abs=1e-3)
assert bic_cond_gauss_score.local_score(variable="B", parents=()) == pytest.approx(-261.61... |
aa5a70a55e61acb2cf650f10a4d14bf09e72459e5196327b2a796a390bb33577 | Python | 2,574 | 81 | import logging
import os
import anndata
import pandas as pd
from scvi.data._download import _pooch_retrieve_with_retries
from scvi.utils import dependencies
logger = logging.getLogger(__name__)
_subtype_to_high_level_mapping = {
"Astrocytes": ("Astrocyte Gfap", "Astrocyte Mfge8"),
"Endothelials": ("Endothel... |
21634f8af8f4d963e8b21b48a371d571ad45157fc2a0cb98ac40266d8dddf462 | Python | 2,575 | 65 | import pandas as pd
from pathlib import Path
# Define root path (update 'root_path' with your base directory)
root_path = Path(r'/data/elevchenko/MovieProject2/')
output_path = Path(root_path / 'bids_data/derivatives')
# Create output directory if it does not exist
output_path.mkdir(parents=True, exist_ok=True)
# In... |
420d8ff6ae50aa4f6f569baaf8d8d164766879e2e867793e3bd8a6df1dc7f92c | Python | 2,575 | 69 | """Tests for the functions in neuropil.py.
Author: Sander Keemink (swkeemink@scimail.eu)
Created: 2015-11-06
"""
import numpy as np
from .base_test import BaseTestCase
from .. import neuropil as npil
class TestNeuropilFuns(BaseTestCase):
"""Neuropil functions testing class."""
def setup_class(self):
... |
df0b3bc734c2f8c986e42eaa7251c40eaa911e4c23100f65e04195c88a087821 | Python | 2,576 | 73 | import os
from os.path import join, basename, dirname
import pandas as pd
from matplotlib import pyplot as plt
from config_path import PLOTS_PATH, PROSTATE_LOG_PATH
base_dir = join(PROSTATE_LOG_PATH, 'review/9hotspot')
files = []
files.append(dict(Model='Mutations with Hotspot adjusted',
file=joi... |
844b6c299f010fc0a0d5b39acc374fb92e1731e942d29324ebba9fee6d8d2fdc | Python | 2,577 | 58 | import gdb.printing
class OutcomeBasicOutcomePrinter(object):
"""Print an outcome::basic_outcome<T>"""
def __init__(self, val):
self.val = val
def children(self):
if self.val['_state']['_status']['status_value'] & 1 == 1:
yield ('value', self.val['_state']['_value'])
i... |
464d79997a74a885868bb82853c24dcdd1c614339abc602a655016068e9a7052 | Python | 2,579 | 94 | import numpy as np
from scipy.fft import fft, ifft
def Surr_Theiler20200723(y,algorithm):
"""
z=Surr_Theiler20200723(y,algorithm)
inputs - y, time series to be surrogated
algorithm - the type of algorithm to be completed
outputs - z, surrogated time series
Remarks
- This code ... |
6c6493ae2cd327c224007182daa236a3e9a2ad4bf68084066b8fc99a51c3dde4 | Python | 2,580 | 75 | """The value-constrained aliases admit their range and refuse the rest."""
import math
import pytest
import torch
from beartype.door import is_bearable
from beartype.roar import BeartypeCallHintParamViolation
from d3text import constraints
from d3text.embeddings_store import StoreProvenance
from d3text.models.base i... |
c3651a55fdd555fc95194f200d0eeedcf17ad966657bc57641789ecd219674d3 | Python | 2,580 | 79 | # -*- coding: utf-8 -*-
"""
@Time:Created on 2019/8/20 20:47
@author: LiFan Chen
@Filename: lookahead.py
@Software: PyCharm
"""
from collections import defaultdict
from itertools import chain
from torch.optim.optimizer import Optimizer
import torch
import warnings
class Lookahead(Optimizer):
def __init__(self, op... |
8ae386843375f4da46aea69f2a7c94570c3cdeb4d7397f8e30b01ce2e27f0b76 | Python | 2,583 | 81 | """A setuptools based setup module.
See:
https://packaging.python.org/guides/distributing-packages-using-setuptools/
https://github.com/pypa/sampleproject
"""
try:
from setuptools import setup, find_packages
except ImportError:
from distutils.core import setup, find_packages
from os import path
from io import... |
a0fbd5a2e96ef78d108ad861fc9c28d72a5a8d35adb64a3ca9af514e886df087 | Python | 2,583 | 68 | #!/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... |
72dac81ab0bc9d9f860b065967e4405893298b00a73b772adc853d9d8016df1d | Python | 2,584 | 82 | """Canonical SegRef3D mask-sequence naming and manifest helpers."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Iterable, Sequence
MASK_MANIFEST_FILENAME = "segref3d-mask-manifest.json"
MASK_SCHEMA_VERSION = 2
MASK_SLICE_ORDER = "segref3d-canonical-v1"
def canonical_... |
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