sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
ff0edcf608744c6d234ea50163a2ea63f794452c507360f0dac5211a82bdf8bc | Python | 424 | 21 | #!/usr/bin/env python
# This script was automatically generated by the MDT GUI.
#
# Generated on: 2017-05-15 22:19:25
# MDT version: 0.9.31
# Purpose: Generate a results figure
import mdt
with open('/tmp/noddi_comparison.conf', 'r') as f:
config = f.read()
mdt.write_view_maps_figure(
'/tmp/noddi_comparis... |
9e27c8bb5f0128550f0d1295417270cb95641a89cba12272c8e483855b7615fa | Python | 425 | 8 | from .permutation_testing import (spin_test, shuf_test, centroid_extraction_sphere,
rotate_parcellation, perm_sphere_p, get_subcortical_distance,
MoranRandomization)
__all__ = ['spin_test', 'shuf_test',
'centroid_extraction_sphere',
... |
42027a93b08e5c17fc70767ed5fbbc20e4fcada405dbfce5c7f41eef7a0b3e72 | Python | 426 | 15 | from mdt import CompartmentTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class Zeppelin(CompartmentTemplate):
parameters = ('g', 'b', 'd', 'dperp0', 'theta', 'phi')
dependencies = ['SphericalToCartesian']
cl_code = '''
... |
4cb9c9d9b4cad52b9d02ff21cfca44a69c35809b16157aecd9471bdd949d62ee | Python | 426 | 17 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
def to_discrete_time(t: torch.Tensor, N: int, T: float) -> torch.LongTensor:
"""Convert continuous time to integer timestep.
Args:
t: continuous time between 0 and T
N: number of timesteps
T: max ti... |
bd0a195dbbdc20217f0e83663f5c2dfc7c6ac622d1ba20244b03a2e48dd0ef6a | Python | 426 | 17 | import polars as pl
from src.utils import read_gtf
pfam = pl.read_csv("export/full_pfam_scan_results.txt")
pbids = read_gtf("nextflow_results/V47/orfanage/orfanage.gtf")\
.filter(
pl.col("feature") == "transcript"
)\
.unique("transcript_id")["transcript_id"]\
.to_list()
pfam\
.filter(
... |
d2c308a724ec7157b8aa9d7596e85b85b5ac2f86dc62837f9ddecf985b0bace3 | Python | 427 | 3 | cn = {'P':0,'M':1,'A':2,'B':3}
d = {'CYS': 'C', 'ASP': 'D', 'SER': 'S', 'GLN': 'Q', 'LYS': 'K','ILE': 'I', 'PRO': 'P', 'THR': 'T', 'PHE': 'F', 'ASN': 'N', 'GLY': 'G', 'HIS': 'H', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W', 'ALA': 'A', 'VAL':'V', 'GLU': 'E', 'TYR': 'Y', 'MET': 'M'}
dn = {'C':0, 'D':1, 'S':2, 'Q':3, 'K':4,'I':5,... |
f23b24dd84933b000cfb0be44c3d58bdb01914c9aae609b2b30a8a028c82468d | Python | 427 | 14 |
# Defines material properties
class MaterialProperties:
def __init__(self, mat_E, mat_nu, w1, l0):
self.mat_E = mat_E
self.mat_nu = mat_nu
self.w1 = w1
self.l0 = l0
self.mat_lmbda = self.mat_E*self.mat_nu/(1+self.mat_nu)/(1-2*self.mat_nu)
self.mat_mu = self.mat_E/(1+... |
12e0e1f2324115aefeb1176055ba0d7dbd20ac9102c9dc787e0aafc4620adf58 | Python | 429 | 14 | from scvi.external.drvi._base_components import DecoderDRVI, SplitFCLayers
from scvi.external.drvi._distributions import LogNegativeBinomial
from scvi.external.drvi._model import DRVI
from scvi.external.drvi._module import DRVIModule
from scvi.external.drvi._utils import StackedLinearLayer
__all__ = [
"DRVI",
... |
97c961acf06d82c9bb847778aa0961d4809c5a24a86728297287f63002c81bcf | Python | 429 | 15 | """Parses config.toml and makes the settings available to the package"""
import tomllib
from importlib import resources
PKGROOT = resources.files("brenda_references")
CONFIG = PKGROOT / "config.toml"
with CONFIG.open(mode="rb") as cf:
config = tomllib.load(cf)
config["documents"] = PKGROOT / config["documents"]... |
abe27abf011cd3018643da7e9f1331f8b990ec3f0169ffe38ec65c57977450b4 | Python | 429 | 20 | from __future__ import annotations
from setuptools import setup
kwargs = dict(
name="Demo",
license="MIT",
version="0.1.2",
description="Demo project.",
author="Sébastien Eustace",
author_email="sebastien@eustace.io",
url="https://github.com/demo/demo",
packages=["demo"],
install_... |
cb8c919de3d52ede5130d7693724939f812b0b0a538f9809156f6c8f59a747af | Python | 431 | 16 | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
impor... |
d668977fe25aeb7963bef49c70dde6cb6d14efa2fe27cf1fed156bbc6c99545c | Python | 431 | 19 | import requests
from bs4 import BeautifulSoup
DOC_URL = "https://yanglab.westlake.edu.cn/gsmap/document/software"
def search_docs(query: str):
html = requests.get(DOC_URL).text
soup = BeautifulSoup(html, "html.parser")
text = soup.get_text()
results = []
for line in text.splitlines():
... |
5e347c331199e11b73f11708fb6af424fbf71b9cb9d47d677c91c529b75e5622 | Python | 432 | 16 |
from tractseg.experiments.base import Config as BaseConfig
class Config(BaseConfig):
EXPERIMENT_TYPE = "endings_segmentation"
CLASSES = "All_endpoints"
LOSS_WEIGHT = 5
LOSS_WEIGHT_LEN = -1
# BATCH_SIZE = 30 # for all 72 (=144) classes we need smaller batch size because of memory limit
... |
ae6c8c864b2477a1ac8cfddeb23c8af6f33772eda35e20ba16c06b05c7af2ce6 | Python | 434 | 19 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""decorators for functions"""
from time import time
def timer_s(func):
# This function shows the execution time of
# the function object passed
def wrap_func(*args, **kwargs):
t1 = time()
result = func(*args, **kwargs)
t2 = time()
... |
481c2db79745148fc2c0d5bbc20e5d9b103168e8b6ad41202c6fc94e939740bb | Python | 435 | 15 | import yaml
import json
from types import SimpleNamespace
from pipelines.data_preprocess import DataPreProcessor
with open('config.yaml', 'r') as file:
p = yaml.safe_load(file)
params = json.loads(json.dumps(p), object_hook=lambda d: SimpleNamespace(**d))
if __name__ == "__main__":
print("--Preprocess ra... |
bacf248b32a571c8f2570c1a5c48689603653033a656b7f31345a7cbd5da7b95 | Python | 435 | 10 | def get_train_dur_replicate_subset_name(
train_dur: int, replicate_num: int
) -> str:
"""Get name of a training set subset for a learning curve,
for a specified training duration and replicate number.
Used when preparing the training set subsets for a learning curve,
and when training models to gen... |
077e5d99c7683a0bf5d6ee52e0a013df98bfbf9d632e570f47c92ea44ff523f2 | Python | 436 | 13 | """AESTETIK: AutoEncoder for Spatial Transcriptomics Expression with
Topology and Image Knowledge."""
from importlib.metadata import PackageNotFoundError
from importlib.metadata import version as _version
from aestetik.AESTETIK import AESTETIK
try:
__version__ = _version("aestetik")
except PackageNotFoundError: ... |
2ef6394019cde7d79573e6f0eaeecff9f9a0c36b2609cc0642e209cc444d5dd5 | Python | 436 | 15 | from sys import platform
__APP_NAME__ = ".IBP_linescan_bin_coverage"
__version__ = "0.2"
PIXEL_SCALING = 0.099 # microns/px
SKIP_FLAGS = ("processed", "done")
SUMMARY_TITLE = "Counts summary"
LENGTHS_TITLE = "ROI lengths"
if platform == "darwin":
FILES_BROWSER_COMMAND = ["open", "--"]
elif platform == "linux2":
FIL... |
4b50cc563799a587f0849f059618c8cb3526258331e4be7642b314764c0ee7fb | Python | 436 | 13 | # 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.
# fmt: off
proteinseq_toks = {
'toks': ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C'... |
89f3d98f4cd2173bcdf2d1ecc3a278e5c7f777f000201684ef83c22be44a16b0 | Python | 438 | 14 | """Utilities for combining namedtuples."""
__copyright__ = "Copyright (C) 2016-2019, DV Klopfenstein, H Tang. All rights reserved."
__author__ = "DV Klopfenstein"
from datetime import date
def get_date_yyyymmdd(yyyymmdd):
"""Return datetime.date given string."""
return date(int(yyyymmdd[:4]), int(yyyymmdd[4... |
a1075756e997b7c234d1671cd67576f00d79de69381d3a1684fe1ed95339493d | Python | 439 | 17 | import polars as pl
from src.utils import read_gtf
signalp_res = pl.read_csv("export/results_20250325-202643.csv")
pbids = read_gtf("nextflow_results/V47/orfanage/orfanage.gtf")\
.filter(
pl.col("feature") == "transcript"
)\
.unique("transcript_id")["transcript_id"]\
.to_list()
signalp_res\
... |
c0a8eeb51ed98a0729666d843c9ef3593502b9ebe092be15104c42fe5f238648 | Python | 439 | 15 | from __future__ import annotations
from types import SimpleNamespace
from src.features.behaviour_alignment.services.plot_service import plot_mean_and_sem_trace
def test_mean_and_sem_display_skips_when_behaviour_has_no_rows():
app = SimpleNamespace(
graph_helper_service=SimpleNamespace(
fetch... |
d0f991d82d6493bfd712920a18e7dc4cab6e0340185cfdb75d2bbfaf9db67752 | Python | 439 | 21 | #!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK
"""Launches the MDT GUI, similar to the mdt-gui command"""
from mdt.cli_scripts.mdt_gui import GUI
__author__ = 'Robbert Harms'
__date__ = "2015-08-18"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class GUI_Shortcut(GUI):
pass
def get_doc_arg_pars... |
8597e2d1543a5545f878b32419fcc36b08f3450707a12447e63ce4acc3fc0875 | Python | 440 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Sachs(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`sachs_2005`
"""
_tags = {
"name": "bnlearn/sachs",
"n_nodes": 11,
"n_edges": 17,
"is_parameterized": True,
"is_dis... |
1bac5be92b782c805692e55f35cd4a69ac2a4a3beeb880b4b9eab7a674ba6d8b | Python | 441 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Pigs(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`bnlearn_pigs`
"""
_tags = {
"name": "bnlearn/pigs",
"n_nodes": 441,
"n_edges": 592,
"is_parameterized": True,
"is_d... |
95df229c3d441b7d6f88334317c3517f8f6c21d4628869d14b8cd964b18ec794 | Python | 441 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Link(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`jensen_kong`
"""
_tags = {
"name": "bnlearn/link",
"n_nodes": 724,
"n_edges": 1125,
"is_parameterized": True,
"is_d... |
ba67463de83208528821f3a54b8875f3a5c22ed3c4426f64eac2aa39ed01a092 | Python | 442 | 17 | from mdt.component_templates.compartment_models import CompartmentTemplate
__author__ = 'Robbert Harms'
__date__ = "2018-08-28"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class AstroSticks(CompartmentTemplate):
parameters = ('G', 'b', 'd')
cl_code = '''
if(b == 0){
re... |
e9b5c827ca05b8e4bbb45e2541d132fd65e9815d94acae75353c8c91e7d836dd | Python | 442 | 18 | # 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
class GradMultiply(torch.autograd.Function):
@staticmethod
def forward(ctx, x, scale):
ctx.scale = scale
... |
1cfdf195dfb28fd2d18526916923275847c67ddc2d9f50e3226d6edadab4d00f | Python | 443 | 17 | import torch
import dadi
import stdpopsim
import os
import numpy as np
from dadi_simulators import *
datadir = snakemake.params.datadir
if not os.path.isdir(f"{datadir}/"):
os.mkdir(f"{datadir}/")
simulator = MODEL_LIST[snakemake.params.demog_model](snakemake)
theta_star = simulator.true_values
theta_star = torc... |
2fe7bbad72322f0623e6000ea2f63c7e8937cca757ab3bb3767f7a1c7ec92b99 | Python | 443 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Andes(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`conati_1997`
"""
_tags = {
"name": "bnlearn/andes",
"n_nodes": 223,
"n_edges": 338,
"is_parameterized": True,
"is_... |
ec5013ba7c88088f6c47e638b1019dff3791ea3f43f1b67fcac3393912402b5f | Python | 443 | 17 | from PyQt5.QtCore import pyqtSignal
from PyQt5.QtWidgets import QMainWindow
class MainWindow(QMainWindow):
"""Subclass of QMainWindow to capture closing event."""
signal_close = pyqtSignal()
def __init__(self, parent=None):
"""Initialize the main window."""
super().__init__(parent)
... |
efe5df48a7d67459ae0229fdc5d176698759ca22d1295ce502dac8a371e9c8be | Python | 444 | 15 | # 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 . import BaseWrapperDataset
class OffsetTokensDataset(BaseWrapperDataset):
def __init__(self, dataset, offset):
super().__i... |
ce60d3d93517e3d47d045521aed1c3c77d413be6b522cbd720e0cfec5122b08c | Python | 445 | 22 | # 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 .task import *
from .vlmtask import *
from .retritask import *
try:
from .fairseqmmtask import *
except ImportError:
pass
try:
... |
0ae2ad6c76bdafdfcbb0af23355bb6cff1a0355e7e9e4ecbe18b1847061a2ac9 | Python | 447 | 19 | from . import pipelines # noqa
from . import nodes # noqa
from . import utils # noqa
from .definitions import ( # noqa
VALID_RECONSTRUCTION,
VALID_SEGMENTATION,
VALID_SURFACE,
VALID_PREPRO_TAGS,
VALID_RECON_TAGS,
VALID_SEG_TAGS,
VALID_SURF_TAGS,
)
__version__ = "unknown"
try:
from .... |
4bca9225baf70e6ebf20c2702ebd5f63721aa3000e07a139c50133de38b3fd80 | Python | 447 | 25 | from . import (
audio_dataset,
constants,
dataset_df_helper,
frame_classification,
parametric_umap,
prep_,
sequence_dataset,
spectrogram_dataset,
unit_dataset,
)
from .prep_ import prep
__all__ = [
"audio_dataset",
"constants",
"dataset_df_helper",
"frame_classificat... |
4bd8e258d3fd31d078ba0b8ccfc34d775c9309387e75dc90796430e504e563f6 | Python | 447 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Water(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`jensen_water_1989`
"""
_tags = {
"name": "bnlearn/water",
"n_nodes": 32,
"n_edges": 66,
"is_parameterized": True,
... |
92d3cd29a3cac03594a021a767e6e234a2993160722ca882dde5a9877534be8b | Python | 447 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Hepar2(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`onisko_thesis`
"""
_tags = {
"name": "bnlearn/hepar2",
"n_nodes": 70,
"n_edges": 123,
"is_parameterized": True,
"... |
cb83bdc7a3369817f4cb5f3463bf68a9dc1fab8f6ea72c299913ad26ca07c5d8 | Python | 447 | 18 | from ... import types
from ...charts.chart import ThreeAxisChart
from ...globals import ChartType
from ...options import InitOpts, RenderOpts
class Bar3D(ThreeAxisChart):
"""
<<< 3D Bar-Chart >>>
"""
def __init__(
self,
init_opts: types.Init = InitOpts(),
render_opts: types.Re... |
e49fe6074a983881c75bb0ac6d5f18a9496926399bd8dc4e384a21bc247a4ab6 | Python | 447 | 12 | # import from plugins/custom_columns.py
"""Show how to customize the columns in the cluster and similarity views."""
from phy import IPlugin, connect
class customizeSelectorStatsPlugin(IPlugin):
def attach_to_controller(self, controller):
@connect
def on_controller_ready(sender):
cont... |
a5a8677803de0e8b6410f9dbee366f89dd8b51522bf70432fa2f548fdff91d00 | Python | 448 | 14 | from st_risk.config import load_config
def test_load_config_reads_dataset_paths(tmp_path):
config_path = tmp_path / "config.yaml"
config_path.write_text(
"dataset:\n"
" visium_h5ad: visium.h5ad\n"
" snrna_h5ad: ref.h5ad\n",
encoding="utf-8",
)
config = load_config(con... |
af6ddc84fac58b9cf1d41fcd6c3780aac6fd9c418eba3e32c003089b954edc8f | Python | 448 | 13 | #定义一个打开电源的函数
def Turnon_voltagesupply(voltage,current):
import visa
import time
rm = visa.ResourceManager()
print(rm.list_resources)
inst = rm.open_resource('USB0::0x1AB1::0x0E11::DP8C221100379::INSTR')
print(type(inst))
inst.write(":APPL CH1," + str(voltage) + ',' + str(current))
... |
4e64d9ebac0202b6f3c38e62a73e35d36a1f7c4bd43681d3b0e1fe5abf5fdbed | Python | 449 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Survey(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`scutari_denis_2021`
"""
_tags = {
"name": "bnlearn/survey",
"n_nodes": 6,
"n_edges": 6,
"is_parameterized": True,
... |
f7be2844dc4f3a9662ad6d7e47e441726cafe8e666e72f4f1db71b83d1668d71 | Python | 449 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Alarm(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`beinlich_alarm_1989`
"""
_tags = {
"name": "bnlearn/alarm",
"n_nodes": 37,
"n_edges": 46,
"is_parameterized": True,
... |
0de05141555b3dc4ee21a013a960d89cb32829e19e243f41448fbd3dd854088a | Python | 450 | 12 | #from Bio import SeqIO
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
def one_hot_encode(sequences):
sequences = sequences
sequences_arry = np.array(list(sequences)).reshape(-1, 1)
lable = np.array(list('ACGU')).reshape(-1, 1)
enc = OneHotEncoder(handle_unknown='ignore'... |
0fc7e48e392eaeffd20b27e7597a05c8a1833f100996b8dc7490c3341cbd9226 | Python | 450 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Munin(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_munin`
"""
_tags = {
"name": "bnlearn/munin",
"n_nodes": 1041,
"n_edges": 1397,
"is_parameterized": True,
... |
4f97d9b1356eef69f14d026017ffe2482ac2b1fb03188c3203fe5261c439c588 | Python | 450 | 14 |
from tractseg.experiments.peak_reg import Config as PeakRegConfig
class Config(PeakRegConfig):
CLASSES = "All_Part1" # All_Part1 | All_Part2 | All_Part3 | All_Part4
LOSS_WEIGHT = 1 # None not possible for PeakReg experiments
LOSS_WEIGHT_LEN = -1
LOSS_FUNCTION = "angle_loss"
METRIC_TYPES = ["los... |
63fdf39423e4c1adbef590792b88b43bf1f5ada4dfe4dd7706fa70ffaf07f032 | Python | 450 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Cancer(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`korb_nicholson_2010`
"""
_tags = {
"name": "bnlearn/cancer",
"n_nodes": 5,
"n_edges": 4,
"is_parameterized": True,
... |
6c5b0874bec307df99910befab5f43e3ebcc0d51cadc6ee0fa3be899fb9bade0 | Python | 450 | 18 | """Create individual experiment files for a parameter sweep."""
from itertools import product
from pathlib import Path
import yaml
FNAME = Path("exp.yaml")
with open(FNAME, "r") as f:
data = yaml.safe_load(f)
INIT_NOISE = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
for i, (noise,) in enumerate(product(INIT_NOISE)):
dat... |
8432cd4ff88a832a53d857180191c6a4be9f0b492e03efd94f813831d44ee481 | Python | 450 | 18 | from ... import types
from ...charts.chart import ThreeAxisChart
from ...globals import ChartType
from ...options import InitOpts, RenderOpts
class Line3D(ThreeAxisChart):
"""
<<< 3D Line-Chart >>>
"""
def __init__(
self,
init_opts: types.Init = InitOpts(),
render_opts: types.... |
28da06d4fa868ac2e7d1e4fc67f839aac052e95067ebc3490807691dcca90573 | Python | 451 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Barley(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`barley_preliminary`
"""
_tags = {
"name": "bnlearn/barley",
"n_nodes": 48,
"n_edges": 84,
"is_parameterized": True,
... |
6ffc10a51c894fc107b6069c4debdf4d9a3dac0e156d026327219ef09b03fcd1 | Python | 451 | 17 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
"""
import fm
def choose_backbone(backbone_name):
if backbone_name == 'rna-fm':
backbone, backbone_alphabet = fm.pretrained.rna_fm_t12()
elif backbone_name == 'mrna-fm':
backbone, backbone_alphabet = fm.pretrained.cds_... |
78d15397d6769bff00f93c15e070ffa858d1fb895aaa9e01bfdc1b04c941a060 | Python | 451 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Munin1(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_munin`
"""
_tags = {
"name": "bnlearn/munin1",
"n_nodes": 186,
"n_edges": 273,
"is_parameterized": True,
... |
24278bd65471177e283b3d0e4cbc756758af200d2bb1a668c3595f311449646f | Python | 452 | 16 | import streamlit as st
st.title("MSI-VISUAL")
st.header("Advanced visualizations and exploration for Mass Spectometry Imaging")
st.markdown(
"""
- Start by importing PyMZL data to MSI-VISUAL.
- Then create a computatioanl pipeline with the pipeline app, and create visualizations.
- Use th... |
5f8aded634342cdc5975ad257cd0e8870a900d3db1e6680310159376fa171aa7 | Python | 452 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Mildew(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`jensen_jensen_midas`
"""
_tags = {
"name": "bnlearn/mildew",
"n_nodes": 35,
"n_edges": 46,
"is_parameterized": True,
... |
9acc5c43c94d1de24d4fd5d66622f07ba5eb4bf8d1e21b0c12f53bf6a347c4b6 | Python | 452 | 13 | """
Pyramid Vision Transformer (PVT-v2) baseline using timm.
Default choice: pvt_v2_b2
You may switch to pvt_v2_b3 or pvt_v2_b4 depending on GPU budget.
"""
from .common import TimmClassifier
class PyramidVisionTransformerClassifier(TimmClassifier):
def __init__(self, num_classes: int = 8, pretrained: bool = Tr... |
ef1ed22449b04f70219221bd3d4f5dffe633199d19f76be8f27ff5154d9f94dc | Python | 452 | 23 | from __future__ import annotations
from abc import abstractmethod
from typing import TYPE_CHECKING
from poetry.plugins.base_plugin import BasePlugin
if TYPE_CHECKING:
from cleo.io.io import IO
from poetry.poetry import Poetry
class Plugin(BasePlugin):
"""
Generic plugin not related to the console... |
18284a3e98923043bc3c5a2d7f628f56cba158aff2a1cebbaaf8220f208a320f | Python | 453 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Munin3(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_munin`
"""
_tags = {
"name": "bnlearn/munin3",
"n_nodes": 1041,
"n_edges": 1306,
"is_parameterized": True,
... |
60dcc597ed26a50cc274a7eec371a7f4a4e70de4636bf846a4dcf176cc614bd7 | Python | 453 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Munin2(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_munin`
"""
_tags = {
"name": "bnlearn/munin2",
"n_nodes": 1003,
"n_edges": 1244,
"is_parameterized": True,
... |
797f249debf051fcbe53d8d8906410fc6e3e5fc337962b68c5939a680593b6df | Python | 453 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Asia(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`lauritzen_spiegelhalter_1988`
"""
_tags = {
"name": "bnlearn/asia",
"n_nodes": 8,
"n_edges": 8,
"is_parameterized": True,
... |
971136e2a0cf8bbc6275b93f63835bae662eccb4cbe146c8a8d5bde1f7392b51 | Python | 453 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Insurance(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`binder_1997`
"""
_tags = {
"name": "bnlearn/insurance",
"n_nodes": 27,
"n_edges": 52,
"is_parameterized": True,
... |
c72a4c768efd4774be390d733dc5b6844238eb7dc857b755c5c2ab2147bd3aa6 | Python | 453 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Munin4(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_munin`
"""
_tags = {
"name": "bnlearn/munin4",
"n_nodes": 1038,
"n_edges": 1388,
"is_parameterized": True,
... |
5c6d48adb90efdc87e91eb5656a30496c3d04977c48b54ac83cbf936781d588d | Python | 454 | 16 | # 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 .block_pair_dataset import BlockPairDataset
from .masked_lm_dataset import MaskedLMDataset
from .masked_lm_dictionary import BertDictiona... |
5b1138fb6984558d295f9a797b7a0fa6eeead583e4e3686f4af3c171a3b27aa5 | Python | 455 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Child(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`spiegelhalter_cowell_1992`
"""
_tags = {
"name": "bnlearn/child",
"n_nodes": 20,
"n_edges": 25,
"is_parameterized": True,
... |
e74b183df3c5fc35cf8be892fffdd34348002d50b563f2bf5c27abf4564451c5 | Python | 455 | 19 | import gl
gl.resetdefaults()
gl.meshload('BrainMesh_ICBM152.lh.mz3')
gl.edgeload('LPBA40.edge')
gl.clipazimuthelevation(0.3, 0, 130)
gl.nodesize(6, 1)
gl.edgesize(3,1)
gl.nodehemisphere(-1)
gl.azimuthelevation(250, 35)
gl.edgecolor('actc',1)
gl.nodecolor('blue',1)
gl.nodethresh(1.0,1.0)
gl.edgethresh(0.5,1.0)
gl.meshcu... |
330398db9c08c1ee3412cf657179b3ba9fa68d0bbab277e9074c05c5d075e1ca | Python | 456 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Win95pts(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`bnlearn_win95pts`
"""
_tags = {
"name": "bnlearn/win95pts",
"n_nodes": 76,
"n_edges": 112,
"is_parameterized": True,
... |
6e072c16932a8b633e3a33424e93b4dd7b1bfccfae8de9f6542145660e103926 | Python | 456 | 12 | # 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 .version import version as __version__ # noqa
from .data import Alphabet, BatchConverter, FastaBatchedDataset # noqa
#from .model impo... |
7b51b1b42fbab569918176f2ba672c2d39ae6639001616d553590c7c1b335344 | Python | 456 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Diabetes(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`andreassen_1991`
"""
_tags = {
"name": "bnlearn/diabetes",
"n_nodes": 413,
"n_edges": 602,
"is_parameterized": True,
... |
ee32552f8c14632ba3b6899bb27bbc8c44b875da509444488db8ebef33af3f9c | Python | 456 | 15 | import json
from pathlib import Path
from jsonschema import validate
from pgmpy.example_models.bnlearn.ecoli70 import Ecoli70
def test_lgbn_schema():
"""Validate data against LGBN schema with semantic checks."""
schema_path = Path(__file__).parent.parent.parent.parent / "devtools" / "schema" / "lgbn_schema... |
8877158d8cb0cd660a93b65b554868545bc5d97526a43329fcbd7033caddd0f6 | Python | 457 | 14 | from . import frame_classification, learncurve, make_splits, validators
from .assign_samples_to_splits import assign_samples_to_splits
from .frame_classification import prep_frame_classification_dataset
from .source_files import get_or_make_source_files
__all__ = [
"assign_samples_to_splits",
"frame_classifica... |
1a952089f25888be5d545b2b3de87ad79c0ce6368657daf32b642609d8aa8b58 | Python | 458 | 18 | """Create individual experiment files for a parameter sweep."""
from itertools import product
from pathlib import Path
import yaml
FNAME = Path("exp.yaml")
with open(FNAME, "r") as f:
data = yaml.safe_load(f)
INIT_NOISE = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
for i, (noise,) in enumerate(product(INIT_NOISE)):
dat... |
53201c0f79e8497d36523e6bb42ecd8253d6b4253f28104b7cc3a5d10247a0fd | Python | 458 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Hailfinder(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`abramson_1996`
"""
_tags = {
"name": "bnlearn/hailfinder",
"n_nodes": 56,
"n_edges": 66,
"is_parameterized": True,
... |
3462c76bee34cfaebe4b296789fc8489b3a17884f13c17b0334c3635161b2242 | Python | 459 | 18 | from ... import types
from ...charts.chart import ThreeAxisChart
from ...globals import ChartType
from ...options import InitOpts, RenderOpts
class Scatter3D(ThreeAxisChart):
"""
<<< 3D Scatter-Chart >>>
"""
def __init__(
self,
init_opts: types.Init = InitOpts(),
render_opts: ... |
443c7cd8c1a7fea3d5c63210d3dc855f76e95f1acdb8dbf425215a2c74ef8889 | Python | 460 | 18 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Reads microscopy image files"""
try:
import numpy as _ # noqa F401 F811
import nd2 as _ # noqa F401 F811
except ImportError:
raise ImportError("Ensure numpy and nd2 are installed")
from .img_reader import nd2_img_reader, get_stain, png_img_reader # noqa... |
0d1b9d9d25128bd64ec7b2d56a524b35012f2deaf0410f57ad2bc2d3d29eed6c | Python | 461 | 20 | from .._base import BaseExampleModel, ContinuousMixin
class MagicIRRI(ContinuousMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`scutari_2016b`
"""
_tags = {
"name": "bnlearn/magic_irri",
"n_nodes": 64,
"n_edges": 102,
"is_parameterized": True... |
44168c92716caa94f19010a73e2f563236e6b16bdc872ef0c1f93d48635a4aa1 | Python | 461 | 15 | """
Medical Image Loading and Preprocessing
load_image: Load any medical image (NIfTI/DICOM)
prepare_for_inference: Convert to model-ready arrays
DatasetMetadata: Manage dataset metadata
CacheManager: Build preprocessing cache
"""
from .io import load_image
from .preprocess import prepare_for_inference
from .metadata... |
93798c4285ffc2151ae8c744c2a61e44cdd4702079e54f53b5107c61cf75df6d | Python | 461 | 15 | from .dataloaders import initialize_dataloaders
from .dataprocessors import edge_level_split, node_level_split_mask, prepare_data
from .datareaders import read_mtx, read_scData, read_multi_scData, concat_data
from .datasets import GraphAnnTorchDataset
__all__ = [
"initialize_dataloaders",
"edge_level_split",
... |
cbbe71930d787732e187688dfba5c4a511583aec026b8c480c1509a623768408 | Python | 461 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Pathfinder(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`heckerman_1992`
"""
_tags = {
"name": "bnlearn/pathfinder",
"n_nodes": 109,
"n_edges": 195,
"is_parameterized": True,... |
1760ed9cc03eaa56e5fcd796b386e74427a2f95d0558b1396fc4400d387c8015 | Python | 462 | 21 | from .._base import BaseExampleModel, DiscreteMixin
class Earthquake(DiscreteMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`korb_nicholson_2010`
"""
_tags = {
"name": "bnlearn/earthquake",
"n_nodes": 5,
"n_edges": 4,
"is_parameterized": True... |
5e700bdf8a0d6f861d4228422f31f5431cc26adadfd079d26f187528c15793f1 | Python | 462 | 8 | from .feature_selection_by_enrichment import FeatureSelectionByEnrichment
from .feature_selection_by_multilevel_enrichment import FeatureSelectionByMultilevelEnrichment
from .feature_selection_by_variance import FeatureSelectionByVariance
from .gsea import GSEA
from .trinarizer import Trinarizer
from .neighborhood_enri... |
342ba95c483a906a4aef1d3856d876e1a41a45e5d1931f8536e77ceb6779e6e3 | Python | 463 | 20 | from .._base import BaseExampleModel, ContinuousMixin
class Ecoli70(ContinuousMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`opgenrhein_strimmer_2007`
"""
_tags = {
"name": "bnlearn/ecoli70",
"n_nodes": 46,
"n_edges": 70,
"is_parameterized":... |
6c6e1d873321a9b8a5b4433152171ae916281d8a8c61e3352dedb44edfe275c7 | Python | 463 | 12 | import numpy as np
from matplotlib.colors import Normalize
class MidpointNormalize(Normalize):
def __init__(self, vmin: float = None, vmax: float = None, midpoint: float = None, clip: bool = False) -> None:
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value: float, cli... |
9a349b57de050adaa216a0b99c3a384ced0f7ab5c3e1f188260e1be192792ce7 | Python | 463 | 12 | from pathlib import Path
from neurovfm.data import DatasetMetadata
data_root = Path("/nfs/turbo/umms-tocho-snr/exp/akhilk/torchmr/raw_data/mri")
raw_dir = data_root / "raw"
# All BraTS studies are MRI, so map every study dir to "mri"
mode_mapping = {d.name: "mri" for d in raw_dir.iterdir() if d.is_dir()}
metadata = ... |
e02be55303deead177e18a83602f92f3a6d00e1ceb593c87b5d1648d3635b27f | Python | 463 | 18 | from .loss import (
compute_omics_recon_mse_loss,
compute_edge_recon_loss,
compute_kl_reg_loss,
compute_contrastive_instanceloss,
compute_contrastive_clusterloss,
compute_omics_recon_mmd_loss,
)
from .GNNModelVAE import GNNModelVAE
__all__ = [
"compute_omics_recon_mse_loss",
"compute_ed... |
62be8ea4a8db131ade52f2c8967cba15802f7026f390372dd82c840f4a94fba6 | Python | 464 | 15 |
import sys
class StructuredExample(object):
def __init__(self, prefix="__structured_test", header=None, dtypes=None):
self.filename = "%s.txt" % (prefix)
self.header = header
if header is not None and dtypes is not None:
if len(header) != len(dtypes):
print("He... |
df6ffa8ca8d77c537d61ef2903b751bf32e52cd5cd02d02f1606cba5b1b342d4 | Python | 464 | 16 | import numpy as np
import pytest
from anndata import AnnData
from novae.utils._validate import _validate_preprocessing
def test_validate_preprocessing():
adata = AnnData(np.array([[int(np.exp(11))]]))
with pytest.raises(AssertionError):
_validate_preprocessing([adata]) # expecting an error because ... |
2d52f7818bc2a240adbb95fe5f8003d63574299981aa959bc230d9a7ceac17d1 | Python | 465 | 15 | from __future__ import annotations
from pathlib import Path
from typing import Any
import yaml
def load_config(path: str | Path) -> dict[str, Any]:
"""Load a YAML configuration file into a plain dictionary."""
with Path(path).open("r", encoding="utf-8") as handle:
config = yaml.safe_load(handle) or ... |
e73af6c44be29f5865980ef30f9a39948312aa05a170325229077bff72862a5e | Python | 465 | 20 | from .._base import BaseExampleModel, ContinuousMixin
class Arth150(ContinuousMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`opgenrhein_strimmer_2007`
"""
_tags = {
"name": "bnlearn/arth150",
"n_nodes": 107,
"n_edges": 150,
"is_parameterized... |
b6e6b3d42782b83b75031d8c1c35b01e38ce908b4e8d6c428504d33e84668a70 | Python | 467 | 18 | from __future__ import annotations
from typing import TYPE_CHECKING
from poetry.mixology.version_solver import VersionSolver
if TYPE_CHECKING:
from poetry.core.packages.project_package import ProjectPackage
from poetry.mixology.result import SolverResult
from poetry.puzzle.provider import Provider
de... |
506b0adb351b1dc50f5778434b6c4a553cd18d23a6100add62fdbf2791764ad6 | Python | 469 | 17 | import torch
from torch.autograd import Function
class GradReverse(Function):
@staticmethod
def forward(ctx, x: torch.Tensor, lambd: float) -> torch.Tensor:
ctx.lambd = lambd
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
return... |
41bb8e8ebe27fea716badce4d0e4b5f6340ed208b1116eed2400b71b089a0782 | Python | 470 | 18 | import pytest
import torch
DEVICES = ["cpu"]
if torch.cuda.is_available():
DEVICES.append("cuda")
@pytest.fixture(params=DEVICES)
def device(request):
"""parametrized device function,
that returns string names of the devices
that ``torch`` considers "available".
causes any test using ``device`` ... |
a574719de17c376e2af8926c388d373dd2d14ee93cd9bc9301e902fc38bbb5b9 | Python | 470 | 16 | from typing import NamedTuple
class _SAVE_KEYS_NT(NamedTuple):
ADATA_FNAME: str = "adata.h5ad"
MDATA_FNAME: str = "mdata.h5mu"
MODEL_FNAME: str = "model.pt"
MODEL_STATE_DICT_KEY: str = "model_state_dict"
VAR_NAMES_KEY: str = "var_names"
ATTR_DICT_KEY: str = "attr_dict"
LEGACY_MODEL_FNAME: ... |
e14bbde81f643070a631082c57642ae121cc1d23b853ce5920b2f87f6d97d927 | Python | 470 | 8 | from .utils import get_ad, calc_var_explained, get_top_loadings, get_gsea_enrichment, get_rmse, get_guide_error, save_model, load_model, get_loadings, get_factors, get_var_explained_per_view_factor
__all__ = ["get_ad", "calc_var_explained",
"get_top_loadings", "get_gsea_enrichment",
"get_rmse"... |
a07b68bad4b80dcf60cbf78b8fa2d73daa95b06664e4d822369a32b264493665 | Python | 471 | 13 | """Per-task dataset policy for Simulation (see README §3.2)."""
from __future__ import annotations
from omegaconf import DictConfig
def assert_task_supported(cfg: DictConfig) -> None:
"""Raise if ``cfg.task.name`` is not supported for this dataset."""
task = str(cfg.task.name)
if task in ("dyn... |
d53f86fea54ff6a5763de719d49da68aad497a783d74a891cdbc0f5d2634084c | Python | 471 | 20 | from .._base import BaseExampleModel, ContinuousMixin
class MagicNIAB(ContinuousMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`opgenrhein_strimmer_2007`
"""
_tags = {
"name": "bnlearn/magic_niab",
"n_nodes": 44,
"n_edges": 66,
"is_parameteri... |
1d0b5ae0e7dbf4286c784e042462181f5640e14b2c3177b7bffa94636ac7d339 | Python | 472 | 21 | from CurrentscapePipeline import CurrentscapePipeline
output_dir = 'output_5s_simulation'
target = 'soma'
partitioning_strategy = 'type'
tmin = 4800
tmax = 4850
filename = f'currentscape_{target}_{partitioning_strategy}_{tmin}_{tmax}.pdf'
pipeline = CurrentscapePipeline.from_preprocessed(
output_dir,
target,
... |
53a5e235b82921f875a40344ba9e70e99f83faf41ee0fd9125ccdcdaa4ac9ba0 | Python | 472 | 15 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ ... |
a96366448bb47d823e68cb5ed735d379fe80444a1c50ba932aaeb0c653d2f400 | Python | 473 | 19 | import numpy as np
import pytest
from oneqmc.analysis.h5_io import ffill0
@pytest.mark.parametrize(
"x,expected",
[
[np.array([1.0, np.nan, 2])[:, None], np.array([1.0, 1, 2])[:, None]],
[
np.array([[np.nan, np.nan], [0.0, 2.0], [1.0, np.nan]]),
np.array([[np.nan, np.n... |
1f21431c2cb598e11ebd823f2cc7bb48b4db64a4715503477663814c8b0d6110 | Python | 474 | 17 | #
# Copyright 2017 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government
# retains certain rights in this software.
#
# See LICENSE for full license details
#
import logging
logging.getLogger(__name__).addHandler(logging.NullHand... |
00cf6077233aa19ddf3e8f9c48fc639f41b9c03a0b347dec175a717d239934ec | Python | 475 | 16 | import generate_counting_table as ct
input_path = "Data/RELISH/Relevance_Matrix/relish_relevance_matrix.csv"
data = ct.load_relevance_matrix(input_path)
data = ct.random_cosine_similarities(data)
counting_table = ct.create_counting_table(data)
print(counting_table)
output_path = "Playground/evaluation/RELISH/count... |
64f86f780942e434bcd939d2a22eed28da9b6a10db903d9317113b642a1fca31 | Python | 475 | 14 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ ... |
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