sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4ba952e1bb67aa96071dd2b122b4ff4150de2e4743e881732d8ac25c50bb6281 | Python | 308 | 12 | #!/usr/bin/env python
"""
# Author: Kangning Dong
# File Name: __init__.py
# Description:
"""
__author__ = "Kangning Dong"
__email__ = "dongkangning16@mails.ucas.ac.cn"
from .Train_STAGATE import train_STAGATE
from .utils import Cal_Spatial_Net, Stats_Spatial_Net, mclust_R, Cal_Spatial_Net_3D
|
5e0c8d21b16ae7ae8b19f5ca1ef381ddd32aeeb552f1c9f1caf9f21c3733ede5 | Python | 308 | 10 | import F02
class F05(F02.F02):
def get_map(self, img, img_f04, eroded_roi):
return super(F05, self).get_map(img, img_f04, eroded_roi)
def get_map_float32(self, img, img_f04, eroded_roi, normalize=True):
return super(F05, self).get_map_float32(img, img_f04, eroded_roi, normalize)
|
5e8798a3ebdf9ddcb1e72f0b6257b3863d405f05774e7363e55645ce76beaf64 | Python | 308 | 9 | try:
import importlib.resources as pkg_resources
except ImportError:
import importlib_resources as pkg_resources
def datafile(filename):
"""Obtain the complete path to the installed file in the src/spitfire/data directory."""
return str(pkg_resources.files('spitfire.data').joinpath(filename))
|
8333d990747d02e48b84f8a459899fc92160911f10ed3cb6b4a55c1c7a22f83f | Python | 308 | 10 | import F03
class F06(F03.F03):
def get_map(self, img, img_f04, eroded_roi):
return super(F06, self).get_map(img, img_f04, eroded_roi)
def get_map_float32(self, img, img_f04, eroded_roi, normalize=True):
return super(F06, self).get_map_float32(img, img_f04, eroded_roi, normalize)
|
b821bab80c1bc91d931ef115e8ccc30d0501b41c1c6943afe92ec52f61c5b210 | Python | 309 | 12 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["sparse_spec"] = dict(
type="SpatiallySparseAcrossChannelsDropMotifs",
sparse_drop_motif_frequency=0.87,
)
msp.extra_params += " --learned-motif-sparsity-threshold-initial 90"
msp.run()
|
4d705533d953c9c94608d552be384391946563388a3cba7f14f5c1734d7106d4 | Python | 310 | 8 | """Spatiotemporal modeling of spatial transcriptomics
"""
from .borderline import get_borderline, grid_borderline
from .boundary_old import *
from .contour import extract_cluster_contours, gen_cluster_image, set_domains
from .grid import digitize, gridit
from .utils import digitize_general, order_borderline
|
4fbda400fd6483799efed12edb3d50fde4521e00df4aaf0d8c64395348c7d05c | Python | 310 | 9 | from tensorflow.python.client import device_lib
def get_available_gpus(config):
r"""
Returns the number of GPUs available on this system.
"""
local_device_protos = device_lib.list_local_devices(session_config=config)
return [x.name for x in local_device_protos if x.device_type == 'GPU']
|
6237f9e8f75e8fec12eec2d0c571ed673b18ccf50471a1c2c021462ec1083460 | Python | 310 | 7 | from keras.src.ops import image
from keras.src.ops import operation_utils
from keras.src.ops.core import * # noqa: F403
from keras.src.ops.linalg import * # noqa: F403
from keras.src.ops.math import * # noqa: F403
from keras.src.ops.nn import * # noqa: F403
from keras.src.ops.numpy import * # noqa: F403
|
5a4b12652a65ce26c57a776febafc8cadba265ef69fac29b316bc75e48a51f68 | Python | 312 | 11 | # membrane noise
from brian2 import mV, ms
from pypet import cartesian_product
input_dict = {'ext_drive': [1.],
'sigma_noise': [5.0]*mV,
'dt': [0.1]*ms}
name = 'hdf5_data'
# explore_dict = n_list(input_dict) # check later if I need that
explore_dict = cartesian_product(input_dict)
|
6fed0233545eb0ba095e0ebe8c69815858a2e49b59ad70217d88801bbd1d5b5c | Python | 312 | 14 | # -*- coding: utf-8 -*-
"""tests for dataset"""
import unittest
from sknetwork.data.base import Dataset
class TestDataset(unittest.TestCase):
def test(self):
dataset = Dataset(name='dataset')
self.assertEqual(dataset.name, 'dataset')
self.assertEqual(dataset['name'], 'dataset')
|
7d4efc01db77bb6ff4b5e38494d06bc6263d1432aae35d7fc12edb737b542920 | Python | 312 | 9 | # pylint: disable=wildcard-import
"""Faster-RCNN Object Detection."""
from __future__ import absolute_import
from .faster_rcnn import *
from .doublehead_rcnn import *
from .predefined_models import *
from .rcnn_target import RCNNTargetGenerator, RCNNTargetSampler
from .data_parallel import ForwardBackwardTask
|
cdbd2342a58dfa7d31e60b7d1b7b4f6ae47097ed75c9a26ba289992c91d54add | Python | 312 | 14 | import os
import sys
sys.path.insert(0, os.getcwd())
# ## Start worker with -P eventlet
# Never use the worker_pool setting as that'll patch
# the worker too late.
broker_url = 'amqp://guest:guest@localhost:5672//'
worker_disable_rate_limits = True
result_expires = 30 * 60
imports = ('tasks', 'webcrawler')
|
505d065edc44d6991d42d020ccf9dce58f58c369ef1545102084b311cb95531c | Python | 313 | 8 | from social_core.backends.auth0_openidconnect import Auth0OpenIdConnectAuth
from galaxy.authnz.oidc import GalaxyOpenIdConnect
class GalaxyAuth0OpenIdConnect(GalaxyOpenIdConnect, Auth0OpenIdConnectAuth):
name = "auth0"
EXTRA_DATA = ["id_token", "refresh_token", ("sub", "id"), "picture", "expires_in"]
|
7a18b9a2734f41c1424991d09e312023e35df887bd8774e797aa32896c65dc84 | Python | 313 | 10 | #!/usr/bin/env python
# =============================================================================
# PACKAGE DOCSTRING
# =============================================================================
"""PyTorch-compatible potential energy functions."""
__all__ = ['base', 'ase', 'openmm', 'psi4', 'tblite']
|
7befb964011c7569d4dd896fad7c62e2b8539003a4f3abb958fca50c1ad33fff | Python | 314 | 10 | try:
import importlib.metadata as importlib_metadata
except ModuleNotFoundError:
import importlib_metadata
package_name = "seqdatasets"
__version__ = importlib_metadata.version(package_name)
from ._datasets import get_dataset_info, random1000
from ._datasets import jores21, ray13, deBoer20, deAlmeida22
|
924a4aeacf9a994bcde48b8c0ee49518d08986f0def28a79d54c21e1e32f97a9 | Python | 315 | 8 | """Custom evaluation metrics"""
from __future__ import absolute_import
from .coco_detection import COCODetectionMetric
from .coco_keypoints import COCOKeyPointsMetric
from .voc_detection import VOCMApMetric, VOC07MApMetric
from .segmentation import SegmentationMetric
from .heatmap_accuracy import HeatmapAccuracy
|
c26d4b77f46020968b19bcdc10349e99f60db06c2c4138b19a1aad8905d74753 | Python | 315 | 13 | from mirp._data_import.generic_file import ImageFile
class BaseWorkflow:
def __init__(
self,
image_file: ImageFile,
write_dir: None | str = None,
**kwargs
):
super().__init__()
self.image_file = image_file
self.write_dir = write_dir
|
06e6f12406bee272ec718b40afab797959d27dd824e382828a189c798c5e0610 | Python | 316 | 13 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et
"""Nipype workflows for xcp_d."""
from xcp_d.workflows import anatomical, base, bold, parcellation, plotting
__all__ = [
'anatomical',
'base',
'bold',
'parcellation',
'plotting',
]
|
416b7468d16addd483512b729910ef121e45f8f25467520ecd3e90b26051893a | Python | 316 | 16 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=3),
)
msp.n_epochs = 10
msp.acc_thresh = 100
msp.run()
|
43af982cfa78c24885cde1048f2a2c762449325d6b03d83a8c7166909f0407f0 | Python | 317 | 16 |
import time
import logging
def profile(func):
def wrap(*args, **kwargs):
started_at = time.time()
result = func(*args, **kwargs)
diff = time.time() - started_at
if isinstance(result, dict):
result['time_ms'] = int(diff * 1000)
return result
return wrap |
5a273776dc4e899710bc9100f2956a8b1ea652de15e619adf059c89448f7b9fb | Python | 318 | 15 | import os.path as osp
from setuptools import find_packages, setup
requirements = ["pytorch-lightning==1.6.0"]
exec(open(osp.join("pointnet2", "_version.py")).read())
setup(
name="pointnet2",
version=__version__,
author="Erik Wijmans",
packages=find_packages(),
install_requires=requirements,
)
|
5fdd85c1c5b90d3b3e873e08a0bed7ce6b845e2f51cb681525dacce71469dd81 | Python | 318 | 16 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=3),
)
msp.window = 10_000
msp.acc_thresh = 100
msp.run()
|
ca8edf5849571bc30fe2e271b22dafdd3b663b4bf2eab85347254ae9dd5aca01 | Python | 318 | 18 |
from ethoscope.hardware.interfaces.odour_delivery_device import OdourDelivererInterface
o = OdourDelivererInterface("/dev/ttyUSB1")
import time
i = 1
while True:
time.sleep(1)
print(i)
i += 1
if i == 15:
o.interact(channel=1, pos=1)
elif i == 17:
o.interact(channel=1, pos=2)
|
f74802a3564b7c433352ad03a3ec9ad5fe7fbda7452b2eeda7df4e81b63b0dde | Python | 318 | 12 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._marker import Marker
from ._textfont import Textfont
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._marker.Marker", "._textfont.Textfont"]
)
|
578b7a1a08bf7e1f29c7922aec20cb949bdb5dfcf6e2669c438dd2b34b4f6f92 | Python | 319 | 15 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for verbose.py"""
import unittest
from sknetwork.log import Log
class TestVerbose(unittest.TestCase):
def test_prints(self):
logger = Log(verbose=True)
logger.print_log('Hello', 42)
self.assertEqual(str(logger.log), 'Hello 42\n')
|
a3f3ee2bff148316722359794e16d7e5bd88fbaa476e5e703213a624fde6e9da | Python | 319 | 17 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=3),
)
msp.acc_thresh = 100
msp.window = 10_000
msp.run()
|
f0fab46af6e4c89b81b8ed8f66722ac569f18dcc7cf3283af340df2493faf0d5 | Python | 319 | 8 | import numpy as np
import imodelsx.augtree.llm
from os.path import join, dirname, abspath
path_to_repo = dirname(dirname(abspath(__file__)))
if __name__ == '__main__':
# should print 'cached!'
out = imodelsx.augtree.llm.expand_keyword('bad', cache_dir=join(path_to_repo, 'results', 'gpt3_cache'), verbose=True) |
3745314bb5acf38efe7ec09a971575e82328051e530b80d20142f6192610fd2d | Python | 320 | 14 | import requests
from celery import shared_task
@shared_task()
def urlopen(url):
print(f"-open: {url}")
try:
response = requests.get(url, timeout=10.0)
except requests.exceptions.RequestException as exc:
print(f"-url {url} gave error: {exc!r}")
return
return len(response.text)
|
3999cffc4157d2987aa9dee1a91eabc04cfabf07d0e92e3dc5b05be1149902cf | Python | 320 | 15 | """
TNS
Author: Lida Kanari
Ecole Polytechnique Federale de Lausanne
Blue Brain Project
(c) 2017-2019. All rights reserved.
"""
from tns.utils import TNSError
from tns.generate.grower import NeuronGrower
from tns.astrocyte.grower import AstrocyteGrower
from tns.version import VERSION as version
|
12d154ba61a15aedaaaea5755d565f550deaeeec882fd8eb745ee1c7d38b4dfc | Python | 321 | 9 | from os.path import dirname, basename, isfile, join
import glob
modules = glob.glob(join(dirname(__file__), "*.py"))
__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
for pkg in __all__:
exec('from . import ' + pkg)
del dirname, basename, isfile, join, glob, modules, pk... |
7638a06ed4cdde8e79b9dfb30edba050b06a3044450395acc68138d1f5d1ca34 | Python | 322 | 8 | """Module defining decoders."""
from bioseq2seq.decoders.decoder import DecoderBase
from bioseq2seq.decoders.transformer import TransformerDecoder
from bioseq2seq.decoders.cnn_decoder import CNNDecoder
str2dec = {"transformer": TransformerDecoder}
__all__ = ["DecoderBase", "TransformerDecoder", "CNNDecoder","str2dec"... |
bb72aabdc3abc2da09fdd4a9b8594ec4aeb6416e585b4f8b284e2811887be3ed | Python | 322 | 13 | import gzip
import tempfile
import requests
def read_gzip(url):
out = requests.get(url)
with tempfile.NamedTemporaryFile(suffix=".gz") as temp:
with open(temp.name, "wb") as f:
f.write(out.content)
with gzip.open(temp.name, "rb") as f:
result = f.read()
return resul... |
c74c16a34701ab5708cfa3e8cce6447eec73a0c63f161e421fc0fcac220fbb0e | Python | 322 | 17 | # ruff: noqa: F401
from ._base_renderers import (
MimetypeRenderer,
PlotlyRenderer,
JsonRenderer,
ImageRenderer,
PngRenderer,
SvgRenderer,
PdfRenderer,
JpegRenderer,
HtmlRenderer,
ColabRenderer,
KaggleRenderer,
NotebookRenderer,
ExternalRenderer,
BrowserRenderer,
... |
d786fc099a2da205b56e8f97732b55cc8587215a3cb51061ed6d1b8aa884fa1c | Python | 322 | 14 | from ._fit import fit, fit_sequence_module
try:
from ._hyperopt import hyperopt
RAY_AVAILABLE = True
except ImportError:
RAY_AVAILABLE = False
def no_ray():
raise ImportError(
"Install ray to use functionality EUGENe's hyperopt functionality."
)
hyperopt = no_ray
... |
d833a160e766e60c66ff1d6dfe1df9b18b1a9ce8fab552bf794a1b984f902ac2 | Python | 323 | 18 | """
This module implement the generic class for the converter
"""
from abc import abstractmethod
class GenericConverter:
"""
This class define what is a converter
"""
@abstractmethod
def convert_output_and_target(
self,
output,
target,
):
raise NotImplementedEr... |
1d00a9d24fe77e16b72c87b569433639955cfd27d8d51a1621f87138fb525da9 | Python | 324 | 5 | from gym.envs.classic_control.acrobot import AcrobotEnv
from gym.envs.classic_control.cartpole import CartPoleEnv
from gym.envs.classic_control.continuous_mountain_car import Continuous_MountainCarEnv
from gym.envs.classic_control.mountain_car import MountainCarEnv
from gym.envs.classic_control.pendulum import Pendulum... |
c124351b43ec2bf14e5696b4d98b81e13ba4f3dc545275f7833340c14e8204f1 | Python | 324 | 16 | import numpy as np
from neuron import h, gui
from neuron.units import ms
s = h.Section()
s.insert("NeuronVariables")
celsius_hoc = h.Vector().record(s(0.5)._ref_range_celsius_NeuronVariables)
h.stdinit()
h.tstop = 5.0 * ms
h.run()
celsius = np.array(celsius_hoc.as_numpy())
assert celsius[-1] == 6.3, f"{celsius[-1]... |
e4846c5dfb10ed6135324ab4d92db0e9b6ef164910cee888540f4a48a17e151f | Python | 324 | 13 | import numpy as np
import mistofrutta as mf
class volume:
def __init__(self, frames, z, dx=0.4, dy=0.4):
self.frames = frames
self.dx = dx
self.dy = dy
self.z = z
def plot(self, wait=False):
mf.plt.hyperstack(self.frames, order='zc', cmap='viridis', wait=wa... |
81fb5618276af06ab2505b42a330092e1e690d682423784e8ea500313aa3a9e6 | Python | 325 | 13 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 80
msp.architecture["motif_model_spec"] = dict(
type="NeuralFixedMotif",
path_format="model/rbns-binary-model-{motif}-2",
removed_motifs=["3P", "5P", "HNRNPA1", "RALY"],
)
msp.r... |
cc5d6a53643a8b868715f515f73f8f8f3e5ffb6d64e368313dcc93d37e2bd559 | Python | 325 | 15 | import requests
from celery import task
@task(ignore_result=True)
def urlopen(url):
print(f'Opening: {url}')
try:
requests.get(url)
except requests.exceptions.RequestException as exc:
print(f'Exception for {url}: {exc!r}')
return url, 0
print(f'Done with: {url}')
return ur... |
cf590b68ee9914183d8373287aec55fba50f99ca8a0314ecd1f8eaa1840e81ce | Python | 325 | 7 | """utils module"""
from sknetwork.data import *
from sknetwork.utils.check import is_symmetric
from sknetwork.utils.format import *
from sknetwork.utils.membership import get_membership, from_membership
from sknetwork.utils.neighbors import get_neighbors, get_degrees, get_weights
from sknetwork.utils.tfidf import get_t... |
a30989e8be352edf742954ce24822641986875929fd27c88416eaf99d9ddd37d | Python | 326 | 20 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.window = 40
msp.batch_size = 150
msp.architecture = dict(
type="SplicePointIdentifier",
cl=40,
asymmetric_cl=(2, 20),
hidden_size=100,
n_layers=4,
)
msp.extra_params += " --only-train donor"
msp.... |
e3bc7fb3257476485c9fe60d98642b4db9bcedccae519dee4a2a6fbbfcdf9dd5 | Python | 326 | 16 | """
Bertalign initialization
"""
__author__ = "Jason (bfsujason@163.com)"
__version__ = "1.1.0"
from bertalign.encoder import Encoder
# See other cross-lingual embedding models at
# https://www.sbert.net/docs/pretrained_models.html
model_name = "LaBSE"
model = Encoder(model_name)
from bertalign.aligner import Bert... |
05faffdc4d4b295257d6d1b540baa268aaac327232ca80f98c07bf0ba743dd31 | Python | 327 | 16 | from celery import Celery
from config import Config
from database import connect_mongo
connect_mongo('Celery_Worker')
celery = Celery(
Config.NAME,
backend=Config.CELERY_RESULT_BACKEND,
broker=Config.CELERY_BROKER_URL
)
celery.autodiscover_tasks(['workers.tasks'])
if __name__ == '__main__':
celery.s... |
1630cefb5d073808e8b4164e4d910f69aee57a93eaee53debb575e7e7aabb83f | Python | 327 | 14 | from flask import Blueprint
from flask import render_template
frontend = Blueprint("frontend", __name__, template_folder="templates")
@frontend.route("/")
def index():
return render_template("frontend/index.html")
@frontend.route("/missing")
def missing_template():
return render_template("missing_template.... |
7e8c26cb4f3b51305d5e35a311df2cdd790a17f656bd113b233dec2c1dda83e5 | Python | 327 | 13 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature_extractor_spec"] = dict(
type="FCMotifFeatureExtractor",
extra_compute=10,
num_compute_layers=1
)
msp... |
8d6c13608bccfcd4b31187b9ad9d6f4c29684cd73e3436fb3f80d37f8d2b199a | Python | 327 | 18 | from tqdm import tqdm
from motif_model import load_model, evaluate_model, MotifModel, MotifModelDataset
def main():
bs = 512
p = 'result_raw_20/'
m = load_model(p)[1]
deval = MotifModelDataset(
'data/rbns_test.h5'
)
evaluate_model(m, deval, limit=float("inf"), bs=bs, quiet=False)
... |
bf93326f569138b8a63e8ea2a88364cf3d0f2b784370c6c9fdf1d587880f9a86 | Python | 328 | 18 | # ruff: noqa: F405
"""Built-in datasets for demonstration, educational and test purposes."""
from plotly.data import * # noqa: F403
__all__ = [
"carshare",
"election",
"election_geojson",
"experiment",
"gapminder",
"iris",
"medals_wide",
"medals_long",
"stocks",
"tips",
"w... |
266015244b0866652f99681cf282aa02923ba5bacbdd46b976c46cb93a5a890c | Python | 329 | 9 | ''' A file to store general basic functionality needed in different modules.'''
import numpy as np
def round_num(num, decimal_places=4):
"""Rounds a number to the selected num of decimal places
This allows consistency throughout the method and avoids
numerical artifacts"""
return np.round(num, decimal... |
3692a72b2663fa49a04a06e163944f505d507549c1eb540bf661ffd5c59c30a0 | Python | 329 | 14 | """
GTN (Galaxy Training Network) Integration Module.
This module provides search and retrieval capabilities for GTN tutorials
through a lightweight SQLite database with FTS5 full-text search.
"""
from .search import (
FAQResult,
GTNSearchDB,
SearchResult,
)
__all__ = ["GTNSearchDB", "SearchResult", "FAQ... |
3817cf0385c75542bc299f554ab6442861af8dea8b942ad20a7757d79eb554b3 | Python | 329 | 20 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.window = 40
msp.batch_size = 150
msp.architecture = dict(
type="SplicePointIdentifier",
cl=40,
asymmetric_cl=(20, 2),
hidden_size=100,
n_layers=4,
)
msp.extra_params += " --only-train acceptor"
m... |
6196acdccfb80775bb6ab1731a8178d2e61040799e8175e388b1edc5d8c69dc7 | Python | 329 | 13 | def should_use_motif(x):
return any(remap_motif(x).startswith(pre) for pre in ("HN", "SR", "TRA"))
def remap_motif(x):
return {
"PTBP1": "HNRNPI",
"PTBP2": "HNRNP: PTBP2",
"PTBP3": "HNRNP: PTBP3",
"FUS": "HNRNPP2",
"PCBP1": "HNRNPE1",
"PCBP2": "HNRNPE2",
}.g... |
a26ad668b9dc9f11b559f347731b45ad01058e2fd0c6041746b631b1e5030a3e | Python | 329 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-67_1",
original_model_step=1459275,
)
m... |
ba1467fec21c655d8d856498ecd01649a7ae69c04a841f6b7fe976929b8ee4f0 | Python | 329 | 10 | def estimate_variance_of_binomial_proportion(kvec, nvec):
"""
Returns the unbiased estimate of the variance associated with the proportion estimator, phat = k/n:
:param kvec:
:param nvec:
:return:
"""
# todo: add checking on argument
phat = kvec / nvec
return (phat * (1 - phat)) / (n... |
cfee427574cb96fa5bbf2313b845da1b3952d90bf2aef2f6c4cbb26430ac07de | Python | 329 | 10 | """Test functions for specparam.modutils.debug"""
from specparam.modutils.debug import *
###################################################################################################
###################################################################################################
def test_sys_info():
sy... |
d7142d178424fed8153223c20264cf21ae38ba93e8284f8ccb1c26a99e73e8b3 | Python | 329 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-67_2",
original_model_step=1184055,
)
m... |
70ddd12678f5c916bf3c532f6b2bd025e01fc04e44c647981da295fa7a58df4a | Python | 330 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-171a1_1",
original_model_step=3920295,
)
... |
9dabc6cbd753cdc98c5ff1736b97a4969baa32f9765b564e4c6dd23d8cbf16a6 | Python | 330 | 10 | import pandas as pd
barcode2id_file = "../resource/barcode2id.csv"
barcode2id = pd.read_csv(barcode2id_file, header = 0)
cluster2size = barcode2id.L1.value_counts()
with open("../resource/sa2_L1_cluster2size.csv", 'w') as f:
f.writelines([f"{c},{s}\n" for c, s in zip(
cluster2size.index.to_list(), cluster2... |
03e1aa50d6a9ad1779e14b5ffe475a79eadd4643f8cb095a26807b5bf1ad52fc | Python | 331 | 15 | """
This module define a converter
"""
import torch
from converter.generic_converter import GenericConverter
from utils.utils import select_output_lm
class ConverterNoChanges(GenericConverter):
"""
This class doesn't convert
"""
def convert_output_and_target(self, output, target):
return outp... |
22bf9b4cbf5c80d281d776296f6e4133ee887a80d36aa6b3f37646db21a13497 | Python | 332 | 13 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._data import Data
from ._layout import Layout
from . import data
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [".data"], ["._data.Data", "._layout.Lay... |
487463f39e80f1e75370b583e684ff5d96589c9cdd4b30becadfe84bfd166d22 | Python | 333 | 10 | """Some basic statistical tests for univariate data."""
__test__ = False
from ._stats.glm import ANOVA
from ._stats.test import (
Correlation,
TTestOneSample, TTestIndependent, TTestRelated, MannWhitneyU, WilcoxonSignedRank,
pairwise, ttest, correlations,
bootstrap_pairwise, lilliefors,
pairwise_cor... |
55cd093824f29b34ac572e8a5f756c9a56369d3493919a76a458242926cdfac6 | Python | 333 | 11 | try:
from ._version import version as __version__
except ImportError:
__version__ = "unknown"
__author__ = "Marcos Obando and Minh Nhat Trinh"
__credits__ = "Teresa Correia and Germán Mato"
__email__ = "marcos.obando@ib.edu.ar"
from ._reconstruction_widget import ReconstructionWidget
from .processors import O... |
a76b34f20aac12ecd33a8832a7fe75ef554be084aa6b9ae1a325170cb2883000 | Python | 333 | 21 | """Merge heads
Revision ID: 75b461a2b24a
Revises: 724237cc4cf0, 98621a25ab75
Create Date: 2025-12-12 18:54:48.572124
"""
# revision identifiers, used by Alembic.
revision = "75b461a2b24a"
down_revision = ("724237cc4cf0", "98621a25ab75")
branch_labels = None
depends_on = None
def upgrade():
pass
def downgrade... |
33b91e3dfbe8e62250ca73c7acb6fbaec80fbe1e740383b15c3bad338a8ae778 | Python | 334 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj2_2",
original_model_step=1546995... |
39441aec221c45b0e66eb19b196d7753b8d0fcd45d6cf87c609c7293a5f4d451 | Python | 334 | 12 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._font import Font
from ._grouptitlefont import Grouptitlefont
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._font.Font", "._grouptitlefont.Grouptitl... |
5e7424052bdb2a0448683c1481cff4170197724bdc4671f2efa8f99c585783ba | Python | 334 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj2_3",
original_model_step=2345595... |
6940e8a9aadd52ba590e213ece77bbc8cb55b4c5100ec757a6fe3f7e62b0aab4 | Python | 334 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj2_1",
original_model_step=2731035... |
7a5269b0b919c8736228a12fd24fc5a480f5ae03f00585a4a4874c4eb0f0b117 | Python | 334 | 20 | #!/usr/bin/env python
# encoding: utf-8
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import os, sys, re
import logging
import argparse
import collections
import numpy
import time
logger = logging.getLogger(__name__)
class GraphCycleException(Exceptio... |
228d66ab791f5949f35844cf54fcaa0fdd2f4e9d0cbf90dbb2cba67d19e2a9c0 | Python | 336 | 11 | from PIL import Image
from lightly.transforms.mae_transform import MAETransform
def test_multi_view_on_pil_image() -> None:
multi_view_transform = MAETransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = multi_view_transform(sample)
assert len(output) == 1
assert output[0].shape... |
cfda58acef3bccee5681a0eb50339f30626359dcc857f12dec55fb456ceb177d | Python | 336 | 19 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from common import sort_items
if len(sys.argv) < 2:
print("Sort the dictionary")
print(("Usage: ", sys.argv[0], "[input] ([output])"))
exit(1)
input = sys.argv[1]
if len(sys.argv) < 3:
output = input
else:
output = sys.argv[2]
sort_items... |
08282ef4246eff0e6a70f94d1f95e6a6f74ab24677d9c186db343cc423e245de | Python | 337 | 13 | import sys
wild_fa = sys.argv[1]
snp_tags = sys.argv[2]
outfile = sys.argv[3]
# Write RNA sequence and SNP tags to a outfile
out = open(outfile, "w")
with open(wild_fa) as in_fa_handle:
for line in in_fa_handle:
out.write(line)
with open(snp_tags) as in_snp_handle:
out.write("*" + in_snp_handle.readlin... |
b665f5d7467c4b857efd418d56a6a9af92459770b886f02820b27bc7512f28cb | Python | 337 | 16 | from .constituitive import Constituitive
from .null import Null
from .alternate_sites import AlternateSites
from .skipped_exon import SkippedExon
from .exclusive_exons import ExclusiveExons
from .other import Other
annotation_types = [
Constituitive,
Null,
AlternateSites,
SkippedExon,
ExclusiveExo... |
f1797bfdd1351a4888ccce259c53f29a68c22ef3995e50cf439cb4bc6d057fcd | Python | 337 | 13 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._fill import Fill
from ._font import Font
from ._line import Line
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._fill.Fill", "._font.Font", "._l... |
d8d6c926fe99b03f3f99ac7d458ba12fe6d4c3e436ac426e48c0315b2f8bb7b2 | Python | 338 | 15 | import pytest
from src import config
def test_config_port():
assert config.PORT == 8000
def test_config_my_region():
assert config.MY_REGION == 'VPM'
def test_config_soma_thr():
assert config.SOMA_THR == 30
def test_config_cpd_params():
assert 'max_it' in config.CPD_PARAMS
assert config.CPD_PARA... |
dfbafae8eed75465ab6c5a322fcdc162e0f520d230fec44b61ebbdcef659a74a | Python | 338 | 17 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=3),
)
msp.data_dir = "../data/gtex_dataset/"
msp.acc_thresh... |
1f88e6cd20b06f28d1de7bb2fa36ed29aa0ae2c069a5b176753dc6520f6db171 | Python | 339 | 11 | """
Authentication module for Ethoscope Node.
Provides PIN-based user authentication with session management,
rate limiting, and security features.
"""
from .middleware import AuthMiddleware, require_admin, require_auth
from .session import SessionManager
__all__ = ["AuthMiddleware", "require_auth", "require_admin",... |
7ce8ba10185346decff4e7b55f68a5c9d1cf510558211379aa7d8c8c3de5ab4e | Python | 339 | 14 | import argparse
parser = argparse.ArgumentParser(
"Explainable Deep Learning for Tumor Dynamic Prediction and Overall Survival using Neural ODE"
)
parser.add_argument(
"--config",
type=str,
default="assets/configs/config.json",
help="paremeter file that specifies TDNODE configuration",
)
args = p... |
89528b876030f8bbeb82ba8848c46f6272efc703db95336daad037651d95178b | Python | 339 | 10 | from sklearn.metrics import mean_absolute_error, mean_squared_error, max_error, r2_score
def get_metrics(y_true, y_pred):
mae = mean_absolute_error(y_true, y_pred)
rmse = mean_squared_error(y_true, y_pred, squared=False)
maxerr = max_error(y_true, y_pred)
r2 = r2_score(y_true, y_pred)
return [mae... |
d8ca8b53e0d43d6fe2dda48e2c86b190c1e2c17b775f53f1bc0e6e9b159da0f3 | Python | 339 | 17 | # Generated by Django 2.1.5 on 2019-01-25 10:31
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0030_auto_20190123_1815'),
]
operations = [
migrations.RemoveField(
model_name='upload',
name='sensitivity'... |
fcb10ef3bd6a920d2e5fc1e4c6fb17185b73c372f7096c80837e927a3d50aa26 | Python | 339 | 7 | import numpy as np
h = np.load("cache/features_healthy.npz", allow_pickle=True)
s = np.load("cache/features_stroke.npz", allow_pickle=True)
for name, d in (("healthy", h), ("stroke", s)):
X = d['X']
print(f"{name}: alpha logPSD {X[:, :29].mean():7.2f} "
f"beta {X[:, 29:58].mean():7.2f} RMS {X[:, 5... |
252e315abbe84b435886746a072d9e3555e0c075d3b05b0e59d2ae3549cb6eb7 | Python | 340 | 9 | from sklearn.metrics import mean_absolute_error, mean_squared_error, max_error, r2_score
def get_metrics(y_true, y_pred):
mae = mean_absolute_error(y_true, y_pred)
rmse = mean_squared_error(y_true, y_pred, squared=False)
maxerr = max_error(y_true, y_pred)
r2 = r2_score(y_true, y_pred)
return([mae,... |
0e16747901d595f2df416cadd1a82ea9fdcaa225f4905283e8d9bf7fc9812aa6 | Python | 341 | 14 | import torch
# define the strategy of weight decay
class Save_Pes():
def __init__(self,PES_Normal,PES_Lammps):
self.PES=PES_Normal
self.Lammps=PES_Lammps
def __call__(self,model):
state = {'reannparam': model.state_dict()}
self.PES(state)
if self.Lammps:
... |
179844b27e1099197ecc8ff0b9f05ff10c2cc189c3aaabc61ccdb8457428f39f | Python | 341 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj13x4-nsp_2",
original_model_step=... |
39a16f0465e25837e095eff0b7466e4e9d6d68e7107b1b22fc63ec0fa1791c50 | Python | 341 | 17 | # Generated by Django 2.1.5 on 2019-04-09 12:12
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0039_auto_20190322_1601'),
]
operations = [
migrations.RemoveField(
model_name='upload',
name='inverse_step... |
b4130b8b08e61986fb2113da5238e4697671333894fe52fa9de769d6ca2a40a9 | Python | 341 | 13 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._button import Button
from ._font import Font
from ._pad import Pad
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._button.Button", "._font.Font"... |
bd78d2cccca18b8b84ba514dfc9a4fc0fe8307e0bb2ab6ec52b19fc7859064c6 | Python | 341 | 10 | """Test functions for specparam.utils.random"""
from specparam.utils.random import *
###################################################################################################
###################################################################################################
def test_set_random_seed():
... |
cd6260904222debc9e2da14717610932bbaac50919e547a040ba2e096ef7021a | Python | 341 | 8 | """Sub-module for model objects & related utilities."""
from .model import SpectralModel
from .group import SpectralGroupModel
from .time import SpectralTimeModel
from .event import SpectralTimeEventModel
from .utils import (compare_model_objs, average_group, average_reconstructions,
combine_model_... |
e7bbeaf7bd02cb5604be2ae7f35c069018eb5207fe65081b3a4ab34697cd32c8 | Python | 341 | 10 | from lightly.cli.config.get_config import get_lightly_config
def test_get_lightly_config() -> None:
conf = get_lightly_config()
# Assert some default values
assert conf.checkpoint == ""
assert conf.loader.batch_size == 16
assert conf.trainer.enable_model_summary is True
assert conf.summary_cal... |
ef2e42a2b11189855504c0a95a2a02c3f9bf363d6881e560a1808956c80175e5 | Python | 341 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj13x4-nsp_1",
original_model_step=... |
f085332174e121d6f5426e4ace21b466a66f8e86b7c0c295d97c5cd456a689c1 | Python | 341 | 14 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj13x4-nsp_3",
original_model_step=... |
2524be904bd9dff8c96c0d02f13b25d55c9c9aaaef8dba2257e0cd717f8d5826 | Python | 342 | 21 | """Merge head revisions
Revision ID: 75348cfb3715
Revises: 9a5207190a4d, cbc46035eba0
Create Date: 2024-11-19 11:48:02.273303
"""
# revision identifiers, used by Alembic.
revision = "75348cfb3715"
down_revision = ("9a5207190a4d", "cbc46035eba0")
branch_labels = None
depends_on = None
def upgrade():
pass
def ... |
05cd8b25b7d163cc9277ccd82f6cabfe9b7e6470e4c3162522ee122559b21873 | Python | 343 | 21 | """Merge migration heads
Revision ID: 98621a25ab75
Revises: 1d1d7bf6ac02, 63dc6cca023f
Create Date: 2025-12-12 13:48:07.888987
"""
# revision identifiers, used by Alembic.
revision = "98621a25ab75"
down_revision = ("1d1d7bf6ac02", "63dc6cca023f")
branch_labels = None
depends_on = None
def upgrade():
pass
def... |
5b307154f4e4d3814180450d5f8fd3e3d169b244ec6906502f055f27f7714f8d | Python | 343 | 11 | import numpy as np
def split_array(X, lengths):
"""
Split an array into subarrays of given lengths along its first dimension.
"""
assert np.sum(lengths) == X.shape[0]
border_indices = np.insert(np.cumsum(lengths), 0, 0)
return [X[start:end, ...] for (start, end) in zip(border_indices[:-1]... |
90ac86f8045b1b1fef2f9e9a57aa272cf948c56d61e6d1c78fbbbe10f8d5bff0 | Python | 343 | 11 | from scipy import ndimage
from .dctsplines import l2spline,l1spline
import numpy as np
def grayscale_msr(img, sigmas=(15,80,250),smoother=l2spline):
out = np.zeros(img.shape,np.float32)
img = img.astype(np.float32)
for s in sigmas:
#smoothed =
out += np.log(img/(smoother(img,s)+0.001))
... |
0dc7436142f4754b2b86f2eed6675c23300b3642335e9eb7d285e6d3b106ef3f | Python | 345 | 14 | """Backward-compatibility shim. Module renamed to pyriemann.utils._check."""
import sys
import warnings
from . import _check as _moved
warnings.warn(
"pyriemann.utils.utils is deprecated and will be removed in 0.14.0; "
"use pyriemann.utils._check instead.",
DeprecationWarning,
stacklevel=2,
)
sys.mo... |
437e495521a1c2e9ce0eae4f4958b633ee9b846c084310a89579cbc5a9ccfc16 | Python | 345 | 12 | from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from mord.datasets.base import load_housing
def test_load_housing():
res = load_housing()
assert_equal(res.data.shape, (1681, 3))
assert_equal(res.target.size, 1681)
assert_equal(len(res.feature_names), 3)
... |
4485c6d7f14378c218de5507835cdc18e0ac41dbd3d9fa74a103f29b2338b3c3 | Python | 346 | 11 | from modular_splicing.eclip.data.pipeline import produce_all_eclips
RCS = "1", "2", "1, 2"
for rc in RCS:
for is_train in False, True:
produce_all_eclips(
replicate_category=rc,
dataset_path="canonical_dataset.txt",
sequence_path="canonical_sequence.txt",
is_... |
734d8844f3449a9ba1998b748a2424958b2e5c8ed4b5e91094e9756ddefd74c7 | Python | 346 | 13 | """A way to run the XCP-D CLI with a Python call."""
from xcp_d.cli.run import main
if __name__ == '__main__':
import sys
from xcp_d import __name__ as module
# `python -m <module>` typically displays the command as __main__.py
if '__main__.py' in sys.argv[0]:
sys.argv[0] = f'{sys.executable... |
adfc25ef357d5b2d894eb2dc24a827949b3fd1cbcb65fd32af29aee6810b0e3c | Python | 346 | 7 | from . import base
from .base._blocks import DenseBlock, Conv1DBlock, RecurrentBlock
from .base._towers import Tower, Conv1DTower
from ._SequenceModule import SequenceModule
from ._ProfileModule import ProfileModule
from ._utils import list_available_layers, get_layer, load_config
from .base._initializers import init_m... |
4b6ad6742a402190e955bffc747bd2e2879e4477d2480b4bc94c650794be700a | Python | 347 | 16 | from afmformats.formats import formats_by_modality
from . import fd
analysis_types = {
"fd": {
"suffixes": [f["suffix"]
for f in formats_by_modality["force-distance"]],
"gui": fd.UiForceDistance,
}
}
known_suffixes = []
for _item in analysis_types.values():
known_suf... |
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