sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
cba4b02fcadcd2f79b0b6716a94de56f0b5bee37858732b280381468580c97bb | Python | 455 | 11 | r"""Cytometry I/O — Flow Cytometry Standard (``.fcs``) files.
A peer of ``io.bulk`` / ``io.single`` / ``io.spatial`` rather than a member of
one of them: an FCS event is a cell, but the file format, its metadata model
($PnN/$PnS, $SPILLOVER) and the instruments that write it are their own world,
shared by conventional... |
e31b6b01617261f39614b1fab51b4834aabf1a20774b6165f5464518533eed7e | Python | 455 | 20 | from dcll.dcll.pytorch_libdcll import *
from dcll.dcll.experiment_tools import *
from dcll.dcll.load_dvsgestures_sparse import *
import torch
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
se... |
bfabbffe3c55b1862a26b72661ddd55c8a86db1560f61cf2e2ca9e2ac4dd9b63 | Python | 456 | 20 | import os
import functools
import warnings
import time
import csv
import matplotlib; matplotlib.use("TkAgg")
from matplotlib import pyplot
import numpy
import skimage.io
#from skimage.external.tifffile import TiffWriter
import yaml
import microscope
import user
import utils
from datetime import date
configs = microsc... |
8a468c13f41059f0f0896ebc829551c26222579a723bc783faecc0e2ee42a9f4 | Python | 457 | 18 | import numpy as np
def rescale_linear(array, new_min, new_max):
"""Rescale an arrary linearly
args:
array: a number array
new_min: the minimum value for new scale
new_max: the maximum value for new scale
"""
minimum, maximum = np.min(array), np.max(array)
if maximum - mini... |
71233d295c0db4a25e20b79e7499bf2aafc2cfd3af0b5c1e4ba5293d3ca9616f | Python | 458 | 19 | """Channel adapters for Jarvis."""
from .telegram import AccessControl, run_bot
from .discord import run_discord_bot
from .feishu import run_feishu_bot, run_feishu_ws_bot
from .imessage import run_imessage_bot
from .qq import run_qq_bot
from .wechat import run_wechat_bot
__all__ = [
"AccessControl",
"run_bot"... |
bef5b353f96bb12a1bd89a4e746ee893c466d058d1fafee881880aeb3aee0069 | Python | 458 | 16 | from pbr.version import VersionInfo
__version__ = VersionInfo("sinabs").release_string()
from . import conversion, utils, validate_memory_speck
from .from_torch import from_model
from .network import Network
from .nir import from_nir, to_nir
from .synopcounter import SNNAnalyzer, SynOpCounter
from .utils import (
... |
39af4a60c71de2bf185475a46652710628f6cd50ea5ae674a8846f999cead839 | Python | 459 | 17 | from abc import ABC
from typing import Optional
from data_profiling.report.presentation.core.renderable import Renderable
class ItemRenderer(Renderable, ABC):
def __init__(
self,
item_type: str,
content: dict,
name: Optional[str] = None,
anchor_id: Optional[str] = None,
... |
e3498f5075fe6bf7c6bf0ab7369baf3e8fdb85304b37df9c3e38cbaa9c702653 | Python | 459 | 7 | from pfrl.explorers.additive_gaussian import AdditiveGaussian # NOQA
from pfrl.explorers.additive_ou import AdditiveOU # NOQA
from pfrl.explorers.boltzmann import Boltzmann # NOQA
from pfrl.explorers.epsilon_greedy import ConstantEpsilonGreedy # NOQA
from pfrl.explorers.epsilon_greedy import ExponentialDecayEpsilon... |
065ac959f2ac0402c2f3776c38f5dc1dd5a6c1723b40c2b7308f5c0d1d222d0a | Python | 460 | 17 | """Configuration constants and paths for SiaPy library.
This module defines important directory paths and configuration constants
used throughout the SiaPy library, including base directories and test data locations.
"""
from pathlib import Path
__all__ = [
"BASE_DIR",
"SIAPY_DIR",
"TEST_DATA_DIR",
]
BA... |
722c60b9847987f5b6688a282107e6a5e401debb2dc390d51057006e0779084c | Python | 460 | 12 | from ray.air.execution.resources.fixed import FixedResourceManager
from ray.air.execution.resources.placement_group import PlacementGroupResourceManager
from ray.air.execution.resources.request import AcquiredResources, ResourceRequest
from ray.air.execution.resources.resource_manager import ResourceManager
__all__ = ... |
a6fe154acd119f96e9c464fbe3b618bd969a909c9c2d616432dc816db31bb793 | Python | 460 | 16 | __author__ = "Horea Christian"
import argh
from samri.pipelines.diagnostics import diagnose
from samri.pipelines.reposit import bru2bids
from samri.pipelines.preprocess import generic, legacy
from samri.pipelines.glm import l1
def main():
# Adapting function names to 1-level hierarchy
generic.__name__ = 'generic-pr... |
b7cf98c14c4bbaf361dc9e76548e62033f54f2b1fadaf8d2c2ac32ece2b55d9a | Python | 460 | 16 | import logging
from torch_geometric.utils.num_nodes import maybe_num_nodes
from torch_scatter import scatter_add, scatter_max
logger = logging.getLogger(__name__)
def pyg_softmax(src, index, num_nodes=None):
num_nodes = maybe_num_nodes(index, num_nodes)
out = src - scatter_max(src, index, dim=0, dim_size=n... |
e9b90bbb5332db440df811e0296bb588e36cd67a4a533f1bdc332e1d2604a659 | Python | 460 | 15 | from siapy.entities import SpectralImage
from siapy.entities.images import SpectralLibImage
# Load from ENVI format (uses spectral python library)
image_sp = SpectralLibImage.open(
header_path="path/to/header.hdr",
image_path="path/to/image.img",
)
image = SpectralImage(image_sp)
# Or you can use the class me... |
2a8154747df3b5b2c51a8802165d3abb1ba4350f6d813e6da33cb5d295ee90b8 | Python | 462 | 15 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
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 annotations
import os
BULK_PKL_PATH = os.path.join(__path__[0], "bulks.pkl")
ADSORBATE_PKL_PATH = os.path.join(__p... |
4c368e9fb34f258d91d66a56dcafb4f7548ae3ac63968357d73fd5e84ff9f4d0 | Python | 462 | 15 | import scrublet as scr
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import os
import random
print(sys.path)
def predict_doublets(matrix):
counts_matrix = scipy.io.mmread(matrix).T.tocsc()
scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.10)
doublet_scores, predicted_double... |
7a33dc89adb5885e6ef2e612fc411996f9869dbcc5f196e3a397c9648d138a38 | Python | 463 | 20 | from torch import nn
class Lambda(nn.Module):
"""Wraps a callable object to make a `torch.nn.Module`.
This can be used to add callable objects to `torch.nn.Sequential` or
`pfrl.nn.RecurrentSequential`, which only accept
`torch.nn.Module`s.
Args:
lambd (callable): Callable object.
"""... |
a329ed353e261c7c6f24e7971171cdc84735302135add9df3eade51e9edb0581 | Python | 464 | 19 | # Generated by Django 4.1.6 on 2023-12-18 01:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("infer", "0020_proteomeresultstats"),
]
operations = [
migrations.AlterField(
model_name="proteometask",
name="id",
... |
e85b63ecfd7ead0b947571813fe2763177bcdda8c06870211be6e257f078de4f | Python | 464 | 16 | import pandas as pd
metadata_final_file = "metadata_final_forLDSC_20221229.csv"
df = pd.read_csv(metadata_final_file, sep=",", index_col=False)
mets_munge_file = "../MetS_GWAS_munge.sumstats.gz"
df2 = df[['Category', 'Trait']]
df2['MetSMunge'] = [mets_munge_file] * len(df)
df2['TraitMunge'] = df['Munged_path'].tol... |
f7d26085877a936e3de3161a2f8be70bf11598ddf007785d2e3b9e117e9b060e | Python | 464 | 25 | #
# Global configuration
#
class MOFAConfig:
"""
MOFA config manager
"""
def __init__(self, use_float32: bool = False):
self.use_float32 = use_float32
@property
def use_float32(self):
return self.float32
@use_float32.setter
def use_float32(self, float32):
if ... |
098e32fb666d601d6bcb4bbcdd9a553434964855f7fe5e9579a725b7c04b264c | Python | 465 | 14 | from data_profiling.report.presentation.core.root import Root
from data_profiling.report.presentation.flavours.html import templates
class HTMLRoot(Root):
def render(self, **kwargs) -> str:
nav_items = [
(section.name, section.anchor_id)
for section in self.content["body"].content[... |
9ee1904e6de44a943610e6c5ba18b887b218c626302236e7493cbe97b6f3ca08 | Python | 465 | 16 | from abc import ABCMeta, abstractmethod
class Explorer(object, metaclass=ABCMeta):
"""Abstract explorer."""
@abstractmethod
def select_action(self, t, greedy_action_func, action_value=None):
"""Select an action.
Args:
t: current time step
greedy_action_func: function ... |
aea0158d66dd5d6f9b0500b79cd9a702a7b45fabb2492afb3be9f70304212271 | Python | 465 | 21 | """
===========
SingleSpike
===========
:class:`~sinabs.activation.SingleSpike` activation function.
"""
import matplotlib.pyplot as plt
import torch
import sinabs.activation as sina
v_mem = torch.linspace(0, 5.5, 500)
spike_threshold = 1.0
activations = sina.SingleSpike.apply(v_mem, spike_threshold, sina.MultiGaus... |
385d8e0b09f6c5e1295b8393ef37df248ec7d95d4ed0253c3a00a1a9f77b957d | Python | 467 | 22 | import pandas as pd
import numpy as np
from scipy.interpolate import splrep, BSpline, make_smoothing_spline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import glob
dir_path = "data/rrls/catalog/"
rrl_list = list()
for file_name in glob.glob(dir_path + '*.csv'):
x = pd.read_csv(file_name, low_memory=... |
94c7a61e9dc1838612f7bf2af531d3c3dd8f71836ef1781bdf79185cea6fc328 | Python | 467 | 18 | from enum import Enum
class VQVAELosses(Enum):
BASELINE = "baseline"
BAUR = "baur"
MSE = "mse"
SPECTRAL = "spectral"
HARTLEY = "hartley"
JUKEBOX = "jukebox"
WAVEGAN = "wavegan"
PERCEPTUAL = "perceptual"
JUKEBOX_PERCEPTUAL = "jukebox_perceptual"
HARTLEY_PERCEPTUAL = "hartley_per... |
c63cbef23d479a7a82ce79759e79800191e5948d77786d0d0232aab350cc12be | Python | 467 | 12 | from pathlib import Path
from shutil import unpack_archive
from . import utils
def download(path: Path, version: str = "4.4.224"):
url = f"https://downloads.thebiogrid.org/Download/BioGRID/Release-Archive/BIOGRID-{version}/BIOGRID-ORGANISM-{version}.mitab.zip"
utils.download_file(
url, path / f"BIOGR... |
1a2187e5bedddfe2cdbfaf7a9e761e89ba5abf863d08d5d305ecfda9159a8623 | Python | 468 | 18 | from mmdet.datasets import CocoDataset
from lqit.registry import DATASETS
RTTS_METAINFO = {
'classes': ('bicycle', 'bus', 'car', 'motorbike', 'person'),
'palette': [(255, 97, 0), (0, 201, 87), (176, 23, 31), (138, 43, 226),
(30, 144, 255)]
}
@DATASETS.register_module()
class RTTSCocoDataset(... |
3bf8ffca8bdfe7f52e591ef06e44316edfc8caf75fc72d7570e184a67fa9fd5e | Python | 468 | 23 | import os
import urllib.request
import zipfile
def download_extract(unzip_dir, url):
outdir = str(unzip_dir)
os.makedirs(outdir, exist_ok=True)
zip_path = os.path.join(outdir, "temp.zip")
urllib.request.urlretrieve("https://" + url, zip_path)
with zipfile.ZipFile(zip_path, "r") as zf:
z... |
6487e26c3642b9ef648e7b47e996e9f66fe6a3168124b35ff9b4b5ce867203bd | Python | 468 | 16 | from typing import Any, List
from data_profiling.report.presentation.core.item_renderer import ItemRenderer
class FrequencyTableSmall(ItemRenderer):
def __init__(self, rows: List[Any], redact: bool, **kwargs):
super().__init__(
"frequency_table_small", {"rows": rows, "redact": redact}, **kwar... |
fe778482bbf1022726365ba0494d2c1d1e344120147bfb59d0100ef87a3fe6cd | Python | 468 | 22 | """
OPIPP: Optimization-based Pairwise Interaction Point Process
=======================
A Python implementation of OPIPP, a method for retinal mosaic simulation.
Classes:
Scope
Feature
AdaptiveSchedule
Mosaic
Pattern
"""
from .scope import Scope
from .feature import Feature
from .cooling impor... |
4e273246a1bef50ad63da30334557981089a40b3eb5f4fad9b07de105df1e1ac | Python | 469 | 11 | # Vendored from `decoupler` (https://github.com/scverse/decoupler) by
# omicverse for in-tree GPU acceleration work. Original copyright by
# the decoupler authors, redistributed under decoupler's GPL-3.0
# license. Cross-module imports rewritten from `decoupler.*` to
# `omicverse.es.*` (see scripts/vendor_decoupler.py)... |
ade5afe82b69fac35021cf6566491a42d5c712454377d7c96955400514c6990c | Python | 469 | 21 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard imports
# Third-party imports
# Internal imports
from .fastx import read
from .kmer import read
from .kmer import write
#------------------- Constants ------------... |
05a84b3b97919faad224ac799ee102a3ba3f714e887214aeb5e2d7f5c0f635ab | Python | 470 | 13 | from typing import List, Union
def is_flat_embedding(x: Union[List[float], List[List[float]]]) -> bool:
"""
Check if the embeddings represent a single flat list of floats
rather than a list of list of float.
Args:
x (List[float] or List[List[float]]): Embeddings to check.
Returns:
... |
186ce445775c26bc57c3705060fc01728bc540c3e0a8ab1f2b83cfe18aab8743 | Python | 470 | 26 | from abc import ABCMeta, abstractmethod
class Dataset(object):
def __init__(self):
__metaclass__ = ABCMeta
@abstractmethod
def __getitem__(self, item):
'''
needs to return a data_dict for the sample at the position item
:param item:
:return:
'''
pas... |
3d9e09c1c4e9402e8d141b4931ff95270c19e4bdaa4e2a54b3874e2de29c62c6 | Python | 470 | 18 | # Generated by Django 4.1.6 on 2023-02-06 04:27
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("infer", "0006_ppidatabase_remove_weights_organisms_dataset_and_more"),
]
operations = [
migrations.AddField(
model_name="ppidatab... |
5e09d65870374817230fd98f928f4d17e29f8161e5ed945dc82c3d44095992c2 | Python | 470 | 23 | from typing import Tuple, Dict
class AdversarialKeys:
"""
A set of common keys for adversarial networks.
"""
REALS = "reals"
FAKES = "fakes"
GLOSS = "g_loss"
DLOSS = "d_loss"
def batch_decomposition(batch: Tuple) -> Tuple:
if len(batch) == 2:
inputs, targets = batch
... |
a8c9aec732d4bb01cc7bb7776593d32a6507c10455fc04df60dbcc59d3026e50 | Python | 470 | 14 | from setuptools import setup
setup(
name='mbeann',
version='0.1.0',
author='Motoaki Hiraga',
author_email='100577843+motoHiraga@users.noreply.github.com',
url='https://github.com/motoHiraga/pyMBEANN',
license='MIT',
description='An implementation of Mutation-Based Evolving Artificial Neural... |
2a681479b0d1a84d1acd5af22f71b8ada2db60c1490240f36317cfa883a86212 | Python | 471 | 15 | """
Test for issue 613:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/613
"""
import numpy as np
import pandas as pd
from data_profiling import ProfileReport
def test_issue613():
df = pd.DataFrame([{"col": "ABCDEF"}, {"col": "DEFGEFGHK"}, {"col": np.nan}])
report = ProfileRep... |
b1e1ecc46eadf1fc4de16d3c42205dfccfb84c90e37ce1dcbd664138a29e40f4 | Python | 471 | 10 | import os
import sys
# train RMBG model
os.system('python train_RMBG_model_conti.py --datasets_folder train_RMBG1 \
--n_epochs 2 --GPU 0 --batch_size 1 \
--fmap 32 --lr 0.0001 \
--denoise_model vol_600_200_rr2 --pth_nam... |
73590e57bbc6c9d843b2cdf03c816b0a50b66257e190c737624c27673784e7e1 | Python | 472 | 16 | import csv
import os
import code
OUT="./PRS"
Traits=['BMI', 'WC', 'T2D', 'FG', 'HTN', 'TG', 'HDL']
for trait in Traits:
file = os.path.join(OUT, trait, "score_sum_indiv.profile")
with open(file, 'r') as f, open(file + '.reformat', 'wt', newline='') as f2:
reader = csv.reader(f, delimiter=' ')
... |
4023b424395399bab3fb6f90994bc4b0b1d19ef6d18491f59b881d1fede40f45 | Python | 473 | 21 | import extraction
import patient
import patient_longitudinal_info
import format
version = "xxxxxx" # please specify version (folder name) of your data
output = "processed_data"
patient.extract_patient_info(version, output)
patient_longitudinal_info.extract_longitudinal_info(output)
extraction.extract_data(version... |
abe3ec30a3198a2cbf2e30f6f4e6bde374a2d5aa38b486adf66198cacbaf9b56 | Python | 473 | 16 | MODEL_CONFIG = {
"weight":{
"protein": "poc_pre_220816.pt",
"molecule_no_h": "mol_pre_no_h_220816.pt",
"molecule_all_h": "mol_pre_all_h_220816.pt",
"crystal": "mp_all_h_230313.pt",
"oled": "oled_pre_no_h_230101.pt",
},
"dict":{
"protein": "poc.dict.txt",
... |
523fd7b1e291844de8279286acb9606a4b4c19abe7716676d0795769f0ab1faf | Python | 474 | 8 | from .trainer_pretrain import TrainerPreTrain
from .trainer_finetune_regression import TrainerFineTuneRegression
from .trainer_finetune_classification import TrainerFineTuneClassification
from .trainer_gan_lit import TrainerGANLit
from .external.ase_dataset import AseReadDataset
from .external.ase_dataset_attribute imp... |
c36d4403a0099427107428ae839aa480f12f69b837f11bec4795e06dff1c7aa9 | Python | 474 | 11 | from samri.pipelines.manipulations import transform_feature
# Takes too long
#def test_transform_feature():
# transform_feature('/usr/share/mouse-brain-templates/dsurqec_200micron_roi-dr.nii', '/usr/share/mouse-brain-templates/dsurqec_200micron_masked.nii', '/usr/share/mouse-brain-templates/ambmc_200micron.nii',
# ta... |
1691aead150d2a580f2b63bc85efc033ef679571473bd585effac187e4498766 | Python | 475 | 21 | from warnings import warn
from fire import Fire
from .helpers import (
categorical_crossval_,
deploy_categorical_model_,
train_categorical_model_,
)
if __name__ == "__main__":
warn(
"feature models are deprecated and may be removed in the future.", FutureWarning
)
Fire(
{
... |
bfb324fbdaf351ef32b4d19ef763b78b569c7f04708a928ec6a36ef6ff8be1ff | Python | 476 | 24 | r"""
Loss function
"""
import torch
def feature_reconstruct_loss(embd:torch.Tensor,
x:torch.Tensor,
recon_model:torch.nn.Module
) -> torch.Tensor:
r"""
Reconstruction loss (MSE)
Parameters
----------
embd
embd of a cell
... |
9a4f9ff7179a7a34b5104bd5082c9665ad090fcdfb6c0eb0d690b8ac5f7e0569 | Python | 477 | 17 | #Training HSI Augmentations
from torchvision import transforms
def train_augmentation(image_size):
"""Torchvision transforms
Args:
image: a torch vision tensor with C, H, W order
Returns:
transformed_image: an augmentated image
"""
transform_list = []
transform_list.append... |
f9555ec2f4652913110ecb4357cf3f321d2a8772b647d31e50d2abeee44adcd5 | Python | 477 | 13 | # TODO: delete after fully support editor metric and datasets.
_base_ = [
'../_base_/models/unet.py',
# '../_base_/datasets/underwater_enhancement.py',
'../_base_/datasets/underwater_enhancement_with_ann.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
train_dataloader = ... |
2f939ea73ce6003c7fb819685e9df83959a82fe5bdfed907c19dc1c13ee12c94 | Python | 478 | 16 | import csv
import os
import code
OUT="./PRS"
Traits=['BMI', 'WC', 'T2D', 'FG', 'HTN', 'TG', 'HDL']
for trait in Traits:
file = os.path.join(OUT, trait, "score_sum_indiv.profile")
with open(file, 'r') as f, open(file + '.KoGES.reformat', 'wt', newline='') as f2:
reader = csv.reader(f, delimiter=' ... |
5d8b34cb18409e0882950ab6630709aff9551df25fb8a0fd5608de5d238468f5 | Python | 478 | 22 | import enum
class DatasetState(enum.IntEnum):
"""Enum representing the possible states of a dataset during execution."""
UNKNOWN = 0
RUNNING = 1
FINISHED = 2
FAILED = 3
PENDING = 4
def __str__(self):
return self.name
@classmethod
def from_string(cls, text):
"""Ge... |
d0cd174c2b2ca560cecf4813619c1a4e8a2ac36cd38c4af1ee93bf90287b1335 | Python | 479 | 13 | import numpy as np
import scipy.stats
def spike_to_rate(spikes, window_std=20):
window_size = np.arange(-3*window_std,3*window_std,1)
window = scipy.stats.norm.pdf(window_size, 0, window_std)
window /= window.sum()
n_units = spikes.shape[0]
estimate = np.zeros_like(spikes) # Create an empty array o... |
7eae3813031d8bfbbc7328b4c0f1874ce72896a706a4b9027bbe67a32a1c9155 | Python | 480 | 21 | """
Test for issue 215:
https://github.com/Data-Centric-AI-Community/data-profiling/issues/215
"""
import pytest
from data_profiling.report.formatters import fmt_percent
@pytest.mark.parametrize(
"ratio, expected_formatting",
[
(0.01, "1.0%"),
(0.001, "0.1%"),
(0.0001, "< 0.1%"),
... |
dde7f08cd012bd8418b2d4e23cf105f41b8d6e7305fd38d49fd2dce580c91695 | Python | 480 | 20 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
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 annotations
from importlib.metadata import PackageNotFoundError, version
try:
__version__ = version("fairchem.... |
3fbac50c636d4101a5ef4641d6a8b1e251cfe3135a7de8ac183404bbf20022e1 | Python | 481 | 20 | def b(
s: 'str | bytes',
encoding: str = 'ascii',
errors: str = 'replace',
) -> bytes:
"""Encode a string to bytes, passing bytes through.
Args:
s: String or bytes input.
encoding: Encoding to use. Defaults to ``'ascii'``.
errors: Error handling strategy. Defaults to
... |
ba2d95ffbe7aa2d2da214d44fc0b547e5b7d2ac26d6b8ca52c7b7cadb297f83c | Python | 483 | 9 | # Ultralytics YOLO 🚀, GPL-3.0 license
from .base import BaseDataset
from .build import build_classification_dataloader, build_dataloader, load_inference_source
from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
from .dataset_wrappers import MixAndRectDataset
__all__ = ('BaseDataset', 'Classific... |
27ded71319e7836f52760c521e83028d6dee6b5042e8562b516c623c605619f9 | Python | 484 | 11 | from .standardize import VCFStandardizer
from .std_delly import DellyStandardizer
from .std_wham import WhamStandardizer
from .std_lumpy import LumpyStandardizer
from .std_manta import MantaStandardizer
from .std_melt import MeltStandardizer
from .std_scramble import ScrambleStandardizer
from .std_dragen import DragenS... |
28d267234f7a7e40b36e3b949a6f7834a8eb382b7b7b23da040031123bba7e2f | Python | 486 | 27 | # flake8: noqa
# __start__
# File name: monitoring.py
from ray import serve
import logging
from starlette.requests import Request
logger = logging.getLogger("ray.serve")
@serve.deployment
class SayHello:
async def __call__(self, request: Request) -> str:
logger.info("Hello world!")
return "hi"
... |
d6023551b5610a49bb77a3cf1a3b261c500cb911fd233441f012f03e87982df9 | Python | 486 | 12 | if __name__ == "__main__":
import argparse
from algo.sz_detection import szDetection
parser = argparse.ArgumentParser(
description="Run a seizure detection algorithm on an EDF file and store computed seizure annotations to a TSV."
)
parser.add_argument("input", help="Path to the input EDF E... |
312845ff54a0e320ff0237c2970b7f336dcd411361e23bf14d5d22c551997ee4 | Python | 487 | 17 | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import get_git_hash
from mmengine.utils.dl_utils import collect_env as collect_base_env
import lqit
def collect_env() -> dict:
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['lqit'] = lq... |
c1417d331b6acef58900044354b64cd83fcb0195a923bf1989be748959917efb | Python | 487 | 10 | from exputils.manage.experimentgenerator import generate_experiment_files
from exputils.manage.experimentstarter import start_experiments
from exputils.manage.experimentstarter import start_slurm_experiments
from exputils.manage.experimentstarter import start_torque_experiments
from exputils.manage.experimentstarter im... |
66b64b1eb00a8fe621ba83598e4031d5416118fede311fee23a258a9e3af3b46 | Python | 488 | 23 | """
===========
MaxSpike
===========
:class:`~sinabs.activation.MaxSpike` activation function.
"""
import matplotlib.pyplot as plt
import torch
import sinabs.activation as sina
v_mem = torch.linspace(0, 5.5, 500)
spike_threshold = 1.0
activations = sina.MaxSpike(max_num_spikes_per_bin=2).apply(
v_mem, spike_thr... |
edc6cbf10b9c67a4e04c31cf971ca5b024f6bd748a2d2c711ccf1546a12b0cca | Python | 488 | 18 | from rising.transforms.abstract import AbstractTransform
def chech_data_preservation(trafo: AbstractTransform, batch: dict, key: str = "data") -> bool:
"""
Checks for inplace modification of input data
Args:
trafo: transforamtion
batch: batch with torch.Tensor data
key: key to exa... |
1b6465324b97f8b052069f933abe09596b71ec9b94a9820c79619f54e1f42700 | Python | 489 | 22 | import torch
from sinabs.layers.channel_shift import ChannelShift
def test_channel_shift_default():
x = torch.rand(1, 10, 5, 5)
cs = ChannelShift()
out = cs(x)
assert out.shape == x.shape
def test_channel_shift():
num_channels = 10
channel_shift = 14
x = torch.rand(1, num_channels, 5, ... |
20781de2d8dd70223c8ba5857e07ec99742686ebc0e4ae1fa2a3932787af226d | Python | 489 | 12 | from .transformer import Transformer
from .vitautoenc import ViTAutoEnc
from .unet import UNet3D
from .unet_3d import UNet3DBase
from .focal_loss import SigmoidFocalLoss
from .unet_img_model import ImageModel as UNet3DModel
from .img_model_wrapper import ImagingModelWrapper
from .resnet_img_model import ResNetModel
fro... |
7207d7f1863390715fed75974fa55caa7de89fe18e661d719d8012760a1a6309 | Python | 490 | 15 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
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 annotations
import os
DISSOCIATION_REACTION_DB_PATH = os.path.join(
__path__[0], "dissociation_reactions_22May2... |
92594103359f61ee1eae311d627e0db7ac45e610a873115b74abd5d09652f08e | Python | 490 | 15 | from pathlib import Path
import os
#list of subj_ids
subj_id = ['c01', 'c02', 'c03', 'c04', 'c05', 'c06', 'c07', 'c08', 'c09', 'c10',
'c11', 'c12', 'c13', 'c14', 'c15', 'c16', 'c17', 'c18', 'c19', 'p01',
'p02', 'p03', 'p04', 'p05', 'p06', 'p07', 'p08', 'p09', 'p10', 'p11',
'p12', 'p13', 'p14'... |
772d6d1f20e1676134fe23fb71dc22c182bbd80775ba5ef081dc590d859cb5d4 | Python | 491 | 16 | from __future__ import annotations
from typing import Any
from ..gateway.routing import GatewaySessionRegistry
from .models import ConversationRoute
class MessageRouter:
"""Resolve runtime conversation routes into Jarvis sessions."""
def __init__(self, session_manager: Any) -> None:
self._registry ... |
93ea2c994abdb9d4ca0fe9ec496c348ff6fbc86104037284880bb53828362d98 | Python | 492 | 22 | """
========================
Integrate and Fire (IAF)
========================
:class:`~sinabs.layers.IAF` layer.
"""
import torch
from utils import plot_evolution
import sinabs.layers as sl
const_current = torch.ones((1, 100, 1)) * 0.03
single_current = torch.zeros((1, 100, 1))
single_current[:, 0] = 0.1
iaf_neuro... |
7b87b2627ce188fc085b7d2d83faf7ecabb0ffa3ef218b839b95c0e1c6557982 | Python | 493 | 8 | from agent.data.entities.config import DEV_ITEM_FILTER, DEV_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="text", _cuis=No... |
e19eb59b73fd354c1b79bb0ae7cc002aceb2df621de946a2491c9e1d7a3779c0 | Python | 493 | 17 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class OnnxRuntimeModel(metaclass=DummyObject):
_backends = ["onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["onnx"])
@classmethod
def from_conf... |
10284812449d7ce0eb5e24aa3f7f4e4284e2b25be923cd3f65d3cdd99d6cc439 | Python | 494 | 23 | from dataclasses import dataclass
import numpy as np
from .node import NIRNode
@dataclass(eq=False)
class Delay(NIRNode):
r"""Simple delay node.
This node implements a simple delay:
.. math::
y(t) = x(t - \tau)
"""
delay: np.ndarray # Delay
def __post_init__(self):
# set... |
2f844548ec07d67e6c7ef2026217b3e59aec1fe72f5a407c82f6aa77365dafb7 | Python | 495 | 16 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.cache import cache_file
if __name__ == "__main__":
file_name = cache_file(
"colors.csv",
"https://github.com/codebrainz/color-names/raw/master/output/colors.csv",
)
df = pd.rea... |
a949ae3fddb6e8ba44c08a0373ffeada7d245ae4b4a7dfa9e8686bd24fae9eec | Python | 495 | 15 | import importlib
def load_function_or_class(path):
"""Load a function or class at runtime given a full path.
Example of the path: mypkg.mysubpkg.myclass
"""
class_data = path.split(".")
if len(class_data) < 2:
raise ValueError("You need to pass a valid path like mymodule.provider_class")
... |
21151db8d70d09f6a671335f3242e5184a665b1577e39bc7168043f55a81d0e3 | Python | 496 | 16 | from ._features import make_dataset, H5TileDataset, train, deploy
__all__ = ["make_dataset", "H5TileDataset", "train", "deploy"]
__author__ = "Marko van Treeck"
__copyright__ = "Copyright 2022, Kather Lab"
__license__ = "MIT"
__version__ = "0.3.0"
__maintainer__ = "Marko van Treeck"
__email__ = "mvantreeck@ukaachen.d... |
5fb284f684a56def38ebda19858fd75e821b178c9c996b7903ca82e7e87e5135 | Python | 496 | 17 | from pathlib import Path
import pandas as pd
from data_profiling import ProfileReport
from data_profiling.utils.cache import cache_file
if __name__ == "__main__":
file_name = cache_file(
"chicago_employees.csv",
"https://data.cityofchicago.org/api/views/xzkq-xp2w/rows.csv?accessType=DOWNLOAD",
... |
0f731956a14ee63ee9ca8d52d60426f41e516600f21739ea444756929cc2ea0f | Python | 497 | 8 | from agent.data.entities.config import TEST_ITEM_FILTER, TEST_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="text", _cuis=... |
1be097f42ef681538e98feb5af49c6fa05ce4fbdab3c13cc1136d8758a1bd952 | Python | 497 | 19 | """Endstate reweighting from MM to NNP"""
# Add imports here
from .endstate_correction import *
# Handle versioneer
from ._version import get_versions
versions = get_versions()
__version__ = versions["version"]
__git_revision__ = versions["full-revisionid"]
del get_versions, versions
import logging
# format loggin... |
6f27adc1fe371d8a61f8be5008474e86add2e68821bc69f1679fbbbed4cf2d6d | Python | 497 | 8 | from case1 import *
import time,os; t0=time.time(); fn="c1_par.json"
d=json.load(open(fn)) if os.path.exists(fn) else {"true":[],"est":[]}
while len(d["true"])<int(sys.argv[1]) and time.time()-t0<230:
p=TRUE+rng.normal(0,0.6,10); p[9]=np.log(0.7); p[8]=TRUE[8]+rng.normal(0,0.8)
D=design(275,60,80,80); Y=simulat... |
34fc01ae75857afcb6efa050869aac89a1eca3d1d7b35a83dadf4abf73081bd1 | Python | 498 | 4 | from ._flow_expressions import construct_flows_from_cellchat, construct_flows_from_commot, construct_flows_from_cellphonedb, construct_flows_from_liana
from ._flow_preprocessing import subset_for_flow_type, filter_flow_vars, determine_differentially_flowing_vars, determine_spatially_flowing_vars, determine_informative_... |
bce812afe6d019165e02668a1a0f82a36ff009eb077051abc179296ab9220f6b | Python | 498 | 25 | # __anti_pattern_start__
import ray
ray.init()
@ray.remote
def f(i):
return i
# Anti-pattern: no parallelism due to calling ray.get inside of the loop.
sequential_returns = []
for i in range(100):
sequential_returns.append(ray.get(f.remote(i)))
# Better approach: parallelism because the tasks are executed... |
010b323895768ac83a78e07facf71dae5426a9f5715ab16a892cb97ba17310c6 | Python | 499 | 15 | import pandas as pd
from matplotlib import pyplot as plt
import argparse
#parser = argparse.ArgumentParser("dir")
dataDir = "6lu7/log336h_6lu7/data10/"
cols = ["Docking_Score", "QED_score", "Toxicity_Probability"]
df = pd.read_csv(dataDir+"present/scores.csv", names=cols)
for col in cols:
fig, ax = plt.... |
5fdc0a940e2793cfbf0d2ed60623c7b9b1d940c6f83ac484d0e91e15e9b13423 | Python | 499 | 21 | import torch.nn as nn
from sinabs.activation import Quantize
class QuantizeLayer(nn.Module):
"""Layer that quantizes the input, i.e. returns floor(input).
Args:
quantize: If False, this layer will pass on the input without modifying it.
"""
def __init__(self, quantize=True):
super()... |
37788b95531a84f2c36213bfcbefc0c0b2c8b8ba8402fbd7fad180f937ae460e | Python | 500 | 23 | import gym
class ScaleReward(gym.RewardWrapper):
"""Scale reward by a scale factor.
Args:
env: Env to wrap.
scale (float): Scale factor.
Attributes:
scale: Scale factor.
original_reward: Reward before casting.
"""
def __init__(self, env, scale):
super()._... |
55fa0e40df59b5815becf0bc4b4018fc825eb18d029da7b30550d0862f1ac84b | Python | 500 | 18 | """
========================================
Adaptive Leaky Integrate and Fire (ALIF)
========================================
:class:`~sinabs.layers.ALIF` layer with an adaptive threshold based on the output spikes.
"""
import torch
from utils import plot_evolution
import sinabs.layers as sl
const_current = torch.o... |
5d361b8f35d0727a67fb35c8422a0c9cd4a0b38e85cb6d10f08a9473c0a43b85 | Python | 500 | 8 | import case3; case3.EXT=True
from case3 import *
import time,os,warnings; warnings.filterwarnings("ignore"); t0=time.time(); fn="c3_par.json"
d=json.load(open(fn)) if os.path.exists(fn) else {"true":[],"est":[]}
while len(d["true"])<int(sys.argv[1]) and time.time()-t0<230:
p=TRUE+np.r_[rng.normal(0,0.35,9),0,0]; dd... |
b3f1586d1adab69d7cddf33d1e246705589b2e8bf07899e35ff9f38dba818875 | Python | 500 | 19 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Read HCP1200 subjects with resting-state fMRI scans based on the directory
structure extracted from DataLad.
Output:
'r_allHCP_subjects.txt' Resting-state HCP subjects are listed at each line.
"""
import os
import numpy as np
#Extract all available subject files.
... |
5d7c454f4100ca1df03bd950e24324e6d0aaf3730094d93d82b3977b87602f85 | Python | 501 | 17 | {
'grid_search': {
'__factory__': 'palladium.fit.with_parallel_backend',
'estimator': {
'__factory__': 'sklearn.model_selection.GridSearchCV',
'estimator': {'__copy__': 'model'},
'param_grid': {'__copy__': 'grid_search.param_grid'},
'scoring': {'__copy... |
4f40c7adcf377428b7201d486e605fb89bc42b09294ea0aadd16da87e9377509 | Python | 503 | 17 | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
raise OptionalDependencyNotAvailable()
except Opt... |
02a7f5dd39992d140e8ed47058acfcb1dfddba48b7a3fda0b3a65abba2d12f19 | Python | 504 | 23 | import pandas as pd
import pytest
from data_profiling import ProfileReport
@pytest.fixture
def correlation_data_num(spark_session):
correlation_testdata = pd.DataFrame(
{
"test_num_1": [1, 2, 3, 5, 7, 8, 9],
"test_num_2": [11, 12, 13, 15, 17, 18, 4],
}
)
return sp... |
17d490aa2e6023a89c72318997d6227e9511fec987280c26a5c6ca69ceb406d6 | Python | 505 | 15 | from samri.pipelines.reposit import bru2bids
BRU_DIR = '/usr/share/samri_bindata/'
# The `20180730_053743_6587_1_1` measurement is acquired with the (incorrect) Bruker ParaVision Prone (i.e. supine) orientation.
# This is relevant for testing the flip_if_needed function
def test_bru2bids(tmp_path):
bru2bids(BRU_DIR,... |
4e90f675378121e6aacdc1aebabeae00567ee8cd5a81b0871a91ec40f5d4272f | Python | 505 | 8 | from agent.data.entities.config import CTT3FR_ITEM_FILTER, CTT3FR_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="text", _c... |
0df7d7e34983cd64cc0b26212ae76021e061b6a4d2a624a4573700311b9ef1d6 | Python | 506 | 17 | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class MidiProcessor(metaclass=DummyObject):
_backends = ["note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["note_seq"])
@classmethod
def from... |
2c3e525d68685ac7d613087e79871953061015c9547539c10b14412ec565cf07 | Python | 507 | 8 | from agent.data.entities.config import CTT3_TEST_ITEM_FILTER, CTT3_LABEL
from agent.classifiers.data.tr_ffnn_fd_dataset_mgmt import get_datasets_from_filter
def get_datasets(pretrained_model, max_lenght, binary_classifier=False,
_expand_tokenizer=False, _use_saved_model=True, _tokenizer_type="text", _... |
30a2967a7158df7cbffd72d5ce2092b8734bbc4c762cab1a48c9e8b1a42801c4 | Python | 507 | 21 | from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0109_replica_job_type"),
]
operations = [
migrations.RenameField(
model_name="task",
old_name="consensus_replicas",
new_name="initial_replicas"... |
d43af0d2bad2ae4505b3c74411a061cc008bfdf78dee69bf5e76b7f09da33c1e | Python | 507 | 5 | # -*- coding: utf-8 -*-
import sys
from mozaik.meta_workflow.parameter_search import CombinationParameterSearch,SlurmSequentialBackend,LocalSequentialBackend
import numpy
#CombinationParameterSearch(LocalSequentialBackend(),{'sheets.exc_layer.ExcExcConnection.weights' : numpy.linspace(0.0,0.006,5),'sheets.inh_layer.Inh... |
e8b40f0fed1188627b7d2f43addb18b75664983cad687a1a64fd4a9322513e1d | Python | 507 | 18 | # copy svimmer merged calls for each sample to my folder
import os
samples = [i.strip() for i in open("samples.txt")]
fdir = "svimmer_result_directory"
odir = "union"
if not os.path.exists(odir): os.mkdir(odir)
w = open("vcf_list_union", 'w')
for i in os.listdir(fdir):
if i[:-7] in samples:
os.system("cp ... |
054ac147666d5acd7afd5a1ffaa10967ee6a763abcd753bfb2697b69bc331aef | Python | 508 | 18 | import argparse
import lib.dataset_config_parser.dataset_config_parser as dcp
import sys
from typing import List
if __name__ == '__main__':
parser = argparse.ArgumentParser('Parse the results of the GLM fit grid search')
parser.add_argument('cfg_path', type=str, help='path to config')
args = parser.pars... |
ba4b5e6786a4beb1898a0e93f8033919d380a6a05a57d8a1dc304e3cddc9eaea | Python | 508 | 10 | from case1 import *
import time,os; t0=time.time()
models=["M0","M1","M2","M2c","M3"]; gi=int(sys.argv[1]); R=int(sys.argv[2]); gm=models[gi]
fn=f"c1_gen{gi}.json"; rows=json.load(open(fn)) if os.path.exists(fn) else []
while len(rows)<R and time.time()-t0<240:
D=design(); p=TRUE.copy()
if gm=="M0": p[[1,4,5]]=... |
7d8212d444b3ea8da75e9531c3df8f105940f40205a1ae626b3f642e18a0509e | Python | 509 | 19 | # Generated by Django 4.1.6 on 2023-02-06 03:53
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("infer", "0004_alter_organism_ncbi_taxon"),
]
operations = [
migrations.RemoveField(model_name="weights", name="path",),
migrations.Ad... |
935d2466d37883aa1d644113904b0d0ba925b1d85463c1655df4c30c71524eed | Python | 510 | 19 |
import os
import argparse
import easygrid
import pdb # DFR add 8-31-21
testOutputPath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_08_27_GWAS_work_nobackup/testEasyGridDir/"
pipeline = easygrid.JobManager(testOutputPath)
testOutFile = testOutputPath + "helloWorld.txt"
testCommand = 'e... |
d6b0918b318cf93200e51e4d5d481e6023dd7dc8899ca3ce3ea79e1d24d66590 | Python | 510 | 21 | """This file add the decorator on the DataFrame object."""
from pandas import DataFrame
from data_profiling.profile_report import ProfileReport
def profile_report(df: DataFrame, **kwargs) -> ProfileReport:
"""Profile a DataFrame.
Args:
df: The DataFrame to profile.
**kwargs: Optional argumen... |
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