sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2d2d64839b3dc9b51e782e45f864d8d2bcdb9a4ebd13d37d34e5439854a30a7d | Python | 2,084 | 54 | from __future__ import print_function, unicode_literals, absolute_import, division
import numpy as np
from scipy.ndimage import zoom
from .care_standard import CARE
from ..data import PercentileNormalizer, PadAndCropResizer
from ..utils import _raise, axes_dict
class UpsamplingCARE(CARE):
"""CARE network for co... |
7da90b06fefcf70746ccc9748d5247cc03f3497218067cb42384e2eb04012029 | Python | 2,084 | 67 | import torch
EPS = 1e-12
def robust_zscore_tensor(x):
x -= torch.median(x, dim=0, keepdim=True)[0]
x /= torch.median(torch.abs(x), dim=0, keepdim=True)[0] * 1.4826 + EPS
x.clamp_(-3, 3)
x -= torch.mean(x, dim=0, keepdim=True)
x /= torch.std(x, unbiased=False, dim=0, keepdim=True) + EPS
return ... |
8a5e14424964060cc75f6eb1e075f710aa1091bb5021b21555039710abe9e4e0 | Python | 2,084 | 71 | """Benchmarking the cfaidx module. Usage::
pytest benchmark/faidx_bench.py
"""
import os
import pysam
from TestUtils import BAM_DATADIR
def iterate_over_fastx(fn, persist=True):
return len(list(pysam.FastxFile(fn, persist=persist)))
def iterate_over_fastx_as_file(fn):
with open(fn) as inf:
return... |
d38cd44728ea8c321e2e9eae8bf23f1dd0b6e7d5b0e9454064b9808948d1def4 | Python | 2,084 | 49 | """Code related to handling annotations."""
import sys
import types
import typing
from inspect import isclass
def is_none_type(value: typing.Any) -> bool:
"""Check if the given value is a NoneType."""
if sys.version_info < (3, 10):
# raise Exception('below 3.10', value, type(None))
return val... |
d5fc152357f46c00c2ea5d245ab5f01609eae66b58ad8d053b4339f4278c96d9 | Python | 2,084 | 61 | """grind-p3 T11.10: validation-copy attr restoration refuses misaligned trees.
``_restore_simple_plain_attrs_on_copy`` zipped ``source.modules()`` against
``copied.modules()`` positionally with no arity check, so a deepcopy that
adds or drops a submodule (a ``__deepcopy__`` hook, lazy child
materialization) silently s... |
f5ad3b253b960787b8ee78088745ccfdd8aee391e867b3382378122114fd428b | Python | 2,084 | 63 | import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
import os
def build_net(ds_kwargs={}, return_metamer_layers=False):
# We need to build... |
a1153f56158996866c23ecea01a2c0f3d5e055de6d432f798d2312002a4b67f0 | Python | 2,085 | 64 | """drop view
Revision ID: 3356bc2ecfc4
Revises: 460d0ecd1dd8
Create Date: 2023-04-10 15:06:01.037416
"""
from alembic import op
# revision identifiers, used by Alembic.
revision = "3356bc2ecfc4"
down_revision = "460d0ecd1dd8"
branch_labels = None
depends_on = None
view_name = "collection_job_state_summary_view"
a... |
deeb254ea83604a1f3a398a74282c65993a8e1772f6c670da4ad47a4964dc99b | Python | 2,085 | 83 | <<<<<<< HEAD
"""
Linear model from github.com/clinicalml/omop-learn
"""
import numpy as np
import scipy.sparse
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
=======
from sklearn.linear_model import LogisticRegression
from... |
6f8a846beb3af8cb49a734b46cdcf4dea9c1472dcb9bddc97dea6c438e147632 | Python | 2,086 | 61 | """R10-15: sparse-runnable ``tensor_slots`` slot_id uniqueness was enforced
nowhere, while every sibling namespace (call ids, witness families, registry
ids, boundary positions) dup-checks. A duplicated slot_id parsed clean, then
last-wins slot indexes disagreed with list-iterating consumers and the binder
dropped one ... |
a84dd6df8a4f327234e50d58b1eb9fa86d4c25e9dec0f9e5058193da3c32acfb | Python | 2,086 | 64 | import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
import os
def build_net(ds_kwargs={}, return_metamer_layers=False):
# We need to build... |
e76921a865faf2ff4665d58658dc111e87860b3b4d2fbde75f88f2e555a45395 | Python | 2,086 | 53 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (leoxiaobin@gmail.com)
# ------------------------------------------------------------------------------
from .build import make_dataloader
from .build impor... |
f68866a2a0a5a0570e8b35a80a5d6baebde6b102794d38889ba30b2a56c0f1c8 | Python | 2,086 | 57 | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 28 16:28:17 2023
@author: cbri3325
"""
import SimpleITK as sitk
import pandas as pd
import os
from ImageAnalysisFunctions import getMaxRoi,getMeanRoi
data_supradir = 'Path to data directory'
subjs_path = [ f.path for f in os.scandir(data_supradir) if f.is... |
0dfaae463801c593f1cfb85e6c87dfae775993bb353781b6c05676639363262e | Python | 2,087 | 59 | from __future__ import annotations
from collections.abc import Generator
from functools import reduce
from typing import Any, Optional
def find_path(path: str, dictionary: dict) -> Optional[Any]:
"""Lookup a path e.g. 'a.b' in dict: {'a': {'b': 2}} will return 2.
If the path doesn't exist, None is returned
... |
21f5c2f17d7f1a701abcf4193d02380be20eb55edc5957042d2afd4538239442 | Python | 2,087 | 64 | import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
import os
def build_net(ds_kwargs={}, return_metamer_layers=False):
# We need to build... |
147d40391c89579d044bf1fc48ad01db4ad35ec5ec516fe1fab8ee0102e85ec8 | Python | 2,088 | 77 | import os
import sys
import math
import pickle
from typing import Dict, List
from dataclasses import dataclass, field
import numpy as np
import pyprojroot
code_root_dir = str(pyprojroot.here())
pack_dir = f"{code_root_dir}/package/python"
sys.path.insert(0, pack_dir)
from leiden import LeidenSum, ScatterPlot
from leid... |
6505413c925b33cc6d723f07937e9f804507c30c37a7401a14f4443774fbaf40 | Python | 2,088 | 70 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightning as p... |
c7155883101aa3accbee78fbf1e71cd29a6e2ba2265b5ec1cae26302e6df6ad2 | Python | 2,088 | 64 | import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
import os
def build_net(ds_kwargs={}, return_metamer_layers=False):
# We need to build... |
f51eb9f4249c9d895560af2ab936aa8435acd5fe21c123d062b5d7d4b7efee12 | Python | 2,088 | 64 | """
core.cohort_pool — Cohort-pool composition helpers.
The paper uses a 6-cohort pool (ds004504, BrainLat, ds003775, LEMON,
figshare 5450293, ds006036) split into three roles:
- Selection cohorts: ds004504, BrainLat, ds003775 — used for in-pool
LOCO and pipeline development.
- Held-out: figshare 5450293 — ne... |
084eb75c1f90871a50542ed7768c6b0ef77667e57f54a77d84f007d0b997b83b | Python | 2,089 | 69 | # This example requires the following dependencies to be installed:
# pip install lightly
import pytorch_lightning as pl
import torch
import torchvision
from torch import nn
from lightly.loss import NTXentLoss
from lightly.models.modules import SimCLRProjectionHead
from lightly.transforms.simclr_transform import SimC... |
3557180eff32c4c005320cff99e0144cd1d90d9e3dc669f86109d39b7654622b | Python | 2,089 | 55 | """Liveness killers for the streamed-bundle finalization step (executor step 18).
r7 R74 executor-family survivor, found by the family's own first sample
campaign: forcing ``_should_run_step_18`` to ``False`` (the streamed bundle
never finalizes) survived the ENTIRE 472-test arming suite -- no suite file
captured with... |
3ea4ad1acb6b5ba8aa91e5338a2439ec5760d9c2fe559cb350c8e1cf37e7b1f5 | Python | 2,089 | 60 | #!/usr/bin/env python
import argparse
import os
from Bio import SeqIO
parser = argparse.ArgumentParser()
parser.add_argument("--records", type=int, default=None)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--num-chunks", type=int, default=0)
parser.add_argument("input_file")
args = pa... |
90c18d7f4cc439332eaf38cfc4b7e4e5254ba0306b27797b566ef14162890113 | Python | 2,089 | 82 | from pathlib import Path
import typer
def main(
store: Path,
batch_size: int = 128,
max_batches: int = 100,
load: bool = False,
):
import gc
import logging
import sys
from time import perf_counter
import numpy as np
import seqdata as sd
import seqpro as sp
import torc... |
c0bfa5eaf0cf9e3f6fe298bc6581138b8f8896735caee0f19a69fb653647742b | Python | 2,089 | 53 | """Regression tests for two capture-robustness fixes surfaced by real models.
1. Integer/bool-dtype ``nn.Parameter`` (e.g. a fixed lookup buffer declared as
``nn.Parameter(torch.arange(n), requires_grad=False)``) is legal PyTorch and
never gradient-capable. Capture prep used to force ``requires_grad = True`` on
... |
5f0f7fd4ca12dacf12f2988133f4472dec3b2c7b2b78fc83b7f4688e6cb8278d | Python | 2,090 | 64 | from bioseq2seq.bin.transforms import CodonTable
import math
from Bio.SeqUtils.CodonUsage import CodonAdaptationIndex
from CAI import CAI as external_CAI
class CAI:
def __init__(self):
self.table = CodonTable()
self.rscu = None
def parse_entry(self,entry):
if any([c == ' ' for c ... |
e3204e33bfd5028a967774c9cc8c63812e2e360391632bd78335ed82cc0452e8 | Python | 2,090 | 58 | import pytest
import gym
from gym.wrappers import RecordEpisodeStatistics, VectorListInfo
ENV_ID = "CartPole-v1"
NUM_ENVS = 3
ENV_STEPS = 50
SEED = 42
def test_usage_in_vector_env():
env = gym.make(ENV_ID, disable_env_checker=True)
vector_env = gym.vector.make(ENV_ID, num_envs=NUM_ENVS, disable_env_checker=... |
ed73fdf29d0ee92df208410f1cba86036a917c27c798b6b2e961c206a3fede82 | Python | 2,090 | 57 | __author__ = 'cenwan'
# The Python implementation of Classifier Two-Sample Tests (CTST) for selecting the optimal synthetic protein feature samples.
import numpy as np
from sklearn.model_selection import LeaveOneOut
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
GOTermI... |
058d0872608191b1a58243675c367132cd3e09a9ae2d19e6e7e41fdca8c01401 | Python | 2,093 | 52 | """
Apply bandpass filters to the data.
"""
import sys
sys.path.append('..')
import argparse
import mne
from config import fname, n_jobs
# Be verbose
mne.set_log_level('INFO')
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', type=int, metavar='sub###... |
061d67ed6dd67b637cbb544858fc84e5040613b7ac9cda1438b2d108bf19c9ee | Python | 2,093 | 50 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for d-iteration"""
import unittest
import numpy as np
from sknetwork.data import house, karate_club
from sknetwork.data.parse import from_edge_list
from sknetwork.data.test_graphs import *
from sknetwork.linalg.operators import Regularizer
from sknetwork.linalg.... |
2673595af29442ea80c0787ae0b083df3a6bcd62154045c2853a365061f356aa | Python | 2,093 | 69 | from __future__ import absolute_import
import logging
from .req_install import InstallRequirement
from .req_set import RequirementSet
from .req_file import parse_requirements
from pip._internal.utils.logging import indent_log
__all__ = [
"RequirementSet", "InstallRequirement",
"parse_requirements", "install... |
1a85c90edb8065458e3c62693d8e09cebdc0d088211626431c2ce2738fede511 | Python | 2,094 | 59 | """
Integration tests for registry bootstrap and configuration behaviour.
These tests rely on the ensure_datasets_ready fixture (auto-applied for the
integration suite) so that the Schleyer processed datasets exist before the
registry is exercised.
"""
from __future__ import annotations
from pathlib import Path
imp... |
8e81eabe72b00b5a5fe83c189e3273e35530c5e936c290f132941d6a40dad929 | Python | 2,095 | 57 | import os
import warnings
import h5py
import numpy as np
import scanpy as sc
from scbig import run_scbig, preprocess, read_data, setup_seed
warnings.filterwarnings('ignore')
for dataset in ['10X_PBMC','mouse_bladder_cell','mouse_ES_cell','human_kidney_counts','Adam','Human_pancreatic_islets','Macosko_mouse_retina']... |
38c2f9f03adce57f95063ec477bfbb184cdbeec1794437893d73f96e7a76c5f1 | Python | 2,096 | 61 | import os
import numpy as np
import sys
sys.path.append('..')
from pyAudioAnalysis import audioBasicIO as aIO
from pyAudioAnalysis import MidTermFeatures as aF
from pyAudioAnalysis import ShortTermFeatures as sF
def feature_extractor(dirs: str,
num_of_classes: int,
mid_win... |
78a9d14c07d30129a8408ec09ead14d20b805ad6f0c16e4ffe64d7ad7b119733 | Python | 2,096 | 63 | """Phase 5(b): _tag_untagged_buffers ordering relative to stack push."""
from __future__ import annotations
import torch
import torchlens as tl
class BufferConsumer(torch.nn.Module):
"""Child module that consumes a dynamically registered parent buffer."""
def forward(self, x: torch.Tensor, scale: torch.Te... |
3749bf04e67b3b2acc289da93d0e0f26706c35e257a7c16b933a18fa0b0a7486 | Python | 2,097 | 68 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
47435dbcf4509f7d8976f2b86277c269ab03c51072bb46c4ef7ad24dc763fc38 | Python | 2,097 | 77 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Marker(_BaseTraceHierarchyType):
_parent_path_str = "histogram2d"
_path_s... |
a146b167e212ca6108e52fb8784d45f4262c7b33e1bca1707afbe43ac0512308 | Python | 2,097 | 76 | import os
import numpy as np
import pandas as pd
def load_annotations(path):
D_dicts = {}
for root, dirs, files, in os.walk(path):
for file in files:
#print(file)
if file.endswith(".txt"):
open_f = open(path + '/' + file, 'r')
list_of_lists = []
for line in open... |
88abaca9ac5e076567edd3b49be44904fd037c80abe2a451a0fb405933d440da | Python | 2,099 | 70 | #!/usr/bin/env python3
import os
import dendropy
import numpy as np
import tskit
def encode(nwk_str, no_taxa, der_ls=None):
dpy_tr = dendropy.Tree.get(data=nwk_str, schema="newick", rooting="default-rooted")
dpy_tr.calc_node_ages(ultrametricity_precision=1e-01) # assuming branch length in generations
... |
b81776bb3dcc562e86bcae634c3a4735f77589dca5cbb6af74565ccd0a9df900 | Python | 2,099 | 67 | from ovseg.augmentation.myRandAugment import torch_myRandAugment
from ovseg.augmentation.MaskAugmentation import MaskAugmentation
from ovseg.augmentation.GridAugmentation import torch_inplane_grid_augmentations
from ovseg.augmentation.GrayValueAugmentation import torch_gray_value_augmentation
import torch.nn as nn
# ... |
cbd6cfda54b404bbfa0ca96c7a20f7abf7c40c409cb2382129e55466749ab46c | Python | 2,099 | 81 | #!/bin/python3
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torch import nn
from torch import optim
from NNucleate.dataset import GNNDataset
from NNucleate.models import initialise_weights, GNNCV
from NNucleate.training import train_gnn_unified, evaluate... |
5287f0694af44c5a6d004f0b80935c0dc87d4ebdf6d5eb509826b5e70bf7e6b0 | Python | 2,102 | 68 | from __future__ import absolute_import
from setuptools import setup, find_packages
from os import path
_dir = path.abspath(path.dirname(__file__))
with open(path.join(_dir,'csbdeep','version.py')) as f:
exec(f.read())
with open(path.join(_dir,'README.md')) as f:
long_description = f.read()
setup(name='csbd... |
7f9648bee3f618e5115603d111b95a8dfe826c076a88fb9a943fdaac30182786 | Python | 2,102 | 81 | """Generate the image lists for data loaders."""
import os
import sys
from os import path as osp
from ..common.logger import logger
def gen_list(
data_root: str,
data_dir: str,
list_dir: str,
phase: str,
list_type: str,
suffix: str = ".jpg",
) -> None:
"""Generate the list."""
phase_... |
abd2a1017139b60c9a353caac36e0362d0ce600d7b967c72a0d11bc728ffcb21 | Python | 2,102 | 62 | # import pandas as pd
# import numpy as np
# import osmnx as ox
# from ..utils import constants, gislib
#
# distance = gislib.getDistance
#
#
# def route(tdf, G=None, index_origin=0, index_destin=-1):
# """Routing.
#
# Create a route on Google Maps between two locations.
#
# Parameters
# ----------
# ... |
2b8c08a7ee126782f56c322b3c2d04063d2f0955aa9ec45a30dac223413ec296 | Python | 2,103 | 59 | from typing import Union, Optional, Tuple, Collection, Sequence, Iterable
from anndata import AnnData
import numpy as np
from scipy.sparse import issparse, isspmatrix_csr, csr_matrix, spmatrix
import scanpy
def filter_genes(
adata: AnnData,
min_counts: Optional[int] = None,
min_cells: Optional[int] = None... |
beebbe4e3493650879e107b3ba50c5ac6058c3933049e23822133001ff0714ac | Python | 2,103 | 62 | """
Author: Derek van Tilborg -- TU/e -- 24-05-2022
Basic 1-D Convolutional Neural Network based on the architecture of [1]
[1] Kimber et al. (2021). Maxsmi: Maximizing molecular property prediction performance with confidence estimation using
SMILES augmentation and deep learning
"""
import torch
import torch.... |
f15546d83c4b1090e68a8ed3ac715d8059a21605efbaf91878f327018be5318f | Python | 2,103 | 62 | """Add pg_notify to history audit triggers
Revision ID: b8d5e2f9a1c7
Revises: f5e9e4bca542
Create Date: 2026-04-17 14:30:00.000000
The SSE-based history update pipeline (see `managers/history_audit_monitor.py`)
depends on a PostgreSQL LISTEN on the `galaxy_history_update` channel. For
existing installations, trigger ... |
0ae9656ab286c02915159f6378eb6317e8d538aa6802d97ca37beb123b1b3e86 | Python | 2,104 | 69 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature_extractor_spec"] = dict(
type="ResidualStack",
depth=3,
)
msp.arch... |
9323dede5c6d7890aae49e24802b2216dde25b35f437aa1274790478d7eee1c9 | Python | 2,104 | 66 | import os
import threading
import time
import logging
from src import sba_api
from src.utils import *
import src.api_server
from src.api_server import run_server
from src import config
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger... |
5b3252a1e6ac16db6403d2e438e72d50c72afc582ebed00f09f0f7e725813a27 | Python | 2,105 | 62 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Multiprocessing helpers."""
import torch
def run(
local_rank,
num_proc,
func,
init_method,
shard_id,
num_shards,
backend,
cfg,
output_queue=None,
):
"""
Runs a function from ... |
c5d944266817d769710ecc2caf7a1c1a9c0ed4eb15f77541c2c70e7d3ac35a35 | Python | 2,105 | 66 | import sys
from robustness import datasets
from robustness.attacker import AttackerModel
from robustness.model_utils import make_and_restore_model
from model_analysis_folders.all_model_info import IMAGENET_PATH, MODEL_BASE_PATH
def build_net(ds_kwargs={}, return_metamer_layers=False):
# We need to build the datase... |
e5a997d0a63d72e3460b72a04f271ab2441a3771327ede719280a29cef3e6575 | Python | 2,105 | 60 | from matplotlib import gridspec
import matplotlib.pyplot as plt
from pycobi import ODESystem
# preparations
##############
# plot settings
plt.rc('text', usetex=True)
plt.rcParams['figure.constrained_layout.use'] = True
plt.rcParams['figure.dpi'] = 400
plt.rcParams['figure.figsize'] = (12, 5)
plt.rcParams['font.size'... |
24ef1e42f5cf0bcaa140dc6de72e200fb688bb77eee5c02b04f3694f69af2657 | Python | 2,106 | 71 | #!/usr/bin/env python
#=========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://... |
3e5c11022aa2e5732e745760eb8961c85a34bd1e8478740d928c4d43d465bba8 | Python | 2,107 | 49 | import unittest
import numpy as np
from Metrics.src.image_quality_estimation import IQS
class TestIQS(unittest.TestCase):
def setUp(self):
# Create a reference image for testing
self.ref_image = np.ones((10, 10))
# Create an instance of the IQS class
self.iqs = IQS(pixel_max=255, ... |
b1357f774441f4ceb3b1722f2d9ed553ab5e51f5f720ad484f29c3a71312dbdf | Python | 2,107 | 61 | """Heartbeat service.
This is the internal thread responsible for sending heartbeat events
at regular intervals (may not be an actual thread).
"""
from celery.signals import heartbeat_sent
from celery.utils.sysinfo import load_average
from .state import SOFTWARE_INFO, active_requests, all_total_count
__all__ = ('Hea... |
b41c72d1a8366fcdd726aac2cb090f4eec539275f5eb9e5ba1e85cf2fa4f6126 | Python | 2,107 | 75 | '''=========================================================================
'
' Copyright NumFOCUS
'
' Licensed under the Apache License, Version 2.0 (the "License");
' you may not use this file except in compliance with the License.
' You may obtain a copy of the License at
'
' http://www.apache.org/licen... |
b3e9422e4d9b151d8614307cfa1a9af2f52187fa492dd6e66ff6449e32c117b6 | Python | 2,108 | 63 | """The ``celery migrate`` command, used to filter and move messages."""
import click
from kombu import Connection
from celery.bin.base import CeleryCommand, CeleryOption, handle_preload_options
from celery.contrib.migrate import migrate_tasks
@click.command(cls=CeleryCommand)
@click.argument('source')
@click.argumen... |
cec395c3164f9b134198239abff44bb06e66234c20c0f26212ccb3bb1b879332 | Python | 2,108 | 56 | from ptic import pmi_tfidf_classifier as ptah
import numpy as np
import pandas as pd
from sklearn import metrics
import torch
from ptic import neuro_ptic
np.random.seed(42)
path = "doc/datasets/"
# Data is taken from here
# https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
data_raw = pd.read_cs... |
e835bb04ce957a81bb27f17e13370613c95aff67cc40c8ecd1735937762870ec | Python | 2,108 | 67 | #!/usr/bin/env python
import sys, csv, gzip
import json
import numpy as np
class MatFlipper(object):
infile = ""
genesfile = ""
outfile = ""
botstrand = []
_coords = []
_matrix = []
def parseArgs(self, args):
self.genesfile = args[0]
self.infile = args[1]
self.... |
f55a24416fdcc34b97b6ae42077c0c6df4ff0f485939ac1e283fa274be8e1960 | Python | 2,108 | 73 | """Plot History from Training"""
import os
class TrainingHistory(object):
r"""Training History Record and Plot
Parameters
----------
labels : list of str
List of names of the labels in the history.
"""
def __init__(self, labels):
self.l = len(labels)
self.history = {}
... |
0a1a817b577e457cd0efdb5ed34186f0ff4d6c0564541260fbac7ff087d31149 | Python | 2,109 | 60 | import pathlib
import os
import logging
import time
import numpy as np
def setup_savedir(exp_name, code_backup=True):
save_dir = '../experiment/%s' % exp_name
pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
if code_backup:
pathlib.Path(f'{save_dir}/code').mkdir... |
ad3d078b07d25a56a14b8d89a8eb5ce071ffd9cb2bd692a8e40800acbd9573cf | Python | 2,109 | 63 | # Demonstrates interoperability of the nfp preprocessing methods with the spektral GNN
# library (https://graphneural.network/). Specifically follows the `DisjointLoader`
# example for QM9 prediction shown here, https://github.com/danielegrattarola/spektral/
# blob/master/examples/graph_prediction/qm9_ecc.py
import pa... |
e85ec558323bacd8774279c8553872b48866af74f5ffb9c16e96068e2ce14d42 | Python | 2,109 | 62 | """2. Test with PSPNet Pre-trained Models
======================================
This is a quick demo of using GluonCV PSPNet model on ADE20K dataset.
Please follow the `installation guide <../../index.html#installation>`__
to install MXNet and GluonCV if not yet.
"""
import mxnet as mx
from mxnet import image
from mx... |
487187ab5e28010c82a02a038c15e95178c4770b5222dc38dc761c75112b6095 | Python | 2,110 | 48 | import numpy as np
import pandas as pd
# 因为原始数据association只包含有关联的circRNA-disease对，所以要加上随机抽一些数据作为没关联的circRNA-disease对，这里已经在excel处理过
trainset = pd.read_csv('data/trainset.csv', header=0, encoding='gb18030').values
meshdisname = pd.read_csv('data/Mesh_disease.csv', header=0, encoding='gb18030')
targetdisease = pd.read... |
6892f402337b587f03da5be09764ce3f5cdfda67f7ce28e0b9c6a7ae709b60fd | Python | 2,110 | 75 | from functools import lru_cache
import os
import pickle
import tqdm.auto as tqdm
import numpy as np
def read_fasta_strings(fasta_path):
with open(fasta_path) as f:
lines = []
for line in f:
assert line[-1] == "\n"
lines.append(line[:-1])
lines.append(">")
resu... |
9c2046b6fe54fd092266a935538feaf0aaab3552b50d58d02d7318c544894f6b | Python | 2,111 | 63 | import pandas as pd
from .gtex_data_table import gene_expr_data, transcript_data
def produce_splice_table(limit=None):
"""
Produces the hg38 splice table, filtered for genes with transcripts. Has paralog
information added to the table.
Parameters
----------
limit : int, optional
If n... |
9ce1e220b64b05615d483b7df6e1b1d56812a839b1d574baa8f795a2e17bb8a5 | Python | 2,111 | 52 | from typing import List, Optional
from carbonserver.api.errors import NotAllowedError, NotAllowedErrorEnum, UserException
from carbonserver.api.infra.repositories.repository_experiments import (
SqlAlchemyRepository as ExperimentSqlRepository,
)
from carbonserver.api.schemas import Experiment, ExperimentCreate, Us... |
b889e19792509e9a37529c92b7b4e352c07390a6f14aad14bcd17accf09308cb | Python | 2,111 | 66 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import pytorch_lightning as p... |
57d24f0f0275e946cf8394655c574503ae9f232ff638d5d4251877c6391dfb31 | Python | 2,112 | 72 | import itertools
import os
from pathlib import Path
import nibabel as nib
import numpy as np
def load_nii(file: Path) -> np.ndarray:
"""
Loads the data from a NIfTI file.
Parameters:
file (Path): The path to the NIfTI file.
Returns:
np.ndarray: The data from the NIfTI file.
"""
... |
9a0ed27bd245b29ad42e6416b316ecbd7292b87809b95636e16509e48b702725 | Python | 2,112 | 67 | import json
import re
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
font = {'family' : 'Times',
'weight' : 'normal',
'size' : 12}
partial_results = []
for filename in ["./storage/predictability_repeated_august19d.json", "./storage/predictability_repeated_august19e.json"]:... |
62de00064d069c2280b4156df7baf3bd5a42cf07d734637e889671b78f629c00 | Python | 2,113 | 81 | """
Tests to make sure the dataload module isn't busted
"""
import os
import numpy as np
import eugene as eu
from pathlib import Path
from eugene.dataload import SeqData
from pathlib import Path
HERE = Path(__file__).parent
dataset_dir = f"{HERE}/../eugene/datasets/janggu_resources"
ref_file = "sample_genome.fa"
roi... |
4df0877c892cc1ae5509cfffe0d380cd62326a9ec94a25d84ea33ddb68e87ff8 | Python | 2,114 | 64 | """
# > Script for measuring quantitative performances in terms of
# - Structural Similarity Metric (SSIM)
# - Peak Signal to Noise Ratio (PSNR)
# > Maintainer: https://github.com/xahidbuffon
"""
## python libs
import numpy as np
from PIL import Image
from glob import glob
from os.path import join
from ntpath imp... |
c347988f2d00498ac26393da2be7ec47815e0cb4e237f81b727885b19b72ad25 | Python | 2,114 | 56 | ######################################################
# This file prepares the NN269 data set for use in #
# CMKN experiments. The script assumes that positive #
# and negative sequences are provided in different #
# files.
# #
# Author: Jonas Ditz ... |
c57b3647de2dc4796b5b3e817e1c1adc1894980971094903546263a059e77f42 | Python | 2,114 | 47 | import numpy as np
import lifelines
import lifelines.statistics
from lifelines import KaplanMeierFitter, plotting
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredText
def plot_survival_function(surv_prob_median, surv_prob_lower=None, surv_prob_upper=None, label='', max_t... |
f32dc9c175220a07db4a8c390c91a561c1461cef32579a04f3dcbb06f9799a12 | Python | 2,114 | 77 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature_extractor_spec"] = dict(
type="ResidualStack",
depth=3,
)
msp.archi... |
118cb4fd294a719b7460ec1299552a43de2a1dddabb28c6d5b21f7a51c33e6e4 | Python | 2,115 | 63 | """Provides functionality to identify corrupt images in a directory.
This module helps users identify corrupt or unreadable image files within a specified
directory. It uses parallel processing to efficiently scan through large collections
of images.
"""
# Copyright (c) 2020. Lightly AG and its affiliates.
# All Righ... |
2670c9046aace874cdb6e746b9085e90efe9ef38a51208ecd3c52bd6c3a238d9 | Python | 2,115 | 53 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
708c117ff3fd57c0ff935aa92e3976761317ea848e1214541a89606668f34359 | Python | 2,117 | 58 | import os
import argparse
import logging
import autogluon.core as ag
from gluoncv.auto.tasks import ObjectDetection
from gluoncv.auto.estimators import FasterRCNNEstimator
from d8.object_detection import Dataset
os.environ['MXNET_ENABLE_GPU_P2P'] = '0'
if __name__ == '__main__':
# user defined arguments
par... |
968af95558bd1766dab322997905820c7b44c8f1df22a7b19f451d1116fb8d4b | Python | 2,117 | 79 | #!/usr/bin/env python
import tempfile
import xml.sax as sax
import readfile
class DictHandler(sax.handler.ContentHandler):
"""
Parse XML files with AID/CID/SID as key and mappings to several other IDs
"""
def __init__(self, key, value):
self.ergebnis = {}
self.schluessel = ""
... |
3431156741f30f4c2fb37da649f0f3cd2d89afaee6a653de9b98ac71102f72e0 | Python | 2,118 | 42 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
7c7c374cf6102fc26fa215e68ab4c4b72efa4b50c91aee7ff884a44298155b4f | Python | 2,118 | 63 | # generated by eelbrain/scripts/make_example_tests.py
import importlib.util
import logging
import os
from pathlib import Path
import re
import mne
import pytest
from eelbrain import configure
from eelbrain.testing import hide_plots, working_directory
DATASETS = {
'mne_sample': bool(mne.datasets.sample.data_path... |
3bc091dad45466c25250c1a5a4e2b984f58dd231ab23f40275a315b25de6667b | Python | 2,120 | 63 | """3. Test with DeepLabV3 Pre-trained Models
======================================
This is a quick demo of using GluonCV DeepLabV3 model on ADE20K dataset.
Please follow the `installation guide <../../index.html#installation>`__
to install MXNet and GluonCV if not yet.
"""
import mxnet as mx
from mxnet import image
f... |
416cd9dbda34fa3a4c88300bd4284d80b419314ef3a3e66f659c04bc5ca48b78 | Python | 2,121 | 58 | import torch
import torch.nn as nn
from modular_splicing.motif_names import get_motif_names
from modular_splicing.utils.io import load_model
class NeuralFixedMotif(nn.Module):
"""
Uses a folder full of Neural fixed motifs as a fixed motif model.
The folder should contain a file for each motif, named by ... |
205a62be5b843896b25c0502d74d968b171a3870157a5ec844ac18ba1f6a7bb5 | Python | 2,123 | 66 | from collections import defaultdict
import json
from modular_splicing.utils.construct import construct
from .sources import (
read_rbns_motifs,
read_rbns_v2p1_motifs,
read_rbns_functional_motifs,
read_rbns_v2p1_functional_motifs,
rbns_rnacompete_motifs_deduplicated,
rna_compete_motifs_for,
)
... |
c1aebc6f587e4da355a4224d2f4e322e2e3bc86808ae5fd2cecb0bea65f1baf0 | Python | 2,123 | 68 | from hydra import compose, initialize
from torch.utils.data import DataLoader
from torchvision.datasets.samplers import DistributedSampler
from data.dataset_3d import Dataset3D, KFoldDataset3D
def get_dataloader3d(cfg, finetune, *modes):
res = list()
for mode in modes:
dataset = Dataset3D(cfg.dataset... |
14f8c18611890659234f52b99bd7262ddd4e980094ef5bc589c3774ce9aadab3 | Python | 2,124 | 61 | from __future__ import division
import unittest
import numpy as np
from wyrm.types import Data
from wyrm.processing import remove_epochs
from wyrm.processing import swapaxes
class TestRemoveEpochs(unittest.TestCase):
def setUp(self):
ones = np.ones((10, 5))
channels = ['ca1', 'ca2', 'cb1', 'cb... |
7ccd3d7e2efc2cf269b920430dc9d3545b44ac148ccc6f19811aad69b6dbe493 | Python | 2,125 | 65 | import argparse
import glob
import os
from mp_dip.obci_readmanager.signal_processing.tags import tags_file_writer as tags_writer
import mne
from tqdm import tqdm
import pandas as pd
from mp_dip.utils.utils import TAG_DEFS
tag_convert_dict = {'spindle': 'Spindle (wrzeciono)',
'segmentViewed': 'se... |
ae00bc451e4f26352290147831e3dfb5f35fbafcac3d8f4b7e3812966ffba3eb | Python | 2,125 | 45 | """ Diffusion weighted MRI: calculate metrics for global cyst seg"""
import pandas as pd
from statsmodels.stats.multitest import multipletests
from scripts.utils.io_utils import get_orig_imgs, get_masks
from scripts.utils.constants import DWMRI_SEQUENCES, CSV_GLOBAL_CYST_ANNOT
from scripts.utils.metrics import calcula... |
f7b77d18e9a4c6586067ececae9d32ef4b8646e0404e60090ffc9e914a4f871f | Python | 2,125 | 69 | import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class InvertedDoublePendulumEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps"... |
fdd3fd36effa5c2574f9611027b1399c6ea9c5958cb5b8fe2d112ed2fb717969 | Python | 2,125 | 76 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in April 2020
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
from typing import Union
import numpy as np
from scipy import sparse
from scipy.sparse.linalg import LinearOperator
from sknetwork.utils.check import check_format, check_square
cla... |
2784e7c5200495f75928e381841ddea41018c85b4c4421b428282b8dd0b2eb7e | Python | 2,126 | 71 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature_extractor_spec"] = dict(
type="ResidualStack",
depth=3,
)
msp.arch... |
a97fb8af8d334fe0bb82f19027e3756a38fe032c63acd821dd2e2540eec57e63 | Python | 2,126 | 68 | """
This module define a specific dataset
"""
import logging
import torch
from datasets_pre_processing.generic_dataset import GenericDataset
class ClassificationPerAminoAcidDataset(GenericDataset):
"""
This class is a dataset where we have one label per protein/example
"""
def __init__(
self,... |
016f4c9f12e70606f4a86f6c3fcfea50b205587de8736e213b58af8ce971166f | Python | 2,128 | 53 | import torch
from models.basic import DeepLearningRelayModel
from strategies.basic import MultiSiteLearningStrategy
class Relay(MultiSiteLearningStrategy):
name = 'relay'
model: DeepLearningRelayModel
def __init__(self, model: DeepLearningRelayModel, config: dict, **kwargs):
super().__init__(mod... |
08bf3fd84c8d5a0a9b5907f0c2788a39ec6c7af4e380efc70a31fbc04d6148a6 | Python | 2,128 | 61 | # Copyright 2023 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... |
1f727271d3a679aa86d2da987b6a23eac303d5c0349058c0dd0e0b28de4ff361 | Python | 2,128 | 63 | """Tests for bdpy.task.core."""
from __future__ import annotations
import unittest
from bdpy.task import core as core_module
class MockCallback(core_module.BaseCallback):
"""Mock callback for testing."""
def __init__(self):
self._storage = []
def on_some_event(self, input_):
sel... |
932c893a8319fdd485993d006022d082e6c0ce3112b3abeadbb67429a62c9079 | Python | 2,129 | 78 | #!/usr/bin/env python
"""
Standalone script to facilitate caching of required TemplateFlow templates.
"""
import argparse
import os
import templateflow.api as tf
def fetch_MNI2009():
template = 'MNI152NLin2009cAsym'
tf.get(template, resolution=1, desc=None, suffix='T1w')
tf.get(template, resolution=1, d... |
db731b4d12a9466493d16b6542c219d4d0706baf6815c2700f220c388b7e79cd | Python | 2,130 | 81 | import datetime
import marisa
import sys
time_begin = datetime.datetime.now()
keys = []
for line in sys.stdin:
keys.append(line.rstrip())
time_end = datetime.datetime.now()
print "input:", time_end - time_begin
time_begin = datetime.datetime.now()
dic = dict()
for i in range(len(keys)):
dic[keys[i]] = i
time_end ... |
477ac5b95219e075eb9fd409e708708988e217120bb4aa41a62100ddb3a1c8d5 | Python | 2,131 | 63 | """Convert BDD100K to Scalabel format."""
from typing import Dict, List, Optional
from scalabel.label.typing import Frame, Label
from scalabel.label.utils import get_leaf_categories
from tqdm import tqdm
from ..common.typing import BDD100KConfig
IGNORED = "ignored"
def deal_bdd100k_category(
label: Label, bdd... |
7d48e18f2c105d1669edc241e56bca9c7af7e2dfb03914f7efb59433337a60e0 | Python | 2,132 | 77 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Marker(_BaseTraceHierarchyType):
_parent_path_str = "histogram2dcontour"
... |
b090c4c15247928e0a9d35098e7c381098038aec58666e462ac1ec55ddce6069 | Python | 2,132 | 68 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Normalizing flow concatenating multiple normalizing flows.
"""
# =============================================... |
bb00c4d6ac2d389e6dfaed4f3eaf9245187f5f64dcda8a4160a3a00daa54180c | Python | 2,132 | 64 | """
# > Script for measuring quantitative performances in terms of
# - Structural Similarity Metric (SSIM)
# - Peak Signal to Noise Ratio (PSNR)
# > Maintainer: https://github.com/xahidbuffon
"""
## python libs
import numpy as np
from PIL import Image
from glob import glob
from os.path import join
from ntpath imp... |
600b81fe8e49d14b4775b0f2fadea2a59607e167f4e5ecdec53b3d45d4e841ee | Python | 2,133 | 42 | # Copyright (c) 2025, Tri Dao.
"""Storage order of the fp32 accumulate tiles the SM100 backward hands to its postprocess.
dQ, and dK / dV for GQA, are accumulated with bulk reduce-adds into flat fp32 buffers of
rows * hdim values per tile, in whatever order the epilogue streams them out of TMEM, and
FlashAttentionBack... |
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