sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a74d53a032361256d763cc46de91647d5beba224f11d79f635a885e87c98903b | Python | 2,552 | 77 | from __future__ import annotations
import logging
from typing import Any
import bream4
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
DEFAULT: dict[str, str] = {}
def reset_context_tag_information(device: BaseDeviceInterface) -> None:
"""
Resets the context tags set in minknow... |
6a08e7506b31bc1d563b8d67507ce0144a45e8c917010b27413896e0edc28b4a | Python | 2,553 | 75 | import tensorflow as tf
from .layers import AccumBatchNormalization
def replace_batchnorm_layers(model, accum_steps, position="replace"):
# Auxiliary dictionary to describe the network graph
network_dict = {"input_layers_of": {}, "new_output_tensor_of": {}}
# Set the input layers of each layer
for l... |
72253a59d80b2a56da7e20e118100e34d2f394af66d4e0d2b648236fde8f2569 | Python | 2,553 | 84 | """Default predicate decision semantics for fastlog."""
from __future__ import annotations
import pytest
import torch
from torch import nn
import torchlens as tl
from torchlens.fastlog import CaptureSpec, RecordingConfigError
class DefaultsModel(nn.Module):
"""Small model for default-decision tests."""
de... |
cfb1276b01bf209d27ffeeb1451196d48324b33fb6da904104bbac3d489e3a55 | Python | 2,553 | 70 | """
core.ranknorm — Inverse-normal quantile for the rank-norm transform.
The rank-norm pipeline maps each subject's feature value through the
empirical CDF of the cohort's HC subjects, then through Φ⁻¹ (the
inverse of the standard normal CDF). This module provides the Φ⁻¹
implementation used by ``proper_cv.apply_rank_... |
111d05c7270f99cbe46a8c9ed4cc5cdf5bd1b525e17ffd0d34a8ecacdabdf364 | Python | 2,554 | 71 | ############################
# it is more efficient to import this module that use it an executable
# FCStd format bearing is too complicated to select (sometime only final visiable is needed) and export
import sys
import os.path
from detectFreeCAD import append_freecad_mod_path
append_freecad_mod_path()
t... |
1db0464ffa400e69bb1ef3195516dfa409a8989b091e9a1b77366d397d8a6647 | Python | 2,554 | 77 | """
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""
import numpy as np
import torch as th
def normal_kl(mean1, logvar1, mean2, logvar... |
a910074e6634770c5256f18bb94f88e49d0b476c178deafff9775d6d7b129bab | Python | 2,554 | 82 | # make barcode dictionaries
import os
import subprocess
import sys
import pandas as pd
from collections import defaultdict
import gzip
from numpy import unique
import numpy as np
import pickle
#import HTSeq
#import pysam
#PATH = './'
PATH = os.path.dirname(os.path.abspath(__file__))
HOME = os.path.expanduser('~')
... |
558b295318e812b7f811fc75e3834d3635997354cbe1caf47ac56c6da7c58cee | Python | 2,555 | 100 | from __future__ import annotations
from dataclasses import dataclass
from larvaworld.portal.landing_registry import ITEMS, LANES, QUICK_START_MODES
from larvaworld.portal.registry_logic import compute_badges, validate_registry
from larvaworld.portal.registry_types import LandingItem, QuickStartModeSpec
@dataclass(f... |
fa7e6c5e261d1c1c0cc99e3cbbc0a2655a904372c571b0fd2dc0f56c05c3adf6 | Python | 2,555 | 89 | from typing import Dict, List
class Parameters:
def __init__(
self,
kfolds: int = 3,
batch_size: int = 64,
epochs: int = 5,
decay: float = 1e-5,
learning_rate: float = 0.0005,
atom_features: int = 32,
bond_features: int = 32,
mol_features: in... |
5591d5798b0f176346dcd727c16161f14f0506c394abdb07d91159ed32fe96e7 | Python | 2,556 | 74 | # MIT License
# Copyright (c) 2018 Adrian Wolny (https://github.com/wolny/pytorch-3dunet)
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... |
6b04138e54edd7affc47e401215b847bcbe0faf1aab99d3fe0577f64d6c184f1 | Python | 2,556 | 72 | import torch.nn as nn
import torch.nn.functional as F
from brainbox.models import BBModel
class LayerModel(BBModel):
def __init__(self, n_in, n_out, rf_len, rf_size, stride, recurrent=False):
super().__init__()
self._n_in = n_in
self._n_out = n_out
self._rf_len = rf_len
se... |
d268acd261d6e41cc092a12807b5e51e2a2b4b15711934fdb864ddb53d3861be | Python | 2,556 | 68 | '''
TNS class : Tree
'''
import copy
from tns.generate.tree import TreeGrower
from tns.astrocyte.section import SectionSpatialGrower
from tns.astrocyte.space_colonization import SpaceColonization
from tns.astrocyte.space_colonization import SpaceColonizationTarget
GROWTH_ALGORITHMS = {'tmd_space_colonization': Spa... |
db2c2656d0d18c1176f0a8491fbfa04fbb02bf3e7ded163064ca3592e4a83fbf | Python | 2,556 | 83 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Projection(_BaseLayoutHierarchyType):
_parent_path_str = "layout.scene.came... |
27ec4a20a9ae69b0524dffc75e5016abd59c85ce8cb0f3f895ddde81b8ec2c55 | Python | 2,557 | 80 | #!/usr/bin/env python
'''Reevaluates *.caffemodel models in specified directory. Return same result as runline. Assume directory was created with do1request/runline.'''
import sys,os
def get_script_path():
return os.path.dirname(os.path.realpath(sys.argv[0]))
sys.path.append(get_script_path()+'/..') #train
... |
435bce7a831bd0bb8a53adddc0e3b0c3f7bbe9b12b439df9e2535a0bc4300899 | Python | 2,557 | 65 | from matplotlib import pyplot as plt
import pickle as pickle
def plot(i, strTitle, strExperiment):
plt.figure()
plt.title(strTitle + ' ' + strExperiment)
linList = []
lgl = []
for ip in range(pairwiseMetrics.shape[1]):
if ip == pairwiseMetrics.shape[1]-1:
leg = 'Oscope'
... |
717af1fc842500f1459f60f6272c5835104bff600c51b4c3461f8041ce9ff8dc | Python | 2,557 | 84 | from .model_zoo import encode, Model
from typing import Optional, Union
from anndata import AnnData
import numpy as np
from .._compat import Literal
from PIL import Image
import pandas as pd
from pathlib import Path
# Test progress bar
from tqdm import tqdm
_CNN_BASE = Literal["resnet50", "vgg16", "inception_v3", "xc... |
a72e75055c48a7b87e2910050132587b521c1ff8387fe67234a4791ee558bf6e | Python | 2,557 | 42 |
import numpy as np
import pandas as pd
import sys
import scipy
import os
import pydot
import pydotplus
import random
import pickle
#for POPname in ["ASW"]:
for POPname in ["CLM","MXL","PEL","PUR","ACB","ASW"]:
with open("/rds/general/user/amassand/home/SLIM_models/MODELS/pops/lengths_counts_rfmix_maxanc_"+str(POP... |
c1597268d6e8313b8f68171b7bc5b6cadedc0aafa4f318e4907b3ab44c6f79c7 | Python | 2,557 | 57 | """1. Export trained GluonCV network to JSON
============================================
It is awesome if you are enjoy using GluonCV in Python for training and testing.
At some point, you might ask: "Is it possible to deploy the existing models to somewhere out of Python environments?"
The answer is "Absolutely!", ... |
af763a89d3e9eb1d36437ec18686f56325bbfdad43417a2342245093c07bddae | Python | 2,558 | 75 | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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 applicab... |
f33b4e142c2209e5baedd7bee5c31fc1f753b2b2ce8377fd2e23b8c4712dab02 | Python | 2,559 | 88 | import torch
import pandas as pd
from tqdm import tqdm
import sys
from pathlib import Path
import numpy as np
from collections import Counter
sys.path.append("models_architectures/")
from wrapper_prot_bert_BFD import WrapperProtBertBFD
def get_ec_weight(path_df_train, class_vocab):
df = pd.read_json(path_df_trai... |
bc0cced49da7703352e18538b33ffcaa64b7b52f94b7d85999eebb61530886d0 | Python | 2,560 | 79 | """Benchmark core pynapple operations touched by the speedups branch.
Run on the PR branch and on `dev`/`main` to compare:
python benchmarks/benchmark_core_ops.py
Times object construction, `restrict` (one and many epochs), `get`, and
`count`, at a few data sizes. Numba is warmed up first so steady-state is
meas... |
c24ad00cc59980940898284175d5b8b2e1099f51c5cc128430ac91b46bd16137 | Python | 2,560 | 88 | #TimesVector
#
#Created by Inuk Jung on 2016-08-3.
#Copyright (c) 2016 Inuk Jung. All rights reserved.
#
#This program is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at you... |
c454e74f7bb6045b9cfe831858a87af4d0cd7569024646324bd858d6b9357fdf | Python | 2,560 | 83 | from os import PathLike
import numpy as np
from anndata import AnnData
def parsing(
adata: AnnData,
coordinates_file: int | str | bytes | PathLike[str] | PathLike[bytes],
copy: bool = True,
) -> AnnData | None:
"""\
Parsing the old spaital transcriptomics data
Parameters
----------
a... |
d3c8b0b99288923fe62c4fd2a06fdd127f1368a276f2a3f1e1c929f54545e58a | Python | 2,560 | 62 | """The pooled `turn` epochs built by the turn annotation.
`turn` is a declared epoch type (see `DatasetConfig.h5_kdic`) and the chunk
the per-turn parameters are tracked in, but `turn_annotation` used to build only
the left and the right epochs. Every analysis reaching for a per-turn quantity -
the `patch` graphgroup ... |
e73597a2190e767e2dd79e7214562f29344626081cbc75a602d4a8dd5d30ec3e | Python | 2,560 | 83 | #!/usr/bin/env python3
import sys
import glob
import os
import json
import subprocess
def substitute_line(lines, starts_with, replacement):
for k, line in enumerate(lines):
if line.startswith(starts_with):
lines[k] = replacement
break
def ensure_directory_exists(filename=None, di... |
ed83dd770fe25644c8ce2119a8352e20274322f32e3cb36fa480cfdea610af9a | Python | 2,561 | 74 | import sys
import os
import argparse
import joblib
import pandas as pd
from tqdm import tqdm
from pmsurv.models.exponential_model import ExponentialModel
from pmsurv.models.weibull_linear import WeibullModelLinear
from pmsurv.models.weibull_nn import WeibullModelNN
def parse_args():
parser = argparse.ArgumentPars... |
b79b67ec3f52e7f708f3840c4129eb65de626028b693cb65324a88133ac634ec | Python | 2,562 | 67 | import unittest
from parameterized import parameterized
import numpy as np
from experiments.msp import starting_point_for_different_number_motifs
from modular_splicing.utils.entropy_calculations import hbern, density_for_same_entropy
class TestDensityForSameEntropy(unittest.TestCase):
def verify_inverse(self, n... |
95da14943e6de1a1712629c39917e322c92691cc766ab6d87d7152826903f698 | Python | 2,563 | 68 | # this file creates a TCR appearance dictionary
# with the key being a TCR in the format of (CDR3, v_allele)
# and the value being a set of individuals that this TCR appears in
# from the data files of 666 individuals that DeWitt_2018 used
# only consider those with frame_type == 'In'
import os
import pickle
import n... |
e4e3b890845c5f908a0afed69d5edb46f6156bce59422d42d69e849b3145f7b6 | Python | 2,563 | 92 | """
Running this script generates a learning rate plot.
The data loaded is be specified by a command line argument.
"""
import argparse
import numpy as np
import matplotlib.pyplot as plt
def style_plot(ax: plt.Axes, num_it: int) -> plt.Axes:
"""
Generate style for Learning Rate plots.
:param ax: Drawing ... |
b6015b09d4ecc55ce9c7403c98c112bf7e834a2398ae4696305c105fe963d020 | Python | 2,564 | 68 | # -*- coding: utf-8 -*-
"""
@author: Dylan Stewart and Sergio Marconi
updated: 05/07/2020
input variables:
data_folder - string
save_folder - string
output:
par - dictionary
to use these parameters:
from parameters import evaluation_parameters
par = evaluation_... |
2f609bfaa226542c46285fedff6ead2fc980b4d12f5a1733a9a2908cf79ab689 | Python | 2,565 | 63 | """
Integration test for memory leak issue #4832.
Reproduces the scenario of a long-running monitor (e.g. ``celery events``
or Flower) consuming real events off the broker: child tasks whose
parent is never observed (because it was scheduled with an ETA/countdown
and its own event hasn't arrived yet, or because it was... |
814048494d40c34f474b51a923af14cabcb342068371b1eb02fc202fe1c662e0 | Python | 2,565 | 83 | """
Author: Derek van Tilborg -- TU/e -- 23-05-2022
A collection of classical machine learning algorithms
-MLmodel: parent class used by all machine learning algorithms
-RF: Random Forest Regressor
-GBM: Gradient Boosting Regressor
-SVM: Support Vector Regressor
... |
953a29b4fa8570b854cdd2e10d5327393e100cc38872b5516b54dd6c33338299 | Python | 2,565 | 83 | #!/usr/bin/env python3
"""This script is an adaptation of the original file "example_4_b.py" created by Roman Martin."""
from GNNSubNet import GNNSubNet as gnn
import ensemble_gnn as egnn
import copy
import random
import time
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import accuracy_s... |
c04c07d48d1779ec5f92a8eb9b5238615d7f098a1c24ca747dc98c92cbcd0edf | Python | 2,565 | 61 | import cv2
import torch # noqa
from .back_prop import GaussianBlurPath, attribution_objective, Path_gradient, saliency_map_PG
from .utils import cv2_to_pil, pil_to_cv2, gini, vis_saliency, vis_saliency_kde, \
click_select_position, grad_abs_norm, grad_norm, prepare_images, make_pil_grid, blend_input, attr_grad, \... |
dc2dd5ec311ee2acf7d5d4fcbd601a2bce3d98739d779fc25be02957cc01a3ed | Python | 2,565 | 64 | import numpy as np
import time
from PCAfold import preprocess
from PCAfold import analysis
angle_list = [i for i in range(0,180)]
n_points = 2000
random_seed = 100
np.random.seed(seed=10)
mean = [0,0]
covariance = [[2.5, 0.52], [0.52, 0.2]]
x_noise, y_noise = np.random.multivariate_normal(mean, covariance, n_points).... |
f8487bd1c5a20688dd62ab9f2f490e2247598ccc6f4646f2704b08ea7ee27c87 | Python | 2,565 | 85 | """Helper functions for I/O"""
import os
from os.path import join as opj
import h5py
import numpy as np
from scripts.utils.constants import MRI_IMG_DIR, DWMRI_IMG_DIR, MRI_ORG_LOC_GT_DIR, DWMRI_ORG_LOC_GT_DIR, MRI_CYST_LOC_GT_DIR, MRI_CYST_LOC_GT_DIR_ALL45
def get_orig_imgs(kind, seq=None):
"""Get MRI / DTI imag... |
9880129b0c4f268ca4a12ead21cc4718f69e56d23f80f4b6b99f3ed3a4b4b01f | Python | 2,567 | 69 | """
Loads and handles the results_display widget.
SimplyFire - Customizable analysis of electrophysiology data
Copyright (C) 2022 Megumi Mori
This program comes with ABSOLUTELY NO WARRANTY
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as publ... |
c5ebbf91594529e4939507d98001e0291eff6c1f8c251699815e95c6bec9dc62 | Python | 2,567 | 93 | import argparse
import math
import os
import numpy as np
from tqdm import tqdm
from PIL import Image
import mxnet as mx
import mxnet.ndarray as nd
from model import StyledGenerator
# pylint: disable-all
def get_mean_style(generator, device):
mean_style = None
for i in range(10):
style = generator.me... |
a4b6a6bef384b5501369c6b3720999a133c1a78d2ad5bc9f1b77043b02d9bacc | Python | 2,568 | 77 | import argparse
import json
import os
import warnings
# Make sure, that `MKL_NUM_THREADS` is set to 1, to ensure reproducibility:
# https://forum.image.sc/t/reproducibility-how-we-spent-years-building-containers-and-then-mkl-decided-to-screw-our-results/109599
if str(os.environ['MKL_NUM_THREADS']) != '1':
warnings... |
bb181f6611aab953573fabb5e6369ff5e58342f4310cfaa02ba4ae174f3882d1 | Python | 2,568 | 91 | # 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... |
f5b7353a222442f27afa587f0c361491bfc348b2fe584ede2816e5f7b1e7b02a | Python | 2,568 | 67 | import attr
@attr.s
class AdaptiveSparsityThresholdManager:
"""
Manages the sparsity threshold of a model.
The threshold is decreased by a fixed amount every epoch, but never below the minimal threshold
The threshold is increased to the accuracy of the model if the accuracy is higher than the curren... |
2dd406fa96fc3e7cdaa66049032709591228286dfe875035788926af3cae114b | Python | 2,569 | 67 | """F11 admission pinning on named real models (collapse memo item 15).
The U-gate makes admission MODE-STABLE: train-mode capture inflates raw op
count 12-40% (the round-1 "ceiling drift" measurement bug), but U is the
rendered universe, so a model admitted in eval mode stays admitted in train
mode. Pinned on named to... |
823121dd9b39b375aad04d4ce983269991db4014e9ac150760ba3f3be417854b | Python | 2,569 | 73 | """Corpus fixture integrity + licensing gates (memo B9; lane F19).
The vendored fast path must stay exactly what its manifest records: every
image byte-hashed, every license unambiguous (CC0 / public domain), the
fixture builder deterministic, and the COCO precision path pinned as a
download-on-demand manifest (URL + ... |
ffadeac2f3b0bbf9de5df790f1c792f605d07f7b6e794609c980ea6dfd234b28 | Python | 2,569 | 86 | #!/usr/bin/python
import os
import sys
import re
import pandas as pd
import numpy as np
import ConfigParser
import tabix
from bisect import bisect_left
##### return unique element in list #####
def unique(a):
unique = []
[unique.append(s) for s in a if s not in unique]
return unique
##### Return index of a1 which... |
4deb239405e764d0e74bced5522391124c235e59785a10390b5eed3cd6fd2621 | Python | 2,570 | 70 | import os
import h5py
import numpy as np
import scanpy as sc
import scvi
from scbig.utils import setup_seed, read_data, louvain, calculate_metric
for dataset in ['10X_PBMC','mouse_bladder_cell','mouse_ES_cell','human_kidney_counts','Adam','Human_pancreatic_islets','Macosko_mouse_retina']:
print('----------------... |
beec8b56451db28d5b562e02d7212b3488f1973c1121db8642cbf4924b0f1489 | Python | 2,571 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 21:59:10 2022
This file contains the functions implementing the AI vs learning speed analysis (reported in Fig. 4D and 8C in the
manuscript).
@author: emilia
"""
import pickle
import torch
import pandas as pd
import numpy as np
import src.learni... |
eed2e293c1bbb77294466224cfb700db18a7e3acffcfa400e8db510a6201cc64 | Python | 2,571 | 97 | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Iterator
import torch
import torch.nn as nn
class BaseLatent(ABC):
"""Latent variable module."""
@abstractmethod
def reset_states(self) -> None:
"""Reset the state of the latent variable."""
... |
4c25c5c0a430364dfb4c97b895d51d8a5cd0abc4966070b67eb18125cfb9e532 | Python | 2,573 | 85 | import numpy as np
from anndata import AnnData
from scipy.spatial.distance import cdist
def local_level(
adata: AnnData,
use_label: str = "leiden",
cluster: int = 9,
w: float = 0.5,
return_matrix: bool = False,
verbose: bool = True,
) -> np.ndarray | None:
"""\
Perform local sptial tra... |
a74b9cbd3601db64d954f693213496c9b0f9a969ac1fc0cfa59cba358e16a73f | Python | 2,573 | 63 | """
_pcm.py — shared PCM decode helper for song_recording's capture-layer
modules (monitor.py, noise_model.py).
pyoperant.interfaces.pyaudio_.PyAudioInterface._open_input_stream() probes
sample formats rather than assuming one (see its docstring for why 24-bit
in particular can't be assumed for a USB Audio Class devic... |
e81978f6b324be56e40882dfeabadf228aeb41675f073051e76cba80dc661ac9 | Python | 2,573 | 79 | """
Standardize Multi-threading computation.
SimplyFire - Customizable analysis of electrophysiology data
Copyright (C) 2022 Megumi Mori
This program comes with ABSOLUTELY NO WARRANTY
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published... |
f79ffecf95ea0e9aae41186788ce5e4b1d603f1f4a2630a4187cf7f43334b359 | Python | 2,573 | 80 | import os
import joblib
import yaml
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline
from pmsurv.models.exponential_model import ExponentialModel
from pmsurv.models.base import BayesianModel
from skopt.space import Real, Categorical, Integer
import utils
import arviz as az
def pr... |
058c089d2dc7eae327b9c703015b36800ccea8a7093d51a4ab587e3ae48847a8 | Python | 2,574 | 71 | import numpy as np
import sympy as sp
from markov_builder.example_models import construct_kemp_model
from scipy.integrate import odeint
from . import common
from . MarkovModel import MarkovModel
class KempModel(MarkovModel):
"""The model described in https://doi.org/10.1085/jgp.202112923
constructed using Ma... |
555abbc31be03e6d84e64c17199a1062836215ae72488a5740728767975de711 | Python | 2,574 | 93 | """
Stimulus action classes for the ComposedStimulator.
Each action encapsulates WHAT stimulus to deliver (hardware command),
independent of WHEN to deliver it (trigger logic). Actions build instruction
dicts that are passed to the OptoMotor hardware interface.
"""
from ethoscope.hardware.interfaces.optomotor import ... |
55e0db9a6f9dc31bdc407322ccad8ac4b397ddbe00f4d1bdc513012017d0be15 | Python | 2,575 | 89 | import numpy as np
import pytest
from Metrics.src.image_quality_estimation import IQS
from NLM.src.denoise import (
nlm,
nlm_CEST,
pad_image,
get_comparison_neighborhood,
get_small_neighborhood,
)
from test_support_function.src.CEST import generate_Z_3D
def test_pad_image():
image = np.array(... |
f9b39ee570a2a3d6c1031d7200b0a47b6d4a021aa0c5098bd1d1aee538094ad3 | Python | 2,575 | 84 | import argparse
import os
import sys
import time
import pandas as pd
from statistics import median
sys.path.append(os.getcwd())
from scripts import extract_workflow_connections
from scripts import prepare_data
from scripts import utils
max_path_len = 25
cut_off = "2017-12-31"
te_share = 0
def find_wf_statistics(un... |
387a429790eff985601bbc556e3fa568597bc6d0a1e0277707fa64207b8e2d99 | Python | 2,576 | 98 | #!/usr/bin/env python
import sys
import pysam
from collections import Counter
from operator import itemgetter
import numpy as np
# python script.py test.bam hg38.fasta hg38.sizes
script, sam_path, fasta_path, size_path, new_bam = sys.argv
def get_pileup_database(sam_path, size_path):
sam = pysam.AlignmentFile(sam_p... |
bea8393485e1f923f6a95de1394954e0e02e68fffec3228490a6f626c2e6a13e | Python | 2,576 | 63 | import glob
import h5py
import numpy as np
import os
import pandas as pd
from sklearn import preprocessing
import sys
"""
hi. this is going to read these in, you basically just need
subjects: path to subjects!
data_path: path to the actual features
phenotypes: path to a csv with the to-be-predicted and the noise
"""
... |
f7c87f0da5b78fef71022b3b1605a465621422e8094cd962a9ede5d20e5c34ae | Python | 2,576 | 93 | from ethoscope.core.roi import ROI
__author__ = 'quentin'
import numpy as np
from ethoscope.utils.description import DescribedObject
import logging
import traceback
class BaseROIBuilder(DescribedObject):
def __init__(self):
"""
Template to design ROIBuilders. Subclasses must implement a ``_roi... |
42a536c004710cd392f762dfb69dee6b8943f4a153ce6dde3d592eb7c60d0944 | Python | 2,577 | 69 | """inseq bridge against the real inseq package (no sys.modules fakes).
The default method must be a name inseq knows, and ``generated_texts`` must
reach inseq's own ``generated_texts=`` so the attributed target is the
caller's text; the result must equal inseq called directly.
"""
from __future__ import annotations
... |
a8597b42a8e5502f5d351c59fc1ef09a3f08fe3b8a60ef20f7b8f3a93e52d1e2 | Python | 2,577 | 77 | """Create the images for the documentation site."""
import shutil
import numpy as np
import matplotlib.pyplot as plt
from specparam import SpectralModel, SpectralGroupModel
from specparam.sim.gen import gen_power_spectrum
from specparam.plts.utils import check_ax
from specparam.plts.spectra import plot_spectrum
from... |
d4ddaee3871ea96bd9ae44ab2b94931e2684db34c80ae19850e95a6b16f93e1f | Python | 2,577 | 77 | import numpy as np
from CEST.src import matlab_style_functions
def cest_correction(
cest_array: np.ndarray,
x_calcentires: np.ndarray,
x: np.ndarray,
x_itp: np.ndarray,
mask: np.ndarray,
offset_map: np.ndarray,
interpolation: str,
cest_range: float,
) -> tuple:
"""
Correct the... |
63666da542dd2bdcb33a070346f458d79811e764cb41d1c565eafeddbf1579d2 | Python | 2,578 | 80 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""
Sample MneExperiment. This experiment can be used with a sample dataset that
treats different parts of the recording from the MNE sample dataset as different
subjects. To produce the data directory for this experiment use (make sure
that the directory you spe... |
a87a39afb7bb1abb20ecc7eecb302add3d6de7df76601f5c237dbbab7daf9abf | Python | 2,578 | 75 | from conftest import requires_matplotlib
import numpy as np
from pyriemann.stats import PermutationDistance, PermutationModel
from pyriemann.spatialfilters import CSP
import pytest
def test_permutation_badmode():
"""Test one way permutation test"""
with pytest.raises(ValueError):
PermutationDistance(m... |
39c50296519b5b0b0192e75f6929cec0c7fec523a1863d1767a5bbbdbdc6e7ce | Python | 2,579 | 61 | import torch
import numpy as np
import sys,os,re
from experiments.analysis.utils import grad_simplex_correction
def pad_to_max_len(array,max_len):
diff = max_len - array.shape[1]
zeros = np.zeros((4,diff),dtype=array.dtype)
padded = np.concatenate([array,zeros],axis=1)
return padded
def make_consensu... |
756cd9604b6c52e78db7a9d2084a48f1eb600e6f04b874060293d4b059cd02b2 | Python | 2,579 | 69 | from Bio.PDB import *
import math
import pandas as pd
import numpy as np
import warnings
# Constants
AMINO_ACIDS = ["ALA", "ARG", "ASN", "ASP", "CYS", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", "MET", "PHE", "PRO", "SER", "THR", "TRP", "TYR", "VAL"]
PDBS_LIST_PATH = "PDBs_list.csv"
NON_OHE_DATASET_PATH ... |
0625c189205ffba79700f18291742bcf82de52801d69d0211204fb8f81427f6d | Python | 2,580 | 70 | import torch
import numpy as np
import pandas as pd
from os.path import join
from pathlib import Path
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
MAX_SPKR_CNT = 259
class DSet(Dataset):
''' This is the raw WSJ parser '''
def __init__(self, path, split):
# Setup
... |
d192bf3601cfb53c021a17cc793d504872d2e63e4ab36f20141d13b363445177 | Python | 2,581 | 78 | from __future__ import annotations
from larvaworld.gui_v2.apps.models_environments.environment_builder import (
build_environment_builder_text,
build_environment_builder_widget,
)
from larvaworld.gui_v2.registry_bridge import GuiEntry, GuiNavigationModel
from PySide6.QtWidgets import QWidget
def build_group_... |
ac0ed9eea214ffd0a6c2c6a916d40e74df6a0a2b3623a95bb7ee5111f64fe0ce | Python | 2,582 | 67 | import pandas as pd
def describe_data(path_json, path_df_train):
# Keep only sequences that have ec present in the train because none of the tested tools can do zero shot predictions
df_train = pd.read_json(path_df_train)
list_train_class_vocab = list(df_train["ec_number"].unique())
print("path_json:... |
f99c5de824c36078d1a88aac8446cde206cbb04f71c375c6eda07cebf996f166 | Python | 2,583 | 66 | class BiomedisaPredictionParameter():
name: str = "BiomedisaPredictionParameter"
path_to_model: str = ""
stride_size: int = 32
batch_size_active: bool = False
batch_size: int = 12
available_devices: int = 1
x_min: int = None
x_max: int = None
y_min: int = None
y_max: int = None
... |
16990c9e2f37d8092125c54ea5f24dd5b683c0b154c6fc202f0a3a1c0a57af62 | Python | 2,584 | 99 | #!/usr/bin/env python
import sys
import pysam
from collections import Counter
from operator import itemgetter
import numpy as np
# python script.py test.bam hg38.fasta hg38.sizes
script, sam_path, fasta_path, size_path, new_bam = sys.argv
def get_pileup_database(sam_path, size_path):
sam = pysam.AlignmentFile(sam_p... |
2e75a372e7a6beb8c266710636e6a49c8cdcbe95fd43af3a8dae3d51d2937768 | Python | 2,584 | 70 | """Constructs a semi-weakly supervised ResNeXt-101 32x8 model pre-trained on 1B weakly supervised
image dataset and finetuned on ImageNet.
`"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
Weights from https://github.com/facebookresearch/semi-supervised-Ima... |
11fa30a304a28b1a75f6d2fcd2252597c6b2d56df2aaaeacf5461c1bd90a7065 | Python | 2,586 | 104 | """I/O utilities."""
import os
###################################################################################################
###################################################################################################
def create_file_path(file_name, file_path, extension):
"""Create the full file pat... |
63de6400b7c02dc2093f6f8afa2ec182a1133f6c0fa5117f55d1c87a3bf27837 | Python | 2,586 | 67 | """
Code here relates to the detection and management of cell membrane properties:
* holding current (Ih)
* access resistance (Ra)
* membrane resistance (Rm)
* membrane capacitance (Cm)
This modules provides two types of membrane capacitance values: ones derived
from step protocols and ones derived from ramp p... |
6cf94258dfb11ff9fa11e26665c8d08002797c508b167a2080cd8bfefab0566f | Python | 2,586 | 86 | """
This script is used to record how to read some supplementary data,
which are published in Nature already.
"""
from typing import List
import os
import re
import numpy as np
import pandas as pd
# * Download Supplementary Data
# You can find the link under the paper website:
# https://www.nature.com/articles/s41586... |
c0fcee0acffe19ae23fe2ee844d87439f6744398574a974673ff55d11bb4e685 | Python | 2,586 | 71 | import numpy as np
from modular_splicing.gtex_data.pipeline.marginal_psis import (
compute_splice_site_psis,
parse_junc,
)
def extract_sequence_info(genes, juncs, gene_ensg, denominator_technique="one_step"):
"""
Extract information about the given gene.
Parameters
----------
genes : Fea... |
e6f970b0b5109202c9f33c94ed5937562e3abe7956395e32203738160bb23c3b | Python | 2,586 | 67 | """Setup script for specparam."""
import os
from setuptools import setup, find_packages
# Get the current version number from inside the module
with open(os.path.join('specparam', 'version.py')) as version_file:
exec(version_file.read())
# Load the long description from the README
with open('README.rst') as read... |
12238765e9e75b8cc81317cc6cac13001ee450e872cd39589e4e2c891e37ac2b | Python | 2,587 | 78 | import logging
import threading
from abc import ABC, abstractmethod
from typing import Any
import grpc
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
class GrpcStreamer(threading.Thread, ABC):
"""Implements a streaming thread that can be stopped and will restart
with temporary ... |
74efbf4675340b024103d45a6d84e9d1fe88f86e5dfa37ee6cd8d474fc7d5e77 | Python | 2,587 | 84 | import copy
from typing import Any
import numpy as np
import pandas as pd
from mirp._images.generic_image import GenericImage
from mirp._imagefilters.generic import GenericFilter
from mirp._images.transformed_image import TransformedImage
from mirp.settings.generic import SettingsClass
class SquareTransformedImage(... |
8bd1d9271605fb77376c221cd3c2e0eba1997c3ef82cfa9747b0ff22a1c755a7 | Python | 2,587 | 80 | """Render a large random graph with rank layout.
Usage:
python scripts/render_large_graph.py NUM_NODES [OPTIONS]
Examples:
python scripts/render_large_graph.py 250000
python scripts/render_large_graph.py 1000000 --format png --seed 123
python scripts/render_large_graph.py 50000 --outdir /tmp/graphs
""... |
5e771342a25c6665219c93a62089a35574e43d4d208aad9b5bc668a3630da02d | Python | 2,588 | 90 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Oct 07 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
from typing import Union
import numpy as np
from scipy import sparse
from sknetwork.linalg import SVDSolver, LanczosSVD
from sknetwork.ranking.base import BaseRanking
from sknetwork... |
eac8d581e6dad71ea6e24b0e62486cb1a652e5a888f8719a2d491d23ba0380db | Python | 2,588 | 78 | import os
from datetime import timedelta
import hydra
import torch
from timm.optim import create_optimizer_v2, optimizer_kwargs
from timm.scheduler import create_scheduler
from torch.distributed import init_process_group
from wandb import AlertLevel
from data.loader import get_kfold_dataloader3d
from initial_setting ... |
2aa0dbed40276381490eaeb5dcd639ec320f6964bc73b711f6c8e26d7d3e9af8 | Python | 2,589 | 75 | from io import BytesIO
import base64
from .png import Writer, from_array
try:
from PIL import Image
pil_imported = True
except ImportError:
pil_imported = False
def image_array_to_data_uri(img, backend="pil", compression=4, ext="png"):
"""Converts a numpy array of uint8 into a base64 png or jpg stri... |
b6d18a899b76ff88651a559a6235534f880dd610b8bfd71811f4079b467632cf | Python | 2,589 | 73 | from typing import Any
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset
class TVTDataModule(LightningDataModule):
"""Simple DataModule creating train, val, and test dataloaders from given train, val, and test dataset.
Example::
from pl_bolts.datamodu... |
cde391e675040607cc9894d4218cfcee27602e38d9b1e99ca0e4a5e1d7ea7352 | Python | 2,589 | 76 | import os
import re
import shutil
import tempfile
from typing import Iterator, List, Tuple
import numpy as np
import pytest
import torchvision
from lightly.core import train_model_and_embed_images
class TestCore:
dataset_dir: str
@pytest.fixture(autouse=True)
def _cleanup(self) -> Iterator[None]:
... |
0559b66f7242d756da6873a8e2e246db36d2805011c5c70396403c7200b6c913 | Python | 2,590 | 58 | import json
import pandas as pd
from tqdm import tqdm, trange
tqdm.pandas()
def main():
test = pd.read_csv('./data/csvs/data/test_new_old.csv')
old_new = pd.read_csv('./data/csvs/data/test_new_old.csv')
old_new = old_new[['Drug2_ID', 'Drug2_SMILES']]
with open('./data/jsons/chemprop_similar_drugs_di... |
73fdf29793ad058809ecf4febfafcfe3ac43ff9b62956563c07371d1491414db | Python | 2,590 | 63 | import numpy as np
from mirp._data_import.dicom_file import ImageDicomFile
from mirp._data_import.dicom_file_stack import ImageDicomFileStack
class ImageDicomFileVolumeStack(ImageDicomFileStack):
def __init__(
self,
image_file_objects: list[ImageDicomFile],
**kwargs
):
... |
90a5da1e2f17ba9488552b5d0743f0c6571ef815327c19a0eb5487a50d3ad59b | Python | 2,590 | 62 | import click
import os
from subprocess import Popen, PIPE
@click.command(help="Build a singularity image from a docker image")
@click.option(
"--target",
default=["docker", "singularity"],
help="Build targets",
type=click.Choice(["base", "docker", "singularity"]),
multiple=True,
)
@click.option("-... |
a986e597adb9ad350205bb7cd9045e74d7c184c55192dc99c2a6531035af2ced | Python | 2,590 | 73 | """Train 3D U-Net"""
import sys
import os
from os.path import join as opj
import yaml
from scripts.utils.constants import WORKING_DIR, MRI_ORG_SEG_FILES_3DUNET, MRI_CYST_SEG_FILES_3DUNET # noqa
os.chdir(WORKING_DIR)
def create_conf_files(kind, create_yml=False):
"""Create 3D U-Net config files for training. On... |
cad978e85a66e4e2629e24f40f2b52990ea5fdfae637d7c137a37d70854bfc1a | Python | 2,590 | 75 | from typing import Optional
import paddle
import paddle.nn as nn
class RotaryPositionalEmbeddings(nn.Layer):
"""
Rotary Positional Embeddings (RoPE)
https://arxiv.org/abs/2104.09864
"""
def __init__(
self,
dim: int,
max_seq_len: int = 4096,
base: int = 10000,
... |
d5245076715d23ba1919af8bc1c4b7966f1fe9b283930d507adfa0a5bc68e04e | Python | 2,590 | 61 | """ SiamRPN benchmark
Code adapted from https://github.com/STVIR/pysot"""
from glob import glob
import argparse
import os
from multiprocessing import Pool
from tqdm import tqdm
from gluoncv.utils.metrics.tracking import OPEBenchmark
from gluoncv.data.otb.tracking import OTBTracking as OTBDataset
def parse_args():
... |
e50020e10349992a654ef21bd2053fc4afe01a0d256e8fda6021246658fd6b68 | Python | 2,590 | 91 | import argparse
import logging
import numpy as np
from atomate.common.powerups import add_tags
from atomate.qchem.fireworks.core import ForceFW, SinglePointFW
from bson.objectid import ObjectId
from fireworks import LaunchPad, Workflow
from instance_mongodb import instance_mongodb_sei
from pymatgen.core import Molecul... |
a520f4be0404e8ca300a3381253bffb12e02b7c0d69fb7852560c0ade4d937c5 | Python | 2,591 | 85 | # --- 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 Line(_BaseTraceHierarchyType):
_parent_path_str = "candlestick"
_path_str... |
f50f1068042a37ac35b18b25c8d5ecf5fed5fb0900437f809dd82483285e0fbd | Python | 2,591 | 96 | import logging
import argparse
from bson.objectid import ObjectId
import numpy as np
from pymatgen.core import Molecule
from atomate.qchem.fireworks.core import SinglePointFW, ForceFW
from atomate.common.powerups import add_tags
from fireworks import LaunchPad, Workflow
from instance_mongodb import instance_mongo... |
ff26258494182af4a10b7ed4741718a61d1b7e39d2afa0a29de0cb16ee5addb7 | Python | 2,592 | 84 | import re
import sys
from Bio import SeqIO
from Bio.Seq import Seq
def main():
fasta_file = sys.argv[1]
output_file = sys.argv[2]
log_file = sys.argv[3]
output_handle = open(output_file, "w")
log_handle = open(log_file, "w")
with open(fasta_file, "r") as fasta_input_handle:... |
13efe072a54e62350f886fe2cfd8f66af561d44340355219b7b032758cf27b28 | Python | 2,595 | 76 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in April 2019
@author: Nathan de Lara <nathan.delara@polytechnique.org>
"""
import warnings
from typing import Optional, Union
import numpy as np
def get_values(shape: tuple, values: Union[np.ndarray, list, dict], default_value: float = -1) -> np.ndarray:
... |
5c3fd60615c4b9504c8c2b052ca9bc322982ecbf5928a1e34b10d25519ee6192 | Python | 2,595 | 117 | import os
import time
import pytest
from caper.nb_subproc_thread import NBSubprocThread
SH_CONTENTS = """#!/bin/bash
echoerr() { echo "$@" 1>&2; }
NUM=$1
if [ -z "$NUM" ]
then
NUM=10
fi
echo $NUM
# NUM kitties (1 kitty per sec)
for i in $(seq 1 $NUM)
do
echo "hello kitty $i-1. (STDOUT)"
sleep 0.25
echoer... |
87792752a86c3d215afcaac1dbe3b24bc239d1294924692bed69ff687dfb4172 | Python | 2,595 | 92 | import random
import numpy as np
import torch
import torchvision.transforms.functional as TF
class Compose(object):
""" Composes several transforms together.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, rendering_images, bounding_box=None):
for... |
af2c3060eecbfcf89227defed78969bb2f46b3af629a860d4e7d0847b81b38b9 | Python | 2,595 | 86 | from typing import Optional, Union
from anndata import AnnData
from matplotlib import pyplot as plt
from pathlib import Path
import os
import sys
import numpy as np
def parsing(
adata: AnnData,
coordinates_file: Union[Path, str],
copy: bool = True,
) -> Optional[AnnData]:
"""\
Parsing the old spa... |
e5047827125e0de0e20eb00ecea35d7c82f882cfcfb2c33f7b103ad9bd735e2c | Python | 2,595 | 67 | import sys
from typing import Any
import numpy as np
try:
from psychopy import visual
except ImportError as e:
msg = "Psychopy is not installed."
raise ImportError(msg) from e
from neurogym.core import TrialEnv
from neurogym.utils import spaces
from neurogym.utils.logging import logger
class PsychopyEn... |
1743546215aa3b271900acf71cf7e7a8dcd9b21b7f5a204abe46ddc576b59f88 | Python | 2,596 | 67 | import os
import utils
import numpy as np
def load_lines(filename, header=False):
header_line = None
lines = []
with utils.reading(filename) as fp:
line_count = 0
if header:
header_line = fp.readline().rstrip()
for line in fp:
lines.append(line.rstrip())
... |
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