sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3364eb67b712b14cb90eb9948731fe5a2028a5ef8cf132f2cfccbf991966084b | Python | 4,392 | 155 | # -*- coding: utf-8 -*-
"""
Created on Fri Dec 8 17:33:43 2023
@author: nlancaster
"""
import matplot.pyplot as plt
#Supplemental Figure 2
def concave_grad(Vf,Vt,Tf,Tt,Te,curve_type):
#pass in the curve_type number and k is determined from the dictionary
if curve_type>=6:
k_dict = {6:0.75,7:0.5,8:0.25... |
e82583725cd4cbb6ebd61bbddbf17c0299bc9d23a28eb9d12ea06068808db06b | Python | 4,393 | 132 | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from .model_utils import cross_entropy
from loguru import logger
# code from https://github.com/tkzeng/Pangolin
L = 32
# convolution window size in residual units
W = np.asarray([11, 11, 11, 11, 11, 11, 11, 11,
21, 21,... |
b6f74fea9cfb0ec69cf554a721261e672933a880de442f15b5d54faec97a442f | Python | 4,395 | 145 | from typing import Hashable, Mapping, Sequence, Union
import torch
from rising.transforms.abstract import AbstractTransform
from rising.transforms.functional.utility import box_to_seg, instance_to_semantic, seg_to_box
__all__ = ["DoNothing", "SegToBox", "BoxToSeg", "InstanceToSemantic"]
class DoNothing(AbstractTra... |
f7cce299ce53246b51e16843537fb718dfd575c0adf443dbcc78a69f9d079895 | Python | 4,395 | 113 | import os
import copy
import math
import numpy as np
from utils.adaptive_blocking import OctTree, cal_feature
def no_partition(devide_type, origin_data):
shape = origin_data.shape
chunk_names = [f'0_{shape[0]}_0_{shape[1]}_0_{shape[2]}']
chunk_datas = [origin_data]
partition_result = copy.deepcopy(orig... |
0efa302a239736e1872b0a5d9143542ed6f5a9f877c8979b6a7c403f8470c066 | Python | 4,396 | 120 | #%%
import torch
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pickle
import itertools
import random
import os
from src.encoders_decoders import *
from src.losses import *
from src.useful_functions import *
from torch.utils.data import DataLoader
from joblib import Parallel,d... |
557d5e077146fb421358eb0f21df83d2f95f5e888a215b897e2273fa3afdac61 | Python | 4,396 | 105 | import requests
from .__config import base_url
from .__aux_funcs import process_response
def fetch_copy_number_segments(sample_study_ids, chromosome=None, projection="SUMMARY"):
"""
Fetch copy number segments from cBioPortal by sample ID. \n
:param sample_study_ids: List of sample identifiers. \n
E... |
5dbafefa9c37300300222e57b309ca16a8474fff221a6adef317736b6f82b22d | Python | 4,396 | 98 | """
import raw data, normalize, smooth, then calculate the threshold for each ROI (0.5 or baselineMean+4*SD, which ever is bigger)
"""
import Utility_working as Utility #custom-made utility file, contains lengthy functions
import numpy as np
import sys
import os
from constant import *
##if want to customize s... |
8fbd5c57dfd48d4cf8d1550530ccb7e39ae52c0321673ce5457b7ca9a6f228cb | Python | 4,396 | 112 | import torch
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from .model.aug import random_aug
from .utils import coords2adjacentmat
from timeit import default_timer as timer
from collections import OrderedDict
from tqdm import trange
def train_seq(graphs, args, dump_epoch_list, out_prefix, mo... |
92354d0952f9961fb4e5338a9c93eea706dd77d0dfc43ea35988c449be47e3a3 | Python | 4,396 | 112 | # Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# 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 L... |
aa648840b5b12dfc1554de0334f6628cdbeb2b73be09d265048ad272a40c5e43 | Python | 4,396 | 140 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
12821b5a07d6280150d1f5211b645e5efdae91d7e438a6a6d89c30735dd7e9b7 | Python | 4,397 | 124 | # Copyright 2023 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
3f11c820706fe420f5d49edd0606e1f81a6ceccb4c38d7feac30b278d5fab737 | Python | 4,398 | 115 | #!/usr/bin/env python3
"""Tests for build_wheel.py"""
import tempfile
import unittest
from pathlib import Path
from unittest import mock
from build_wheel import (
BuildConfig,
BuildError,
main,
)
class TestBuildConfigPlatformDetection(unittest.TestCase):
"""Test _detect_platform() returns correct va... |
45dc49b60ab7aac3a81fa5cbca3a18891a71e48bd2cc3352a6e12297fdcb4ac3 | Python | 4,398 | 102 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
55b5e593428afa6de480175a734bb16ee0f7d5ee4e8ab822c2ba295167e6edf6 | Python | 4,398 | 127 | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def... |
611be991cd8abe0cdc39d3e3ef1677089018dc0331a49119f6ca7b5c547c8666 | Python | 4,398 | 126 | '''!
@file calibration.py
Defines a Calibration class that is a container for a device calibration.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
Kinect For Azure SDK.
'''
import ctypes as _ctypes
from .k4atypes import _Calibration, EStatus, EDepthMode, EColorResolution, ... |
a08c731538766e0478f8e4a9a726610f027c4f3aadb6da4a1aa7e4ad81e2d289 | Python | 4,398 | 153 | """
Methods for feeding syllable data to the VAE.
Meant to be used with `ava.models.vae.VAE`.
"""
__date__ = "November 2018 - July 2020"
import h5py
import numpy as np
from scipy.io import wavfile
from torch.utils.data import Dataset, DataLoader
from ava.models.utils import _get_sylls_per_file, numpy_to_tensor, \
... |
05950f06ed7298191798f2ad90ce73a0582835e60d5e4f51244891aadb97a627 | Python | 4,399 | 127 | r"""Feature-level transformations for metabolomics.
Between-feature scaling is separate from between-sample normalization
(`_norm.py`) because the aims are different: sample normalization
removes dilution bias, feature transformation makes features
statistically comparable regardless of their absolute intensity range.... |
5efdb7874ef80958c7daa100e445bfc45c139ede54a12ea3b246e528ca484381 | Python | 4,399 | 117 | # rag_adk_agent.py — Google ADK agent wrapping the two‑stage RAG system
# -----------------------------------------------------------------------------
# This agent exposes your existing two‑stage code RAG as **function tools** in
# Google’s Agent Development Kit (ADK). Launch with any of the following:
#
# adk web ... |
05c5695a510ee5e0f4a22867c1feed9efe357970df91444cb487e97ba811d81d | Python | 4,400 | 125 | """
Exact copy of wavelet calculation in Gordon Berman's MotionMapper code.
Written by :
Kanishk Jain
kanishkbjain@gmail.com
"""
import time
import warnings
def findWavelets(projections, pcaModes, omega0, numPeriods, samplingFreq, maxF, minF, numProcessors, useGPU,
verbose=True):
"""
findWave... |
3b2ac5eeb15c41bf85ed95e4e0a1005dc6be557c447819641682659809f6ba6d | Python | 4,400 | 128 | import scanpy as sc
import numpy as np
import pyliger
from tensorflow_probability import math as tm
tfk = tm.psd_kernels
import spatial_factorization as sf
from typing import Optional
from scipy.sparse import csr_matrix
from ._townes_nsf_utils import *
from sklearn.decomposition import NMF
def construct_gems_using_pyl... |
b27b0bc89138f1de18290b71c860fc841504d5c90c7ce557ae87c88949b36ae4 | Python | 4,403 | 119 | """Script to build a small sample EDF from an EDF recording. The recording header and list of signals are kept the same.
The content of data is white noise. The output file is named input_sample.edf
"""
import argparse
import os
import shutil
from itertools import islice
from pathlib import Path
from makeSampleEdf im... |
dc13dee8858e49f3fc825fd660e0eef105a20c9a6136bf686d4fd52f20fbf6aa | Python | 4,403 | 111 | import sys
sys.path.insert(0, '..')
import matplotlib.pyplot as plt
import numpy as np
import time
from glmhmm import glm, glm_hmm
from glmhmm.utils import permute_states, find_best_fit, compare_top_weights
from glmhmm.visualize import plot_model_params, plot_loglikelihoods, plot_weights
import matplotlib as mpl
mpl.... |
8d2e4b555baad4eeebdce97c53839bc127ef84b277a0ba45645c88e4d3cedd50 | Python | 4,404 | 133 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
93e265d06af3989c02bf23d91d38f40fb91aad641301305da48c6df8fa33c8a4 | Python | 4,404 | 106 | import anndata
import pandas as pd
import numpy as np
import random
import torch
# 固定随机种子
def set_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if hasattr(tor... |
9efa4f4b9684ea8804760c8fe33efe159349e3ffddbb27569e643cc7ff63d27c | Python | 4,404 | 169 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 24 09:37:30 2022
To create the paper figure from here, run several times with different values
of dirnameB and the ROI file (muscle/prostate)
@author: pbolan
"""
import numpy as np
import matplotlib.pyplot as plt
from os import path, getcwd, maked... |
4733518d8dabf277eb10b0a787f47c0150e4ea6046dfa7ffe1240a19f01c8165 | Python | 4,405 | 122 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model.warplayer import warp
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Con... |
a7e89b8112caf642248975a80adab849a9e04d910151a3483b48602906382842 | Python | 4,405 | 146 | # encoding: utf-8
"""
This module provides the base definition for patterns.
"""
import re
from .compat import unicode
class Pattern(object):
"""
The :class:`Pattern` class is the abstract definition of a pattern.
"""
# Make the class dict-less.
__slots__ = ('include',)
def __init__(self, include):
"""
... |
dfae16d928027d6de90c81493a365251bbd9cdb2611ca66043d17f0997622b2a | Python | 4,406 | 127 | import os
from math import ceil
from os.path import join
from typing import Dict, List
import lightning.pytorch as pl
import merlin.io
from merlin.dataloader.torch import Loader
from merlin.dtypes import boolean
from merlin.dtypes import float32, int64
from merlin.schema import ColumnSchema, Schema
PARQUET_SCHEMA = ... |
f7be44a47d6e48f6555651e64dce06e85c4a3946d2ed15f3acdb8895153faa2c | Python | 4,406 | 182 | import pdb
from discontinuous_galerkin.base.base_model import BaseModel
import numpy as np
class AdvectionEquation(BaseModel):
"""Advection equation model class."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model_params = {
'advection_velocity': 0.5*np.pi,
... |
9b4662319b5973c7c92caef1aa7fc7fbb72e3a53553b70d0a4fcc85b0487e02d | Python | 4,407 | 115 | import logging, os, sys
import numpy as np
from numba import njit
class EarlyStopper:
def __init__(self, patience=10):
self.patience = patience
self.counter = 0
self.max_acc = 0
def early_stop(self, acc):
if acc > self.max_acc:
self.max_acc = acc
self.c... |
155abb711130218541cccde5fc03e86b16bc01196a38be2fa1aba81e3993a29d | Python | 4,409 | 86 | # Case 3: female post-mating switch — recursive state (sperm store depleted by oviposition), signed access
import numpy as np, json, sys
from scipy.optimize import minimize
from scipy.special import gammaln
rng=np.random.default_rng(31)
EXT=False
DAYS=10; RDAYS=[1,2,4,7,10]
NAMES=["ln_lv","ln_lmax","ln_K","ln_kappa","l... |
9aa6c5c133b8743d9d0b56435d35291fcb30a3a94dfb248b14d07cde57b75637 | Python | 4,411 | 159 | """
a TabularShortCut for predicting the context, use a lookup table
input specific
"""
import torch
# import torch.nn as nn
import numpy as np
from collections import Counter
from utils import to_sqpth, to_sqnp
class TabularShortCutIS():
def __init__(self, buffer_size=5):
self.buffer_size = buffer_size
... |
62cfdd59ee642640e15a0432b24da4127fa15967e594545d2e62d709b5bc6ed1 | Python | 4,412 | 118 | """
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 logging
import os
import shutil
import time
from typing import TYPE_CHECKING
from torchtnt.fram... |
9a42372e410af91645c319728b8d19792b5c97d0e63b7ac5f26466e3c4c640d2 | Python | 4,412 | 144 | r"""
Preprocessing utilities for single-cell and bulk omics data.
This module provides essential preprocessing functions for quality control,
normalization, dimensionality reduction, and neighborhood analysis of omics data.
It supports both CPU and GPU computation for large-scale datasets.
Core preprocessing function... |
367ae64a9e1d0d05a88f2a8ffdbca7213dfed8bbe63892d6790e587bc8b8c877 | Python | 4,414 | 131 | """
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 contextlib import contextmanager
from copy import deepcopy
from typing import TYPE_CHECKING
impor... |
57b983ad2b7ce0d3943142f2ec88b1c906226be39a7d33f702112f6ee4405c1a | Python | 4,414 | 146 | # Copyright 2023 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
88c032ae6d9f8a5edfd4f2e296e4ded1b5dff7c26f5bb6b3278063520663494a | Python | 4,414 | 147 | import logging
import sys
import torch
import torch.nn.functional as F
from scipy.stats.stats import pearsonr
from sklearn.metrics import average_precision_score
logging.basicConfig(
format="[%(asctime)s] %(levelname)s:%(name)s: %(message)s",
level=logging.INFO,
stream=sys.stdout,
)
logger = logging.getLo... |
045eacb647ef80241b8e175affc3b31ea73201caf0c9a1a9310e9d2dd659d41d | Python | 4,415 | 107 | r"""
Microbiome analysis utilities.
Downstream 16S / amplicon analysis on top of the AnnData produced by
:mod:`omicverse.alignment.amplicon_16s_pipeline`
(samples × ASVs, ``var`` carries 7-rank SINTAX taxonomy + ASV sequence).
Classes
-------
Alpha : Alpha-diversity (Shannon / Simpson / Chao1 / Observed / Faith PD)
B... |
ae2802d7360c7bb5d2c7dff16e826f4e951a0b2773f2e3b53b055a6f6ec3c961 | Python | 4,415 | 141 | import signal
import warnings
from multiprocessing import Pipe, Process
import numpy as np
from torch.distributions.utils import lazy_property
import pfrl
def worker(remote, env_fn):
# Ignore CTRL+C in the worker process
signal.signal(signal.SIGINT, signal.SIG_IGN)
env = env_fn()
try:
while ... |
558a3dbcc95895c0344545434f1176b94a8e87a85f6e4ce7d1de930665a0bd54 | Python | 4,418 | 103 | """
Plot example ellipse plot(s) in dDR space for A1 and dPEG
each 2x2 inches
"""
import os
rdir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
import sys
sys.path.append(rdir)
from settings import RESULTS_DIR
from helpers.path_helpers import local_results_file
import helpers.loaders as loaders
impo... |
c2c6fd43514941f5de356526d832837989eb12c2dbc3b09f7cf61270c67bec96 | Python | 4,420 | 102 | import logging
import time
from typing import Dict, List
from ray.autoscaler._private.constants import RAY_AUTOSCALER_AVAILABILITY_RECOVERY_S
from ray.autoscaler.v2.instance_manager.instance_manager import (
InstanceUpdatedSubscriber,
)
from ray.autoscaler.v2.schema import NodeType
from ray.core.generated.instance... |
05170666589f68e5211cc70f616893d227678cf94f8e31841ac5da5ff35dd1ef | Python | 4,421 | 114 | import re, glob, os
import tensorflow as tf
from create_tfrecords import parse_tfrecord_fn
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report, cohen_kappa_score, confusion_matrix, f1_score
import seaborn as sns
import scipy.io as sio
def calculate_metrics(y_trues, y_pr... |
534ca6367e9b112723507fc90edd8ec1b40745535fa5809f3bb8d4577cb48b88 | Python | 4,422 | 121 | import torch
import torch.nn as nn
from transformers import BertConfig, BertModel
class AttentionPool(nn.Module):
"""Attention-based pooling layer."""
def __init__(self, hidden_size):
super(AttentionPool, self).__init__()
self.attention_weights = nn.Parameter(torch.randn(hidden_size, 1))
... |
734259252e3c5f5291e835077f2cacedc8967faebaa8df30fcfbf235f4b4b160 | Python | 4,422 | 114 | import numpy as np
import pandas as pd
from tqdm import tqdm
def determine_metacell_open_peaks(
atac_meta_ad,
peak_set=None,
low_dim_embedding="X_svd",
pval_cutoff=1e-2,
read_len=147,
n_neighbors=3,
n_jobs=1,
):
"""Determine the set of peaks that are open in each metacell.
:param ... |
64c818a6fc0fb85d250a1a50194e0d0e89f69ef165f8321373b111c5449ae407 | Python | 4,423 | 102 | # Ultralytics YOLO 🚀, GPL-3.0 license
import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
class DetectionPre... |
9b6cb5d2b8a4716676121c90516b0694bde3f92c04d3b59a85bcf5cdb0f74a6d | Python | 4,426 | 78 | import os
import subprocess
import scipy.ndimage
import numpy as np
import nibabel as nib
from utils import orientation, find_bbox
from skimage.morphology import dilation, cube
class CreateInput():
def __init__(self):
self.atlas_path = 'mni_icbm152_t1_tal_nlin_asym_09c.nii.gz'
self.atlas_seg_p... |
d1b00ee58ad1981a24c696c1015d2c5bf7a7be5b51b87f2685d8093cef703a9d | Python | 4,426 | 138 | import os
import pandas as pd
from pyfaidx import Fasta
from progress.bar import Bar
'''Get the length of chromosomes'''
def get_chrom_size(path):
chrs = {}
with open(path, 'r') as file:
# read the file line by line
for line in file:
if line.startswith('#'):
conti... |
77f26765d87384f3e0d70e0c3e7d24be0b0fe5fc893c67dd6502725d06c1d159 | Python | 4,427 | 102 | import os
import argparse
import numpy as np
def modified_zscore(signal, mad_threshold=3.5, consistency_correction=1.4826):
median = np.median(signal)
dev_from_med = np.array(signal) - median
mad = np.median(np.abs(dev_from_med))
mad_score = dev_from_med / (consistency_correction * mad)
x = np.where(np.abs(mad_... |
1e619b8961764f348fe0f0c637daea5b786aad8521d8dd6c47101e2dc198e2fc | Python | 4,428 | 104 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import pickle
import numpy as np
from model.model import Encoders, NN
from model.layers import Losses
from model import ... |
7937fc5540dbdbdaffb3662dcf918a352e185d58f4d2b7bb8b87c61483b3489d | Python | 4,429 | 133 | import torch.nn as nn
from monai.networks.blocks import Convolution
from monai.networks.layers import Norm
class TamingDiscriminator(nn.Module):
"""
MONAI based implementation of the PatchGAN Discriminator in [1] which is based on [2].
Args:
dimensions: number of spatial dimensions.
in_c... |
f5332b64d9cbe7855a7fa52fbdf2bf4923086a973f97d4bf4019484e065ea38f | Python | 4,429 | 115 | #!/usr/bin/env python
#!/usr/local/bin/python
from __future__ import print_function
import numpy as np
import tensorflow as tf
import layer
import speech_data
from speech_data import Source,Target
from layer import net
import time
start=int(time.time())
display_step = 1
test_step = 10
save_step = 100
learning_rate = 0.... |
1d88ab5b145a747558865f424ad867ebaac653623d6bad04cd5fe211569fc256 | Python | 4,430 | 131 | # Copyright 2023 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
6c72bf39d402a668001018794a863ba11eb5bf57de95204d0a4129ec0ee9635b | Python | 4,430 | 131 | # Copyright 2023 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
f0bc293427ce8d14fa8a5bdb254df8249b2494323db69460f21e6a20dcd71810 | Python | 4,430 | 116 | import nibabel as nib
import numpy as np
from scipy import ndimage
import sys
import csv
import os
import glob
def count_nonzero_voxels_in_atlas(atlas_data, image_path):
# Load the second NIfTI image
image_img = nib.load(image_path)
image_data = image_img.get_fdata()
# Extract connected components for... |
4ff6378b3b630c08bae2ef632577e6bddbdea5bfdc859afbaae8d39aba0c55ae | Python | 4,433 | 128 | ####################################################################################################
# Copyright (c) 2020 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
98ccfa5e60df649c971280173e1e0b8ffaf83149e0c7b35d90f0e76205cae86f | Python | 4,434 | 118 | from __future__ import annotations
import numpy as np
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
class Scope:
"""
The rectangle area
Parameters
----------
min_x, max_x, min_y, max_y: float
Axis of th... |
fb9ab189b38af08e75c02a317de1f36ff6b2dcf2f7e06c70814c86e255b9f04d | Python | 4,436 | 147 | from unittest.mock import Mock, patch
import pytest
from data_profiling.model.expectation_algorithms import (
categorical_expectations,
datetime_expectations,
file_expectations,
generic_expectations,
image_expectations,
numeric_expectations,
path_expectations,
url_expectati... |
0e1f0f3908ccf2db0b39a5e7a11e89fff9305789d41d5cdd019c653dff3998d2 | Python | 4,437 | 126 | import collections
import numpy as np
from pfrl.collections.prioritized import PrioritizedBuffer
from pfrl.replay_buffers.replay_buffer import ReplayBuffer # NOQA
class PriorityWeightError(object):
"""For proportional prioritization
alpha determines how much prioritization is used.
beta determines ho... |
9739de5b6c6f7e9f549b99ab10969e9b1b29eadb62e7e1bebd11fd358d433688 | Python | 4,438 | 124 | # --------------------------------------------- #
# This script performs endstate corrections
# --------------------------------------------- #
system_name = "test1"
n_samples = 1_000
n_steps_per_sample = 1_000
run_id = 1
traj_base = f"{system_name}/equilibrium_samples/run{run_id:0>2d}" # define directory containing MM... |
d747772d2b5552fcef816ffa0287f94e2aa3ceb97471b6afd53bbc3b2662d143 | Python | 4,438 | 130 | import os
import time
import torch
from scipy import spatial
from torch.utils.data import Dataset
from .fourier import apply_fourier_mask_to_tomo
from .missing_wedge import (get_missing_wedge_mask,
get_rotated_missing_wedge_mask)
from .rotation import rotate_vol_around_axis
BASE_SEED = 88... |
b3eca3bcd31390b52916eded98f4397883f4ad1f7739b5f471654772e55acff1 | Python | 4,439 | 132 | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
#In order for this code to be executed one needs the detailed spreadsheets that are created after each model's training
#and testing process. The code can then read the spreadsheets and calculate the confidence intervals based on the method
#described in Appendix A1. Th... |
ae9cc150ec34a3fe2191b91c5938a10eed7d311bd623dc4ad1006e9f06993dfd | Python | 4,440 | 123 | #!/usr/bin/env python3
"""Functions for plotting gene-gene graphs."""
import logging
import numpy as np
from matplotlib import pyplot as plt
# Set a standard visual style:
standard_visual_style = {}
standard_visual_style["vertex_size"] = 10
standard_visual_style["vertex_color"] = "slateblue"
standard_visual_style["... |
fad714b9c85de1431453b3d26f52f4f517bf84018cfe80743d24bac17504387d | Python | 4,440 | 129 | # -*- coding: utf-8 -*-
# File : comm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import queue
import collections
import threading
... |
03612dd47a621d2c46c598cbb5f32f39ecc541e3372f19ea45da853cb346d363 | Python | 4,441 | 99 | from typing import Any, Dict, List, Literal
import numpy as np
import pandas as pd
from .distance import perceptual_distance
from .logging import logger_manager as lm
from .mapping import embed_graph, map_graph
from .utils import extract_palette
def assign_color(
cluster_distance_matrix: pd.DataFrame,
color... |
41825f758bdb56d7f694234ae1645699d407b91a02260deaadd3656834c3a8af | Python | 4,443 | 149 | import numpy as np
from scipy.interpolate import interp1d
from lib5c.util.mathematics import gmean
from hic3defdr.util.binning import equal_bin
from hic3defdr.util.progress import tqdm_maybe as tqdm
def no_scaling(data):
"""
Computes dummy size factors of 1 for each replicate.
Parameters
----------... |
0e9b0206b8859266673db7323571c0adfaadfcc0704b69100e01ecfa1466b38c | Python | 4,444 | 89 | import logging
from datetime import datetime
from transformers import AlbertForSequenceClassification, BertForSequenceClassification, FunnelForSequenceClassification
from agent.classifiers.data.transformer_text_triple_fc_dataset_mgmt import get_datasets, annotate_dataset
from agent.classifiers.transformer_classifier_... |
2e13fc5b8a332338b102fffee47499e9f157c93b5313d19ba5f71af9e49dac49 | Python | 4,444 | 129 | import logging
import os.path as osp
from pathlib import Path
from typing import Dict, Optional, Union
import tabulate
from mmengine.dist import master_only
from mmengine.hooks import Hook
from mmengine.logging import print_log
from lqit.registry import HOOKS
@HOOKS.register_module()
class SummarizeHook(Hook):
... |
9b9ebaef1b270ee2f9b4bf985ca711483a97544978fa73c8e1531a8286715630 | Python | 4,445 | 127 | import logging
import uuid
from collections import defaultdict
from typing import List, Optional
from ray.autoscaler.v2.instance_manager.instance_manager import (
InstanceUpdatedSubscriber,
)
from ray.autoscaler.v2.instance_manager.node_provider import ICloudInstanceProvider
from ray.autoscaler.v2.metrics_reporter... |
1fbe1d1ede25ba2b61c8b10133a8e01a1988f5473944f2c51d3f379c9e60bae3 | Python | 4,448 | 145 | import os
from itertools import repeat
from tqdm import tqdm
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.utils import subgraph, to_networkx
from .molecule_motif_datasets import rdkit_functional_group_label_features_generator
class Molecule3DMaskingDatas... |
484fccfa483022619bfa7812e74412c5256ab741ade2e836f66bae74f6c35a43 | Python | 4,449 | 120 | #!/usr/bin/python
"""Class for handling an adjacency matrix."""
# --------------------------------------
# Class for handling an adjacency matrix
# Updated: 06/28/21
# --------------------------------------
from .utils_adjacency import (
prune_degree,
prune_scale,
prune_knn,
zscore_from_bivariate_cutoff... |
8c819b1366071f1545301fbcb1385a63cce47542af1b9e58a602c3dfbad584fa | Python | 4,449 | 115 | '''
image_transformations.py
A simple program that transforms images from one camera coordinate to another.
Requirements:
Users should install the following python packages before using this module:
matplotlib
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
Kinect For Azu... |
d87e6b4d043d95ad6654e36156fcf59b46d94091e51bbe5045cecad2e4ef3b05 | Python | 4,449 | 169 | from unittest import TestCase
import xarray as xr
import numpy as np
from shapely.geometry.polygon import Polygon
from shapely.geometry import LineString
from scipy.interpolate import RegularGridInterpolator
import neuron_morphology.layered_point_depths.__main__ as lpd
class TestUtilities(TestCase):
def test_tr... |
2694c7e59dead457f21409268b8ad5fc949076fe5c2ebfc9610f41994b50a9ee | Python | 4,450 | 123 | """Options for calculating uncorrected p-values."""
__copyright__ = (
"Copyright (C) 2016-2018, DV Klopfenstein, H Tang et al., All rights reserved."
)
__author__ = "DV Klopfenstein"
import collections as cx
import sys
class PvalCalcBase(object):
"""Base class for initial p-value calculations."""
def _... |
5506dac9894318f6b8f62e858165167464b22ad9cadb348486b2d5dc0dfef3bd | Python | 4,450 | 140 | # Copyright 2023 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
530f5eaac2d534ee34daefd4fe74fce20345cd226350efc41a7d5296cb077ba3 | Python | 4,451 | 144 | from tqdm import tqdm
import numpy as np
import torch
from torch.nn.functional import cross_entropy
from utils.mask_utils import generate_tgt_mask
from utils.data_utils import (
correct_trans_output,
convert_log_into_label
)
from utils.data_utils import eval_trans as data_eval_trans
def warmup_lr_scheduler(o... |
099450a5fdbbb361668abfccc37e36eb20149987e6788c945c9e2cdf0d40b33b | Python | 4,453 | 129 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as td
__all__ = [
'BerpoDecoder',
]
class BernoulliDecoder(nn.Module):
def __init__(self, num_nodes, num_edges, balance_loss=False):
"""Base class for Bernoulli decoder.
Args:
... |
e43672e87ca56ec049a9d67cd7c7b127f63581098132af8e1d35e4a120c44fad | Python | 4,455 | 107 | # import the main libraries required for the preprocessing
from mne_bids import read_raw_bids
import mne
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
# import homemade function
from technical_validation_utils import get_raw_condition, get_emg_power, plot_power, subject_files
mne.set_log... |
d7b4f23cdd64d635f25e8625d04a9174070888aac59e5a62c2f20f853980a08e | Python | 4,456 | 138 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
# pylint: disable=too-many-lines
"""RPU configurations presets for resistive processing units."""
from typing import Optional
from dataclasses import dataclass,... |
59c2c9d2b1db018b6f16f8ab5677a498bf76cbd092a609017b3174d16a642dea | Python | 4,459 | 108 | #!/usr/bin/env python3
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Invokes clang-format and reports formatting errors
# Optionally can cause clang-format to run the format in place with --reformat
import re, argparse, sys, subprocess, os.path, xml.dom.minidom
pars... |
68258c6eee355197fc19db251bd81640f02d714f1742f48d1f4ea87a5ca6b616 | Python | 4,460 | 143 | import os
import json
import numpy as np
import darts.models
import torch.optim.lr_scheduler
import torch.nn
from copy import deepcopy
import argparse
import pickle as pk
from sklearn.metrics import r2_score
from src.data.load_data import load_params, load_darts_timeseries
from src.tools import check_path
def comput... |
4c01a5333406ac4a0565d7df4b8ecffeea41605f520e68bc654ef719fcf33e62 | Python | 4,461 | 122 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
# mypy: disable-error-code=attr-defined
"""Defines configuration parameters and conversions to dict
structures for the quantized module base classes"""
from da... |
6b5108b3fddb16a6d961f1560a346216400384cb4d218da17635356be46602d5 | Python | 4,461 | 134 | from data_profiling.config import Settings
from data_profiling.report.formatters import fmt, fmt_numeric
from data_profiling.report.presentation.core import Container, FrequencyTable, Table
from data_profiling.report.presentation.frequency_table_utils import freq_table
from data_profiling.report.structure.variables.ren... |
ca0925428bf4d2bdd33a67494ed3f5383577bbe124dec6ea363f84ab73632461 | Python | 4,461 | 169 | # flake8: noqa
# fmt: off
# __stopping_example_trainable_start__
from ray import tune
import time
def my_trainable(config):
i = 1
while True:
# Do some training...
time.sleep(1)
# Report some metrics for demonstration...
tune.report({"mean_accuracy": min(i / 10, 1.0)})
... |
107d0256183742913d4ec4473f8181102f6d0f682e7ae4d3ae30bd3b67e132c9 | Python | 4,462 | 135 | import logging
import pytest
from sklearn.base import BaseEstimator
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score, make_scorer
from sklearn.model_selection import KFold, train_test_split
from sklearn.svm import SVC
from siapy.core.exceptions import (
InvalidInputError,
M... |
110e7756bf77c69d2e4141977431dcc07b00fd4f5405865a82c8eded55e2fb8e | Python | 4,462 | 132 | """Functions for training"""
import argparse
import json
import logging
from pathlib import Path
import pandas as pd
from lefshift import constants, utils
from lefshift.application_utils import validate_training_input
from lefshift.model import FluorineModel
def add_train_subparser(subparsers):
"""Add train arg... |
b6203217edbd9ec7e6a978870e629c7ecbf6f6bb171c1e85fde7098d42b88b54 | Python | 4,462 | 118 | """Util functions to manage dependency requirements."""
import logging
import os
import tempfile
from contextlib import asynccontextmanager
from typing import List, Optional, Tuple
from ray._private.runtime_env import virtualenv_utils
from ray._private.runtime_env.utils import check_output_cmd
INTERNAL_PIP_FILENAME ... |
26a7cf628b0777b651ef80a8d96fd818b4d7eac3b8a449b4b68aedfd850e27a7 | Python | 4,463 | 153 | import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import pdb
from PIL import Image
from scipy.io import loadmat
from torch.autograd import Variable
from torchvision import transforms
from torchvision.models.segmentation import segmentation as segmentation
impor... |
045ab33bf1aef187fbf5dfa4ebedaa61fd1dd16444aaf5a801d11b0d9024b834 | Python | 4,464 | 160 | import itertools
from math import pi
from typing import Optional, Union
import torch
def points_to_homogeneous(batch: torch.Tensor) -> torch.Tensor:
"""
Transforms points from cartesian to homogeneous coordinates
Args:
batch: the batch of points to transform. Should be of shape
BATCH... |
412752328e7912160f346b7423070a024a39f81ce6676c68663f22cb3338cd18 | Python | 4,467 | 101 | import os
import numpy as np
import pandas as pd
from typing import Optional
from tensorflow.keras import optimizers
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
#from tensorflow_addons.optimizers import RectifiedAdam, TriangularCyclicalLearningRate, Triangular2CyclicalLearningRat... |
9affe83d9b8eeacce06cf540b7f6b02de16f8e397b55a2bbf6213534e6d1a761 | Python | 4,468 | 136 | """Write file into txt file.
If use `split`, the script will base on `proportion` to split the total file
to train and val set, and save `train.txt` and `val.txt` file.
If do not use `split`, the script will write all files into txt file.
Here is an example to run this script.
Example:
python tools/misc/get_uwdata_... |
a0e9dfa2793094d6c608727e8ed6fe25c62089d629fea9f486943e16d83b8c10 | Python | 4,468 | 136 | from pickle import FALSE
from datagenerator_pd import DataGenerator
from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import seaborn as sns
import SimpleITK as sitk
import pandas as pd
import numpy as n... |
2f7e695b3ca90b0fa0a96e1941daa999a8a56146be11f806f6dac0f0455e647e | Python | 4,470 | 124 | """
This script performs a parameter search of the concentration parameter and
alignment jitter under the conjunctive grid by head-direction cell hypothesis.
The results are saved and used by the file 'Produce_mainfigure_conjunctive.py'.
"""
import os
import pickle
import matplotlib
import matplotlib.pyplot as plt
impo... |
48b45a873ca3356202f505b62a0911738b016a35593dfa76c00617300accac6f | Python | 4,473 | 107 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
from run_QTL_analysis import run_QTL_analysis
from qtl_utilities import merge_QTL_results
import subprocess
import numpy as np
import pandas as pd
import pytest
def hdf5_results_checking(filename,fun=lambda df: np.mean(df['beta']) ):
'''For a given hdf5 results file,... |
048da275d709ca6a2b5d6b375812ffd298194ec6352571ba7843c48f9c2b2adf | Python | 4,474 | 115 | # -*- coding: utf-8 -*-
"""
PMLB was primarily developed at the University of Pennsylvania by:
- Randal S. Olson (rso@randalolson.com)
- William La Cava (lacava@upenn.edu)
- Weixuan Fu (weixuanf@upenn.edu)
- Trang Le (ttle@pennmedicine.upenn.edu)
- and many more generous open source contributors
P... |
3f5754e25f87d297155ed014662931595191ddf33ea8a57baab64e0bd030d914 | Python | 4,475 | 117 | from model.models import PepCA
from model.models import PeptideComplexes, PepCA_dataset
from transformers import BertModel, BertTokenizer, pipeline
from time import time
import numpy as np
import torch.nn as nn
import torch
import argparse
import torch.nn.functional as F
import datetime
import warnings
warnings.filterw... |
da09d028c091dea28b4661ad3e6114dc83186ba5d5f717f3eb11c08e660cdaa1 | Python | 4,475 | 123 | import numpy as np
from spinnaker2.neuron_models.application import BaseApplication
from spinnaker2.neuron_models.lif_neuron import LIFApplication
from spinnaker2.mapper import SynapseWordSize, SynapseWordSpec
from spinnaker2.coordinates import ByteAddr
class LIFCurrExpApplication(LIFApplication):
default_params... |
ed2c91a6a8971f87ee87cfc78848baf83f7a2ae264ccdb34a2c1b18ae46864be | Python | 4,477 | 128 | import collections
import os
import imageio
import numpy as np
import torch
from pytorch3dunet.augment import transforms
from pytorch3dunet.datasets.utils import ConfigDataset, calculate_stats
from pytorch3dunet.unet3d.utils import get_logger
logger = get_logger('DSB2018Dataset')
def dsb_prediction_collate(batch):... |
56adac3493d585bf06b6d38574e2d558f7217837fb66b9bc9e3c097d765a7cb0 | Python | 4,481 | 146 | r"""
Various helper functions.
"""
import hashlib
import re
import numpy
import numpy.random
from numpy import pi, sqrt, exp, power
def sample_from_bin_distribution(bins, number_of_samples, seed):
r"""
Samples from a distribution defined by a vector the sum in. The vector doesn't have to add up to one
i... |
65c64c77e881b9be0a15881f8e99cd59baf28da2554c4adea7ae8ec31bdfe989 | Python | 4,483 | 139 | import unittest
import numpy as np
from neuron_morphology.morphology import Morphology
from neuron_morphology.constants import (
AXON, APICAL_DENDRITE, BASAL_DENDRITE, SOMA
)
from neuron_morphology.feature_extractor.data import Data
from neuron_morphology.features.statistics.coordinates import COORD_TYPE_SPECIALI... |
969d9fa9203afaf3a060c17fe6b66eae630c70c6cc5b655329243cb6f3adfd04 | Python | 4,484 | 113 | ####################################################################################################
# Copyright (c) 2020, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is free softw... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.