sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fc8f9dc3ff79a3f85887c8d93ad1669396738cb127ac6b4a95581bc36aed06ae | Python | 6,225 | 205 | import functools
import logging
import os
import sys
import numpy
import wx
from cellprofiler_core.image import FileImage
from cellprofiler_core.preferences import reset_cpfigure_position
from cellprofiler_core.preferences import get_interpolation_mode
from cellprofiler_core.preferences import IM_NEAREST
from cellprof... |
3c853a734df63cda9cd86814e48e27b3bee6d6130e8e976bb206f4eaf2849480 | Python | 6,226 | 223 | from __future__ import division
import numpy as np
from scipy.stats import sem
from matplotlib.pylab import *
import sys
import random
import scipy
from scipy.signal import gaussian
from scipy.ndimage import filters
from joblib import Parallel, delayed
import multiprocessing
from scikits import bootstrap
import seabo... |
65186d53f20b0882a28f3816620e628f02dae91e2fb7040c46e0fa09c0c6cf69 | Python | 6,226 | 181 | import os.path as osp
import sys, os
# sys.path.insert(0, '..')
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.datasets import MNISTSuperpixels
import torch_geometric.transforms as T
from torch_geometric.data impor... |
dcbe658de0658d96451221834ad508470f17fd0de1f088029bbcb1fdb5232324 | Python | 6,234 | 148 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 17 17:56:34 2021
@author: Manuel
Create ImageDataGenerator and define options for training the CNN.
"""
__author__ = "Manuel R. Popp"
os.chdir(wd)
# import modules---------------------------------------------------------------
import tensorflow as... |
af7421a1ad9957f1cc13af19b3e2a2baf520962b652d76b52abba704c9d8f874 | Python | 6,235 | 186 | import wx
import wx.grid
from cellprofiler_core.preferences import get_error_color
from cellprofiler_core.setting import Table
class TableController(wx.grid.GridTableBase):
DEFAULT_ATTR = wx.grid.GridCellAttr()
ERROR_ATTR = wx.grid.GridCellAttr()
ERROR_ATTR.SetTextColour(get_error_color())
def __init... |
882a9f856afc33c1218e075774c4062c10423813342ab1f8339a1e31e6844e98 | Python | 6,237 | 176 | """Per-landmark rater-reliability prior for the guided-learning tutor.
Loads ``rater_reliability.json`` — the distribution of trained-rater anatomical
fiducial localization error (AFLE) for each of the 32 AFIDs, computed from the
afids-data multi-rater release by ``compute_reliability.py`` — and exposes it in
three fo... |
36cdcac2f7b07723257b73e7d05d42b94e0a135532ac46e1ecff83f4d174043c | Python | 6,241 | 212 | # -*- coding: utf-8 -*-
"""plots.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1pehuxZfPEnI5XcBclFG_WmMH0JHWsbOl
"""
!pip install cufflinks
import pandas as pd
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, i... |
1a8081bd19b3c7942c8edba55fc932bc8e22a6fa88a9cdef9c3eaf9e98b10ba8 | Python | 6,250 | 170 | #!/usr/bin/env python3
"""
Produces a csv file mean voxel/vertex time series from a functional file <func>
within a seed mask <seed>.
Usage:
ciftify_meants [options] <func> <seed>
Arguments:
<func> functional data can be (nifti or cifti)
<seed> seed mask (nifti, cifti or gifti)
Options:... |
4e1a5be1c15d48e350132f2a4e66a0b94b9b899beb6c86c6f309de72946156a1 | Python | 6,250 | 157 | import matplotlib.pyplot as plt
import numpy as np
import logging
from dragen.utilities.InputInfo import RveInfo
from dragen.utilities.Helpers import HelperFunctions
class Tesselation2D(HelperFunctions):
def __init__(self, grains_df):
super().__init__()
self.grains_df = grains_df
self.a ... |
d76195bc09859a5a9c7980093350827f810f10302abbf7cd8c14d53036beeab0 | Python | 6,256 | 185 | """
Script containing tools for working with reference grid.
Created on 26 May 2023
@author: Nejc Čož, ZRC SAZU, Novi trg 2, 1000 Ljubljana, Slovenia
"""
from pathlib import Path
import geopandas as gpd
import numpy as np
import pandas as pd
import rasterio
from rasterio.features import shapes
from shapely.geometry i... |
68330762e92335e3fd86885708a5b7db639f46957e0dba6ea9142e1ffff7a18a | Python | 6,258 | 197 | #!/bin/env python
"""
STEPS nowcast
=============
This tutorial shows how to compute and plot an ensemble nowcast using Swiss
radar data.
"""
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
from pprint import pprint
from pysteps import io, nowcasts, rcparams
from pysteps.motion.luca... |
b1798f42c711f1f7535a549a64cf780991c1a6a24351addd6e58f823440ce279 | Python | 6,259 | 120 | #! /usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys, os, glob, gzip, time
from functools import partial
from importlib import import_module
## Math
import numpy as np
## tensorflow imports
import tensorflow.compat.v1 as tf
tf.... |
862e2c012994feca926ec20917a882f88db7592c426239e3bed00d3f2fd901ea | Python | 6,274 | 196 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 20 22:39:20 2019
@author: nchapochnikov
Here we are testing that the linear ORN-LN model circuit is converging to
the theoretical pca solution in the linear case
ALL TESTS PASS
"""
import numpy as np
import matplotlib.pylab as plt
import time
i... |
38e19d15bf677c43b9b52df224f4218975753683f1fcd4f79253e79d3dc41d0f | Python | 6,275 | 179 | """
Miscellanous functions to handle models.
Date: September 2018
Author: Ignacio Heredia
Email: iheredia@ifca.unican.es
Github: ignacioheredia
"""
import os
import json
import numpy as np
from tensorflow import keras
from tensorflow.keras import applications
from tensorflow.keras import regularizers
from tensorflow... |
d1da9ee60ff91824c6298ed56c329f0923021a68c7cc057a267d3544676860b2 | Python | 6,281 | 200 | #!/usr/bin/env python3
import time
import bct
import numpy as np
import pandas as pd
from brainspace.gradient import GradientMaps
from nilearn.connectome import vec_to_sym_matrix
from scipy.stats import zscore
def get_edge_timeseries(timeseries):
"""This function will take a timeseries of brain areas, and calcu... |
3d338b9a997649aa9c53b03e95da271ccc4efce2929c5e9055c83be3a37a3267 | Python | 6,290 | 165 | import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
#learn f(x) such that x is the wt and f(x) is the CTCFKO - then i have to clean and label in a different way.
#or x is the CTCFKO and f(x) is the DKO
class LastLayerNN(nn.Module):
def __init__(self):
super(LastLayerNN, se... |
6623a89d9bedd73234cfc62184d39b618342d85b1e41a8c728ca9f47910485ba | Python | 6,305 | 170 | import torch
import torch.nn as nn
#import torch.nn.functional as F
import torch.utils.data
#from torchvision import transforms, models
from torch.utils.data import TensorDataset, DataLoader
import copy
from sklearn import metrics
#import wfdb
#import ast
import pandas as pd
import numpy as np
from vajiraGAN_original... |
5e34b559720843f2f7f56ddeb679c18daac283f2714247b506579c2b02f4fc62 | Python | 6,307 | 137 | from unittest import TestCase
import numpy as np
from skimage.measure import regionprops
from numpy.testing import assert_equal, assert_almost_equal
from .form_feature_extraction import FormFeatureExtraction
def _convert(props, key):
p = np.array([prop[key] for prop in props])
if (p.ndim > 0):
p = p... |
7876bd04025bfedbc6f1e8751fdbc7ebdd233fbc60b4061e9faaef76f4049807 | Python | 6,307 | 195 | import numpy as np
from netCDF4 import Dataset
import os
#===========================================================================
# Function for converting from longitude to X-dimension
#===========================================================================
def lon_to_xdim(lon_st, lon_nd, lon_int=2.5, lon_ini... |
8829c690e3caa8cba7e0a058ee0ed0f9b90ae1f860dbc7a7b8a6dcbc0b31f6ca | Python | 6,310 | 170 | """
Gather all module's test
Date: December 2019
Author: Ignacio Heredia
Email: iheredia@ifca.unican.es
Github: ignacioheredia
"""
import glob
import json
import os
import shutil
import subprocess
import time
from urllib.parse import quote_plus
from imgclas import paths
module_name = 'imgclas'
test_url = 'https://f... |
343cd40e50cbcd8951cde5cb6e63ad80347646bf7830da88789e6485d816c149 | Python | 6,315 | 184 | #!/bin/env python
"""
Precipitation downscaling with RainFARM
=======================================
This example script shows how to use the stochastic downscaling method RainFARM
available in pysteps.
RainFARM is a downscaling algorithm for rainfall fields developed by Rebora et
al. (2006). The method can... |
b8636363ac0554a28f5a8f091b98a10bce15d630258bd21bcdb98240b38028a4 | Python | 6,315 | 233 | #
# nate berry
# 2023-04-01
#
# ---------------------------------------------------------
# NOTES:
# - this script contains a varity of functions that are
# called in the brain-body analyses...
# - the included functions are specific to importing, exporting,
# processing, etc.
# - all functions associa... |
c8955159b10326c8950b36673feebf7d2f5503bbf7eadc40dc30046d576ce7d5 | Python | 6,315 | 169 | # coding=utf-8
# Copyright 2020 The SimCLR 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 applicable law or... |
ed60b2f1a008731c734ad7cb77a0add3eb926f017e365edf9d0060cb989b6bd9 | Python | 6,318 | 168 | import os.path as osp
import time
import pytest
import h5py
import numpy as np
from ptsa.data.readers.hdf5 import H5RawReader
from ptsa.data.readers.binary import BinaryRawReader
from ptsa.data.readers.events import CMLEventReader
from ptsa.data.readers import EEGReader
from tests.utils import get_rhino_root, skip_wi... |
568fbfcb8eba26e4f4458dab23f1173a8586339ae82debcc6354a4e86b29c715 | Python | 6,320 | 165 | # coding=utf-8
# Copyright 2020 The SimCLR 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 applicable law or... |
e4913e48aacdf227e66aa47cee2a9a571a70deacf451540d76ef731f865c1619 | Python | 6,324 | 215 | import os
import numpy as np
import pkg_resources
from scipy.stats import zscore
from sklearn.model_selection import GroupKFold, RepeatedKFold
from etspredict.data.hcp_ya_pheno.get_data import load_behavioural_data
from etspredict.pipelines.ioutils import configure_output_name
from etspredict.prediction.model_grids i... |
ba8b4f5ba878c955c5251cc967603c58371dac44763664d0314d36643ab753e0 | Python | 6,326 | 135 | import numpy
from numpy.typing import NDArray
from pydantic import Field, validate_call, ConfigDict, BaseModel
from typing import Annotated, Tuple, Union, Optional, List, Any
from cellprofiler_library.opts.measureobjectoverlap import DecimationMethod, Feature, C_IMAGE_OVERLAP
from cellprofiler_library.types import Obje... |
29fa192fa5b6ef3d5079fe7dd63960e6ce5eafb3c695773c6db11fc1c99b92a1 | Python | 6,337 | 191 | import warnings
import pdb2sql
from deeprank.features import FeatureClass
try:
import freesasa
except ImportError:
warnings.warn('freesasa module not found')
raise
class BSA(FeatureClass):
def __init__(self, pdb_data, chain1='A', chain2='B'):
"""Compute the burried surface area feature.
... |
c0e437a634225a546f136cfda7e1471b0731df341fe8112a0b6a6dd5202987f2 | Python | 6,339 | 190 | """
This file implements a Hopfield network. It provides functions to
set and retrieve the network state, store patterns.
Relevant book chapters:
- http://neuronaldynamics.epfl.ch/online/Ch17.S2.html
"""
# This file is part of the exercise code repository accompanying
# the book: Neuronal Dynamics (see http://ne... |
a9c29f84dc7326c7e02906c63f7eb0a2dab780216eebf5348d4514318c8ac015 | Python | 6,341 | 193 | import pickle
import numpy as np
import ray
import torch
import yaml
from yaml.loader import SafeLoader
import pdb
import os
import matplotlib.pyplot as plt
from tqdm import tqdm
import wntr
import networkx as nx
from DT_for_WDN_leak_localization.inference.forward_model import ForwardModel
from DT_for_WDN_leak_localiz... |
f2063acf3899860ab4ee5ec6126e17ef18bd75945c7a9cd3c533bb96d31f713a | Python | 6,341 | 196 | import os
import shutil
import csv
import sys
def getMax(list, gene, position):
aaList = ["A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"]
ind = 0
max = 0
for i in range(0,len(list)):
if(float(list[i]) > max):
ind = i
max = float(list[i])
... |
0cfabeefc08adc56ddfe72aa0c4da02ca67c9f091719d18d15d975edae919a8b | Python | 6,351 | 157 | """
File name: plot_results.py
Author: Esra Zihni
Date created: 27.07.2018
This script creates plots for performance assessment and feature importance
assessment. It reads the implementation and path options from the config.yml script.
It saves the created plots as .png files.
"""
import itertools
import os
import p... |
213b773d04f14c86a850946db4e8b6e33dd8788c801147e742a769628255c4e4 | Python | 6,351 | 197 | """
Top-level trial data bandpower processing
Authored by Eric Easthope
"""
import os
import warnings
import numpy as np
import scipy.signal as sig
import resampy
import multiprocess as mp
from pathlib import Path
from utils import log_scale_bands
# Filter warnings
warnings.filterwarnings("ignore")
DATA = "data"
DE... |
8bf28da727ae11d952d3cc58512d8271335cb0e29acebed3d8df640ff007a6b7 | Python | 6,355 | 156 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 5 15:58:38 2021
@author: filtsem
"""
from pathlib import Path
import numpy as np
import eelbrain
import re
import os
import mne
import pandas as pd
from mne.stats import (spatio_temporal_cluster_test, f_threshold_mway_rm, f_mway_rm)
from mne impo... |
a2e8d865bb4d3dd7b4c21beab257b5eea49d85f76b01abdd9211276da0d31a99 | Python | 6,356 | 164 | #
# nate berry
# 2023-04-01
#
# ---------------------------------------------------------
# NOTES:
# - this script calcualtes cross-RQA for COM and EEG data
#
# ---------------------------------------------------------
# ---------------------------------------------------------
import pandas as pd
import numpy as... |
4984f699ac8283cd9684b3bf92b9991fca2a74fab12cf68abe717c478fa34be3 | Python | 6,362 | 191 | # -*- coding: utf-8 -*-
import os
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert
pytest.importorskip("h5py")
# tests for three OPERA products:
# Odyssey rain rate composite (production discontinued on October 30th 2024)
# CIRRUS max. reflectivity composites
# NIMBUS rain rate composite... |
79200a4e06a23426c08d1f145ed6caee56657e35f9a12a7e9b1a01df86ec91b6 | Python | 6,365 | 271 | # Copyright (C) 2014 SWIPO Project
#
# Authors (this file):
# Stefan Schinkel <stefan.schinkel@gmail.com>
"""
Provides sanity checks for basic for parallel and serial circiuts.
"""
import numpy as np
import networkx as nx
from pyunicorn import ResNetwork
from .ResistiveNetwork_utils import \
makeNW, parallelCop... |
ffd76b758a07b22bde70d4f58122a81f4f136e0639d46d123249a039543e3650 | Python | 6,372 | 184 | # -*- coding: utf-8 -*-
#
# experiment.py
#
"""
Features selection and classifications
"""
__author__ = "Ahmed Albuni"
__email__ = "ahmed.albuni@gmail.com"
import datetime
import logging
import os
import time
from collections import Counter
from pathlib import Path
import matplotlib.pyplot as plt
import numpy
impo... |
955986e7d9020da167f1cdfb149f5cbbd4feac965d5395254d5ba96339528c04 | Python | 6,377 | 173 | import os
import sys
import unittest
from time import time
import shutil
from tempfile import mkdtemp
import logging
from unittest.mock import patch, MagicMock
import numpy
import h5py
from nose.tools import eq_, ok_
from deeprank.generate import *
from deeprank.models.variant import PdbVariantSelection
from deeprank... |
88c42ce5ebe739cc06b998c789c9cd38abfa09852559a9476d3cbc017e08acea | Python | 6,384 | 208 | from collections import namedtuple
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.image
from ...util import dtype as dtypes
from ...exposure import is_low_contrast
from ..._shared.utils import warn
from math import floor, ceil
_default_colormap = 'gray'
_nonstandard_color... |
c82fd5bc5e44c0b2adf1165261cb2f318aa76db3d0a831304e3dd9e5538efe97 | Python | 6,393 | 183 | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... |
351500d420fea048c1a8e7a4bc270264f4295299961217cb1c4ac845b0cb6ff2 | Python | 6,398 | 216 | import os
import subprocess
import sys
from pathlib import Path
import pytest
import xgboost
from xgboost import testing as tm
pytestmark = tm.timeout(30)
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
CLI_DEMO_DIR = os.path.join(DEMO_DIR, "CLI")
PYTHON = sys.executable
... |
b0610c815d2e9711f6f9fbe6f0505833ce39a60e8b8148d3f4cebb7b4a232874 | Python | 6,398 | 216 | import time
import sys
import traceback
import codecs
from copy import deepcopy
from io import UnsupportedOperation
from multiprocessing import Queue
from queue import Empty, Full
from subprocess import CalledProcessError, PIPE, Popen
from threading import Thread
import os
sys_kwargs = {}
if not os.name == 'nt':
f... |
46d670d3859c77e9ebd0335374bb2db5c3e3e6631b0797cf05f99290410f8c86 | Python | 6,399 | 174 | import numpy as np
import sys
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os.path as osp
import os
sys.path.append('..')
from functions.def_ROIs_WangParcels import roi
from functions.def_ROIs_EarlyVisualAreas import roi as ROI
from functions.individual_variability import grab_data... |
90e4fef668acf6b8f65ade1ac9387d99115d8fd3572e1d0dfdb74a0be902368b | Python | 6,404 | 140 | """
Author: Mingjian He <mh1@stanford.edu>
gen module contains general Gaussian state-space model methods used in SOMATA
"""
from somata.basic_models import StateSpaceModel as Ssm
from somata.exact_inference import inverse
import numpy as np
from scipy.linalg import block_diag
class GeneralSSModel(Ssm):
"""
... |
b28fb2820dca0235e002ca755410e68c627bdd7d36be288b397846aff01f142d | Python | 6,407 | 169 | """Torch modules for graph related softmax."""
# pylint: disable= no-member, arguments-differ
import torch as th
from dgl.function import TargetCode
from dgl.base import ALL, is_all
from dgl import backend as F
from dgl import utils
from dgl.graph import DGLGraph
from dgl.heterograph import DGLHeteroGraph
__all__ = [... |
57e4ff5162e3bf9b3640c5152db04d7f1000dcb40ddeea1bb91fa20c1a969882 | Python | 6,408 | 130 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
Main pipeline script for processing ASL MRI data.
Repository: https://github.com/JSIERO/ClinicalASL
Author: Jeroen Siero
Institution: UMCU (University Medical Center Utrecht), The Netherlands
Contact: j.c.w.siero@umcutrecht.nl
Description:
Out... |
b02f6c39870892412590b6c26495efb2d14866589f47e0c354b3a6c9ef4f9ae9 | Python | 6,408 | 188 | import torch
from torch.utils.data import DataLoader
from rdkit import DataStructs
from rdkit import Chem
from multiprocessing import Pool, cpu_count
import pandas as pd
from rdkit.Chem import AllChem
from sklearn.metrics import r2_score
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib im... |
074d87415deb3cb0ef52b002c8c6daf941b9e7c246b28ecdefb11bca785bcdad | Python | 6,427 | 200 | import os
import pickle
import logging
import torch
import numpy as np
import time
from tqdm import tqdm
from torch import nn
from sklearn.metrics.pairwise import cosine_similarity
from .metric_learning import *
from transformers import (
AutoTokenizer,
AutoModel,
)
LOGGER = logging.getLogger()
class Mode... |
2d2168fd3b421f01614e127d8bb6a05858f1571d5a0011c2ddb21454b45e14fb | Python | 6,437 | 149 | import collections.abc as abc
from functools import partial
from torch.utils.data._utils.collate import default_collate
from torch.utils.data import DataLoader
from typing import Union, Callable, Tuple
import copy
import numpy as np
import torch
from opacus import PrivacyEngine
from opacus.validators import ModuleVali... |
e88445fccd8bf5603a2a1f9491a2b63d5956a967b76af2181b8e2a3924437ce6 | Python | 6,439 | 196 | import numpy as np
import torch.nn as nn
import torch
import pdb
import math
import torch.nn.functional as F
#from flash_attn.flash_attention import FlashMHA
class PositionalEmbedding(nn.Module):
def __init__(self, embed_dim, seq_len=1000):
super().__init__()
pe = torch.zeros(seq_len, embed_dim)
... |
66ae8d189129f88b072fe839fd000494dbe28830db88222593f3fc1f22f5d073 | Python | 6,445 | 238 | #!/usr/bin/env python3
import os
import sys
from itertools import combinations, product
import numpy as np
import pandas as pd
import pkg_resources
from etspredict.data import load_struct_connectomes
from etspredict.pipelines.ioutils import IOManager
from etspredict.prepare_data import prepare_connectomes as pc
de... |
887f5a24b9c1061d37304a369975fd428ab989bc09c3f1b710f2417a906a33d6 | Python | 6,457 | 140 | import numpy as np
import pandas as pd
import torch
from torchmetrics import Specificity, Recall
from monai.data import DataLoader, Dataset
from monai.transforms import Compose, MapTransform, EnsureChannelFirstd, \
Resized, ScaleIntensityd, ToTensord
from models.siamese3D import Siamese3... |
3fe341d83307a75992ca5882d7aebb0d9cb6dfc1eacac75bf7326acd1a48f006 | Python | 6,465 | 195 | # -*- coding: utf-8 -*-
import matplotlib as mpl
from matplotlib.testing.compare import compare_images
from tempfile import NamedTemporaryFile
import os.path
import pygenometracks.plotTracks
mpl.use('agg')
ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_data")
browser_tracks ... |
899c7cbbae29dd8a73a2aa7fb84f1987f358ac838e3762e2b6d502fc17888050 | Python | 6,467 | 171 | import torch
from torch import nn
import cv2
import multiprocessing
import numpy as np
import glob
import os
import warnings
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import make_grid
import json
import pandas as pd
from tqdm import tq... |
30d224350777e0cb376ce3724206ba698bf7cbcf045eb25f816c8c4f261a3817 | Python | 6,468 | 151 | from pathlib import Path
from datetime import datetime
from shutil import copyfile
import logging
import numpy as np
import torch
import torch.nn.functional as F
import SimpleITK as sitk
import torchio as tio
from breaststudies.data import BreastDatasetCreator
from breaststudies.models import UNet, nnUNet, SwinUN... |
1e156a85da101afbdc2c740961e2ece77735d3557911fd25c70e379d456b9be0 | Python | 6,470 | 211 | import sys, os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# Load the correct matplotlibrc
# mpl.rc_file(os.path.join(os.path.dirname(__file__), 'matplotlibrc'))
# mpl.rcParams["text.usetex"] = True
# mpl.rcParams["text.latex.preamble"] = r"""
# \renewcom... |
aa91ebabc86e19aa4b5dd290ebed5acf42064b307395e5f51db5ad31acdb6951 | Python | 6,470 | 221 | import json
import os
import stat
import sys
import warnings
from jsmin import jsmin
from jsonschema import Draft4Validator
# import subpackages
from . import blending
from . import cascade
from . import datasets
from . import decorators
from . import downscaling
from . import exceptions
from . import extrapolation
f... |
cce297acd94f1cebad78f50f39e1bfac07c240cc56877e8ed8efe01629b061d7 | Python | 6,478 | 222 | # -- coding: utf-8 --
"""
pysteps.verification.plots
==========================
Methods for plotting verification results.
.. autosummary::
:toctree: ../generated/
plot_intensityscale
plot_rankhist
plot_reldiag
plot_ROC
"""
from matplotlib import cm
import matplotlib.pylab as plt
from mpl_toolki... |
e12cdc77ff8458676c3e8735d3166442b4ac1ffabd7ab3c76ef99c41a7a57907 | Python | 6,484 | 194 | import csv
import json
import numpy as np
import torch
import pdb
from tqdm import tqdm
def check_k(queries):
return len(queries[0]['mentions'][0]['candidates'])
def evaluate_topk_acc(data):
"""
evaluate acc@1~acc@k
"""
queries = data['queries']
k = check_k(queries)
for i in range(0, k):
... |
d124d8187d3e298d51cea30acd3bc426e5662abf974832fe6543b1caf6ca804b | Python | 6,485 | 183 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 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.
#
# Any... |
103ee26be86d7e9fb42722e578d6f1a57df51a71abca67bcfb213da93e2c4d07 | Python | 6,488 | 142 | from __future__ import annotations
from typing import Any, Callable, Hashable, Iterable, Sequence
import numpy as np
import xarray
from xarray.core import dtypes
from ptsa.data.timeseries import TimeSeries
__all__ = ["concat"]
# ``xarray.concat`` types ``coords`` as ``Literal['minimal', 'different',
# 'all'] | lis... |
2f10a89a4b782223f000ada76d00dab0c9a1da88c2e3feda77c9d484ed831d0a | Python | 6,488 | 126 | import glob
import json
import os
import sys
import time
import warnings
from typing import Literal
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import cm
from scipy import integrate
import Functions.globalFunctions.ExtracellularField as EcF
from Analyses.SDC.tools import VTA... |
10e8d970601f11cb855cb3be7ffd7744a7ac88157a2f4a268f115c0987c7cd45 | Python | 6,504 | 155 | import numpy as np
from pysteps import utils
def check_norain(
precip_arr, precip_thr=None, norain_thr=0.0, win_fun=None, printmsg=True
):
"""
Parameters
----------
precip_arr: array-like
An at least 2 dimensional array containing the input precipitation field
precip_thr: float, optio... |
66b4d096cb0c5ff7eeb903def5699f1492bcf52bc6b474ff545bf0b195372655 | Python | 6,508 | 211 | from __future__ import division
from matplotlib.pylab import *
import glob
from pickle import load
import seaborn as sns
from matplotlib import gridspec
import bz2
def despine(ax,top=False,right=False,bottom=True,left=True):
ax.spines["top"].set_visible(top)
ax.spines["right"].set_visible(right)
ax.spines... |
e91f2de99ebe8d5a4982716f24386cae1705f832d22fc6ba4c95d6a5408b45d5 | Python | 6,508 | 163 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 5 15:58:38 2021
@author: filtsem
"""
from pathlib import Path
import numpy as np
import eelbrain
import re
import os
import mne
import pandas as pd
from mne.stats import (spatio_temporal_cluster_test, f_threshold_mway_rm, f_mway_rm)
from mne imp... |
73b26cc62b869b6615d5adea644d5bbee08bd1940e918b4e6638f5140ea0e4e3 | Python | 6,511 | 205 | import pandas as pd
import numpy as np
from scipy.stats import pearsonr, norm
from scipy import special
def get_calibration(forecast):
def calc_in_range(pred, z):
return(np.sum(np.logical_and((pred['Pred'] - z * pred['Std']) < pred['True'],
(pred['Pred'] + z * pred['St... |
0fd888f789b3eaad5e9e5404d2a2bc157136b7035ba3c1d19fa23f8e6ea0ee43 | Python | 6,515 | 144 | #### Master Script 4c: Calculate validation set calibration and discrimination for dropout ####
#
# Shubhayu Bhattacharyay
# University of Cambridge
# email address: sb2406@cam.ac.uk
#
### Contents:
# I. Initialisation
# II. Calculate validation set calibration and discrimination based on provided bootstrapping resampl... |
3df8641009873eb7aee5cbc2e1cd37564d42049c977392768a64e390a274344a | Python | 6,519 | 157 | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 7 18:23:18 2021
@author: Jing Li, Small steps make changes. dnt_seq@163.com
"""
from sklearn.manifold import TSNE
# from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
import pand... |
63c843c2c4fbbd5d0578fe9b3aaec3f6b67160704756935aa7da699047a98fc5 | Python | 6,529 | 161 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 10 14:40:03 2021
@author: Nasibeh Mohammadi
@email: nasibeh.mohammadi@gmail.com
"""
import numpy as np
import pandas as pd
import six
from collections import Counter
import nibabel as nib
import pkg_resources
class LBPFeature():
"""
This code calculates LBP numb... |
0c1fadeec6e1caa852f3a9e2b74489b349e40317b154edf245630bfeeba86829 | Python | 6,530 | 157 | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 7 18:23:18 2021
@author: Jing Li, Small steps make changes. dnt_seq@163.com
"""
from sklearn.manifold import TSNE
# from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
import pand... |
17d55b084b540ec6470607dead5950189b352bef90b12589941eea13a1ca1995 | Python | 6,538 | 162 | import os
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import data.mapping_utils as mapping_utils
# the cmap below is based on cmap = plt.cm.Set1 (1st and 3rd colors)
my_cmap=np.array(
[[0. , 0. , 0. , 0.],
[0.89411765, 0.10196078, 0.10980392, ... |
25df515b6ed05d9e012f6c51698764b31312208c5e172bef2ef21a62ce6cd602 | Python | 6,538 | 226 | import pandas as pd
import sys
from typing import Tuple, Dict
def generate_seed_list(seed_file: str) -> list:
"""Read a single column seed file into a python list
Parameters
----------
seed_file (str): File containing list of proteins, one protein id per
line, to ... |
0d43e22ff978743d5644d4f50c3c290b119d0e8a242a0e4d1454c650d948f5f3 | Python | 6,540 | 139 | from matplotlib import colors
import numpy as np
from . HiCMatrixLikeTrack import HiCMatrixLikeTrack
from .. utilities import get_region
import logging
DEFAULT_MATRIX_COLORMAP = 'RdYlBu_r'
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger(__name__)
class HiCMatrixSquareTrack(HiCMatrixLikeTrack):
#... |
dbd2457e2d415c29ee98f4664913de8ef4a3c59924570bc447899cad00447517 | Python | 6,553 | 182 | from ._setting import Setting
class Color(Setting):
"""Represents a choice of color
These are coded in hex unless a valid HTML name is available.
"""
def __init__(self, text, value="gray", *args, **kwargs):
super(Color, self).__init__(text, value, *args, **kwargs)
def to_rgb(self):
... |
01e2c712fc52c7445f9c76c2ba2ab45a03f8de7df33aa0991fc1fcaef24c930c | Python | 6,567 | 165 | # Pytorch Tabular
# Author: Manu Joseph <manujoseph@gmail.com>
# For license information, see LICENSE.TXT
# Inspired by https://github.com/rixwew/pytorch-fm/blob/master/torchfm/model/afi.py
"""AutomaticFeatureInteraction Model"""
import logging
from typing import Dict
import pytorch_lightning as pl
import torch
import... |
8b6629a1c01d77eb98d9cbbf810bbf3650e29a65e0daa5a8c485af3422198ac3 | Python | 6,567 | 167 | """Prepare and check registry snapshots on Versionary release branches."""
from __future__ import annotations
import argparse
import json
import re
import shutil
import sys
import tempfile
from pathlib import Path
import dm_index
import yaml
ROOT = Path(__file__).resolve().parents[1]
_VERSION = re.compile(r"(?:0|[1... |
c311354a302a6f8df9def92619c2eef6df1dc01f37ae50482c00ecba276d6e8c | Python | 6,572 | 213 | # -*- coding: utf-8 -*-
#
# comparison_schemes.py
#
"""
Schemes for model comparison experiments.
Parts of the code taken from Severin Langberg:
https://github.com/gsel9/biorad
"""
__author__ = "Ahmed Albuni"
__email__ = "ahmed.albuni@gmail.com"
import os
from collections import OrderedDict
from datetime import dat... |
adbc02ebaa81a8149a393fe5091380d56475ca9812e0c2ef6df0d644182ea18d | Python | 6,578 | 204 | '''
JHK
Python script for calculating internal variability range of linear trend of HF PRCP variability.
'''
import numpy as np
from netCDF4 import Dataset
import os
#==========================================================
# Set main directory
#==========================================================
hdir = '/hom... |
a6b7f0161b16d4befc25cde7f578f596a42ceaf74fe874c41fae99e2cbe07805 | Python | 6,584 | 183 | import cv2
import numpy as np
import tensorflow as tf
from scipy.stats import halfnorm
from scipy.ndimage import rotate
from sklearn.utils import shuffle
def _random_gaussian_blur(img, kernel_size=5):
choice = np.random.choice([0, 1], replace=True, p=[0.666, 0.334])
kernel_size = int(halfnorm.rvs(loc=kernel_s... |
f4c449f27afe0b9d3728f72a4ac3ed1cb0b461f9a49c7089134ec3ef27a785ff | Python | 6,587 | 173 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 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.
#
# Any... |
3cb8eba4488f0f8bf5d105e7f6c0b45af09f607427ff4db7cd69c3f90c5fa2e5 | Python | 6,596 | 179 | import matplotlib.colors as mcolors
# import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import numpy as np
import os.path as op
from src.utils import utils
from src.utils import figures_utils as figu
LINKS_DIR = utils.get_links_dir()
MMVT_DIR = utils.get_link_dir(LINKS_DIR, 'mmvt')
def create_YlOrRd_... |
44f11a51f5da2151b188310df96eef6630ac38364d8554b4d292ca19c2da48a3 | Python | 6,601 | 197 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in 2022
@author: nchapochnikov
Here we are testing that the ORN-LN circuit without LN-LN connection
is converging to the theoretical pca solution in the linear case
TESTS ALL PASS
"""
import numpy as np
import time
import scipy.linalg as LA
import collectio... |
573b677eece957045f90a11588fbd6cdd43677aaf8b7ab5f3f350303cec6d942 | Python | 6,601 | 189 | #!/usr/bin/env python
"""
Calculates fractional amplitude of low-frequency fluctuations (fALFF)
Usage:
falff_nifti.py <func.nii.gz> <output.nii.gz> [options]
Arguments:
<func.nii.gz> The functional 4D nifti files
<output.nii.gz> Output filename
Options:
--min-low-freq 0.01 Min low frequency range v... |
2c83ca0766262a9a88a9172b0a650686ec147501a6a85a241a67e69fc7a29034 | Python | 6,605 | 182 | import cv2
import torch
import numpy as np
import torchvision.utils
import torch.nn.functional as F
import matplotlib.pyplot as plt
import itertools
def createRGBGrid(tensor_list, nrow):
"""Creates a grid of rgb values based on the tensor stored in tensor_list"""
vis_tensor_list = []
for tensor in tensor_... |
4fb496e5e55add42f3592bb967a3b02669745dbc0bc3c203a57f3ec5e30e07df | Python | 6,610 | 73 | # -*- coding: utf-8 -*-
import argparse
import train
import setup
import os
import torch
import numpy as np
import time
def mkd(args):
if not os.path.isdir('output/' + args.codename.split('-')[0]+'/'+args.codename):
os.makedirs('output/' + args.codename.split('-')[0]+'/'+args.codename)
if... |
5f2f4a37abbd657cca1e07d209d1aa65578f9d2a342e2414da8749e85e706ec5 | Python | 6,624 | 193 | # Created by: Deserno, M. et al., 2022
# Calculates the DSB mAP for Instance Segmentation results from MMDetection
import itertools
import pickle
import scipy.optimize as opt
import numpy as np
import pycocotools.mask as mask
import cv2
from pycocotools.coco import COCO
import argparse
from alive_progress import aliv... |
b9e582a1bd031f562c9d0b12397c2879c8434499a815590d7cd31f8c5a573628 | Python | 6,627 | 210 | import pickle
import numpy as np
import ray
import torch
import yaml
from yaml.loader import SafeLoader
import pdb
import os
import matplotlib.pyplot as plt
from tqdm import tqdm
import wntr
import networkx as nx
from DT_for_WDN_leak_localization.inference.forward_model import ForwardModel
from DT_for_WDN_leak_localiz... |
022aa319136a57ee1e4cc03618c91039eb5a6de87c29386c48ca831323a406fe | Python | 6,631 | 226 | # Mappings for converting NBDC data dictionary to ReproSchema
# NBDC column names to ReproSchema internal names
# Based on NBDCtoolsData data dictionary structure
NBDC_COLUMN_MAP = {
# Primary identifiers
"name": "item_name", # Variable name -> item ID
"table_name": "activity_name", # Table/assessment ->... |
73a183cf1d51e10471d9a62cd2a8c02bfc9f13b0298087bdaf318ae5ef4d0853 | Python | 6,633 | 235 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# DeepRank documentation build configuration file, created by
# sphinx-quickstart on Mon Feb 26 14:44:07 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# a... |
8f37131c7ee4bff8a4ae263121eedf6d02649b039067378f5b828b096d851963 | Python | 6,645 | 195 | # -*- coding: utf-8 -*-
"""
Implementation of UNet
"""
from GANDLF.models.seg_modules.DownsamplingModule import DownsamplingModule
from GANDLF.models.seg_modules.EncodingModule import EncodingModule
from GANDLF.models.seg_modules.DecodingModule import DecodingModule
from GANDLF.models.seg_modules.UpsamplingModule impo... |
5b09a74c3a1b2296e9a4326f86a3ee0538d5d5ef9cb5c04649da2809e248f6be | Python | 6,650 | 175 | import prov.model as pm
from .Core import Core, getUUID
from ..core import Constants
class Project(pm.ProvActivity, Core):
"""Class for NIDM-Experiment Project-Level Objects.
Default constructor uses empty graph with namespaces added from NIDM/Scripts/Constants.py.
Additional alternate constructors for u... |
de8ad42a254b1960612008c6d4246bb07a9e20d479a2f518c227c36b64901945 | Python | 6,650 | 146 | from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... |
58ac38a821f5a89b86038a5e63ed6140cd7931a10b01b3105d6fe0cf3c63282b | Python | 6,652 | 184 | import torch
import math
import warnings
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out
def is_meta_device(device) -> bool:
"""Check if targeting meta device (explicit arg or context manager)."""
if device is not None:
return str(device) == 'meta'
# Check context mana... |
9fac227f0acaa858299756fd6e43a67ee2f0878c9b9804d41d4c135d8472c6c7 | Python | 6,652 | 166 | # -*- coding: utf-8 -*-
"""
Implementation of UNet
"""
import torch
from torch.nn import ModuleList
from GANDLF.models.seg_modules.DownsamplingModule import DownsamplingModule
from GANDLF.models.seg_modules.EncodingModule import EncodingModule
from GANDLF.models.seg_modules.DecodingModule import DecodingModule
from GA... |
39e61aa4c728ca729ce025bde87557aa5cb9e024171add26e9ef6eb14fc09c1d | Python | 6,653 | 133 | """Shared assertions for tests/scenarios/Case_*.py scenarios.
Each Case_*.py script defines its parameters (phase_ratio, dimension, abaqus_flag, damask_flag,
moose_flag, ...) as module-level globals before calling Run(...).run(). run_all_test_cases.py
imports each scenario via importlib, so those globals are readable ... |
379e183a207677dfd551ab767d1209514011a097fa5227546bb5d96611b14c3e | Python | 6,654 | 236 | """Route requests with Flask."""
from __future__ import annotations
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
import wtforms as wtf
from flask import (
Blueprint,
current_app,
jsonify,
redirect,
render_template,
reque... |
eecd81dfcadf39e90b72f47acb0bb6ba9a84da0c0f1ec7d717d4bfc0c61f5aff | Python | 6,668 | 174 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 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.
#
# Any... |
bcab5afbe33fd9cbeca837de18b15c1395193727c76243b28eac65737c5958af | Python | 6,680 | 155 | import warnings
from typing import Dict, Tuple, Union
from GANDLF.losses import global_losses_dict
from GANDLF.metrics import global_metrics_dict
import torch
import torch.nn.functional as nnf
from GANDLF.utils import one_hot, reverse_one_hot, get_linear_interpolation_mode
def get_metric_output(
metric_function:... |
68eef189e08045bdadeaaa0674577cb40ebd39a036ed2b3cec1aed1f65ac7b8f | Python | 6,697 | 177 | # -*- coding: utf-8 -*-
"""
pysteps.utils.pca
Principal component analysis for pysteps.
.. autosummary::
:toctree: ../generated/
pca_transform
pca_backtransform
"""
import numpy as np
from pysteps.exceptions import MissingOptionalDependency
try:
from sklearn import decomposition
SKLEARN_IMPORT... |
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