sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1d7797d509fb62a760904d2debeb91e24212de449247933494e967df5e5405ad | Python | 5,131 | 176 | from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
import argparse
from rdkit.Chem import AllChem
import pandas as pd
from rdkit import Chem
from rdkit import DataStructs
import numpy as np
import matplotlib.pyplot as plt
import math
import random
import os
from ... |
7fd352cff9511980f985a56b27cef92570d7bd458d801c0bfa3a24fd9aa54dc6 | Python | 5,135 | 155 | import csv
import math
import os
import sys
#get_natrial_stats_all
#This program takes in multiple sequence alignments and calculates the entropy for each aa position
'''
1) Calculates aa freq (20 values) for each col in alignment
2) Calculates entropy for each col (H)
3) Calculates e^H
4) Records into CSV for each co... |
c29db54295cdaf2ef8af8172f26a46ea439fc53310095a6730f9099cd70b0b31 | Python | 5,137 | 148 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import yaml
import shutil
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from utils.parallel import auto_select_device, get_dist_info, init_dist, s... |
3b002df60bb4d2e8e7be660a9430f4de6b21581d7736d165a745d3c2c0f073ae | Python | 5,141 | 160 | import torch
from torch.optim.optimizer import Optimizer
import pytorch_lightning as pl
#from ..losses import SinkPITLossWrapper
class BaseScheduler(object):
"""Base class for the step-wise scheduler logic.
Args:
optimizer (Optimize): Optimizer instance to apply lr schedule on.
Subclass this an... |
5551cbd36581f57c23b1a4d5b3d6b6c3b52344e49d18e07d8736cde8389de8e9 | Python | 5,153 | 66 | import pandas
from deeprank.models.residue import Residue
from deeprank.config.chemicals import AA_codes_1to3
# >>> pandas.read_hdf("GPCR_variantsv2_increased_coverage.hdf5", "conservation")
# sequence_residue_number amino_acid alignment_position alignment_name sub_sequencecount ... sub_consv_W sub_... |
2b98a32a7403a6f449fb8a1bfbcc5670f4ccf8f2e2c9448d1bcc7bc72af38ade | Python | 5,156 | 151 | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
References for added functionality:
Cautious Optimizers:... |
d2b250b497e03e1d3d9a6dce85222af0d670d62fe4f0fb4189bc2ebbd25e926c | Python | 5,156 | 120 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 9 10:23:18 2018
@author: saskiad
"""
import numpy as np
import pandas as pd
import os, h5py
import scipy.stats as st
import core
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
def get_movie_correlations():
save_path = ... |
6d4071975c158046262e59974c1dd55ed360ae9638cd1e484c7cf45ed250fe0a | Python | 5,159 | 81 | import skimage.measure
import numpy as np
from typing import Annotated, Optional, Tuple, List, Union
from pydantic import Field, validate_call, ConfigDict, BaseModel
from cellprofiler_library.types import ImageBinary, ObjectSegmentation, ImageAnyMask
from cellprofiler_library.functions.measurement import measure_area_o... |
846acbaadfae796505350963a9942ec9d2da64f95777a99b96f1a5d15e852903 | Python | 5,161 | 128 | #!/usr/bin/env python3
"""
Will concatenate PINT output summaries into one concatenated file.
Also, calculates all distances from template vertex on standard surface
Usage:
ciftify_postPINT1_concat [options] <concatenated-pint> <PINT_summary.csv>...
Arguments:
<concatenated-pint> The concatenated PINT output... |
a9abf66c8ab4d93e3b992b7cdae93484035a5973bf83e286728561691075478a | Python | 5,163 | 157 | #!/usr/bin/python3
def qc_reads(reads, n_gene, nc_gene, ncp_gene, n_cell, nt_cell, ntp_cell):
"""Quality control by bounding read counts.
Quality control is perform separately on genes based on their cell statisics and on cells based on their gene statistics, iteratively until dataset remains unchanged. A gene or ... |
5e07eb4dec81799d71f1fd3d55eb1575b512337526e6516dcb3cf439f1758934 | Python | 5,166 | 137 | #!/usr/bin/env python3
"""
Makes pictures of standard views from ciftify_recon_all outputs and pastes them
together into a html page for quality assurance.
Usage:
cifti_vis_recon_all snaps [options] <subject>
cifti_vis_recon_all subject [options] <subject>
cifti_vis_recon_all index [options]
Arguments:
... |
8ee9a9760c517b2579c731786176568647a2f4243d1ad583f95502b8587e1593 | Python | 5,168 | 154 | import os
import numpy
import skimage
def save_object_image_crops(
input_image,
input_objects,
save_dir,
file_format="tiff8",
nested_save=False,
save_names = {"input_filename": None, "input_objects_name": None},
volumetric=False
):
"""
For a given input_objects array, save crop... |
a14ba410aafeab2e9fabe5a49147e2ba63d8c5bdd3b776db40026b709e426af8 | Python | 5,171 | 116 | import time
import numpy as np
import pandas as pd
from dragen.utilities.InputInfo import RveInfo
import cv2
class shape:
def __init__(self):
pass
def thresh_callback(self, img, max_id, phaseID):
#cv2.imwrite(f'{RveInfo.fig_path}/test_{time.time()}.png', img)
if phaseID in [6,7]: #Rv... |
9b2c225ba53b464d98b57bbd3a9e41523394d8d8fb8146e9d904053f6c6408e8 | Python | 5,174 | 144 | import io
import pickle
from ._image_set import ImageSet
from ..utilities.image import make_dictionary_key
class ImageSetList:
"""Represents the list of image sets in a pipeline run
"""
def __init__(self, test_mode=False):
self.__image_sets = []
self.__image_sets_by_key = {}
sel... |
40700bc7bc00896f6ca7e822e8f662f13f5803ab5a44e6a93ed2a9327411eb64 | Python | 5,177 | 135 | import torch
import torch.nn as nn
## Channel Attention Layer
class CALayer(nn.Module):
def __init__(self, n_feat, reduction=16, bias=False):
super(CALayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_du = nn.Sequential(
nn.Conv2d(in_channels=n_feat, ... |
d1165f63d50767b9562a797b7fbaa24b9c41de93e29c7d3ff52042775ddbdf9f | Python | 5,177 | 173 | #!/usr/bin/env python
import os
from time import time
import numpy as np
from cleandata import *
from deeprank.generate import *
##########################################################################
#
# GENERATE THE DATA BASE AT ONCE
# --> assemble the pdbs
# --> compute the features on the fly
# --> c... |
ffe39e8f3c6d7a872247834cef1aed38a7d605f5268d1bcdaff48c99d7190987 | Python | 5,179 | 131 | from typing import Union
from pydantic import BaseModel, model_validator, Field, AfterValidator
from GANDLF.configuration.default_config import DefaultParameters
from GANDLF.configuration.differential_privacy_config import DifferentialPrivacyConfig
from GANDLF.configuration.nested_training_config import NestedTraining
... |
95ee984323388d37a78b7a3dd990126bd19254b96691bd1449a4659105c27947 | Python | 5,184 | 169 | """
Created on Tue Dec 19 14:55:23 2017
@author: Nikolai M Chapochnikov
This is what is present in this file:
- calculating the PCA
- calculating the correlations and the significance tests for each correlation
coefficient independently (no multi-hypothesis testing in the first place)
- exporting the corr and sign d... |
22829724c6c32c46c569fe6f2fa7db2dbe3203554d5404dc2fca79ebe5c52faf | Python | 5,185 | 145 | # -*- coding: utf-8 -*-
"""
pysteps.extrapolation.interface
===============================
The functions in the extrapolation module implement the following interface::
``extrapolate(extrap, precip, velocity, timesteps, outval=np.nan, **keywords)``
where *extrap* is an extrapolator object returned by the initia... |
2bfd2dbd4ac0be8821d1ef5bb0aeca117e6b9fc849e417c914cbd7a4a0e5bab1 | Python | 5,185 | 178 | from attr import dataclass
from torch import nn
import torch
import pdb
from DT_for_WDN_leak_localization.optimizers import GANOptimizers
class GANTrainStepper():
def __init__(
self,
model: nn.Module,
optimizer: GANOptimizers,
gradient_penalty_regu: str,
num_critic_steps: ... |
c4aa5864361282a61477b9b59173920119850a6ee4e389ad70c03908f1c42164 | Python | 5,189 | 141 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
Repository: https://github.com/JSIERO/ClinicalASL
Script Author: Jeroen Siero
Institution: UMCU (University Medical Center Utrecht), The Netherlands
Contact: j.c.w.siero@umcutrecht.nl
Algorithm code author: Thomas Kirk, Quantified Imaging
Descript... |
d635be4fd1d33978dce55055c358bf79fb6e81e503f9fd234cb5260480895c7c | Python | 5,192 | 144 | import os
import pickle
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
class Mathgreek(Dataset):
def __init__(self, train_or_test, transform=None, target_transform=None):
super(Mathgreek, self).__init__()
roots = '../../Dataset/mathgreek'
... |
5ba1b86885931c04bdeb0352fe50b5a41351b5c64dbd74cd8fac7b9ea66980c1 | Python | 5,194 | 158 | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import numpy as np
from scipy.stats import gaussian_kde
from matplotlib import rc
import numpy as np
import sys
import zipfile
import pandas as pd
import os
from matplotlib import cm
import seaborn as ... |
55c6b2e62f39df309710df84fec532d765f3bc216c110c00748dfea2b7b334cb | Python | 5,198 | 111 | # -*- coding: utf-8 -*-
"""
pysteps.motion.interface
========================
Interface for the motion module. It returns a callable optical flow routine for
computing the motion field.
The methods in the motion module implement the following interface:
``motion_method(precip, **keywords)``
where precip is a (T... |
d21d7125775cba315d964ccb53cb31823d8494a117c550a1c09c63ebb0f9ef4b | Python | 5,204 | 144 | # This file is part of pyunicorn.
# Copyright (C) 2008--2026 Jonathan F. Donges and pyunicorn authors
# URL: <https://www.pik-potsdam.de/members/donges/software-2/software>
# License: BSD (3-clause)
#
# Please acknowledge and cite the use of this software and its authors
# when results are used in publications or publi... |
37d26ef9795707972d20441c63588bdd60cc991f9a5ac75bcea15321b8cfc6eb | Python | 5,206 | 163 | import torch
import random
from braincog.base.node import node
import numpy as np
import pylab as pl
class LIFNeuron(node.LIFNode):
def __init__(self, tau_ref = 0, vthresh = 5, Rm = 2, Cm = 0.2,dt = 0.1,*args, **kwargs):
super().__init__(threshold=vthresh, tau=Rm*Cm, dt=dt, *args, **kwargs)
self.la... |
23883d8d1240ba60dfe7b33ea9786b6451545dce69481ac78153a5f16051eed3 | Python | 5,208 | 152 | # -*- 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... |
6d45bc824a71872e0035c25b2efe32feced5180ef5f702e46b39fb675065d4e5 | Python | 5,208 | 135 | """ Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Squeeze-Excitation (ESE).
Paper: `CenterMa... |
a3f6ce5da3d04a549397fd64cf582d993f5941f1425d6ef094cad83cde097ab5 | Python | 5,212 | 154 | # -*- 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... |
ed9353e510b0cca3348cc5ce1ab5a80b0f9716411dfa2efc5d1207a36f5fd32f | Python | 5,212 | 180 | import torch
import torch.nn as nn
import torch.nn.functional as F
from rdkit.Chem import AllChem
import pandas as pd
from rdkit import Chem
from rdkit import DataStructs
import numpy as np
import matplotlib.pyplot as plt
import math
import random
import os
from torch.utils.data import DataLoader
from tqdm import tqdm
... |
2c93e403c387de0c36ce9f14d8716c394ed7513ccdfb6b6f29da6aabe2995907 | Python | 5,215 | 127 | import numpy
import numpy as np
import torch
def generate_input_representation(events, event_representation, shape, nr_temporal_bins=5, separate_pol=True):
"""
Events: N x 4, where cols are x, y, t, polarity, and polarity is in {-1, 1}. x and y correspond to image
coordinates u and v.
"""
if event... |
d4fe3470960051a9c6ca877166ec031ab26b28e1fdab1df35d95b60c613c87b4 | Python | 5,217 | 134 | import numpy
import cellprofiler_core.image
import cellprofiler_core.image.abstract_image._callback_image
class TestImageSetList:
def test_init(self):
x = cellprofiler_core.image.ImageSetList()
assert (
x.count() == 0
), "# of elements of an empty image set list is %d, not zer... |
0f14e4b849403489b9c232b9ccbdd9490000fab3d40b5fe533411546f11257a8 | Python | 5,225 | 141 | import lzma
import os
import csv
from typing import List, Tuple, Any
from math import sqrt
from torch import argmax, tensor
from torch.nn.functional import cross_entropy
from torch.utils.tensorboard import SummaryWriter
from sklearn.metrics import roc_auc_score
class MetricsExporter:
"The class implements an obj... |
b78088b5fc4413d8e294ca4762b97f3dc95b11fc9e1a20af20751baedb5132f7 | Python | 5,225 | 143 | #!/usr/bin/env python3
# Copyright 2021 Alexander Meulemans, Matilde Tristany, Maria Cervera
#
# 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
... |
d745daf8893df2c879a7c5fb7f3cc058e1135036204dabc67f3252c9833feb66 | Python | 5,231 | 128 | """Tools for working with NIDM-Experiment files"""
import re
import click
from rdflib import Graph, Literal, Namespace, URIRef, util
from rdflib.namespace import RDF, split_uri
from nidm.core import Constants
from nidm.experiment.Core import getUUID
from nidm.experiment.Query import GetProjectsMetadata, getProjectAcq... |
9d307aba94357d4d235e86b7995e5a20e2085254ac28717ed573dd79ffc455e3 | Python | 5,237 | 146 | #!/usr/bin/env python
""" test_haralick -- tests for centriole.haralick
"""
import unittest
import centriole.haralick as haralick
import numpy as np
gray4 = np.array([[0, 0, 1, 1], [0, 0, 1, 1], [0, 2, 2, 2], [2, 2, 3, 3]])
gray = gray4 / (1.0 * gray4.max())
labels = np.ones((4, 4), dtype="int32")
# labels = np.arr... |
fc0c5c3837b4109c615f7c057af51810fc27ef1a33cb315b006b6c48259d3d10 | Python | 5,237 | 106 | # Place this file in src/ of the DIPS folder preprocessed as in
# https://github.com/amorehead/DIPS-Plus up to and including the step prune_pairs.py (but not beyond that step).
import logging
import os
import random
from pathlib import Path
import atom3.pair as pa
import click
import pandas as pd
from atom3 import da... |
f2d7806b2d80e61888481b0274108d872a11af196453d6cdad9b6cba7fa3300f | Python | 5,251 | 146 | from ._integer_range import IntegerRange
from .._range import Range
from ..._validation_error import ValidationError
class IntegerOrUnboundedRange(IntegerRange):
"""A setting that specifies an integer range where the minimum and maximum
can be set to unbounded by the user.
The maximum value can be relati... |
bccf4db31b14f718818fb9d363cd131e451f52b35970b493173294a242627a13 | Python | 5,253 | 184 | #
# nate berry
# 2023-04-01
#
# ---------------------------------------------------------
print('''
\n\n
# -------------------------------------------------
author: nate berry (nberry11 at gmail dot com)
citation:
<tbd>
# -------------------------------------------------
... |
e59fbc8e70b70eff42adaf1a2e75324f9d95a0cd84b426da9a826be17e3ca021 | Python | 5,253 | 156 | """
Helper tools to visualize patterns and network state
"""
# This file is part of the exercise code repository accompanying
# the book: Neuronal Dynamics (see http://neuronaldynamics.epfl.ch)
# located at http://github.com/EPFL-LCN/neuronaldynamics-exercises.
# This free software: you can redistribute it and/or mod... |
c473c514c31aca2062deff04dd9557d2ad41f83ce53f6d30af4eba6be4e4e16f | Python | 5,254 | 114 | # python -m src.test_all_methods.eval_pdb_outputset
import os
from biopandas.pdb import PandasPdb
import numpy as np
import torch
from src.utils.eval import Meter_Unbound_Bound
import scipy.spatial as spa
def get_CA_coords(pdb_file):
ppdb_model = PandasPdb().read_pdb(pdb_file)
df = ppdb_model.df['ATOM']
... |
f62563d04fbd315740e902edbf3c1414dc587916e1f3fc13c2b7983cf94be155 | Python | 5,260 | 123 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 13 13:45:49 2021
@author: andrey
"""
import numpy as np
from megsimutils.utils import xyz2pol
from megsimutils.arrays import BarbuteArray
from mne.preprocessing.maxwell import _get_n_moments
class BarbuteArraySL(BarbuteArray):
"""
Single ... |
4b7615150dd11d7ea6000b9be43d8c3df489e38d1d8a9c54c491f0171712211a | Python | 5,263 | 181 | #!/usr/bin/env python
import os
from time import time
from cleandata import *
from deeprank.generate import *
##########################################################################
#
# GENERATE THE DATA BASE AT ONCE
# --> assemble the pdbs
# --> compute the features on the fly
# --> compute the targets o... |
9b8df89ce77676b02940dba8e94de4edd071099327cc87506e3ad0f9fc7aa1af | Python | 5,269 | 144 | import numpy as np
from PIL import Image
def DepthNorm(x, maxDepth):
return maxDepth / x
def predict(model, images, minDepth=10, maxDepth=1000, batch_size=2):
# Support multiple RGBs, one RGB image, even grayscale
if len(images.shape) < 3: images = np.stack((images,images,images), axis=2)
if len(imag... |
6cab795dd3fdc78c60d26e188256e167ae5a39885983770b4096a9ee83b7af9d | Python | 5,276 | 176 | # study: .sqlite3
# fragment: 136.0618
# PPM: 20.0
# RT_TOL: 0.5
# output: .features
import sys
import time
import bisect
import sqlite3
start_time = time.time()
__version__ = "0.5"
class Thing:
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
setattr... |
da318698f5f26a4bf4a466fed46aa620a660950303e4ede76b0699964e71f3f0 | Python | 5,285 | 83 | import argparse
from config import VESSEL_DIR, ENDOSTROKE_DIR, ISLES2018_DIR
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argpars... |
52fd6fe91d7d8965ef9f4e8c46f7157fea9fa6128fc6bcdb555136dbefefbb57 | Python | 5,286 | 113 | import numpy as np
from .base import BayesianSpikingModel
from .srm import SRM
from ..masks import shift_mask
from ..utils.time import get_dt
class VGLM(SRM, BayesianSpikingModel):
def __init__(self, kappa=None, eta=None, gamma=None, vr=None, vt=None, dv=None, lam=None):
super().__init__(vr=vr, kappa=ka... |
729f20a95deac88708dc7128ef0a4955fa7e18ba2308082df10cdd426dceb56b | Python | 5,291 | 126 | from pathlib import Path
import rvt.tile
import rvt.vis
import rvt.default
import numpy as np
# pytest rvt.tile.save_visualization_tile_by_tile
dem_path = Path(r"test_data\TM1_564_146.tif")
tile_size_x = 100
tile_size_y = 100
def test_slope_tile_by_tile() -> None:
out_slope_path = Path(r"test_data\TM1_564_146_t... |
9cb010551b3c6b3dd686a9f4d2f0e45fbd0128665de50bd244bdfa54a4344cb6 | Python | 5,292 | 152 | import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
from io import BytesIO
import random
def _is_pil_image(img):
return isinstance(img, Image.Image)
def _is_numpy_image(img):
return isinstance(img,... |
5c7545995a9eaeb205f2c2b6d70e6e3e51c4c319b44a9d8e1e0d9323d43e9b08 | Python | 5,295 | 126 | """ PolyLoss (Poly-1)
Paper: `PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions`
- Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov
- ICLR 2022, https://arxiv.org/abs/2204.12511
Poly-1 adds epsilon * (1 - p_t) to the loss, adjus... |
1c590ae5fc6e62d9421e0535d13b76ba40c2a205760a3f70610bf6c37bc48d44 | Python | 5,300 | 121 | import os
import random
import torch
import argparse
import numpy as np
from util import (clamp, seed_torch, prepare,
PairIoHandler, PlayerIoHandler, SampleIoHandler, set_args)
def gen_pairs(grid_size: int, pair_num: int, stride: int = 1) -> np.ndarray:
"""
Input:
grid_size: int, th... |
b212e2bd05710014233b73d54cf085eecbf7ec506be300bf131cb34c88318554 | Python | 5,303 | 150 | #!/usr/bin/env python
"""The setup script."""
import sys, re, os
from setuptools import setup, find_packages
try:
with open("README.md") as readme_file:
readme = readme_file.read()
except Exception as error:
readme = "No README information found."
sys.stderr.write("Warning: Could not open '%s' ... |
e5bd66d75b6d1fb1feadbf5453f7da7c1adb71ebf688a7aa20cf02015f9714ca | Python | 5,318 | 141 | from pathlib import Path
from .utils import pass_method, map_values
import numpy as np
from skimage.io import imsave
import os
# from pathlib import Path
from zarr.core import Array
class Patch:
def __init__(
self,
slide_path: str,
slide_object: Array,
manager,
coordinates... |
e90d7e9d1cdf2156e666ce55223b168607550c46048514168204edda4b29a208 | Python | 5,323 | 156 | # Utility functions for the plasclass module
import numpy as np
import itertools
complements = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A'}
nt_bits = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
def readfq(fp): # this is a generator function
''' Adapted from https://github.com/lh3/readfq
'''
last = None # this is a bu... |
b9aa2128362b0f80efa5c603be987a44a5f57af4c2477138cd0a7ef4e81e0807 | Python | 5,324 | 142 | import json
import time
from datetime import datetime
import numpy as np
from ..globalFunctions.utils import MyEncoder, applysigniftoall, get_size
def SaveResults(input, cell, t, vsoma, traces, apcounts, aptimevectors, apinfo, totales, totalos, iOptogenx, succes_ratio, amps_SDeVstim, amps_SDoptogenx, pos_VTAeVstim,... |
2573e83b2091d3b1ed5e7351af18ab6545d1fb01f130c0d894828fd74d1a8f4c | Python | 5,327 | 137 | # This file is part of pyunicorn.
# Copyright (C) 2008--2026 Jonathan F. Donges and pyunicorn authors
# URL: <https://www.pik-potsdam.de/members/donges/software-2/software>
# License: BSD (3-clause)
#
# Please acknowledge and cite the use of this software and its authors
# when results are used in publications or publi... |
53c74aedfd0b1bc9039384ec395ad9a02962948436c10e6aaee57557882f8d35 | Python | 5,327 | 128 | """
Author: "Keitaro Yamashita, Garib N. Murshudov"
MRC Laboratory of Molecular Biology
This software is released under the
Mozilla Public License, version 2.0; see LICENSE.
"""
from __future__ import absolute_import, division, print_function, generators
import gemmi
import numpy
import scipy.integrate
import os
i... |
07ac60b236af04deb19ed9402669ff96e8cec6bca2136e2a45ef4e8ce39539ad | Python | 5,335 | 124 | import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from lib.utils import *
import tqdm
tfd = tfp.distributions
def random_gaussian_initializer(shape, dtype):
n = int(shape / 2)
loc_norm = tf.random_normal_initializer(mean=0., stddev=0.1)
loc = tf.Variable(
initial_val... |
d5ec9336d192f3713b3eec5ded3c9ead9997847498b6572bbeb967db81403d0c | Python | 5,339 | 212 | import numpy
import numpy.testing
import pytest
import skimage.morphology
import cellprofiler.modules.fillobjects
instance = cellprofiler.modules.fillobjects.FillObjects()
@pytest.fixture(scope="module")
def image_labels():
labels = numpy.zeros((20, 20), dtype=numpy.uint8)
labels[2:8, 2:8] = 1
labels[... |
0b74cba70557c32b612aa4f28f112cf03a5ff894c017d8e84139fbef3aab7bcd | Python | 5,344 | 140 | import numpy as np
import matplotlib.pyplot as plt
import random
import datetime
import logging
from scipy.ndimage import convolve
from dragen.utilities.Helpers import HelperFunctions
from dragen.utilities.InputInfo import RveInfo
class DiscreteRsa2D(HelperFunctions):
def __init__(self, a, b, alpha):
se... |
251be1c898f55a0f3c7a674a381d4f99c394c778a325473982812cdf2c411490 | Python | 5,345 | 162 | # -*- 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... |
47d3899e863bb847c4bd2d33f31d321a7a92b21f80086445bb893346ed24eae2 | Python | 5,346 | 167 | # -*- 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... |
a0911b47c69db681b1810333e2d9bbb9d5fe99da4944b554c1584b455a42d860 | Python | 5,350 | 140 | import numpy as np
from netCDF4 import Dataset
import os
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
#==========================================================
# Set output path ... |
d437e159f023b19b35b305e13587c9070a2e50ac2430098727cc737377241a9f | Python | 5,356 | 183 | """Data processing functions module for pulldown assay data and SVAE model
Author: Hikari Sorensen - Vector Engineering Team (hsorense@broadinstitute.org)
Notes:
"""
import random
import numpy as np
import pandas as pd
import tensorflow as tf
AAs = np.array(
[
"A",
"C",
"D",
"E",
... |
a557fe116f8db091c32e1b8d638ae80093f90439eb27ca31bbf5b3b5c49e4970 | Python | 5,366 | 149 | from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, cm
import os
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
o... |
a610d260d83826468ab802f7857865c394ef1e07375965cd39541b9d1aba0733 | Python | 5,388 | 186 | #!/bin/env python
"""
LINDA nowcasts
==============
This example shows how to compute and plot a deterministic and ensemble LINDA
nowcasts using Swiss radar data.
"""
from datetime import datetime
import warnings
warnings.simplefilter("ignore")
import matplotlib.pyplot as plt
from pysteps import io, rcparams
from... |
f9a643b3c6673de0bab1d73468a483a7a53eff3b4d108d73e18222eb466f7e3d | Python | 5,393 | 160 | from cellprofiler_core.setting.subscriber import LabelSubscriber
from ._image_segmentation import ImageSegmentation
from ...constants.measurement import COLTYPE_INTEGER
from ...constants.measurement import C_CHILDREN
from ...constants.measurement import C_NUMBER
from ...constants.measurement import C_PARENT
from ...co... |
56a4a1e32883d0c6e56b066843b45d4c72ec856c16dc734af33078e5c50e253e | Python | 5,399 | 146 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
Figure saving utility module.
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:
Utility function to sa... |
87a5a2d055e2c9e0125faf29af6b0b15b495b3f4836b6591977c82e12efaab8f | Python | 5,399 | 110 | # TODO Sep 08 version
"""General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dat... |
34e8a021436470c381fc1a4f117f0098fdb214f337f06ca5ffb747568d7bcf8c | Python | 5,411 | 170 | """test_fastemd.py test the FastEMD library wrapper
CellProfiler is distributed under the GNU General Public License,
but this file is licensed under the more permissive BSD license.
See the accompanying file LICENSE for details.
Copyright (c) 2003-2009 Massachusetts Institute of Technology
Copyright (c) 2009-2015 Br... |
43f0f049524b835a08b0491e66177c7858a4d6bc1c00f3aba47fcdffe94ddd8a | Python | 5,412 | 192 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pysteps.utils import cleansing
def test_decluster_empty():
"""
Decluster an empty input"""
X = np.empty((0, 2))
V = np.empty((0, 2))
X_dec, V_dec = cleansing.decluster(X, V, 20, 1)
assert X_dec.ndim == 2
assert V_dec.ndim ==... |
87f480544507fd86db5b320c8e56d661602a4dcf2321ecc09c76e5becdbfcfa7 | Python | 5,416 | 127 | import numpy as np
import scipy.io
import os.path as osp
import sys
sys.path.append('..')
from nilearn import plotting
from functions.def_ROIs_DorsalEarlyVisualCortex import roi as ROI
from functions.def_ROIs_EarlyVisualAreas import roi
def polarAngle_plot(subject_id, path, dorsal_only=False, binarize=False, save=Fa... |
d91eec1bf0551fde92d5c227013e0e7e8b6e991ba8d52bc2727da5cc93caa0d3 | Python | 5,416 | 129 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import models
from tqdm import tqdm
from torch.autograd import Variable
from tensorboardX import SummaryWriter
import os
loss_function = nn.MSELoss()
def train... |
46aa51460166adb8517937ae938beab20b29fdde5a8ac447eea3e1466ed1d7a3 | Python | 5,417 | 161 | import numpy as np
import os.path as osp
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import pandas as pd
import os
sys.path.append('..')
from numpy.random import seed
from sklearn.metrics import jaccard_score
from functions.def_ROIs_EarlyVisualAreas import roi as ROI
from functions.d... |
59b1394de288a666546e5f897eb37fdc8019d85c85e525f3e9aea68eae06ed7e | Python | 5,417 | 128 | import logging
from typing import Annotated, Optional, List, Dict, Any
from pydantic import validate_call, Field, ConfigDict
from ..opts.morph import MorphFunction
from ..functions.image_processing import (
apply_branchpoints,
apply_bridge,
apply_clean,
apply_convex_hull,
apply_diag,
apply_dis... |
5de7dd27cb9b3653dc47657a4934a417ab52ef065c234c710f7d93c9123cf3ea | Python | 5,417 | 145 | import collections
import json
import warnings
from os.path import abspath, basename, dirname, isfile, join, splitext
from typing import TYPE_CHECKING, Any
import traits.api
from ptsa.data.readers import BaseReader
__all__ = [
'ParamsReader',
]
class ParamsReader(BaseReader, traits.api.HasTraits):
"""
... |
675a9680ef3c769229482a8b36c3662b0faa022c710ac44a19a855dc0ed25ae7 | Python | 5,420 | 153 | '''
JHK
Python script for generating ED figure 5.
'''
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, cm
import os
#==========================================================
# Set main directory
#=================================================... |
a17a65e5f72c7938cdcd2ca7e54e779548e49c06af8f488c1666bf8c174cc60b | Python | 5,427 | 208 | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use
basic versions of the... |
8f44870af8548a42bba8e4aa7f00b1fa3b0274080ba5d5e28b254f1457bdbedf | Python | 5,431 | 182 | # study: .sqlite3
# PPM: 20.0
# RT_TOL: 2.0
# MIN_SIGNAL: 100000.0
# MIN_RANGE: 10.0
import sys
import time
import bisect
import sqlite3
start_time = time.time()
batchmode = True
__version__ = "0.7"
#
# Based on: https://github.com/suzaku/plain_obj
#
def make_constructor(fields):
assignments = "\n".join([f" ... |
c7a147fe96bccf3f66ab70dc13cbfc38c3524a7dc93d40cb8dd420b78f7ccf6d | Python | 5,438 | 144 | '''
JHK
Python script for calculating the ratio between dSTD and dClim. of HF PRCP.
'''
import numpy as np
from netCDF4 import Dataset
import os
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_de... |
2d18520aa2905d23e3bd5cce62fef3b41ba795b2d41e2ac04fa0bc08028a0759 | Python | 5,439 | 144 | import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from tqdm import tqdm
from utils import WarmUpLR, downLR, custom_loss, get_time_dif
import numpy as np
from sklearn.metrics import classification_report,confusion_matrix,\
precision_score, f1_score, accuracy_score, recall_score,\
r... |
07298baec0e23a376bfa5de40e420dc3e4dd8f8804caba4563695ade5916445f | Python | 5,442 | 150 | import keras.backend as K
import theano
from keras.layers import TimeDistributed, RepeatVector, Permute
from os.path import join, realpath, dirname
from model.layers_custom import AttLayer
theano.config.openmp = False
from keras.layers.core import Flatten, Reshape, Lambda
from keras.layers import Dense, merge
def ge... |
67926235cc9ed3758ef8d0c69a47cc4ff9ac248340e1ef56f7bc6a01ff40e147 | Python | 5,445 | 146 | import numpy as np
import scipy.io
import os.path as osp
import sys
import argparse
sys.path.append('..')
from nilearn import plotting
from functions.def_ROIs_EarlyVisualAreas import roi as ROI
from functions.def_ROIs_DorsalEarlyVisualCortex import roi
def topographic_map_plot(subject_id, path, modality, hemisphere,... |
f72225dec1b8996f3515c786c04cb7d6d11c4feecb96cfbf21b6cb827d0bae2e | Python | 5,446 | 134 | """Compare every converted Gisette value and label with the pinned UCI archive."""
from __future__ import annotations
import argparse
import hashlib
import json
import sys
from pathlib import Path
from zipfile import ZipFile
import numpy as np
import yaml
ROOT = Path(__file__).resolve().parents[3]
sys.path.insert(0... |
bb9f7803d48f8e18a3733970123ac5304f929a47e0295b1ed5218f1293775725 | Python | 5,447 | 155 | import cv2
import faiss as f
import numpy as np
import pyarrow.parquet as pq
from configs import *
def build_index(emb):
xb = emb
d = emb.shape[1]
# res = faiss.StandardGpuResources()
# flat_config = faiss.GpuIndexFlatConfig()
# flat_config.useFloat16 = False
# flat_config.device = 0 #check num... |
55bcd468dec9cf285783daedaebd7d56b856ad637736e844fe7f08027177d75e | Python | 5,451 | 160 | # -*- 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... |
be10fe8e1796e71781df9ec5536324ffd1bd3efb3c4592fffdbb6e11ea85c683 | Python | 5,451 | 164 | import itertools
import multiprocessing as mp
import time
from multiprocessing import Manager
import numpy as np
from Utils.cal_utils import count_kmers, readfq, mer2bits, get_rc
def compute_kmer_inds(ks):
''' Get the indeces of each canonical kmer in the kmer count vectors
'''
kmer_list = ... |
7bee5054fb3d54098305e79b0feb080c3782cce5fb5c1342d25c6c0221139f05 | Python | 5,456 | 149 | import unittest
import numpy as np
import pkg_resources
from deeprank.features import AtomicFeature
class TestAtomicFeature(unittest.TestCase):
"""Test StructureSimialrity."""
@staticmethod
def test_atomic_haddock():
# in case you change the ref don't forget to:
# - comment the first l... |
e27f26d6ff471dd2e8320a93de2c1a50a42225e9af2abb392d1240ff15f4d5d3 | Python | 5,456 | 126 | #!/usr/bin/env python3
"""
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: UMC Utrecht (University Medical Center Utrecht), The Netherlands
Contact: j.c.w.siero@umcut... |
93130031a42c5e57fb6d6a426e7bb26007ac72a5664ca8e7de488caea9074226 | Python | 5,457 | 178 | import json
import os
from pathlib import Path
import pytest
from pyld import jsonld
from ...jsonldutils import load_file
from ...utils import start_server, stop_server
from .. import Activity, Item, Protocol, ResponseOption
from ..utils import write_obj_jsonld
contextfile_url = "https://raw.githubusercontent.com/Re... |
fbfbd8362d5a33654196952426ea71be8da6023f9e7f47b0bd9c490eaee3d6f9 | Python | 5,462 | 188 | from email.mime import base
import pdb
from re import S
from unittest import TextTestRunner
import numpy as np
import os
import wntr
import networkx as nx
import copy
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import newton, curve_fit
import torch
import ray
def get_demand_time_series_noi... |
b22d81fe0a8aaa429efd54e6b2762b66f5323d4556430511afd71b132bc080a2 | Python | 5,464 | 163 | from ._fx import (
create_feature_extractor,
get_graph_node_names,
register_notrace_function,
register_notrace_module,
is_notrace_module,
is_notrace_function,
get_notrace_modules,
get_notrace_functions,
)
from .activations import *
from .adaptive_avgmax_pool import (
adaptive_avgmax_... |
e0e4b65ee0cc0ca4a9eb077911b9b20308a7797fde441726d3d7033b69576e90 | Python | 5,466 | 117 | import matplotlib.pyplot as plt
import numpy as np
from ..spiketrain import SpikeTrain
from .spiketrain import SpikeTrainsPlotter
from ..utils.time import get_dt, searchsorted
class DecodingPlotter:
def __init__(self, t=None, model=None, optimizer=None, mask_spikes=None, neurons=None, files=None, stim_dec=None,
... |
6b685e2b53ddb8e27775afe9b16c6910cf08cce6076c1ff8298821216ed260a3 | Python | 5,480 | 140 | # Pytorch Tabular
# Author: Manu Joseph <manujoseph@gmail.com>
# For license information, see LICENSE.TXT
"""Tabular Model"""
import logging
from typing import Dict
import pytorch_lightning as pl
import torch
import torch.nn as nn
from omegaconf import DictConfig
from ..base_model import BaseModel
from . import utils... |
cfe85e75001d9695e1817ed424031d2c37ce581934d5021ec701f6320b9494de | Python | 5,480 | 154 | '''
This script was a quick hack to make simulated Illumina and PacBio reads for circular sequences.
PBSIM and Wgsim do not seem to have a 'circular reference' option, nor did it have an option for
different depths for different references. This script simply runs PBSIM over and over, each time
randomly rotating the r... |
c12fa118d6e61cfc47d3a805b32fc7a393a99c0adaca61a4210d0055011a22f4 | Python | 5,489 | 138 | # -*- coding: utf-8 -*-
"""
@author: danielm
Allen Institute Software License - This software license is the 2-clause BSD license
plus a third clause that prohibits redistribution for commercial purposes without further permission.
Copyright (c) 2018. Allen Institute. All rights reserved.
Redistribution an... |
d8f868f4e517206575cf66a00ab861979d90249006a7da489685e579046012a9 | Python | 5,492 | 131 | from tensorflow.keras import backend as K
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv3D, Activation, Dense, concatenate, add
from tensorflow.keras.layers import Conv3DTranspose, LeakyReLU
from tensorflow.keras import regularizers
from tensorflow_addons.layers import InstanceN... |
de223d0d53f9335ceb60c184a9f4b7bc3c78ebe03952e7fbdc7d76dcfd8b78da | Python | 5,497 | 153 | import torch
from torch import nn
from abc import ABC, abstractmethod
from typing import List
class AbstractLossFunction(nn.Module, ABC):
def __init__(self, params: dict):
nn.Module.__init__(self)
self.params = params
self.num_classes = len(params["model"]["class_list"])
self._init... |
1860f476e583a05a8700fcaf39f964477fd91821d9a86b3ed69f54dc94e05ad2 | Python | 5,501 | 137 | import PyDSTool
import numpy as np
import pickle
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter,ScalarFormatter
import matplotlib as mpl
from matplotlib.lines import Line2D
'''
ANT flux at steady state. This script generates the figure 4a in the paper
08.11.23
GCG
'''
params = {'axes.... |
5736148321a66189e9f95214ffe588c40b74d01ace9f21cb466f35c6f4aef24d | Python | 5,502 | 154 | """
Implementation of the Adaptive Exponential Integrate-and-Fire model.
See Neuronal Dynamics
`Chapter 6 Section 1 <http://neuronaldynamics.epfl.ch/online/Ch6.S1.html>`_
"""
# This file is part of the exercise code repository accompanying
# the book: Neuronal Dynamics (see http://neuronaldynamics.epfl.ch)
# located... |
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