BF_analysis.BF_cell_features() - Code Metrics - Inspection of "bunch o` change" - ccurtis7/diff_classifier - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 97b43f...ffcb6f )

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created 2019-07-12 04:41 UTC

BF_analysis.BF_cell_features() B

↳ Parent: BF_analysis

Complexity

Conditions

Size

Total Lines	54
Code Lines	44

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	44
nop	3
dl	0
loc	54
rs	8.824
c	0
b	0
f	0

How to fix Long Method

import sys
import matplotlib
matplotlib.use('Agg')
import skimage.io as sio
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import diff_classifier.aws as aws
from skimage.filters import roberts, sobel, scharr, prewitt, median, rank
from skimage import img_as_ubyte
from skimage.morphology import erosion, dilation, opening, closing, white_tophat, disk, reconstruction
from scipy.ndimage.morphology import distance_transform_edt as EuclideanTransform
from operator import itemgetter

to_track = []
result_futures = {}

remote_folder = 'Cell_Studies/10_16_18_cell_study' #Folder in AWS S3 containing files to be analyzed
bucket = 'ccurtis.data'
vids = 5
types = ['PS', 'PEG']
slices = [1, 2]

for typ in types:
    for slic in slices:
        for num in range(1, vids+1):

            #to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num))
            to_track.append('{}_{}_XY{}'.format(typ, slic, num))


def binary_BF(image, meanse=disk(10), edgefilt='prewitt', opense=disk(10),
          fill_first=False, bi_thresh=0.000025, tophatse=disk(20)):
    
    #convertim = img_as_ubyte(image)
    meanim = rank.mean(image, meanse)
    if edgefilt is 'prewitt':
        edgeim = prewitt(meanim)
    elif edgefilt is 'sobel':
        edgeim = sobel(meanim)
    elif edgefilt is 'scharr':
        edgeim = scharr(meanim)
    elif edgefilt is 'roberts':
        edgeim = roberts(meanim)
    
    closeim = closing(edgeim, opense)

    openim = opening(closeim, opense)
    if fill_first:
        seed = np.copy(openim)
        seed[1:-1, 1:-1] = openim.max()
        mask = openim
        filledim = reconstruction(seed, mask, method='erosion')
        binarim = filledim > bi_thresh
    else:
        binarim = openim > bi_thresh*np.mean(openim)
        seed = np.copy(binarim)
        seed[1:-1, 1:-1] = binarim.max()
        mask = binarim
        filledim = reconstruction(seed, mask, method='erosion')

    tophim = filledim - closing(white_tophat(filledim, tophatse), opense)>0.01

    fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))
    ax[0][0].imshow(image, cmap='gray')
    ax[0][1].imshow(meanim, cmap='gray')
    ax[0][2].imshow(edgeim, cmap='gray', vmax=4*np.mean(edgeim))
    ax[0][3].imshow(closeim, cmap='gray', vmax=4*np.mean(closeim))
    ax[1][0].imshow(openim, cmap='gray', vmax=4*np.mean(openim))
    ax[1][1].imshow(binarim, cmap='gray')
    ax[1][2].imshow(filledim, cmap='gray')
    ax[1][3].imshow(tophim, cmap='gray')
    for axes in ax:
        for axe in axes:
            axe.axis('off')
    fig.tight_layout()
    
    return tophim


def BF_cell_features(prefix, folder, bucket='ccurtis.data'):
    
    ffilename = 'features_{}.csv'.format(prefix)
    mfilename = 'msd_{}.csv'.format(prefix)
    bffilename = 'BF_cells_{}.tif'.format(prefix)
    biim = 'bi_BF_cells_{}.tif'.format(prefix)
    bimages = 'biproc_BF_cells_{}.png'.format(prefix)
    
    aws.download_s3('{}/{}'.format(folder, ffilename), ffilename, bucket_name=bucket)
    aws.download_s3('{}/{}'.format(folder, mfilename), mfilename, bucket_name=bucket)
    aws.download_s3('{}/{}'.format(folder, bffilename), bffilename, bucket_name=bucket)
    print('Successfully downloaded files')
    
    fstats = pd.read_csv(ffilename, encoding = "ISO-8859-1")
    msds = pd.read_csv(mfilename, encoding = "ISO-8859-1")
    bfimage = plt.imread(bffilename)
    tophimage = binary_BF(bfimage, opense=disk(12), bi_thresh=1.2, tophatse=disk(20))
    plt.savefig(bimages)
    euimage = EuclideanTransform(tophimage)+EuclideanTransform(~tophimage)
    print('Successfully performed image processing')
    
    xa = -np.reshape(np.clip((fstats.Y.values-1).astype(int), a_min=0, a_max=2043), newshape=(fstats.Y.shape[0], 1))
    ya = np.reshape(np.clip((fstats.X.values-1).astype(int), a_min=0, a_max=2043), newshape=(fstats.X.shape[0], 1))
    xya = [tuple(l) for l in np.concatenate((xa, ya), axis=1).tolist()]
    fstats['Cell Status'] = itemgetter(*xya)(tophimage)
    fstats['Cell Distance'] = itemgetter(*xya)(euimage)

    print('Successfully calculated Cell Status Params')
    
    frames = 651
    xb = -np.reshape(np.clip((msds.Y.values-1).astype(int), a_min=0, a_max=2043), newshape=(int(msds.Y.shape[0]), 1))
    yb = np.reshape(np.clip((msds.X.values-1).astype(int), a_min=0, a_max=2043), newshape=(int(msds.X.shape[0]), 1))
    xyb = [tuple(l) for l in np.concatenate((xb, yb), axis=1).tolist()]
    msds['Cell Status'] = itemgetter(*xyb)(tophimage)
    msds['Cell Distance'] = itemgetter(*xyb)(euimage)
    
    msds_cell_status = np.reshape(msds['Cell Status'].values, newshape=(int(msds.X.shape[0]/frames), frames))
    msds_cell_distance = np.reshape(msds['Cell Distance'].values, newshape=(int(msds.X.shape[0]/frames), frames))
    fstats['Membrane Xing'] = np.sum(np.diff(msds_cell_status, axis=1) == True, axis=1)
    fstats['Distance Towards Cell'] = np.sum(np.diff(msds_cell_distance, axis=1), axis=1)
    fstats['Percent Towards Cell'] = np.mean(np.diff(msds_cell_distance, axis=1) > 0, axis=1)
    print('Successfully calculated Membrane Xing Params')
    
    fstats.to_csv(ffilename, sep=',', encoding = "ISO-8859-1")
    msds.to_csv(mfilename, sep=',', encoding = "ISO-8859-1")
    plt.imsave(biim, tophimage, cmap='gray')
    
    aws.upload_s3(ffilename, '{}/{}'.format(folder, ffilename), bucket_name=bucket)
    aws.upload_s3(mfilename, '{}/{}'.format(folder, mfilename), bucket_name=bucket)
    aws.upload_s3(biim, '{}/{}'.format(folder, biim), bucket_name=bucket)
    aws.upload_s3(bimages, '{}/{}'.format(folder, bimages, bucket_name=bucket))
    print('Successfully uploaded files')
    
    return fstats


for prefix in to_track[int(sys.argv[1]):int(sys.argv[2])]:
    fstats = BF_cell_features(prefix, remote_folder, bucket=bucket)
    print('Successfully output cell features for {}'.format(prefix))
    


1			import sys
2			import matplotlib
3			matplotlib.use('Agg')
4			import skimage.io as sio
5			import matplotlib.pyplot as plt
6			import numpy as np
7			import pandas as pd
8			import diff_classifier.aws as aws
9			from skimage.filters import roberts, sobel, scharr, prewitt, median, rank
10			from skimage import img_as_ubyte
11			from skimage.morphology import erosion, dilation, opening, closing, white_tophat, disk, reconstruction
12			from scipy.ndimage.morphology import distance_transform_edt as EuclideanTransform
13			from operator import itemgetter
14
15			to_track = []
16			result_futures = {}
17
18			remote_folder = 'Cell_Studies/10_16_18_cell_study' #Folder in AWS S3 containing files to be analyzed
19			bucket = 'ccurtis.data'
20			vids = 5
21			types = ['PS', 'PEG']
22			slices = [1, 2]
23
24			for typ in types:
25			for slic in slices:
26			for num in range(1, vids+1):
			0 ignored issues – show Comprehensibility Best Practice introduced 2019-07-12 04:49 UTC by Report Bug Copy Issue Report The variable `range` does not seem to be defined. Loading history...
27			#to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num))
28			to_track.append('{}_{}_XY{}'.format(typ, slic, num))
29
30
31			def binary_BF(image, meanse=disk(10), edgefilt='prewitt', opense=disk(10),
32			fill_first=False, bi_thresh=0.000025, tophatse=disk(20)):
33
34			#convertim = img_as_ubyte(image)
35			meanim = rank.mean(image, meanse)
36			if edgefilt is 'prewitt':
37			edgeim = prewitt(meanim)
38			elif edgefilt is 'sobel':
39			edgeim = sobel(meanim)
40			elif edgefilt is 'scharr':
41			edgeim = scharr(meanim)
42			elif edgefilt is 'roberts':
43			edgeim = roberts(meanim)
44
45			closeim = closing(edgeim, opense)
			0 ignored issues – show introduced 2019-07-12 04:49 UTC by Report Bug Copy Issue Report The variable `edgeim` does not seem to be defined for all execution paths. Loading history...
46			openim = opening(closeim, opense)
47			if fill_first:
48			seed = np.copy(openim)
49			seed[1:-1, 1:-1] = openim.max()
50			mask = openim
51			filledim = reconstruction(seed, mask, method='erosion')
52			binarim = filledim > bi_thresh
53			else:
54			binarim = openim > bi_thresh*np.mean(openim)
55			seed = np.copy(binarim)
56			seed[1:-1, 1:-1] = binarim.max()
57			mask = binarim
58			filledim = reconstruction(seed, mask, method='erosion')
59
60			tophim = filledim - closing(white_tophat(filledim, tophatse), opense)>0.01
61
62			fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))
63			ax[0][0].imshow(image, cmap='gray')
64			ax[0][1].imshow(meanim, cmap='gray')
65			ax[0][2].imshow(edgeim, cmap='gray', vmax=4*np.mean(edgeim))
66			ax[0][3].imshow(closeim, cmap='gray', vmax=4*np.mean(closeim))
67			ax[1][0].imshow(openim, cmap='gray', vmax=4*np.mean(openim))
68			ax[1][1].imshow(binarim, cmap='gray')
69			ax[1][2].imshow(filledim, cmap='gray')
70			ax[1][3].imshow(tophim, cmap='gray')
71			for axes in ax:
72			for axe in axes:
73			axe.axis('off')
74			fig.tight_layout()
75
76			return tophim
77
78
79			def BF_cell_features(prefix, folder, bucket='ccurtis.data'):
80
81			ffilename = 'features_{}.csv'.format(prefix)
82			mfilename = 'msd_{}.csv'.format(prefix)
83			bffilename = 'BF_cells_{}.tif'.format(prefix)
84			biim = 'bi_BF_cells_{}.tif'.format(prefix)
85			bimages = 'biproc_BF_cells_{}.png'.format(prefix)
86
87			aws.download_s3('{}/{}'.format(folder, ffilename), ffilename, bucket_name=bucket)
88			aws.download_s3('{}/{}'.format(folder, mfilename), mfilename, bucket_name=bucket)
89			aws.download_s3('{}/{}'.format(folder, bffilename), bffilename, bucket_name=bucket)
90			print('Successfully downloaded files')
91
92			fstats = pd.read_csv(ffilename, encoding = "ISO-8859-1")
93			msds = pd.read_csv(mfilename, encoding = "ISO-8859-1")
94			bfimage = plt.imread(bffilename)
95			tophimage = binary_BF(bfimage, opense=disk(12), bi_thresh=1.2, tophatse=disk(20))
96			plt.savefig(bimages)
97			euimage = EuclideanTransform(tophimage)+EuclideanTransform(~tophimage)
98			print('Successfully performed image processing')
99
100			xa = -np.reshape(np.clip((fstats.Y.values-1).astype(int), a_min=0, a_max=2043), newshape=(fstats.Y.shape[0], 1))
101			ya = np.reshape(np.clip((fstats.X.values-1).astype(int), a_min=0, a_max=2043), newshape=(fstats.X.shape[0], 1))
102			xya = [tuple(l) for l in np.concatenate((xa, ya), axis=1).tolist()]
103			fstats['Cell Status'] = itemgetter(*xya)(tophimage)
104			fstats['Cell Distance'] = itemgetter(*xya)(euimage)
105
106			print('Successfully calculated Cell Status Params')
107
108			frames = 651
109			xb = -np.reshape(np.clip((msds.Y.values-1).astype(int), a_min=0, a_max=2043), newshape=(int(msds.Y.shape[0]), 1))
110			yb = np.reshape(np.clip((msds.X.values-1).astype(int), a_min=0, a_max=2043), newshape=(int(msds.X.shape[0]), 1))
111			xyb = [tuple(l) for l in np.concatenate((xb, yb), axis=1).tolist()]
112			msds['Cell Status'] = itemgetter(*xyb)(tophimage)
113			msds['Cell Distance'] = itemgetter(*xyb)(euimage)
114
115			msds_cell_status = np.reshape(msds['Cell Status'].values, newshape=(int(msds.X.shape[0]/frames), frames))
116			msds_cell_distance = np.reshape(msds['Cell Distance'].values, newshape=(int(msds.X.shape[0]/frames), frames))
117			fstats['Membrane Xing'] = np.sum(np.diff(msds_cell_status, axis=1) == True, axis=1)
118			fstats['Distance Towards Cell'] = np.sum(np.diff(msds_cell_distance, axis=1), axis=1)
119			fstats['Percent Towards Cell'] = np.mean(np.diff(msds_cell_distance, axis=1) > 0, axis=1)
120			print('Successfully calculated Membrane Xing Params')
121
122			fstats.to_csv(ffilename, sep=',', encoding = "ISO-8859-1")
123			msds.to_csv(mfilename, sep=',', encoding = "ISO-8859-1")
124			plt.imsave(biim, tophimage, cmap='gray')
125
126			aws.upload_s3(ffilename, '{}/{}'.format(folder, ffilename), bucket_name=bucket)
127			aws.upload_s3(mfilename, '{}/{}'.format(folder, mfilename), bucket_name=bucket)
128			aws.upload_s3(biim, '{}/{}'.format(folder, biim), bucket_name=bucket)
129			aws.upload_s3(bimages, '{}/{}'.format(folder, bimages, bucket_name=bucket))
130			print('Successfully uploaded files')
131
132			return fstats
133
134
135			for prefix in to_track[int(sys.argv[1]):int(sys.argv[2])]:
136			fstats = BF_cell_features(prefix, remote_folder, bucket=bucket)
137			print('Successfully output cell features for {}'.format(prefix))
138
139

ccurtis7 / diff_classifier

Push — master ( 97b43f...ffcb6f )

BF_analysis.BF_cell_features() B

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