from __future__ import division

%run NBCONFIG.ipynb

c=Canvas()
c.available()

c.add('dimer', overlap=.3) #Default to image center (256, 256)
c.add('dimer', center=(100,100), phi=25.)
c.add('circle', center=(100,400), phi=60)
c.add('ellipse', center=(400,100))
c.add('triangle', center=(200,300), length=50)
c.add('ellipse', center=(400, 400), yradius=5, xradius=50)

print c.particles
c.show();

from skimage.morphology import label

ax1, ax2, ax3 = splot(1, 3, figsize=(10,8))

# We can access grayimage immediately from canvas
labels = label(c.grayimage)

showim(c.image, ax1, title='RGB')
showim(c.grayimage, 'gray', ax2, title='Grayscale')
showim(labels, 'jet', ax3, title='skimage.morphology labels');

print labels.shape
print np.unique(labels)
labels

print '  '.join(['INDEX', 'NAME', 'CSTART', 'CEND', 'CENTER'])+'\n'
for idx, p in enumerate(c.particles):
    print idx, p.name, labels[p.rr_cc][0:3], labels[p.rr_cc][-3:], p.center

ax1, ax2 = splot(1,2)

c.show('pbinary', ax1, title='pbinary')
showim(c.binaryimage, ax2, 'gray', title='%s' % c.threshfcn);
print 'Canvas thresholding function is "%s()"' % c.threshfcn

clab = c.from_labels(exclude='black') 
clab.show(title='Particles from labels')
clab.particles[0:5]

ax1, ax2, ax3 = splot(1, 3, figsize=(12,8))

clab = c.from_labels(prefix='FOOPREFIX', colorbynum=True, exclude='black')

clab.show(ax1, title='Black to Yellow Ascending')
clab.show('jet', ax2, title="1-Channel coloring")

clab.background='black'
clab.show('jet', ax3, title='Original Coloring');
clab.particles[0:5]

len(c), len(clab), 'Dimer labeled as 2 particles'

clab.sortby('eccentricity', inplace=True, ascending=False)
clab.eccentricity

from pyparty.data import lena_who

ax1, ax2 = splot(1,2)

c=Canvas(background=lena_who())
c.show('pbinary', ax1, title='No particles; pbinary is blank');

c.set_threshfcn('adaptive', block_size=199, method='gaussian')
showim(c.binarybackground, ax2, 'gray', title='Adaptive threshold');

#This cell may take a bit to load
ax1, ax2, ax3, ax4 = splot(2,2, figsize=(15,15))

c1=c.from_labels(pbinary=False) #background = None, neighbors = 4
c2=c.from_labels(neighbors=8, pbinary=False)  #background = none
c3=c.from_labels(exclude=0, pbinary=False) # neighbors = 4
c4=c.from_labels(exclude=1, pbinary=False) # neighbors = 4

c1.show(ax1, title='%s particles; no background; 4 neighbors' % len(c1))
c2.show(ax2, title='%s particles; no background; 8 neighbors' % len(c2))
c3.show(ax3, title='%s particles; black bg cut; 4 neighbors' % len(c3))
c4.show(ax4, title='%s particles; white bg cut; 4 neighbors' % len(c4));

ax1, ax2 = splot(1,2, figsize=(15,15))

c1=c.from_labels(pbinary=False, exclude='black', bgout='white') 
c2=c.from_labels(pbinary=False, exclude='black', bgout=c.graybackground) 

c1.show(ax1, title='Labels on a white background')
c2.show(ax2, title='Labels on a gray image');

ax1, ax2 = splot(1,2, figsize=(15,15))

c1=c.from_labels(exclude='black', pmax=1000, pbinary=False) 
c2=c.from_labels(exclude='black', pmin=5000, pbinary=False) 

c1.show(ax1, title='Small labels (< 1000 pixels)')
c2.show(ax2, title='Large labels (> 5000 pixels)');

#Sort particles by largest area; keep top 3
c2.sortby('area', inplace=True, ascending=False) #large to small
c2.particles=c2.particles[0:3]

# To set attributes on indivdual particles, access plist, not particles
c2.plist[0].color = 'r'
c2.plist[1].color = 'b'
c2.plist[2].color = 'w'

#Superimpose on a black background
c2.background = 'black'
c2.show();

def rotate(p):
    p.phi = p.phi + 45.0
    return p

c2.pmap(rotate)
c2.show();