%run NBCONFIG.ipynb

from pyparty.data import nanolabels, nanogold

NANOLABELS = nanolabels()
ax1, ax2 = splot(1,2)

showim(nanogold(), ax1, title='SEM nanoparticles')
showim(NANOLABELS, ax2, 'spectral', 
       title='size-segmented nanoparticles');

print 'unique colors:', np.unique(NANOLABELS)

from pyparty.multi import multi_mask
from collections import OrderedDict

NAMES = ('singles', 'dimers', 'trimers', 'clusters')

axes = splot(2,2, figsize=(10,6))

masks = multi_mask(NANOLABELS, *NAMES, astype=OrderedDict)

for idx, (name, image) in enumerate(masks.items()):
    showim(image, axes[idx], 'gray', title=name)

type(masks), masks.keys()[0], masks.values()[0].shape

L, W = NANOLABELS.shape
dimer_area = sum(masks['dimers'])

print 'Dimer coverage: %.1f%%' % \
    (100.0 * ( float(dimer_area) / (L * W)) )

from pyparty import MultiCanvas

mc = MultiCanvas.from_labeled(NANOLABELS, *NAMES)
mc

mc.set_colors('r','g','y', 'magenta')
mc.mycolors

# Take out singles, take mean
mu = np.mean(mc['singles'].area)
'Mean single particle area: %s%%'  % round(mu,1)

ax1, ax2 = splot(1, 2, figsize=(10,5))

chartkwds = {'autopct':'percent', 'shadow':True}

mc.pie(ax1, **chartkwds);
mc.pie(ax2, attr='area', explode=(0,0,0,0.1), **chartkwds);

del mc['clusters']
mc.names

BINS = 30
YMAX = XMAX = 300
BINS=30

mc.hist(attr='area', bins=BINS);

# Add vline at 1, 2, 3 times mean
plt.vlines((mu, 2*mu, 3*mu), 0, YMAX, linestyles='--')

# Add text to plot
amu = '$A_\mu$'
textkwds = {'color':'blue', 'bbox':{'facecolor':'gray', 'alpha':.5}}

plt.text(25, 275, 'x < %s' % amu, **textkwds)
plt.text(80, 275, '%s < x < 2%s' % (amu, amu), **textkwds)
plt.text(158, 275, '2%s < x <2%s' % (amu,amu), **textkwds)
plt.text(250, 200, 'x > 3%s' % amu, **textkwds)

plt.xlim(0,XMAX)
plt.ylim(0,YMAX);

mc['singles'].patchshow(gcolor='red', title='Singles patches');

c_dimers = mc['dimers']
c_dimers.background = nanogold()
c_dimers.patchshow(pmap='jet');