#!/usr/bin/env python3
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
from __future__ import print_function # Python 2/3 compatibility
__doc__ = """
This scipt makes a noisy activation image and extracts the blobs from it.

Requires matplotlib

Author : Bertrand Thirion, 2009--2012
"""
print(__doc__)

import numpy as np

try:
    import matplotlib.pyplot as plt
except ImportError:
    raise RuntimeError("This script needs the matplotlib library")
import matplotlib as mpl

import nipy.labs.utils.simul_multisubject_fmri_dataset as simul
import nipy.labs.spatial_models.hroi as hroi
from nipy.labs.spatial_models.discrete_domain import grid_domain_from_shape

# ---------------------------------------------------------
# simulate an activation image
# ---------------------------------------------------------

shape = (60, 60)
pos = np.array([[12, 14], [20, 20], [30, 20]])
ampli = np.array([3, 4, 4])
dataset = simul.surrogate_2d_dataset(n_subj=1, shape=shape, pos=pos,
                                     ampli=ampli, width=10.0).squeeze()
values = dataset.ravel()

#-------------------------------------------------------
# Computations
#-------------------------------------------------------

# create a domain descriptor associated with this
domain = grid_domain_from_shape(shape)
nroi = hroi.HROI_as_discrete_domain_blobs(domain, values, threshold=2.0, smin=3)

# create an average activaion image
activation = [values[nroi.select_id(id, roi=False)] for id in nroi.get_id()]
nroi.set_feature('activation', activation)
bmap = nroi.feature_to_voxel_map(
    'activation', roi=True, method="mean").reshape(shape)

#--------------------------------------------------------
# Result display
#--------------------------------------------------------

aux1 = (0 - values.min()) / (values.max() - values.min())
aux2 = (bmap.max() - values.min()) / (values.max() - values.min())
cdict = {'red': ((0.0, 0.0, 0.7), 
                 (aux1, 0.7, 0.7),
                 (aux2, 1.0, 1.0),
                 (1.0, 1.0, 1.0)),
         'green': ((0.0, 0.0, 0.7),
                   (aux1, 0.7, 0.0),
                   (aux2, 1.0, 1.0),
                   (1.0, 1.0, 1.0)),
         'blue': ((0.0, 0.0, 0.7),
                  (aux1, 0.7, 0.0),
                  (aux2, 0.5, 0.5),
                  (1.0, 1.0, 1.0))}
my_cmap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 256)

plt.figure(figsize=(12, 3))
plt.subplot(1, 3, 1)
plt.imshow(dataset, interpolation='nearest', cmap=my_cmap)
cb = plt.colorbar()
for t in cb.ax.get_yticklabels():
    t.set_fontsize(16)

plt.axis('off')
plt.title('Thresholded data')

# plot the blob label image
plt.subplot(1, 3, 2)
plt.imshow(nroi.feature_to_voxel_map('id', roi=True).reshape(shape),
          interpolation='nearest')
plt.colorbar()
plt.title('Blob labels')

# plot the blob-averaged signal image
aux = 0.01
cdict = {'red': ((0.0, 0.0, 0.7), (aux, 0.7, 0.7), (1.0, 1.0, 1.0)),
         'green': ((0.0, 0.0, 0.7), (aux, 0.7, 0.0), (1.0, 1.0, 1.0)),
         'blue': ((0.0, 0.0, 0.7), (aux, 0.7, 0.0), (1.0, 0.5, 1.0))}
my_cmap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 256)

plt.subplot(1, 3, 3)
plt.imshow(bmap, interpolation='nearest', cmap=my_cmap)
cb = plt.colorbar()
for t in cb.ax.get_yticklabels():
    t.set_fontsize(16)
plt.axis('off')
plt.title('Blob average')
plt.show()
