#!/usr/bin/env python
import argparse
import shutil

import dipy.core.optimize as opt
import dipy.tracking.life as life
from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors

import matplotlib
import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1 import AxesGrid

import nibabel as nib

import numpy as np

parser = argparse.ArgumentParser()
parser.add_argument('--input', dest='input', help='Track Visualization Header dataset')
parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
parser.add_argument('--output_life_optimized', dest='output_life_optimized', help='Output life optimized streamlines')
parser.add_argument('--output_beta_histogram', dest='output_beta_histogram', help='Output beta histogram')
parser.add_argument('--output_error_histograms', dest='output_error_histograms', help='Output error histograms')
parser.add_argument('--output_spatial_errors', dest='output_spatial_errors', help='Output spatial errors')

args = parser.parse_args()

# We'll need to know where the corpus callosum is from these variables.
hardi_img, gtab, labels_img = read_stanford_labels()
labels = labels_img.get_data()
cc_slice = labels == 2
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
data = hardi_img.get_data()

# Read the candidates from file in voxel space:
candidate_sl = [s[0] for s in nib.trackvis.read(args.input, points_space='voxel')[0]]
# Visualize the initial candidate group of streamlines
# in 3D, relative to the anatomical structure of this brain.
candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl))
cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
vol_actor = fvtk.slicer(t1_data)
vol_actor.display(40, None, None)
vol_actor2 = vol_actor.copy()
vol_actor2.display(None, None, 35)
# Add display objects to canvas.
ren = fvtk.ren()
fvtk.add(ren, candidate_streamlines_actor)
fvtk.add(ren, cc_ROI_actor)
fvtk.add(ren, vol_actor)
fvtk.add(ren, vol_actor2)
fvtk.record(ren, n_frames=1, out_path="life_candidates.png", size=(800, 800))
shutil.move("life_candidates.png", args.output_life_candidates)
# Initialize a LiFE model.
fiber_model = life.FiberModel(gtab)
# Fit the model, producing a FiberFit class instance,
# that stores the data, as well as the results of the
# fitting procedure.
fiber_fit = fiber_model.fit(data, candidate_sl, affine=np.eye(4))
fig, ax = plt.subplots(1)
ax.hist(fiber_fit.beta, bins=100, histtype='step')
ax.set_xlabel('Fiber weights')
ax.set_ylabel('# fibers')
fig.savefig("beta_histogram.png")
shutil.move("beta_histogram.png", args.output_beta_histogram)
# Filter out these redundant streamlines and
# generate an optimized group of streamlines.
optimized_sl = list(np.array(candidate_sl)[np.where(fiber_fit.beta > 0)[0]])
ren = fvtk.ren()
fvtk.add(ren, fvtk.streamtube(optimized_sl, line_colors(optimized_sl)))
fvtk.add(ren, cc_ROI_actor)
fvtk.add(ren, vol_actor)
fvtk.record(ren, n_frames=1, out_path="optimized.png", size=(800, 800))
shutil.move("optimized.png", args.output_life_optimized)
model_predict = fiber_fit.predict()
# Focus on the error in prediction of the diffusion-weighted
# data, and calculate the root of the mean squared error.
model_error = model_predict - fiber_fit.data
model_rmse = np.sqrt(np.mean(model_error[:, 10:] ** 2, -1))
# Calculate another error term by assuming that the weight for each streamline
# is equal to zero. This produces the naive prediction of the mean of the
# signal in each voxel.
beta_baseline = np.zeros(fiber_fit.beta.shape[0])
pred_weighted = np.reshape(opt.spdot(fiber_fit.life_matrix, beta_baseline), (fiber_fit.vox_coords.shape[0], np.sum(~gtab.b0s_mask)))
mean_pred = np.empty((fiber_fit.vox_coords.shape[0], gtab.bvals.shape[0]))
S0 = fiber_fit.b0_signal
# Since the fitting is done in the demeaned S/S0 domain,
# add back the mean and then multiply by S0 in every voxel:
mean_pred[..., gtab.b0s_mask] = S0[:, None]
mean_pred[..., ~gtab.b0s_mask] = (pred_weighted + fiber_fit.mean_signal[:, None]) * S0[:, None]
mean_error = mean_pred - fiber_fit.data
mean_rmse = np.sqrt(np.mean(mean_error ** 2, -1))
# Compare the overall distribution of errors
# between these two alternative models of the ROI.
fig, ax = plt.subplots(1)
ax.hist(mean_rmse - model_rmse, bins=100, histtype='step')
ax.text(0.2, 0.9, 'Median RMSE, mean model: %.2f' % np.median(mean_rmse), horizontalalignment='left', verticalalignment='center', transform=ax.transAxes)
ax.text(0.2, 0.8, 'Median RMSE, LiFE: %.2f' % np.median(model_rmse), horizontalalignment='left', verticalalignment='center', transform=ax.transAxes)
ax.set_xlabel('RMS Error')
ax.set_ylabel('# voxels')
fig.savefig("error_histograms.png")
shutil.move("error_histograms.png", args.output_error_histograms)
# Show the spatial distribution of the two error terms,
# and of the improvement with the model fit:
vol_model = np.ones(data.shape[:3]) * np.nan
vol_model[fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]] = model_rmse
vol_mean = np.ones(data.shape[:3]) * np.nan
vol_mean[fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]] = mean_rmse
vol_improve = np.ones(data.shape[:3]) * np.nan
vol_improve[fiber_fit.vox_coords[:, 0], fiber_fit.vox_coords[:, 1], fiber_fit.vox_coords[:, 2]] = mean_rmse - model_rmse
sl_idx = 49
fig = plt.figure()
fig.subplots_adjust(left=0.05, right=0.95)
ax = AxesGrid(fig, 111, nrows_ncols=(1, 3), label_mode="1", share_all=True, cbar_location="top", cbar_mode="each", cbar_size="10%", cbar_pad="5%")
ax[0].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[0].matshow(np.rot90(vol_model[sl_idx, :, :]), cmap=matplotlib.cm.hot)
ax.cbar_axes[0].colorbar(im)
ax[1].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[1].matshow(np.rot90(vol_mean[sl_idx, :, :]), cmap=matplotlib.cm.hot)
ax.cbar_axes[1].colorbar(im)
ax[2].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[2].matshow(np.rot90(vol_improve[sl_idx, :, :]), cmap=matplotlib.cm.RdBu)
ax.cbar_axes[2].colorbar(im)
for lax in ax:
    lax.set_xticks([])
    lax.set_yticks([])
fig.savefig("spatial_errors.png")
shutil.move("spatial_errors.png", args.output_spatial_errors)