import nibabel as ni
import numpy as np
import osmosis.model.analysis as oza
import osmosis.model.sparse_deconvolution as ssd
import osmosis.model.dti as dti
import osmosis.viz.mpl as mpl
import matplotlib.pyplot as plt
%matplotlib inline

import os
import osmosis as oz
import osmosis.io as oio
oio.data_path = os.path.join(oz.__path__[0], 'data')

subject = 'SUB1'
data_1k_1, data_1k_2 = oio.get_dwi_data(1000, subject)
data_2k_1, data_2k_2 = oio.get_dwi_data(2000, subject)
data_4k_1, data_4k_2 = oio.get_dwi_data(4000, subject)

wm_mask = np.zeros(ni.load(data_1k_1[0]).shape[:3])
wm_nifti = ni.load(oio.data_path + '/%s/%s_wm_mask.nii.gz'%(subject, subject)).get_data()
wm_mask[np.where(wm_nifti==1)] = 1

# Start working on a small chunk:
#wm_mask = np.zeros(wm_mask.shape)
#wm_mask[40:42, 40:42, 40:42] = 1

ad_rd = oio.get_ad_rd(subject, 2000)

SD_2k_1_nnls_demean = ssd.SparseDeconvolutionModel(*data_2k_1, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[0]['AD'], radial_diffusivity=ad_rd[0]['RD'], 
                                       solver='nnls')

SD_2k_2_nnls_demean = ssd.SparseDeconvolutionModel(*data_2k_2, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[1]['AD'], radial_diffusivity=ad_rd[1]['RD'], 
                                       solver='nnls')

SD_2k_1_nnls = ssd.SparseDeconvolutionModel(*data_2k_1, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[0]['AD'], radial_diffusivity=ad_rd[0]['RD'], 
                                       solver='nnls', demean=False)

SD_2k_2_nnls = ssd.SparseDeconvolutionModel(*data_2k_2, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[1]['AD'], radial_diffusivity=ad_rd[1]['RD'], 
                                       solver='nnls', demean=False)

SD_2k_1_EN_demean = ssd.SparseDeconvolutionModel(*data_2k_1, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[0]['AD'], radial_diffusivity=ad_rd[0]['RD'])

SD_2k_2_EN_demean = ssd.SparseDeconvolutionModel(*data_2k_2, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[1]['AD'], radial_diffusivity=ad_rd[1]['RD'])

SD_2k_1_EN = ssd.SparseDeconvolutionModel(*data_2k_1, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[0]['AD'], radial_diffusivity=ad_rd[0]['RD'],
                                       demean=False)

SD_2k_2_EN = ssd.SparseDeconvolutionModel(*data_2k_2, mask=wm_mask,  params_file ='temp', 
                                       axial_diffusivity=ad_rd[1]['AD'], radial_diffusivity=ad_rd[1]['RD'],
                                       demean=False)

rrmse_2k_nnls = oza.cross_predict(SD_2k_1_nnls, SD_2k_2_nnls)
rrmse_2k_nnls_demean = oza.cross_predict(SD_2k_1_nnls_demean, SD_2k_2_nnls_demean)
rrmse_2k_EN = oza.cross_predict(SD_2k_1_EN, SD_2k_2_EN)
rrmse_2k_EN_demean = oza.cross_predict(SD_2k_1_EN, SD_2k_2_EN_demean)


#print(rrmse_2k_nnls[np.where(wm_mask==1)])
#print(rrmse_2k_nnls_demean[np.where(wm_mask==1)])
#print(rrmse_2k_EN_demean[np.where(wm_mask==1)])
#print(rrmse_2k_EN[np.where(wm_mask==1)])

rrmse_2k_nnls_demean[np.isfinite(rrmse_2k_nnls_demean)]

fig = mpl.probability_hist(rrmse_2k_nnls[np.isfinite(rrmse_2k_nnls)], label='NNLS')
fig = mpl.probability_hist(rrmse_2k_nnls_demean[np.isfinite(rrmse_2k_nnls_demean)], fig=fig, label='NNLS (demean)')

fig.axes[0].plot([1,1], fig.axes[0].get_ylim(), '--k')
fig.axes[0].plot([1/np.sqrt(2),1/np.sqrt(2)], fig.axes[0].get_ylim(), '--k')
fig.axes[0].set_xlim([0.6,1.4])
plt.legend()

fig = mpl.probability_hist(rrmse_2k_EN[np.isfinite(rrmse_2k_EN)], label='Elastic Net')
fig = mpl.probability_hist(rrmse_2k_EN_demean[np.isfinite(rrmse_2k_EN_demean)], fig=fig, label='Elastic Net (demean)')
fig.axes[0].plot([1,1], fig.axes[0].get_ylim(), '--k')
fig.axes[0].plot([1/np.sqrt(2),1/np.sqrt(2)], fig.axes[0].get_ylim(), '--k')
fig.axes[0].set_xlim([0.6,1.4])
plt.legend()


whos

a = 1

fig = mpl.probability_hist(rrmse_2k_nnls[np.isfinite(rrmse_2k_nnls)], label='NNLS')
fig = mpl.probability_hist(rrmse_2k_EN[np.isfinite(rrmse_2k_EN)], fig=fig, label='Elastic Net ')
fig.axes[0].plot([1,1], fig.axes[0].get_ylim(), '--k')
fig.axes[0].plot([1/np.sqrt(2),1/np.sqrt(2)], fig.axes[0].get_ylim(), '--k')
fig.axes[0].set_xlim([0.6,1.4])
plt.legend()