"""

===========================
Read/Write streamline files
===========================

Overview
========

DIPY_ can read and write many different file formats. In this example
we give a short introduction on how to use it for loading or saving
streamlines. The new stateful tractogram class was made to reduce errors
caused by spatial transformation and complex file format convention.

Read :ref:`faq`

"""

import os

import nibabel as nib
import numpy as np
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import load_tractogram, save_tractogram
from dipy.io.utils import (create_nifti_header, get_reference_info,
                           is_header_compatible)
from dipy.tracking.streamline import select_random_set_of_streamlines
from dipy.tracking.utils import density_map

from dipy.data.fetcher import (fetch_file_formats,
                               get_file_formats)

###############################################################################
# First fetch the dataset that contains 5 tractography file of 5 file formats:
# 
# - cc_m_sub.trk
# - laf_m_sub.tck
# - lpt_m_sub.fib
# - raf_m_sub.vtk
# - rpt_m_sub.dpy
# 
# And their reference anatomy, common to all 5 files:
# 
# - template0.nii.gz


fetch_file_formats()
bundles_filename, ref_anat_filename = get_file_formats()
for filename in bundles_filename:
    print(os.path.basename(filename))
reference_anatomy = nib.load(ref_anat_filename)

###############################################################################
# Load tractogram will support 5 file formats, functions like load_trk or
# load_tck will simply be restricted to one file format
# 
# TRK files contain their own header (when written properly), so they
# technically do not need a reference. (See how below)
# 
# ``cc_trk = load_tractogram(bundles_filename[0], 'same')``


cc_sft = load_tractogram(bundles_filename[0], reference_anatomy)
print(cc_sft)
laf_sft = load_tractogram(bundles_filename[1], reference_anatomy)
raf_sft = load_tractogram(bundles_filename[3], reference_anatomy)

###############################################################################
# These files contain invalid streamlines (negative values once in voxel space)
# This is not considered a valid tractography file, but it is possible to load
# it anyway.


lpt_sft = load_tractogram(bundles_filename[2], reference_anatomy,
                          bbox_valid_check=False)
rpt_sft = load_tractogram(bundles_filename[4], reference_anatomy,
                          bbox_valid_check=False)

###############################################################################
# The function ``load_tractogram`` requires a reference, any of the following
# inputs is considered valid (as long as they are in the same share space)
# - Nifti filename
# - Trk filename
# - nib.nifti1.Nifti1Image
# - nib.streamlines.trk.TrkFile
# - nib.nifti1.Nifti1Header
# - Trk header (dict)
# - Stateful Tractogram
# 
# The reason why this parameter is required is to guarantee all information
# related to space attributes is always present.


affine, dimensions, voxel_sizes, voxel_order = get_reference_info(
    reference_anatomy)
print(affine)
print(dimensions)
print(voxel_sizes)
print(voxel_order)

###############################################################################
# If you have a Trk file that was generated using a particular anatomy,
# to be considered valid all fields must correspond between the headers.
# It can be easily verified using this function, which also accept
# the same variety of input as ``get_reference_info``


print(is_header_compatible(reference_anatomy, bundles_filename[0]))

###############################################################################
# If a TRK was generated with a valid header, but the reference NIFTI was lost
# a header can be generated to then generate a fake NIFTI file.
# 
# If you wish to manually save Trk and Tck file using nibabel streamlines
# API for more freedom of action (not recommended for beginners) you can
# create a valid header using create_tractogram_header


nifti_header = create_nifti_header(affine, dimensions, voxel_sizes)
nib.save(nib.Nifti1Image(np.zeros(dimensions), affine, nifti_header),
         'fake.nii.gz')
nib.save(reference_anatomy, os.path.basename(ref_anat_filename))

###############################################################################
# Once loaded, no matter the original file format, the stateful tractogram is
# self-contained and maintains a valid state. By requiring a reference the
# tractogram's spatial transformation can be easily manipulated.
# 
# Let's save all files as TRK to visualize in TrackVis for example.
# However, when loaded the lpt and rpt files contain invalid streamlines and
# for particular operations/tools/functions it is safer to remove them


save_tractogram(cc_sft, 'cc.trk')
save_tractogram(laf_sft, 'laf.trk')
save_tractogram(raf_sft, 'raf.trk')

print(lpt_sft.is_bbox_in_vox_valid())
lpt_sft.remove_invalid_streamlines()
print(lpt_sft.is_bbox_in_vox_valid())
save_tractogram(lpt_sft, 'lpt.trk')

print(rpt_sft.is_bbox_in_vox_valid())
rpt_sft.remove_invalid_streamlines()
print(rpt_sft.is_bbox_in_vox_valid())
save_tractogram(rpt_sft, 'rpt.trk')

###############################################################################
# Some functions in DIPY require streamlines to be in voxel space so computation
# can be performed on a grid (connectivity matrix, ROIs masking, density map).
# The stateful tractogram class provides safe functions for such manipulation.
# These functions can be called safely over and over, by knowing in which state
# the tractogram is operating, and compute only necessary transformations
# 
# No matter the state, functions such as ``save_tractogram`` or
# ``removing_invalid_coordinates`` can be called safely and the transformations
# are handled internally when needed.


cc_sft.to_voxmm()
print(cc_sft.space)
cc_sft.to_rasmm()
print(cc_sft.space)

###############################################################################
# Now let's move them all to voxel space, subsample them to 100 streamlines,
# compute a density map and save everything for visualisation in another
# software such as Trackvis or MI-Brain.
# 
# To access volume information in a grid, the corner of the voxel must be
# considered the origin in order to prevent negative values.
# Any operation doing interpolation or accessing a grid must use the
# function 'to_vox()' and 'to_corner()'


cc_sft.to_vox()
laf_sft.to_vox()
raf_sft.to_vox()
lpt_sft.to_vox()
rpt_sft.to_vox()

cc_sft.to_corner()
laf_sft.to_corner()
raf_sft.to_corner()
lpt_sft.to_corner()
rpt_sft.to_corner()

cc_streamlines_vox = select_random_set_of_streamlines(cc_sft.streamlines,
                                                      1000)
laf_streamlines_vox = select_random_set_of_streamlines(laf_sft.streamlines,
                                                       1000)
raf_streamlines_vox = select_random_set_of_streamlines(raf_sft.streamlines,
                                                       1000)
lpt_streamlines_vox = select_random_set_of_streamlines(lpt_sft.streamlines,
                                                       1000)
rpt_streamlines_vox = select_random_set_of_streamlines(rpt_sft.streamlines,
                                                       1000)

# Same dimensions for every stateful tractogram, can be re-use
affine, dimensions, voxel_sizes, voxel_order = cc_sft.space_attributes
cc_density = density_map(cc_streamlines_vox, np.eye(4), dimensions)
laf_density = density_map(laf_streamlines_vox, np.eye(4), dimensions)
raf_density = density_map(raf_streamlines_vox, np.eye(4), dimensions)
lpt_density = density_map(lpt_streamlines_vox, np.eye(4), dimensions)
rpt_density = density_map(rpt_streamlines_vox, np.eye(4), dimensions)

###############################################################################
# Replacing streamlines is possible, but if the state was modified between
# operations such as this one is not recommended:
# -> cc_sft.streamlines = cc_streamlines_vox
# 
# It is recommended to re-create a new StatefulTractogram object and
# explicitly specify in which space the streamlines are. Be careful to follow
# the order of operations.
# 
# If the tractogram was from a Trk file with metadata, this will be lost.
# If you wish to keep metadata while manipulating the number or the order
# look at the function StatefulTractogram.remove_invalid_streamlines() for more
# details
# 
# It is important to mention that once the object is created in a consistent state
# the ``save_tractogram`` function will save a valid file. And then the function
# ``load_tractogram`` will load them in a valid state.
# 


cc_sft = StatefulTractogram(cc_streamlines_vox, reference_anatomy, Space.VOX)
laf_sft = StatefulTractogram(laf_streamlines_vox, reference_anatomy, Space.VOX)
raf_sft = StatefulTractogram(raf_streamlines_vox, reference_anatomy, Space.VOX)
lpt_sft = StatefulTractogram(lpt_streamlines_vox, reference_anatomy, Space.VOX)
rpt_sft = StatefulTractogram(rpt_streamlines_vox, reference_anatomy, Space.VOX)

print(len(cc_sft), len(laf_sft), len(raf_sft), len(lpt_sft), len(rpt_sft))
save_tractogram(cc_sft, 'cc_1000.trk')
save_tractogram(laf_sft, 'laf_1000.trk')
save_tractogram(raf_sft, 'raf_1000.trk')
save_tractogram(lpt_sft, 'lpt_1000.trk')
save_tractogram(rpt_sft, 'rpt_1000.trk')

nib.save(nib.Nifti1Image(cc_density, affine, nifti_header),
         'cc_density.nii.gz')
nib.save(nib.Nifti1Image(laf_density, affine, nifti_header),
         'laf_density.nii.gz')
nib.save(nib.Nifti1Image(raf_density, affine, nifti_header),
         'raf_density.nii.gz')
nib.save(nib.Nifti1Image(lpt_density, affine, nifti_header),
         'lpt_density.nii.gz')
nib.save(nib.Nifti1Image(rpt_density, affine, nifti_header),
         'rpt_density.nii.gz')