"""

===========================
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.

Read :ref:`faq`

"""

import numpy as np
from dipy.data import get_data
from dipy.io.streamline import load_trk, save_trk
from dipy.tracking.streamline import Streamlines

"""
1. Read/write streamline files with DIPY.
"""

fname = get_fnames('fornix')
print(fname)

# Read Streamlines
streams, hdr = load_trk(fname)
streamlines = Streamlines(streams)

# Save Streamlines
save_trk("my_streamlines.trk", streamlines=streamlines, affine=np.eye(4))


"""
2. We also work on our HDF5 based file format which can read/write massive datasets
   (as big as the size of you free disk space). With `Dpy` we can support

  * direct indexing from the disk
  * memory usage always low
  * extensions to include different arrays in the same file

Here is a simple example.
"""

from dipy.io.dpy import Dpy
dpw = Dpy('fornix.dpy', 'w')

"""
Write many streamlines at once.
"""

dpw.write_tracks(streamlines)

"""
Write one track
"""

dpw.write_track(streamlines[0])

"""
or one track each time.
"""

for t in streamlines:
    dpw.write_track(t)

dpw.close()

"""
Read streamlines directly from the disk using their indices

.. include:: ../links_names.inc
"""

dpr = Dpy('fornix.dpy', 'r')
some_streamlines = dpr.read_tracksi([0, 10, 20, 30, 100])
dpr.close()


print(len(streamlines))
print(len(some_streamlines))

"""

===========================
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.

Read :ref:`faq`

"""

import numpy as np
from dipy.data import get_fnames
from dipy.io.streamline import load_trk, save_trk
from dipy.tracking.streamline import Streamlines

"""
1. Read/write streamline files with DIPY.
"""

fname = get_fnames('fornix')
print(fname)

# Read Streamlines
streams, hdr = load_trk(fname)
streamlines = Streamlines(streams)

# Save Streamlines
save_trk("my_streamlines.trk", streamlines=streamlines, affine=np.eye(4))


"""
2. We also work on our HDF5 based file format which can read/write massive 
   datasets (as big as the size of you free disk space). With `Dpy` we can
   support

  * direct indexing from the disk
  * memory usage always low
  * extensions to include different arrays in the same file

Here is a simple example.
"""

from dipy.io.dpy import Dpy
dpw = Dpy('fornix.dpy', 'w')

"""
Write many streamlines at once.
"""

dpw.write_tracks(streamlines)

"""
Write one track
"""

dpw.write_track(streamlines[0])

"""
or one track each time.
"""

for t in streamlines:
    dpw.write_track(t)

dpw.close()

"""
Read streamlines directly from the disk using their indices

.. include:: ../links_names.inc
"""

dpr = Dpy('fornix.dpy', 'r')
some_streamlines = dpr.read_tracksi([0, 10, 20, 30, 100])
dpr.close()


print(len(streamlines))
print(len(some_streamlines))

fix example issue