"""
=====================
Neo All - example 1
=====================

This example shows how to use create a NeoAll instance.

.. note::

    NeoAll instances are created from the SpykingCircus files. See `here <https://spyking-circus.readthedocs.io/en/latest/advanced/files.html>`_
    for details about these results files. NeoAll needs at least three arguments to be initiated :

    * spikesorting_dirpath : the path of the directory containng the spyking circus result file
    * spikesorting_filename : the name of the Spiking Circus result file
    * probe_filepath : the path to the Spiking Circus probe file


For instance, the Spyking Cirucs files may look like these :

.. image:: ./../../_static/images/spykingcircus_files.png

Details of the files :
 * The *000_AA.prb* file is the probe file, it contains information about how contacts are organized on the electrodes, see `here <https://spyking-circus.readthedocs.io/en/latest/code/probe.html>`_ for more info. This file is needed for launching the spike-sorting. It is used in NeoAll for creating channel indexes and knowing the number of tetrodes.
 * The *.result.hdf5* file is used to store the spike times for each unit, at the end of the spike sorting procedure.
 * The *.template.hdf5* file contains the preffered contact of each unit.
 * In order to generate the units template, three files are used : *.basis.hdf5*, *.templates.hdf5* and *.clusters.hdf5* files. Unit template is computed using the :func:`~spykingcircus_utils.get_templates_from_spykingcircus_file` function.

"""

##################################
# First import the neoStructures :
from neoStructures import *
import matplotlib.pyplot as plt
from os.path import isdir, join

#######################
# Import the data and create the NeoAll instance
data_dir = join('pySpikeAnalysis', 'sample_data') if isdir('pySpikeAnalysis') else join('..', '..', 'pySpikeAnalysis', 'sample_data')
spykingcircus_dir = r'SpykingCircus_results'
probe_filename = r'000_AA.prb'
results_filename = r'spykingcircusres'

neoAll = NeoAll(join(data_dir, spykingcircus_dir), results_filename, join(data_dir, probe_filename), save_fig=0)

##############################
# See information about NeoAll
print(neoAll)

#############################
# neoAll contains a list of Neo Segments (1 segment per unit)
print(type(neoAll.segments[0]))

##############################
# Computing the mean firing rate is easy, e.g. for unit 3:
mean_spkrate_3 = len(neoAll.segments[3].spiketrains[0]) / (neoAll.segments[3].t_stop - neoAll.segments[3].t_start)
print(mean_spkrate_3)

#################################
# The method :func:`neoStructures.NeoAll.plot_spikerate_evolution` can be used to visualize the firing-rate over time
# The firing rate is computed by kernel convolution. The kernel is gaussian and the standard deviation can be set using
# the sigma_gauss_kernel parameter.
# The package `Elephant <http://elephant.readthedocs.io/en/latest/index.html>`_ is used for the estimation.
neoAll.plot_spikerate_evolution(unit_pos=3, sigma_gauss_kernel=30*s)

#######################
# The shape of each unit can be plotted using the plot_unit_shape method :
neoAll.plot_unit_shape(2)

#######################
# As well as the ISI (Inter-Spike Interval), e.g. for unit 22 :
neoAll.plot_isi(unit_pos=22)

##########################
# Zoom on the x-origin and increase the number of bins for the histogram
neoAll.plot_isi(22, bin_duration_ms=0.1, tmax_ms=15)