#!/usr/bin/env python
# coding: utf-8

# # NWB use-case pvc-7

# --- Data courtesy of Aleena Garner, Allen Institute for Brain Sciences ---
# 
# Here we demonstrate how data from the NWB pvc-7 use-case can be stored in NIX files.
# 
# ### Context:
# - *In vivo* calcium imaging of layer 4 cells in mouse primary visual cortex.
# - Two-photon images sampled @ 30 Hz
# - Visual stimuli of sinusoidal moving gratings were presented.
# 
# 
# - In this example, we use a subset of the original data file.
# - We only use image frames 5000 to frame 6000.
# - Image data was 10 times down-sampled.

# In[1]:


from nixio import *
import numpy as np
import matplotlib.pylab as plt
get_ipython().run_line_magic('matplotlib', 'inline')
from utils.notebook import print_stats
from utils.plotting import Plotter


# ## Open a file and inspect its content

# In[2]:


f = File.open("data/pvc-7.nix.h5", FileMode.ReadOnly)

print_stats(f.blocks)


# In[3]:


block = f.blocks[0]

print_stats(block.data_arrays)
print_stats(block.tags)


# ## Explore Stimulus

# In[4]:


# get recording tag
recording = block.tags[0]

# stimulus combinations array
stimulus = recording.features[0].data


# In[5]:


# display the stimulus conditions
for label in stimulus.dimensions[0].labels:
    print label + ' :',
print '\n'

# actual stimulus condition values
for cmb in stimulus.data[:]:
    for x in cmb:
        print "%.2f\t" % x,
    print '\n'


# In[6]:


# get particular stimulus combination
index = 2
print "a stimulus combination %s" % str(stimulus.data[index])


# In[7]:


# find out when stimulus was displayed
start = recording.position[index]
end = recording.extent[index]

print "was displayed from frame %d to frame %d" % (start, end)


# ## Explore video and imaging data

# In[8]:


# get movie arrays from file
movies = filter(lambda x: x.type == 'movie', recording.references)
print_stats(movies)


# In[9]:


# get mouse image at the beginning of the selected stimulus
mouse = movies[1]
image_index = int(np.where(np.array(mouse.dimensions[0].ticks) > start)[0][0])

plt.imshow(mouse.data[image_index])


# In[10]:


# get eye image at the end of the selected stimulus
eye = movies[0]
image_index = int(np.where(np.array(eye.dimensions[0].ticks) > end)[0][0])

plt.imshow(eye.data[image_index])


# In[11]:


# get 2-photon image at the beginning of the selected stimulus
imaging = filter(lambda x: x.type == 'imaging', recording.references)[0]

image_index = int(np.where(np.array(imaging.dimensions[0].ticks) > start)[0][0])

plt.imshow(imaging.data[image_index])


# In[12]:


# plot mouse speed in the whole window (TODO: add stimulus events)
speeds = filter(lambda x: x.type == 'runspeed', recording.references)[0]

p = Plotter()
p.add(speeds)
p.plot()


# In[13]:


f.close()


# In[13]: