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

# #Testing out PlanetaryImage

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
from glob import glob
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from planetaryimage import PDS3Image, CubeFile
from scipy.misc import imsave    # requires pillow as well
from scipy import ndimage


# ### Working with PDS Images
# 
# Read in a list of PDS image files.

# In[2]:


pdsfiles = glob("tests/data/*.img")
images = []
for pdsfile in pdsfiles:
    images.append(PDS3Image.open(pdsfile))
    print pdsfile

images


# What are the types and shapes?

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for i in images:
    print i.data.dtype, i.data.shape, i.shape


# Displaying a sample Pancam and Mastcam Image

# In[4]:


pancam1 = images[3]
plt.imshow(pancam1.data, cmap='gray')


# In[5]:


mcam1 = images[0]
plt.imshow(mcam1.data, cmap='gray')


# Displaying a subframe

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plt.imshow(mcam1.data[700:1100, 600:1000])


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plt.imshow(np.hstack([
    mcam1.data[700:1100, 600:1000, 0],
    mcam1.data[700:1100, 600:1000, 1],
    mcam1.data[700:1100, 600:1000, 2],            
]), cmap='gray')


# In[8]:


caltarget = mcam1.data[700:1100, 600:1000]
plt.imshow(caltarget)


# In[9]:


imsave('caltarget.png', caltarget)
i = mpimg.imread('caltarget.png')
plt.imshow(i)


# #### Doing some Image Processing

# In[10]:


plt.hist(pancam1.data.flatten(), 4096)


# In[11]:


plt.imshow(pancam1.data, cmap='gray', vmin=1400, vmax=2500)
print pancam1.data.mean(), pancam1.data.min(), pancam1.data.max()


# In[12]:


plt.imshow(ndimage.gaussian_filter(pancam1.data, 6), cmap='gray', vmin=1400, vmax=2500)


# ### Working with a Tiled Isis CubeFile

# In[13]:


isisimage = CubeFile.open("tests/data/pattern.cub")


# In[14]:


isisimage.data.dtype, isisimage.data.shape, isisimage.shape


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plt.imshow(isisimage.data, cmap='gray')


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