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

# # Example script for reading MAIA cloud product files.
# 
# 
# Created with:
# 
# Package | Version
# --- | ---
# SatPy | 0.9.0
# PyResample | 1.10.1
# Trollimage | 1.5.3
# PyKdtree | 1.3.1
# 
# 
# MAIA files can be created with AAPP with scripts MAIA4_RUN and MAIA4_RUN_AVHRR scripts
# 
# for BOTH VIIRS an AVHRR imagers
# 
# - Install satpy and pyresample
# - pycoast can also be installed if you want to generate plots with coast lines
# - Here is an example of a minimal script that reads MAIA files
# 
# MAIA files are named with a specific syntax for example:
# 
#    - avhCT_M02_GL_20151012_S005503_E005802_DES_D_La-11_Lo0126_00000.h5
#     
#    - viiCT_npp_DB_20121010_S132824_E132947_ASC_D_La050_Lo-012_00001.h5
#     
# References :   
# 
# https://nwpsaf.eu/site/software/aapp/
# 
#       [NWPSAF-MF-UD-003] DATA Formats
#       
#       [NWPSAF-MF-UD-009] MAIA version 4 Scientific User Manual
#       
# This example uses the MAIA cloud product which can be found in the MAIA4_test.tgz tar test case file available with the AAPP software.

# In[1]:


from satpy import Scene
from satpy.utils import debug_on
import numpy as np
from satpy.composites import PaletteCompositor
from satpy.writers import to_image


# In[2]:


# define palette (matplotlib style)
cpool = ['#4e7791',  # not processed 0
         '#008c30',  # Cloud free land 1
         '#000000',  # Cloud free sea 2
         '#ffbbff',  # Snow over ice 3
         '#dda0dd',  # Sea ice 4
         '#ffa500',  # Very low cumuliforme 5      # non utilisé par maia
         '#ff6600',  # Very low no cumuliforme 6
         '#ffd800',  # Low cumuliforme 7           # non utilisé par maia
         '#ffa500',  # Low cumuliforme 8
         '#ffff00',  # Mid-level cumuliforme 9         # non utilisé par maia
         '#d8ff00',  # Mid-level no cumuliforme 10
         '#cccc00',  # High opaque cumuliforme 11    # on utilisé par maia
         '#d8b575',  # High opaque no cumuliforme 12
         '#ffffff',  # Very High opaque cumuliforme 13 # non utilisé par maia
         '#ffe0aa',  # Very High opaque no cumuliforme 14
         '#0000ff',  # Semi transp. thin 15
         "#00b2ff",  # Semi transp. meanly thick 16
         '#00ffe5',  # Semi transp. thick 17
         '#00ffb2',  # semi transp abobe others 18
         '#d800ff',  # fractionnal
         '#660f00',  # Not classified 20
         ]


# In[3]:


# function to convert it in satpy style
def hex_to_rgb(value):
    value = value.lstrip('#')
    lv = len(value)
    return [int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]


# In[4]:


fnmaia = "/home/a000680/data/maia/viiCT_npp_DB_20121010_S132824_E132947_ASC_D_La050_Lo-012_00001.h5"


# In[5]:


maia_scene = Scene(sensor='viirs', reader='maia', filenames=[fnmaia])


# In[6]:


maia_scene.all_dataset_names()


# In[7]:


maia_scene.load(["CloudType", "ct", "cma", "cma_conf",
                     'opaq_cloud', "CloudTopPres", "CloudTopTemp", "Alt_surface"])


# 
#     The CloudType is a bit field containing the actual "ct" with values from 0 to 20
#     which can be interpreted according to the cpool colormap
# 
#     "ct" can be display in black and white:
# 

# In[8]:


maia_scene.show("ct")
maia_scene.save_dataset("ct", filename="ct_bw.png")


# <img src="ct_bw.png">

# but it is better to palettize the image:
# 
# step 1: creation of the palette

# In[9]:


mycolors = []
for i in range(21):
    mycolors.append(hex_to_rgb(cpool[i]))
arr = np.array(mycolors)
np.save("/tmp/binary_maia_ct_colormap.npy", arr)


# step2: creation of the composite

# In[10]:


compositor = PaletteCompositor("test", standard_name="maia_ct")
composite = compositor((maia_scene["ct"], arr))
img = to_image(composite)
img.show()
img.pil_save("ct.png")


# <img src="ct.png">
#