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

# Dependencies | Version
# --- | ---
# SatPy | 0.9.0
# PyResample | 1.10.1
# Trollimage | 1.5.3
# PyKdtree | 1.3.1
# 
# 
# # Quickstart with EARS-NWC

# <p>In December 2012 <a class="reference external" href="http://www.eumetsat.int/">EUMETSAT</a> extended the <a class="reference external" href="http://www.eumetsat.int/home/main/satellites/groundnetwork/earssystem/index.htm">EARS</a> services to include a new service with the purpose of supporting European users with cloud information
# from polar orbiting satellites in near real time.</p>
# <p>This <em>EARS-NWC</em> service provides the parameters <em>Cloud Mask</em>, <em>Cloud Type</em> and
# <em>Cloud Top Temperature and Height (CTTH)</em> as derived using the Polar Platform
# System (<a class="reference external" href="http://nwcsaf.smhi.se/">PPS</a>) software package from the <a class="reference external" href="http://www.nwcsaf.org/">NWC SAF</a>. The products are derived
# from AVHRR data received at the EARS core stations with a reception coverage including Europe and the North Atlantic. Products are disseminated on <a class="reference external" href="http://www.eumetsat.int/home/main/dataaccess/eumetcast/index.htm">EUMETCast</a> (EUMETSAT <a class="reference external" href="http://www.eumetsat.int/home/main/dataaccess/eumetcast/receptionstationset-up/sp_20100623124251305?l=en">data channel 1</a>) with a timeliness better than 30
# minutes, and available in netCDF4 format. The geolocation information is available on a tie-point grid and stored in each product.
# <p>At the moment the satellites contributing to the service are Metop-B and NOAA-19.</p>

# ## Loading and concatenating the cloud type granules

# In[1]:


from satpy import Scene
from satpy import find_files_and_readers
from datetime import datetime

DATA_DIR = "/home/a000680/laptop/Nordisk/EARS_NWC/data/case_20180314/ears"

myfiles = find_files_and_readers(base_dir=DATA_DIR,
                                 start_time=datetime(2018, 3, 14, 10, 5),
                                 end_time=datetime(2018, 3, 14, 10, 11),
                                 reader='nc_nwcsaf_pps')
scene = Scene(filenames=myfiles)
scene.load(['ct'])


# Now we have loaded and concatenated the <em>ct</em> field of the Cloudtype product granules in the time window given by the start amd end times above

# In[2]:


print(scene['ct'].data.shape)
print(scene['ct'].data.compute())


# <p>Also the geolocation has been unpacked. That is the the full resolution
# geolocation information has been recreated from the tie point grid by
# interpolating and extrapolating the longitudes and latitudes on the tie point
# grid. This is accomplished using the <a class="reference external" href="http://www.github.com/adybbroe/python-geotiepoints">python-geotiepoints</a> tool, but this is
# transparent to the user:</p>

# In[3]:


print(scene['ct'].area.lats.shape)


# Now let’s visualise the cloudtype data using the Nowcasting SAF palette read from the file:

# In[4]:


scene.load(['cloudtype'])
scene.show('cloudtype')


# <img src='https://github.com/pytroll/pytroll-examples/raw/master/satpy/ears_nwc_cloudtype_20180314T1005Z_20180314T1010Z_thumb.png'>

# # Load all CTTH granules, concatenate, reproject, generate composite image, and save to disk

# In[5]:


from glob import glob
import os.path


# In[6]:


DATA_DIR = "/home/a000680/laptop/Nordisk/EARS_NWC/data/case_20180314/ears"


# ## Make a list of all CTTH files in the directory, both bzipped and unpacked ones:

# In[7]:


myfiles = glob(os.path.join(DATA_DIR, "*CTTH_C*.nc.bz2"))
myfiles = myfiles + glob(os.path.join(DATA_DIR, "*CTTH_C*.nc"))


# ## Generate the scene object

# In[8]:


scene = Scene(filenames=myfiles, reader='nc_nwcsaf_pps')


# ## Load the cloud height composite image

# In[9]:


scene.load(['cloud_top_height'])


# ## Resample the data to the pre-defined "euron1" area

# In[10]:


lscn = scene.resample('euron1', radius_of_influence=5000)


# In[11]:


lscn.show('cloud_top_height')


# <img src='https://github.com/pytroll/pytroll-examples/raw/master/satpy/ears_nwc_ctth_20180314T1000Z_20180314T1014Z_thumbnail.png'>

# ## Save the image to file

# In[11]:


dt_start = lscn.attrs['start_time'].strftime('%Y%m%dT%H%MZ')
dt_end = lscn.attrs['end_time'].strftime('%Y%m%dT%H%MZ')
lscn.save_dataset(
    'cloud_top_height', filename='./ears_nwc_ctth_{starttime}_{endtime}.png'.format(starttime=dt_start, endtime=dt_end))