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

# # Example: Radar processing
# 
# The processing of radar data - often referred to as SAR data, which stands for Synthetice Aperture Radar - is a topic of its own and quite different from optical data that uses Red, Green, Blue, NIR and similar bands.
# This is the first of a series of jupyter notebooks where example SAR workflows will be explained that can be run on UP42.
# All of the examples are based on a specific type of analysis which is called polarimetry and processes one image at a time.
# In comparison to polarimetric there also is interferometric analysis which leverages the information encoded in suitable pairs of radar image. 
# 
# In this notebook we want to have a look at the Sentinel-1 GRD full scene block, followed by SNAP Sentinel-1 Polarimetric Processing. Polarimetric SAR Processing turns a Sentinel-1 GRD image into a multichannel GeoTIFF that is ready for analysis. 
# 
#  - Prepare workflow
#  - Define aoi and input parameters
#  - Run Job
#  - Visualize results

# In[1]:


import up42


# In[3]:


# Authenticate with UP42
up42.authenticate(cfg_file="config.json")
#up42.authenticate(project_id=12345, project_api_key=12345)


# ## Prepare UP42 workflows
# 
# Create a new project on UP42 or use an existing one.

# In[5]:


S1_SNAP_project = up42.initialize_project()


# Create workflow and check available blocks and data

# In[6]:


workflow = S1_SNAP_project.create_workflow(name="S1-GRD_SNAP", 
                                   use_existing=True)
print(up42.get_blocks(basic=True))


# In[7]:


# Fill the workflow with tasks
input_tasks= ['sobloo-s1-grd-fullscene', 'snap-polarimetric']
workflow.add_workflow_tasks(input_tasks=input_tasks)
workflow.get_parameters_info()


# ## Define aoi and input parameters
# 
# The S1 GRD block always delivers the complete images as they are delivered in SAFE format which cannot be clipped.
# The image can be clipped though by the SNAP polarimetric processing blocks. For this it is necessary to supply the same geometry (in this case a bbox) to that block as well. 
# 
# 

# In[8]:


input_parameters = {
  "sobloo-s1-grd-fullscene:1": {
    "bbox": [13.371037, 52.512799, 13.382624, 52.518747],
    "ids": None,
    "time": "2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00",
    "limit": 1,
    "zoom_level": 14,
  },
    "snap-polarimetric:1": {
    "bbox": [13.371037, 52.512799, 13.382624, 52.518747],
    "mask": None,
    "contains": None,
    "intersects": None,
    "clip_to_aoi": True,
    "tcorrection": True,
    "linear_to_db": True,
    "polarisations": [
        "VV"
   ],
   "speckle_filter": True,
   "calibration_band": [
         "sigma"
   ]
  }
}


# ## Run test query
# Run a test job to query data availability and check the configuration.

# In[9]:


test_job = workflow.test_job(input_parameters=input_parameters, track_status=True)
test_results = test_job.get_results_json()
print(test_results)


# ## Run the job

# In[10]:


job = workflow.run_job(input_parameters=input_parameters, track_status=True)


# ## Visualize the results

# In[12]:


# Download results:
job.download_results()

# Visualize downloaded results
job.plot_results()


# ## Next processing steps
# The output of the workflow is a GeoTIFF file which can be processed by different algorithms. It is for example possible to apply raster tiling and then do some machine learning based analysis at the end.
# 
#