Add analyze task for global land cover

This task takes the output of the STAC Histogram Query and
calculates the area and coverage percent from it, and
formats it into the standard MMW output.

We remove IO Class 0 NOADAT from the standard set of
outputs so that we don't include it in the final output.

src/mmw/apps/geoprocessing_api/tasks.py

@@ -184,6 +184,46 @@ def area(dictionary, key, default=0):
     }
 
 
+@shared_task(throws=Exception)
+def analyze_global_land(result, year):
+    if 'error' in result:
+        raise Exception(f'[analyze_global_land_{year}] {result["error"]}')
+
+    histogram = parse(result['result'])
+    pixel_size = result['pixel_size']
+
+    # Count up all the legitimate IO LULC classes
+    # This excludes NODATA which is IO LULC 0
+    total_count = sum(
+        [
+            count
+            for ioclass, count in histogram.items()
+            if ioclass in layer_classmaps.IO_LULC
+        ]
+    )
+
+    categories = []
+
+    for ioclass, (code, name) in layer_classmaps.IO_LULC.items():
+        categories.append(
+            {
+                'area': histogram.get(ioclass, 0) * pixel_size,
+                'code': code,
+                'coverage': float(histogram.get(ioclass, 0)) / total_count,
+                'ioclass': ioclass,
+                'type': name,
+            }
+        )
+
+    return {
+        'survey': {
+            'name': f'global_land_io_{year}',
+            'displayName': f'Global Land Use/Cover {year}',
+            'categories': categories,
+        }
+    }
+
+
 @shared_task(throws=Exception)
 def analyze_soil(result, area_of_interest=None):
     if 'error' in result:

src/mmw/mmw/settings/layer_classmaps.py

@@ -55,7 +55,7 @@
 # Map for the Impact Observatory Land Use Land Classification scheme
 # https://www.impactobservatory.com/maps-for-good/
 IO_LULC = {
-    0: ['no_data', 'No Data'],
+    # 0: ['no_data', 'No Data'],
     1: ['water', 'Water'],
     2: ['trees', 'Trees'],
     4: ['flooded_vegetation', 'Flooded vegetation'],