deconvolute landcover l34 tests

odc/stats/plugins/l34_utils/lc_level3.py

@@ -21,6 +21,7 @@ def lc_level3(xx: xr.Dataset, urban_mask):
     )
 
     # Mask urban results with bare sfc (210)
+    # and urban mask
 
     res = expr_eval(
         "where((a==_u)&(c>0), b, a)",

tests/conftest.py

@@ -215,3 +215,13 @@ def urban_shape():
     ) as dst:
         dst.write(data)
     return filename
+
+
+@pytest.fixture()
+def veg_threshold():
+    return [1, 4, 15, 40, 65, 100]
+
+
+@pytest.fixture()
+def watper_threshold():
+    return [1, 4, 7, 10]

tests/test_lc_l34.py

@@ -172,7 +172,7 @@ def image_groups():
     return xx
 
 
-def test_l4_classes(image_groups):
+def test_l4_classes(image_groups, urban_shape):
     expected_l3 = [[216, 216, 215], [216, 216, 216], [220, 215, 215], [220, 220, 220]]
 
     expected_l4 = [[95, 97, 93], [97, 96, 96], [100, 93, 93], [101, 101, 101]]

tests/test_lc_l4_ctv.py

@@ -2,7 +2,6 @@
 import xarray as xr
 import dask.array as da
 
-from odc.stats.plugins.lc_level34 import StatsLccsLevel4
 from odc.stats.plugins.l34_utils import (
     l4_cultivated,
     lc_level3,
@@ -31,7 +30,7 @@ def image_groups(l34, urban, cultivated, woody, pv_pc_50):
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-        "urban_classes": xr.DataArray(
+        "artificial_surface": xr.DataArray(
             da.from_array(urban, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
@@ -57,7 +56,7 @@ def image_groups(l34, urban, cultivated, woody, pv_pc_50):
     return xx
 
 
-def test_ctv_classes_woody():
+def test_ctv_classes_woody(veg_threshold):
 
     expected_cultivated_classes = [
         [13, 10, 9],
@@ -126,17 +125,17 @@ def test_ctv_classes_woody():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
 
     assert (l4_ctv.compute() == expected_cultivated_classes).all()
 
 
-def test_ctv_classes_herbaceous():
+def test_ctv_classes_herbaceous(veg_threshold):
 
     expected_cultivated_classes = [
         [18, 15, 14],
@@ -205,16 +204,16 @@ def test_ctv_classes_herbaceous():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
     assert (l4_ctv.compute() == expected_cultivated_classes).all()
 
 
-def test_ctv_classes_woody_herbaceous():
+def test_ctv_classes_woody_herbaceous(veg_threshold):
 
     expected_cultivated_classes = [
         [13, 10, 9],
@@ -283,17 +282,17 @@ def test_ctv_classes_woody_herbaceous():
         dtype="int",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
 
     assert (l4_ctv.compute() == expected_cultivated_classes).all()
 
 
-def test_ctv_classes_no_vegcover():
+def test_ctv_classes_no_vegcover(veg_threshold):
 
     expected_cultivated_classes = [
         [2, 2, 2],
@@ -363,10 +362,11 @@ def test_ctv_classes_no_vegcover():
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
 
     assert (l4_ctv.compute() == expected_cultivated_classes).all()

tests/test_lc_l4_natural_surface.py

@@ -40,7 +40,7 @@ def image_groups(
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-        "urban_classes": xr.DataArray(
+        "artificial_surface": xr.DataArray(
             da.from_array(urban, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
@@ -81,7 +81,7 @@ def image_groups(
     return xx
 
 
-def test_ns():
+def test_ns(veg_threshold):
     expected_l4_srf_classes = [
         [95, 97, 93],
         [97, 96, 96],
@@ -188,9 +188,9 @@ def test_ns():
         l34, urban, woody, bs_pc_50, pv_pc_50, cultivated, water_frequency, water_season
     )
 
-    level3 = lc_level3.lc_level3(xx)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_threshold = [1, 4, 15, 40, 65, 100]
     veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     # Apply cultivated to match the code in Level4 processing

tests/test_lc_l4_nav.py

@@ -6,7 +6,6 @@
 import xarray as xr
 import dask.array as da
 
-from odc.stats.plugins.lc_level34 import StatsLccsLevel4
 from odc.stats.plugins.l34_utils import (
     l4_cultivated,
     lc_level3,
@@ -39,7 +38,7 @@ def image_groups(
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-        "urban_classes": xr.DataArray(
+        "artificial_surface": xr.DataArray(
             da.from_array(urban, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
@@ -75,7 +74,7 @@ def image_groups(
     return xx
 
 
-def test_ntv_classes_woody_herbaceous():
+def test_ntv_classes_woody_herbaceous(veg_threshold):
     expected_l4_ntv_classes = [[56, 56, 56], [57, 57, 57], [56, 56, 56], [57, 57, 57]]
 
     l34 = np.array(
@@ -165,10 +164,10 @@ def test_ntv_classes_woody_herbaceous():
     xx = image_groups(
         l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
     )
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     # Apply cultivated to match the code in Level4 processing
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
@@ -181,7 +180,7 @@ def test_ntv_classes_woody_herbaceous():
     assert (l4_ctv_ntv_nav.compute() == expected_l4_ntv_classes).all()
 
 
-def test_ntv_herbaceous_seasonal_water_veg_cover():
+def test_ntv_herbaceous_seasonal_water_veg_cover(veg_threshold):
     expected_l4_ntv_classes = [
         [91, 83, 79],
         [80, 82, 83],
@@ -275,10 +274,10 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
     xx = image_groups(
         l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
     )
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     # Apply cultivated to match the code in Level4 processing
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)
@@ -291,7 +290,7 @@ def test_ntv_herbaceous_seasonal_water_veg_cover():
     assert (l4_ctv_ntv_nav.compute() == expected_l4_ntv_classes).all()
 
 
-def test_ntv_woody_seasonal_water_veg_cover():
+def test_ntv_woody_seasonal_water_veg_cover(veg_threshold):
     expected_l4_ntv_classes = [
         [76, 68, 64],
         [65, 67, 68],
@@ -385,10 +384,10 @@ def test_ntv_woody_seasonal_water_veg_cover():
     xx = image_groups(
         l34, urban, cultivated, woody, pv_pc_50, water_frequency, water_season
     )
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     # Apply cultivated to match the code in Level4 processing
     l4_ctv = l4_cultivated.lc_l4_cultivated(xx.level_3_4, level3, xx.woody, veg_cover)

tests/test_lc_l4_ntv.py

@@ -6,7 +6,6 @@
 import xarray as xr
 import dask.array as da
 
-from odc.stats.plugins.lc_level34 import StatsLccsLevel4
 from odc.stats.plugins.l34_utils import (
     lc_level3,
     l4_veg_cover,
@@ -35,7 +34,7 @@ def image_groups(l34, urban, cultivated, woody, pv_pc_50):
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
         ),
-        "urban_classes": xr.DataArray(
+        "artificial_surface": xr.DataArray(
             da.from_array(urban, chunks=(1, -1, -1)),
             dims=("spec", "y", "x"),
             attrs={"nodata": 255},
@@ -61,7 +60,7 @@ def image_groups(l34, urban, cultivated, woody, pv_pc_50):
     return xx
 
 
-def test_ntv_classes_herbaceous():
+def test_ntv_classes_herbaceous(veg_threshold):
 
     expected_natural_terrestrial_veg_classes = [
         [36, 33, 32],
@@ -130,16 +129,16 @@ def test_ntv_classes_herbaceous():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ntv = l4_natural_veg.lc_l4_natural_veg(xx.level_3_4, level3, xx.woody, veg_cover)
     assert (l4_ntv.compute() == expected_natural_terrestrial_veg_classes).all()
 
 
-def test_ntv_classes_woody():
+def test_ntv_classes_woody(veg_threshold):
 
     expected_natural_terrestrial_veg_classes = [
         [31, 28, 27],
@@ -208,16 +207,16 @@ def test_ntv_classes_woody():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
 
     l4_ntv = l4_natural_veg.lc_l4_natural_veg(xx.level_3_4, level3, xx.woody, veg_cover)
     assert (l4_ntv.compute() == expected_natural_terrestrial_veg_classes).all()
 
 
-def test_ntv_classes_no_veg():
+def test_ntv_classes_no_veg(veg_threshold):
 
     expected_natural_terrestrial_veg_classes = [
         [20, 20, 20],
@@ -286,16 +285,16 @@ def test_ntv_classes_no_veg():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ntv = l4_natural_veg.lc_l4_natural_veg(xx.level_3_4, level3, xx.woody, veg_cover)
     assert (l4_ntv.compute() == expected_natural_terrestrial_veg_classes).all()
 
 
-def test_ntv_classes_no_lifeform():
+def test_ntv_classes_no_lifeform(veg_threshold):
 
     expected_natural_terrestrial_veg_classes = [
         [26, 23, 22],
@@ -364,10 +363,10 @@ def test_ntv_classes_no_lifeform():
         dtype="uint8",
     )
     xx = image_groups(l34, urban, cultivated, woody, pv_pc_50)
+    mock_urban_mask = da.ones(xx.artificial_surface.shape)
 
-    stats_l4 = StatsLccsLevel4()
-    level3 = lc_level3.lc_level3(xx)
+    level3 = lc_level3.lc_level3(xx, mock_urban_mask)
 
-    veg_cover = l4_veg_cover.canopyco_veg_con(xx, stats_l4.veg_threshold)
+    veg_cover = l4_veg_cover.canopyco_veg_con(xx, veg_threshold)
     l4_ntv = l4_natural_veg.lc_l4_natural_veg(xx.level_3_4, level3, xx.woody, veg_cover)
     assert (l4_ntv.compute() == expected_natural_terrestrial_veg_classes).all()