added pytests and .lax-File for Netzkater demo data

src/pyForMetrix/metricCalculators/publications.py

@@ -78,7 +78,7 @@ def __call__(self, points_in_poly, rumple_pixel_size=1):
             outArray[3] = scipy.stats.kurtosis(points[:, 2])
             outArray[4:6] = np.percentile(points[:, 2], [10, 90])
             outArray[6] = np.count_nonzero(points[:, 2] > outArray[0]) / points.shape[0]
-            rumple = rumple_index(points, rumple_pixel_size)
+            rumple = rumple_index(points_in_poly, rumple_pixel_size)
             outArray[7] = rumple
 
         return outArray

tests/test_plot_metrics.py

@@ -0,0 +1,62 @@
+import geopandas, pathlib
+datadir = pathlib.Path(__file__).parent / '../demo'
+
+def ensure_netzkater_data():
+    import os, wget, zipfile
+    if not os.path.exists(datadir / 'las_623_5718_1_th_2014-2019.laz'):
+        if not os.path.exists(datadir / 'data_netzkater.zip'):
+            print('Downloading file')
+            wget.download(
+                'https://geoportal.geoportal-th.de/hoehendaten/LAS/las_2014-2019/las_623_5718_1_th_2014-2019.zip',
+                str((datadir / 'data_netzkater.zip').absolute()))
+        print('Unzipping file')
+        zipfile.ZipFile(str((datadir / 'data_netzkater.zip').absolute())).extractall(str(datadir.absolute()))
+
+
+def test_paper_metrics():
+    from pyForMetrix.metricCalculators.publications import MCalc_Hollaus_et_al_2009, MCalc_White_et_al_2015, \
+        MCalc_Xu_et_al_2019, MCalc_Woods_et_al_2009
+    from pyForMetrix.metrix import PlotMetrics
+    ensure_netzkater_data()
+    polys = geopandas.read_file(datadir / 'netzkater_polygons.gpkg')
+    pm = PlotMetrics([datadir / 'las_623_5718_1_th_2014-2019.laz'], polys)
+    mcs = [MCalc_Hollaus_et_al_2009(), MCalc_White_et_al_2015(), MCalc_Xu_et_al_2019(), MCalc_Woods_et_al_2009()]
+    results = pm.calc_custom_metrics(mcs)
+    assert results.shape == (7,62)
+    print(results)
+
+def test_lidRmetrics():
+    from pyForMetrix.metricCalculators.types import \
+        MCalc_lidRmetrics_lad, \
+        MCalc_lidRmetrics_kde, \
+        MCalc_lidRmetrics_dispersion, \
+        MCalc_lidRmetrics_voxels, \
+        MCalc_lidRmetrics_HOME, \
+        MCalc_lidRmetrics_percabove, \
+        MCalc_lidRmetrics_echo, \
+        MCalc_lidRmetrics_basic, \
+        MCalc_lidRmetrics_Lmoments, \
+        MCalc_lidRmetrics_rumple, \
+        MCalc_lidRmetrics_percentiles, \
+        MCalc_lidRmetrics_interval, \
+        MCalc_lidRmetrics_canopydensity
+
+    from pyForMetrix.metrix import PlotMetrics
+    ensure_netzkater_data()
+    polys = geopandas.read_file(datadir / 'netzkater_polygons.gpkg')
+    pm = PlotMetrics([datadir / 'las_623_5718_1_th_2014-2019.laz'], polys)
+    mcs = [MCalc_lidRmetrics_lad(),
+           MCalc_lidRmetrics_kde(),
+           MCalc_lidRmetrics_dispersion(),
+           MCalc_lidRmetrics_voxels(),
+           MCalc_lidRmetrics_HOME(),
+           MCalc_lidRmetrics_percabove(),
+            MCalc_lidRmetrics_echo(),
+           MCalc_lidRmetrics_basic(),
+           MCalc_lidRmetrics_rumple(),
+           MCalc_lidRmetrics_percentiles(),
+           MCalc_lidRmetrics_interval(),
+           MCalc_lidRmetrics_canopydensity()]
+    results = pm.calc_custom_metrics(mcs)
+    assert results.shape == (7,78)
+    print(results)

tests/test_raster_metrics.py

@@ -0,0 +1,27 @@
+from test_plot_metrics import ensure_netzkater_data, datadir
+
+
+def test_group_metrics():
+    from pyForMetrix.metricCalculators.types import MCalc_VisMetrics, MCalc_DensityMetrics, \
+        MCalc_HeightMetrics, MCalc_EchoMetrics, MCalc_CoverMetrics, MCalc_VarianceMetrics
+    from pyForMetrix.metrix import RasterMetrics
+    from pyForMetrix.normalizer import normalize
+    ensure_netzkater_data()
+    import laspy
+    data = laspy.read(datadir / 'las_623_5718_1_th_2014-2019.laz')
+    points = {
+        'points': data.xyz,
+        'classification': data.classification,
+        'echo_number': data.return_number,
+        'scan_angle_rank': data.scan_angle_rank,
+        'pt_src_id': data.point_source_id
+    }
+    normalize(points)
+    rm = RasterMetrics(points, raster_size=25)
+    mcs = [MCalc_EchoMetrics(), MCalc_DensityMetrics(), MCalc_CoverMetrics(),
+           MCalc_VarianceMetrics(), MCalc_HeightMetrics(), MCalc_VisMetrics()]
+    results = rm.calc_custom_metrics(mcs)
+    assert results.shape == (40, 40, 35)
+    assert abs(results.sel({'val':'p100'}).data.max() - 105.63799999) < 0.0001
+
+    print(results)