resolve #3, update pytests

test/test_array.py

@@ -45,4 +45,4 @@ def test_search_point():
 
 def test_sel_modern_basin():
     data = create_testdata()
-    assert data.sel_modern_basin(50,norm_lon_method='').mean().data.item() == 0.5019781051132972
+    assert data.sel_modern_basin(50,norm_lon_method='').mean().data.item() == 0.5019781051132972

test/test_colormap.py

@@ -1,8 +1,22 @@
 from cgeniepy.plot import CommunityPalette
+import matplotlib.colors
 
-def test_get_palette():
 
+def test_txt_palette():
     cmap = CommunityPalette().get_palette('ODV')
-    assert cmap.colors[0] == '#FEB483'
+    assert matplotlib.colors.to_hex(cmap.colors[0]) == '#feb483'
 
 
+def test_spk_palette():
+    cmap = CommunityPalette().get_palette('medium_rainbow')
+    assert matplotlib.colors.to_hex(cmap.colors[0]) == '#cc00ff'
+
+
+def test_xml_palette():
+    cmap = CommunityPalette().get_palette('Section')
+    assert matplotlib.colors.to_hex(cmap.colors[0]) == '#c2ccb8'
+
+
+def test_alt_init():
+    hex_codes = CommunityPalette('medium_rainbow').to_hex()
+    assert hex_codes[0] == '#cc00ff'

test/test_model.py

@@ -1,136 +1,17 @@
-from cgeniepy.model import GenieModel
-from importlib.resources import files
+import cgeniepy
 
 def test_model_getvar():
-    model_path = str(files("data").joinpath("muffin.CBE.worlg4.BASESFeTDTL.SPIN"))
-    model = GenieModel(model_path, gemflag=['ecogem'])
-    data= model.get_var('eco2D_Plankton_C_Total').isel(time=-1).mean().data.item()
-    assert data  == 1.690475344657898
+    model = cgeniepy.sample_model()
+    assert model.get_var("ocn_sur_temp").mean().data.values.item() == 18.055744171142578
+
+def test_eco_pft():
+    model = cgeniepy.sample_model(model_type='EcoModel',gemflag=['ecogem'])
+    ## 1st PFT
+    data = model.get_pft(1).isel(time=-1).mean().data.values.item()
+    assert data  == 0.12259095907211304
 
-def tets_model_ncvardict():
-    model_path = str(files("data").joinpath("muffin.CBE.worlg4.BASESFeTDTL.SPIN"))
-    model = GenieModel(model_path, gemflag=['ecogem'])
-
-    data = model._ncvar_dict
-    baseline = {'./muffin.CBE.worlg4.BASESFeTDTL.SPIN/ecogem/fields_ecogem_2d.nc': ['time',
-    'year',
-    'lon',
-    'lat',
-    'zt',
-    'lon_edges',
-    'lat_edges',
-    'zt_edges',
-    'grid_level',
-    'grid_mask',
-    'grid_topo',
-    'eco2D_Plankton_C_001',
-    'eco2D_Plankton_C_002',
-    'eco2D_Plankton_C_003',
-    'eco2D_Plankton_C_004',
-    'eco2D_Plankton_C_005',
-    'eco2D_Plankton_C_006',
-    'eco2D_Plankton_C_007',
-    'eco2D_Plankton_C_008',
-    'eco2D_Plankton_C_009',
-    'eco2D_Plankton_C_010',
-    'eco2D_Plankton_C_011',
-    'eco2D_Plankton_C_012',
-    'eco2D_Plankton_C_013',
-    'eco2D_Plankton_C_014',
-    'eco2D_Plankton_C_015',
-    'eco2D_Plankton_C_016',
-    'eco2D_Plankton_C_017',
-    'eco2D_Plankton_C_018',
-    'eco2D_Plankton_C_019',
-    'eco2D_Plankton_C_Total',
-    'eco2D_Uptake_Fluxes_C',
-    'eco2D_Plankton_P_001',
-    'eco2D_xGamma_P_001',
-    'eco2D_Plankton_P_002',
-    'eco2D_xGamma_P_002',
-    'eco2D_Plankton_P_003',
-    'eco2D_xGamma_P_003',
-    'eco2D_Plankton_P_004',
-    'eco2D_xGamma_P_004',
-    'eco2D_Plankton_P_005',
-    'eco2D_xGamma_P_005',
-    'eco2D_Plankton_P_006',
-    'eco2D_xGamma_P_006',
-    'eco2D_Plankton_P_007',
-    'eco2D_xGamma_P_007',
-    'eco2D_Plankton_P_008',
-    'eco2D_xGamma_P_008',
-    'eco2D_Plankton_P_009',
-    'eco2D_Plankton_P_010',
-    'eco2D_Plankton_P_011',
-    'eco2D_Plankton_P_012',
-    'eco2D_Plankton_P_013',
-    'eco2D_Plankton_P_014',
-    'eco2D_Plankton_P_015',
-    'eco2D_Plankton_P_016',
-    'eco2D_Plankton_P_017',
-    'eco2D_Plankton_P_018',
-    'eco2D_xGamma_P_018',
-    'eco2D_Plankton_P_019',
-    'eco2D_xGamma_P_019',
-    'eco2D_Plankton_P_Total',
-    'eco2D_Uptake_Fluxes_P',
-    'eco2D_Plankton_Fe_001',
-    'eco2D_xGamma_Fe_001',
-    'eco2D_Plankton_Fe_002',
-    'eco2D_xGamma_Fe_002',
-    'eco2D_Plankton_Fe_003',
-    'eco2D_xGamma_Fe_003',
-    'eco2D_Plankton_Fe_004',
-    'eco2D_xGamma_Fe_004',
-    'eco2D_Plankton_Fe_005',
-    'eco2D_xGamma_Fe_005',
-    'eco2D_Plankton_Fe_006',
-    'eco2D_xGamma_Fe_006',
-    'eco2D_Plankton_Fe_007',
-    'eco2D_xGamma_Fe_007',
-    'eco2D_Plankton_Fe_008',
-    'eco2D_xGamma_Fe_008',
-    'eco2D_Plankton_Fe_009',
-    'eco2D_Plankton_Fe_010',
-    'eco2D_Plankton_Fe_011',
-    'eco2D_Plankton_Fe_012',
-    'eco2D_Plankton_Fe_013',
-    'eco2D_Plankton_Fe_014',
-    'eco2D_Plankton_Fe_015',
-    'eco2D_Plankton_Fe_016',
-    'eco2D_Plankton_Fe_017',
-    'eco2D_Plankton_Fe_018',
-    'eco2D_xGamma_Fe_018',
-    'eco2D_Plankton_Fe_019',
-    'eco2D_xGamma_Fe_019',
-    'eco2D_Plankton_Fe_Total',
-    'eco2D_Uptake_Fluxes_Fe',
-    'eco2D_Plankton_Chl_001',
-    'eco2D_Plankton_Chl_002',
-    'eco2D_Plankton_Chl_003',
-    'eco2D_Plankton_Chl_004',
-    'eco2D_Plankton_Chl_005',
-    'eco2D_Plankton_Chl_006',
-    'eco2D_Plankton_Chl_007',
-    'eco2D_Plankton_Chl_008',
-    'eco2D_Plankton_Chl_018',
-    'eco2D_Plankton_Chl_019',
-    'eco2D_Plankton_Chl_Total',
-    'eco2D_xGamma_T',
-    'eco2D_Nutrients_DIC',
-    'eco2D_Nutrients_PO4',
-    'eco2D_Nutrients_Fe',
-    'eco2D_Size_Mean',
-    'eco2D_Size_Stdev',
-    'eco2D_Size_Minimum',
-    'eco2D_Size_Maximum',
-    'eco2D_Diversity_Threshold',
-    'eco2D_Diversity_Shannon',
-    'eco2D_Diversity_Simpson',
-    'eco2D_Diversity_Berger',
-    'eco2D_Size_Frac_Pico_Chl',
-    'eco2D_Size_Frac_Nano_Chl',
-    'eco2D_Size_Frac_Micro_Chl']}
-    assert data == baseline
-
+def test_eco_multipft():
+    model = cgeniepy.sample_model(model_type='EcoModel',gemflag=['ecogem'])
+    ## 1st and 2nd PFT
+    data = model.get_pft([1,2]).isel(time=-1).sum(dim='variable').mean().data.values.item()
+    assert data  == 0.16739805042743683

test/test_plot.py

@@ -1,34 +1,27 @@
-from cgeniepy.array import GriddedData
-import numpy as np
-import xarray as xr
-import cartopy.crs as ccrs
 from matplotlib.testing.decorators import image_comparison
-import matplotlib.pyplot as plt
 from cgeniepy.table import ScatterData
 from importlib.resources import files
+import cgeniepy
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
 
-
-def create_testdata():
-    lat = np.linspace(-89.5,89.5,180)
-    lon = np.linspace(0,359,360)
-    np.random.seed(12349)
-    data = np.random.rand(lat.size,lon.size)
-    xdata = xr.DataArray(data, coords=[('lat',lat),('lon',lon)],
-                         attrs={'long_name':'random data', 'units':'uniteless'})   
-    return GriddedData(xdata,False, attrs=xdata.attrs)
+def create_sample_data():
+    model = cgeniepy.sample_model()
+    return model.get_var('ocn_sur_temp').isel(time=-1)
 
 @image_comparison(baseline_images=['test_map'], remove_text=True,
                   extensions=['png'], style='mpl20')
-def test_map():
-    data = create_testdata()
+def test_map():    
     fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Mollweide()})
+    data = create_sample_data()
     data.plot(ax=ax)
+
     return fig
 
 @image_comparison(baseline_images=['test_line'], remove_text=True,
                   extensions=['png'], style='mpl20')
 def test_line():
-    data = create_testdata()
+    data = create_sample_data()
     fig, ax = plt.subplots()
     data.mean(dim='lon').plot(ax=ax)
     return fig

test/test_skill.py

@@ -8,19 +8,19 @@ def create_testdata():
     ## calculate skill score
     return ArrComparison(x, y)    
 
-
 def test_mscore():    
     ac = create_testdata()
     assert ac.mscore()==1.0
 
 def test_pearson_r():
     ac = create_testdata()
-    assert ac.pearson_r()==0.9999999999999999
+    diff = ac.pearson_r().item() - 1.0
+    assert diff < 1E-8
 
 def test_cos_sim():    
     ac = create_testdata()
     assert ac.cos_similarity()==1.0
 
 def test_rmse():
     ac = create_testdata()
-    assert ac.rmse()==0.0
+    assert ac.rmse()==0.0