Updated nosetests for forecast.py

pvlib · wholmgren · Oct 2, 2015 · Oct 2, 2015 · Oct 8, 2015 · Oct 13, 2015
commit befb83b7271501d607efc7eee2cf701612c12420
diff --git a/pvlib/forecast.py b/pvlib/forecast.py
@@ -304,12 +304,12 @@ def __init__(self,res='Half'):
        super(GFS, self).__init__(model_type,model,data_labels)


-class NAM(ForecastModel):
+class GSD(ForecastModel):
    '''
-    Subclass of the ForecastModel class representing NAM forecast model.
+    Subclass of the ForecastModel class representing NCEP forecast model.

-    Model data corresponds to NAM CONUS 12km resolution forecasts
-    from CONDUIT.
+    Model data corresponds to NOAA/GSD HRRR CONUS 3km resolution
+    surface forecasts.

    Attributes
    ----------
@@ -324,36 +324,35 @@ class NAM(ForecastModel):
        Name of the UNIDATA forecast model.
    model_type: string
        UNIDATA category in which the model is located.
-    res: string
-        Determines which resolution of the GFS to use, as 'Half' or 'Quarter'
    variables: list
        Names of default variables specific to the model.

    '''
    def __init__(self):
+        import warnings
+        warnings.warn('Experimental model. May not be available.')       
        model_type = 'Forecast Model Data'
-        model = 'NAM CONUS 12km from CONDUIT'
+        model = 'GSD HRRR CONUS 3km surface'
        description = ''
        variables = ['Temperature_surface',
-                     'Temperature_isobaric',
-                     'Total_cloud_cover_entire_atmosphere_single_layer',
-                     'Low_cloud_cover_low_cloud',
-                     'Medium_cloud_cover_middle_cloud',
-                     'High_cloud_cover_high_cloud',
-                     'Downward_Short-Wave_Radiation_Flux_surface',
-                     'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average']
-        cols = super(NAM, self).columns
-        idx = [0,1,3,4,5,6,9,10]
+                     'Total_cloud_cover_entire_atmosphere',
+                     'Low_cloud_cover_UnknownLevelType-214',
+                     'Medium_cloud_cover_UnknownLevelType-224',
+                     'High_cloud_cover_UnknownLevelType-234',
+                     'Downward_short-wave_radiation_flux_surface']
+        cols = super(GSD, self).columns
+        idx = [0,3,4,5,6,9]
        data_labels = dict(zip(cols[idx],variables))
-        super(NAM, self).__init__(model_type,model,data_labels)
+        super(GSD, self).__init__(model_type,model,data_labels)


-class RAP(ForecastModel):
+
+class NAM(ForecastModel):
    '''
-    Subclass of the ForecastModel class representing RAP forecast model.
+    Subclass of the ForecastModel class representing NAM forecast model.

-    Model data corresponds to Rapid Refresh CONUS 20km resolution 
-    forecasts.
+    Model data corresponds to NAM CONUS 12km resolution forecasts
+    from CONDUIT.

    Attributes
    ----------
@@ -368,23 +367,28 @@ class RAP(ForecastModel):
        Name of the UNIDATA forecast model.
    model_type: string
        UNIDATA category in which the model is located.
+    res: string
+        Determines which resolution of the GFS to use, as 'Half' or 'Quarter'
    variables: list
        Names of default variables specific to the model.

    '''
    def __init__(self):
        model_type = 'Forecast Model Data'
-        model = 'Rapid Refresh CONUS 20km'
+        model = 'NAM CONUS 12km from CONDUIT'
        description = ''
        variables = ['Temperature_surface',
+                     'Temperature_isobaric',
                     'Total_cloud_cover_entire_atmosphere_single_layer',
                     'Low_cloud_cover_low_cloud',
                     'Medium_cloud_cover_middle_cloud',
-                     'High_cloud_cover_high_cloud']
-        cols = super(RAP, self).columns
-        idx = [0,3,4,5,6]
+                     'High_cloud_cover_high_cloud',
+                     'Downward_Short-Wave_Radiation_Flux_surface',
+                     'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average']
+        cols = super(NAM, self).columns
+        idx = [0,1,3,4,5,6,9,10]
        data_labels = dict(zip(cols[idx],variables))
-        super(RAP, self).__init__(model_type,model,data_labels)
+        super(NAM, self).__init__(model_type,model,data_labels)


 class NCEP(ForecastModel):
@@ -425,12 +429,11 @@ def __init__(self):
        super(NCEP, self).__init__(model_type,model,data_labels)


-class GSD(ForecastModel):
+class NDFD(ForecastModel):
    '''
-    Subclass of the ForecastModel class representing NCEP forecast model.
+    Subclass of the ForecastModel class representing NDFD forecast model.

-    Model data corresponds to NOAA/GSD HRRR CONUS 3km resolution
-    surface forecasts.
+    Model data corresponds to NWS CONUS CONDUIT forecasts.

    Attributes
    ----------
@@ -449,28 +452,26 @@ class GSD(ForecastModel):
        Names of default variables specific to the model.

    '''
-    def __init__(self):
-        model_type = 'Forecast Model Data'
-        model = 'GSD HRRR CONUS 3km surface'

+    def __init__(self):
+        model_type = 'Forecast Products and Analyses'
+        model = 'National Weather Service CONUS Forecast Grids (CONDUIT)'
        description = ''
        variables = ['Temperature_surface',
-                     'Total_cloud_cover_entire_atmosphere',
-                     'Low_cloud_cover_UnknownLevelType-214',
-                     'Medium_cloud_cover_UnknownLevelType-224',
-                     'High_cloud_cover_UnknownLevelType-234',
-                     'Downward_short-wave_radiation_flux_surface']
-        cols = super(GSD, self).columns
-        idx = [0,3,4,5,6,9]
+                     'Wind_speed_surface',
+                     'Total_cloud_cover_surface']
+        cols = super(NDFD, self).columns
+        idx = [0,2,3]
        data_labels = dict(zip(cols[idx],variables))
-        super(GSD, self).__init__(model_type,model,data_labels)
+        super(NDFD, self).__init__(model_type,model,data_labels)


-class NDFD(ForecastModel):
+class RAP(ForecastModel):
    '''
-    Subclass of the ForecastModel class representing NDFD forecast model.
+    Subclass of the ForecastModel class representing RAP forecast model.

-    Model data corresponds to NWS CONUS CONDUIT forecasts.
+    Model data corresponds to Rapid Refresh CONUS 20km resolution 
+    forecasts.

    Attributes
    ----------
@@ -489,15 +490,16 @@ class NDFD(ForecastModel):
        Names of default variables specific to the model.

    '''
-
    def __init__(self):
-        model_type = 'Forecast Products and Analyses'
-        model = 'National Weather Service CONUS Forecast Grids (CONDUIT)'
+        model_type = 'Forecast Model Data'
+        model = 'Rapid Refresh CONUS 20km'
        description = ''
        variables = ['Temperature_surface',
-                     'Wind_speed_surface',
-                     'Total_cloud_cover_surface']
-        cols = super(NDFD, self).columns
-        idx = [0,2,3]
+                     'Total_cloud_cover_entire_atmosphere_single_layer',
+                     'Low_cloud_cover_low_cloud',
+                     'Medium_cloud_cover_middle_cloud',
+                     'High_cloud_cover_high_cloud']
+        cols = super(RAP, self).columns
+        idx = [0,3,4,5,6]
        data_labels = dict(zip(cols[idx],variables))
-        super(NDFD, self).__init__(model_type,model,data_labels)
+        super(RAP, self).__init__(model_type,model,data_labels)
diff --git a/pvlib/test/test_forecast.py b/pvlib/test/test_forecast.py
@@ -40,110 +40,6 @@ def test_fmcreation():
        _models[model[0]] = amodel


-def test_GFSdict():
-
-    gfs = _models['GFS']
-
-    varlist = ['Temperature_isobaric',
-               'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average',
-               'Total_cloud_cover_low_cloud_Mixed_intervals_Average',
-               'Total_cloud_cover_middle_cloud_Mixed_intervals_Average',
-               'Total_cloud_cover_high_cloud_Mixed_intervals_Average',
-               'Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average',
-               'Total_cloud_cover_convective_cloud']
-
-    idx = [1,3,4,5,6,7,8]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in gfs.data_labels.iteritems():
-        assert label_dict[key] == gfs.data_labels[key]
-
-
-def test_GSDdict():
-
-    gsd = _models['GFS']
-
-    varlist = ['Temperature_surface',
-               'Total_cloud_cover_entire_atmosphere',
-               'Low_cloud_cover_UnknownLevelType-214',
-               'Medium_cloud_cover_UnknownLevelType-224',
-               'High_cloud_cover_UnknownLevelType-234',
-               'Downward_short-wave_radiation_flux_surface']
-
-    idx = [0,3,4,5,6,9]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in gsd.data_labels.iteritems():
-        assert label_dict[key] == gsd.data_labels[key]
-
-
-def test_NAMdict():
-
-    nam = _models['GFS']
-
-    varlist = ['Temperature_surface',
-                 'Temperature_isobaric',
-                 'Total_cloud_cover_entire_atmosphere_single_layer',
-                 'Low_cloud_cover_low_cloud',
-                 'Medium_cloud_cover_middle_cloud',
-                 'High_cloud_cover_high_cloud',
-                 'Downward_Short-Wave_Radiation_Flux_surface',
-                 'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average']
-    idx = [0,1,3,4,5,6,9,10]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in nam.data_labels.iteritems():
-        assert label_dict[key] == nam.data_labels[key]
-
-
-def test_NCEPdict():
-
-    ncep = _models['GFS']
-
-    varlist = ['Total_cloud_cover_entire_atmosphere',
-               'Low_cloud_cover_low_cloud',
-               'Medium_cloud_cover_middle_cloud',
-               'High_cloud_cover_high_cloud',]
-
-    idx = [3,4,5,6]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in ncep.data_labels.iteritems():
-        assert label_dict[key] == ncep.data_labels[key]
-
-
-def test_NDFDdict():
-
-    ndfd = _models['GFS']
-
-    varlist = ['Temperature_surface',
-               'Wind_speed_surface',
-               'Total_cloud_cover_surface']
-
-    idx = [0,2,3]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in ndfd.data_labels.iteritems():
-        assert label_dict[key] == ndfd.data_labels[key]
-
-
-def test_RAPdict():
-
-    rap = _models['GFS']
-
-    varlist = ['Temperature_surface',
-               'Total_cloud_cover_entire_atmosphere_single_layer',
-               'Low_cloud_cover_low_cloud',
-               'Medium_cloud_cover_middle_cloud',
-               'High_cloud_cover_high_cloud']
-
-    idx = [0,3,4,5,6]
-    label_dict = dict(zip(_variables[idx],varlist))
-
-    for key, _ in rap.data_labels.iteritems():
-        assert label_dict[key] == rap.data_labels[key]
-
-
 def test_data_query():

    for name, model in _models.items():