tabel met alle mnp soorten

sample/dk_plant.py

@@ -89,7 +89,7 @@ for row in dk.itertuples():
     else:
         mean_freq = sel.Frequentie.mean()
         mean_trouw = sel.Trouw.mean()
-        dk_ww = hlp.draagkracht_ww(frequentie=mean_freq, trouw=mean_trouw)
+        dk_ww = hlp.draagkracht_ww(frequentie=mean_freq, trouw=mean_trouw, th=0.01)
         print('{0} X {1}: n={2} freq={3} trouw={4} dk={5}'
               .format(row.Scientific_name, row.Land_type_code, sel.shape[0], mean_freq, mean_trouw, dk_ww))
 
@@ -98,16 +98,25 @@ for row in dk.itertuples():
     dk.loc[row.Index, 'Land_type_quality'] = dk_ww
 
 '''Write draagkracht file per species'''
-# TODO: minimale draagkracht is 0.01
-# TODO:
-
+fails = []
 for species_code in list(set(dk.Species_code)):
     local_name = code2localname[species_code].replace(' ', '_')
-    sel = dk.loc[(dk.Species_code == species_code) & (dk.Land_type_quality.notna() & dk.Land_type_quality >= 0.01),
+    sel = dk.loc[(dk.Species_code == species_code) & (dk.Land_type_quality.notna()),
            ['Species_code', 'Land_type_code', 'Land_type_quality']].round({'Land_type_quality': 3})
     if not sel.empty:
         sel.to_csv(r'c:\apps\z_temp\mnp_dk\03_MNP_versie7_par_density_factors_{}.csv'.format(local_name),
-                                                   index=False, header=True, sep=',')
+                   index=False, header=True, sep=',')
+        print('To file: {}'.format(local_name))
+    else:
+        sel = pd.Series({'Species_code': species_code, 'Local_name': local_name,
+               'Scientific_name': code2scientificname[species_code], 'msg': 'failed'})
+        fails.append(sel)
+        print('Fail: {}'.format(local_name))
+if len(fails) > 0:
+    fails_df = pd.concat(fails, axis=1)
+    fails_df.T.to_csv(r'c:\apps\z_temp\mnp_dk\03_MNP_versie7_par_density_factors_MISSING.csv',
+                      index=True, header=True, sep='\t')
+
 
 
 '''5. Reporting

sample/dk_vogel_vlinder.py

@@ -54,7 +54,7 @@ for i, species in enumerate(species, start=1):
         sp = hlp.Species(species)
         ndff = hlp.query_ndff(gdf=ndff_all, species=sp, max_area=10000)  # NDFF shapefile for selected species.
     except (AssertionError, ImportError) as e:
-        hlp.write2file(df=pd.Series({sp.naam_ned: e}), out_name='error_{}'.format(species), out_dir=args.out_dir)
+        hlp.write2file(df=pd.Series({species: e}), out_name='error_{}'.format(species), out_dir=args.out_dir)
         continue
 
     out_name = '03_MNP_versie7_par_density_factors_{0}'.format(sp.naam_ned)
@@ -131,12 +131,17 @@ for i, species in enumerate(species, start=1):
             draagkracht = areas.loc[:, '{}_ndffpxls'.format(beheertype)].divide(areas.loc[:, beheertype]).max().round(3)
             if draagkracht >= args.th:
                 dks[beheertype] = {'Species_code': sp.mnp_species_code, 'Land_type_code': beheertype,
-                                   'Land_type_quality': draagkracht}
+                               'Land_type_quality': draagkracht}
             else:
                 continue
         except KeyError:
             continue
+
     out = pd.DataFrame.from_dict(dks, orient='index')
-    hlp.write2file(df=out, out_name=out_name, out_dir=args.out_dir)
+    if not out.empty:
+        print('  {0} matched to {1} beheertypen'.format(sp.naam_ned, out.shape[0]))
+        hlp.write2file(df=out, out_name=out_name, out_dir=args.out_dir)
+    else:
+        print('  zero dk for {}'.format(sp.naam_ned))
 
 

sample/mnp/helpers.py

@@ -450,6 +450,7 @@ def ndff_sources():
             'dagvlinder': r'W:\PROJECTS\MNP2020\c_fases\f7_draagkracht\a_source_data\vlinders_ndff_shapefiles\ndff_vlinders_merged_okt2020.shp',
             'test': r'c:\apps\temp_geodata\ndff\ndff_vogel_sample.shp'}
 
+
 class Species:
     """
     Class holding all info related to a species
@@ -594,15 +595,19 @@ def fix_bt(code_in, as_mnp=False):
         return '{0}{1}.{2}'.format(pre, head, tail)
 
 
-def draagkracht_ww(frequentie, trouw):
+def draagkracht_ww(frequentie, trouw, th):
     """
     Draagkracht formule volgens Wieger Wamelink. Zie MS Teams discussie 16-07-2020 MNP2020/Fase 7 Objectivering Draagk..
     :param frequentie: gemiddelde frequentie van soort X binnen beheertype Y
     :param trouw: gemiddelde trouw van soort X binnen beheertype Y
+    :param th: minumum draagkracht value
     :return: draagkracht soort X - beheertype Y combinatie, gemaximaliseerd tot 1.
     """
     dk = np.min([np.multiply(np.divide(np.max([frequentie, trouw]), 100), 5), 1])
-    return dk
+    if dk >= th:
+        return dk
+    else:
+        return np.nan
 
 
 def valid_dir(x):