Issue88 durack1 add mae annual

src/python/pcmdi/mean_climate_metrics_calculations.py

@@ -1,79 +1,76 @@
 import cdms2 as cdms
 import pcmdi_metrics
-#import cdutil
-#import MV2 as MV
-#from genutil import statistics
 
 def compute_metrics(var,dm_glb,do_glb):
-
- cdms.setAutoBounds('on')
-
- metrics_dictionary = {}
-
- domains = ['GLB','NHEX','TROPICS','SHEX']
-
- for dom in domains: 
-
- dm = dm_glb
- do = do_glb
-
- if dom == 'NHEX':
- dm = dm_glb(latitude = (30.,90))
- do = do_glb(latitude = (30.,90))
- if dom == 'SHEX':
- dm = dm_glb(latitude = (-90.,-30))
- do = do_glb(latitude = (-90.,-30))
- if dom == 'TROPICS':
- dm = dm_glb(latitude = (-30.,30))
- do = do_glb(latitude = (-30.,30))
-
- ### ANNUAL CYCLE SPACE-TIME RMS AND CORRELATION
- print '---- shapes ', dm.shape,' ', do.shape
- rms_xyt = pcmdi_metrics.pcmdi.rms_xyt.compute(dm,do)
- cor_xyt = pcmdi_metrics.pcmdi.cor_xyt.compute(dm,do)
-
- ### CALCUALTE ANNUAL MEANS OF DATA
- do_am, dm_am = pcmdi_metrics.pcmdi.annual_mean.compute(dm,do)
-
- ### ANNUAL MEAN BIAS
- bias_xy = pcmdi_metrics.pcmdi.bias.compute(dm_am,do_am)
- print var,' ', 'annual mean bias is ' , bias_xy
-
- ### MEAN ABSOLOUTE ERROR 
- mae_xy = pcmdi_metrics.pcmdi.meanabs_xy.compute(dm_am,do_am)
-
- ### ANNUAL MEAN RMS
- rms_xy = pcmdi_metrics.pcmdi.rms_xy.compute(dm_am,do_am)
-
- conv = 1.
- if var == 'pr': conv = 1.e5
-
- sig_digits = '.2f'
- if var in ['hus']: sig_digits = '.5f'
-
- for m in ['rms_xyt','rms_xy','bias_xy','cor_xyt','mae_am']:
- if m == 'rms_xyt': metrics_dictionary[m + '_ann_' + dom] = format(rms_xyt*conv,sig_digits) 
- if m == 'rms_xy': metrics_dictionary[m + '_ann_' + dom] =  format(rms_xy*conv,sig_digits)
- if m == 'bias_xy': metrics_dictionary[m + '_ann_' + dom] = format(bias_xy*conv,sig_digits)
- if m == 'mae_xy': metrics_dictionary[m + '_ann_' + dom] = format(mae_xy*conv,sig_digits)
- if m == 'cor_xyt': metrics_dictionary[m + '_ann_' + dom] = format(cor_xyt,'.2f')
-
- #### SEASONAL MEANS ######
-
- for sea in ['djf','mam','jja','son']:
-
- dm_sea = pcmdi_metrics.pcmdi.seasonal_mean.compute(dm,sea) 
- do_sea = pcmdi_metrics.pcmdi.seasonal_mean.compute(do,sea)
-
- ### SEASONAL RMS AND CORRELATION
- rms_sea = pcmdi_metrics.pcmdi.rms_xy.compute(dm_sea,do_sea)
- cor_sea = pcmdi_metrics.pcmdi.cor_xy.compute(dm_sea,do_sea) 
- mae_sea = pcmdi_metrics.pcmdi.meanabs_xy.compute(dm_sea,do_sea)
- bias_sea = pcmdi_metrics.pcmdi.bias.compute(dm_sea,do_sea)
-
- metrics_dictionary['bias_xy_' + sea + '_' + dom] = format(bias_sea*conv,sig_digits)
- metrics_dictionary['rms_xy_' + sea + '_' + dom] = format(rms_sea*conv,sig_digits) 
- metrics_dictionary['cor_xy_' + sea + '_' + dom] = format(cor_sea,'.2f')
- metrics_dictionary['mae_xy_' + sea + '_' + dom] = format(mae_sea*conv,sig_digits)
-
- return metrics_dictionary 
+ cdms.setAutoBounds('on')
+  metrics_dictionary = {}
+ 
+  domains = ['GLB','NHEX','TROPICS','SHEX']
+ 
+  for dom in domains:
+ 
+  dm = dm_glb
+ do = do_glb
+  
+  if dom == 'NHEX':
+ dm = dm_glb(latitude = (30.,90))
+  do = do_glb(latitude = (30.,90))
+  if dom == 'SHEX':
+  dm = dm_glb(latitude = (-90.,-30))
+  do = do_glb(latitude = (-90.,-30))
+  if dom == 'TROPICS':
+  dm = dm_glb(latitude = (-30.,30))
+  do = do_glb(latitude = (-30.,30))
+  
+  ### CALCULATE ANNUAL CYCLE SPACE-TIME RMS AND CORRELATIONS
+ print '---- shapes ', dm.shape,' ', do.shape
+  rms_xyt = pcmdi_metrics.pcmdi.rms_xyt.compute(dm,do)
+  cor_xyt = pcmdi_metrics.pcmdi.cor_xyt.compute(dm,do)
+  
+  ### CALCULATE ANNUAL MEANS
+ do_am, dm_am = pcmdi_metrics.pcmdi.annual_mean.compute(dm,do)
+  
+  ### CALCULATE ANNUAL MEAN BIAS
+ bias_xy = pcmdi_metrics.pcmdi.bias.compute(dm_am,do_am)
+  print var,' ', 'annual mean bias is ' , bias_xy
+  
+  ### CALCULATE MEAN ABSOLUTE ERROR
+ mae_xy = pcmdi_metrics.pcmdi.meanabs_xy.compute(dm_am,do_am)
+   
+  ### CALCULATE ANNUAL MEAN RMS
+ rms_xy = pcmdi_metrics.pcmdi.rms_xy.compute(dm_am,do_am)
+  
+  # SET CONDITIONAL ON INPUT VARIABLE
+ if var == 'pr':
+  conv = 1.e5
+  else:
+ conv = 1.
+  
+   sig_digits = '.2f'
+ if var in ['hus']: sig_digits = '.5f'
+  
+  for m in ['rms_xyt','rms_xy','bias_xy','cor_xyt','mae_xy']:
+  if m == 'rms_xyt': metrics_dictionary[m + '_ann_' + dom] = format(rms_xyt*conv,sig_digits)
+  if m == 'rms_xy': metrics_dictionary[m + '_ann_' + dom] =  format(rms_xy*conv,sig_digits)
+  if m == 'bias_xy': metrics_dictionary[m + '_ann_' + dom] = format(bias_xy*conv,sig_digits)
+  if m == 'mae_xy': metrics_dictionary[m + '_ann_' + dom] = format(mae_xy*conv,sig_digits)
+ if m == 'cor_xyt': metrics_dictionary[m + '_ann_' + dom] = format(cor_xyt,'.2f')
+  
+ ### CALCULATE SEASONAL MEANS
+  for sea in ['djf','mam','jja','son']:
+ 
+  dm_sea = pcmdi_metrics.pcmdi.seasonal_mean.compute(dm,sea)
+  do_sea = pcmdi_metrics.pcmdi.seasonal_mean.compute(do,sea)
+ 
+  ### CALCULATE SEASONAL RMS AND CORRELATION
+  rms_sea = pcmdi_metrics.pcmdi.rms_xy.compute(dm_sea,do_sea)
+  cor_sea = pcmdi_metrics.pcmdi.cor_xy.compute(dm_sea,do_sea)
+  mae_sea = pcmdi_metrics.pcmdi.meanabs_xy.compute(dm_sea,do_sea)
+  bias_sea = pcmdi_metrics.pcmdi.bias.compute(dm_sea,do_sea)
+ 
+  metrics_dictionary['bias_xy_' + sea + '_' + dom] = format(bias_sea*conv,sig_digits)
+  metrics_dictionary['rms_xy_' + sea + '_' + dom] = format(rms_sea*conv,sig_digits)
+  metrics_dictionary['cor_xy_' + sea + '_' + dom] = format(cor_sea,'.2f')
+  metrics_dictionary['mae_xy_' + sea + '_' + dom] = format(mae_sea*conv,sig_digits)
+  
+  return metrics_dictionary