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Issue88 durack1 add mae annual #102

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merged 5 commits into from
Oct 16, 2014
Merged

Issue88 durack1 add mae annual #102

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f87f3f9
Merge branch 'issue53_durack1_TAS_units_hardcode'
durack1 Sep 2, 2014
fb088fb
Merge branch 'pcmdi_master'
durack1 Sep 10, 2014
96f0444
Merge branch 'pcmdi_master'
durack1 Sep 23, 2014
78d9b67
Merge branch 'pcmdi_master'
durack1 Oct 16, 2014
981a5b6
fix #88 add MAE annual to outputs, cleanup indentation
durack1 Oct 16, 2014
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fix #88 add MAE annual to outputs, cleanup indentation
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@durack1
durack1 committed Oct 16, 2014
commit 981a5b6e0295cd37d3a2e8a02b6085f837a6379b
147 changes: 72 additions & 75 deletions src/python/pcmdi/mean_climate_metrics_calculations.py
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Original file line number Diff line number Diff line change
@@ -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