def save(self, path):
com.save(self, path)
# read in the output of "modvsobs.py"
df1=pd.read_csv(site[0]+'botttemp_mc_mod_mc_obs.csv',index_col=0)
ts1=pd.read_csv(site[0]+'botttemp_mc_obs.csv',index_col=0,names=['yy','mm','dd','count','mean','median','min','max','std','rms'])
dfmean1=df1.mean()
df2=df1['mean'].fillna(0)
dfmean1['max']=max(df2)
dfmean1['min']=min(df2)
dfmean1['std']=ts1['std'].mean()
pdmean1=pd.DataFrame(dfmean1)
pdmean1=pdmean1.drop('median')
pdmean1.columns=[site[0]]
for k in range(len(site)):
if k!=0:
#df=pd.read_csv(site[k]+'_wtmp_mc_mod_mc_obs.csv',index_col=0)
df=pd.read_csv(site[k]+'botttemp_mc_mod_mc_obs.csv',index_col=0)
ts=pd.read_csv(site[k]+'botttemp_mc_obs.csv',index_col=0,names=['yy','mm','dd','count','mean','median','min','max','std','rms'])
dfmean=df.mean()
df3=df['mean'].fillna(0)
dfmean['max']=max(df3)
dfmean['min']=min(df3)
dfmean['std']=ts['std'].mean()
pdmean=pd.DataFrame(dfmean)
pdmean=pdmean.drop('median')
pdmean.columns=[site[k]]
pdmean1=pdmean1.join(pdmean)
pdmean1=pdmean1.T
pdmean1.to_csv('totalcaculate_10Dec2013.csv',index=True)
htmlmean=pdmean1.to_html(header=True,index=True,float_format=lambda x: '%10.2f' % x)
save(htmlmean,'/net/nwebserver/epd/ocean/MainPage/lob/emoltvsnecofs_diff.html')
#save(htmlmean,'emoltvsnecofs_diff.html')
def save(self, path):
com.save(self, path)
def save(self, path):
save(self, path)
dfmean2 = df2['mean']
pdmean2 = pd.DataFrame(dfmean2, index=df2.index)
pdmean2.columns = [site2[0]]
for k in range(len(site2)):
if k != 0:
df3 = pd.read_csv(site2[k] + 'temp_mc_mod_mc_obs.csv', index_col=0)
dfmean3 = df3['mean']
pdmean3 = pd.DataFrame(dfmean3, index=df3.index)
pdmean3.columns = [site2[k]]
pdmean2 = pdmean2.join(pdmean3)
print pdmean2
pdmean1.to_csv('totalplot.csv', index=True)
htmlmean = pdmean1.to_html(header=True, index=True)
save(htmlmean, 'totalplot.html')
fig = plt.figure()
ax = fig.add_subplot(211)
#ax.plot(pdmean1.index,pdmean1.values)
ax.plot(pdmean1.index, pdmean1['AG01surf'], color='r')
ax.plot(pdmean1.index, pdmean1['AG01bott'], color='b')
ax.set_ylabel('Temperature difference (degC)')
plt.title('observed minus model at AG01')
plt.grid()
#ax.set_xlabel('Month')
plt.setp(ax.get_xticklabels(), visible=False)
plt.legend(['surface', 'bottom'],
loc='upper right',
ncol=3,
fancybox=True,
dfmean1['std'] = ts1['std'].mean()
pdmean1 = pd.DataFrame(dfmean1)
pdmean1 = pdmean1.drop('median')
pdmean1.columns = [site[0]]
for k in range(len(site)):
if k != 0:
df = pd.read_csv(site[k] + 'botttemp_mc_mod_mc_obs.csv', index_col=0)
ts = pd.read_csv(site[k] + 'botttemp_mc_obs.csv',
index_col=0,
names=[
'yy', 'mm', 'dd', 'count', 'mean', 'median',
'min', 'max', 'std'
])
dfmean = df.mean()
df3 = df['mean'].fillna(0)
dfmean['max'] = max(df3)
dfmean['min'] = min(df3)
dfmean['std'] = ts['std'].mean()
pdmean = pd.DataFrame(dfmean)
pdmean = pdmean.drop('median')
pdmean.columns = [site[k]]
pdmean1 = pdmean1.join(pdmean)
pdmean1 = pdmean1.T
pdmean1.to_csv('totalcaculate.csv', index=True)
htmlmean = pdmean1.to_html(header=True,
index=True,
float_format=lambda x: '%10.2f' % x)
save(htmlmean,
'/net/nwebserver/epd/ocean/MainPage/lob/emoltvsnecofs_diff.html')
save(htmlmean, 'emoltvsnecofs_diff.html')
pdmean2=pd.DataFrame(dfmean2,index=df2.index)
pdmean2.columns=[site2[0]]
for k in range(len(site2)):
if k!=0:
df3=pd.read_csv(site2[k]+'temp_mc_mod_mc_obs.csv',index_col=0)
dfmean3=df3['mean']
pdmean3=pd.DataFrame(dfmean3,index=df3.index)
pdmean3.columns=[site2[k]]
pdmean2=pdmean2.join(pdmean3)
print pdmean2
pdmean1.to_csv('totalplot.csv',index=True)
htmlmean=pdmean1.to_html(header=True,index=True)
save(htmlmean,'totalplot.html')
fig=plt.figure()
ax=fig.add_subplot(211)
#ax.plot(pdmean1.index,pdmean1.values)
ax.plot(pdmean1.index,pdmean1['AG01surf'],color='r')
ax.plot(pdmean1.index,pdmean1['AG01bott'],color='b')
ax.set_ylabel('Temperature difference (degC)')
plt.title('observed minus model at AG01')
plt.grid()
#ax.set_xlabel('Month')
plt.setp(ax.get_xticklabels(),visible=False)
plt.legend(['surface','bottom'],loc='upper right',
ncol=3, fancybox=True, shadow=True)
for i in range(len(ax.lines)):#plot in different ways
def save(self, path):
save(self, path)
tmi=tsdc['min'].resample('m',how=['mean'],loffset=timedelta(days=-15)).values
tma=tsdc['max'].resample('m',how=['mean'],loffset=timedelta(days=-15)).values
tstd=tsdc['std'].resample('m',how=['mean'],loffset=timedelta(days=-15)).values
for kk in range(len(tsmc)):
tsmc['count'].values[kk]=tcount[kk]
tsmc['min'].values[kk]=tmi[kk]
tsmc['max'].values[kk]=tma[kk]
tsmc['std'].values[kk]=tstd[kk]
tsmc['yy']=0
tsmc['mm']=tsmc.index.month
tsmc['dd']=0
output_fmt=['yy','mm','dd','count','mean','median','min','max','std']
tsmcp=tsmc.reindex(columns=output_fmt)# found I needed to generate a new dataframe to print in this order
tsmcp.to_csv(outdir+site[k]+'_wtmp_mc_'+str(int(depth))+'.csv',index=False,header=False,na_rep='NaN',float_format='%10.2f')
output_fmt_html=['mm','count','mean','median','min','max','std']
outhtml=tsmcp.to_html(header=True,index=False,na_rep='NaN',float_format=lambda x: '%10.2f' % x,columns=output_fmt_html)
save(outhtml,'/net/home3/ocn/jmanning/py/'+site[k]+'_wtmp_mc_'+str(int(depth))+'.html')
#make some plots
plt.figure()
tsod['mean'].plot() #plot daily mean
plt.title(site[k]+' daily means')
plt.show()
plt.savefig('/net/nwebserver/epd/ocean/MainPage/lob/'+site[k]+'_jt.png')
plt.figure()
tt=tsmc['mean'].plot() #plot mthly clim
tt.xaxis.set_major_formatter(DateFormatter('%b'))
plt.title(site[k]+' Monthly Climatology')
plt.show()
plt.savefig('/net/nwebserver/epd/ocean/MainPage/lob/'+site[k]+'_seacycle.png')
# DO THE SAME AS ABOVE FOR MULTIPLE SITES
for k in range(len(site)):
print site[k]
#if (k!=0) and (os.path.isfile("/net/data5/jmanning/modvsobs/roms/"+site[k]+'botttemp_mc_mod_mc_obs.csv')):
if (k!=0) and (os.path.isfile("/net/data5/jmanning/modvsobs/"+site[k]+'botttemp_mc_mod_mc_obs'+mode+'.csv')):
df=pd.read_csv("/net/data5/jmanning/modvsobs/"+site[k]+'botttemp_mc_mod_mc_obs'+mode+'.csv',index_col=0)
dfmean=df.mean().dropna()
dfmean['max']=np.nanmax(df['mean'])
dfmean['min']=np.nanmin(df['mean'])
if len(dfmean)>4: # cases where there is std
dfmean=dfmean.drop('std')
dfmean=dfmean.drop('median')
std=df['mean'].std()
rms=sqrt((np.sum(np.square(df['mean'])))/(np.count_nonzero(df['mean'])))
row= pd.DataFrame([dict(rms=rms,std=std)])
row=row.T
pdmean=pd.DataFrame(dfmean)
pdmean=pdmean.append(row)
#pdmean=pdmean.drop('median')
pdmean.columns=[site[k]]
pdmean1=pdmean1.join(pdmean)
pdmean1=pdmean1.T
#pdmean1.to_csv('/data5/jmanning/modvsobs/roms/totalcalculate.csv',index=True,float_format='%8.3f')
pdmean1.to_csv('/data5/jmanning/modvsobs/totalcalculate'+mode+'.csv',index=True,float_format='%8.3f')
htmlmean=pdmean1.to_html(header=True,index=True,float_format=lambda x: '%10.2f' % x)
save(htmlmean,'/net/nwebserver/epd/ocean/MainPage/modvsobs/emoltvsnecofs_diff'+mode+'.html')
print 'mean='+str(np.mean(pdmean1['mean']))
print 'rms ='+str(np.mean(pdmean1['rms']))
