import pylab import mflst import bro cfd_2_mgd = 7.481 / 1.0e6 #--use maxentries to process the file if model is still running - otherwise a race condition between time and budget maxentries = None file_name = 'bro.list' ltime = mflst.lsttime(file_name,start=bro.start) ltime.load(maxentries=maxentries) df_time = ltime.to_pandas() df_time.to_csv('list_time.csv',index_label=('ts','sp')) mfb = mflst.mfbudget(file_name) mfb.load(maxentries=maxentries) df_flux,df_cumu = mfb.to_pandas() df_flux.to_csv('list_flux.csv',index_label=('ts','sp')) df_cumu.to_csv('list_cumu.csv',index_label=('ts','sp')) df_flux.index = df_time['datetime'] df_cumu.index = df_time['datetime'] #df_flux = df_flux.merge(df_time,left_index=True,right_index=True) #df_cumu = df_cumu.merge(df_time,left_index=True,right_index=True) #--sum all the outs and ins df_fluxin = df_flux['in'].sum(axis=1) df_fluxout = df_flux['out'].sum(axis=1)
import numpy as np import pylab import pandas import mflst modelnames = ['_model\\simple','_model\\simple_l','_model\\simple_rc'] names = ['base','layer','rowcol'] colors = ['k','b','g'] dfs = [] for mname in modelnames: lst = mflst.mfbudget(mname+'.list') lst.load() df_flux,df_vol = lst.to_pandas() df_flux_diff = df_flux['in'] - df_flux['out'] df_flux_diff.index = df_flux_diff.index.get_level_values(1) dfs.append(df_flux_diff) for ftype in dfs[0].columns: print ftype fig = pylab.figure(figsize=(6,6)) ax = pylab.subplot(111) for df,name,color in zip(dfs,names,colors): ax.plot(df[ftype].index,df[ftype].values,color=color) ax.set_title(ftype) ax.legend(names) pylab.savefig('png\\'+ftype+'.png',fmt='png',dpi=300,bbox_inches='tight')
import pylab import mflst import bro cfd_2_mgd = 7.481 / 1.0e6 #--use maxentries to process the file if model is still running - otherwise a race condition between time and budget maxentries = None file_name = 'bro.list' ltime = mflst.lsttime(file_name, start=bro.start) ltime.load(maxentries=maxentries) df_time = ltime.to_pandas() df_time.to_csv('list_time.csv', index_label=('ts', 'sp')) mfb = mflst.mfbudget(file_name) mfb.load(maxentries=maxentries) df_flux, df_cumu = mfb.to_pandas() df_flux.to_csv('list_flux.csv', index_label=('ts', 'sp')) df_cumu.to_csv('list_cumu.csv', index_label=('ts', 'sp')) df_flux.index = df_time['datetime'] df_cumu.index = df_time['datetime'] #df_flux = df_flux.merge(df_time,left_index=True,right_index=True) #df_cumu = df_cumu.merge(df_time,left_index=True,right_index=True) #--sum all the outs and ins df_fluxin = df_flux['in'].sum(axis=1) df_fluxout = df_flux['out'].sum(axis=1)
import numpy as np import pylab import pandas import mflst from bro import flow '''compare the wel+swr flux to the wel flux ''' maxentries=749 swr_lst = mflst.mfbudget('flow.list') wel_lst = mflst.mfbudget('flow_noriv.list') ltime = mflst.lsttime('flow.list',start=flow.start) ltime.load(maxentries=maxentries) swr_time = ltime.to_pandas() swr_lst.load(maxentries=maxentries) swr_flux,swr_cumu = swr_lst.to_pandas() swr_flux.index = swr_time['datetime'] wel_lst.load(maxentries=maxentries) wel_flux,swr_cumu = wel_lst.to_pandas() wel_flux.index = swr_time['datetime'] tot_swr_flux = -1.0 * np.loadtxt('tot_vol_record.dat',usecols=[1]) fig = pylab.figure() ax1 = pylab.subplot(311) ax2 = pylab.subplot(312) axt = pylab.twinx() ax3 = pylab.subplot(313)