Use of the wflow OpenStreams framework API

This ipython notebook demonstrates how to load an openstreams python model and execute it step-by-step and investigate the (intermediate) results. The first steps is to load the model and framework:



In [1]:

    
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
# First import the model. Here we use the HBV version
from wflow.wflow_sbm import *
import IPython
from IPython.display import display, clear_output

#clear_output = IPython.core.display.clear_output

Set model run-time parameters

Set the:

start and time time
set the runid (this is where the results are stored, relative to the casename)
set the name of the configfile (stire in the case directory
set the clone mape (usually the wflow_subcatch.map)
set the casename. This is where all the model the model resides



In [3]:

    
# define start and stop time of the run
startTime = 1
stopTime = 200
currentTime = 1
     
# set runid, cl;onemap and casename. Also define the ini file
runId = "memtest"
#configfile="wflow_hbv_mem.ini"
configfile="wflow_sbm.ini"
wflow_cloneMap = 'wflow_subcatch.map' 

# the casename points to the complete model setup with both static and dynamic input
caseName="../examples/wflow_rhine_sbm/"



In [4]:

    
#make a usermodel object     
myModel = WflowModel(wflow_cloneMap, caseName,runId,configfile)

# initialise the framework
dynModelFw = wf_DynamicFramework(myModel, stopTime,startTime)



In [5]:

    
dynModelFw.createRunId(NoOverWrite=False,level=logging.ERROR)
dynModelFw.setQuiet(1)
# Run the initial part of the model (reads parameters and sets initial values)
dynModelFw._runInitial() # Runs initial part



In [6]:

    
dynModelFw._runResume() # gets the state variables from disk
# Get list of variables supplied by the model
#print dynModelFw.wf_supplyVariableNamesAndRoles()

Investigate a couple of model variables



In [7]:

    
#
sm = dynModelFw.wf_supplyMapAsNumpy("UStoreDepth")
sm[sm == -999] = np.nan
uz = dynModelFw.wf_supplyMapAsNumpy("FirstZoneCapacity")
uz[uz == -999] = np.nan

imshow(sm)
title("UStoreDepth")
colorbar()









    Out[7]:





<matplotlib.colorbar.Colorbar instance at 0x000000000EA75688>



In [8]:

    
imshow(uz)
title("FirstZoneCapacity")
colorbar()









    Out[8]:





<matplotlib.colorbar.Colorbar instance at 0x000000000EFD2508>



In [9]:

    
dynModelFw.wf_setValueLdd("TopoLdd",5.0,6.46823,51.6821)
npmap0 = dynModelFw.wf_supplyMapAsNumpy("TopoLdd")
ltt = dynModelFw.wf_supplyMapAsList("SurfaceRunoff")









    



c:\repos\wflow\wflow\wflow_lib.py:524: RuntimeWarning: invalid value encountered in absolute
  col_ =  numpy.absolute(diffx) <= (XX[0,0] * tolerance)  # cellsize
c:\repos\wflow\wflow\wflow_lib.py:524: RuntimeWarning: invalid value encountered in less_equal
  col_ =  numpy.absolute(diffx) <= (XX[0,0] * tolerance)  # cellsize
c:\repos\wflow\wflow\wflow_lib.py:525: RuntimeWarning: invalid value encountered in absolute
  row_ =  numpy.absolute(diffy) <= (XX[0,0] * tolerance)# cellsize
c:\repos\wflow\wflow\wflow_lib.py:525: RuntimeWarning: invalid value encountered in less_equal
  row_ =  numpy.absolute(diffy) <= (XX[0,0] * tolerance)# cellsize

Run for a number of timesteps



In [10]:

    
f, ax = plt.subplots(1,3,figsize=(16, 6))

plotar = []

for ts in range(20,80):
         # Get value at pit
      
        inflowQ = dynModelFw.wf_supplyScalar("SurfaceRunoff",5.68962,50.7307)
        plotar.append(inflowQ)   
        # Add inflow to outflow
        #dynModelFw.wf_setValue("ForecQ_qmec", -1.0 * inflowQ  ,6.46823,51.6821)
        Resoutflow = inflowQ
        dynModelFw.wf_setValues("PET",scalar(2.0))
        #dynModelFw.wf_setValue("ForecQ_qmec",inflowQ * 1000 ,6.47592,51.7288)
        # update runoff ONLY NEEDED IF YOU FIDDLE WITH THE KIN_WAVE RESERVOIR
        myModel.updateRunOff()
        dynModelFw._runDynamic(ts,ts) # runs for all timesteps
        #dynModelFw.wf_setValue("SurfaceRunoff",0.0,6.46823,51.6821)
        #dynModelFw.wf_setValue("SurfaceRunoff",0.0,6.11535,51.8425)
        npmap0 = dynModelFw.wf_supplyMapAsNumpy("ForecQ_qmec")
        npmap1 = dynModelFw.wf_supplyMapAsNumpy("P")
        run = dynModelFw.wf_supplyMapAsNumpy("SurfaceRunoff")
        uz = dynModelFw.wf_supplyMapAsNumpy("FirstZoneCapacity")
        sm = dynModelFw.wf_supplyMapAsNumpy("UStoreDepth")
        
        sm[sm == -999] = np.nan
        #dynModelFw.wf_setValues("UpperZoneStorage",uz * 10.1)
        uz[uz == -999] = np.nan
        run[run == -999] = np.nan
        ax[0].imshow(uz)
        ax[1].imshow(sm)
        #ax[2].imshow(log(run))
        ax[2].plot(plotar,'b')
        ax[2].set_title(str(ts))
        clear_output()
        display(f)
        
plt.close()



In [ ]:

    
dynModelFw._runSuspend() # saves the state variables
dynModelFw._wf_shutdown()



In [ ]:

    
run = dynModelFw.wf_supplyMapAsNumpy("SurfaceRunoff")
run[run == -999] = np.nan



In [ ]:

    
imshow(log(run))



In [11]:

    
plotar









    Out[11]:





[nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan,
 nan]



In [ ]: