def main():
# First we will create an instance of PrecipitationDistribution
PD = PrecipitationDistribution()
# Because the values for storm duration, interstorm duration, storm
# depth and intensity are set stochastically in the initialization
# phase, we should see that they seem reasonable.
print("Mean storm duration is: ", PD.mean_storm, " hours, while the value from the Poisson distribution is: ", PD.storm_duration)
print("Mean interstorm Duration is: ", PD.mean_interstorm, 'hours, while the value from the Poisson distribution is: ', PD.interstorm_duration)
print("Mean storm depth is: ", PD.mean_storm_depth, 'mm, while the value from the Poisson distribution is: ', PD.storm_depth)
print("Mean intensity is: ", PD.mean_intensity, 'mm/hr, while the value from the Poisson distribution is: ', PD.intensity)
print('\n')
# If we update the values, we can verify they are changing.
PD.update()
print("Storm Duration is: ", PD.storm_duration, 'hours.')
print("Interstorm Duration is: ", PD.interstorm_duration, 'hours.')
print("Storm Depth is: ", PD.storm_depth, 'mm.')
print("Intensity is: ", PD.intensity, 'mm.')
# If we generate a time series we can plot a precipitation distribution
PD.get_storm_time_series()
# And get the storm array from the component..
storm_arr = PD.storm_time_series
# And now to call the plotting method.
create_precip_plot(storm_arr)
def main():
# First we will create an instance of PrecipitationDistribution
PD = PrecipitationDistribution()
# Because the values for storm duration, interstorm duration, storm
# depth and intensity are set stochastically in the initialization
# phase, we should see that they seem reasonable.
print "Mean storm duration is: ", PD.mean_storm, " hours, while the value from the Poisson distribution is: ", PD.storm_duration
print "Mean interstorm Duration is: ", PD.mean_interstorm, 'hours, while the value from the Poisson distribution is: ', PD.interstorm_duration
print "Mean storm depth is: ", PD.mean_storm_depth, 'mm, while the value from the Poisson distribution is: ', PD.storm_depth
print "Mean intensity is: ", PD.mean_intensity, 'mm/hr, while the value from the Poisson distribution is: ', PD.intensity
print '\n'
# If we update the values, we can verify they are changing.
PD.update()
print "Storm Duration is: ", PD.storm_duration, 'hours.'
print "Interstorm Duration is: ", PD.interstorm_duration, 'hours.'
print "Storm Depth is: ", PD.storm_depth, 'mm.'
print "Intensity is: ", PD.intensity, 'mm.'
# If we generate a time series we can plot a precipitation distribution
PD.get_storm_time_series()
# And get the storm array from the component..
storm_arr = PD.storm_time_series
# And now to call the plotting method.
create_precip_plot(storm_arr)
def main():
print 'We are going to use TrialRun as our class instance.'
TrialRun = PrecipitationDistribution()
print 'TrialRun = PrecipitationDistribution()'
print '\n'
print "TrialRun's values before initiation..."
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm/hr.'
print '\n'
print 'We should initialize TrialRun... TrialRun.initialize()'
TrialRun.initialize()
print '\n'
print 'What are the mean values read in from the input file?'
print "Mean Storm Duration is: ", TrialRun.mean_storm, 'hours.'
print "Interstorm Duration is: ", TrialRun.mean_interstorm, 'hours.'
print "Storm Depth is: ", TrialRun.mean_storm_depth, 'mm.'
print "Intensity is: ", TrialRun.mean_intensity, 'mm/hr.'
print '\n'
print "Let's see what what the class members are after the first initialization..."
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm/hr.'
print '\n'
print 'Now we will update these values using TrialRun.update()'
TrialRun.update()
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm.'
print '\n'
print 'Now we are going to generate a time series:'
TrialRun.get_storm_time_series()
print TrialRun.storm_time_series
def main():
print 'We are going to use TrialRun as our class instance.'
TrialRun = PrecipitationDistribution()
print 'TrialRun = PrecipitationDistribution()'
print '\n'
print "TrialRun's values before initiation..."
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm/hr.'
print '\n'
print 'We should initialize TrialRun... TrialRun.initialize()'
TrialRun.initialize()
print '\n'
print 'What are the mean values read in from the input file?'
print "Mean Storm Duration is: ", TrialRun.mean_storm, 'hours.'
print "Interstorm Duration is: ", TrialRun.mean_interstorm, 'hours.'
print "Storm Depth is: ", TrialRun.mean_storm_depth, 'mm.'
print "Intensity is: ", TrialRun.mean_intensity, 'mm/hr.'
print '\n'
print "Let's see what what the class members are after the first initialization..."
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm/hr.'
print '\n'
print 'Now we will update these values using TrialRun.update()'
TrialRun.update()
print "Storm Duration is: ", TrialRun.storm_duration, 'hours.'
print "Interstorm Duration is: ", TrialRun.interstorm_duration, 'hours.'
print "Storm Depth is: ", TrialRun.storm_depth, 'mm.'
print "Intensity is: ", TrialRun.intensity, 'mm.'
print '\n'
print 'Now we are going to generate a time series:'
TrialRun.get_storm_time_series()
print TrialRun.storm_time_series
EP30[i] = np.mean(PET_[i-30:i], axis = 0)
time_check = 0. # Buffer to store current_time at previous storm
i_check = 0 #
yrs = 0
Start_time = time.clock() # Recording time taken for simulation
WS = 0.
## Run Time Loop
for i in range(0, n):
# Update objects
Julian = np.floor( (current_time - np.floor( current_time)) * 365.)
if Julian < 182 or Julian > 273: # Dry Season
PD_D.update()
P[i] = PD_D.storm_depth
Tr[i] = PD_D.storm_duration
Tb[i] = PD_D.interstorm_duration
else: # Wet Season - Jul to Sep - NA Monsoon
PD_W.update()
P[i] = PD_W.storm_depth
Tr[i] = PD_W.storm_duration
Tb[i] = PD_W.interstorm_duration
grid['cell']['PotentialEvapotranspiration'] = PET_[Julian]
grid['cell']['PotentialEvapotranspiration30'] = EP30[Julian]
current_time = SM.update( current_time, P = P[i], Tr = Tr[i], Tb = Tb[i] )
if Julian != 364:
if EP30[Julian + 1,0] > EP30[Julian,0]:
time_check = 0. # Buffer to store current_time at previous storm
i_check = 0 #
yrs = 0
Start_time = time.clock() # Recording time taken for simulation
WS = 0.
Threshold = [data['ETTdwn'], data['ETTup']]
count = 0
## Run Time Loop
for i in range(0, n):
# Update objects
Julian = np.floor((current_time - np.floor(current_time)) * 365.)
if Julian < 182 or Julian > 273: # Dry Season
PD_D.update()
P[i] = PD_D.storm_depth
Tr[i] = PD_D.storm_duration
Tb[i] = PD_D.interstorm_duration
else: # Wet Season - Jul to Sep - NA Monsoon
PD_W.update()
P[i] = PD_W.storm_depth
Tr[i] = PD_W.storm_duration
Tb[i] = PD_W.interstorm_duration
grid['cell']['PotentialEvapotranspiration'] = PET_[Julian]
grid['cell']['PotentialEvapotranspiration30'] = EP30[Julian]
current_time = SM.update(current_time, P=P[i], Tr=Tr[i], Tb=Tb[i])
PETn_[i] = SM._PET
if Julian != 364:
