def main():
# First we will create an instance of PrecipitationDistribution
PD = PrecipitationDistribution(mean_storm_duration=2.0,
mean_interstorm_duration=50.0,
mean_storm_depth=0.05,
total_t=37000.)
# 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_duration, " hours, while",
"the value from the Poisson distribution is: ", PD.storm_duration)
print("Mean interstorm Duration is: ", PD.mean_interstorm_duration,
'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 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(mean_storm_duration = 2.0,
mean_interstorm_duration = 50.0,
mean_storm_depth = 0.05, total_t = 37000.)
# 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_duration, " hours, while",
"the value from the Poisson distribution is: ", PD.storm_duration)
print("Mean interstorm Duration is: ", PD.mean_interstorm_duration,
'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 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)
import os from matplotlib import pyplot as plt from landlab.components.uniform_precip import PrecipitationDistribution from landlab.components.fire_generator import FireGenerator import numpy as np from math import ceil # Input text file name and location filename = os.path.join(os.path.dirname(__file__), 'fireraininput.txt') # Initializing the PrecipitationDistribution class using the default file # and getting the time series needed for comparison against the fire time series. Rain = PrecipitationDistribution(filename) Rain.get_storm_time_series() storm= Rain.storm_time_series # Initializing the FireGenerator class using the default file and getting the # time series needed for comparison against the precipitation time series. # As an additional step, we should find the scale parameter and set it. # The default value is set to 0. Fire = FireGenerator(filename) Fire.get_scale_parameter() Fire.generate_fire_time_series() fires = Fire.fire_events ## Methods used to find these potentially erosion-inducing events.
import os from matplotlib import pyplot as plt from landlab.components.uniform_precip import PrecipitationDistribution from landlab.components.fire_generator import FireGenerator import numpy as np from math import ceil # Input text file name and location filename = os.path.join(os.path.dirname(__file__), 'fireraininput.txt') # Initializing the PrecipitationDistribution class using the default file # and getting the time series needed for comparison against the fire time series. Rain = PrecipitationDistribution(filename) Rain.get_storm_time_series() storm = Rain.storm_time_series # Initializing the FireGenerator class using the default file and getting the # time series needed for comparison against the precipitation time series. # As an additional step, we should find the scale parameter and set it. # The default value is set to 0. Fire = FireGenerator(filename) Fire.get_scale_parameter() Fire.generate_fire_time_series() fires = Fire.fire_events ## Methods used to find these potentially erosion-inducing events.
