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
import os from matplotlib import pyplot as plt from landlab.components.uniform_precip.generate_uniform_precip import PrecipitationDistribution from landlab.components.fire_generator.generate_fire 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.generate_uniform_precip import PrecipitationDistribution from landlab.components.fire_generator.generate_fire 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.generate_uniform_precip import PrecipitationDistribution from landlab.components.fire_generator.generate_fire import FireGenerator import numpy as np from math import ceil ## INPUT TXT FILE WITH NECESSARY PARAMETERS ## filename = os.path.join(os.path.dirname(__file__), 'fireraininput.txt') ## INITIALIZING THE CLASSES IN LANDLAB ## Rain = PrecipitationDistribution() Rain.initialize(filename) Rain.get_storm_time_series() ## UNITS IN DAYS storm= Rain.storm_time_series Fire = FireGenerator() Fire.initialize(filename) Fire.get_scale_parameter() Fire.generate_fire_time_series() fires = Fire.fire_events ## FUNCTIONS TO GET POTENTIAL EROSION EVENTS ## set_threshold() ## ## ## GETS THRESHOLD BASED ON ## CANNON ET AL., 2008 RELATIONSHIP ## FOR THE COAL SEAM FIRE, EAST OF
import os from matplotlib import pyplot as plt from landlab.components.uniform_precip.generate_uniform_precip import PrecipitationDistribution from landlab.components.fire_generator.generate_fire import FireGenerator import numpy as np from math import ceil ## INPUT TXT FILE WITH NECESSARY PARAMETERS ## filename = os.path.join(os.path.dirname(__file__), 'fireraininput.txt') ## INITIALIZING THE CLASSES IN LANDLAB ## Rain = PrecipitationDistribution() Rain.initialize(filename) Rain.get_storm_time_series() ## UNITS IN DAYS storm = Rain.storm_time_series Fire = FireGenerator() Fire.initialize(filename) Fire.get_scale_parameter() Fire.generate_fire_time_series() fires = Fire.fire_events ## FUNCTIONS TO GET POTENTIAL EROSION EVENTS ## set_threshold() ## ## ## GETS THRESHOLD BASED ON ## CANNON ET AL., 2008 RELATIONSHIP ## FOR THE COAL SEAM FIRE, EAST OF
