import matplotlib.pyplot as plt # import the real params # all are in standard units of meters; fluxes in m**3 num_pres_strata = 74 channel_prop_median = 0.18135607321131447 # using extract_ch_proportions.py not_subaerial_prop_median = 0.61619800332778696 font = {'size': 16} sect_comps = np.loadtxt('section_completenesses.txt') SL_trajectory = np.loadtxt('real_SL.txt')[1:] Q_real = np.loadtxt('real_Qs.txt')[1:] # use the 1st step as the initial condition... mydelta = delta() ins = mydelta.read_input_file('real_inputs.txt') nt = int(ins['nt']) completenesses = [] tscales, completenesses = mydelta.execute( 'real_inputs.txt', SL_trajectory, completeness_records=completenesses, graphs=True, Q=Q_real, initial_topo=(1.04, 0.065)) #, save_strat=(20,(3.5,0.4))) final_pres = mydelta.final_preserved completeness_subsampled = [] for i in xrange(10): condition = np.random.rand( final_pres.size) < (float(num_pres_strata - 1) / nt)
import matplotlib.pyplot as plt # import the real params # all are in standard units of meters; fluxes in m**3 num_pres_strata = 74 channel_prop_median = 0.18135607321131447 # using extract_ch_proportions.py not_subaerial_prop_median = 0.61619800332778696 font = {'size':16} sect_comps = np.loadtxt('section_completenesses.txt') SL_trajectory = np.loadtxt('real_SL.txt')[1:] Q_real = np.loadtxt('real_Qs.txt')[1:] # use the 1st step as the initial condition... mydelta = delta() ins = mydelta.read_input_file('real_inputs.txt') nt = int(ins['nt']) completenesses = [] tscales, completenesses = mydelta.execute('real_inputs.txt', SL_trajectory, completeness_records=completenesses, graphs=True, Q=Q_real, initial_topo=(1.04, 0.065)) #, save_strat=(20,(3.5,0.4))) final_pres = mydelta.final_preserved completeness_subsampled = [] for i in xrange(10): condition = np.random.rand(final_pres.size)<(float(num_pres_strata-1)/nt) new_pres_strata = np.logical_and(final_pres, condition) tsc, comp = mydelta.full_completeness(record=new_pres_strata) completeness_subsampled.append(comp.copy()) figure(7)
from delta_obj2 import delta import numpy as np from matplotlib.pyplot import colorbar, figure, show, plot, imshow, legend, title import matplotlib.pyplot as plt num_versions = 10 mydelta = delta() ins = mydelta.read_input_file('sensitivity_inputs.txt') nt = int(ins['nt']) SL_trajectory3 = np.sin(np.arange(nt) / 2.3) SL_trajectory3 *= np.sin(np.arange(nt) / 2.1) SL_trajectory3 *= np.sin(np.arange(nt) / 4.1) * 0.5 SL_trajectory3 *= 2. # SL_trajectory3 += np.arange(nt)*0.01 + 0.7 SL_trajectory3 += 2. SL_miller = (np.loadtxt('Miller_SL_1000y.txt')[:nt])[::-1] + 150. # ^note the reversal of order!!! completenesses_walking = [] completenesses_whole = [] completenesses_restricted = [] completenesses_noE = [] completenesses_compensated = [] # for i in xrange(num_versions): # tscales, completenesses_walking = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_walking, graphs=False, walking_erosion_depo=True) # tscales, completenesses_restricted = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_restricted, graphs=False, restricted_channel_mass_conserved=True) # tscales, completenesses_compensated = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_compensated, graphs=False, compensation=True) tscales, completenesses_whole = mydelta.execute( 'sensitivity_inputs.txt', SL_trajectory=SL_miller,
from delta_obj2 import delta import numpy as np from matplotlib.pyplot import colorbar, figure, show, plot, imshow, legend, title import matplotlib.pyplot as plt num_versions = 10 mydelta = delta() ins = mydelta.read_input_file('sensitivity_inputs.txt') nt = int(ins['nt']) SL_trajectory3 = np.sin(np.arange(nt)/2.3) SL_trajectory3 *= np.sin(np.arange(nt)/2.1) SL_trajectory3 *= np.sin(np.arange(nt)/4.1)*0.5 SL_trajectory3 *= 2. # SL_trajectory3 += np.arange(nt)*0.01 + 0.7 SL_trajectory3 += 2. SL_miller = (np.loadtxt('Miller_SL_1000y.txt')[:nt])[::-1]+150. # ^note the reversal of order!!! completenesses_walking = [] completenesses_whole = [] completenesses_restricted = [] completenesses_noE = [] completenesses_compensated = [] # for i in xrange(num_versions): # tscales, completenesses_walking = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_walking, graphs=False, walking_erosion_depo=True) # tscales, completenesses_restricted = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_restricted, graphs=False, restricted_channel_mass_conserved=True) # tscales, completenesses_compensated = mydelta.execute('synth_inputs.txt', SL_trajectory3, completeness_records=completenesses_compensated, graphs=False, compensation=True) tscales, completenesses_whole = mydelta.execute('sensitivity_inputs.txt', SL_trajectory=SL_miller, completeness_records=completenesses_whole, graphs=True, initial_topo=(3000.,0.)) # tscales, completenesses_noE = mydelta.execute('synth_inputs.txt', SL_trajectory3, graphs=False, completeness_records=completenesses_noE, never_erosion=True) # mean_comp_walking = np.mean(completenesses_walking, axis=0)