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)
