def test_sed_dep():
input_file = os.path.join(_THIS_DIR, "sed_dep_params.txt")
inputs = ModelParameterDictionary(input_file, auto_type=True)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
uplift_rate = inputs.read_float("uplift_rate")
runtime = inputs.read_float("total_time")
dt = inputs.read_float("dt")
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
mg = RasterModelGrid((nrows, ncols), xy_spacing=(dx, dx))
mg.add_zeros("topographic__elevation", at="node")
z = np.loadtxt(os.path.join(_THIS_DIR, "seddepinit.txt"))
mg["node"]["topographic__elevation"] = z
mg.set_closed_boundaries_at_grid_edges(True, False, True, False)
fr = FlowAccumulator(mg, flow_director="D8")
sde = SedDepEroder(mg, **inputs)
for i in range(nt):
mg.at_node["topographic__elevation"][mg.core_nodes] += uplift_per_step
mg = fr.run_one_step()
mg, _ = sde.erode(dt)
z_tg = np.loadtxt(os.path.join(_THIS_DIR, "seddepz_tg.txt"))
assert_array_almost_equal(
mg.at_node["topographic__elevation"][mg.core_nodes], z_tg[mg.core_nodes]
)
def __init__(self, grid, input_stream):
self.grid = grid
inputs = ModelParameterDictionary(input_stream)
# User sets:
self.K = inputs.read_float("K_sp")
self.m = inputs.read_float("m_sp")
try:
self.n = inputs.read_float("n_sp")
except:
self.n = 1.0
try:
self.dt = inputs.read_float("dt")
except: # if dt isn't supplied, it must be set by another module, so look in the grid
print "Set dynamic timestep from the grid. You must call gear_timestep() to set dt each iteration."
else:
try:
self.r_i = inputs.read_float("dt")
except:
self.r_i = 1.0
try:
self.value_field = inputs.read_str("value_field")
except:
self.value_field = "planet_surface__elevation"
# make storage variables
self.A_to_the_m = grid.create_node_array_zeros()
self.alpha = grid.empty(centering="node")
if self.n != 1.0:
raise ValueError("The Braun Willett stream power algorithm requires n==1. at the moment, sorry...")
def __init__(self, grid, input_stream):
#grid here is a true model field. i.e., we should be able to do grid.at_node['topographic__elevation']
#input_stream is a text file, entered in format './my_file.txt'
self.grid = grid
inputs = ModelParameterDictionary(input_stream)
self.n = inputs.read_float('n') # roughness coefficient (Manning's n)
self.g = inputs.read_float('g') # gravitational acceleration (m/s2)
self.alpha = inputs.read_float('alpha') # time-step factor (ND; from Bates et al., 2010)
self.tau_crit = inputs.read_float('tau_crit') # critical shear stress, pascals
self.mpm = inputs.read_float('mpm') # sed trans coefficient
self.erode_start_time = inputs.read_float('erode_start_time') # allows an offset between when flow starts and when we start to allow erosion
#test the necessary fields are all already present:
try:
self.z = grid.at_node['topographic__elevation']
except:
warnings.warn('elevations not found in grid!')
try:
self.h = grid.at_node['planet_surface__water_depth']
except:
warnings.warn('initial water depths not found in grid!')
#build the internally necessary params:
self.rhog = 9810. # water unit weight, kg/m2s2 (N/m3)
self.q = grid.zeros(at='active_link') # unit discharge (m2/s)
self.dhdt = grid.zeros(at='node') # rate of water-depth change
self.tau = grid.zeros(at='active_link') # shear stress (Pa)
self.qs = grid.zeros(at='active_link') # sediment flux (m2/s)
self.dqsds = grid.zeros(at='node')
self.dzdt = self.dhdt
self.dzaccum = grid.zeros(at='node')
self.zm = grid.zeros(at='node')
self.zm[:] = self.z[:]
def test_storms():
input_file_string = os.path.join(_THIS_DIR, 'drive_sp_params_storms.txt')
inputs = ModelParameterDictionary(input_file_string)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
uplift = inputs.read_float('uplift_rate')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node')
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
precip = PrecipitationDistribution(input_file=input_file_string)
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
for (interval_duration, rainfall_rate) in \
precip.yield_storm_interstorm_duration_intensity():
if rainfall_rate != 0.:
mg.at_node['water__unit_flux_in'].fill(rainfall_rate)
mg = fr.route_flow()
sp.run_one_step(dt)
mg.at_node['topographic__elevation'][
mg.core_nodes] += uplift * interval_duration
def __init__(self, grid, input_stream):
self.grid = grid
inputs = ModelParameterDictionary(input_stream)
#User sets:
self.K = inputs.read_float('K_sp')
self.m = inputs.read_float('m_sp')
try:
self.n = inputs.read_float('n_sp')
except:
self.n = 1.
try:
self.dt = inputs.read_float('dt')
except: #if dt isn't supplied, it must be set by another module, so look in the grid
print 'Set dynamic timestep from the grid. You must call gear_timestep() to set dt each iteration.'
else:
try:
self.r_i = inputs.read_float('rainfall_intensity')
except:
self.r_i = 1.
try:
self.value_field = inputs.read_str('value_field')
except:
self.value_field = 'planet_surface__elevation'
#make storage variables
self.A_to_the_m = grid.create_node_array_zeros()
self.alpha = grid.empty(centering='node')
if self.n != 1.:
raise ValueError('The Braun Willett stream power algorithm requires n==1. at the moment, sorry...')
def initialize(self, input_stream):
# Create a ModelParameterDictionary for the inputs
if type(input_stream)==ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Read input/configuration parameters
self.kd = inputs.read_float('DIFMOD_KD')
try:
self.uplift_rate = inputs.read_float('DIFMOD_UPLIFT_RATE')
except:
self.uplift_rate = inputs.read_float('uplift_rate')
try:
self.values_to_diffuse = inputs.read_str('values_to_diffuse')
except:
self.values_to_diffuse = 'planet_surface__elevation'
try:
self.timestep_in = inputs.read_float('dt')
except:
print 'No fixed timestep supplied, it must be set dynamically somewhere else. Be sure to call input_timestep(timestep_in) as part of your run loop.'
# Create grid if one doesn't already exist
if self.grid==None:
self.grid = create_and_initialize_grid(input_stream)
# Set internal time step
# ..todo:
# implement mechanism to compute time-steps dynamically if grid is
# adaptive/changing
dx = self.grid.min_active_link_length() # smallest active link length
self.dt = _ALPHA*dx*dx/self.kd # CFL condition
try:
self.tstep_ratio = self.timestep_in/self.dt
except:
pass
# Get a list of interior cells
self.interior_cells = self.grid.get_core_cell_node_ids()
# Here we're experimenting with different approaches: with
# 'make_all_data', we create and manage all the data we need and embed
# it all in the grid. With 'explicit', we require the caller/user to
# provide data.
if _VERSION=='make_all_data':
#print 'creating internal data'
self.z = self.grid.add_zeros('node', 'landscape_surface__elevation')
self.g = self.grid.add_zeros('active_link', 'landscape_surface__gradient') # surface gradients
self.qs = self.grid.add_zeros('active_link','unit_sediment_flux') # unit sediment flux
self.dqds = self.grid.add_zeros('node', 'sediment_flux_divergence') # sed flux derivative
elif _VERSION=='explicit':
pass
else:
# Create data arrays for variables that won't (?) be shared with other
# components
self.g = self.grid.create_active_link_array_zeros() # surface gradients
self.qs = self.grid.create_active_link_array_zeros() # unit sediment flux
self.dqds = self.grid.create_node_array_zeros() # sed flux derivative
def initialize( self ):
MPD = ModelParameterDictionary()
MPD.read_from_file( _DEFAULT_INPUT_FILE )
# Reading Input Parameters
self._N = MPD.read_float( 'CLOUDINESS' )
self._latitude = MPD.read_float( 'LATITUDE' )
self._A = MPD.read_float( 'ALBEDO' )
def __init__(self, input_file=None, mean_storm=None, mean_interstorm=None, mean_storm_depth=None, total_t=None, delta_t=None):
""" This reads in information from the input_file (either default or user
assigned, and creates an instantaneous storm event drawn from the Poisson distribution
"""
# First we create an instance of the Model Parameter Dictionary
MPD = ModelParameterDictionary()
# If no input_file is given,the default file is used
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
# This reads in the file information
MPD.read_from_file(input_file)
# And now we set our different parameters
# using the model parameter dictionary...
if mean_storm == None:
self.mean_storm = MPD.read_float( 'MEAN_STORM')
else:
self.mean_storm = mean_storm
if mean_interstorm == None:
self.mean_interstorm = MPD.read_float( 'MEAN_INTERSTORM')
else:
self.mean_interstorm =mean_interstorm
if mean_storm_depth== None:
self.mean_storm_depth = MPD.read_float( 'MEAN_DEPTH')
else:
self.mean_storm_depth =mean_storm_depth
if total_t== None:
self.run_time = MPD.read_float( 'RUN_TIME')
else:
self.run_time =total_t
if delta_t== None:
self.delta_t = MPD.read_int( 'DELTA_T')
else:
self.detla_t =delta_t
# Mean_intensity is not set by the MPD, but can be drawn from
# the mean storm depth and mean storm duration.
self.mean_intensity = self.mean_storm_depth / self.mean_storm
# If a time series is created later, this blank list will be used.
self.storm_time_series =[]
# Given the mean values assigned above using either the model
# parameter dictionary or the init function, we can call the
# different methods to assign values from the Poisson distribution.
self.storm_duration = self.get_precipitation_event_duration()
self.interstorm_duration = self.get_interstorm_event_duration()
self.storm_depth = self.get_storm_depth()
self.intensity = self.get_storm_intensity()
def initialize(self, grid=None, input_file=None, intensity=None, stormduration=None):
self.grid = grid
if self.grid==None:
self.grid = create_and_initialize_grid(input_file)
##self.current_time = current_time <- this isn't being used yet.
# Create a ModelParameterDictionary for the inputs
MPD = ModelParameterDictionary()
## I am not sure we want to use an instance of MPD as an input_file. I'm confused about this. SO for
## now I changed this to reflect other component types
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
MPD.read_from_file(input_file)
## This was the old way... where an instance of MPD is an input_file
#if type(input_file)==MPD:
# inputs = input_file
#else:
# inputs = MPD(input_file)
# Read input/configuration parameters
self.m_n = MPD.read_float('MANNINGS_N')
if intensity == None:
self.rainfall_mmhr = MPD.read_float( 'RAINFALL_RATE')
else:
self.rainfall_mmhr = intensity
if stormduration == None:
self.rain_duration = MPD.read_float( 'RAIN_DURATION' )
else:
self.rain_duration = stormduration
#self.h_init = 0.001 # initial thin layer of water (m)
#self.g = 9.8 # gravitational acceleration (m/s2)
#self.alpha = 0.2 # time-step factor (ND; from Bates et al., 2010)
#self.m_n_sq = self.m_n*self.m_n # manning's n squared
#self.rho = 1000 # density of water, kg/m^3
# Derived parameters ## THIS IS IMPORTANT - UNITS!!!! Double check precip component...
self.rainfall_rate = (self.rainfall_mmhr/1000.)/3600. # rainfall in m/s
# Set up state variables
self.hstart = grid.zeros(centering='node') + self.h_init
self.h = grid.zeros(centering='node') + self.h_init # water depth (m)
#NG why is water depth at each node and not at cells, which are only active
self.q = grid.zeros(centering='active_link') # unit discharge (m2/s)
self.dhdt = grid.zeros(centering='active_cell') # rate of water-depth change
def __init__(self, grid, input_stream):
self.grid = grid
inputs = ModelParameterDictionary(input_stream)
#User sets:
try:
self.K = inputs.read_float('K_sp')
except ParameterValueError:
self.use_K = True
else:
self.use_K = False
self.m = inputs.read_float('m_sp')
try:
self.n = inputs.read_float('n_sp')
except:
self.n = 1.
try:
self.dt = inputs.read_float('dt')
except: #if dt isn't supplied, it must be set by another module, so look in the grid
print('Set dynamic timestep from the grid. You must call gear_timestep() to set dt each iteration.')
try:
self.r_i = inputs.read_float('rainfall_intensity')
except:
self.r_i = 1.
try:
self.value_field = inputs.read_str('value_field')
except:
self.value_field = 'topographic__elevation'
#make storage variables
self.A_to_the_m = grid.create_node_array_zeros()
self.alpha = grid.empty(centering='node')
self.alpha_by_flow_link_lengthtothenless1 = numpy.empty_like(self.alpha)
self.grid.node_diagonal_links() #calculates the number of diagonal links
if self.n != 1.:
#raise ValueError('The Braun Willett stream power algorithm requires n==1. at the moment, sorry...')
self.nonlinear_flag = True
if self.n<1.:
print("***WARNING: With n<1 performance of the Fastscape algorithm is slow!***")
else:
self.nonlinear_flag = False
def func_for_newton(x, last_step_elev, receiver_elev, alpha_by_flow_link_lengthtothenless1, n):
y = x - last_step_elev + alpha_by_flow_link_lengthtothenless1*(x-receiver_elev)**n
return y
def func_for_newton_diff(x, last_step_elev, receiver_elev, alpha_by_flow_link_lengthtothenless1, n):
y = 1. + n*alpha_by_flow_link_lengthtothenless1*(x-receiver_elev)**(n-1.)
return y
self.func_for_newton = func_for_newton
self.func_for_newton_diff = func_for_newton_diff
def test_sp_new():
"""
Tests new style component instantiation and run.
"""
input_str = os.path.join(_THIS_DIR, 'drive_sp_params.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
uplift = inputs.read_float('uplift_rate')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid((nrows, ncols), spacing=(dx, dx))
mg.set_closed_boundaries_at_grid_edges(False, False, True, True)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
numpy.random.seed(0)
mg['node']['topographic__elevation'] = z + \
numpy.random.rand(len(z)) / 1000.
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
elapsed_time = 0.
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
fr.route_flow()
sp.run_one_step(dt)
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift * dt
elapsed_time += dt
z_trg = numpy.array([5.48813504e-04, 7.15189366e-04, 6.02763376e-04,
5.44883183e-04, 4.23654799e-04, 6.45894113e-04,
1.02783376e-02, 9.66667235e-03, 6.15060782e-03,
3.83441519e-04, 7.91725038e-04, 1.00905776e-02,
8.98955843e-03, 5.32836181e-03, 7.10360582e-05,
8.71292997e-05, 9.96377080e-03, 9.63738797e-03,
7.31213677e-03, 8.70012148e-04, 9.78618342e-04,
1.02124693e-02, 8.78386002e-03, 4.88161060e-03,
1.18274426e-04, 6.39921021e-04, 1.00377580e-02,
9.54340293e-03, 6.05173814e-03, 4.14661940e-04,
2.64555612e-04, 1.02160196e-02, 8.61600088e-03,
4.77005225e-03, 1.87898004e-05, 6.17635497e-04,
9.30445558e-03, 9.48713993e-03, 7.25689742e-03,
6.81820299e-04, 3.59507901e-04, 5.79161813e-03,
6.83777542e-03, 6.18063842e-03, 6.66766715e-04,
6.70637870e-04, 2.10382561e-04, 1.28926298e-04,
3.15428351e-04, 3.63710771e-04])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_trg)
def test_sp_new():
"""
Tests new style component instantiation and run.
"""
input_str = os.path.join(_THIS_DIR, 'drive_sp_params.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
uplift = inputs.read_float('uplift_rate')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid((nrows, ncols), spacing=(dx, dx))
mg.set_closed_boundaries_at_grid_edges(False, False, True, True)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
numpy.random.seed(0)
mg['node']['topographic__elevation'] = z + \
numpy.random.rand(len(z)) / 1000.
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
elapsed_time = 0.
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
fr.route_flow()
sp.run_one_step(dt)
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift * dt
elapsed_time += dt
z_trg = numpy.array([5.48813504e-04, 7.15189366e-04, 6.02763376e-04,
5.44883183e-04, 4.23654799e-04, 6.45894113e-04,
1.01830760e-02, 9.58036770e-03, 6.55865452e-03,
3.83441519e-04, 7.91725038e-04, 1.00142749e-02,
8.80798884e-03, 5.78387585e-03, 7.10360582e-05,
8.71292997e-05, 9.81911417e-03, 9.52243406e-03,
7.55093226e-03, 8.70012148e-04, 9.78618342e-04,
1.00629755e-02, 8.49253798e-03, 5.33216680e-03,
1.18274426e-04, 6.39921021e-04, 9.88956320e-03,
9.47119567e-03, 6.43790696e-03, 4.14661940e-04,
2.64555612e-04, 1.00450743e-02, 8.37262908e-03,
5.21540904e-03, 1.87898004e-05, 6.17635497e-04,
9.21286940e-03, 9.34022513e-03, 7.51114450e-03,
6.81820299e-04, 3.59507901e-04, 6.19166921e-03,
7.10456176e-03, 6.62585507e-03, 6.66766715e-04,
6.70637870e-04, 2.10382561e-04, 1.28926298e-04,
3.15428351e-04, 3.63710771e-04])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_trg)
def initialize(self):
""" Reading data from input file """
MPD = ModelParameterDictionary()
MPD.read_from_file(_DEFAULT_INPUT_FILE)
""" Priestly Taylor Constant """
self._alpha = MPD.read_float("alpha")
""" Surface Albedo """
self._a = MPD.read_float("a")
""" Latent Heat of Vaporization """
self._pwhv = MPD.read_float("pwhv")
""" Psychometric Constant """
self._y = MPD.read_float("y")
""" Stefan Boltzmann Constant """
self._sigma = MPD.read_float("sigma")
""" Solar Constant """
self._Gsc = MPD.read_float("Gsc")
""" Latitude of Location under Study """
self._lat = MPD.read_float("lat")
""" Elevation of Location under Study """
self._z = MPD.read_float("z")
""" Adjustment Coefficient """
self._Krs = MPD.read_float("Krs")
""" Initialize Arrays """
""" Net Short Wave Radiation """
self._Rns = 0
""" Net Radiation """
self._Rn = 0
""" Clear Sky Solar Radiation """
self._Rso = 0
""" Short Wave Radiation """
self._Rs = 0
""" Relative cloudiness factor """
self._u = 0
""" Priestly Taylor evapotranspiration """
self._ETp = 0
""" Extraterrestrial Radiation """
self._Ra = 0
""" Julian Day """
self._J = 0
""" Sunset hour angle """
self._ws = 0
self._x = 0
""" Solar declination angle """
self._sdecl = 0
""" Inverse relative distance factor """
self._dr = 0
""" Cloudiness function """
self._fcd = 0
""" Latitude in radians """
self._phi = (3.14 / 180) * self._lat
def __init__(self, input_file=None):
"""All initial values are set to zero until initalized
using the initialize() method which reads in data using
the ModelParameterDictionary and sets the random variables
according to their respective distribution.
REQUIRED PARAMETERS
------------------
Shape Parameter: Describes the skew of the Weibull distribution.
If shape < 3.5, data skews left.
If shape == 3.5, data is normal.
If shape > 3.5, data skews right.
Scale Parameter: Describes the peak of the Weibull distribution,
located at 63.5% value of the cumulative distribution function. If unknown,
it can be found using mean fire recurrence value and the get_scale_parameter()
method described later.
Mean Fire Recurrence : Average recurrence for a given area, elevation, veg type, etc.
Total Run Time : Total model run time
Delta T : Model time step.
-------
Time to Next Fire: Value generated from the random.weibullvariate() function based
on the scale and shape parameters
"""
# We import methods from ModelParameterDictionary
# to read the parameters from the input file.
#If the scale parameter is unknown, it can be found using the mean
# fire recurrence value, which MUST be known or estimated to run the
# get_scale_parameter() method."""
MPD = ModelParameterDictionary()
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
MPD.read_from_file(input_file)
self.shape_parameter = MPD.read_float("SHAPE_PARAMETER")
self.scale_parameter = MPD.read_float("SCALE_PARAMETER")
self.mean_fire_recurrence = MPD.read_float("MEAN_FIRE_RECURRENCE")
self.total_run_time = MPD.read_float("RUN_TIME")
self.delta_t = MPD.read_int("DELTA_T")
self.time_to_next_fire = 0.0
def __init__(self, grid, input_file=None, rain_duration=None, rainfall_intensity=None, model_run_time=None):
self.h_init = 0.001
self.g = 9.8
self.alpha = 0.2
self.rho = 1000
self.ten_thirds = 10./3.
# Create a ModelParameterDictionary instance for the inputs
MPD = ModelParameterDictionary()
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
MPD.read_from_file(input_file)
self.current_time = 0.0
self.m_n = MPD.read_float('MANNINGS_N')
self.m_n_sq = self.m_n*self.m_n
if rainfall_intensity == None:
self.rainfall_mmhr = MPD.read_float( 'RAINFALL_RATE')
if rain_duration == None:
self.rain_duration = MPD.read_float( 'RAIN_DURATION' )
if model_run_time == None:
self.total_time = self.rain_duration
# Conversion to m/s from mm/hr
self.rainfall_rate = (self.rainfall_mmhr/1000.)/3600.
# Set up grid arrays for state variables
# Water Depth
self.hstart = grid.zeros(centering='node') + self.h_init
self.h = grid.zeros(centering='node') + self.h_init
self.dhdt = grid.zeros(centering='cell')
# Discharge
self.q = grid.zeros(centering='active_link') # unit discharge (m2/s)
# Tau
self.tau = grid.zeros(centering='node') # shear stress (Pascals)
def test_sp_discharges_new():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_discharge_new.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
# perform the loop (once!)
for i in range(1):
fr.route_flow()
sp.run_one_step(dt)
z_tg = np.array([5. , 5. , 0. , 5. ,
5. , 5. , 1.47759225, 0.43050087,
1.47759225, 5. , 5. , 2.32883687,
1.21525044, 2.32883687, 5. , 5. ,
3.27261262, 3.07175015, 3.27261262, 5. ,
5. , 5. , 5. , 5. ,
5. ])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_tl_fluvial():
input_file = os.path.join(_THIS_DIR, 'stream_power_params_ideal.txt')
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('node', 'topographic__elevation')
z = np.loadtxt(os.path.join(_THIS_DIR, 'tl_init.txt'))
mg['node']['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(True, False, True, False)
mg.set_fixed_value_boundaries_at_grid_edges(
False, True, False, True, value_of='topographic__elevation')
fr = FlowRouter(mg)
tl = TransportLimitedEroder(mg, input_file)
for i in range(nt):
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_per_step
mg = fr.route_flow()
mg, _ = tl.erode(mg, dt, stability_condition='loose')
z_tg = np.loadtxt(os.path.join(_THIS_DIR, 'tlz_tg.txt'))
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def initialize(self, input_file=None):
MPD = ModelParameterDictionary()
"""We imported methods from ModelParameterDictionary
to read the parameters from the input file.
Necessary parameters to run this code include:
mean_storm (type: float)
mean_intensity (type: float)
mean_interstorm (type: float)
mean_storm_depth is calculated using mean storm and intensity data
The random variables of storm_duration, interstorm_duration, storm_depth
and intensity are found using methods declared later in the class
"""
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
MPD.read_from_file(input_file)
self.mean_storm = MPD.read_float( "MEAN_STORM" )
self.mean_interstorm = MPD.read_float( "MEAN_INTERSTORM" )
self.mean_storm_depth = MPD.read_float( "MEAN_DEPTH" )
self.mean_intensity = self.mean_storm_depth / self.mean_storm
self.storm_duration = self.get_precipitation_event_duration()
self.interstorm_duration = self.get_interstorm_event_duration()
self.storm_depth = self.get_storm_depth()
self.intensity = self.get_storm_intensity()
self.run_time = MPD.read_float("RUN_TIME")
try: #DEJH thinks this is redundant
self.delta_t = MPD.read_int("DELTA_T")
except:
pass
def initialize( self ):
MPD = ModelParameterDictionary()
MPD.read_from_file(_DEFAULT_INPUT_FILE)
self._vegcover = MPD.read_float( 'VEG_COV' )
self._WUE = MPD.read_float( 'WUE' )
self._LAI_max = MPD.read_float( 'LAI_MAX' )
self._cb = MPD.read_float( 'CB' )
self._cd = MPD.read_float( 'CD' )
self._ksg = MPD.read_float( 'KSG' )
self._kdd = MPD.read_float( 'KDD' )
self._Blive_ini = MPD.read_float( 'BLIVE_INI' )
self._Bdead_ini = MPD.read_float( 'BDEAD_INI' )
def test_sp_widths():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_widths.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
widths = np.ones(mg.number_of_nodes, dtype=float)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, use_W=widths, **inputs)
# perform the loop (once!)
for i in range(1):
fr.route_flow()
sqrt_A = mg.at_node['drainage_area']**0.5
widths[mg.core_nodes] = sqrt_A[mg.core_nodes]/sqrt_A[
mg.core_nodes].mean()
# so widths has mean=1.
# note the issue with drainage_area not defined at perimeter => nans
# if not careful...
sp.run_one_step(dt)
z_tg = np.array([ 5. , 5. , 0. , 5. ,
5. , 5. , 1.37222369, 0.36876358,
1.37222369, 5. , 5. , 2.17408606,
1.07986038, 2.17408606, 5. , 5. ,
3.08340277, 2.85288049, 3.08340277, 5. ,
5. , 5. , 5. , 5. ,
5. ])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_storms():
input_file_string = os.path.join(_THIS_DIR, "drive_sp_params_storms.txt")
inputs = ModelParameterDictionary(input_file_string, auto_type=True)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
uplift = inputs.read_float("uplift_rate")
mean_duration = inputs.read_float("mean_storm")
mean_interstorm = inputs.read_float("mean_interstorm")
mean_depth = inputs.read_float("mean_depth")
storm_run_time = inputs.read_float("storm_run_time")
delta_t = inputs.read_float("delta_t")
mg = RasterModelGrid(nrows, ncols, xy_spacing=dx)
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node")
mg["node"]["topographic__elevation"] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros("water__unit_flux_in", at="node")
precip = PrecipitationDistribution(
mean_storm_duration=mean_duration,
mean_interstorm_duration=mean_interstorm,
mean_storm_depth=mean_depth,
total_t=storm_run_time,
delta_t=delta_t,
)
fr = FlowAccumulator(mg, flow_director="D8")
sp = StreamPowerEroder(mg, **inputs)
for (
interval_duration,
rainfall_rate,
) in precip.yield_storm_interstorm_duration_intensity():
if rainfall_rate != 0.:
mg.at_node["water__unit_flux_in"].fill(rainfall_rate)
fr.run_one_step()
sp.run_one_step(dt)
mg.at_node["topographic__elevation"][mg.core_nodes] += (
uplift * interval_duration
)
def test_storms():
input_file_string = os.path.join(_THIS_DIR, 'drive_sp_params_storms.txt')
inputs = ModelParameterDictionary(input_file_string, auto_type=True)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
uplift = inputs.read_float('uplift_rate')
mean_duration = inputs.read_float('mean_storm')
mean_interstorm = inputs.read_float('mean_interstorm')
mean_depth = inputs.read_float('mean_depth')
storm_run_time = inputs.read_float('storm_run_time')
delta_t = inputs.read_float('delta_t')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node')
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
precip = PrecipitationDistribution(mean_storm_duration = mean_duration,
mean_interstorm_duration = mean_interstorm,
mean_storm_depth = mean_depth,
total_t = storm_run_time, delta_t = delta_t)
fr = FlowAccumulator(mg, flow_director='D8')
sp = StreamPowerEroder(mg, **inputs)
for (interval_duration, rainfall_rate) in \
precip.yield_storm_interstorm_duration_intensity():
if rainfall_rate != 0.:
mg.at_node['water__unit_flux_in'].fill(rainfall_rate)
fr.run_one_step()
sp.run_one_step(dt)
mg.at_node['topographic__elevation'][
mg.core_nodes] += uplift * interval_duration
def test_sp_discharges():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_discharge.txt')
inputs = ModelParameterDictionary(input_str)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, input_str)
# perform the loop (once!)
for i in range(1):
fr.route_flow(method='D8')
my_Q = mg.at_node['water__volume_flux'] * 1.
sp.erode(mg, dt, node_drainage_areas='drainage_area',
slopes_at_nodes='topographic__steepest_slope',
Q_if_used=my_Q)
z_tg = np.array([5.00000000e+00, 5.00000000e+00, 0.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
1.29289322e+00, 1.00000000e-06, 1.29289322e+00,
5.00000000e+00, 5.00000000e+00, 2.29289322e+00,
1.00000000e+00, 2.29289322e+00, 5.00000000e+00,
5.00000000e+00, 3.29289322e+00, 3.00000000e+00,
3.29289322e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_sp_discharges_new():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_discharge_new.txt')
inputs = ModelParameterDictionary(input_str)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
# perform the loop (once!)
for i in range(1):
fr.route_flow()
sp.run_one_step(dt)
z_tg = np.array([5.00000000e+00, 5.00000000e+00, 0.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
1.29289322e+00, 1.00000000e-06, 1.29289322e+00,
5.00000000e+00, 5.00000000e+00, 2.29289322e+00,
1.00000000e+00, 2.29289322e+00, 5.00000000e+00,
5.00000000e+00, 3.29289322e+00, 3.00000000e+00,
3.29289322e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
5.00000000e+00])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_sp_discharges_old():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_discharge.txt')
inputs = ModelParameterDictionary(input_str)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowAccumulator(mg, flow_director='D8')
my_Q = mg.at_node['surface_water__discharge']
sp = StreamPowerEroder(mg, input_str, use_Q=my_Q)
# perform the loop (once!)
for i in range(1):
fr.run_one_step()
my_Q[:] = mg.at_node['surface_water__discharge'] * 1.
sp.run_one_step(dt)
z_tg = np.array([5. , 5. , 0. , 5. ,
5. , 5. , 1.47759225, 0.43050087,
1.47759225, 5. , 5. , 2.32883687,
1.21525044, 2.32883687, 5. , 5. ,
3.27261262, 3.07175015, 3.27261262, 5. ,
5. , 5. , 5. , 5. ,
5. ])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_sed_dep():
input_file = os.path.join(_THIS_DIR, 'sed_dep_params.txt')
inputs = ModelParameterDictionary(input_file, auto_type=True)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
mg = RasterModelGrid((nrows, ncols), (dx, dx))
mg.add_zeros('topographic__elevation', at='node')
z = np.loadtxt(os.path.join(_THIS_DIR, 'seddepinit.txt'))
mg['node']['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(True, False, True, False)
fr = FlowAccumulator(mg, flow_director='D8')
sde = SedDepEroder(mg, **inputs)
for i in range(nt):
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_per_step
mg = fr.run_one_step()
mg, _ = sde.erode(dt)
z_tg = np.loadtxt(os.path.join(_THIS_DIR, 'seddepz_tg.txt'))
assert_array_almost_equal(mg.at_node['topographic__elevation'][
mg.core_nodes], z_tg[mg.core_nodes])
def test_sp_widths():
input_str = os.path.join(_THIS_DIR, "test_sp_params_widths.txt")
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = 5
ncols = 5
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
mg = RasterModelGrid(nrows, ncols, xy_spacing=dx)
widths = np.ones(mg.number_of_nodes, dtype=float)
mg.add_zeros("topographic__elevation", at="node")
z = np.array(
[
5.0,
5.0,
0.0,
5.0,
5.0,
5.0,
2.0,
1.0,
2.0,
5.0,
5.0,
3.0,
2.0,
3.0,
5.0,
5.0,
4.0,
4.0,
4.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
]
)
mg["node"]["topographic__elevation"] = z
fr = FlowAccumulator(mg, flow_director="D8")
sp = StreamPowerEroder(mg, use_W=widths, **inputs)
# perform the loop (once!)
for i in range(1):
fr.run_one_step()
sqrt_A = mg.at_node["drainage_area"] ** 0.5
widths[mg.core_nodes] = sqrt_A[mg.core_nodes] / sqrt_A[mg.core_nodes].mean()
# so widths has mean=1.
# note the issue with drainage_area not defined at perimeter => nans
# if not careful...
sp.run_one_step(dt)
z_tg = np.array(
[
5.0,
5.0,
0.0,
5.0,
5.0,
5.0,
1.37222369,
0.36876358,
1.37222369,
5.0,
5.0,
2.17408606,
1.07986038,
2.17408606,
5.0,
5.0,
3.08340277,
2.85288049,
3.08340277,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
]
)
assert_array_almost_equal(mg.at_node["topographic__elevation"], z_tg)
from landlab import RasterModelGrid
from landlab import ModelParameterDictionary
from landlab.utils import structured_grid as sgrid
from landlab.components.sed_trp_shallow_flow.transport_sed_in_shallow_flow import SurfaceFlowTransport
import time
import pylab
import numpy as np
#get the needed properties to build the grid:
inputs = ModelParameterDictionary('./stof_params_basin.txt')
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
h_init = inputs.read_float('h_init')
z_boundary = inputs.read_float('z_boundary') #now the outlet height
drop_ht = inputs.read_float('drop_ht') #Now the inlet gradient
initial_slope = inputs.read_float('initial_slope')
time_to_run = inputs.read_int('run_time')
inlet_nodes = [0, 1, 2, ncols, 2 * ncols]
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_inactive_boundaries(False, True, False, True)
#mg.node_status[inlet_nodes] = 1
#mg.node_status[-5] = 1 #Fixed lip outlet
#print sgrid.node_tolink_index(mg.shape)[1]
#mg.reset_list_of_active_links()
#node_distances_to_inlet = mg.get_distances_of_nodes_to_point((mg.node_x[0], mg.node_y[0]))
#z0 = initial_slope*(np.amax(node_distances_to_inlet) - node_distances_to_inlet)
def initialize(self,
grid=None,
input_file=None,
intensity=None,
stormduration=None):
""" Initialize the model and grid using either an input file
or default values.
"""
self.grid = grid
if self.grid == None:
self.grid = create_and_initialize_grid(
input_file
) ##<- this is the same input file used for parameters.
## this seems wrong to me...
##self.current_time = current_time <- this isn't being used yet.
# Create a ModelParameterDictionary for the inputs
MPD = ModelParameterDictionary()
## I am not sure we want to use an instance of MPD as an input_file. I'm confused about this. SO for
## now I changed this to reflect other component types
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
MPD.read_from_file(input_file)
## This was the old way... where an instance of MPD is an input_file
#if type(input_file)==MPD:
# inputs = input_file
#else:
# inputs = MPD(input_file)
# Read input/configuration parameters
self.m_n = MPD.read_float('MANNINGS_N')
if intensity == None:
self.rainfall_mmhr = MPD.read_float('RAINFALL_RATE')
else:
self.rainfall_mmhr = intensity
if stormduration == None:
self.rain_duration = MPD.read_float('RAIN_DURATION')
else:
self.rain_duration = stormduration
#self.h_init = 0.001 # initial thin layer of water (m)
#self.g = 9.8 # gravitational acceleration (m/s2)
#self.alpha = 0.2 # time-step factor (ND; from Bates et al., 2010)
#self.m_n_sq = self.m_n*self.m_n # manning's n squared
#self.rho = 1000 # density of water, kg/m^3
# Derived parameters ## THIS IS IMPORTANT - UNITS!!!! Double check precip component...
## IF WE CLEARLY STATE UNIT REQUIREMENTS IN DOCS, THIS SHOULDN'T BE A PROBLEM
## BUT IT MUST BE CLEAR THAT ALL INPUT FILES HAVE STANDARD UNITS THROUGHOUT.
self.rainfall_rate = (self.rainfall_mmhr /
1000.) / 3600. # rainfall in m/s
# Set up state variables
self.hstart = grid.zeros(centering='node') + self.h_init
self.h = grid.zeros(centering='node') + self.h_init # water depth (m)
#NG why is water depth at each node and not at cells, which are only active
self.q = grid.zeros(centering='active_link') # unit discharge (m2/s)
self.dhdt = grid.zeros(centering='cell') # rate of water-depth change
self.tau = grid.zeros(centering='node') # shear stress (Pascals)
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', or a string giving the name of the model field
where capacity values are stored on nodes.
At the moment, only 'MPM' is permitted as a way to set the
capacity automatically, but expansion would be trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width and
basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
The equation for depth used to derive shear stress and hence
carrying capacity contains a prefactor:
mannings_n*(k_Q**(1-b)/K_w)**0.6
(so shear = fluid_density*g*depth_equation_prefactor*A**(0.6*c*(1-b)*S**0.7 !)
Don't know what to set these values to? k_w=0.002, k_Q=1.e-9,
mannings_n=0.03 give vaguely plausible numbers (e.g., for a
drainage area ~400km2, like Boulder Creek at Boulder,
=> depth~2.5m, width~35m, shear stress ~O(1000Pa)).
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each node,
don't set, and use the Dchar_if_used argument in erode()
instead.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set True,
becomes a weak function of local slope following Lamb et al
(2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation. An exception will be raised if threshold_shields is
also set.
*Parker_epsilon -> float, defaults to 0.4. This is Parker's (1978)
epsilon, which is used in the relation
tau - tauc = tau * (epsilon/(epsilon+1))
The 0.4 default is appropriate for coarse (gravelly) channels.
The value approaches infinity as the river banks become more
cohesive.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False.
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge'].
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
*return_capacity -> bool (default False). NOT YET IMPLEMENTED.
If True, this component
will save the calculated capacity in the field
'fluvial_sediment_transport_capacity'. (Requires some additional
math, so is suppressed for speed by default).
'''
self.grid = grid
# needs to be filled with values in execution
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links + 1)
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.rho_g = self.fluid_density * self.g
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc == 'MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float('threshold_shields')
# flag for sed_flux_dep_incision to see if the threshold was
# manually set.
self.set_threshold = True
# print("Found a threshold to use: ", self.shields_crit)
assert self.lamb_flag == False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self.return_capacity = inputs.read_bool('return_capacity')
except MissingKeyError:
self.return_capacity = False
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
if self.calc_cap_flag:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
if self.calc_cap_flag:
raise NameError('c was not set')
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
pass
# assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6 * self._c * (1. - self._b)
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
if mannings_n < 0. or mannings_n > 0.2:
warnings.warn("Manning's n outside it's typical range")
self.depth_prefactor = self.rho_g * mannings_n * \
(self.k_Q**(1. - self._b) / self.k_w)**0.6
# Note the depth_prefactor we store already holds rho*g
try:
epsilon = inputs.read_float('Parker_epsilon')
except MissingKeyError:
epsilon = 0.4
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
try:
self.shields_prefactor = 1. / \
((self.sed_density - self.fluid_density) * self.g * self.Dchar_in)
self.MPM_prefactor = 8. * self.C_MPM * \
np.sqrt(self.relative_weight * self.Dchar_in *
self.Dchar_in * self.Dchar_in)
self.MPM_prefactor_alt = 4. * \
self.g**(-2. / 3.) / self.excess_SG / \
self.fluid_density / self.sed_density
except AttributeError:
# have to set these manually as needed
self.shields_prefactor_noD = 1. / \
((self.sed_density - self.fluid_density) * self.g)
self.diffusivity_prefactor = 8. * np.sqrt(
8. * self.g) / (self.sed_density / self.fluid_density - 1.) * (
epsilon / (epsilon + 1.)
)**1.5 * mannings_n**(5. / 6.) * self.k_w**-0.9 * self.k_Q**(
0.9 *
(1. - self._b)) # ...this is multiplied by A**c(1-0.1*(1-b))
# we consciously skip out a factor of S**0.05-->1. in the diffusion prefactor, to avoid delinearizing the diffusion. Only a possible problem at tiny S (20% error @S==0.01; 37% error @S==10**-4)
# we could include this as a static adjustment in the actual looping code (i.e., just multiply by S**0.05, and don't work with it as part of the problem)
# in reality, Manning's n changes downstream too, so... whatever
self.diffusivity_power_on_A = 0.9 * self._c * \
(1. - self._b) # i.e., q/D**(1/6)
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.area_of_cell))
self.cell_areas[grid.node_at_cell] = grid.area_of_cell
self.dx2 = grid.dx**2
self.dy2 = grid.dy**2
self.bad_neighbor_mask = np.equal(
grid.get_active_neighbors_at_node(bad_index=-1), -1)
"""
from __future__ import print_function
import numpy as np
import pylab
from landlab import ModelParameterDictionary, RasterModelGrid
from landlab.components.flow_routing import FlowAccumulator
from landlab.components.gFlex.flexure import gFlex
from landlab.components.stream_power import FastscapeEroder, StreamPowerEroder
from landlab.plot.imshow import imshow_node_grid
inputs = ModelParameterDictionary("./coupled_SP_gflex_params.txt")
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
time_to_run = inputs.read_float("run_time")
init_elev = inputs.read_float("init_elev")
uplift_perstep = inputs.read_float("uplift_rate") * dt
rock_stress_param = inputs.read_float("rock_density") * 9.81
mg = RasterModelGrid(nrows, ncols, xy_spacing=dx)
# create the fields in the grid
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
mg["node"]["topographic__elevation"] = z + np.random.rand(len(z)) / 1000.
# make some surface load stresses in a field to test
mg.at_node["surface_load__stress"] = np.zeros(nrows * ncols, dtype=float)
from landlab.plot import channel_profile as prf
from landlab import RasterModelGrid
import numpy as np
import pylab
from time import time
import random
#get the needed properties to build the grid:
input_file = './drive_sp_params.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('run_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
#instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
boundary_node_list = mg.get_boundary_nodes()
#set up its boundary conditions (bottom, right, top, left is inactive)
mg.set_inactive_boundaries(True, True, True, True)
mg.set_closed_boundaries_at_grid_edges(False, False, False, False)
mg.set_fixed_value_boundaries(20)
def test_diffusion():
infile = os.path.join(_THIS_DIR, 'diffusion_params.txt')
inputs = ModelParameterDictionary(infile, auto_type=True)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid((nrows, ncols), (dx, dx))
uplift_rate = mg.node_y[mg.core_cells] / 100000.
# create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
np.random.seed(0)
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.set_fixed_value_boundaries_at_grid_edges(True, True, True, True)
# instantiate:
dfn = LinearDiffuser(mg, **inputs)
# perform the loop:
elapsed_time = 0. # total time in simulation
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
dfn.run_one_step(dt)
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_rate * dt
elapsed_time += dt
z_target = np.array([
5.48813504e-04, 7.15189366e-04, 6.02763376e-04, 5.44883183e-04,
4.23654799e-04, 6.45894113e-04, 4.37587211e-04, 8.91773001e-04,
9.63662761e-04, 3.83441519e-04, 7.91725038e-04, 9.18166135e-04,
1.02015039e-03, 1.10666198e-03, 1.14866514e-03, 1.20224288e-03,
1.12953135e-03, 1.12966219e-03, 1.00745155e-03, 8.70012148e-04,
9.78618342e-04, 1.12628772e-03, 1.41663596e-03, 2.66338249e-03,
2.80420703e-03, 2.82445061e-03, 2.69263914e-03, 2.44620140e-03,
2.04237613e-03, 4.14661940e-04, 2.64555612e-04, 2.15073330e-03,
2.77965579e-03, 3.22134736e-03, 3.45859244e-03, 4.47224671e-03,
4.25371135e-03, 3.82941648e-03, 3.25127747e-03, 6.81820299e-04,
3.59507901e-04, 3.36577718e-03, 4.20490812e-03, 4.81467159e-03,
5.14099588e-03, 5.15029835e-03, 4.83533539e-03, 5.22312276e-03,
4.37284689e-03, 3.63710771e-04, 5.70196770e-04, 4.65122535e-03,
5.67854747e-03, 6.44757828e-03, 6.85985389e-03, 6.86464781e-03,
6.45159799e-03, 5.65255723e-03, 4.54258827e-03, 2.44425592e-04,
1.58969584e-04, 5.85971567e-03, 7.16648352e-03, 8.10954246e-03,
8.61082386e-03, 8.61350727e-03, 8.10597021e-03, 7.12594182e-03,
5.75483957e-03, 9.60984079e-05, 9.76459465e-04, 6.29476234e-03,
7.70594852e-03, 9.79504842e-03, 1.03829367e-02, 1.03869062e-02,
9.79374998e-03, 8.65447904e-03, 7.07179252e-03, 1.18727719e-04,
3.17983179e-04, 7.43078552e-03, 9.18353155e-03, 1.04682910e-02,
1.11542648e-02, 1.21643980e-02, 1.14930584e-02, 1.02184219e-02,
8.53727126e-03, 9.29296198e-04, 3.18568952e-04, 8.68034110e-03,
1.06702554e-02, 1.21275181e-02, 1.29049224e-02, 1.29184938e-02,
1.21616788e-02, 1.17059081e-02, 9.66728348e-03, 4.69547619e-06,
6.77816537e-04, 1.00128306e-02, 1.21521279e-02, 1.37494046e-02,
1.46053573e-02, 1.46205669e-02, 1.37908840e-02, 1.22146332e-02,
1.01165765e-02, 9.52749012e-04, 4.47125379e-04, 1.12069867e-02,
1.35547122e-02, 1.52840440e-02, 1.62069802e-02, 1.62196380e-02,
1.53169489e-02, 1.35997836e-02, 1.12818577e-02, 6.92531590e-04,
7.25254280e-04, 1.14310516e-02, 1.38647655e-02, 1.66771925e-02,
1.76447108e-02, 1.76515649e-02, 1.66885162e-02, 1.48507549e-02,
1.23206170e-02, 2.90077607e-04, 6.18015429e-04, 1.24952067e-02,
1.49924260e-02, 1.68435913e-02, 1.78291009e-02, 1.88311310e-02,
1.78422046e-02, 1.59665841e-02, 1.34122052e-02, 4.31418435e-04,
8.96546596e-04, 1.34612553e-02, 1.58763600e-02, 1.76887976e-02,
1.86526609e-02, 1.86492669e-02, 1.76752679e-02, 1.68480793e-02,
1.44368883e-02, 9.98847007e-04, 1.49448305e-04, 1.40672989e-02,
1.64140227e-02, 1.81162514e-02, 1.90091351e-02, 1.89959971e-02,
1.80757625e-02, 1.63425116e-02, 1.39643530e-02, 6.91669955e-05,
6.97428773e-04, 1.47340964e-02, 1.66453353e-02, 1.80835612e-02,
1.88335770e-02, 1.88048458e-02, 1.80022362e-02, 1.65110248e-02,
1.44854151e-02, 1.71629677e-04, 5.21036606e-04, 1.40633664e-02,
1.54867652e-02, 1.75865008e-02, 1.81309867e-02, 1.80760242e-02,
1.74501109e-02, 1.63343931e-02, 1.48208186e-02, 3.18389295e-05,
1.64694156e-04, 1.41550038e-02, 1.49870334e-02, 1.57563641e-02,
1.60213295e-02, 1.69074625e-02, 1.64888825e-02, 1.58787450e-02,
1.50671910e-02, 3.11944995e-04, 3.98221062e-04, 2.09843749e-04,
1.86193006e-04, 9.44372390e-04, 7.39550795e-04, 4.90458809e-04,
2.27414628e-04, 2.54356482e-04, 5.80291603e-05, 4.34416626e-04
])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_target)
def initialize(self, grid, params_file):
"""
This module implements sediment flux dependent channel incision following:
E = f(Qs, Qc) * stream_power - sp_crit,
where stream_power is the stream power (often ==K*A**m*S**n) provided by the
stream_power.py component. Note that under this incision paradigm, sp_crit
is assumed to be controlled exclusively by sediment mobility, i.e., it is
not a function of bedrock resistance. If you want it to represent a bedrock
resistance term, be sure to set Dchar if you use the MPM transport capacity
relation, and do not use the flag 'slope_sensitive_threshold'.
The component currently assumes that the threshold on bed incision is
controlled by the threshold of motion of its sediment cover. This means
there is assumed interplay between the supplied Shields number,
characteristic grain size, and shear stress threshold.
This calculation has a tendency to be slow, and can easily result in
numerical instabilities. These instabilities are suppressed by retaining a
memory of what the sediment flux was in the last time step, and weighting
the next timestep by that value. XXXmore detail needed. Possibilities:
1. weight by last timestep/2timesteps (what about early ones?)
2. do it iteratively; only do incision one the sed flux you are using
stabilises (so the previous iter "seed" becomes less important)
Parameters needed in the initialization file follow those for
stream_power.py. However, we now require additional input terms for the
f(Qs,Qc) term:
REQUIRED:
*Set the stream power terms using a, b, and c NOT m, n.
*...remember, any threshold set is set as tau**a, not just tau.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*sed_dependency_type -> 'None', 'linear_decline', 'parabolic',
'almost_parabolic', 'generalized_humped'. For definitions, see Gasparini
et al., 2006; Hobley et al., 2011.
*Qc -> This input controls the sediment capacity used by the component.
It can calculate sediment carrying capacity for itself if this
parameter is a string 'MPM', which will cause the component to use a
slightly modified version of the Meyer-Peter Muller equation (again, see
Hobley et al., 2011). Alternatively, it can be another string denoting
the grid field name in which a precalculated capacity is stored.
Depending on which options are specified above, further parameters may be
required:
*If sed_dependency_type=='generalized_humped', need the shape parameters
used by Hobley et al:
kappa_hump
nu_hump
phi_hump
c_hump
Note the onus is on the user to ensure that these parameters result
in a viable shape, i.e., one where the maximum is 1 and there is
indeed a hump in the profile. If these parameters are NOT specified,
they will default to the form of the curve for Leh valley as found
in Hobley et al 2011: nu=1.13; phi=4.24; c=0.00181; kappa=13.683.
*If Qc=='MPM', these parameters may optionally be provided:
Dchar -> characteristic grain size (i.e., D50) on the bed, in m.
C_MPM -> the prefactor in the MPM relation. Defaults to 1, as in the
relation sensu stricto, but can be modified to "tune" the
equations to a known point where sediment deposition begins.
In cases where k_Q and k_w are not known from real data, it
is recommended these parameters be tuned in preference to C.
*...if Dchar is NOT provided, the component will attempt to set (and will
report) an appropriate characteristic grain size, such that it is
consistent both with the threshold provided *and* a critical Shields
number of 0.05. (If you really, really want to, you can override this
critical Shields number too; use parameter *threshold_Shields*).
OPTIONAL:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shear_stress -> a float.
If not provided, may be overridden by the following parameters.
If it is not, defaults to 0.
*slope_sensitive_threshold -> a boolean, defaults to FALSE.
In steep mountain environments, the critical Shields number for particle
motion appears to be weakly sensitive to the local slope, as
taustar_c=0.15*S**0.25 (Lamb et al, 2008). If this flag is set to TRUE,
the critical threshold in the landscape is allowed to become slope
sensitive as well, in order to be consistent with this equation.
This modification was used by Hobley et al., 2011.
*set_threshold_from_Dchar -> a boolean, defaults to FALSE.
Use this flag to force an appropriate threshold value from a
provided Dchar. i.e., this is the inverse of the procedure that is
used to find Dchar if it isn't provided. No threshold can be
specified in the parameter file, and Dchar must be specified.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
"""
#this is the fraction we allow any given slope in the grid to evolve by in one go (suppresses numerical instabilities)
self.fraction_gradient_change = 0.25
self.pseudoimplicit_repeats = 5
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(
grid.number_of_links +
1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.thresh = inputs.read_float('threshold_shear_stress')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a shear stress threshold to use: ", self.thresh
except MissingKeyError:
print "Found no incision threshold to use."
self.thresh = 0.
self.set_threshold = False
try:
self._a = inputs.read_float('a_sp')
except:
print "a not supplied. Setting power on shear stress to 1..."
self._a = 1.
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
#if self.use_W:
# self._b = 0.
#else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
#if self.use_Q:
# self._c = 1.
#else:
raise NameError(
'c was not set') #we need to restore this functionality later
#'To use the sed flux dependent model, you must set a,b,c not m,n. Try a=1,b=0.5,c=1...?'
self._K_unit_time = inputs.read_float(
'K_sp') / 31557600. #...because we work with dt in seconds
#set gravity
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.relative_weight = (
self.sed_density - self.fluid_density
) / self.fluid_density * self.g #to accelerate MPM calcs
self.rho_g = self.fluid_density * self.g
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
self.mannings_n = mannings_n
if mannings_n < 0. or mannings_n > 0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside its typical range. Did you mean it? Proceeding...***"
sleep(2)
self.diffusivity_power_on_A = 0.9 * self._c * (1. - self._b
) #i.e., q/D**(1/6)
self.type = inputs.read_string('sed_dependency_type')
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
self.Qc = None
else:
if self.Qc == 'MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.override_threshold = inputs.read_bool(
'set_threshold_from_Dchar')
except MissingKeyError:
self.override_threshold = False
try:
self.shields_crit = inputs.read_float('threshold_Shields')
except MissingKeyError:
self.shields_crit = 0.05
try:
self.return_ch_props = inputs.read_bool('return_stream_properties')
except MissingKeyError:
self.return_ch_props = False
#now conditional inputs
if self.type == 'generalized_humped':
try:
self.kappa = inputs.read_float('kappa_hump')
self.nu = inputs.read_float('nu_hump')
self.phi = inputs.read_float('phi_hump')
self.c = inputs.read_float('c_hump')
except MissingKeyError:
self.kappa = 13.683
self.nu = 1.13
self.phi = 4.24
self.c = 0.00181
print 'Adopting inbuilt parameters for the humped function form...'
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
#this is going to be a nightmare to implement...
except:
self.lamb_flag = False
if self.Qc == 'MPM':
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
assert self.thresh > 0., "Can't automatically set characteristic grain size if threshold is 0 or unset!"
#remember the threshold getting set is already tau**a
self.Dchar_in = self.thresh / self.g / (
self.sed_density - self.fluid_density) / self.shields_crit
print 'Setting characteristic grain size from the Shields criterion...'
print 'Characteristic grain size is: ', self.Dchar_in
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
if self.override_threshold:
print "Overriding any supplied threshold..."
try:
self.thresh = self.shields_crit * self.g * (
self.sed_density - self.fluid_density) * self.Dchar_in
except AttributeError:
self.thresh = self.shields_crit * self.g * (
self.sed_density -
self.fluid_density) * inputs.read_float('Dchar')
print "Threshold derived from grain size and Shields number is: ", self.thresh
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
#new 11/12/14
self.point6onelessb = 0.6 * (1. - self._b)
self.shear_stress_prefactor = self.fluid_density * self.g * (
self.mannings_n / self.k_w)**0.6
if self.set_threshold is False or self.override_threshold:
try:
self.shields_prefactor_to_shear = (
self.sed_density -
self.fluid_density) * self.g * self.Dchar_in
except AttributeError: #no Dchar
self.shields_prefactor_to_shear_noDchar = (
self.sed_density - self.fluid_density) * self.g
twothirds = 2. / 3.
self.Qs_prefactor = 4. * self.C_MPM**twothirds * self.fluid_density**twothirds / (
self.sed_density - self.fluid_density)**twothirds * self.g**(
twothirds /
2.) * mannings_n**0.6 * self.k_w**(1. / 15.) * self.k_Q**(
0.6 + self._b / 15.) / self.sed_density**twothirds
self.Qs_thresh_prefactor = 4. * (
self.C_MPM * self.k_w * self.k_Q**self._b /
self.fluid_density**0.5 / (self.sed_density - self.fluid_density) /
self.g / self.sed_density)**twothirds
#both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c * (0.6 + self._b / 15.)
self.Qs_power_onAthresh = twothirds * self._b * self._c
import copy
import time
import numpy
import pylab
from landlab import ModelParameterDictionary, RasterModelGrid
from landlab.components import FlowAccumulator, StreamPowerEroder
from landlab.components.stream_power import FastscapeEroder as Fsc
from landlab.plot import channel_profile as prf, imshow as llplot
from landlab.plot.video_out import VideoPlotter
inputs = ModelParameterDictionary("./drive_sp_params.txt")
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
time_to_run = inputs.read_float("run_time")
# nt needs defining
uplift = inputs.read_float("uplift_rate")
init_elev = inputs.read_float("init_elev")
mg = RasterModelGrid(nrows, ncols, dx)
# create the fields in the grid
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
mg["node"]["topographic__elevation"] = z + numpy.random.rand(len(z)) / 1000.
# make some K values in a field to test
mg.at_node["K_values"] = 1.e-6 + numpy.random.rand(nrows * ncols) * 1.e-8
@author: danhobley
"""
from __future__ import print_function
import numpy as np
import pylab
from landlab import ModelParameterDictionary, RasterModelGrid
from landlab.components.gflex.flexure import gFlex
from landlab.plot.imshow import imshow_node_grid
inputs = ModelParameterDictionary("./AW_gflex_params.txt")
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
time_to_run = inputs.read_float("run_time")
init_elev = inputs.read_float("init_elev")
mg = RasterModelGrid(nrows, ncols, dx)
# create the fields in the grid
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
mg["node"]["topographic__elevation"] = z + np.random.rand(len(z)) / 1000.
# make some surface load stresses in a field to test
mg.at_node["surface_load__stress"] = np.zeros(nrows * ncols, dtype=float)
square_qs = mg.at_node["surface_load__stress"].view().reshape((nrows, ncols))
square_qs[10:40, 10:40] += 1.e6
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
Module erodes where channels are, implemented as
E = K * A**m * S**n - sp_crit,
and if E<0, E=0.
If 'use_W' is declared and True, the module instead implements:
E = K * A**m * S**n / W - sp_crit
***Parameters for input file***
OBLIGATORY:
K_sp -> positive float, the prefactor. This is defined per unit
time, not per tstep.
ALTERNATIVES:
*either*
m_sp -> positive float, the power on A
and
n_sp -> positive float, the power on S
*or*
sp_type -> String. Must be one of 'Total', 'Unit', or 'Shear_stress'.
and (following Whipple & Tucker 1999)
a_sp -> +ve float. The power on the SP/shear term to get the erosion
rate.
b_sp -> +ve float. The power on discharge to get width, "hydraulic
geometry". Unnecessary if sp_type='Total'.
c_sp -> +ve float. The power on area to get discharge, "basin
hydology".
... If 'Total', m=a*c, n=a.
... If 'Unit', m=a*c*(1-b), n=a.
... If 'Shear_stress', m=2*a*c*(1-b)/3, n = 2*a/3.
OPTIONS:
threshold_sp -> +ve float; the threshold sp_crit. Defaults to 0.
dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. If you set sp_m and sp_n, follows the
equation given above. If you set sp_type, it will be ignored if
'Total', but used directly if you want 'Unit' or 'Shear_stress'.
use_Q -> Bool. If true, the equation becomes E=K*Q**m*S**n.
Effectively sets c=1 in Wh&T's 1999 derivation, if you are
setting m and n through a, b, and c.
prevent_erosion -> Bool. If True, stream powers are calculated and
stored in the grid, but incision is NOT IMPLEMENTED. i.e., values of
elevation are NOT updated. Use if you wish to derive stream power
values for some other purpose, but do not wish to actually model
stream power dependent incision. Defaults to False.
'''
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
self._K_unit_time = inputs.read_float('K_sp')
try:
self.sp_crit = inputs.read_float('threshold_sp')
print "Found a threshold to use: ", self.sp_crit
except MissingKeyError:
self.sp_crit = 0.
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self.no_erode = inputs.read_bool('prevent_erosion')
except MissingKeyError:
self.no_erode = False
try:
self._m = inputs.read_float('m_sp')
except MissingKeyError:
self._type = inputs.read_string('sp_type')
self._a = inputs.read_float('a_sp')
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
raise NameError('c was not set')
if self._type == 'Total':
self._n = self._a
self._m = self._a*self._c #==_a if use_Q
elif self._type == 'Unit':
self._n = self._a
self._m = self._a*self._c*(1.-self._b) #==_a iff use_Q&use_W etc
elif self._type == 'Shear_stress':
self._m = 2.*self._a*self._c*(1.-self._b)/3.
self._n = 2.*self._a/3.
else:
raise MissingKeyError('Not enough information was provided on the exponents to use!')
else:
self._n = inputs.read_float('n_sp')
#m and n will always be set, but care needs to be taken to include Q and W directly if appropriate
self.stream_power_erosion = grid.zeros(centering='node')
from landlab.components.diffusion.diffusion import LinearDiffuser
from landlab import RasterModelGrid, CLOSED_BOUNDARY, FIXED_VALUE_BOUNDARY
from landlab import ModelParameterDictionary
from landlab.io.netcdf import write_netcdf
import numpy as np
#get needed properties to build grid:
input_file = 'create_topo.txt'
#initialize an object that will supply the parameters:
inputs = ModelParameterDictionary(input_file)
#load grid properties
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
total_t = inputs.read_float('total_time')
uplift_rate = inputs.read_float('uplift_rate')
nt = int(total_t // dt) #this is how many loops we'll need
uplift_per_step = uplift_rate * dt
#build the grid
mg = RasterModelGrid((nrows, ncols), dx)
z = mg.add_zeros('node', 'topographic__elevation')
# add some roughness as the initial topography, this lets "natural" channel planforms arise
initial_roughness = np.random.rand(z.size) / 100000.
z += initial_roughness
#set the boundary conditions - here we have closed boundaries at the top/bottom and fixed boundaries at the left/right
##elapsed_time = 0. #total time in simulation
##while elapsed_time < time_to_run:
## #add uplift
## mg.at_node['topographic__elevation'][mg.core_nodes] += uplift*dt
## print elapsed_time
## if elapsed_time+dt>time_to_run:
## print "Short step!"
## dt = time_to_run - elapsed_time
## mg = fr.route_flow(grid=mg)
## mg,_,_ = sp.erode(mg)
## elapsed_time += dt
inputs = ModelParameterDictionary("./pot_fr_params.txt")
nrows = 200 # inputs.read_int('nrows')
ncols = 200 # inputs.read_int('ncols')
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
time_to_run = inputs.read_float("run_time")
# nt needs defining
uplift = inputs.read_float("uplift_rate")
init_elev = inputs.read_float("init_elev")
mg = RasterModelGrid(nrows, ncols, dx)
# create the fields in the grid
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
mg["node"]["topographic__elevation"] = z + np.random.rand(len(z)) / 1000.0
# make some K values in a field to test
mg.at_node["K_values"] = 0.00001 + np.random.rand(nrows * ncols) / 100000.0
def initialize(self, input_stream):
# Create a ModelParameterDictionary for the inputs
if type(input_stream)==ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Read input/configuration parameters
self.kd = inputs.read_float('linear_diffusivity')
try:
self.uplift_rate = inputs.read_float('uplift_rate')
except MissingKeyError:
self.uplift_rate = 0.
try:
self.values_to_diffuse = inputs.read_string('values_to_diffuse')
except MissingKeyError:
self.values_to_diffuse = 'topographic__elevation'
else:
#take switch in the new field name in the class properties
for mysets in (self._input_var_names, self._output_var_names):
mysets.remove('topographic__elevation')
mysets.add(self.values_to_diffuse)
for mydicts in (self._var_units, self._var_mapping, self._var_doc):
mydicts[self.values_to_diffuse] = mydicts.pop('topographic__elevation')
try:
self.timestep_in = inputs.read_float('dt')
except MissingKeyError:
pass
# Create grid if one doesn't already exist
if self._grid is None:
self._grid = create_and_initialize_grid(input_stream)
# Set internal time step
# ..todo:
# implement mechanism to compute time-steps dynamically if grid is
# adaptive/changing
dx = self._grid.min_active_link_length() # smallest active link length
self.dt = _ALPHA*dx*dx/self.kd # CFL condition
try:
self.tstep_ratio = self.timestep_in/self.dt
except AttributeError:
pass
# Get a list of interior cells
self.interior_cells = self._grid.node_at_core_cell
##DEJH bites the bullet and forces the 2015 style with fields
# # Here we're experimenting with different approaches: with
# # 'make_all_data', we create and manage all the data we need and embed
# # it all in the grid. With 'explicit', we require the caller/user to
# # provide data.
# if _VERSION=='make_all_data':
# #print('creating internal data')
# self.z = self._grid.add_zeros('node', 'landscape_surface__elevation')
# self.g = self._grid.add_zeros('active_link', 'landscape_surface__gradient') # surface gradients
# self.qs = self._grid.add_zeros('active_link','unit_sediment_flux') # unit sediment flux
# self.dqds = self._grid.add_zeros('node', 'sediment_flux_divergence') # sed flux derivative
# elif _VERSION=='explicit':
# pass
# else:
# # Create data arrays for variables that won't (?) be shared with other
# # components
# self.g = self._grid.create_active_link_array_zeros() # surface gradients
# self.qs = self._grid.create_active_link_array_zeros() # unit sediment flux
# self.dqds = self._grid.create_node_array_zeros() # sed flux derivative
self.z = self._grid.at_node[self.values_to_diffuse]
g = self._grid.zeros(centering='link')
qs = self._grid.zeros(centering='link')
try:
self.g = self._grid.add_field('link', 'surface__gradient', g, noclobber=True) #note this will object if this exists already
except FieldError:
pass #field exists, so no problem
try:
self.qs = self._grid.add_field('link', 'unit_flux', qs, noclobber=True)
except FieldError:
pass
import pylab
from pylab import colorbar, figure, loglog, plot, show
from six.moves import range
from landlab import ModelParameterDictionary, RasterModelGrid
from landlab.components import FlowAccumulator, SedDepEroder
from landlab.plot import channel_profile as prf, imshow
from landlab.plot.imshow import imshow_node_grid
from landlab.plot.video_out import VideoPlotter
# get the needed properties to build the grid:
input_file = "./sed_dep_params_reliable.txt" # './sed_dep_NMGparams2.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
leftmost_elev = inputs.read_float("leftmost_elevation")
initial_slope = inputs.read_float("initial_slope")
uplift_rate = inputs.read_float("uplift_rate")
runtime = inputs.read_float("total_time")
dt = inputs.read_float("dt")
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
print("uplift per step: ", uplift_per_step)
# instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
##create the elevation field in the grid:
from landlab import ModelParameterDictionary
from landlab.plot import channel_profile as prf
from landlab.plot.imshow import imshow_node_grid
from landlab import RasterModelGrid
import numpy as np
import pylab
from time import time
#get the needed properties to build the grid:
input_file = './stream_power_params.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
#instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
##create the elevation field in the grid:
#create the field
mg.create_node_array_zeros('topographic_elevation')
z = mg.create_node_array_zeros() + leftmost_elev
def test_sp_discharges_new():
input_str = os.path.join(_THIS_DIR, "test_sp_params_discharge_new.txt")
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = 5
ncols = 5
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
mg = RasterModelGrid(nrows, ncols, xy_spacing=dx)
mg.add_zeros("topographic__elevation", at="node")
z = np.array(
[
5.,
5.,
0.,
5.,
5.,
5.,
2.,
1.,
2.,
5.,
5.,
3.,
2.,
3.,
5.,
5.,
4.,
4.,
4.,
5.,
5.,
5.,
5.,
5.,
5.,
]
)
mg["node"]["topographic__elevation"] = z
fr = FlowAccumulator(mg, flow_director="D8")
sp = StreamPowerEroder(mg, **inputs)
# perform the loop (once!)
for i in range(1):
fr.run_one_step()
sp.run_one_step(dt)
z_tg = np.array(
[
5.,
5.,
0.,
5.,
5.,
5.,
1.47759225,
0.43050087,
1.47759225,
5.,
5.,
2.32883687,
1.21525044,
2.32883687,
5.,
5.,
3.27261262,
3.07175015,
3.27261262,
5.,
5.,
5.,
5.,
5.,
5.,
]
)
assert_array_almost_equal(mg.at_node["topographic__elevation"], z_tg)
def test_sp_discharges_old():
input_str = os.path.join(_THIS_DIR, "test_sp_params_discharge.txt")
inputs = ModelParameterDictionary(input_str)
nrows = 5
ncols = 5
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
mg = RasterModelGrid((nrows, ncols), xy_spacing=dx)
mg.add_zeros("topographic__elevation", at="node")
z = np.array([
5.0,
5.0,
0.0,
5.0,
5.0,
5.0,
2.0,
1.0,
2.0,
5.0,
5.0,
3.0,
2.0,
3.0,
5.0,
5.0,
4.0,
4.0,
4.0,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
])
mg["node"]["topographic__elevation"] = z
fr = FlowAccumulator(mg, flow_director="D8")
my_Q = mg.at_node["surface_water__discharge"]
sp = StreamPowerEroder(mg, input_str, use_Q=my_Q)
# perform the loop (once!)
for i in range(1):
fr.run_one_step()
my_Q[:] = mg.at_node["surface_water__discharge"] * 1.0
sp.run_one_step(dt)
z_tg = np.array([
5.0,
5.0,
0.0,
5.0,
5.0,
5.0,
1.47759225,
0.43050087,
1.47759225,
5.0,
5.0,
2.32883687,
1.21525044,
2.32883687,
5.0,
5.0,
3.27261262,
3.07175015,
3.27261262,
5.0,
5.0,
5.0,
5.0,
5.0,
5.0,
])
assert_array_almost_equal(mg.at_node["topographic__elevation"], z_tg)
def __init__(self, input_stream):
"""
Reads in parameters and initializes the model.
Example
-------
>>> from six import StringIO
>>> p = StringIO('''
... model_grid_row__count: number of rows in grid
... 4
... model_grid_column__count: number of columns in grid
... 4
... plot_interval: interval for plotting to display, s
... 2.0
... model__run_time: duration of model run, s
... 1.0
... model__report_interval: time interval for reporting progress, real-time seconds
... 1.0e6
... surface_bleaching_time_scale: time scale for OSL bleaching, s
... 2.42
... light_attenuation_length: length scale for light attenuation, cells (1 cell = 1 mm)
... 2.0
... ''')
>>> tsbm = TurbulentSuspensionAndBleachingModel(p)
>>> tsbm.node_state
array([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
>>> tsbm.grid.at_node['osl']
array([ 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0.,
0., 0., 0.])
>>> tsbm.n_xn
array([0, 1, 1, 0, 0, 1, 1, 0])
>>> tsbm.fluid_surface_height
3.5
"""
# Get a source for input parameters.
params = ModelParameterDictionary(input_stream)
# Read user-defined parameters
nr = params.read_int('model_grid_row__count'
) # number of rows (CSDMS Standard Name [CSN])
nc = params.read_int(
'model_grid_column__count') # number of cols (CSN)
self.plot_interval = params.read_float(
'plot_interval') # interval for plotting output, s
self.run_duration = params.read_float(
'model__run_time') # duration of run, sec (CSN)
self.report_interval = params.read_float(
'model__report_interval') # report interval, in real-time seconds
self.bleach_T0 = params.read_float(
'surface_bleaching_time_scale'
) # time scale for bleaching at fluid surface, s
self.zstar = params.read_float(
'light_attenuation_length'
) # length scale for light attenuation in fluid, CELLS
# Derived parameters
self.fluid_surface_height = nr - 0.5
# Calculate when we next want to report progress.
self.next_report = time.time() + self.report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
ns_dict = {0: 'fluid', 1: 'particle'}
xn_list = self.setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Initialize the node-state array: here, the initial condition is a pile of
# resting grains at the bottom of a container.
bottom_rows = where(mg.node_y < 0.4 * nr)[0]
node_state_grid[bottom_rows] = 1
# Create a data array for bleaching.
# Here, osl=optically stimulated luminescence, normalized to the original
# signal (hence, initially all unity). Over time this signal may get
# bleached out due to exposure to light.
self.osl = mg.add_zeros('node', 'osl')
self.osl[bottom_rows] = 1.0
self.osl_display = mg.add_zeros('node', 'osl_display')
self.osl_display[bottom_rows] = 1.0
# We'll need an array to track the last time any given node was
# updated, so we can figure out the duration of light exposure between
# update events
self.last_update_time = mg.add_zeros('node', 'last_update_time')
# Call the base class (RasterCTS) init method
super(TurbulentSuspensionAndBleachingModel, \
self).__init__(mg, ns_dict, xn_list, node_state_grid, prop_data=self.osl)
# Set up plotting (if plotting desired)
if self.plot_interval <= self.run_duration:
self.initialize_plotting()
from six.moves import range
from landlab import RasterModelGrid, ModelParameterDictionary
from landlab.plot.imshow import imshow_node_grid
import numpy as np
from pylab import imshow, show, contour, figure, clabel, quiver, plot, close
from landlab.components.potentiality_flowrouting import PotentialityFlowRouter
from landlab.components.flow_routing import FlowRouter
from landlab.components.stream_power import FastscapeEroder
from landlab.grid.mappers import map_link_end_node_max_value_to_link
inputs = ModelParameterDictionary('./pot_fr_params.txt')
nrows = 50 #inputs.read_int('nrows')
ncols = 50 #inputs.read_int('ncols')
dx = inputs.read_float('dx')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
# attempt to implement diffusion with flow routing...
#modify the fields in the grid
z = mg.zeros(at='node') + init_elev
mg.at_node['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
#Set boundary conditions
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
mg.set_fixed_value_boundaries_at_grid_edges(False, False, False, True)
inlet_node = np.array((mg.number_of_node_columns + 1))
from landlab.components.craters.dig_craters import impactor
from landlab import ModelParameterDictionary
from landlab import RasterModelGrid
import numpy as np
import time
import pylab
#get the needed properties to build the grid:
input_file = './craters_params.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
nt = inputs.read_int('number_of_craters_per_loop')
loops = inputs.read_int('number_of_loops')
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_inactive_boundaries(False, False, False, False)
#create the fields in the grid
mg.create_node_array_zeros('topographic_elevation')
mg['node']['topographic_elevation'] = np.load('init.npy')
# Display a message
print('Running ...')
start_time = time.time()
#instantiate the component:
@author: danhobley
"""
from __future__ import print_function
from landlab.components.gflex.flexure import gFlex
import numpy as np
import pylab
from landlab import RasterModelGrid
from landlab import ModelParameterDictionary
from landlab.plot.imshow import imshow_node_grid
inputs = ModelParameterDictionary('./AW_gflex_params.txt')
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
#create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
#make some surface load stresses in a field to test
mg.at_node['surface_load__stress'] = np.zeros(nrows * ncols, dtype=float)
square_qs = mg.at_node['surface_load__stress'].view().reshape((nrows, ncols))
square_qs[10:40, 10:40] += 1.e6
def run_model(input_file=None,
savepath=None,
initial_topo_file=None,
initial_seed_file=None):
from landlab.components.flow_routing.route_flow_dn_JL import FlowRouter
from landlab.components.stream_power.fastscape_stream_power_JL import FastscapeEroder
#from landlab.components.stream_power.stream_power import StreamPowerEroder
from landlab.components.sink_fill.pit_fill_pf import PitFiller
from landlab.components.diffusion.diffusion import LinearDiffuser
from landlab import ModelParameterDictionary
#from landlab.plot import channel_profile as prf
from landlab.plot.imshow import imshow_node_grid
from landlab.io.esri_ascii import write_esri_ascii
from landlab.io.esri_ascii import read_esri_ascii
from landlab import RasterModelGrid
#from analysis_method import analyze_drainage_percentage
#from analysis_method import analyze_drainage_percentage_each_grid
#from analysis_method import analyze_mean_erosion
#from analysis_method import elev_diff_btwn_moraine_upland
#from analysis_method import label_catchment
#from analysis_method import cross_section
#from analysis_method import analyze_node_below_threshold
#from analysis_method import identify_drained_area
#from analysis_method import save_result
from analysis_method import shiftColorMap
import copy
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os
import time
import sys
import shutil
sys.setrecursionlimit(5000)
#===============================================================================
#get the needed properties to build the grid:
if input_file is None:
input_file = './coupled_params_sp.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
initial_slope = inputs.read_float('initial_slope')
rightmost_elevation = initial_slope * ncols * dx
#rightmost_elevation = inputs.read_float('rightmost_elevation')
uplift_rate = inputs.read_float('uplift_rate')
incision_rate = inputs.read_float('incision_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
k_sp = inputs.read_float('K_sp')
m_sp = inputs.read_float('m_sp')
uplift_per_step = uplift_rate * dt
incision_per_step = incision_rate * dt
moraine_height = inputs.read_float('moraine_height')
moraine_width = inputs.read_float('moraine_width')
#valley_width = inputs.read_float('valley_width')
valley_depth = inputs.read_float('valley_depth')
num_outs = inputs.read_int('number_of_outputs')
output_interval = int(nt // num_outs)
diff = inputs.read_float('linear_diffusivity')
#threshold_stream_power = inputs.read_float('threshold_stream_power')
threshold_AS = inputs.read_float('threshold_AS')
#threshold_erosion = dt*threshold_stream_power
gw_coeff = inputs.read_float('gw_coeff')
all_dry = inputs.read_bool('all_dry')
fill_sink_with_water = inputs.read_bool('fill_sink_with_water')
#===============================================================================
#===============================================================================
if initial_topo_file is None:
#instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
##create the elevation field in the grid:
#create the field
#specifically, this field has a triangular ramp
#moraine at the north edge of the domain.
mg.add_zeros('node', 'topographic__elevation', units='m')
z = mg.at_node['topographic__elevation']
moraine_start_y = np.max(mg.node_y) - moraine_width
moraine_ys = np.where(mg.node_y > moraine_start_y)
z[moraine_ys] += (mg.node_y[moraine_ys] - np.min(
mg.node_y[moraine_ys])) * (moraine_height / moraine_width)
#set valley
#valley_start_x = np.min(mg.node_x)+valley_width
#valley_ys = np.where((mg.node_x<valley_start_x)&(mg.node_y<moraine_start_y-valley_width))
#z[valley_ys] -= (np.max(mg.node_x[valley_ys])-mg.node_x[valley_ys])*(valley_depth/valley_width)
#set ramp (towards valley)
upland = np.where(mg.node_y < moraine_start_y)
z[upland] -= (np.max(mg.node_x[upland]) -
mg.node_x[upland]) * (rightmost_elevation / (ncols * dx))
z += rightmost_elevation
#set ramp (away from moraine)
#upland = np.where(mg.node_y<moraine_start_y)
#z[upland] -= (moraine_start_y-mg.node_y[upland])*initial_slope
#put these values plus roughness into that field
if initial_seed_file is None:
z += np.random.rand(len(z)) / 1
else:
(seedgrid,
seed) = read_esri_ascii(initial_seed_file,
name='topographic__elevation_seed')
z += seed
mg.at_node['topographic__elevation'] = z
#set river valley
river_valley, = np.where(
np.logical_and(
mg.node_x == 0,
np.logical_or(mg.status_at_node == 1, mg.status_at_node == 2)))
mg.at_node['topographic__elevation'][river_valley] = -valley_depth
else:
(mg, z) = read_esri_ascii(initial_topo_file,
name='topographic__elevation')
#set river valley
river_valley, = np.where(
np.logical_and(
mg.node_x == 0,
np.logical_or(mg.status_at_node == 1, mg.status_at_node == 2)))
mg.at_node['topographic__elevation'][river_valley] = -valley_depth
#set up grid's boundary conditions (right, top, left, bottom) is inactive
mg.set_closed_boundaries_at_grid_edges(True, True, False, True)
#set up boundary along moraine
#moraine_start_y = np.max(mg.node_y)-moraine_width
#bdy_moraine_ids = np.where((mg.node_y > moraine_start_y) & (mg.node_x == 0))
#mg.status_at_node[bdy_moraine_ids]=4
#mg._update_links_nodes_cells_to_new_BCs()
#===============================================================================
#===============================================================================
#instantiate the components:
fr = FlowRouter(mg)
pf = PitFiller(mg)
sp = FastscapeEroder(mg, input_file, threshold_sp=threshold_AS * k_sp)
#sp = StreamPowerEroder(mg, input_file, threshold_sp=threshold_erosion, use_Q=True)
#diffuse = PerronNLDiffuse(mg, input_file)
#lin_diffuse = LinearDiffuser(mg, input_file, method='on_diagonals')
lin_diffuse = LinearDiffuser(mg, input_file)
#===============================================================================
#===============================================================================
#instantiate plot setting
plt.close('all')
output_time = output_interval
plot_num = 0
mycmap = shiftColorMap(matplotlib.cm.gist_earth, 'mycmap')
#folder name
if savepath is None:
if all_dry:
name_tag = 'All_dry'
elif fill_sink_with_water:
name_tag = 'Not_all_dry_no_sink'
else:
name_tag = 'Not_all_dry_sink'
savepath = 'results/sensitivity_test_threshold_same_seed/'+name_tag+'_dt=' + str(dt) + '_total_time=' + str(runtime) + '_k_sp=' + str(k_sp) + \
'_uplift_rate=' + str(uplift_rate) + '_incision_rate=' + str(incision_rate) + '_initial_slope=' + str(initial_slope) + \
'_threshold=' + str(threshold_stream_power)
if not os.path.isdir(savepath):
os.makedirs(savepath)
#copy params_file
if not os.path.isfile(savepath + '/coupled_params_sp.txt'):
shutil.copy(input_file, savepath + '/coupled_params_sp.txt')
# Display a message
print 'Running ...'
print savepath
#save initial topography
write_esri_ascii(savepath + '/Topography_t=0.0.txt', mg,
'topographic__elevation')
#time
start_time = time.time()
#===============================================================================
#===============================================================================
#perform the loops:
for i in xrange(nt):
#note the input arguments here are not totally standardized between modules
'''
# simulate changing climate
if (((i+1)*dt // 5000.) % 2) == 0.:
all_dry = False
else:
all_dry = True
'''
#sp = FastscapeEroder(mg, input_file, threshold_sp = threshold_stream_power)
#update elevation
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_per_step
mg.at_node['topographic__elevation'][river_valley] -= incision_per_step
#mg = lin_diffuse.diffuse(dt)
#route and erode
original_topo = mg.at_node['topographic__elevation'].copy()
slope = mg.at_node['topographic__steepest_slope'].copy()
filled_nodes = None
if all_dry or fill_sink_with_water:
mg = pf.pit_fill()
filled_nodes, = np.where(
(mg.at_node['topographic__elevation'] - original_topo) > 0)
mg = fr.route_flow(routing_flat=all_dry)
old_topo = mg.at_node['topographic__elevation'].copy()
#mg, temp_z, temp_sp = sp.erode(mg, dt, Q_if_used='water__volume_flux')
mg = sp.erode(mg, dt, flooded_nodes=filled_nodes)
new_topo = mg.at_node['topographic__elevation'].copy()
mg.at_node[
'topographic__elevation'] = original_topo + new_topo - old_topo
#diffuse
#for j in range(10):
# mg = lin_diffuse.diffuse(dt/10)
mg = lin_diffuse.diffuse(dt)
if i + 1 == output_time:
print 'Saving data...'
plot_num += 1
plt.figure(plot_num)
im = imshow_node_grid(mg, 'topographic__elevation', plot_name='t = '+str(int((i+1)*dt))+' years', \
grid_units = ['m','m'], cmap=mycmap, allow_colorbar=True, \
vmin=0-valley_depth-incision_rate*runtime, vmax=5.+moraine_height+uplift_rate*runtime)
plt.savefig(savepath + '/Topography_t=' + str(
(i + 1) * dt) + '.jpg',
dpi=300)
write_esri_ascii(
savepath + '/Topography_t=' + str((i + 1) * dt) + '.txt', mg,
'topographic__elevation')
output_time += output_interval
plt.close('all')
print("--- %.2f minutes ---" %
((time.time() - start_time) / 60)), 'Completed loop', i + 1
plt.close('all')
print 'Finished simulating.'
#===============================================================================
#===============================================================================
#analyze_drainage_percentage(savepath, True)
#analyze_mean_erosion(savepath, True)
#elev_diff_btwn_moraine_upland(savepath, True)
#label_catchment(savepath)
#cross_section(savepath)
#===============================================================================
print 'Done!'
print '\n'
from landlab.components.flow_routing import (FlowAccumulator,
DepressionFinderAndRouter)
from landlab.components.stream_power import StreamPowerEroder, FastscapeEroder
import numpy
from landlab import RasterModelGrid
from landlab import ModelParameterDictionary
from landlab.plot.imshow import imshow_node_grid
import pylab
import time
inputs = ModelParameterDictionary('./drive_sp_params.txt')
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
#nt needs defining
uplift = 10. * inputs.read_float('uplift_rate')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
#create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
#z += mg.node_x*0.001
mg['node']['topographic__elevation'] = z + numpy.random.rand(len(z)) / 1000.
#make some K values in a field to test
from time import time
import numpy as np
import pylab
from six.moves import range
from landlab import FIXED_VALUE_BOUNDARY, ModelParameterDictionary, RasterModelGrid
from landlab.components import FastscapeEroder, FlowAccumulator
from landlab.plot import channel_profile as prf
# get the needed properties to build the grid:
input_file = "./drive_sp_params.txt"
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
uplift_rate = inputs.read_float("uplift_rate")
runtime = inputs.read_float("run_time")
dt = inputs.read_float("dt")
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
# instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
boundary_node_list = mg.boundary_nodes
# set up its boundary conditions (bottom, right, top, left is inactive)
mg.set_inactive_boundaries(True, True, True, True)
mg.set_closed_boundaries_at_grid_edges(False, False, False, False)
mg.status_at_node[20] = FIXED_VALUE_BOUNDARY
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', or a string giving the name of the model field
where capacity values are stored on nodes.
At the moment, only 'MPM' is permitted as a way to set the
capacity automatically, but expansion would be trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width and
basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
The equation for depth used to derive shear stress and hence
carrying capacity contains a prefactor:
mannings_n*(k_Q**(1-b)/K_w)**0.6
(so shear = fluid_density*g*depth_equation_prefactor*A**(0.6*c*(1-b)*S**0.7 !)
Don't know what to set these values to? k_w=0.002, k_Q=1.e-9,
mannings_n=0.03 give vaguely plausible numbers (e.g., for a
drainage area ~400km2, like Boulder Creek at Boulder,
=> depth~2.5m, width~35m, shear stress ~O(1000Pa)).
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each node,
don't set, and use the Dchar_if_used argument in erode()
instead.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set True,
becomes a weak function of local slope following Lamb et al
(2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation. An exception will be raised if threshold_shields is
also set.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False.
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge'].
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
'''
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.relative_weight = (self.sed_density-self.fluid_density)/self.fluid_density*self.g #to accelerate MPM calcs
self.excess_SG = (self.sed_density-self.fluid_density)/self.fluid_density
self.rho_g = self.fluid_density*self.g
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc=='MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float('threshold_shields')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a threshold to use: ", self.shields_crit
assert self.lamb_flag == False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
if self.calc_cap_flag:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
if self.calc_cap_flag:
raise NameError('c was not set')
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
pass
#assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6*self._c*(1.-self._b)
try:
self.k_Q = inputs.read_float('k_Q')
except MissingKeyError:
self.depth_prefactor = self.rho_g*inputs.read_float('depth_equation_prefactor')
else:
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
if mannings_n<0. or mannings_n>0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside it's typical range. Did you mean it? Proceeding...***"
sleep(2)
self.depth_prefactor = self.rho_g*mannings_n*(self.k_Q**(1.-self._b)/self.k_w)**0.6
##Note the depth_prefactor we store already holds rho*g
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
try:
self.shields_prefactor = 1./((self.sed_density-self.fluid_density)*self.g*self.Dchar_in)
self.MPM_prefactor = 8.*self.C_MPM*np.sqrt(self.relative_weight*self.Dchar_in*self.Dchar_in*self.Dchar_in)
self.MPM_prefactor_alt = 4.*self.g**(-2./3.)/self.excess_SG/self.fluid_density/self.sed_density
except AttributeError:
#have to set these manually as needed
self.shields_prefactor_noD = 1./((self.sed_sensity-self.fluid_density)*self.g)
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
def initialize(self, input_stream):
# Create a ModelParameterDictionary for the inputs
if type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Read input/configuration parameters
self.kd = inputs.read_float('DIFMOD_KD')
try:
self.uplift_rate = inputs.read_float('DIFMOD_UPLIFT_RATE')
except:
self.uplift_rate = inputs.read_float('uplift_rate')
try:
self.values_to_diffuse = inputs.read_str('values_to_diffuse')
except:
self.values_to_diffuse = 'planet_surface__elevation'
try:
self.timestep_in = inputs.read_float('dt')
except:
print 'No fixed timestep supplied, it must be set dynamically somewhere else. Be sure to call input_timestep(timestep_in) as part of your run loop.'
# Create grid if one doesn't already exist
if self.grid == None:
self.grid = create_and_initialize_grid(input_stream)
# Set internal time step
# ..todo:
# implement mechanism to compute time-steps dynamically if grid is
# adaptive/changing
dx = self.grid.min_active_link_length() # smallest active link length
self.dt = _ALPHA * dx * dx / self.kd # CFL condition
try:
self.tstep_ratio = self.timestep_in / self.dt
except:
pass
# Get a list of interior cells
self.interior_cells = self.grid.get_core_cell_node_ids()
# Here we're experimenting with different approaches: with
# 'make_all_data', we create and manage all the data we need and embed
# it all in the grid. With 'explicit', we require the caller/user to
# provide data.
if _VERSION == 'make_all_data':
#print 'creating internal data'
self.z = self.grid.add_zeros('node',
'landscape_surface__elevation')
self.g = self.grid.add_zeros(
'active_link',
'landscape_surface__gradient') # surface gradients
self.qs = self.grid.add_zeros(
'active_link', 'unit_sediment_flux') # unit sediment flux
self.dqds = self.grid.add_zeros(
'node', 'sediment_flux_divergence') # sed flux derivative
elif _VERSION == 'explicit':
pass
else:
# Create data arrays for variables that won't (?) be shared with other
# components
self.g = self.grid.create_active_link_array_zeros(
) # surface gradients
self.qs = self.grid.create_active_link_array_zeros(
) # unit sediment flux
self.dqds = self.grid.create_node_array_zeros(
) # sed flux derivative
def __init__(self,
input_file=None,
mean_storm=None,
mean_interstorm=None,
mean_storm_depth=None,
total_t=None,
delta_t=None):
""" This reads in information from the input_file (either default or user
assigned, and creates an instantaneous storm event drawn from the Poisson distribution
"""
# First we create an instance of the Model Parameter Dictionary
MPD = ModelParameterDictionary()
# If no input_file is given,the default file is used
if input_file is None:
input_file = _DEFAULT_INPUT_FILE
# This reads in the file information
MPD.read_from_file(input_file)
# And now we set our different parameters
# using the model parameter dictionary...
if mean_storm == None:
self.mean_storm = MPD.read_float('MEAN_STORM')
else:
self.mean_storm = mean_storm
if mean_interstorm == None:
self.mean_interstorm = MPD.read_float('MEAN_INTERSTORM')
else:
self.mean_interstorm = mean_interstorm
if mean_storm_depth == None:
self.mean_storm_depth = MPD.read_float('MEAN_DEPTH')
else:
self.mean_storm_depth = mean_storm_depth
if total_t == None:
self.run_time = MPD.read_float('RUN_TIME')
else:
self.run_time = total_t
if delta_t == None:
if input_file != _DEFAULT_INPUT_FILE:
try:
self.delta_t = MPD.read_float('DELTA_T')
except MissingKeyError:
self.delta_t = None
else:
self.delta_t = None
else:
self.delta_t = delta_t
# Mean_intensity is not set by the MPD, but can be drawn from
# the mean storm depth and mean storm duration.
self.mean_intensity = self.mean_storm_depth / self.mean_storm
# If a time series is created later, this blank list will be used.
self.storm_time_series = []
# Given the mean values assigned above using either the model
# parameter dictionary or the init function, we can call the
# different methods to assign values from the Poisson distribution.
self.storm_duration = self.get_precipitation_event_duration()
self.interstorm_duration = self.get_interstorm_event_duration()
self.storm_depth = self.get_storm_depth()
self.intensity = self.get_storm_intensity()
self._elapsed_time = 0.
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
Module erodes where channels are, implemented as
E = K * A**m * S**n - sp_crit,
and if E<0, E=0.
If 'use_W' is declared and True, the module instead implements:
E = K * A**m * S**n / W - sp_crit
***Parameters for input file***
OBLIGATORY:
K_sp -> positive float, the prefactor. This is defined per unit
time, not per tstep. Type the string 'array' to cause the
component's erode method to look for an array of values of K
(see documentation for 'erode').
ALTERNATIVES:
*either*
m_sp -> positive float, the power on A
and
n_sp -> positive float, the power on S
*or*
sp_type -> String. Must be one of 'Total', 'Unit', or 'Shear_stress'.
and (following Whipple & Tucker 1999)
a_sp -> +ve float. The power on the SP/shear term to get the erosion
rate.
b_sp -> +ve float. The power on discharge to get width, "hydraulic
geometry". Unnecessary if sp_type='Total'.
c_sp -> +ve float. The power on area to get discharge, "basin
hydology".
... If 'Total', m=a*c, n=a.
... If 'Unit', m=a*c*(1-b), n=a.
... If 'Shear_stress', m=2*a*c*(1-b)/3, n = 2*a/3.
OPTIONS:
threshold_sp -> +ve float; the threshold sp_crit. Defaults to 0.
This threshold is assumed to be in "stream power" units, i.e.,
if 'Shear_stress', the value should be tau**a.
dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. If you set sp_m and sp_n, follows the
equation given above. If you set sp_type, it will be ignored if
'Total', but used directly if you want 'Unit' or 'Shear_stress'.
use_Q -> Bool. If true, the equation becomes E=K*Q**m*S**n.
Effectively sets c=1 in Wh&T's 1999 derivation, if you are
setting m and n through a, b, and c.
'''
self.grid = grid
self.fraction_gradient_change = 1.
self.link_S_with_trailing_blank = np.zeros(
grid.number_of_links +
1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self._K_unit_time = inputs.read_float('K_sp')
except ParameterValueError: #it was a string
self.use_K = True
else:
self.use_K = False
try:
self.sp_crit = inputs.read_float('threshold_sp')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a threshold to use: ", self.sp_crit
except MissingKeyError:
self.sp_crit = 0.
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self._m = inputs.read_float('m_sp')
except MissingKeyError:
self._type = inputs.read_string('sp_type')
self._a = inputs.read_float('a_sp')
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
raise NameError('c was not set')
if self._type == 'Total':
self._n = self._a
self._m = self._a * self._c #==_a if use_Q
elif self._type == 'Unit':
self._n = self._a
self._m = self._a * self._c * (1. - self._b
) #==_a iff use_Q&use_W etc
elif self._type == 'Shear_stress':
self._m = 2. * self._a * self._c * (1. - self._b) / 3.
self._n = 2. * self._a / 3.
else:
raise MissingKeyError(
'Not enough information was provided on the exponents to use!'
)
else:
self._n = inputs.read_float('n_sp')
#m and n will always be set, but care needs to be taken to include Q and W directly if appropriate
self.stream_power_erosion = grid.zeros(centering='node')
##Flags for self-building of derived data:
#self.made_link_gradients = False
##This will us the MPD once finalized
##Now perform checks for existance of needed data items:
#try:
# _ = self.grid.at_link['planet_surface__derivative_of_elevation']
#except FieldError:
# self.made_link_gradients = True
self.weave_flag = grid.weave_flag
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', or a string giving the name of the model field
where capacity values are stored on nodes.
At the moment, only 'MPM' is permitted as a way to set the
capacity automatically, but expansion would be trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width and
basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each node,
don't set, and use the Dchar_if_used argument in erode()
instead.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set True,
becomes a weak function of local slope following Lamb et al
(2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation,
An exception will be raised if threshold_shields is
also set.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. NOT YET IMPLEMENTED
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge']. NOT YET IMPLEMENTED
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
'''
#this is the fraction we allow any given slope in the grid to evolve by in one go (suppresses numerical instabilities)
self.fraction_gradient_change = 0.25
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.rho_g = self.fluid_density * self.g
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc=='MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.return_ch_props = inputs.read_bool('return_stream_properties')
except MissingKeyError:
self.return_ch_props = False
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float('threshold_shields')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a threshold to use: ", self.shields_crit
assert self.lamb_flag == False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self.return_capacity = inputs.read_bool('return_capacity')
except MissingKeyError:
self.return_capacity = False
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
if self.calc_cap_flag:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
if self.calc_cap_flag:
raise NameError('c was not set')
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
pass
#assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6*self._c*(1.-self._b)
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
self.mannings_n = mannings_n
if mannings_n<0. or mannings_n>0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside its typical range. Did you mean it? Proceeding...***"
sleep(2)
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
self.diffusivity_power_on_A = 0.9*self._c*(1.-self._b) #i.e., q/D**(1/6)
#new for v3:
#set thresh in shear stress if poss at this stage:
try: #fails if no Dchar provided, or shields crit is being set dynamically from slope
self.thresh = self.shields_crit*(self.sed_density-self.fluid_density)*self.g*self.Dchar_in
except AttributeError:
try:
self.shields_prefactor_to_shear = (self.sed_density-self.fluid_density)*self.g*self.Dchar_in
except AttributeError: #no Dchar
self.shields_prefactor_to_shear_noDchar = (self.sed_density-self.fluid_density)*self.g
twothirds = 2./3.
self.Qs_prefactor = 4.*self.C_MPM**twothirds*self.fluid_density**twothirds/(self.sed_density-self.fluid_density)**twothirds*self.g**(twothirds/2.)*mannings_n**0.6*self.k_w**(1./15.)*self.k_Q**(0.6+self._b/15.)/self.sed_density**twothirds
self.Qs_thresh_prefactor = 4.*(self.C_MPM*self.k_w*self.k_Q**self._b/self.fluid_density**0.5/(self.sed_density-self.fluid_density)/self.g/self.sed_density)**twothirds
#both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c*(0.6+self._b/15.)
self.Qs_power_onAthresh = twothirds*self._b*self._c
if RasterModelGrid in inspect.getmro(grid.__class__):
self.cell_areas = grid.node_spacing_horizontal*grid.node_spacing_vertical
else:
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
self.bad_neighbor_mask = np.equal(grid.get_neighbor_list(bad_index=-1),-1)
self.routing_code = """
from pylab import colorbar, show, plot, loglog, figure
from landlab import RasterModelGrid
import numpy as np
import pylab
from pylab import show
from copy import copy, deepcopy
from time import time
#get the needed properties to build the grid:
input_file = './sed_dep_params.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime//dt)
uplift_per_step = uplift_rate * dt
print 'uplift per step: ', uplift_per_step
#instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
##create the elevation field in the grid:
def test_fastscape():
input_str = os.path.join(_THIS_DIR, "drive_sp_params.txt")
inputs = ModelParameterDictionary(input_str)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
dt = inputs.read_float("dt")
time_to_run = inputs.read_float("run_time")
uplift = inputs.read_float("uplift_rate")
init_elev = inputs.read_float("init_elev")
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_closed_boundaries_at_grid_edges(False, False, True, True)
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
numpy.random.seed(0)
mg["node"]["topographic__elevation"] = z + numpy.random.rand(len(z)) / 1000.
fr = FlowAccumulator(mg, flow_director="D8")
fsp = Fsc(mg, input_str, method="D8")
elapsed_time = 0.
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
mg = fr.run_one_step()
mg = fsp.erode(mg, dt=dt)
mg.at_node["topographic__elevation"][mg.core_nodes] += uplift * dt
elapsed_time += dt
z_trg = numpy.array(
[
5.48813504e-04,
7.15189366e-04,
6.02763376e-04,
5.44883183e-04,
4.23654799e-04,
6.45894113e-04,
1.01830760e-02,
9.58036770e-03,
6.55865452e-03,
3.83441519e-04,
7.91725038e-04,
1.00142749e-02,
8.80798884e-03,
5.78387585e-03,
7.10360582e-05,
8.71292997e-05,
9.81911417e-03,
9.52243406e-03,
7.55093226e-03,
8.70012148e-04,
9.78618342e-04,
1.00629755e-02,
8.49253798e-03,
5.33216680e-03,
1.18274426e-04,
6.39921021e-04,
9.88956320e-03,
9.47119567e-03,
6.43790696e-03,
4.14661940e-04,
2.64555612e-04,
1.00450743e-02,
8.37262908e-03,
5.21540904e-03,
1.87898004e-05,
6.17635497e-04,
9.21286940e-03,
9.34022513e-03,
7.51114450e-03,
6.81820299e-04,
3.59507901e-04,
6.19166921e-03,
7.10456176e-03,
6.62585507e-03,
6.66766715e-04,
6.70637870e-04,
2.10382561e-04,
1.28926298e-04,
3.15428351e-04,
3.63710771e-04,
]
)
assert_array_almost_equal(mg.at_node["topographic__elevation"], z_trg)
from __future__ import print_function
from landlab.components.craters.dig_craters import impactor
from landlab import ModelParameterDictionary
from landlab import RasterModelGrid
import numpy as np
import time
import pylab
#get the needed properties to build the grid:
input_file = './craters_params_degrade.txt'
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
nt = inputs.read_int('number_of_craters_per_loop')
loops = inputs.read_int('number_of_loops')
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_looped_boundaries(True, True)
#create the fields in the grid
mg.create_node_array_zeros('topographic__elevation')
mg['node'][ 'topographic__elevation'] = np.load('init_topo.npy')
# Display a message
print( 'Running ...' )
start_time = time.time()
from __future__ import print_function
import numpy as np
import pylab
from landlab.components.gFlex.flexure import gFlex
from landlab.components.flow_routing import FlowAccumulator
from landlab.components.stream_power import StreamPowerEroder, FastscapeEroder
from landlab import RasterModelGrid
from landlab import ModelParameterDictionary
from landlab.plot.imshow import imshow_node_grid
inputs = ModelParameterDictionary('./coupled_SP_gflex_params.txt')
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
init_elev = inputs.read_float('init_elev')
uplift_perstep = inputs.read_float('uplift_rate')*dt
rock_stress_param = inputs.read_float('rock_density')*9.81
mg = RasterModelGrid(nrows, ncols, dx)
#create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
mg['node'][ 'topographic__elevation'] = z + np.random.rand(len(z))/1000.
#make some surface load stresses in a field to test
mg.at_node['surface_load__stress'] = np.zeros(nrows*ncols, dtype=float)
