Source code documentation¶
Model classes¶
The AeoLiS model is based on two main model classes:
AeoLiS
and
AeoLiSRunner
. The former is the actual,
lowlevel, BMIcompatible class that implements the basic model
functions and numerical schemes. The latter is a convenience class
that implements a time loop, netCDF4 output, a progress indicator and
a callback function that allows the used to interact with the model
during runtime.
The additional WindGenerator
class to generate
realistic wind time series is available from the same module.
AeoLiS¶

class
model.
AeoLiS
(configfile)[source]¶ AeoLiS model class
AeoLiS is a processbased model for simulating supplylimited aeolian sediment transport. This model class is compatible with the Basic Model Interface (BMI) and provides basic model operations, like initialization, time stepping, finalization and data exchange. For higher level operations, like a progress indicator and netCDF4 output is refered to the AeoLiS model runner class, see
AeoLiSRunner
.Examples
>>> with AeoLiS(configfile='aeolis.txt') as model: >>> while model.get_current_time() <= model.get_end_time(): >>> model.update()
>>> model = AeoLiS(configfile='aeolis.txt') >>> model.initialize() >>> zb = model.get_var('zb') >>> model.set_var('zb', zb + 1) >>> for i in range(10): >>> model.update(60.) # step 60 seconds forward >>> model.finalize()

__init__
(configfile)[source]¶ Initialize class
Parameters: configfile (str) – Model configuration file. See read_configfile()
.

crank_nicolson
()[source]¶ Convenience function for implicit solver based on CrankNicolson scheme
See also

static
dimensions
(var=None)[source]¶ Static method that returns named dimensions of all spatial grids
Parameters: var (str, optional) – Name of spatial grid Returns: Tuple with named dimensions of requested spatial grid or dictionary with all named dimensions of all spatial grids. Returns nothing if requested spatial grid is not defined. Return type: tuple or dict

euler_backward
()[source]¶ Convenience function for implicit solver based on Euler backward scheme
See also

euler_forward
()[source]¶ Convenience function for explicit solver based on Euler forward scheme
See also

get_var
(var)[source]¶ Returns spatial grid or model configuration parameter
If the given variable name matches with a spatial grid, the spatial grid is returned. If not, the given variable name is matched with a model configuration parameter. If a match is found, the parameter value is returned. Otherwise, nothing is returned.
Parameters: var (str) – Name of spatial grid or model configuration parameter Returns: Spatial grid or model configuration parameter Return type: np.ndarray or int, float, str or list Examples
>>> # returns bathymetry grid ... model.get_var('zb')
>>> # returns simulation duration ... model.get_var('tstop')
See also

get_var_name
(i)[source]¶ Returns name of spatial grid by index (in alphabetical order)
Parameters: i (int) – Index of spatial grid Returns: Name of spatial grid or 1 in case index exceeds the number of grids Return type: str or 1

get_var_rank
(var)[source]¶ Returns rank of spatial grid
Parameters: var (str) – Name of spatial grid Returns: Rank of spatial grid or 1 if not found Return type: int

get_var_shape
(var)[source]¶ Returns shape of spatial grid
Parameters: var (str) – Name of spatial grid Returns: Dimensions of spatial grid or 1 if not found Return type: tuple or int

get_var_type
(var)[source]¶ Returns variable type of spatial grid
Parameters: var (str) – Name of spatial grid Returns: Variable type of spatial grid or 1 if not found Return type: str or int

initialize
()[source]¶ Initialize model
Read model configuration file and initialize parameters and spatial grids dictionary and load bathymetry and bed composition.

set_timestep
(dt=1.0)[source]¶ Determine optimal time step
If no time step is given the optimal time step is determined. For explicit numerical schemes the time step is based in the CourantFrierichsLewy (CFL) condition. For implicit numerical schemes the time step specified in the model configuration file is used. Alternatively, a preferred time step is given that is maximized by the CFL condition in case of an explicit numerical scheme.
Returns True except when:
1. No time step could be determined, for example when there is no wind and the numerical scheme is explicit. In this case the time step is set arbitrarily to one second.
2. Or when the time step is smaller than 1. In this case the time is updated with the absolute value of the time step, but no model execution is performed. This funcionality can be used to skip fastforward in time.
Parameters: df (float, optional) – Preferred time step Returns: False if determination of time step was unsuccessful, True otherwise Return type: bool

set_var
(var, val)[source]¶ Sets spatial grid or model configuration parameter
If the given variable name matches with a spatial grid, the spatial grid is set. If not, the given variable name is matched with a model configuration parameter. If a match is found, the parameter value is set. Otherwise, nothing is set.
Parameters:  var (str) – Name of spatial grid or model configuration parameter
 val (np.ndarray or int, float, str or list) – Spatial grid or model configuration parameter
Examples
>>> # set bathymetry grid ... model.set_var('zb', np.array([[0.,0., ... ,0.]]))
>>> # set simulation duration ... model.set_var('tstop', 3600.)
See also

set_var_index
(i, val)[source]¶ Set spatial grid by index (in alphabetical order)
Parameters:  i (int) – Index of spatial grid
 val (np.ndarray) – Spatial grid

solve
(alpha=0.5, beta=1.0)[source]¶ Implements the explicit Euler forward, implicit Euler backward and semiimplicit CrankNicolson numerical schemes
Determines weights of sediment fractions, sediment pickup and instantaneous sediment concentration. Returns a partial spatial grid dictionary that can be used to update the global spatial grid dictionary.
Parameters:  alpha (float, optional) – Implicitness coefficient (0.0 for Euler forward, 1.0 for Euler backward or 0.5 for CrankNicolson, default=0.5)
 beta (float, optional) – Centralization coefficient (1.0 for upwind or 0.5 for centralized, default=1.0)
Returns: Partial spatial grid dictionary
Return type: dict
Examples
>>> model.s.update(model.solve(alpha=1., beta=1.) # euler backward
>>> model.s.update(model.solve(alpha=.5, beta=1.) # cranknicolson

update
(dt=1)[source]¶ Time stepping function
Takes a single step in time. Interpolates wind and hydrodynamic time series to the current time, updates the soil moisture, mixes the bed due to wave action, computes wind velocity threshold and the equilibrium sediment transport concentration. Subsequently runs one of the available numerical schemes to compute the instantaneous sediment concentration and pickup for the next time step and updates the bed accordingly.
For explicit schemes the time step is maximized by the CourantFriedrichsLewy (CFL) condition. See
set_timestep()
.Parameters: dt (float, optional) – Time step in seconds. The time step specified in the model configuration file is used in case dt is smaller than zero. For explicit numerical schemes the time step is maximized by the CFL confition.

AeoLiSRunner¶

class
model.
AeoLiSRunner
(configfile='aeolis.txt')[source]¶ AeoLiS model runner class
This runner class is a convenience class for the BMIcompatible AeoLiS model class (
AeoLiS()
). It implements a time loop, a progress indicator and netCDF4 output. It also provides the definition of a callback function that can be used to interact with the AeoLiS model during runtime.The commandline function
aeolis
is available that uses this class to start an AeoLiS model run.Examples
>>> # run with default settings ... AeoLiSRunner().run()
>>> AeoLiSRunner(configfile='aeolis.txt').run()
>>> model = AeoLiSRunner(configfile='aeolis.txt') >>> model.run(callback=lambda model: model.set_var('zb', zb))
>>> model.run(callback='bar.py:add_bar')
See also

__init__
(configfile='aeolis.txt')[source]¶ Initialize class
Reads model configuration file without parsing all referenced files for the progress indicator and netCDF output. If no configuration file is given, the default settings are used.
Parameters: configfile (str, optional) – Model configuration file. See read_configfile()
.

get_statistic
(var, stat='avg')[source]¶ Return statistic of spatial grid
Parameters:  var (str) – Name of spatial grid
 stat (str) – Name of statistic (avg, sum, var, min or max)
Returns: Statistic of spatial grid
Return type: numpy.ndarray

get_var
(var, clear=True)[source]¶ Returns spatial grid, statistic or model configuration parameter
Overloads the
get_var()
function and extends it with the functionality to return statistics on spatial grids by adding a postfix to the variable name (e.g. Ct_avg). Supported statistics are avg, sum, var, min and max.Parameters:  var (str) – Name of spatial grid or model configuration parameter. Spatial grid name can be extended with a postfix to request a statistic (_avg, _sum, _var, _min or _max).
 clear (bool) – Clear output statistics afterwards.
Returns: Spatial grid, statistic or model configuration parameter
Return type: np.ndarray or int, float, str or list
Examples
>>> # returns average sediment concentration ... model.get_var('Ct_avg')
>>> # returns variance in wave height ... model.get_var('Hs_var')
See also

initialize
()[source]¶ Initialize model
Overloads the
initialize()
function, but also initializes output statistics.

load_hotstartfiles
()[source]¶ Load model state from hotstart files
Hotstart files are plain text representations of model state variables that can be used to hotstart the (partial) model state. Hotstart files should have the name of the model state variable it contains and have the extension .hotstart. Hotstart files differ from restart files in that restart files contain entire model states and are pickled Python objects.

load_restartfile
(restartfile)[source]¶ Load model state from restart file
Parameters: restartfile (str) – Path to previously written restartfile.

output_clear
()[source]¶ Clears output statistics dictionary
Creates a matrix for minimum, maximum, variance and summed values for each output variable and sets the time step counter to zero.

output_update
()[source]¶ Updates output statistics dictionary
Updates matrices with minimum, maximum, variance and summed values for each output variable with current spatial grid values and increases time step counter with one.

output_write
()[source]¶ Appends output to netCDF4 output file
If the time since the last output is equal or larger than the set output interval, append current output to the netCDF4 output file. Computes the average and variance values based on available output statistics and clear output statistics dictionary.

parse_callback
(callback)[source]¶ Parses callback definition and returns function
The callback function can be specified in two formats:
 As a native Python function
 As a string refering to a Python script and function,
separated by a colon (e.g.
example/callback.py:function
)
Parameters: callback (str or function) – Callback definition Returns: Python callback function Return type: function

print_progress
(fraction=0.1, min_interval=1.0, max_interval=60.0)[source]¶ Print progress to screen
Parameters:  fraction (float, optional) – Fraction of simulation at which to print progress (default: 10%)
 min_interval (float, optional) – Minimum time in seconds between subsequent progress prints (default: 1s)
 max_interval (float, optional) – Maximum time in seconds between subsequent progress prints (default: 60s)

run
(callback=None, restartfile=None)[source]¶ Start model time loop
Changes current working directory to the model directory, prints model configuration parameters and progress indicator to the screen, writes netCDF4 output and calls a callback function upon request.
Parameters:  callback (str or function) – The callback function is called at the start of every
single time step and takes the AeoLiS model object as
input. The callback function can be used to interact with
the model during simulation (e.g. update the bed with new
measurements). See for syntax
parse_callback()
.  restartfile (str) – Path to previously written restartfile. The model state is loaded from this file after initialization of the model.
See also
 callback (str or function) – The callback function is called at the start of every
single time step and takes the AeoLiS model object as
input. The callback function can be used to interact with
the model during simulation (e.g. update the bed with new
measurements). See for syntax

update
(dt=1)[source]¶ Time stepping function
Overloads the
update()
function, but also updates output statistics and clears output statistics upon request.Parameters: dt (float, optional) – Time step in seconds.

WindGenerator¶

class
model.
WindGenerator
(mean_speed=9.0, max_speed=30.0, dt=60.0, n_states=30, shape=2.0, scale=2.0)[source]¶ Wind velocity time series generator
Generates a random wind velocity time series with given mean and maximum wind speed, duration and time resolution. The wind velocity time series is generated using a Markov Chain Monte Carlo (MCMC) approach based on a Weibull distribution. The wind time series can be written to an AeoLiScompatible wind input file assuming a constant wind direction of zero degrees.
The commandline function
aeoliswind
is available that uses this class to generate AeoLiS wind input files.Examples
>>> wind = WindGenerator(mean_speed=10.).generate(duration=24*3600.) >>> wind.write_time_series('wind.txt') >>> wind.plot() >>> wind.hist()
See also
Physics modules¶
Bathymetry and bed composition¶

bed.
initialize
(s, p)[source]¶ Initialize bathymetry and bed composition
Initialized bathymetry, computes cell sizes and orientation, bed layer thickness and bed composition.
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

bed.
mixtoplayer
(s, p)[source]¶ Mix grain size distribution in top layer of the bed
Simulates mixing of the top layers of the bed by wave action. The wave action is represented by a local wave height maximized by a maximum wave hieght over depth ratio
gamma
. The mixing depth is a fraction of the local wave height indicated byfacDOD
. The mixing depth is used to compute the number of bed layers that should be included in the mixing. The grain size distribution in these layers is then replaced by the average grain size distribution over these layers.Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

bed.
prevent_negative_mass
(m, dm, pickup)[source]¶ Handle situations in which negative mass may occur due to numerics
Negative mass may occur by moving sediment to lower layers down to accomodate deposition of sediments. In particular two cases are important:
A net deposition cell has some erosional fractions.
In this case the top layer mass is reduced according to the existing sediment distribution in the layer to accomodate deposition of fresh sediment. If the erosional fraction is subtracted afterwards, negative values may occur. Therefore the erosional fractions are subtracted from the top layer beforehand in this function. An equal mass of deposition fractions is added to the top layer in order to keep the total layer mass constant. Subsequently, the distribution of the sediment to be moved to lower layers is determined and the remaining deposits are accomodated.
Deposition is larger than the total mass in a layer.
In this case a nonuniform distribution in the bed may also lead to negative values as the abundant fractions are reduced disproportionally as sediment is moved to lower layers to accomodate the deposits. This function fills the top layers entirely with fresh deposits and moves the existing sediment down such that the remaining deposits have a total mass less than the total bed layer mass. Only the remaining deposits are fed to the routine that moves sediment through the layers.
Parameters:  m (np.ndarray) – Sediment mass in bed (nx*ny, nl, nf)
 dm (np.ndarray) – Total sediment mass exchanged between layers (nx*ny, nf)
 pickup (np.ndarray) – Sediment pickup (nx*ny, nf)
Returns:  np.ndarray – Sediment mass in bed (nx*ny, nl, nf)
 np.ndarray – Total sediment mass exchanged between layers (nx*ny, nf)
 np.ndarray – Sediment pickup (nx*ny, nf)
Note
The situations handled in this function can also be prevented by reducing the time step, increasing the layer mass or increasing the adaptation time scale.

bed.
update
(s, p)[source]¶ Update bathymetry and bed composition
Update bed composition by moving sediment fractions between bed layers. The total mass in a single bed layer does not change as sediment removed from a layer is repleted with sediment from underlying layers. Similarly, excess sediment added in a layer is moved to underlying layers in order to keep the layer mass constant. The lowest bed layer exchanges sediment with an infinite sediment source that follows the original grain size distribution as defined in the model configuration file by
grain_size
andgrain_dist
. The bathymetry is updated following the cummulative erosion/deposition over the fractions ifbedupdate
isTrue
.Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict
Wind velocity and direction¶

wind.
get_velocity_at_height
(u, z, z0, z1=None)[source]¶ Compute shear velocity from wind velocity following PrandlKarman’s Law of the Wall
Parameters:  u (numpy.ndarray) – Spatial wind field
 z (float) – Height above bed where
u
is measured  z0 (float) – Roughness length
 z1 (float, optional) – Height above bed for which to return wind speeds. Returns wind shear if not given.
Returns: Array of size
u
with wind speeds at heightz1
Return type: numpy.ndarray

wind.
interpolate
(s, p, t)[source]¶ Interpolate wind velocity and direction to current time step
Interpolates the wind time series for velocity and direction to the current time step. The cosine and sine of the direction angle are interpolated separately to prevent zerocrossing errors. The wind velocity is decomposed in two grid components based on the orientation of each individual grid cell. In case of a onedimensional model only a single positive component is used.
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
 t (float) – Current time
Returns: Spatial grids
Return type: dict

class
shear.
WindShear
(x, y, z, dx=1.0, dy=1.0, buffer_width=100.0, buffer_relaxation=None, L=100.0, z0=0.001, l=10.0)[source]¶ Class for computation of 2DH wind shear perturbations over a topography.
The class implements a 2D FFT solution to the wind shear perturbation on curvilinear grids. As the FFT solution is only defined on an equidistant rectilinear grid with circular boundary conditions that is aligned with the wind direction, a rotating computational grid is automatically defined for the computation. The computational grid is extended in all directions using a logistic sigmoid function as to ensure full coverage of the input grid for all wind directions, circular boundaries and preservation of the alongshore uniformity. An extra buffer distance can be used as to minimize the disturbence from the borders in the input grid. The results are interpolated back to the input grid when necessary.
Frequencies related to wave lengths smaller than a computational grid cell are filtered from the 2D spectrum of the topography using a logistic sigmoid tapering. The filtering aims to minimize the disturbance as a result of discontinuities in the topography that may physically exists, but cannot be solved for in the computational grid used.
Example
>>> w = WindShear(x, y, z) >>> w(u0=10., udir=30.).add_shear(taux, tauy)
Notes
To do:
 Actual resulting values are still to be compared with the results
 from Kroy et al. (2002)
 Grid interpolation can still be optimized
 Separation bubble is still to be implemented
 Avalanching is still to be implemented

__call__
(u0, udir)[source]¶ Compute wind shear for given wind speed and direction
Parameters:  u0 (float) – Freeflow wind speed
 udir (float) – Wind direction in degrees

add_shear
(taux, tauy)[source]¶ Add wind shear perturbations to a given wind shear field
Parameters:  taux (numpy.ndarray) – Wind shear in xdirection
 tauy (numpy.ndarray) – Wind shear in ydirection
Returns:  taux (numpy.ndarray) – Wind shear including perturbations in xdirection
 tauy (numpy.ndarray) – Wind shear including perturbations in ydirection

compute_shear
(u0, nfilter=(1.0, 2.0))[source]¶ Compute wind shear perturbation for given freeflow wind speed on computational grid
Parameters:  u0 (float) – Freeflow wind speed
 nfilter (2tuple) – Wavenumber range used for logistic sigmoid filter. See
filter_highfrequencies()

filter_highfrequenies
(kx, ky, hs, nfilter=(1, 2), p=0.01)[source]¶ Filter high frequencies from a 2D spectrum
A logistic sigmoid filter is used to taper higher frequencies from the 2D spectrum. The range over which the sigmoid runs from 0 to 1 with a precision
p
is given by the 2tuplenfilter
. The range is defined as wavenumbers in terms of gridcells, i.e. a value 1 corresponds to a wave with lengthdx
.Parameters:  kx (numpy.ndarray) – Wavenumbers in xdirection
 ky (numpy.ndarray) – Wavenumbers in ydirection
 hs (numpy.ndarray) – 2D spectrum
 nfilter (2tuple) – Wavenumber range used for logistic sigmoid filter
 p (float) – Precision of sigmoid range definition
Returns: hs – Filtered 2D spectrum
Return type: numpy.ndarray

static
get_exact_grid
(xmin, xmax, ymin, ymax, dx, dy)[source]¶ Returns a grid with given gridsizes approximately within given bounding box

get_shear
()[source]¶ Returns wind shear perturbation field
Returns:  dtaux (numpy.ndarray) – Wind shear perturbation in xdirection
 dtauy (numpy.ndarray) – Wind shear perturbation in ydirection

get_sigmoid
(x)[source]¶ Get sigmoid function value
Get bed level multiplication factor in buffer area based on buffer specificationa and distance to input grid boundary.
Parameters: x (float or numpy.ndarray) – Distance(s) to input grid boundary Returns: Bed level multiplication factor (z = factor * z_boundary) Return type: float or numpy.ndarray

static
interpolate_projected_point
(a, b, p)[source]¶ Project point to line segment and return distance and interpolated value
Parameters:  a (iterable) – Start vector for line segment
 b (iterable) – End vector for line segment
 p (iterable) – Point vector to be projected
Returns:  d (numpy.ndarray) – Distance from point p to projected point q
 z (float) – Interpolated value at projected point q

plot
(ax=None, cmap='Reds', stride=10, computational_grid=False, **kwargs)[source]¶ Plot wind shear perturbation
Parameters:  ax (matplotlib.pyplot.Axes, optional) – Axes to plot onto
 cmap (matplotlib.cm.Colormap or string, optional) – Colormap for topography (default: Reds)
 stride (int, optional) – Stride to apply to wind shear vectors (default: 10)
 computational_grid (bool, optional) – Plot on computational grid rather than input grid (default: False)
 kwargs (dict) – Additional arguments to
matplotlib.pyplot.quiver()
Returns: ax – Axes used for plotting
Return type: matplotlib.pyplot.Axes

populate_computational_grid
(alpha)[source]¶ Interpolate input topography to computational grid
Rotates computational grid to current wind direction and interpolates the input topography to the rotated grid. Any grid cells that are not covered by the input grid are filled using a sigmoid function.
Parameters: alpha (float) – Rotation angle in degrees

static
rotate
(x, y, alpha, origin=(0, 0))[source]¶ Rotate a matrix over given angle around given origin
Wind velocity threshold¶

threshold.
compute
(s, p)[source]¶ Compute wind velocity threshold based on bed surface properties
Computes wind velocity threshold based on grain size fractions, bed slope, soil moisture content, air humidity and the presence of roughness elements. All bed surface properties increase the current wind velocity threshold, except for the grain size fractions. Therefore, the computation is initialized by the grain size fractions and subsequently altered by the other bed surface properties.
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

threshold.
compute_bedslope
(s, p)[source]¶ Modify wind velocity threshold based on bed slopes following Dyer (1986)
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

threshold.
compute_grainsize
(s, p)[source]¶ Compute wind velocity threshold based on grain size fractions following Bagnold (1937)
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

threshold.
compute_humidity
(s, p)[source]¶ Modify wind velocity threshold based on air humidity following Arens (1996)
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

threshold.
compute_moisture
(s, p)[source]¶ Modify wind velocity threshold based on soil moisture content following Belly (1964) or Hotta (1984)
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

threshold.
compute_roughness
(s, p)[source]¶ Modify wind velocity threshold based on the presence of roughness elements following Raupach (1993)
Raupach (1993) presents the following amplification factor for the shear velocity threshold due to the presence of roughness elements.
\[R_t = \frac{u_{*,th,s}}{u_{*,th,r}} = \sqrt{\frac{\tau_s''}{\tau}} = \frac{1}{\sqrt{\left( 1  m \sigma \lambda \right) \left( 1 + m \beta \lambda \right)}}\]\(m\) is a constant smaller or equal to unity that accounts for the difference between the average stress on the bed surface \(\tau_s\) and the maximum stress on the bed surface \(\tau_s''\).
\(\beta\) is the stress partition coefficient defined as the ratio between the drag coefficient of the roughness element itself \(C_r\) and the drag coefficient of the bare surface without roughness elements \(C_s\).
\(\sigma\) is the shape coefficient defined as the basal area divided by the frontal area: \(\frac{A_b}{A_f}\). For hemispheres \(\sigma = 2\), for spheres \(\sigma = 1\).
\(\lambda\) is the roughness density defined as the number of elements per surface area \(\frac{n}{S}\) multiplied by the frontal area of a roughness element \(A_f\), also known as the frontal area index:
\[\lambda = \frac{n b h}{S} = \frac{n A_f}{S}\]If multiplied by \(\sigma\) the equation simplifies to the mass fraction of nonerodible elements:
\[\sigma \lambda = \frac{n A_b}{S} = \sum_{k=n_0}^{n_k} \hat{w}^{\mathrm{bed}}_k\]where \(k\) is the fraction index, \(n_0\) is the smallest nonerodible fraction, \(n_k\) is the largest nonerodible fraction and \(\hat{w}^{\mathrm{bed}}_k\) is the mass fraction of sediment fraction \(k\). It is assumed that the fractions are ordered by increasing size.
Substituting the derivation in the Raupach (1993) equation gives the formulation implemented in this function:
\[u_{*,th,r} = u_{*,th,s} * \sqrt{\left( 1  m \sum_{k=n_0}^{n_k} \hat{w}^{\mathrm{bed}}_k \right) \left( 1 + m \frac{\beta}{\sigma} \sum_{k=n_0}^{n_k} \hat{w}^{\mathrm{bed}}_k \right)}\]Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict
Tides, meteorology and soil moisture content¶

hydro.
interpolate
(s, p, t)[source]¶ Interpolate hydrodynamic and meteorological conditions to current time step
Interpolates the hydrodynamic and meteorological time series to the current time step, if available. Meteorological parameters are stored as dictionary rather than a single value.
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
 t (float) – Current time
Returns: Spatial grids
Return type: dict

hydro.
saturation_pressure
(T)[source]¶ Compute saturation pressure based on air temperature
Parameters: T (float) – Air temperature in degrees Celcius Returns: Saturation pressure Return type: float

hydro.
update
(s, p, dt)[source]¶ Update soil moisture content
Updates soil moisture content in all cells. The soil moisure content in flooded cells is set to the porosity. The soil moisture content in nonflooded cells is decreased by simulating infiltration using an exponential decay function with a rate
F
following:\[M = M \cdot e^{F \cdot \Delta t}\]Cells are considered flooded if the water depth is larger than
eps
. Aditionally, is meteorological conditions are provided the soil moisture content is decreased by simulating evaporation following the Penman equation:\[M = M  \frac{m \cdot R + \gamma \cdot 6.43 \cdot (1 + 0.536 \cdot u]) \cdot \delta}{\lambda \cdot (m + \gamma)}\]Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
 dt (float) – Current time step
Returns: Spatial grids
Return type: dict
Sediment transport¶

transport.
compute_weights
(s, p)[source]¶ Compute weights for sediment fractions
Multifraction sediment transport needs to weigh the transport of each sediment fraction to prevent the sediment transport to increase with an increasing number of sediment fractions. The weighing is not uniform over all sediment fractions, but depends on the sediment availibility in the air and the bed and the bed interaction parameter
bi
.Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Array with weights for each sediment fraction
Return type: numpy.ndarray

transport.
equilibrium
(s, p)[source]¶ Compute equilibrium sediment concentration following Bagnold (1937)
Parameters:  s (dict) – Spatial grids
 p (dict) – Model configuration parameters
Returns: Spatial grids
Return type: dict

transport.
renormalize_weights
(w, ix)[source]¶ Renormalizes weights for sediment fractions
Renormalizes weights for sediment fractions such that the sum of all weights is unity. To ensure that the erosion of specific fractions does not exceed the sediment availibility in the bed, the normalization only modifies the weights with index equal or larger than
ix
.Parameters:  w (numpy.ndarray) – Array with weights for each sediment fraction
 ix (int) – Minimum index to be modified
Returns: Array with weights for each sediment fraction
Return type: numpy.ndarray
Helper modules¶
Input/Output¶

inout.
check_configuration
(p)[source]¶ Check model configuration validity
Checks if required parameters are set and if the references files for bathymetry, wind, tide and meteorological input are valid. Throws an error if one or more requirements are not met.
Parameters: p (dict) – Model configuration dictionary with parsed files See also

inout.
parse_value
(val, parse_files=True, force_list=False)[source]¶ Casts a string to the most appropriate variable type
Parameters:  val (str) – String representation of value
 parse_files (bool) – If True, files referred to by string parameters are parsed by
numpy.loadtxt
 force_list – If True, interpret the value as a list, even if it consists of a single value
Returns: Casted value
Return type: misc
Examples
>>> type(parse_value('T')) bool >>> type(parse_value('F')) bool >>> type(parse_value('123')) int >>> type(parse_value('123.2')) float >>> type(parse_value('euler_forward')) str >>> type(parse_value('')) NoneType >>> type(parse_value('zb zs Ct')) numpy.ndarray >>> type(parse_value('zb', force_list=True)) numpy.ndarray >>> type(parse_value('0.1 0.2 0.3')[0]) float >>> type(parse_value('wind.txt'), parse_files=True) numpy.ndarray >>> type(parse_value('wind.txt'), parse_files=False) str

inout.
read_configfile
(configfile, parse_files=True, load_defaults=True)[source]¶ Read model configuration file
Updates default model configuration based on a model configuration file. The model configuration file should be a text file with one parameter on each line. The parameter name and value are seperated by an equal sign (=). Any lines that start with a percent sign (%) or do not contain an equal sign are omitted.
Parameters are casted into the best matching variable type. If the variable type is
str
it is optionally interpreted as a filename. If the corresponding file is found it is parsed using thenumpy.loadtxt
function.Parameters:  configfile (str) – Model configuration file
 parse_files (bool) – If True, files referred to by string parameters are parsed
 load_defaults (bool) – If True, default settings are loaded and overwritten by the settings from the configuration file
Returns: Dictionary with casted and optionally parsed model configuration parameters
Return type: dict
See also

inout.
write_configfile
(configfile, p=None)[source]¶ Write model configuration file
Writes model configuration to file. If no model configuration is given, the default configuration is written to file. Any parameters with a name ending with _file and holding a matrix are treated as separate files. The matrix is then written to an ASCII file using the
numpy.savetxt
function and the parameter value is replaced by the name of the ASCII file.Parameters:  configfile (str) – Model configuration file
 p (dict, optional) – Dictionary with model configuration parameters
Returns: Dictionary with casted and optionally parsed model configuration parameters
Return type: dict
See also
netCDF4 output¶

netcdf.
append
(outputfile, variables)[source]¶ Append variables to existing netCDF4 output file
Increments the time axis length with one and appends the provided spatial grids along the time axis. The
variables
dictionary should at least have thetime
field indicating the current simulation time. The CF time bounds are updated accordingly.Parameters:  outputfile (str) – Name of netCDF4 output file
 variables (dict) – Dictionary with spatial grids and time
Examples
>>> netcdf.append('aeolis.nc', {'time', 3600., ... 'Ct', np.array([[0.,0., ... ,0.]]), ... 'Cu', np.array([[1.,1., ... ,1.]]))
See also

netcdf.
dump
(outputfile, dumpfile, var='mass', ix=1)[source]¶ Dumps time slice from netCDF4 output file to ASCII file
This function can be used to use a specific time slice from a netCDF4 output file as input file for another AeoLiS model run. For example, the bed composition from a spinup run can be used as initial composition for other runs reducing the spinup time.
Parameters:  outputfile (str) – Name of netCDF4 output file
 dumpfile (str) – Name of ASCII dump file
 var (str, optional) – Name of spatial grid to be dumped (default: mass)
 ix (int) – Time slice index to be dumped (default: 1)
Examples
>>> # use bedcomp_file = bedcomp.txt in model configuration file ... netcdf.dump('aeolis.nc', 'bedcomp.txt', var='mass')

netcdf.
initialize
(outputfile, outputvars, s, p, dimensions)[source]¶ Create empty CFcompatible netCDF4 output file
Parameters:  outputfile (str) – Name of netCDF4 output file
 outputvars (dictionary) – Spatial grids to be written to netCDF4 output file
 s (dict) – Spatial grids
 p (dict) – Model configuration parameters
 dimensions (dict) – Dictionary that specifies a tuple with the named dimensions for each spatial grid (e.g. (‘ny’, ‘nx’, ‘nfractions’))
Examples
>>> netcdf.initialize('aeolis.nc', ... ['Ct', 'Cu', 'zb'], ... ['avg', 'max'], ... s, p, {'Ct':('ny','nx','nfractions'), ... 'Cu':('ny','nx','nfractions'), ... 'zb':('ny','nx')})

netcdf.
parse_metadata
(outputvars)[source]¶ Parse metadata from constants.py
Parses the Python comments in constants.py to extract meta data, like units, for the model state variables that can be used as netCDF4 meta data.
Parameters: outputvars (dictionary) – Spatial grids to be written to netCDF4 output file Returns: meta – Dictionary with meta data for the output variables Return type: dict
Plotting¶
Commandline tools¶

console.
aeolis
()[source]¶ aeolis : a processbased model for simulating supplylimited aeolian sediment transport
 Usage:
 aeolis <config> [options]
 Positional arguments:
 config configuration file
 Options:
h, help show this help message and exit callback=FUNC reference to callback function (e.g. example/callback.py:callback) restart=FILE model restart file verbose=LEVEL logging verbosity [default: 20] debug write debug logs

console.
wind
()[source]¶ aeoliswind : a wind time series generation tool for the aeolis model
 Usage:
 aeoliswind <file> [–mean=MEAN] [–max=MAX] [–duration=DURATION] [–timestep=TIMESTEP]
 Positional arguments:
 file output file
 Options:
h, help show this help message and exit mean=MEAN mean wind speed [default: 10] max=MAX maximum wind speed [default: 30] duration=DURATION duration of time series [default: 3600] timestep=TIMESTEP timestep of time series [default: 60]
Miscellaneous¶

utils.
apply_mask
(arr, mask)[source]¶ Apply complex mask
The real part of the complex mask is multiplied with the input array. Subsequently the imaginary part is added and the result returned.
The shape of the mask is assumed to match the first few dimensions of the input array. If the input array is larger than the mask, the mask is repeated for any additional dimensions.
Parameters:  arr (numpy.ndarray) – Array or matrix to which the mask needs to be applied
 mask (numpy.ndarray) – Array or matrix with complex mask values
Returns: arr – Array or matrix to which the mask is applied
Return type: numpy.ndarray

utils.
format_log
(msg, ncolumns=2, **props)[source]¶ Format log message into columns
Prints log message and additional data into a column format that fits into a 70 character terminal.
Parameters:  msg (str) – Main log message
 ncolumns (int) – Number of columns
 props (key/value pairs) – Properties to print in column format
Returns: Formatted log message
Return type: str
Note
Properties names starting with
min
,max
ornr
are respectively replaced bymin.
,max.
or#
.

utils.
interp_array
(x, xp, fp, circular=False, **kwargs)[source]¶ Interpolate multiple time series at once
Parameters:  x (array_like) – The xcoordinates of the interpolated values.
 xp (1D sequence of floats) – The xcoordinates of the data points, must be increasing.
 fp (2D sequence of floats) – The ycoordinates of the data points, same length as
xp
.  circular (bool) – Use the
interp_circular()
function rather than thenumpy.interp()
function.  kwargs (dict) – Keyword options to the
numpy.interp()
function
Returns: The interpolated values, same length as second dimension of
fp
.Return type: ndarray

utils.
interp_circular
(x, xp, fp, **kwargs)[source]¶ Onedimensional linear interpolation.
Returns the onedimensional piecewise linear interpolant to a function with given values at discrete datapoints. Values beyond the limits of
x
are interpolated in circular manner. For example, a value ofx > x.max()
evaluates asf(xx.max())
assuming thatx.max()  x < x.max()
.Parameters:  x (array_like) – The xcoordinates of the interpolated values.
 xp (1D sequence of floats) – The xcoordinates of the data points, must be increasing.
 fp (1D sequence of floats) – The ycoordinates of the data points, same length as
xp
.  kwargs (dict) – Keyword options to the
numpy.interp()
function
Returns: y – The interpolated values, same shape as
x
.Return type: {float, ndarray}
Raises: ValueError
– Ifxp
andfp
have different length

utils.
normalize
(x, ref=None, axis=0, fill=0.0)[source]¶ Normalize array
Normalizes an array to make it sum to unity over a specific axis. The procedure is safe for dimensions that sum to zero. These dimensions return the
fill
value instead.Parameters:  x (array_like) – The array to be normalized
 ref (array_like, optional) – Alternative normalization reference, if not specified, the sum of x is used
 axis (int, optional) – The normalization axis (default: 0)
 fill (float, optional) – The return value for allzero dimensions (default: 0.)

utils.
prevent_tiny_negatives
(x, max_error=1e10, replacement=0.0)[source]¶ Replace tiny negative values in array
Parameters:  x (np.ndarray) – Array with potential tiny negative values
 max_error (float) – Maximum absolute value to be replaced
 replacement (float) – Replacement value
Returns: Array with tiny negative values removed
Return type: np.ndarray