MizerSim
MizerSim(_r_obj, _env)Python wrapper around an R MizerSim object.
MizerSim stores time-resolved output from a projection. The wrapper exposes common summaries as labelled pandas and xarray objects.
Examples
import pymizer as mz
params = mz.new_community_params(no_w=20)
sim = params.project(t_max=5, dt=0.1, t_save=1, progress_bar=False)
biomass = sim.biomass()Attributes
| Name | Description |
|---|---|
| r | Access the underlying R object. |
Methods
| Name | Description |
|---|---|
| abundance | Return total abundance through time as a pandas DataFrame. |
| biomass | Return biomass through time as a pandas DataFrame. |
| biomass_tidy | Return biomass through time in tidy long-form pandas format. |
| community_slope | Return the fitted community size-spectrum slope through time. |
| diet | Return diet composition at the final simulated state. |
| f_mort | Return fishing mortality through time. |
| f_mort_gear | Return gear-resolved fishing mortality through time. |
| feeding_level | Return feeding levels through time. |
| growth_curves | Return growth curves evaluated from the final simulation state. |
| initial_n | Return the initial fish abundance density spectrum used by the simulation. |
| initial_n_resource | Return the initial resource spectrum used by the simulation. |
| mean_max_weight | Return the mean maximum weight through time. |
| mean_weight | Return mean community weight through time. |
| n | Return the species abundance array. |
| n_resource | Return the resource abundance array. |
| params | Return the MizerParams used to create the simulation. |
| plot_biomass | Plot biomass through time with matplotlib and return the axes. |
| pred_mort | Return predation mortality through time. |
| pred_rate | Return predation rate at the final simulated state. |
| proportion_of_large_fish | Return the proportion of large fish through time. |
| save_rds | Serialise the wrapped simulation object as a generic .rds file. |
| ssb | Return spawning stock biomass through time as a pandas DataFrame. |
| times | Return saved times as a NumPy array. |
| trophic_level | Return trophic level at size at the final simulated state. |
| trophic_level_by_species | Return the species-level trophic level at the final simulated state. |
| yield_ | Return fisheries yield through time as a pandas DataFrame. |
| yield_gear | Return gear-resolved fisheries yield through time. |
abundance
MizerSim.abundance(min_w=None, max_w=None, min_l=None, max_l=None)Return total abundance through time as a pandas DataFrame.
biomass
MizerSim.biomass(
use_cutoff=False,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return biomass through time as a pandas DataFrame.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| use_cutoff | bool | Use the biomass_cutoff species parameter when available. |
False |
| min_w | float | list[float] | None | Minimum weight filter. | None |
| max_w | float | list[float] | None | Maximum weight filter. | None |
| min_l | float | list[float] | None | Minimum length filter. | None |
| max_l | float | list[float] | None | Maximum length filter. | None |
biomass_tidy
MizerSim.biomass_tidy(
species=None,
use_cutoff=False,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return biomass through time in tidy long-form pandas format.
The returned DataFrame has time, species, and biomass columns, which makes it easy to use with seaborn-style APIs or custom notebook analysis pipelines.
community_slope
MizerSim.community_slope(
species=None,
biomass=True,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return the fitted community size-spectrum slope through time.
diet
MizerSim.diet(proportion=True, as_xarray=True)Return diet composition at the final simulated state.
This method evaluates diet on a params object rebuilt from the final simulated state.
f_mort
MizerSim.f_mort(as_xarray=True)Return fishing mortality through time.
f_mort_gear
MizerSim.f_mort_gear(as_xarray=True)Return gear-resolved fishing mortality through time.
feeding_level
MizerSim.feeding_level(as_xarray=True)Return feeding levels through time.
growth_curves
MizerSim.growth_curves(species=None, max_age=20, percentage=False)Return growth curves evaluated from the final simulation state.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| species | str | list[str] | tuple[str, …] | None | Optional species subset. | None |
| max_age | float | Maximum age to evaluate. | 20 |
| percentage | bool | Return size as a percentage of w_max. |
False |
initial_n
MizerSim.initial_n(as_xarray=True)Return the initial fish abundance density spectrum used by the simulation.
initial_n_resource
MizerSim.initial_n_resource()Return the initial resource spectrum used by the simulation.
mean_max_weight
MizerSim.mean_max_weight(
measure='both',
*,
species=None,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return the mean maximum weight through time.
mean_weight
MizerSim.mean_weight(
species=None,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return mean community weight through time.
n
MizerSim.n(as_xarray=True)Return the species abundance array.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| as_xarray | bool | When True, return an xarray.DataArray with dimensions ("time", "sp", "w"). |
True |
n_resource
MizerSim.n_resource(as_xarray=True)Return the resource abundance array.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| as_xarray | bool | When True, return an xarray.DataArray with dimensions ("time", "w"). |
True |
params
MizerSim.params()Return the MizerParams used to create the simulation.
plot_biomass
MizerSim.plot_biomass(
species=None,
use_cutoff=False,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
ax=None,
**kwargs,
)Plot biomass through time with matplotlib and return the axes.
This is an opinionated convenience wrapper around sim.biomass() for quick notebook exploration. For more control, call biomass() or biomass_tidy() directly and build the plot yourself.
pred_mort
MizerSim.pred_mort(as_xarray=True)Return predation mortality through time.
pred_rate
MizerSim.pred_rate(as_xarray=True)Return predation rate at the final simulated state.
This uses setInitialValues(getParams(sim), sim) under the hood so that the predation rate is evaluated on the final simulated state.
proportion_of_large_fish
MizerSim.proportion_of_large_fish(
species=None,
threshold_w=100,
threshold_l=None,
biomass_proportion=True,
min_w=None,
max_w=None,
min_l=None,
max_l=None,
)Return the proportion of large fish through time.
save_rds
MizerSim.save_rds(path)Serialise the wrapped simulation object as a generic .rds file.
ssb
MizerSim.ssb()Return spawning stock biomass through time as a pandas DataFrame.
times
MizerSim.times()Return saved times as a NumPy array.
trophic_level
MizerSim.trophic_level(as_xarray=True)Return trophic level at size at the final simulated state.
trophic_level_by_species
MizerSim.trophic_level_by_species()Return the species-level trophic level at the final simulated state.
yield_
MizerSim.yield_()Return fisheries yield through time as a pandas DataFrame.
yield_gear
MizerSim.yield_gear(as_xarray=True)Return gear-resolved fisheries yield through time.