seapopym.function package

Subpackages

Submodules

seapopym.function.apply_coefficient_to_primary_production module

Wrapper for the application of the transfert cooeficient to primary production. Use xarray.map_block.

seapopym.function.apply_coefficient_to_primary_production.primary_production_by_fgroup(state: SeapopymState) → xr.Dataset

It is equivalent to generate the fisrt cohort of pre-production.

Input

  • primary_production [time, latitude, longitude]

  • functional_group_coefficient [functional_group]

Output

  • primary_production [functional_group, time, latitude, longitude]

seapopym.function.apply_mask_to_state module

This module provides a function to apply a mask to a Seapopym state dataset.

The apply_mask_to_state function checks if a global mask is present in the state dataset and applies it to filter the dataset accordingly. If no mask is found, the state dataset is returned unchanged.

seapopym.function.apply_mask_to_state.apply_mask_to_state(state: SeapopymState) → SeapopymState

Apply a mask to a state dataset.

seapopym.function.average_acidity module

An average acidity by fgroup computation wrapper. Use xarray.map_block.

seapopym.function.average_acidity.average_acidity(state: SeapopymState) → Dataset

In the open ocean, pH does not exhibit significant temporal or spatial variability. However, in coastal shelf and estuarine regions, pH can fluctuate both diurnally and spatially due to biological activity and freshwater input. Additionally, pH varies with depth. This function is designed to capture such potential variability (e.g. could be useful in a future version of the model that includes vertical diurnal migration of pteropods.).

Depend on: - compute_daylength - mask_by_fgroup.

Input

  • mask_by_fgroup() [time, latitude, longitude]

  • compute_daylength() [functional_group, latitude, longitude] in day

  • day/night_layer [functional_group]

  • acidity [time, latitude, longitude, layer] dimensionless (pH)

Output

  • average_acidity [functional_group, time, latitude, longitude] dimensionless (pH)

seapopym.function.average_temperature module

An average temperature by fgroup computation wrapper. Use xarray.map_block.

seapopym.function.average_temperature.average_temperature(state: SeapopymState) → xr.Dataset

Depend on: - compute_daylength - mask_by_fgroup.

Input

  • mask_by_fgroup() [time, latitude, longitude]

  • compute_daylength() [functional_group, latitude, longitude] in day

  • day/night_layer [functional_group]

  • temperature [time, latitude, longitude, layer] in degC

Output

  • avg_temperature [functional_group, time, latitude, longitude] in degC

seapopym.function.biomass module

This module contains the post-production function used to compute the biomass. They are run after the production process.

seapopym.function.biomass.biomass(state: SeapopymState) → xr.Dataset

Wrap the biomass computation arround the Numba function biomass_sequence.

seapopym.function.cell_area module

Module specific to the cell surface area computation.

seapopym.function.cell_area.cell_area(state: SeapopymState) → xr.Dataset

Compute the cell area from the latitude and longitude.

Input

  • latitude [latitude]

  • longitude [longitude]

  • resolution

Output

  • cell_area [latitude, longitude]s

seapopym.function.day_length module

Functions used to calculate the length of the day.

seapopym.function.day_length.day_length(state: SeapopymState) → xr.Dataset

seapopym.function.global_mask module

Funcitons used to generate a landmask from any forcing data.

seapopym.function.global_mask.global_mask(state: SeapopymState) → xr.Dataset

Create a global mask from temperature forcing in the state of the model.

seapopym.function.limits module

Compute the layers depth. Based on the O. Titaud work from 2018.

seapopym.function.limits.compute_limits_from_zeu_lat_sst(zeu: DataArray, sst: DataArray, layer_bounds: dict | None = None) → Dataset

Compute the layers limits from zeu and definition. Apply a correction based on latitude and sst.

Based on 2020 report evolution for cmems.

Parameters

zeuxarray.DataArray

3D DataArray containing euphotic depth data.

sstxarray.DataArray

3D DataArray countaining sea surface temperature data.

layer_boundsdict

layer bounds definition w.r.t. zeu Default: {“epipelagic”: 1.5, “upper_mesopelagic”: 4.5, “lower_mesopelagic”: 10.5, “treshold”: 1000.0}

Returns

xarray.Dataset

A Dataset containing limits of layers wrt to euphotic depth.

seapopym.function.mask_by_functional_group module

Funcitons used to generate a landmask from any forcing data.

seapopym.function.mask_by_functional_group.mask_by_fgroup(state: SeapopymState) → xr.Dataset

The mask_by_fgroup has at least 3 dimensions (lat, lon, layer) and is a boolean array.

Output

  • mask_by_fgroup [functional_group, latitude, longitude] -> boolean

seapopym.function.mask_temperature module

A temperature mask computation wrapper. Use xarray.map_block.

seapopym.function.mask_temperature.mask_temperature(state: SeapopymState) → xr.Dataset

It uses the min_temperature.

Depend on

  • min_temperature()

  • average_temperature()

Input

  • min_temperature [cohort_age]

  • average_temperature [functional_group, time, latitude, longitude]

Output

  • mask_temperature_by_cohort_by_functional_group [functional_group, time, latitude, longitude, cohort_age]

NOTE(Jules): Warning : average temperature by functional group (because of daily vertical migration) and not by layer. We therefore have a function with a high cost in terms of computation and memory space.

seapopym.function.min_temperature module

An average temperature by fgroup computation wrapper. Use xarray.map_block.

seapopym.function.min_temperature.min_temperature_by_cohort(state: SeapopymState) → xr.Dataset

Define the minimal temperature of a cohort to be recruited.

Input

  • mean_timestep [functional_group, cohort_age]

  • tr_max [functional_group]

  • tr_rate [functional_group]

Output

  • min_temperature [functional_group, cohort_age] : a datarray with cohort_age as coordinate and

minimum temperature as value.

Note:

The minimal temperature for recruitment is defined as: - Temperature = log(Tau_r / Tau_r_0) / Gamma_Tau_r Which is calculated from the equation Tau_r = Tau_r_0 * exp(Gamma_Tau_r * Temperature) Where Tau_r is equal to the cohorte age (delta_t -> Tau_r_0).

seapopym.function.mortality_acidity_field module

Mortality field calculations for ocean acidification effects.

seapopym.function.mortality_acidity_field.mortality_acidity_bed_field(state: SeapopymState) → xr.Dataset

Generate mortality field using Bednarsek et al. (2022) equation.

Uses the linear relationship between temperature, pH and mortality from Bednarsek equation: mortality = lambda_0_bed + gamma_lambda_temperature_bed * T + gamma_lambda_acidity_bed * pH The result is converted to a daily mortality rate.

Parameters

stateSeapopymState

The model state containing forcing and configuration data.

Returns

xr.Dataset

Dataset containing the mortality_field variable with Bednarsek mortality rates.

Notes

  • Original Bednarsek equation gives weekly mortality percentage

  • Converted to daily rate by dividing by (100 * 7)

  • Negative values are clipped to 0

seapopym.function.mortality_acidity_field.mortality_acidity_field(state: SeapopymState) → xr.Dataset

Use the relation between temperature, pH and mortality to generate the mortality field.

Depend on

  • average_temperature()

  • acidity

Input

  • average_temperature [functional_group, time, latitude, longitude]

  • acidity [time, latitude, longitude] (pH)

  • inv_lambda_max [functional_group]

  • inv_lambda_rate [functional_group]

Output

  • mortality_field [functional_group, time, latitude, longitude]

seapopym.function.mortality_field module

A temperature mask computation wrapper. Use xarray.map_block.

seapopym.function.mortality_field.mortality_field(state: SeapopymState) → xr.Dataset

Use the relation between temperature and mortality to generate the mortality field.

Depend on

  • average_temperature()

Input

  • average_temperature [functional_group, time, latitude, longitude]

  • lambda_temperature_0 [functional_group]

  • gamma_lambda_temperature [functional_group]

Output

  • mortality_field [functional_group, time, latitude, longitude]

Note:

The mortality field is computed as follow: - lambda = lambda_temperature_0 * exp(gamma_lambda_temperature * T) - B_t = B_(t-1) * exp(-dt * lambda)

Which is equivalent to: - tau_m = tau_m_0 * exp(gamma_tau_m * T) Where tau_m is equal to 1/lambda, tau_m_0 is equal to 1/lambda_temperature_0 and gamma_tau_m is equal to -gamma_lambda_temperature.

seapopym.function.production module

This module contains the function used to compute the recruited population in the NotTransport model. They are run in sequence in timeseries order.

seapopym.function.production.production(state: SeapopymState) → xr.Dataset

Compute the production using a numba jit function.

seapopym.function.production.production_initial_condition(state: SeapopymState) → xr.Dataset

Compute the production using a numba jit function. Export the initial conditions.

seapopym.function.production.production_space_optimized(state: SeapopymState) → xr.Dataset

Compute the production using a numba jit function.

seapopym.function.production.production_unrecruited(state: SeapopymState) → xr.Dataset

Compute the production using a numba jit function. Export the unrecruited production (pre-production).

Module contents