Light (memory) model

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr

from seapopym.configuration.no_transport import (
    ForcingParameter,
    ForcingUnit,
    FunctionalGroupParameter,
    FunctionalGroupUnit,
    FunctionalTypeParameter,
    MigratoryTypeParameter,
    NoTransportConfiguration,
)
from seapopym.configuration.no_transport.environment_parameter import ChunkParameter, EnvironmentParameter
from seapopym.model.no_transport_model import NoTransportLightModel
from seapopym.standard import coordinates
from seapopym.standard.units import StandardUnitsLabels

Generating data for the 1D simulation

Let’s generate some data for the 1D simulation. In this NoTransport model, only temperature and primary production are required. The temperature is generated as a sine wave with a period of 1 year and the primary production is randomly generated.

time_axis = coordinates.new_time(pd.date_range("2000-01-01", "2001-01-01", freq="D"))
n = int(time_axis.size)
t = np.linspace(0, 1, n)
temperature = 15 + 15 * np.sin(2 * np.pi * t)
primary_production = 10 + 5 * np.sin(2 * np.pi * t * 365)

temperature = xr.DataArray(
    dims=["time", "latitude", "longitude", "layer"],
    coords={
        "time": coordinates.new_time(pd.date_range("2000-01-01", "2001-01-01", freq="D")),
        "latitude": coordinates.new_latitude([0]),
        "longitude": coordinates.new_longitude([0]),
        "layer": coordinates.new_layer([0]),
    },
    attrs={"units": StandardUnitsLabels.temperature},
    data=temperature[:, np.newaxis, np.newaxis, np.newaxis],
)

plt.figure(figsize=(9, 3))
temperature[:, 0, 0].cf.plot.line(x="T")
plt.title("Temperature")
plt.show()

primary_production = xr.DataArray(
    dims=["time", "latitude", "longitude"],
    coords={
        "time": coordinates.new_time(pd.date_range("2000-01-01", "2001-01-01", freq="D")),
        "latitude": coordinates.new_latitude([0]),
        "longitude": coordinates.new_longitude([0]),
    },
    attrs={"units": StandardUnitsLabels.production},
    data=np.random.rand(367, 1, 1),
)

plt.figure(figsize=(9, 3))
primary_production.plot()
plt.title("Primary Production")
plt.show()

dataset = xr.Dataset({"temperature": temperature, "primary_production": primary_production})

Initialize the model

First we set up the model parameters. We will define a single functional group with the commonly used parameters for the zooplankton in Seapodym LMTL.

day_layer = 0
night_layer = 0
tr_0 = 10.38
gamma_tr = -0.11
lambda_temperature_0 = 1 / 150
gamma_lambda_temperature = 0.15

f_groups = FunctionalGroupParameter(
    functional_group=[
        FunctionalGroupUnit(
            name=f"D{day_layer}N{night_layer}",
            energy_transfert=0.1668,
            migratory_type=MigratoryTypeParameter(day_layer=day_layer, night_layer=night_layer),
            functional_type=FunctionalTypeParameter(
                lambda_temperature_0=lambda_temperature_0,
                gamma_lambda_temperature=gamma_lambda_temperature,
                tr_0=tr_0,
                gamma_tr=gamma_tr,
            ),
        ),
        FunctionalGroupUnit(
            name=f"D{day_layer}N{night_layer}_BIS",
            energy_transfert=0.1668 / 2,
            migratory_type=MigratoryTypeParameter(day_layer=day_layer, night_layer=night_layer),
            functional_type=FunctionalTypeParameter(
                lambda_temperature_0=lambda_temperature_0 / 2,
                gamma_lambda_temperature=gamma_lambda_temperature / 2,
                tr_0=tr_0 / 2,
                gamma_tr=gamma_tr / 2,
            ),
        ),
    ]
)

p_param = ForcingParameter(
    temperature=ForcingUnit(forcing=dataset["temperature"]),
    primary_production=ForcingUnit(forcing=dataset["primary_production"]),
)

parameters = NoTransportConfiguration(forcing=p_param, functional_group=f_groups)
no_transport_model = NoTransportLightModel.from_configuration(configuration=parameters)
no_transport_model.state

<xarray.Dataset> Size: 10kB
Dimensions:                     (time: 367, latitude: 1, longitude: 1,
                                 layer: 1, functional_group: 2, cohort: 11)
Coordinates:
  * time                        (time) datetime64[ns] 3kB 2000-01-01 ... 2001...
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * layer                       (layer) int64 8B 0
  * cohort                      (cohort) int64 88B 0 1 2 3 4 5 6 7 8 9 10
  * functional_group            (functional_group) int64 16B 0 1
Data variables: (12/18)
    temperature                 (time, latitude, longitude, layer) float64 3kB ...
    primary_production          (time, latitude, longitude) float64 3kB 0.793...
    name                        (functional_group) <U8 64B 'D0N0' 'D0N0_BIS'
    energy_transfert            (functional_group) float64 16B 0.1668 0.0834
    lambda_temperature_0        (functional_group) float64 16B 0.006667 0.003333
    gamma_lambda_temperature    (functional_group) float64 16B 0.15 0.075
    ...                          ...
    max_timestep                (functional_group, cohort) float64 176B 1.0 ....
    mean_timestep               (functional_group, cohort) float64 176B 1.0 ....
    timestep                    float64 8B 1.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • time: 367
  • latitude: 1
  • longitude: 1
  • layer: 1
  • functional_group: 2
  • cohort: 11
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2001-01-01

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2000-12-30T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-01T00:00:00.000000000'],
          shape=(367,), dtype='datetime64[ns]')

  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

  • cohort
    (cohort)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    long_name :

    cohort

    standard_name :

    cohort

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

• Data variables: (18)
  • temperature
    (time, latitude, longitude, layer)
    float64
    15.0 15.26 15.51 ... 14.74 15.0

    units :

    degree_Celsius

    array([[[[1.50000000e+01]]],
    
    
           [[[1.52574949e+01]]],
    
    
           [[[1.55149140e+01]]],
    
    
           [[[1.57721813e+01]]],
    
    
           [[[1.60292211e+01]]],
    
    
           [[[1.62859575e+01]]],
    
    
           [[[1.65423150e+01]]],
    
    ...
    
           [[[1.34576850e+01]]],
    
    
           [[[1.37140425e+01]]],
    
    
           [[[1.39707789e+01]]],
    
    
           [[[1.42278187e+01]]],
    
    
           [[[1.44850860e+01]]],
    
    
           [[[1.47425051e+01]]],
    
    
           [[[1.50000000e+01]]]])

  • primary_production
    (time, latitude, longitude)
    float64
    0.7936 0.1527 ... 0.8473 0.3407

    units :

    kilogram / day / meter ** 2

    array([[[0.79355271]],
    
           [[0.15271976]],
    
           [[0.656222  ]],
    
           [[0.5724233 ]],
    
           [[0.60933407]],
    
           [[0.72385632]],
    
           [[0.79391143]],
    
           [[0.54772632]],
    
           [[0.56883569]],
    
           [[0.2173121 ]],
    
    ...
    
           [[0.70014506]],
    
           [[0.33254986]],
    
           [[0.13774381]],
    
           [[0.07987521]],
    
           [[0.12176378]],
    
           [[0.08526655]],
    
           [[0.11642395]],
    
           [[0.89875684]],
    
           [[0.8472787 ]],
    
           [[0.34066477]]])

  • name
    (functional_group)
    <U8
    'D0N0' 'D0N0_BIS'

    array(['D0N0', 'D0N0_BIS'], dtype='<U8')

  • energy_transfert
    (functional_group)
    float64
    0.1668 0.0834

    units :

    dimensionless

    array([0.1668, 0.0834])

  • lambda_temperature_0
    (functional_group)
    float64
    0.006667 0.003333

    units :

    1 / day

    array([0.00666667, 0.00333333])

  • gamma_lambda_temperature
    (functional_group)
    float64
    0.15 0.075

    units :

    1 / delta_degree_Celsius

    array([0.15 , 0.075])

  • tr_0
    (functional_group)
    float64
    10.38 5.19

    units :

    day

    array([10.38,  5.19])

  • gamma_tr
    (functional_group)
    float64
    -0.11 -0.055

    units :

    1 / delta_degree_Celsius

    array([-0.11 , -0.055])

  • day_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • night_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • timesteps_number
    (functional_group, cohort)
    float64
    1.0 1.0 1.0 1.0 ... nan nan nan nan

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    array([[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.,  1.,  1., nan, nan, nan, nan, nan]])

  • min_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The minimum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • max_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The maximum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • mean_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The mean timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • timestep
    ()
    float64
    1.0

    units :

    day

    array(1.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
                   '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
                   '2000-01-09', '2000-01-10',
                   ...
                   '2000-12-23', '2000-12-24', '2000-12-25', '2000-12-26',
                   '2000-12-27', '2000-12-28', '2000-12-29', '2000-12-30',
                   '2000-12-31', '2001-01-01'],
                  dtype='datetime64[ns]', name='time', length=367, freq='D'))

  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='cohort'))

  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

• Attributes: (0)

no_transport_model.expected_memory_usage

'The expected memory usage is 0.05 MB.'

Run the model

no_transport_model.run()
no_transport_model.state

<xarray.Dataset> Size: 10kB
Dimensions:                     (time: 367, latitude: 1, longitude: 1,
                                 functional_group: 2, cohort: 11, layer: 1)
Coordinates:
  * time                        (time) datetime64[ns] 3kB 2000-01-01 ... 2001...
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * functional_group            (functional_group) int64 16B 0 1
  * cohort                      (cohort) int64 88B 0 1 2 3 4 5 6 7 8 9 10
  * layer                       (layer) int64 8B 0
Data variables: (12/17)
    biomass                     (functional_group, time, latitude, longitude) float64 6kB ...
    name                        (functional_group) <U8 64B 'D0N0' 'D0N0_BIS'
    energy_transfert            (functional_group) float64 16B 0.1668 0.0834
    lambda_temperature_0        (functional_group) float64 16B 0.006667 0.003333
    gamma_lambda_temperature    (functional_group) float64 16B 0.15 0.075
    tr_0                        (functional_group) float64 16B 10.38 5.19
    ...                          ...
    max_timestep                (functional_group, cohort) float64 176B 1.0 ....
    mean_timestep               (functional_group, cohort) float64 176B 1.0 ....
    timestep                    float64 8B 1.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • time: 367
  • latitude: 1
  • longitude: 1
  • functional_group: 2
  • cohort: 11
  • layer: 1
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2001-01-01

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2000-12-30T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-01T00:00:00.000000000'],
          shape=(367,), dtype='datetime64[ns]')

  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

  • cohort
    (cohort)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    long_name :

    cohort

    standard_name :

    cohort

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

• Data variables: (17)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    0.0 0.1324 0.1491 ... 5.297 5.318

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    array([[[[0.        ]],
    
            [[0.13236459]],
    
            [[0.14909579]],
    
            [[0.24833224]],
    
            [[0.326143  ]],
    
            [[0.40369743]],
    
            [[0.49350487]],
    
            [[0.58669707]],
    
            [[0.62967303]],
    
            [[0.67069197]],
    
    ...
    
            [[5.5105205 ]],
    
            [[5.52725041]],
    
            [[5.53627392]],
    
            [[5.51360554]],
    
            [[5.47392679]],
    
            [[5.42880709]],
    
            [[5.38661294]],
    
            [[5.34077481]],
    
            [[5.29696753]],
    
            [[5.31781607]]]])

  • name
    (functional_group)
    <U8
    'D0N0' 'D0N0_BIS'

    array(['D0N0', 'D0N0_BIS'], dtype='<U8')

  • energy_transfert
    (functional_group)
    float64
    0.1668 0.0834

    units :

    dimensionless

    array([0.1668, 0.0834])

  • lambda_temperature_0
    (functional_group)
    float64
    0.006667 0.003333

    units :

    1 / day

    array([0.00666667, 0.00333333])

  • gamma_lambda_temperature
    (functional_group)
    float64
    0.15 0.075

    units :

    1 / delta_degree_Celsius

    array([0.15 , 0.075])

  • tr_0
    (functional_group)
    float64
    10.38 5.19

    units :

    day

    array([10.38,  5.19])

  • gamma_tr
    (functional_group)
    float64
    -0.11 -0.055

    units :

    1 / delta_degree_Celsius

    array([-0.11 , -0.055])

  • day_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • night_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • timesteps_number
    (functional_group, cohort)
    float64
    1.0 1.0 1.0 1.0 ... nan nan nan nan

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    array([[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.,  1.,  1., nan, nan, nan, nan, nan]])

  • min_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The minimum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • max_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The maximum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • mean_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The mean timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • timestep
    ()
    float64
    1.0

    units :

    day

    array(1.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
                   '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
                   '2000-01-09', '2000-01-10',
                   ...
                   '2000-12-23', '2000-12-24', '2000-12-25', '2000-12-26',
                   '2000-12-27', '2000-12-28', '2000-12-29', '2000-12-30',
                   '2000-12-31', '2001-01-01'],
                  dtype='datetime64[ns]', name='time', length=367, freq='D'))

  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='cohort'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

• Attributes: (0)

Plotting the results

The biomass evolution over time

plt.figure(figsize=(9, 3))
no_transport_model.state["biomass"].mean(["latitude", "longitude"]).plot(
    label="Zoo Seapopym", x="time", hue="functional_group"
)
plt.legend()
plt.title("Evolution of the biomass")
plt.show()

And now let’s do it in parallel

from dask.distributed import Client

client = Client()
client

Client

Client-85ead76e-51d3-11f0-a89a-36240d3e2cc2

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

4bde85bc

Dashboard: http://127.0.0.1:8787/status	Workers: 4
Total threads: 12	Total memory: 48.00 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-8f12a22d-01a3-4d90-a937-877a69920ebf

Comm: tcp://127.0.0.1:59375	Workers: 0
Dashboard: http://127.0.0.1:8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

Worker: 0

Comm: tcp://127.0.0.1:59386	Total threads: 3
Dashboard: http://127.0.0.1:59388/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:59378
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-tq35pxlm

Worker: 1

Comm: tcp://127.0.0.1:59387	Total threads: 3
Dashboard: http://127.0.0.1:59389/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:59380
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-x2fjybze

Worker: 2

Comm: tcp://127.0.0.1:59392	Total threads: 3
Dashboard: http://127.0.0.1:59393/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:59382
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-kmoium0a

Worker: 3

Comm: tcp://127.0.0.1:59395	Total threads: 3
Dashboard: http://127.0.0.1:59396/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:59384
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-ldsoxipn

p_param = ForcingParameter(
    temperature=ForcingUnit(forcing=dataset["temperature"]),
    primary_production=ForcingUnit(forcing=dataset["primary_production"]),
)
environment = EnvironmentParameter(chunk=ChunkParameter())

parameters = NoTransportConfiguration(forcing=p_param, functional_group=f_groups, environment=environment)
no_transport_model = NoTransportLightModel.from_configuration(configuration=parameters)
no_transport_model.state

<xarray.Dataset> Size: 10kB
Dimensions:                     (time: 367, latitude: 1, longitude: 1,
                                 layer: 1, functional_group: 2, cohort: 11)
Coordinates:
  * time                        (time) datetime64[ns] 3kB 2000-01-01 ... 2001...
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * layer                       (layer) int64 8B 0
  * cohort                      (cohort) int64 88B 0 1 2 3 4 5 6 7 8 9 10
  * functional_group            (functional_group) int64 16B 0 1
Data variables: (12/18)
    temperature                 (time, latitude, longitude, layer) float64 3kB dask.array<chunksize=(367, 1, 1, 1), meta=np.ndarray>
    primary_production          (time, latitude, longitude) float64 3kB dask.array<chunksize=(367, 1, 1), meta=np.ndarray>
    name                        (functional_group) <U8 64B dask.array<chunksize=(1,), meta=np.ndarray>
    energy_transfert            (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    lambda_temperature_0        (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    gamma_lambda_temperature    (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    ...                          ...
    max_timestep                (functional_group, cohort) float64 176B dask.array<chunksize=(1, 11), meta=np.ndarray>
    mean_timestep               (functional_group, cohort) float64 176B dask.array<chunksize=(1, 11), meta=np.ndarray>
    timestep                    float64 8B 1.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • time: 367
  • latitude: 1
  • longitude: 1
  • layer: 1
  • functional_group: 2
  • cohort: 11
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2001-01-01

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2000-12-30T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-01T00:00:00.000000000'],
          shape=(367,), dtype='datetime64[ns]')

  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

  • cohort
    (cohort)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    long_name :

    cohort

    standard_name :

    cohort

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

• Data variables: (18)
  • temperature
    (time, latitude, longitude, layer)
    float64
    dask.array<chunksize=(367, 1, 1, 1), meta=np.ndarray>

    units :

    degree_Celsius

    Array Chunk Bytes 2.87 kiB 2.87 kiB Shape (367, 1, 1, 1) (367, 1, 1, 1) Dask graph 1 chunks in 1 graph layer Data type float64 numpy.ndarray

  • primary_production
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(367, 1, 1), meta=np.ndarray>

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 2.87 kiB 2.87 kiB Shape (367, 1, 1) (367, 1, 1) Dask graph 1 chunks in 1 graph layer Data type float64 numpy.ndarray

  • name
    (functional_group)
    <U8
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 64 B 32 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type

  • energy_transfert
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    dimensionless

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • lambda_temperature_0
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / day

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • gamma_lambda_temperature
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / delta_degree_Celsius

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • tr_0
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    day

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • gamma_tr
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / delta_degree_Celsius

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • day_layer
    (functional_group)
    int64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type int64 numpy.ndarray

  • night_layer
    (functional_group)
    int64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type int64 numpy.ndarray

  • timesteps_number
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 11), meta=np.ndarray>

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    Array Chunk Bytes 176 B 88 B Shape (2, 11) (1, 11) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • min_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 11), meta=np.ndarray>

    description :

    The minimum timestep index.

    units :

    day

    Array Chunk Bytes 176 B 88 B Shape (2, 11) (1, 11) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • max_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 11), meta=np.ndarray>

    description :

    The maximum timestep index.

    units :

    day

    Array Chunk Bytes 176 B 88 B Shape (2, 11) (1, 11) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • mean_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 11), meta=np.ndarray>

    description :

    The mean timestep index.

    units :

    day

    Array Chunk Bytes 176 B 88 B Shape (2, 11) (1, 11) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • timestep
    ()
    float64
    1.0

    units :

    day

    array(1.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
                   '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
                   '2000-01-09', '2000-01-10',
                   ...
                   '2000-12-23', '2000-12-24', '2000-12-25', '2000-12-26',
                   '2000-12-27', '2000-12-28', '2000-12-29', '2000-12-30',
                   '2000-12-31', '2001-01-01'],
                  dtype='datetime64[ns]', name='time', length=367, freq='D'))

  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='cohort'))

  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

• Attributes: (0)

no_transport_model.run()

no_transport_model.state.load()

<xarray.Dataset> Size: 10kB
Dimensions:                     (functional_group: 2, time: 367, latitude: 1,
                                 longitude: 1, cohort: 11, layer: 1)
Coordinates:
  * functional_group            (functional_group) int64 16B 0 1
  * time                        (time) datetime64[ns] 3kB 2000-01-01 ... 2001...
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * cohort                      (cohort) int64 88B 0 1 2 3 4 5 6 7 8 9 10
  * layer                       (layer) int64 8B 0
Data variables: (12/17)
    biomass                     (functional_group, time, latitude, longitude) float64 6kB ...
    name                        (functional_group) <U8 64B 'D0N0' 'D0N0_BIS'
    energy_transfert            (functional_group) float64 16B 0.1668 0.0834
    lambda_temperature_0        (functional_group) float64 16B 0.006667 0.003333
    gamma_lambda_temperature    (functional_group) float64 16B 0.15 0.075
    tr_0                        (functional_group) float64 16B 10.38 5.19
    ...                          ...
    max_timestep                (functional_group, cohort) float64 176B 1.0 ....
    mean_timestep               (functional_group, cohort) float64 176B 1.0 ....
    timestep                    float64 8B 1.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • functional_group: 2
  • time: 367
  • latitude: 1
  • longitude: 1
  • cohort: 11
  • layer: 1
• Coordinates: (6)
  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2001-01-01

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2000-12-30T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-01T00:00:00.000000000'],
          shape=(367,), dtype='datetime64[ns]')

  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • cohort
    (cohort)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    long_name :

    cohort

    standard_name :

    cohort

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

• Data variables: (17)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    0.0 0.1324 0.1491 ... 5.297 5.318

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    array([[[[0.        ]],
    
            [[0.13236459]],
    
            [[0.14909579]],
    
            [[0.24833224]],
    
            [[0.326143  ]],
    
            [[0.40369743]],
    
            [[0.49350487]],
    
            [[0.58669707]],
    
            [[0.62967303]],
    
            [[0.67069197]],
    
    ...
    
            [[5.5105205 ]],
    
            [[5.52725041]],
    
            [[5.53627392]],
    
            [[5.51360554]],
    
            [[5.47392679]],
    
            [[5.42880709]],
    
            [[5.38661294]],
    
            [[5.34077481]],
    
            [[5.29696753]],
    
            [[5.31781607]]]])

  • name
    (functional_group)
    <U8
    'D0N0' 'D0N0_BIS'

    array(['D0N0', 'D0N0_BIS'], dtype='<U8')

  • energy_transfert
    (functional_group)
    float64
    0.1668 0.0834

    units :

    dimensionless

    array([0.1668, 0.0834])

  • lambda_temperature_0
    (functional_group)
    float64
    0.006667 0.003333

    units :

    1 / day

    array([0.00666667, 0.00333333])

  • gamma_lambda_temperature
    (functional_group)
    float64
    0.15 0.075

    units :

    1 / delta_degree_Celsius

    array([0.15 , 0.075])

  • tr_0
    (functional_group)
    float64
    10.38 5.19

    units :

    day

    array([10.38,  5.19])

  • gamma_tr
    (functional_group)
    float64
    -0.11 -0.055

    units :

    1 / delta_degree_Celsius

    array([-0.11 , -0.055])

  • day_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • night_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • timesteps_number
    (functional_group, cohort)
    float64
    1.0 1.0 1.0 1.0 ... nan nan nan nan

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    array([[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.,  1.,  1., nan, nan, nan, nan, nan]])

  • min_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The minimum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • max_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The maximum timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • mean_timestep
    (functional_group, cohort)
    float64
    1.0 2.0 3.0 4.0 ... nan nan nan nan

    description :

    The mean timestep index.

    units :

    day

    array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.],
           [ 1.,  2.,  3.,  4.,  5.,  6., nan, nan, nan, nan, nan]])

  • timestep
    ()
    float64
    1.0

    units :

    day

    array(1.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
                   '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
                   '2000-01-09', '2000-01-10',
                   ...
                   '2000-12-23', '2000-12-24', '2000-12-25', '2000-12-26',
                   '2000-12-27', '2000-12-28', '2000-12-29', '2000-12-30',
                   '2000-12-31', '2001-01-01'],
                  dtype='datetime64[ns]', name='time', length=367, freq='D'))

  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='cohort'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

• Attributes: (0)

The biomass evolution over time

plt.figure(figsize=(9, 3))
no_transport_model.state["biomass"].mean(["latitude", "longitude"]).plot(
    label="Zoo Seapopym", x="time", hue="functional_group"
)
plt.legend()
plt.title("Evolution of the biomass")
plt.show()

Finaly, let’s try in weekly time step

weekly_dataset = dataset.resample(time="1W").mean().interpolate_na()
weekly_dataset

<xarray.Dataset> Size: 1kB
Dimensions:             (latitude: 1, longitude: 1, layer: 1, time: 54)
Coordinates:
  * latitude            (latitude) int64 8B 0
  * longitude           (longitude) int64 8B 0
  * layer               (layer) int64 8B 0
  * time                (time) datetime64[ns] 432B 2000-01-02 ... 2001-01-07
Data variables:
    temperature         (time, latitude, longitude, layer) float64 432B 15.13...
    primary_production  (time, latitude, longitude) float64 432B 0.4731 ... 0...

xarray.Dataset

• Dimensions:
  • latitude: 1
  • longitude: 1
  • layer: 1
  • time: 54
• Coordinates: (4)
  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    axis :

    X

    array([0])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

  • time
    (time)
    datetime64[ns]
    2000-01-02 ... 2001-01-07

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-02T00:00:00.000000000', '2000-01-09T00:00:00.000000000',
           '2000-01-16T00:00:00.000000000', '2000-01-23T00:00:00.000000000',
           '2000-01-30T00:00:00.000000000', '2000-02-06T00:00:00.000000000',
           '2000-02-13T00:00:00.000000000', '2000-02-20T00:00:00.000000000',
           '2000-02-27T00:00:00.000000000', '2000-03-05T00:00:00.000000000',
           '2000-03-12T00:00:00.000000000', '2000-03-19T00:00:00.000000000',
           '2000-03-26T00:00:00.000000000', '2000-04-02T00:00:00.000000000',
           '2000-04-09T00:00:00.000000000', '2000-04-16T00:00:00.000000000',
           '2000-04-23T00:00:00.000000000', '2000-04-30T00:00:00.000000000',
           '2000-05-07T00:00:00.000000000', '2000-05-14T00:00:00.000000000',
           '2000-05-21T00:00:00.000000000', '2000-05-28T00:00:00.000000000',
           '2000-06-04T00:00:00.000000000', '2000-06-11T00:00:00.000000000',
           '2000-06-18T00:00:00.000000000', '2000-06-25T00:00:00.000000000',
           '2000-07-02T00:00:00.000000000', '2000-07-09T00:00:00.000000000',
           '2000-07-16T00:00:00.000000000', '2000-07-23T00:00:00.000000000',
           '2000-07-30T00:00:00.000000000', '2000-08-06T00:00:00.000000000',
           '2000-08-13T00:00:00.000000000', '2000-08-20T00:00:00.000000000',
           '2000-08-27T00:00:00.000000000', '2000-09-03T00:00:00.000000000',
           '2000-09-10T00:00:00.000000000', '2000-09-17T00:00:00.000000000',
           '2000-09-24T00:00:00.000000000', '2000-10-01T00:00:00.000000000',
           '2000-10-08T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-10-22T00:00:00.000000000', '2000-10-29T00:00:00.000000000',
           '2000-11-05T00:00:00.000000000', '2000-11-12T00:00:00.000000000',
           '2000-11-19T00:00:00.000000000', '2000-11-26T00:00:00.000000000',
           '2000-12-03T00:00:00.000000000', '2000-12-10T00:00:00.000000000',
           '2000-12-17T00:00:00.000000000', '2000-12-24T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-07T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • temperature
    (time, latitude, longitude, layer)
    float64
    15.13 16.29 18.07 ... 13.97 15.0

    units :

    celsius

    array([[[[15.12874747]]],
    
    
           [[[16.28519966]]],
    
    
           [[[18.06647279]]],
    
    
           [[[19.80351661]]],
    
    
           [[[21.47127687]]],
    
    
           [[[23.0456986 ]]],
    
    
           [[[24.50407313]]],
    
    ...
    
           [[[ 5.69634386]]],
    
    
           [[[ 7.17262685]]],
    
    
           [[[ 8.76180803]]],
    
    
           [[[10.44096584]]],
    
    
           [[[12.18588092]]],
    
    
           [[[13.97138547]]],
    
    
           [[[15.        ]]]])

  • primary_production
    (time, latitude, longitude)
    float64
    0.4731 0.6389 ... 0.3267 0.3407

    units :

    kilogram / meter**2 / day

    array([[[0.47313624]],
    
           [[0.6389013 ]],
    
           [[0.52021274]],
    
           [[0.56546703]],
    
           [[0.45849896]],
    
           [[0.41606559]],
    
           [[0.57825358]],
    
           [[0.65369131]],
    
           [[0.56892123]],
    
           [[0.69145984]],
    
    ...
    
           [[0.36897297]],
    
           [[0.41363068]],
    
           [[0.45677646]],
    
           [[0.59490269]],
    
           [[0.5871194 ]],
    
           [[0.4053234 ]],
    
           [[0.53023786]],
    
           [[0.34229642]],
    
           [[0.32672984]],
    
           [[0.34066477]]])

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-02', '2000-01-09', '2000-01-16', '2000-01-23',
                   '2000-01-30', '2000-02-06', '2000-02-13', '2000-02-20',
                   '2000-02-27', '2000-03-05', '2000-03-12', '2000-03-19',
                   '2000-03-26', '2000-04-02', '2000-04-09', '2000-04-16',
                   '2000-04-23', '2000-04-30', '2000-05-07', '2000-05-14',
                   '2000-05-21', '2000-05-28', '2000-06-04', '2000-06-11',
                   '2000-06-18', '2000-06-25', '2000-07-02', '2000-07-09',
                   '2000-07-16', '2000-07-23', '2000-07-30', '2000-08-06',
                   '2000-08-13', '2000-08-20', '2000-08-27', '2000-09-03',
                   '2000-09-10', '2000-09-17', '2000-09-24', '2000-10-01',
                   '2000-10-08', '2000-10-15', '2000-10-22', '2000-10-29',
                   '2000-11-05', '2000-11-12', '2000-11-19', '2000-11-26',
                   '2000-12-03', '2000-12-10', '2000-12-17', '2000-12-24',
                   '2000-12-31', '2001-01-07'],
                  dtype='datetime64[ns]', name='time', freq='W-SUN'))

• Attributes: (0)

p_param = ForcingParameter(
    temperature=ForcingUnit(forcing=weekly_dataset["temperature"]),
    primary_production=ForcingUnit(forcing=weekly_dataset["primary_production"]),
)
parameters = NoTransportConfiguration(forcing=p_param, functional_group=f_groups, environment=environment)
no_transport_model = NoTransportLightModel.from_configuration(configuration=parameters)
no_transport_model.state

<xarray.Dataset> Size: 2kB
Dimensions:                     (time: 54, latitude: 1, longitude: 1, layer: 1,
                                 functional_group: 2, cohort: 2)
Coordinates:
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * layer                       (layer) int64 8B 0
  * time                        (time) datetime64[ns] 432B 2000-01-02 ... 200...
  * cohort                      (cohort) int64 16B 0 1
  * functional_group            (functional_group) int64 16B 0 1
Data variables: (12/18)
    temperature                 (time, latitude, longitude, layer) float64 432B dask.array<chunksize=(54, 1, 1, 1), meta=np.ndarray>
    primary_production          (time, latitude, longitude) float64 432B dask.array<chunksize=(54, 1, 1), meta=np.ndarray>
    name                        (functional_group) <U8 64B dask.array<chunksize=(1,), meta=np.ndarray>
    energy_transfert            (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    lambda_temperature_0        (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    gamma_lambda_temperature    (functional_group) float64 16B dask.array<chunksize=(1,), meta=np.ndarray>
    ...                          ...
    max_timestep                (functional_group, cohort) float64 32B dask.array<chunksize=(1, 2), meta=np.ndarray>
    mean_timestep               (functional_group, cohort) float64 32B dask.array<chunksize=(1, 2), meta=np.ndarray>
    timestep                    float64 8B 7.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • time: 54
  • latitude: 1
  • longitude: 1
  • layer: 1
  • functional_group: 2
  • cohort: 2
• Coordinates: (6)
  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

  • time
    (time)
    datetime64[ns]
    2000-01-02 ... 2001-01-07

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-02T00:00:00.000000000', '2000-01-09T00:00:00.000000000',
           '2000-01-16T00:00:00.000000000', '2000-01-23T00:00:00.000000000',
           '2000-01-30T00:00:00.000000000', '2000-02-06T00:00:00.000000000',
           '2000-02-13T00:00:00.000000000', '2000-02-20T00:00:00.000000000',
           '2000-02-27T00:00:00.000000000', '2000-03-05T00:00:00.000000000',
           '2000-03-12T00:00:00.000000000', '2000-03-19T00:00:00.000000000',
           '2000-03-26T00:00:00.000000000', '2000-04-02T00:00:00.000000000',
           '2000-04-09T00:00:00.000000000', '2000-04-16T00:00:00.000000000',
           '2000-04-23T00:00:00.000000000', '2000-04-30T00:00:00.000000000',
           '2000-05-07T00:00:00.000000000', '2000-05-14T00:00:00.000000000',
           '2000-05-21T00:00:00.000000000', '2000-05-28T00:00:00.000000000',
           '2000-06-04T00:00:00.000000000', '2000-06-11T00:00:00.000000000',
           '2000-06-18T00:00:00.000000000', '2000-06-25T00:00:00.000000000',
           '2000-07-02T00:00:00.000000000', '2000-07-09T00:00:00.000000000',
           '2000-07-16T00:00:00.000000000', '2000-07-23T00:00:00.000000000',
           '2000-07-30T00:00:00.000000000', '2000-08-06T00:00:00.000000000',
           '2000-08-13T00:00:00.000000000', '2000-08-20T00:00:00.000000000',
           '2000-08-27T00:00:00.000000000', '2000-09-03T00:00:00.000000000',
           '2000-09-10T00:00:00.000000000', '2000-09-17T00:00:00.000000000',
           '2000-09-24T00:00:00.000000000', '2000-10-01T00:00:00.000000000',
           '2000-10-08T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-10-22T00:00:00.000000000', '2000-10-29T00:00:00.000000000',
           '2000-11-05T00:00:00.000000000', '2000-11-12T00:00:00.000000000',
           '2000-11-19T00:00:00.000000000', '2000-11-26T00:00:00.000000000',
           '2000-12-03T00:00:00.000000000', '2000-12-10T00:00:00.000000000',
           '2000-12-17T00:00:00.000000000', '2000-12-24T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-07T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • cohort
    (cohort)
    int64
    0 1

    long_name :

    cohort

    standard_name :

    cohort

    array([0, 1])

  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

• Data variables: (18)
  • temperature
    (time, latitude, longitude, layer)
    float64
    dask.array<chunksize=(54, 1, 1, 1), meta=np.ndarray>

    units :

    degree_Celsius

    Array Chunk Bytes 432 B 432 B Shape (54, 1, 1, 1) (54, 1, 1, 1) Dask graph 1 chunks in 1 graph layer Data type float64 numpy.ndarray

  • primary_production
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(54, 1, 1), meta=np.ndarray>

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 432 B 432 B Shape (54, 1, 1) (54, 1, 1) Dask graph 1 chunks in 1 graph layer Data type float64 numpy.ndarray

  • name
    (functional_group)
    <U8
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 64 B 32 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type

  • energy_transfert
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    dimensionless

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • lambda_temperature_0
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / day

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • gamma_lambda_temperature
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / delta_degree_Celsius

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • tr_0
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    day

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • gamma_tr
    (functional_group)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    1 / delta_degree_Celsius

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • day_layer
    (functional_group)
    int64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type int64 numpy.ndarray

  • night_layer
    (functional_group)
    int64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 16 B 8 B Shape (2,) (1,) Dask graph 2 chunks in 1 graph layer Data type int64 numpy.ndarray

  • timesteps_number
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    Array Chunk Bytes 32 B 16 B Shape (2, 2) (1, 2) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • min_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    description :

    The minimum timestep index.

    units :

    day

    Array Chunk Bytes 32 B 16 B Shape (2, 2) (1, 2) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • max_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    description :

    The maximum timestep index.

    units :

    day

    Array Chunk Bytes 32 B 16 B Shape (2, 2) (1, 2) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • mean_timestep
    (functional_group, cohort)
    float64
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    description :

    The mean timestep index.

    units :

    day

    Array Chunk Bytes 32 B 16 B Shape (2, 2) (1, 2) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • timestep
    ()
    float64
    7.0

    units :

    day

    array(7.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-02', '2000-01-09', '2000-01-16', '2000-01-23',
                   '2000-01-30', '2000-02-06', '2000-02-13', '2000-02-20',
                   '2000-02-27', '2000-03-05', '2000-03-12', '2000-03-19',
                   '2000-03-26', '2000-04-02', '2000-04-09', '2000-04-16',
                   '2000-04-23', '2000-04-30', '2000-05-07', '2000-05-14',
                   '2000-05-21', '2000-05-28', '2000-06-04', '2000-06-11',
                   '2000-06-18', '2000-06-25', '2000-07-02', '2000-07-09',
                   '2000-07-16', '2000-07-23', '2000-07-30', '2000-08-06',
                   '2000-08-13', '2000-08-20', '2000-08-27', '2000-09-03',
                   '2000-09-10', '2000-09-17', '2000-09-24', '2000-10-01',
                   '2000-10-08', '2000-10-15', '2000-10-22', '2000-10-29',
                   '2000-11-05', '2000-11-12', '2000-11-19', '2000-11-26',
                   '2000-12-03', '2000-12-10', '2000-12-17', '2000-12-24',
                   '2000-12-31', '2001-01-07'],
                  dtype='datetime64[ns]', name='time', freq='W-SUN'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='cohort'))

  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

• Attributes: (0)

Run the model

no_transport_model.run()
no_transport_model.state.load()

<xarray.Dataset> Size: 2kB
Dimensions:                     (functional_group: 2, time: 54, latitude: 1,
                                 longitude: 1, cohort: 2, layer: 1)
Coordinates:
  * functional_group            (functional_group) int64 16B 0 1
  * time                        (time) datetime64[ns] 432B 2000-01-02 ... 200...
  * latitude                    (latitude) int64 8B 0
  * longitude                   (longitude) int64 8B 0
  * cohort                      (cohort) int64 16B 0 1
  * layer                       (layer) int64 8B 0
Data variables: (12/17)
    biomass                     (functional_group, time, latitude, longitude) float64 864B ...
    name                        (functional_group) <U8 64B 'D0N0' 'D0N0_BIS'
    energy_transfert            (functional_group) float64 16B 0.1668 0.0834
    lambda_temperature_0        (functional_group) float64 16B 0.006667 0.003333
    gamma_lambda_temperature    (functional_group) float64 16B 0.15 0.075
    tr_0                        (functional_group) float64 16B 10.38 5.19
    ...                          ...
    max_timestep                (functional_group, cohort) float64 32B 7.0 .....
    mean_timestep               (functional_group, cohort) float64 32B 7.0 .....
    timestep                    float64 8B 7.0
    angle_horizon_sun           float64 8B 0.0
    compute_initial_conditions  bool 1B False
    compute_preproduction       bool 1B False

xarray.Dataset

• Dimensions:
  • functional_group: 2
  • time: 54
  • latitude: 1
  • longitude: 1
  • cohort: 2
  • layer: 1
• Coordinates: (6)
  • functional_group
    (functional_group)
    int64
    0 1

    flag_values :

    range(0, 2)

    flag_meanings :

    D0N0 D0N0_BIS

    standard_name :

    functional_group

    long_name :

    functional group

    array([0, 1])

  • time
    (time)
    datetime64[ns]
    2000-01-02 ... 2001-01-07

    long_name :

    time

    standard_name :

    time

    axis :

    T

    array(['2000-01-02T00:00:00.000000000', '2000-01-09T00:00:00.000000000',
           '2000-01-16T00:00:00.000000000', '2000-01-23T00:00:00.000000000',
           '2000-01-30T00:00:00.000000000', '2000-02-06T00:00:00.000000000',
           '2000-02-13T00:00:00.000000000', '2000-02-20T00:00:00.000000000',
           '2000-02-27T00:00:00.000000000', '2000-03-05T00:00:00.000000000',
           '2000-03-12T00:00:00.000000000', '2000-03-19T00:00:00.000000000',
           '2000-03-26T00:00:00.000000000', '2000-04-02T00:00:00.000000000',
           '2000-04-09T00:00:00.000000000', '2000-04-16T00:00:00.000000000',
           '2000-04-23T00:00:00.000000000', '2000-04-30T00:00:00.000000000',
           '2000-05-07T00:00:00.000000000', '2000-05-14T00:00:00.000000000',
           '2000-05-21T00:00:00.000000000', '2000-05-28T00:00:00.000000000',
           '2000-06-04T00:00:00.000000000', '2000-06-11T00:00:00.000000000',
           '2000-06-18T00:00:00.000000000', '2000-06-25T00:00:00.000000000',
           '2000-07-02T00:00:00.000000000', '2000-07-09T00:00:00.000000000',
           '2000-07-16T00:00:00.000000000', '2000-07-23T00:00:00.000000000',
           '2000-07-30T00:00:00.000000000', '2000-08-06T00:00:00.000000000',
           '2000-08-13T00:00:00.000000000', '2000-08-20T00:00:00.000000000',
           '2000-08-27T00:00:00.000000000', '2000-09-03T00:00:00.000000000',
           '2000-09-10T00:00:00.000000000', '2000-09-17T00:00:00.000000000',
           '2000-09-24T00:00:00.000000000', '2000-10-01T00:00:00.000000000',
           '2000-10-08T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-10-22T00:00:00.000000000', '2000-10-29T00:00:00.000000000',
           '2000-11-05T00:00:00.000000000', '2000-11-12T00:00:00.000000000',
           '2000-11-19T00:00:00.000000000', '2000-11-26T00:00:00.000000000',
           '2000-12-03T00:00:00.000000000', '2000-12-10T00:00:00.000000000',
           '2000-12-17T00:00:00.000000000', '2000-12-24T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-07T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • latitude
    (latitude)
    int64
    0

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    array([0])

  • longitude
    (longitude)
    int64
    0

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    array([0])

  • cohort
    (cohort)
    int64
    0 1

    long_name :

    cohort

    standard_name :

    cohort

    array([0, 1])

  • layer
    (layer)
    int64
    0

    long_name :

    layer

    standard_name :

    layer

    positive :

    down

    axis :

    Z

    flag_values :

    [0]

    flag_meanings :

    epipelagic upper-mesopelagic lower-mesopelagic

    array([0])

• Data variables: (17)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    0.07892 0.1527 ... 0.7816 0.7558

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    array([[[[0.07891912]],
    
            [[0.15270155]],
    
            [[0.16249885]],
    
            [[0.1597229 ]],
    
            [[0.12611051]],
    
            [[0.09810352]],
    
            [[0.11200115]],
    
            [[0.12088975]],
    
            [[0.10322835]],
    
            [[0.11996741]],
    
    ...
    
            [[0.75800233]],
    
            [[0.77040116]],
    
            [[0.78397656]],
    
            [[0.80604424]],
    
            [[0.82343705]],
    
            [[0.8209951 ]],
    
            [[0.824351  ]],
    
            [[0.80629351]],
    
            [[0.78164136]],
    
            [[0.75584618]]]])

  • name
    (functional_group)
    <U8
    'D0N0' 'D0N0_BIS'

    array(['D0N0', 'D0N0_BIS'], dtype='<U8')

  • energy_transfert
    (functional_group)
    float64
    0.1668 0.0834

    units :

    dimensionless

    array([0.1668, 0.0834])

  • lambda_temperature_0
    (functional_group)
    float64
    0.006667 0.003333

    units :

    1 / day

    array([0.00666667, 0.00333333])

  • gamma_lambda_temperature
    (functional_group)
    float64
    0.15 0.075

    units :

    1 / delta_degree_Celsius

    array([0.15 , 0.075])

  • tr_0
    (functional_group)
    float64
    10.38 5.19

    units :

    day

    array([10.38,  5.19])

  • gamma_tr
    (functional_group)
    float64
    -0.11 -0.055

    units :

    1 / delta_degree_Celsius

    array([-0.11 , -0.055])

  • day_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • night_layer
    (functional_group)
    int64
    0 0

    array([0, 0])

  • timesteps_number
    (functional_group, cohort)
    float64
    7.0 7.0 7.0 nan

    description :

    The number of timesteps represented in the cohort. If there is no aggregation, all values are equal to 1.

    units :

    day

    array([[ 7.,  7.],
           [ 7., nan]])

  • min_timestep
    (functional_group, cohort)
    float64
    7.0 14.0 7.0 nan

    description :

    The minimum timestep index.

    units :

    day

    array([[ 7., 14.],
           [ 7., nan]])

  • max_timestep
    (functional_group, cohort)
    float64
    7.0 14.0 7.0 nan

    description :

    The maximum timestep index.

    units :

    day

    array([[ 7., 14.],
           [ 7., nan]])

  • mean_timestep
    (functional_group, cohort)
    float64
    7.0 14.0 7.0 nan

    description :

    The mean timestep index.

    units :

    day

    array([[ 7., 14.],
           [ 7., nan]])

  • timestep
    ()
    float64
    7.0

    units :

    day

    array(7.)

  • angle_horizon_sun
    ()
    float64
    0.0

    array(0.)

  • compute_initial_conditions
    ()
    bool
    False

    array(False)

  • compute_preproduction
    ()
    bool
    False

    array(False)

• Indexes: (6)
  • functional_group
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='functional_group'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-02', '2000-01-09', '2000-01-16', '2000-01-23',
                   '2000-01-30', '2000-02-06', '2000-02-13', '2000-02-20',
                   '2000-02-27', '2000-03-05', '2000-03-12', '2000-03-19',
                   '2000-03-26', '2000-04-02', '2000-04-09', '2000-04-16',
                   '2000-04-23', '2000-04-30', '2000-05-07', '2000-05-14',
                   '2000-05-21', '2000-05-28', '2000-06-04', '2000-06-11',
                   '2000-06-18', '2000-06-25', '2000-07-02', '2000-07-09',
                   '2000-07-16', '2000-07-23', '2000-07-30', '2000-08-06',
                   '2000-08-13', '2000-08-20', '2000-08-27', '2000-09-03',
                   '2000-09-10', '2000-09-17', '2000-09-24', '2000-10-01',
                   '2000-10-08', '2000-10-15', '2000-10-22', '2000-10-29',
                   '2000-11-05', '2000-11-12', '2000-11-19', '2000-11-26',
                   '2000-12-03', '2000-12-10', '2000-12-17', '2000-12-24',
                   '2000-12-31', '2001-01-07'],
                  dtype='datetime64[ns]', name='time', freq='W-SUN'))

  • latitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='longitude'))

  • cohort
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='cohort'))

  • layer
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='layer'))

• Attributes: (0)

Plotting the results

The biomass evolution over time

plt.figure(figsize=(9, 3))
no_transport_model.state["biomass"].mean(["latitude", "longitude"]).plot(
    label="Zoo Seapopym", x="time", hue="functional_group"
)
plt.legend()
plt.title("Evolution of the biomass")
plt.show()