A simple 1D model implementation

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 import NoTransportModel
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 = NoTransportModel.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 0.409...
    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
    0.4097 0.8611 ... 0.2935 0.1967

    units :

    kilogram / day / meter ** 2

    array([[[0.40971724]],
    
           [[0.86110761]],
    
           [[0.78992291]],
    
           [[0.39577809]],
    
           [[0.66765583]],
    
           [[0.89406038]],
    
           [[0.01360631]],
    
           [[0.58055348]],
    
           [[0.29145258]],
    
           [[0.97425778]],
    
    ...
    
           [[0.6783646 ]],
    
           [[0.41868511]],
    
           [[0.81550287]],
    
           [[0.87888598]],
    
           [[0.3247767 ]],
    
           [[0.47070832]],
    
           [[0.5236295 ]],
    
           [[0.36007249]],
    
           [[0.29354815]],
    
           [[0.19670505]]])

  • 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: 50kB
Dimensions:                       (time: 367, latitude: 1, longitude: 1,
                                   functional_group: 2, cohort: 11, layer: 1)
Coordinates:
  * time                          (time) datetime64[ns] 3kB 2000-01-01 ... 20...
  * 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: (12/28)
    biomass                       (functional_group, time, latitude, longitude) float64 6kB ...
    recruited                     (functional_group, time, latitude, longitude) float64 6kB ...
    mortality_field               (functional_group, time, latitude, longitude) float64 6kB ...
    mask_temperature              (functional_group, time, latitude, longitude, cohort) bool 8kB ...
    min_temperature               (functional_group, cohort) float64 176B 21....
    primary_production_by_fgroup  (functional_group, time, latitude, longitude) float64 6kB ...
    ...                            ...
    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: (28)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    0.0 0.06834 0.2075 ... 6.184 6.151

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    array([[[[0.        ]],
    
            [[0.06834083]],
    
            [[0.20745979]],
    
            [[0.32499636]],
    
            [[0.36788786]],
    
            [[0.45208791]],
    
            [[0.56657765]],
    
            [[0.52380588]],
    
            [[0.57744403]],
    
            [[0.57666319]],
    
    ...
    
            [[6.29832963]],
    
            [[6.25474021]],
    
            [[6.2554494 ]],
    
            [[6.23341722]],
    
            [[6.24358368]],
    
            [[6.25782081]],
    
            [[6.22456387]],
    
            [[6.20263469]],
    
            [[6.18415211]],
    
            [[6.15101191]]]])

  • recruited
    (functional_group, time, latitude, longitude)
    float64
    0.0 0.06834 ... 0.04367 0.03003

    standard_name :

    production

    long_name :

    production

    units :

    kilogram / day / meter ** 2

    array([[[[0.00000000e+00]],
    
            [[6.83408349e-02]],
    
            [[1.43632750e-01]],
    
            [[1.31759142e-01]],
    
            [[6.60157861e-02]],
    
            [[1.11364992e-01]],
    
            [[1.49129272e-01]],
    
            [[2.26953188e-03]],
    
            [[9.68363212e-02]],
    
            [[4.86142898e-02]],
    
    ...
    
            [[7.57285902e-02]],
    
            [[1.16038006e-02]],
    
            [[5.65756076e-02]],
    
            [[3.49183384e-02]],
    
            [[6.80129393e-02]],
    
            [[7.32990909e-02]],
    
            [[2.70863767e-02]],
    
            [[3.92570735e-02]],
    
            [[4.36707007e-02]],
    
            [[3.00300459e-02]]]])

  • mortality_field
    (functional_group, time, latitude, longitude)
    float64
    0.9387 0.9364 0.934 ... 0.99 0.9898

    standard_name :

    mortality

    long_name :

    mortality coefficient

    description :

    Mortality coefficient according to the temperature.

    array([[[[0.93870729]],
    
            [[0.93637203]],
    
            [[0.9339517 ]],
    
            [[0.93144421]],
    
            [[0.92884755]],
    
            [[0.92615971]],
    
            [[0.92337878]],
    
            [[0.92050287]],
    
            [[0.91753018]],
    
            [[0.91445901]],
    
    ...
    
            [[0.99140198]],
    
            [[0.99123685]],
    
            [[0.99106815]],
    
            [[0.99089586]],
    
            [[0.99071994]],
    
            [[0.99054038]],
    
            [[0.99035713]],
    
            [[0.99017019]],
    
            [[0.98997954]],
    
            [[0.98978514]]]])

  • mask_temperature
    (functional_group, time, latitude, longitude, cohort)
    bool
    False True True ... False False

    standard_name :

    mask

    long_name :

    cohort recruitment mask by functional group

    description :

    Mask to recruit a cohort according to the temperature.

    flag_values :

    [0, 1]

    flag_meanings :

    0:not recruited, 1:recruited

    array([[[[[False,  True,  True, ...,  True,  True,  True]]],
    
    
            [[[False,  True,  True, ...,  True,  True,  True]]],
    
    
            [[[False,  True,  True, ...,  True,  True,  True]]],
    
    
            ...,
    
    
            [[[False, False,  True, ...,  True,  True,  True]]],
    
    
            [[[False, False,  True, ...,  True,  True,  True]]],
    
    
            [[[False,  True,  True, ...,  True,  True,  True]]]],
    
    ...
           [[[[False, False,  True, ..., False, False, False]]],
    
    
            [[[False, False,  True, ..., False, False, False]]],
    
    
            [[[False, False,  True, ..., False, False, False]]],
    
    
            ...,
    
    
            [[[False, False,  True, ..., False, False, False]]],
    
    
            [[[False, False,  True, ..., False, False, False]]],
    
    
            [[[False, False,  True, ..., False, False, False]]]]],
          shape=(2, 367, 1, 1, 11))

  • min_temperature
    (functional_group, cohort)
    float64
    21.27 14.97 11.28 ... nan nan nan

    standard_name :

    minimum temperature

    long_name :

    minimum temperature by cohort

    description :

    Minimum temperature to recruit a cohort according to its age.

    units :

    degree_Celsius

    array([[21.27164434, 14.97030634, 11.2842599 ,  8.66896833,  6.64039059,
             4.9829219 ,  3.58155208,  2.36763033,  1.29687546,  0.33905259,
            -0.52740359],
           [29.94061268, 17.33793667,  9.96584379,  4.73526066,  0.67810518,
            -2.63683222,         nan,         nan,         nan,         nan,
                    nan]])

  • primary_production_by_fgroup
    (functional_group, time, latitude, longitude)
    float64
    0.06834 0.1436 ... 0.02448 0.01641

    standard_name :

    primary production

    long_name :

    primary production by functional group

    description :

    Primary production by functional group according to their energy transfert coefficient.

    units :

    kilogram / day / meter ** 2

    array([[[[0.06834083]],
    
            [[0.14363275]],
    
            [[0.13175914]],
    
            [[0.06601579]],
    
            [[0.11136499]],
    
            [[0.14912927]],
    
            [[0.00226953]],
    
            [[0.09683632]],
    
            [[0.04861429]],
    
            [[0.1625062 ]],
    
    ...
    
            [[0.05657561]],
    
            [[0.03491834]],
    
            [[0.06801294]],
    
            [[0.07329909]],
    
            [[0.02708638]],
    
            [[0.03925707]],
    
            [[0.0436707 ]],
    
            [[0.03003005]],
    
            [[0.02448192]],
    
            [[0.0164052 ]]]])

  • average_temperature
    (functional_group, time, latitude, longitude)
    float64
    15.0 15.26 15.51 ... 14.74 15.0

    long_name :

    average sea temperature by fonctional group

    standard_name :

    sea water temperature

    description :

    Average temperature by functional group according to their layer position during day and night.

    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]],
    
            [[1.67982179e+01]],
    
            [[1.70535909e+01]],
    
            [[1.73083587e+01]],
    
    ...
    
            [[1.26916413e+01]],
    
            [[1.29464091e+01]],
    
            [[1.32017821e+01]],
    
            [[1.34576850e+01]],
    
            [[1.37140425e+01]],
    
            [[1.39707789e+01]],
    
            [[1.42278187e+01]],
    
            [[1.44850860e+01]],
    
            [[1.47425051e+01]],
    
            [[1.50000000e+01]]]])

  • day_length
    (time, latitude, longitude)
    float64
    0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5

    long_name :

    Day length

    standard_name :

    day_length

    description :

    Day length at the surface using Forsythe's method.

    units :

    day

    array([[[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
    ...
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]],
    
           [[0.5]]])

  • mask_fgroup
    (functional_group, latitude, longitude)
    bool
    True True

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

    array([[[ True]],
    
           [[ True]]])

  • mask
    (latitude, longitude, layer)
    bool
    True

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

    array([[[ True]]])

  • 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
    0.4097 0.8611 ... 0.2935 0.1967

    units :

    kilogram / day / meter ** 2

    array([[[0.40971724]],
    
           [[0.86110761]],
    
           [[0.78992291]],
    
           [[0.39577809]],
    
           [[0.66765583]],
    
           [[0.89406038]],
    
           [[0.01360631]],
    
           [[0.58055348]],
    
           [[0.29145258]],
    
           [[0.97425778]],
    
    ...
    
           [[0.6783646 ]],
    
           [[0.41868511]],
    
           [[0.81550287]],
    
           [[0.87888598]],
    
           [[0.3247767 ]],
    
           [[0.47070832]],
    
           [[0.5236295 ]],
    
           [[0.36007249]],
    
           [[0.29354815]],
    
           [[0.19670505]]])

  • 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-a36e381c-51ce-11f0-a19c-36240d3e2cc2

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

Cluster Info

LocalCluster

9a95c16e

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-7b22cd8d-c774-46e0-94ca-26aa1dd8826f

Comm: tcp://127.0.0.1:58431	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://127.0.0.1:58443	Total threads: 3
Dashboard: http://127.0.0.1:58446/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:58434
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-zrcqmuqu

Worker: 1

Comm: tcp://127.0.0.1:58442	Total threads: 3
Dashboard: http://127.0.0.1:58445/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:58436
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-xzlcrucw

Worker: 2

Comm: tcp://127.0.0.1:58444	Total threads: 3
Dashboard: http://127.0.0.1:58447/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:58438
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-8t6336ji

Worker: 3

Comm: tcp://127.0.0.1:58451	Total threads: 3
Dashboard: http://127.0.0.1:58452/status	Memory: 12.00 GiB
Nanny: tcp://127.0.0.1:58440
Local directory: /var/folders/z_/8j3qx1mn0299kkpjgz9g53780000gq/T/dask-scratch-space/worker-9f3wbr2f

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 = NoTransportModel.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

<xarray.Dataset> Size: 50kB
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 ... 20...
  * 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: (12/28)
    biomass                       (functional_group, time, latitude, longitude) float64 6kB dask.array<chunksize=(1, 367, 1, 1), meta=np.ndarray>
    recruited                     (functional_group, time, latitude, longitude) float64 6kB dask.array<chunksize=(1, 367, 1, 1), meta=np.ndarray>
    mortality_field               (functional_group, time, latitude, longitude) float64 6kB dask.array<chunksize=(1, 367, 1, 1), meta=np.ndarray>
    mask_temperature              (functional_group, time, latitude, longitude, cohort) bool 8kB dask.array<chunksize=(1, 367, 1, 1, 11), meta=np.ndarray>
    min_temperature               (functional_group, cohort) float64 176B dask.array<chunksize=(1, 11), meta=np.ndarray>
    primary_production_by_fgroup  (functional_group, time, latitude, longitude) float64 6kB dask.array<chunksize=(1, 367, 1, 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:
  • 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: (28)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 367, 1, 1), meta=np.ndarray>

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    Array Chunk Bytes 5.73 kiB 2.87 kiB Shape (2, 367, 1, 1) (1, 367, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    standard_name :

    production

    long_name :

    production

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 5.73 kiB 2.87 kiB Shape (2, 367, 1, 1) (1, 367, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    standard_name :

    mortality

    long_name :

    mortality coefficient

    description :

    Mortality coefficient according to the temperature.

    Array Chunk Bytes 5.73 kiB 2.87 kiB Shape (2, 367, 1, 1) (1, 367, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • mask_temperature
    (functional_group, time, latitude, longitude, cohort)
    bool
    dask.array<chunksize=(1, 367, 1, 1, 11), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    cohort recruitment mask by functional group

    description :

    Mask to recruit a cohort according to the temperature.

    flag_values :

    [0, 1]

    flag_meanings :

    0:not recruited, 1:recruited

    Array Chunk Bytes 7.88 kiB 3.94 kiB Shape (2, 367, 1, 1, 11) (1, 367, 1, 1, 11) Dask graph 2 chunks in 1 graph layer Data type bool numpy.ndarray

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

    standard_name :

    minimum temperature

    long_name :

    minimum temperature by cohort

    description :

    Minimum temperature to recruit a cohort according to its age.

    units :

    degree_Celsius

    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

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

    standard_name :

    primary production

    long_name :

    primary production by functional group

    description :

    Primary production by functional group according to their energy transfert coefficient.

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 5.73 kiB 2.87 kiB Shape (2, 367, 1, 1) (1, 367, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    long_name :

    average sea temperature by fonctional group

    standard_name :

    sea water temperature

    description :

    Average temperature by functional group according to their layer position during day and night.

    units :

    degree_Celsius

    Array Chunk Bytes 5.73 kiB 2.87 kiB Shape (2, 367, 1, 1) (1, 367, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    long_name :

    Day length

    standard_name :

    day_length

    description :

    Day length at the surface using Forsythe's method.

    units :

    day

    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

  • mask_fgroup
    (functional_group, latitude, longitude)
    bool
    dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

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

  • mask
    (latitude, longitude, layer)
    bool
    dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

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

  • 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)
  • 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 0.6354 ... 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
    0.6354 0.519 ... 0.5239 0.1967

    units :

    kilogram / meter**2 / day

    array([[[0.63541242]],
    
           [[0.51900423]],
    
           [[0.60242434]],
    
           [[0.34507644]],
    
           [[0.57073973]],
    
           [[0.31830055]],
    
           [[0.44753185]],
    
           [[0.52828377]],
    
           [[0.38946036]],
    
           [[0.53819221]],
    
    ...
    
           [[0.56392826]],
    
           [[0.69588573]],
    
           [[0.64146919]],
    
           [[0.58629306]],
    
           [[0.63952293]],
    
           [[0.47883573]],
    
           [[0.55693056]],
    
           [[0.60475491]],
    
           [[0.52387486]],
    
           [[0.19670505]]])

• 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 = NoTransportModel.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

<xarray.Dataset> Size: 7kB
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 ... 2...
  * latitude                      (latitude) int64 8B 0
  * longitude                     (longitude) int64 8B 0
  * cohort                        (cohort) int64 16B 0 1
  * layer                         (layer) int64 8B 0
Data variables: (12/28)
    biomass                       (functional_group, time, latitude, longitude) float64 864B dask.array<chunksize=(1, 54, 1, 1), meta=np.ndarray>
    recruited                     (functional_group, time, latitude, longitude) float64 864B dask.array<chunksize=(1, 54, 1, 1), meta=np.ndarray>
    mortality_field               (functional_group, time, latitude, longitude) float64 864B dask.array<chunksize=(1, 54, 1, 1), meta=np.ndarray>
    mask_temperature              (functional_group, time, latitude, longitude, cohort) bool 216B dask.array<chunksize=(1, 54, 1, 1, 2), meta=np.ndarray>
    min_temperature               (functional_group, cohort) float64 32B dask.array<chunksize=(1, 2), meta=np.ndarray>
    primary_production_by_fgroup  (functional_group, time, latitude, longitude) float64 864B dask.array<chunksize=(1, 54, 1, 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:
  • 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: (28)
  • biomass
    (functional_group, time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 54, 1, 1), meta=np.ndarray>

    long_name :

    biomass

    units :

    kilogram / meter ** 2

    description :

    The biomass of the recruited individuals.

    Array Chunk Bytes 864 B 432 B Shape (2, 54, 1, 1) (1, 54, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    standard_name :

    production

    long_name :

    production

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 864 B 432 B Shape (2, 54, 1, 1) (1, 54, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    standard_name :

    mortality

    long_name :

    mortality coefficient

    description :

    Mortality coefficient according to the temperature.

    Array Chunk Bytes 864 B 432 B Shape (2, 54, 1, 1) (1, 54, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

  • mask_temperature
    (functional_group, time, latitude, longitude, cohort)
    bool
    dask.array<chunksize=(1, 54, 1, 1, 2), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    cohort recruitment mask by functional group

    description :

    Mask to recruit a cohort according to the temperature.

    flag_values :

    [0, 1]

    flag_meanings :

    0:not recruited, 1:recruited

    Array Chunk Bytes 216 B 108 B Shape (2, 54, 1, 1, 2) (1, 54, 1, 1, 2) Dask graph 2 chunks in 1 graph layer Data type bool numpy.ndarray

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

    standard_name :

    minimum temperature

    long_name :

    minimum temperature by cohort

    description :

    Minimum temperature to recruit a cohort according to its age.

    units :

    degree_Celsius

    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

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

    standard_name :

    primary production

    long_name :

    primary production by functional group

    description :

    Primary production by functional group according to their energy transfert coefficient.

    units :

    kilogram / day / meter ** 2

    Array Chunk Bytes 864 B 432 B Shape (2, 54, 1, 1) (1, 54, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    long_name :

    average sea temperature by fonctional group

    standard_name :

    sea water temperature

    description :

    Average temperature by functional group according to their layer position during day and night.

    units :

    degree_Celsius

    Array Chunk Bytes 864 B 432 B Shape (2, 54, 1, 1) (1, 54, 1, 1) Dask graph 2 chunks in 1 graph layer Data type float64 numpy.ndarray

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

    long_name :

    Day length

    standard_name :

    day_length

    description :

    Day length at the surface using Forsythe's method.

    units :

    day

    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

  • mask_fgroup
    (functional_group, latitude, longitude)
    bool
    dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

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

  • mask
    (latitude, longitude, layer)
    bool
    dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

    standard_name :

    mask

    long_name :

    mask

    flag_values :

    [0, 1]

    flag_meanings :

    0:land, 1:ocean

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

  • 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)
  • 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()