Plant Communities
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from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from pyrealm.demography.flora import PlantFunctionalType, Flora
from pyrealm.demography.community import Cohorts, Community
short_pft = PlantFunctionalType(
name="short", h_max=15, m=1.5, n=1.5, f_g=0, ca_ratio=380
)
tall_pft = PlantFunctionalType(name="tall", h_max=30, m=1.5, n=2, f_g=0.2, ca_ratio=500)
# Create the flora
flora = Flora([short_pft, tall_pft])
# Create a simply community with three cohorts
# - 15 saplings of the short PFT
# - 5 larger stems of the short PFT
# - 2 large stems of tall PFT
community = Community(
flora=flora,
cell_area=32,
cell_id=1,
cohorts=Cohorts(
dbh_values=np.array([0.02, 0.20, 0.5]),
n_individuals=np.array([15, 5, 2]),
pft_names=np.array(["short", "short", "tall"]),
),
)
/home/docs/checkouts/readthedocs.org/user_builds/pyrealm/checkouts/latest/pyrealm/core/experimental.py:72: ExperimentalFeatureWarning: 'Be aware that Cohorts is an experimental feature of pyrealm and the implementation and API may change within major versions.'
warn(qualname, ExperimentalFeatureWarning)
/home/docs/checkouts/readthedocs.org/user_builds/pyrealm/checkouts/latest/pyrealm/core/experimental.py:72: ExperimentalFeatureWarning: 'Be aware that Community is an experimental feature of pyrealm and the implementation and API may change within major versions.'
warn(qualname, ExperimentalFeatureWarning)
/home/docs/checkouts/readthedocs.org/user_builds/pyrealm/checkouts/latest/pyrealm/core/experimental.py:72: ExperimentalFeatureWarning: 'Be aware that StemAllometry is an experimental feature of pyrealm and the implementation and API may change within major versions.'
warn(qualname, ExperimentalFeatureWarning)
community
Community(cell_id=1, cell_area=32, flora=Flora with 2 functional types: short, tall, cohorts=Cohorts(n_individuals=array([15, 5, 2]), pft_names=array(['short', 'short', 'tall'], dtype='<U5'), _cohort_id=array(['e608c8db-29d6-4118-bbf8-fb616068a552',
'ba9ad3b4-8e0e-4d3c-a3ed-d60823073fbc',
'75888b44-e171-45d4-bf27-7cdd6cd5e9ff'], dtype='<U36'), _dbh_values=array([0.02, 0.2 , 0.5 ]), n_cohorts=3), n_cohorts=3, stem_traits=StemTraits(name=array(['short', 'short', 'tall'], dtype='<U5'), a_hd=array([116., 116., 116.]), ca_ratio=array([380, 380, 500]), h_max=array([15, 15, 30]), rho_s=array([200., 200., 200.]), lai=array([1.8, 1.8, 1.8]), sla=array([14., 14., 14.]), tau_f=array([4., 4., 4.]), tau_rt=array([1., 1., 1.]), tau_r=array([1.04, 1.04, 1.04]), par_ext=array([0.5, 0.5, 0.5]), yld=array([0.6, 0.6, 0.6]), zeta=array([0.17, 0.17, 0.17]), resp_r=array([0.913, 0.913, 0.913]), resp_s=array([0.044, 0.044, 0.044]), resp_f=array([0.1, 0.1, 0.1]), resp_rt=array([0., 0., 0.]), m=array([1.5, 1.5, 1.5]), n=array([1.5, 1.5, 2. ]), f_g=array([0. , 0. , 0.2]), q_m=array([1.28413734, 1.28413734, 1.5 ]), z_max_prop=array([0.54288352, 0.54288352, 0.70710678]), p_foliage_for_reproductive_tissue=array([0., 0., 0.]), gpp_topslice=array([0., 0., 0.]), validate=False, _n_stems=3), stem_allometry=StemAllometry: Prediction for 3 stems at 1 DBH values.)