spatiocoexistence.py_tools module¶
- spatiocoexistence.py_tools.get_BA_and_k(CI_CS, CI_C, CI_HS, CI_H, species, dbh, max_x, max_y, radius)[source]¶
BA stands for basal area, and k represents aggregation. ff is for comparing a focal species with conspecifics. fh is for comparing a focal species with heterospecifics. Returns numpy arrays for all outputs.
- Return type:
tuple[ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[int64]], ndarray[tuple[Any, …], dtype[int64]]]
- spatiocoexistence.py_tools.reduced_growth(CI_CS, CI_HS, beta_gr=None)[source]¶
Calculate the reduced growth using the formula from Cython.
- Return type:
ndarray[tuple[Any, …], dtype[float64]]
- spatiocoexistence.py_tools.survival(CI_CS, CI_HS, CI_CS_dead, CI_HS_dead, dbh, reduced=True)[source]¶
Calculate the reduced survival using the formula from Cython.
- Return type:
ndarray[tuple[Any, …], dtype[float64]]
- spatiocoexistence.py_tools.reduced_recruitment(CI_CS, CI_HS, CI_CS_d, CI_HS_d, dbh, species)[source]¶
Calculate the reduced recruitment using the formula from Cython.
- Return type:
ndarray[tuple[Any, …], dtype[float64]]
- spatiocoexistence.py_tools.size_class(bins=32, delta_size=0.2558)[source]¶
Calculate the size classes, that are needed for further analysis.
- Return type:
ndarray
- spatiocoexistence.py_tools.mean_count_size_class(size_class, parameter, dbh, count=False)[source]¶
Get the mean values of the parameter specified for a size class.
- Return type:
ndarray
- spatiocoexistence.py_tools.create_initial_inventory(n_species=80, n_repro=22646, n_saplings=10000, initial_dbh_rp=6.64, initial_dbh_sa=0.397, dim_x=1000, dim_y=500, radius=20, num_threads=1)[source]¶
Create an initial inventory for a uniformly distributed forest. The abundance per species is calculated by the mean density of species of BCI. Then for each species the trees/individuals are located randomly according to their abundance.
the initial dbh, can be also calculated as: # np.sqrt(4*mean_BA/np.pi) mean_BA: float = 6.64,
ToDo: -2 (dead, memory), -1(just died), 0(alive), 1(recruit), 2(reproud)
- Return type:
ndarray
- spatiocoexistence.py_tools.read_initial_inventory(file_name, radius=10)[source]¶
- Return type:
ndarray
- spatiocoexistence.py_tools.read_param_file(file, skip_header=0, skip_footer=0)[source]¶
- Return type:
dict[str, float | int | str]
- spatiocoexistence.py_tools.map_params(params)[source]¶
Map the parameters to pythonic naming and scale certain values.
- Return type:
dict
- spatiocoexistence.py_tools.species_abundance_distribution(species, abundance_class)[source]¶
Get the amount of species per abundance class.