pywasp.LandCoverTable

class pywasp.LandCoverTable(*args, **kwargs)

Class with methods to work with landcover tables

Methods

__init__(*args, **kwargs)
add_colors_to_table([levels, colors, html, ...])	Return the landover table with an additional / updated color column.
clear()	Remove all items from the dict.
copy()	Return a shallow copy of the dict.
dic_from_matrix(outlctypes)	Creates dictionary from matrix that results from fortran routine
from_dict_ora(dic[, z0frac, dfrac, makecopy])	Use ORA model to convert tree height to LandCoverTable.
from_dict_raupach(dic)	Use Raupach model to convert treeheight and LAI
from_dict_scadis(dic[, alpha, beta, z0s])	Use SCADIS 1D model to convert treeheight and LAI
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
get_table(dataset[, table])	Get landcover table from dataset and table name.
items()	Return a set-like object providing a view on the dict's items.
keys()	Return a set-like object providing a view on the dict's keys.
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
read_json(filename)	Create LandCoverTable from json file.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
to_json(filename)	Write landcover table to json.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()	Return an object providing a view on the dict's values.