Main community class.
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Returns: | an Community object |
Examples:
# python list
abd = Community([1, 1, 2, 3, 4, 8, 12])
# text file
abd = Community("bci.txt", j_tot=256987)
# finally a saved Community object
abd = Community("abundance_bci_first_try.pik")
Draw Relative Species Abundances curve.
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save params and kda with pickle force option is for writing pickle with no consideration if existing
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Fit Community to model. Extra arguments can be pssed depending on the model to fit. See doc of its corresponding optimize function.
Parameters: | name (Ewens) – name of the model, between Etienne, Ewens or Log-normal |
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Returns: | model |
return distribution of abundance, according to a given model and a given community size. if none of them are given, values of current Community are used.
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Returns: | random neutral distribution of abundances |
Parameters: | name – name of a computed model |
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Returns: | a EcologicalModel object corresponding to one of the computed models |
load params and kda with pickle from infile WARNING: do not overwrite values of params.
Parameters: | infile – path of the outfile |
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likelihood-ratio-test between two neutral models
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Returns: | p-value of rejection of alternative model |
eg: usually ewens, etienne. And if pval < 0.05 than use etienne
Draw Relative Species Abundances curve (ASCII format).
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Returns: | string corresponding to plot |
set on model as default/current model.
Parameters: | name – model name of precomputed model |
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add one model computed externally to the computed models of current Community object
Parameters: | model – model object |
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test for neutrality comparing Shannon entropy if (Hobs > Hrand_neut) then evenness of observed data is higher then neutral
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Returns: | p_value if full=True also returns Shannon entropy (or likelihoods if method=’loglike’) of all random neutral abundances generated |