o
    ˷e                     @   sZ   d Z ddlZddlmZ g dZeddddZeddd	d
ZeddddZdS )zTrophic levels    N)not_implemented_for)trophic_levelstrophic_differencestrophic_incoherence_parameter
undirectedweightc              
   C   s"  ddl }tj| |dj }|j|dd}||dk dd|dkf }|||dk dd|jf  }|jd }||}z
|j	
|| }W n |j	jy\ }	 zd}
t|
|	d}	~	ww |jddd }i }dd | jD }|D ]}d||< qqd	d | jD }t|D ]
\}}|| ||< q|S )
a  Compute the trophic levels of nodes.

    The trophic level of a node $i$ is

    .. math::

        s_i = 1 + \frac{1}{k^{in}_i} \sum_{j} a_{ij} s_j

    where $k^{in}_i$ is the in-degree of i

    .. math::

        k^{in}_i = \sum_{j} a_{ij}

    and nodes with $k^{in}_i = 0$ have $s_i = 1$ by convention.

    These are calculated using the method outlined in Levine [1]_.

    Parameters
    ----------
    G : DiGraph
        A directed networkx graph

    Returns
    -------
    nodes : dict
        Dictionary of nodes with trophic level as the value.

    References
    ----------
    .. [1] Stephen Levine (1980) J. theor. Biol. 83, 195-207
    r   Nr      )axiszTrophic levels are only defined for graphs where every node has a path from a basal node (basal nodes are nodes with no incoming edges).c                 s   s     | ]\}}|d kr|V  qdS r   N .0node_iddegreer   r   ]/var/www/ideatree/venv/lib/python3.10/site-packages/networkx/algorithms/centrality/trophic.py	<genexpr>G       z!trophic_levels.<locals>.<genexpr>c                 s   s     | ]\}}|d kr|V  qdS r   r   r   r   r   r   r   L   r   )numpynxadjacency_matrixTtoarraysumnewaxisshapeeyelinalginvLinAlgErrorNetworkXError	in_degree	enumerate)Gr   nparowsumpnninerrmsgylevelszero_node_idsr   nonzero_node_idsr   r   r   r      s0   "


r   c                 C   s<   t | |d}i }| jD ]\}}|| ||  |||f< q|S )as  Compute the trophic differences of the edges of a directed graph.

    The trophic difference $x_ij$ for each edge is defined in Johnson et al.
    [1]_ as:

    .. math::
        x_ij = s_j - s_i

    Where $s_i$ is the trophic level of node $i$.

    Parameters
    ----------
    G : DiGraph
        A directed networkx graph

    Returns
    -------
    diffs : dict
        Dictionary of edges with trophic differences as the value.

    References
    ----------
    .. [1] Samuel Johnson, Virginia Dominguez-Garcia, Luca Donetti, Miguel A.
        Munoz (2014) PNAS "Trophic coherence determines food-web stability"
    r   )r   edges)r#   r   r.   diffsuvr   r   r   r   S   s
   r   Fc                 C   sb   ddl }|rt| |d}ntt| }|r |  }|| n| }t||d}|t| S )a+  Compute the trophic incoherence parameter of a graph.

    Trophic coherence is defined as the homogeneity of the distribution of
    trophic distances: the more similar, the more coherent. This is measured by
    the standard deviation of the trophic differences and referred to as the
    trophic incoherence parameter $q$ by [1].

    Parameters
    ----------
    G : DiGraph
        A directed networkx graph

    cannibalism: Boolean
        If set to False, self edges are not considered in the calculation

    Returns
    -------
    trophic_incoherence_parameter : float
        The trophic coherence of a graph

    References
    ----------
    .. [1] Samuel Johnson, Virginia Dominguez-Garcia, Luca Donetti, Miguel A.
        Munoz (2014) PNAS "Trophic coherence determines food-web stability"
    r   Nr   )	r   r   listr   selfloop_edgescopyremove_edges_fromstdvalues)r#   r   cannibalismr$   r2   
self_loopsG_2r   r   r   r   u   s   r   r   )r   F)	__doc__networkxr   networkx.utilsr   __all__r   r   r   r   r   r   r   <module>   s    J!