import numpy as np
from scipy.stats import gaussian_kde

def shorten_species(name):
    genus, species = name.split('_')
    return genus[0] + '. ' + species

def unsort_unique(array):
    values, inds = np.unique(array, return_index=True)
    return values[np.argsort(inds)]

def get_kde(data, sigma, axis=None, n=1000, pad=10):
    if axis is None:
        axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)
    pdf = gaussian_kde(data, sigma)(axis)
    return pdf, axis

def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
                   condense=None):
    if condense == 'norm' and sigmoid.ndim == 2:
        sigmoid = np.linalg.norm(sigmoid, axis=1)

    min_value = sigmoid[0] if first else sigmoid.min(axis=0)
    max_value = sigmoid[-1] if last else sigmoid.max(axis=0)
    
    span = max_value - min_value
    low_value = min_value + low * span
    high_value = min_value + high * span

    low_mask = sigmoid <= low_value
    high_mask = sigmoid <= high_value
    if sigmoid.ndim == 1:
        low_ind = np.nonzero(low_mask)[0][-1]
        high_ind = np.nonzero(high_mask)[0][-1]
    elif condense == 'all':
        low_ind = np.nonzero(low_mask.all(axis=1))[0][-1]
        high_ind = np.nonzero(high_mask.all(axis=1))[0][-1]
    else:
        low_ind, high_ind = [], []
        for i in range(sigmoid.shape[1]):
            low_ind.append(np.nonzero(low_mask[:, i])[0][-1])
            high_ind.append(np.nonzero(high_mask[:, i])[0][-1])
    return low_ind, high_ind