'''

Dendrogram statistics as described in Burkhart et al. (2013)

Two statistics are contained:

* number of leaves + branches vs. $\delta$ parameter

* statistical moments of the intensity histogram

Requires the astrodendro package (http://github.com/astrodendro/dendro-core)

'''

from astrodendro import Dendrogram

import numpy as np

from matplotlib import pyplot as p

import matplotlib.cm as cm

import os

#class DendroStats(object):

# """#

# docstring for DendroStats#

# Parameters

# **********#

# save : bool, optional

# Sets whether each dendrogram should be saved (in HDF5). If True,

# dendrograms are loaded in only when needed. If False, the entire

# dendrogram is kept open.#

# """

# def __init__(self, cube, header, deltas=None, save=True, save_name=None, \

# save_path=None, verbose=True):

# super(DendroStats, self).__init__()

# self.cube = cube

# self.header = header

# self.save = save#

# self.dendrograms = {}#

# if deltas is None:

# pass

# ## define some criterion to set uniform delta values to test

# else:

# assert isinstance(deltas, np.ndarray)

# self.deltas = deltas#

# if self.save:

# if save_name is None:

# print "Specify save name for dendrogram output. Set to \

# generic name with header's object name if available."

# try:

# save_name = header["OBJECT"]

# except KeyError:

# print "No object name found in the header."

# save_name = "generic_savename"#

#

# for delta in self.deltas:

# dendro_file = load_dendro(delta, save_name, save_path=save_path)

# if save:

# if dendro_file is None:

# self.dendrograms[delta] = compute_dendro(cube, header, delta,

# save=save, save_name=save_name, save_path=save_path, verbose=verbose)

# else:

# self.dendrograms[delta] = dendro_file

# else:

# if dendro_file is None:

# self.dendrograms[delta] = compute_dendro(cube, header, delta,

# save=save, save_name=save_name, save_path=save_path, verbose=verbose)

# else:

# self.dendrograms[delta] = Dendrogram.load_from(dendro_file)#

#

# self.num_features = np.empty((1, max(self.deltas.shape)))

# self.histogram = {}#

#

#

# def num_features(self):

# '''#

# Compute the number of leaves and branches in a dendrogram at all

# specified values of delta.#

# '''#

# for delta, i in enumerate(self.deltas):

# if self.save:

# d = Dendrogram.load_from(self.dendrograms[delta])

# else:

# d = self.dendrograms[delta]#

# self.num_features[:,i] = d.__len__()#

# return self#

# def make_histogram(self):

# '''#

# Returns a histogram of the intensities of the leaves and branches.#

# '''#

# for delta, i in enumerate(self.deltas):

# if self.save:

# d = Dendrogram.load_from(self.dendrograms[delta])

# else:

# d = self.dendrograms[delta]#

# self.histogram[delta] =[value.vmax for value in d.branches]

# self.histogram[delta].extend([value.vmax for value in d.leaves])#

#

# return self#

# def run(self, verbose=False):#

# self.num_features()

# self.make_histogram()#

# if verbose:

# import matplotlib.pyplot as p

# colours = cm.rainbow(np.linspace(0, 1, num_fids+1))

# p.subplot(2,1,1)

# for delta,i in enumerate(deltas):

# p.hist(self.histogram[delta], colours[i], bins=50, label=str(delta))

# p.legend()#

# p.subplot(2,1,2)

# p.plot(self.deltas, self.num_features, "kD-")#

# p.show#

# return self

def compute_dendro(cube, header, delta, save_name, verbose=False, save=True,\

return dendrogram

def load_dendro(delta, save_name, save_path=None):