#!/usr/bin/python
# -*- coding: utf-8 -*-
# \file dfc_sdaps.py
# \brief TODO
# \author Florent Guiotte <florent.guiotte@gmail.com>
# \version 0.1
# \date 27 août 2018
#
# TODO details

import numpy as np
import ld2dap

def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):
    """DFC Self Dual Attribute Profiles

    Compute description vectors for parameters. Rasters can be splitted along
    `split_dim` before description proceeds.

    """

    # Parse attribute type
    treshold = float(treshold)
    areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)
    sd = None if sd is None else np.array(sd).astype(np.float)
    moi = None if moi is None else np.array(moi).astype(np.float)

    # Load and filter 
    loader = ld2dap.LoadTIFF(rasters)
    dfc_filter = ld2dap.Treshold(treshold)
    normalize = ld2dap.Normalize(dtype=np.uint8)
    raw_out = ld2dap.RawOutput()

    raw_out.input = normalize
    normalize.input = dfc_filter
    dfc_filter.input = loader
    raw_out.run()

    # Split
    n = split; d = split_dim

    step = int(raw_out.data.shape[d] / n)
    view = np.moveaxis(raw_out.data, d, 0)
    cuts = list()
    for i in range(n):
        cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)
        cuts.append(cut)

    # Describe
    dcuts = list()
    for cut in cuts:
        rinp = ld2dap.RawInput(cut, raw_out.metadata)
        aps = ld2dap.SelfDualAttributeProfiles(areas, sd, moi, normalize_to_dtype=False)
        vout = ld2dap.RawOutput()
        
        vout.input = aps
        aps.input = rinp
        vout.run()
        
        dcuts.append(vout.data)

    # Merge
    descriptors = np.zeros(raw_out.data.shape[:2] + (dcuts[0].shape[-1],))
    view = np.moveaxis(descriptors, d, 0)

    for i, cut in enumerate(dcuts):
        view[i*step:(i+1)*step+1] = np.moveaxis(cut, 0, d)

    return descriptors

def version():
    return 'v0.0'