# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd

import sys
sys.path.insert(0, './Modules')

from gratings import grating_model
from plotting import plot_data, plot_mean_against_index, show_feature
from persistence import persistence
from decoding import cohomological_parameterization, remove_feature
from noisereduction import PCA_reduction, z_cutoff



## Generate data
data = grating_model(Nn=8, Np=(18,1,18,1), deltaT=55, random_neurons=True)

## Apply noise reduction
# data = PCA_reduction(data, 5)
# data = z_cutoff(data,2)

## Analyze shape
persistence(data,homdim=2,coeff=2)
persistence(data,homdim=2,coeff=3)

## Decode first parameter
decoding1 = cohomological_parameterization(data, coeff=23)
show_feature(decoding1)
plot_mean_against_index(data,decoding1,"orientation")
plot_mean_against_index(data,decoding1,"phase")
# plot_data(data,transformation="PCA", labels=decoding1,
#           colors=["Twilight","Viridis","Twilight","Viridis","Twilight"])

## Decode second parameter
# reduced_data = remove_feature(data, decoding1, cut_amplitude=0.5)
# decoding2 = cohomological_parameterization(reduced_data, coeff=23)
# show_feature(decoding2)
# plot_mean_against_index(data,decoding2,"orientation")
# plot_mean_against_index(data,decoding2,"phase")
# plot_data(data,transformation="PCA", labels=decoding2,
#           colors=["Twilight","Viridis","Twilight","Viridis","Twilight"])