# Prepare Python environment
import numpy as np
import scipy.io as sio
from pathlib import Path

data_dir = Path("../../../data/03-T2")
data_file = "multiexpo_T2_curves.mat"

#Load either archived or generated plot variables
mat_contents = sio.loadmat(data_dir / data_file)

# Get the signals and parameters from Matlab

# Signals
signal_mono_MW = np.array(mat_contents['signal_mono_MW'][0])
signal_mono_IEW = np.array(mat_contents['signal_mono_IEW'][0])
signal_multi_MWF = np.array(mat_contents['signal_multi_MWF'][0])

# MWF from simulation
FitResult = mat_contents['FitResult']
MWF = np.round(FitResult['MWF'][0][0]/100, 2)

# TE 
params = mat_contents['params']
TE = params['TE'][0][0][0]

# Initialize MWF values for interactive multi-expo curve
interactive_multiexpo_signal = MWF*signal_mono_MW + (1-MWF)*signal_mono_IEW

## Plot
import matplotlib.pyplot as plt
import chart_studio.plotly as py
import plotly.graph_objs as go
import numpy as np
from plotly import __version__
from plotly.offline import iplot

config={'showLink': False, 'displayModeBar': False}

# Mono-exponential myelin water (MW) signal

MW_T2 = go.Scatter(
    x = TE,
    y = signal_mono_MW,
    name = 'Mono-exponential : Myelin water (MW)',
    text = 'Myelin Water (MW)',
    hoverinfo = 'x+y+text',
    line=dict(color='#2ca02c'),
    visible = True
)

# Mono-exponential intra- and extracellular water (IEW) signal

IEW_T2 = go.Scatter(
    x = TE,
    y = signal_mono_IEW,
    name = 'Mono-exponential : Intra- and extracellular water (IEW)',
    text = 'Intra- and Extracellular Water (IEW)',
    hoverinfo = 'x+y+text',
    line=dict(color='#ff7f0e'),
    visible = True
)

# Interactive multi-exponential signal (with slider on interactive figure)

multiexpo_T2_inter = go.Scatter(
    x = TE,
    y = interactive_multiexpo_signal[0],
    name = f'Multi-exponential : MW + IEW',
    text = f'Interactive multi-expo T2',
    hoverinfo = 'x+y+text', 
    line=dict(color='#9467bd',dash='dot'),
    visible = True,
)

data = [MW_T2, IEW_T2, multiexpo_T2_inter]

# Define steps for slider
steps = [] 
for value_slider in np.arange(0, 1.05, 0.05):  # For slider with 5% increments
    interactive_multiexpo_signal = value_slider* signal_mono_MW + (1-value_slider) * signal_mono_IEW
    steps.append(
        dict(
            method='update',
            args=[
                {'y': [signal_mono_MW, signal_mono_IEW, interactive_multiexpo_signal]},
                {'visible': [True, True, True]}, 
                {'name': f'{int(value_slider*100)}% MW   {int((1-value_slider)*100)}% IEW'},
            ],
            label=f'<b>{int(value_slider*100)}% MW {int((1-value_slider)*100)}% IEW</b>',
        )
    )


layout = go.Layout(
    width=670,
    height=475,
    margin=go.layout.Margin(
        l=100,
        r=50,
        b=30,
        t=30,
    ),
    sliders = [
            dict(
            steps=steps,
            active = int(MWF[0][0] * 20),
            len = 1.0,
            pad = {'t':50},
        )
    ],
    annotations=[
        dict(
            x=0.5004254919715793,
            y=-0.175,
            showarrow=False,
            text='Echo Time – TE (ms)',
            font=dict(
                family='Times New Roman',
                size=22
            ),
            xref='paper',
            yref='paper'
        ),
        dict(
            x=-0.15,
            y=0.50,
            showarrow=False,
            text='Transverse Magnetization (M<sub>xy</sub>)',
            font=dict(
                family='Times New Roman',
                size=22
            ),
            textangle=-90,
            xref='paper',
            yref='paper'
        ),
    ],
    xaxis=dict(
        showgrid=False,
        linecolor='black',
        linewidth=2,
    ),
    yaxis=dict(
        showgrid=False,
        linecolor='black',
        linewidth=2
    ),
    legend=dict(
        x=0.36,
        y=0.97,
        traceorder='normal',
        font=dict(
            family='Times New Roman',
            size=12,
            color='#000'
        ),
        bordercolor='#000000',
        borderwidth=2
    )
) 

fig = dict(data=data, layout=layout)

iplot(fig, filename = 'ir_fig_2.html', config = config)