5. Results

from erddapy import ERDDAP
import os
import pandas as pd
import numpy as np

import panel as pn
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')
pn.extension()

df = pd.read_csv('dataset.csv', parse_dates=True, index_col=0)

from imputeMF import imputeMF

df_imputed = pd.DataFrame(imputeMF(df.values, 10, print_stats=True), columns=df.columns, index=df.index)

df_imputed.to_csv('dataset_imputed.csv')

# Create a dropdown selector
site_selector = pn.widgets.Select(name='Site', options=list(df.columns))

def highlight_imputed_regions(label):

    series = df[label]
    series_imputed = df_imputed[label]
    
    # Identify NaN regions
    is_nan = series.isna()
    nan_ranges = []
    current_start = None

    for date, missing in is_nan.items():
        if missing and current_start is None:
            current_start = date
        elif not missing and current_start is not None:
            nan_ranges.append((current_start, date))
            current_start = None
    if current_start is not None:
        nan_ranges.append((current_start, series.index[-1]))

    # Create shaded regions
    spans = [
        hv.VSpan(start, end).opts(color='red', alpha=0.2)
        for start, end in nan_ranges
    ]

    curve = hv.Curve(series_imputed, label=label).opts(
        width=900, height=250, tools=['hover', 'box_zoom', 'pan', 'wheel_zoom'],
        show_grid=True, title=label
    )

    return curve * hv.Overlay(spans)
    
interactive_plot = hv.DynamicMap(pn.bind(highlight_imputed_regions, site_selector))

pn.Column(site_selector, interactive_plot)