libs/dashapp.py

# -*- coding: utf-8 -*-
import gc
import glob
import os
from datetime import timedelta
from typing import Dict

import fiona
import geopandas as gpd
import netCDF4
import numpy as np
import pandas as pd
import rasterio
from rasterio.mask import mask
from shapely.validation import make_valid

from libs.utils import verbose as vprint

V = 1
V_IGNORE = []  # Debug, Warning, Error


def read_shape_file(
    file_name: str,
    epsg_num: int = 4326,
    dissolve=True,
    # buffer: float = 100,
    *args,
    **kwargs,
) -> gpd.GeoDataFrame:
    """Read a shape file and return a geo-dataframe object.

    Parameters
    ----------
    file_name : str
        path to shape file
    epsg_num : int, optional
        coordinate system number
    dissolve : bool, optional
        Dissolve to one geometry, e.g. get the most external shape.
        Useful when interested in the

    Returns
    -------
    gpd.GeoDataFrame
        pandas geo-dataframe with the geometry data and other infos

    Raises
    ------
    FileNotFoundError
        error if file is not found
    """
    if not os.path.exists(file_name):
        raise FileNotFoundError

    geo_df = gpd.read_file(file_name)
    if epsg_num is not None:
        geo_df["geometry"] = geo_df["geometry"].to_crs(epsg=epsg_num)

    crs = geo_df.crs
    if crs is None:
        crs = 4326

    print("Initial CRS:", crs)

    # check for multi
    geo_df.geometry = geo_df.apply(
        lambda row: make_valid(row.geometry)
        if not row.geometry.is_valid
        else row.geometry,
        axis=1,
    )
    return geo_df


def find_match_raster(model_path, farm_name):
    # navigate one directory up in model_path
    while not os.path.basename(model_path) == farm_name:
        model_path = os.path.dirname(model_path)
    root = model_path
    print("Looking for a matching raster in:", root)
    for root, dirs, _ in os.walk(root):
        # print(root,dirs,"\n\n")
        if "et_pp" in dirs:
            real_path = os.path.join(root, "et_pp")
            print("raster_path is", real_path)
            # for file in os.listdir(real_path):
            #     if file.endswith(".tif"):
            #         match_raster = os.path.join(real_path,file)
            #         print("Found match raster:",match_raster)
            return real_path
    return None


def find_shape_file(model_path, farm_name):
    # navigate one directory up in model_path until you reach the farm_name directory
    while not os.path.basename(model_path) == farm_name:
        model_path = os.path.dirname(model_path)
    print("Looking for a shape file in:", model_path)
    root = model_path
    print("Looking for a shape file in:", root)
    for root, dirs, files in os.walk(root):
        # print(root,dirs,"\n\n")
        for file in files:
            if file.endswith(".shp"):
                real_path = os.path.join(root, file)
                return real_path
    return None


def get_range_agg(
    input_dir: str,
    window_start: str,
    window_end: str,
    layer_name: str,
    agg: str = "mean",
) -> np.ndarray:
    """Get the mean for a given window_start and window_end dates.

    Parameters
    ----------
    input_dir : str
        Path to the directory containing the netcdf files.
    window_start : str
        Start date of the window. Format: YYYY-MM-DD.
    window_end : str
        End date of the window. Format: YYYY-MM-DD.
    layer_name : str
        Soil layer to consider for the mean.
    agg : str
        Aggregation method to use. Possible values: mean, median, max, min, std, or None.

    Returns
    -------
    np.ndarray
        Mean raster for the given window_start and window_end dates.
    """

    # Get the list of dates between two dates if date_from and date_to
    dates = pd.DataFrame(
        pd.date_range(
            pd.to_datetime(window_start),
            pd.to_datetime(window_end) - timedelta(days=1),
            freq="d",
        ),
        columns=["date"],
    )  # .strftime('%Y-%m-%d')
    dates["dayofyear"] = dates["date"].dt.dayofyear - 1
    dates["year"] = dates["date"].dt.year
    dates["str_dates"] = dates["date"].dt.strftime("%Y-%m-%d")

    yearly_dates = dates.groupby("year")["dayofyear"].apply(list).to_dict()

    data_l = list()
    # For each year, get the data for layer_name for the dates specified in yearly_dates
    for year in yearly_dates:
        # read the year file
        nc_y = netCDF4.Dataset(os.path.join(input_dir, f"model_{year}.nc"))

        vprint(
            1,
            V,
            V_IGNORE,
            Debug=f"getting data for year: {year} from layer: {layer_name}...",
        )
        # Get the data for the layer_name
        data = nc_y.variables[layer_name][:, :, :]

        # Get the data for the dates
        days = yearly_dates[year]

        data = data[days, :, :]

        data_l.append(data)
        nc_y.close()
        del data
        gc.collect()

    # Concat data for all years
    data_concat = np.concatenate(data_l, axis=0)
    data_concat.shape
    if agg == "mean":
        # Get the mean raster for the range
        data_agg = np.mean(data_concat, axis=0)
    elif agg == "median":
        # Get the median raster for the range
        data_agg = np.median(data_concat, axis=0)
    elif agg == "max":
        # Get the max raster for the range
        data_agg = np.max(data_concat, axis=0)
    elif agg == "min":
        # Get the min raster for the range
        data_agg = np.min(data_concat, axis=0)
    elif agg == "std":
        # Get the std raster for the range
        data_agg = np.std(data_concat, axis=0)
    elif agg == "var":
        # Get the var raster for the range
        data_agg = np.var(data_concat, axis=0)
    elif agg == "sum":
        # Get the sum raster for the range
        data_agg = np.sum(data_concat, axis=0)
    elif agg is None:
        data_agg = data_concat.copy()
    else:
        raise ValueError(
            f"agg should be one of 'mean', 'median', 'max', 'min', 'std', 'var', 'sum', or a None value. {agg} was provided."
        )
    print("done.")
    return data_agg


def get_historic_agg(
    input_dir: str,
    historic_years: int,
    current_window_start: str,
    current_window_end: str,
    layer_name: str,
    agg_window: str = "mean",
    agg_history: str = "mean",
) -> np.ndarray:
    """Get the historic mean for a given window_start and window_end dates.

    Parameters
    ----------
    input_dir : str
        Path to the directory containing the netcdf files.
    historic_years : int
        Number of historic years to consider for the mean.
    current_window_start : str
        Start date of the current window. Format: YYYY-MM-DD.
    current_window_end : str
        End date of the current window. Format: YYYY-MM-DD.
    layer_name : str
        Soil layer to consider for the mean.
    agg_window : str
        Aggregation method for the window (applies to both current and historic). Default is "mean". Possible values: "mean", "median", "max", "min", "std", "var", None (None will just make a 3D matrix for each year).
    agg_history : str
        Aggregation method for the historic years. This defines the way all years are combined into one. Default is "mean". Possible values: "mean", "median", "max", "min", "std", "var", "quantiles".
        If `quantile` is selected, the historic cube will be sent back as a 3D matrix (with non-agregated window and concatenated on axis 0 instead) for quantile comparison.
    Returns
    -------
    np.ndarray
        Array of the historic mean for the given window_start and window_end dates for the historic years.

    Raises
    ------
    FileNotFoundError
        If the file for the historic year is not found. Possible solutions:
          - The historic year should be modelled before calling this function.
          - The path to the historic year should be changed.
          - Calculate for a more recent historic year by reducing historic_years value.
    """

    # Get the window_start year
    window_start_year = pd.to_datetime(current_window_start).year
    window_end_year = pd.to_datetime(current_window_end).year

    # Get the first year
    first_year = window_start_year - historic_years

    # Check if file exists for this year
    if os.path.exists(os.path.join(input_dir, f"model_{first_year}.nc")):
        # Get the list of historic windows
        historic_agg = {}
        for year in range(1, historic_years + 1):
            args = {
                "input_dir": input_dir,
                "window_start": f"{window_start_year-year}{current_window_start[4:]}",
                "window_end": f"{window_end_year-year}{current_window_end[4:]}",
                "layer_name": layer_name,
                "agg": agg_window,
            }
            # Get the range mean
            historic_agg[window_start_year - year] = get_range_agg(**args)
        historic_agg_np = np.array([historic_agg[year] for year in historic_agg])
        # Get the aggregation of the historic years
        if agg_history == "mean":
            historic_agg_np = np.mean(historic_agg_np, axis=0)
        elif agg_history == "median":
            historic_agg_np = np.median(historic_agg_np, axis=0)
        elif agg_history == "max":
            historic_agg_np = np.max(historic_agg_np, axis=0)
        elif agg_history == "min":
            historic_agg_np = np.min(historic_agg_np, axis=0)
        elif agg_history == "std":
            historic_agg_np = np.std(historic_agg_np, axis=0)
        elif agg_history == "var":
            historic_agg_np = np.var(historic_agg_np, axis=0)
        elif agg_history == "sum":
            historic_agg_np = np.sum(historic_agg_np, axis=0)
        elif agg_history is None:
            historic_agg_np = np.concatenate(list(historic_agg.values()), axis=0)
        else:
            raise ValueError(
                f"Invalid aggregation method: {agg_history}. Possible values: mean, median, max, min, std, var, sum."
            )
        return historic_agg_np
    else:
        raise FileNotFoundError(
            f"File not found for the historic data: {os.path.join(input_dir,f'model_{first_year}.nc')}. Make sure the path is correct and the historic year for the requested year is modelled before calling this function."
        )


def save(path, array, profile):
    """Save the array as a raster.

    Parameters
    ----------
    path : str
        Path to the raster to save.
    array : np.ndarray
        Array to save as a raster.
    profile : dict
        Profile of the raster to save.
    """

    with rasterio.open(path, "w", **profile) as dst:
        dst.write(array, 1)


def analyse(
    input,
    window_start,
    window_end,
    historic_years: int,
    layer: str,
    match_raster: str = None,
    output: str = None,
    agg_history: str = "mean",
    agg_window: str = "mean",
    farm_name: str = None,
    **kwargs,
) -> Dict[str, str]:
    """Main function to run the script.

    Parameters
    ----------
    input : str
        Path to the input raster.
    window_start : str
        Start date of the window. Format: YYYY-MM-DD.
    window_end : str
        End date of the window. Format: YYYY-MM-DD.
    historic_years : int
        Number of historic years to use for the comparison.
    layer : str
        Soil layer to consider for the comparison.
    match_raster : str
        Path to the match raster. Default: None. If None, the match raster will be searched in the et_pp directory based on the input directory.
    output : str
        Path to the output raster. Default: None. If None, the output raster will be saved in the same directory as the input raster.
    agg_history : str
        Aggregation method to use for the historic years. Possible values: 'mean', 'median', 'max', 'min', 'std', None. Default: 'mean'.
    agg_window : str
        Aggregation method to use for the window. Possible values: 'mean', 'median', 'max', 'min', 'std', None. Default: 'mean'.
    farm_name : str
        Name of the farm. Should be provided. Default: None.

    Returns
    -------
    Dict[str,str]
        Dictionary with the path to the output rasters.
    """

    if output is None:
        output = os.path.join(input, "analysis")

    # Create the output directory if it does not exist
    if not os.path.exists(output):
        os.makedirs(output)

    if match_raster is None:
        match_raster = find_match_raster(input, farm_name)
    print("match_raster is:", match_raster)

    if match_raster is not None:
        print("Found match raster:", match_raster)

    files = glob.glob(os.path.join(match_raster, f"{window_start[:7]}*.tif"))
    if len(files) == 0:
        files = glob.glob(os.path.join(match_raster, f"{window_end[:7]}*.tif"))
    if len(files) == 0:
        vprint(
            1,
            V,
            V_IGNORE,
            Debug=f"Expanding the search for match raster file to find e closer date to {window_start[:5]}...",
        )
        files = glob.glob(os.path.join(match_raster, f"{window_start[:5]}*.tif"))
    if len(files) == 0:
        vprint(
            1,
            V,
            V_IGNORE,
            Debug=f"Expanding the search further for match raster file to find e closer date  to {window_end[:5]}...",
        )
        files = glob.glob(os.path.join(match_raster, f"{window_end[:5]}*.tif"))
    if len(files) == 0:
        raise FileNotFoundError(
            f"Could not find any matching raster in {match_raster} for the rage of dates given at {window_start} / {window_end}!"
        )
    print(f"Found {len(files)} matching raster file {files[0]}.")
    match_raster = files[0]

    with rasterio.open(match_raster) as src:
        profile = src.profile

    # Get the layers
    layer = layer
    # Get the historic aggregated data

    # Get aggregated current window data
    current_data = get_range_agg(
        input_dir=input,
        window_start=window_start,
        window_end=window_end,
        agg=agg_window,
        layer_name=layer,
    )

    historic_data = get_historic_agg(
        input_dir=input,
        historic_years=historic_years,
        current_window_start=window_start,
        current_window_end=window_end,
        agg_window=agg_window,
        agg_history=agg_history,
        layer_name=layer,
    )

    historic_data_quant = get_historic_agg(
        input_dir=input,
        historic_years=historic_years,
        current_window_start=window_start,
        current_window_end=window_end,
        agg_window=None,
        agg_history=None,
        layer_name=layer,
    )

    # Compare the two rasters
    delta = current_data - historic_data

    quantile = current_data.copy()
    print("\nCalculating quantiles...", "\n=========================")
    print("Data shape:", current_data.shape)
    print("Historic data shape:", historic_data_quant.shape)
    print("=========================\n")
    pixel_now = []
    pixel_hist = []
    pixel_quant = []
    for i in range(current_data.shape[0]):
        for j in range(current_data.shape[1]):
            sorted_scores = np.array(sorted(historic_data_quant[:, i, j]))
            quantile[i, j] = (
                (sorted_scores.searchsorted(current_data[i, j]))
                / sorted_scores.shape[0]
                * 100
            )
            pixel_now.append(current_data[i, j])
            pixel_hist.append(sorted_scores)
            pixel_quant.append(quantile[i, j])

    df_quants = pd.DataFrame(pixel_hist)
    df_quants["pixel_now"] = pixel_now
    df_quants["pixel_quant"] = pixel_quant
    print("shapes are:", len(pixel_now), len(pixel_quant))
    df_quants = df_quants.sort_values(by=["pixel_quant"], ascending=False)
    print(df_quants)
    df_quants.to_csv(
        os.path.join(
            output,
            f"quantiles-{window_start.replace('-','_')}-{window_end.replace('-','_')}-{layer}-w_{agg_window}-h_concat-y_{historic_years}.csv",
        )
    )

    # Search for a shape file ending with .shp
    shape_file = find_shape_file(input, farm_name)
    print("Shape file:", shape_file)

    # Save the rasters
    historic_raster = os.path.join(
        output,
        f"historic-{window_start.replace('-','_')}-{window_end.replace('-','_')}-{layer}-w_{agg_window}-h_{agg_history}-y_{historic_years}.tif",
    )
    current_raster = os.path.join(
        output,
        f"current-{window_start.replace('-','_')}-{window_end.replace('-','_')}-{layer}-w_{agg_window}.tif",
    )
    delta_raster = os.path.join(
        output,
        f"delta-{window_start.replace('-','_')}-{window_end.replace('-','_')}-{layer}-w_{agg_window}-h_{agg_history}-y_{historic_years}.tif",
    )
    quant_raster = os.path.join(
        output,
        f"quantile-{window_start.replace('-','_')}-{window_end.replace('-','_')}-{layer}-w_{agg_window}-h_concat-y_{historic_years}.tif",
    )

    save(historic_raster, historic_data, profile)
    save(current_raster, current_data, profile)
    save(delta_raster, delta, profile)
    save(quant_raster, quantile, profile)

    # Open the rasters
    # with rasterio.open(historic_raster) as src:
    #     historic_raster = src.read(1)

    # Clip the rasters to the shape file
    if shape_file is not None:
        print("Found shape file:", shape_file)
        # shapes = read_shape_file(
        #     shape_file, epsg_num=None, dissolve=False
        #     ).geometry
        try:
            with fiona.open(shape_file, "r") as shapefile:
                shapes = [feature["geometry"] for feature in shapefile]
        except Exception as e:
            print("Error reading shape file:", e)

        try:
            with rasterio.open(historic_raster) as src:
                out_image, transformed = mask(src, shapes, crop=True, filled=True)
                out_profile = src.profile.copy()
            out_profile.update(
                {
                    "width": out_image.shape[2],
                    "height": out_image.shape[1],
                    "transform": transformed,
                }
            )
            with rasterio.open(historic_raster, "w", **out_profile) as dst:
                dst.write(out_image)
        except Exception as e:
            print("Error clipping historic raster:", e)

        try:
            with rasterio.open(current_raster) as src:
                out_image, transformed = mask(src, shapes, crop=True, filled=True)
                out_profile = src.profile.copy()
            out_profile.update(
                {
                    "width": out_image.shape[2],
                    "height": out_image.shape[1],
                    "transform": transformed,
                }
            )
            with rasterio.open(current_raster, "w", **out_profile) as dst:
                dst.write(out_image)
        except Exception as e:
            print("Error clipping current raster:", e)

        try:
            with rasterio.open(delta_raster) as src:
                out_image, transformed = mask(src, shapes, crop=True, filled=True)
                out_profile = src.profile.copy()
            out_profile.update(
                {
                    "width": out_image.shape[2],
                    "height": out_image.shape[1],
                    "transform": transformed,
                }
            )
            with rasterio.open(delta_raster, "w", **out_profile) as dst:
                dst.write(out_image)
        except Exception as e:
            print("Error clipping delta raster:", e)

        try:
            with rasterio.open(quant_raster) as src:
                out_image, transformed = mask(src, shapes, crop=True, filled=True)
                out_profile = src.profile.copy()
            out_profile.update(
                {
                    "width": out_image.shape[2],
                    "height": out_image.shape[1],
                    "transform": transformed,
                }
            )
            with rasterio.open(quant_raster, "w", **out_profile) as dst:
                dst.write(out_image)
        except Exception as e:
            print("Error clipping quantile raster:", e)

        # current_data, _ = rasterio.mask.mask(current_data, shapes, crop=True)
        # historic_data, _ = rasterio.mask.mask(historic_data, shapes, crop=True)
        # delta, _ = rasterio.mask.mask(delta, shapes, crop=True)
        # quantile, _ = rasterio.mask.mask(quantile, shapes, crop=True)
        print("done.")

    return {
        "historic_raster": historic_raster,
        "current_raster": current_raster,
        "delta_raster": delta_raster,
        "quant_raster": quant_raster,
    }


def find_analyses(path):
    """Find all the analysis files in a directory.

    Parameters
    ----------
    path: str
        Path to the directory containing the analysis files

    Returns
    -------
    files: list
        List of analysis files
    """
    files = [f for f in os.listdir(path) if f.endswith(".tif")]
    return files


def open_image(path):
    """Open a raster image and return the data and coordinates.

    Parameters
    ----------
    path: str
        path to the raster image

    Returns
    -------
    band1: np.array
        The raster data
    lons: np.array
        The longitude coordinates
    lats: np.array
        The latitude coordinates
    """
    with rasterio.open(path) as src:
        band1 = src.read(1)
        print("Band1 has shape", band1.shape)
        height = band1.shape[0]
        width = band1.shape[1]
        cols, rows = np.meshgrid(np.arange(width), np.arange(height))
        xs, ys = rasterio.transform.xy(src.transform, rows, cols)
        lons = np.array(xs)
        lats = np.array(ys)

    return band1, lons, lats


def perform_analysis(
    input,
    window_start,
    window_end,
    historic_years: int,
    layer: str,
    match_raster: str = None,
    output: str = None,
    agg_history: str = "mean",
    agg_window: str = "mean",
    comparison: str = "diff",
    farm_name: str = None,
    **args,
) -> Dict[str, str]:
    """Perform the analysis.

    This is a wrapper function for the analysis module. It takes the input parameters and passes them to the analysis module.

    Parameters
    ----------
    input : str
        path to the input data
    window_start : str
        start date of the window
    window_end : str
        end date of the window
    historic_years : int
        number of years to use for the historic data
    layer : str
        layer to use for the analysis
    match_raster : str, optional
        path to the raster to match the output to, by default None
    output : str, optional
        path to the output file, by default None
    agg_history : str, optional
        aggregation method for the historic data, by default "mean"
    agg_window : str, optional
        aggregation method for the window data, by default "mean"
    comparison : str, optional
        comparison method for the window and historic data, by default "diff"

    Returns
    -------
        files: dict
            Dict of analysis files
    """
    files = analyse(
        input=input,
        window_start=window_start,
        window_end=window_end,
        historic_years=historic_years,
        agg_window=agg_window,
        agg_history=agg_history,
        comparison=comparison,
        layer=layer,
        output=output,
        match_raster=match_raster,
        farm_name=farm_name,
    )
    return files


def layout(WAIT_IMAGE):
    import datetime

    import plotly.express as px
    from dash import dcc, html

    today = datetime.datetime.today()

    colorscales = px.colors.named_colorscales()

    layout = html.Div(
        [
            # html.Div(
            # className="dashapp-header",
            # children=[
            #     html.Div('Soil Moisture Comparison Tool', className="dashapp-header--title")
            # ]
            # ),
            dcc.Store(id="farm-name-session", storage_type="session"),
            html.Div(
                [
                    html.P(
                        """This tool will use the produced datacubes to compare the soil moisture of a farm against historic data.
                        Please select the desired comaprison method and dates to make the comparison as in section A.
                        Then choose the visualisation in section B to see the results.""",
                        style={"font-size": "larger"},
                    ),
                    html.Hr(),
                    html.H3("A"),
                ],
                className="col-lg-12",
                style={"padding-top": "1%", "padding-left": "1%"},
            ),
            html.Div(
                [
                    html.Div(
                        [
                            # html.P("Write farm name/ID:"),
                            dcc.Input(
                                id="farm-name",
                                type="text",
                                placeholder="Farm name",
                                style={"width": "80%"},
                            ),
                            html.Img(
                                id="farm-image",
                                src=WAIT_IMAGE,
                                style={"width": "30px", "margin-left": "15px"},
                            ),
                        ],
                        className="col-lg-5",
                        # style = {'padding-top':'1%', 'padding-left':'1%'}
                    ),
                    html.Div(
                        [
                            html.P(),
                        ],
                        className="col-lg-7",
                        # style = {'padding-top':'1%', 'padding-left':'1%'}
                    ),
                ],
                className="row",
                style={"padding-top": "1%", "padding-left": "1%"},
            ),
            html.Div(
                [
                    html.Div(
                        [
                            html.P("Select soil layer:"),
                            dcc.Dropdown(
                                id="layer-dropdown",
                                options=[
                                    {"label": "SM1", "value": "SM1"},
                                    {"label": "SM2", "value": "SM2"},
                                    {"label": "SM3", "value": "SM3"},
                                    {"label": "SM4", "value": "SM4"},
                                    {"label": "SM5", "value": "SM5"},
                                    {"label": "DD", "value": "DD"},
                                ],
                                value="SM2",
                            ),
                        ],
                        className="col-lg-4",
                        style={"padding": "1%"},
                    ),
                    html.Div(
                        [
                            html.P("Select the historic years to compare against:"),
                            dcc.Dropdown(
                                id="historic-dropdown",
                                options=[
                                    {"label": year, "value": year}
                                    for year in range(1, 20)
                                ],
                                value=2,
                            ),
                        ],
                        className="col-lg-4",
                        style={"padding": "1%"},
                    ),
                    html.Div(
                        [
                            html.P(
                                "Select the most recent window of dates to analyse:"
                            ),
                            dcc.DatePickerRange(
                                id="window-select",
                                min_date_allowed=datetime.date(2000, 1, 1),
                                max_date_allowed=today.strftime("%Y-%m-%d"),
                                initial_visible_month=datetime.date(2023, 1, 1),
                                clearable=False,
                                display_format="YYYY-MM-DD",
                                start_date_placeholder_text="Start date",
                                end_date_placeholder_text="End date",
                                style={"width": "100%"},
                            ),
                        ],
                        className="col-lg-4",
                        style={"padding": "1%"},
                    ),
                ],
                className="row",
                style={"padding-top": "1%"},
            ),
            html.Div(
                [
                    html.Div(
                        [
                            html.P("Select window aggregation method:"),
                            dcc.Dropdown(
                                id="w-aggregation-dropdown",
                                options=[
                                    {"label": "Mean", "value": "mean"},
                                    {"label": "Median", "value": "median"},
                                    {"label": "Max", "value": "max"},
                                    {"label": "Min", "value": "min"},
                                    {"label": "Sum", "value": "sum"},
                                    {"label": "std", "value": "std"},
                                    {"label": "var", "value": "var"},
                                ],
                                value="mean",
                            ),
                        ],
                        className="col-lg-6",
                        style={"padding": "1%"},
                    ),
                    html.Div(
                        [
                            html.P("Select historic aggregation method:"),
                            dcc.Dropdown(
                                id="h-aggregation-dropdown",
                                options=[
                                    {"label": "Mean", "value": "mean"},
                                    {"label": "Median", "value": "median"},
                                    {"label": "Max", "value": "max"},
                                    {"label": "Min", "value": "min"},
                                    {"label": "Sum", "value": "sum"},
                                    {"label": "std", "value": "std"},
                                    {"label": "var", "value": "var"},
                                    {"label": "quantile", "value": "quantile"},
                                ],
                                value="mean",
                            ),
                        ],
                        className="col-lg-6",
                        style={"padding": "1%"},
                    ),
                ],
                className="row",
                # style = {'padding-top':'1%'}
            ),
            html.Div(
                [
                    html.Button("Generate Images", id="generate-button"),
                    html.Br(),
                    html.Hr(),
                ],
                className="col-lg-12",
                style={"margin-bottom": "1%"},
            ),
            html.Div(
                [
                    html.H3("B"),
                ],
                className="col-lg-12",
                style={"padding-top": "1%", "padding-left": "1%"},
            ),
            html.Div(
                [
                    html.Div(
                        [
                            html.P("Select visualisation name:"),
                            dcc.Dropdown(id="visualisation-select"),
                        ],
                        className="col-lg-6",
                        style={"padding": "1%"},
                    ),
                    html.Div(
                        [
                            html.P("Select your palette:"),
                            dcc.Dropdown(
                                id="platter-dropdown",
                                options=colorscales,
                                value="viridis",
                            ),
                        ],
                        className="col-lg-6",
                        style={"padding": "1%"},
                    ),
                ],
                className="row",
                # style = {'padding-top':'1%'}
            ),
            html.Div(
                [
                    html.Hr(),
                    html.H3("Results"),
                    dcc.Graph(id="graph"),
                ],
                className="col-lg-12",
                style={"padding-top": "1%"},
            ),
            # html.Div(
            # className="dashapp-footer",
            # children=[
            #     html.Div(f"Copyright @ {today.strftime('%Y')} Sydney Informatics Hub (SIH)", className="dashapp-footer--copyright")
            # ]
            # ),
        ],
        className="container-fluid",
    )

    return layout