Zooming in does not resample when Y-value are identical about plotly-resampler HOT 9 OPEN

import dash
from dash import html, dcc, Input, Output, State, no_update
import plotly.graph_objects as go
import pandas as pd

# For plain dash apps you need to use the FigureResampler class
# (the register function is for notebooks only)
from plotly_resampler import FigureResampler, ASSETS_FOLDER

FigureResampler(create_overview=True, verbose=True)
GRAPH_ID = "graph-id"
OVERVIEW_GRAPH_ID = "overview-graph"

# 0. Load the data
df_hourly = pd.read_csv("data.csv", index_col=0)

# 1. Create the figure and add data
# fmt: off
hourly_fig = FigureResampler(
    go.Figure(
        layout=dict(
            dragmode="pan",
            hovermode="x unified",
            xaxis=dict(
                rangeselector=dict(
                    buttons=list( [
                        dict(count=1, label="1 day", step="day", stepmode="backward"),
                        dict(count=1, label="1 month", step="month", stepmode="backward"),
                        dict(count=1, label="1 year", step="year", stepmode="backward",),
                    ])
                ),
            ),
        )
    ),
)
hourly_fig.add_trace(go.Scattergl(x=df_hourly["Date"], y=df_hourly["MWh"], name="Hourly position", mode="lines"))

# 1.1 Create the overview figure
coarse_fig = hourly_fig._create_overview_figure()

# Create the app in which the figure will be displayed
app = dash.Dash(
    __name__,
    meta_tags=[
        {"name": "viewport", "content": "width=device-width, initial-scale=1.0"}
    ],
    assets_folder=ASSETS_FOLDER,
    external_scripts=["https://cdn.jsdelivr.net/npm/lodash/lodash.min.js"],
)
# NOTE: you need to create both a coars and 
app.layout = html.Div(
    children=[
        dcc.Graph(id=GRAPH_ID, figure=hourly_fig),
        dcc.Graph(id=OVERVIEW_GRAPH_ID, figure=coarse_fig),
    ]
)

# -------------------- Callbacks --------------------
# --- Clientside callbacks used to bidirectionally link the overview and main graph ---
app.clientside_callback(
    dash.ClientsideFunction(namespace="clientside", function_name="main_to_coarse"),
    dash.Output(OVERVIEW_GRAPH_ID, "id", allow_duplicate=True),
    dash.Input(GRAPH_ID, "relayoutData"),
    [dash.State(OVERVIEW_GRAPH_ID, "id"), dash.State(GRAPH_ID, "id")],
    prevent_initial_call=True,
)

app.clientside_callback(
    dash.ClientsideFunction(namespace="clientside", function_name="coarse_to_main"),
    dash.Output(GRAPH_ID, "id", allow_duplicate=True),
    dash.Input(OVERVIEW_GRAPH_ID, "selectedData"),
    [dash.State(GRAPH_ID, "id"), dash.State(OVERVIEW_GRAPH_ID, "id")],
    prevent_initial_call=True,
)


# --- FigureResampler update callback ---
# The plotly-resampler callback to update the graph after a relayout event (= zoom/pan)
# As we use the figure again as output, we need to set: allow_duplicate=True
@app.callback(
    Output(GRAPH_ID, "figure", allow_duplicate=True),
    Input(GRAPH_ID, "relayoutData"),
    prevent_initial_call=True,
)
def update_fig(relayoutdata: dict):
    if relayoutdata is None:
        return no_update
    return hourly_fig.construct_update_data_patch(relayoutdata)


# Start the app
app.run(debug=False, host="localhost")