### A Pluto.jl notebook ###
# v1.0.4

using Markdown
using InteractiveUtils

# ╔═╡ 82290cf0-cc7e-11f1-afb5-679eb5954384
begin
	using Downloads
	using CodecZlib
	using CSV
	using DataFrames
	using Dates
	using GLM
	using LinearAlgebra
	using Statistics
	using JSON
	using CairoMakie
	using GeoMakie
	using FileIO
    using ImageIO
end

# ╔═╡ f6602dd0-292c-4a63-8247-94d626d87f39
# ╠═╡ disabled = true
#=╠═╡

  ╠═╡ =#

# ╔═╡ 57fe870c-3366-4aaf-b2a3-014edf609e7a
md"""
# Ilmatar Energy

# Alajärvi wind farm complex
"""

# ╔═╡ 88ef8628-e883-4113-abd3-080e2b2376d8
md"""
## Weather Stations
- Alajarvi Moksy / Huosianmaa (02787)
"""

# ╔═╡ 5c21db41-78f6-4db7-ad1e-ff1900afdca3
md"""
Rather than relying on distant municipal weather stations with historical recording gaps, this analysis leverages high-precision telemetry from the Finnish Meteorological Institute (FMI) automated synoptic station at Alajärvi Möksy (02787), positioned directly at the wind park site. The sample spans January 2021 through October 2026 ($N = 2,078$ days), providing unbroken daily wind records that match the operational aerodynamic roughness of the modern 221 MW complex while offering abundant degrees of freedom for robust regression estimation.
"""

# ╔═╡ d61dc3ca-fccc-4770-af6d-aa5082c55369
"""
Fetch daily weather time-series for a given Meteostat station ID.
Meteostat bulk daily files do not contain headers; columns follow the v2 standard schema.
"""
function fetch_meteostat_daily(station_id::String)
    url = "https://bulk.meteostat.net/v2/daily/$(station_id).csv.gz"
    
    # In-memory buffer for the compressed stream
    io_buffer = IOBuffer()
    Downloads.download(url, io_buffer)
    seekstart(io_buffer)
    
    # Meteostat Daily v2 standard schema
    col_names = [
        "date", "tavg", "tmin", "tmax", "prcp", 
        "snow", "wdir", "wspd", "wpgt", "pres", "tsun"
    ]
    
    # Decompress in-memory and read into DataFrame
    decompressed = GzipDecompressorStream(io_buffer)
    df = CSV.read(
        decompressed, 
        DataFrame; 
        header = col_names,
        missingstring = ["", "NA", "null"],
        types = Dict(
            "date" => Date, 
            "tavg" => Union{Float64, Missing},
            "wspd" => Union{Float64, Missing},
            "pres" => Union{Float64, Missing}
        )
    )
    close(decompressed)
    
    # Meteostat records wspd in km/h; convert to m/s for wind turbine modeling
    df.wspd_ms = df.wspd ./ 3.6
    
    return df
end

# ╔═╡ fc5e12f2-3978-4979-935c-0d9c83d8d2e0
"""
Fetch recent daily data from Meteostat's annual partition endpoint.
Unlike the legacy v2 bulk file, these annual files include a header row.
"""
function fetch_meteostat_year(station_id::String, year::Int)
    url = "https://data.meteostat.net/daily/$(year)/$(station_id).csv.gz"
    
    io = IOBuffer()
    try
        Downloads.download(url, io)
    catch e
        @warn "Could not fetch annual data for $year: $e"
        return DataFrame()
    end
    seekstart(io)
    
    decompressed = GzipDecompressorStream(io)
    df = CSV.read(decompressed, DataFrame)
    close(decompressed)
    
    # 1. Reconstruct date from split year/month/day columns
    if :year in propertynames(df) && :month in propertynames(df) && :day in propertynames(df)
        df.date = Date.(df.year, df.month, df.day)
    elseif :date in propertynames(df)
        df.date = Date.(string.(df.date))
    else
        error("Unrecognized date schema. Available columns: $(names(df))")
    end
    
    if :wspd in propertynames(df)
        df.wspd_ms = df.wspd ./ 3.6
    end
    
    return df
end

# ╔═╡ faaa0eb8-be12-42a4-abf8-c9ffbb07df9a
function fetch_station_series(station_id::String)
    # 1. Fetch legacy archive (up to early 2026)
    df_hist = fetch_meteostat_daily(station_id)
    
    # 2. Fetch 2026 stream
    df_2026 = fetch_meteostat_year(station_id, 2026)
    
    # Standardize columns to :date and :wspd_ms
    s_hist = select(df_hist, :date, :wspd_ms)
    s_2026 = select(df_2026, :date, :wspd_ms)
    
    # Combine, deduplicate, and sort
    df_all = unique(vcat(s_hist, s_2026), :date)
    sort!(df_all, :date)
    return df_all
end

# ╔═╡ 02acee4c-f198-43f5-ab21-939f30969361
df = fetch_station_series("02787") # 02787 02913

# ╔═╡ 4e23c813-6e54-4f47-9345-40d6a698913e
last(df, 5)

# ╔═╡ 2f2ad881-4177-462c-b2e3-6c2ebc9d8fa1
let
    df_valid = dropmissing(df, :wspd_ms)
    println("First non-missing wind record: ", minimum(df_valid.date))
    println("Last non-missing wind record:  ", maximum(df_valid.date))
    println("Total days with wind data:     ", nrow(df_valid))
end

# ╔═╡ 7a3ad16b-4d08-434e-82dd-23f186aa1d9b
let
    # 1. Synchronize the start boundary to the first recorded date
    start_date = Date(2021, 1, 2)
    df_valid   = filter(r -> r.date >= start_date && !ismissing(r.wspd_ms), df)
    end_date   = maximum(df_valid.date) # 2026-10-14
    
    # 2. Build continuous calendar grid
    all_dates = DataFrame(date = collect(start_date:Day(1):end_date))
    
    # 3. Identify missing dates
    missing_dates_df = antijoin(all_dates, df_valid, on = :date)
    sort!(missing_dates_df, :date)
    
    total_expected = nrow(all_dates)
    total_valid    = nrow(df_valid)
    total_missing  = nrow(missing_dates_df)
    completeness   = round(total_valid / total_expected * 100, digits = 2)
    
    println("--- Overview ($start_date to $end_date) ---")
    println("Expected Calendar Days:  ", total_expected)
    println("Recorded Days (Valid):   ", total_valid)
    println("Total Days Missing:      ", total_missing, " ($completeness% complete)\n")
    
    # 4. Group missing dates into contiguous streaks
    if total_missing > 0
        missing_dates = missing_dates_df.date
        streaks = DataFrame(start_date = Date[], end_date = Date[], days_lost = Int[])
        
        cur_start = missing_dates[1]
        cur_prev  = missing_dates[1]
        
        for d in missing_dates[2:end]
            if d == cur_prev + Day(1)
                cur_prev = d
            else
                push!(streaks, (cur_start, cur_prev, Dates.value(cur_prev - cur_start) + 1))
                cur_start = d
                cur_prev  = d
            end
        end
        push!(streaks, (cur_start, cur_prev, Dates.value(cur_prev - cur_start) + 1))
        
        # 5. Annual breakdown
        missing_dates_df.year = Dates.year.(missing_dates_df.date)
        annual_summary = combine(groupby(missing_dates_df, :year), nrow => :missing_days)
        
        (
            Annual_Missing = annual_summary,
            Contiguous_Gaps = sort(streaks, :days_lost, rev = true),
            All_Missing_Dates = missing_dates_df.date
        )
    else
        "No missing days found!"
    end
end

# ╔═╡ 76184222-dd79-4e3b-877a-8eced64fe6ae
df_target_clean = let
    # Continuous calendar sequence from the first non-missing day
    full_calendar = DataFrame(date = collect(Date(2021, 1, 2):Day(1):Date(2026, 10, 14)))
    
    # Merge valid records
    raw_valid = filter(r -> r.date >= Date(2021, 1, 2) && !ismissing(r.wspd_ms), df)
    merged = leftjoin(full_calendar, select(raw_valid, :date, :wspd_ms => :wind_target), on = :date)
    
    # Linear interpolation across isolated missing days
    w = merged.wind_target
    for i in 1:length(w)
        if ismissing(w[i])
            prev_idx = findlast(!ismissing, w[1:i-1])
            next_offset = findfirst(!ismissing, w[i+1:end])
            next_idx = isnothing(next_offset) ? nothing : i + next_offset
            
            if !isnothing(prev_idx) && !isnothing(next_idx)
                p_val, n_val = w[prev_idx], w[next_idx]
                w[i] = p_val + (n_val - p_val) * (i - prev_idx) / (next_idx - prev_idx)
            elseif !isnothing(prev_idx)
                w[i] = w[prev_idx]
            end
        end
    end
    merged.wind_target = Float64.(w)
    merged
end

# ╔═╡ c9c823ad-9875-4efa-82b4-d6189a141b12
network_master_df = let
    predictors = Dict(
        # Inner Ring (~30-80 km)
        :inner_N => "02725", # Halsua Purola
        :inner_E => "02915", # Viitasaari
        :inner_S => "02924", # Ähtäri
        :inner_W => "02913", # Kauhava
        
        # Outer Ring (~200-400 km)
        :outer_N => "02875", # Oulu
        :outer_E => "02917", # Kuopio 
        :outer_S => "02974", # Helsinki-Vantaa
        :outer_W => "02286", # Umeå
    )
    
    df_net = copy(df_target_clean)
    
    for (role, st_id) in predictors
        st_data = filter(r -> r.date >= Date(2021, 1, 2), fetch_station_series(st_id))
        rename!(st_data, :wspd_ms => Symbol("wind_", role))
        df_net = leftjoin(df_net, select(st_data, :date, Symbol("wind_", role)), on = :date)
    end
    
    # Drop rows where any predictor has missing records to guarantee a balanced regression panel
    dropmissing!(df_net)
    sort!(df_net, :date)
    df_net
end

# ╔═╡ 3b777682-5faa-47c6-bd70-98a06f368d1a
begin
    n_days = nrow(network_master_df)
    
    model_df = DataFrame(
        date          = network_master_df.date[3:end],
        target        = network_master_df.wind_target[3:end],
        
        # Target Autoregressive memory
        lag1_target   = network_master_df.wind_target[2:end-1],
        lag2_target   = network_master_df.wind_target[1:end-2],
        
        # Inner Ring (t-1 and t-2)
        lag1_inner_N  = network_master_df.wind_inner_N[2:end-1],
        lag2_inner_N  = network_master_df.wind_inner_N[1:end-2],
        lag1_inner_E  = network_master_df.wind_inner_E[2:end-1],
        lag2_inner_E  = network_master_df.wind_inner_E[1:end-2],
        lag1_inner_S  = network_master_df.wind_inner_S[2:end-1],
        lag2_inner_S  = network_master_df.wind_inner_S[1:end-2],
        lag1_inner_W  = network_master_df.wind_inner_W[2:end-1],
        lag2_inner_W  = network_master_df.wind_inner_W[1:end-2],
        
        # Outer Ring (t-1 and t-2)
        lag1_outer_N  = network_master_df.wind_outer_N[2:end-1],
        lag2_outer_N  = network_master_df.wind_outer_N[1:end-2],
        lag1_outer_E  = network_master_df.wind_outer_E[2:end-1],
        lag2_outer_E  = network_master_df.wind_outer_E[1:end-2],
        lag1_outer_S  = network_master_df.wind_outer_S[2:end-1],
        lag2_outer_S  = network_master_df.wind_outer_S[1:end-2],
        lag1_outer_W  = network_master_df.wind_outer_W[2:end-1],
        lag2_outer_W  = network_master_df.wind_outer_W[1:end-2]
    )
    
    const latest_date = maximum(model_df.date)
    const split_date  = latest_date - Day(365)
    
    train_df   = filter(r -> r.date <= split_date, model_df)
    holdout_df = filter(r -> r.date >  split_date, model_df)
    
    println("Training: ", nrow(train_df), " days | Holdout: ", nrow(holdout_df), " days")
end

# ╔═╡ c126fdcc-e149-4834-af5c-503465ffdede
# fit_model0_and_model1
begin
    # --- Model 0: Baseline Persistence (Y_t = Y_{t-1}) ---
    m0_preds = holdout_df.lag1_target
    m0_errors = holdout_df.target .- m0_preds
    m0_mae  = mean(abs.(m0_errors))
    m0_rmse = sqrt(mean(m0_errors.^2))
    
    # --- Model 1: Static 4-Direction OLS ---
    formula_m1 = @formula(target ~ lag1_target + lag2_target +
                                   lag1_inner_W + lag2_inner_W +
                                   lag1_inner_N + lag2_inner_N +
                                   lag1_inner_E + lag2_inner_E +
                                   lag1_inner_S + lag2_inner_S)
    
    ols_m1 = lm(formula_m1, train_df)
    
    m1_preds = predict(ols_m1, holdout_df)
    m1_errors = holdout_df.target .- m1_preds
    m1_mae  = mean(abs.(m1_errors))
    m1_rmse = sqrt(mean(m1_errors.^2))
    
    (
        Baseline_M0 = (Holdout_MAE = round(m0_mae, digits=3), Holdout_RMSE = round(m0_rmse, digits=3)),
        Static_M1   = (Train_R2 = round(r2(ols_m1), digits=4), Adj_R2 = round(adjr2(ols_m1), digits=4),
                       Holdout_MAE = round(m1_mae, digits=3), Holdout_RMSE = round(m1_rmse, digits=3))
    )
end

# ╔═╡ 7aa0f225-948d-4d1f-8b5a-2262f879783b
# fit_model2_dynamic
begin    
    # All 18 lagged features (2 target lags + 16 station lags)
    feature_symbols = [
        :lag1_target, :lag2_target,
        :lag1_inner_N, :lag2_inner_N, :lag1_inner_E, :lag2_inner_E,
        :lag1_inner_S, :lag2_inner_S, :lag1_inner_W, :lag2_inner_W,
        :lag1_outer_N, :lag2_outer_N, :lag1_outer_E, :lag2_outer_E,
        :lag1_outer_S, :lag2_outer_S, :lag1_outer_W, :lag2_outer_W
    ]
    
    X_mat = hcat(ones(nrow(model_df)), Matrix(model_df[:, feature_symbols]))
    y_vec = Vector(model_df.target)
    
    window_len = 365
    n_total = nrow(model_df)
    m2_all_preds = fill(NaN, n_total)
    beta_history = fill(NaN, n_total, size(X_mat, 2))
    
    for t in (window_len + 1):n_total
        # Trailing 365-day calibration window
        X_w = X_mat[(t - window_len):(t - 1), :]
        y_w = y_vec[(t - window_len):(t - 1)]
        
        # Closed-form OLS solution via QR decomposition
        b_t = X_w \ y_w
        beta_history[t, :] = b_t
        
        # 1-day ahead out-of-sample prediction
        m2_all_preds[t] = dot(X_mat[t, :], b_t)
    end
    
    model_df.pred_m2 = m2_all_preds
    
    # Evaluate Model 2 strictly over the exact same holdout period
    m2_holdout = filter(r -> r.date in holdout_df.date, model_df)
    m2_errors  = m2_holdout.target .- m2_holdout.pred_m2
    m2_mae     = mean(abs.(m2_errors))
    m2_rmse    = sqrt(mean(m2_errors.^2))
    
    (Holdout_MAE = round(m2_mae, digits=3), Holdout_RMSE = round(m2_rmse, digits=3))
end

# ╔═╡ 4aa84d18-d2ec-4219-b85d-6ba5e08bc7e8
# inspect_m1_coefficients
coeftable(ols_m1)

# ╔═╡ 73ed2f4e-38d7-4f64-9ac7-b53acc046200
# model2_coefficient_summary
let
    # All 19 parameter names (Intercept + 18 lagged features)
    param_names = vcat(
        :Intercept,
        [
            :lag1_target, :lag2_target,
            :lag1_inner_N, :lag2_inner_N, :lag1_inner_E, :lag2_inner_E,
            :lag1_inner_S, :lag2_inner_S, :lag1_inner_W, :lag2_inner_W,
            :lag1_outer_N, :lag2_outer_N, :lag1_outer_E, :lag2_outer_E,
            :lag1_outer_S, :lag2_outer_S, :lag1_outer_W, :lag2_outer_W
        ]
    )
    
    # Filter only rows where the rolling window had enough history (non-NaN)
    valid_mask = .!isnan.(beta_history[:, 1])
    valid_betas = beta_history[valid_mask, :]
    
    # Calculate distribution metrics across all estimation windows
    summary_df = DataFrame(
        Parameter   = param_names,
        Mean_Beta   = [round(mean(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)],
        Std_Beta    = [round(std(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)],
        Min_Beta    = [round(minimum(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)],
        Max_Beta    = [round(maximum(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)]
    )
    
    summary_df
end

# ╔═╡ 92ea415f-c0d8-4f98-8981-a6680e0999d7
# model2_latest_snapshot
let
    param_names = vcat(
        :Intercept,
        [
            :lag1_target, :lag2_target,
            :lag1_inner_W, :lag2_inner_W, :lag1_inner_N, :lag2_inner_N,
            :lag1_inner_E, :lag2_inner_E, :lag1_inner_S, :lag2_inner_S,
            :lag1_outer_W, :lag2_outer_W, :lag1_outer_SW, :lag2_outer_SW,
            :lag1_outer_N, :lag2_outer_N, :lag1_outer_S, :lag2_outer_S
        ]
    )
    
    latest_betas = beta_history[end, :]
    
    DataFrame(
        Parameter = param_names,
        Active_Weight = round.(latest_betas, digits = 4)
    )
end

# ╔═╡ 399edbd0-61e2-4c72-a3a0-a1147cf868fc
# model2_seasonal_shift
let
    valid_mask = .!isnan.(beta_history[:, 1])
    valid_dates = model_df.date[valid_mask]
    valid_betas = beta_history[valid_mask, :]
    
    months = Dates.month.(valid_dates)
    winter_mask = (months .== 12) .| (months .== 1) .| (months .== 2)
    summer_mask = (months .== 6)  .| (months .== 7) .| (months .== 8)
    
    param_names = vcat(
        :Intercept,
        [
            :lag1_target, :lag2_target,
            :lag1_inner_W, :lag2_inner_W, :lag1_inner_N, :lag2_inner_N,
            :lag1_inner_E, :lag2_inner_E, :lag1_inner_S, :lag2_inner_S,
            :lag1_outer_W, :lag2_outer_W, :lag1_outer_SW, :lag2_outer_SW,
            :lag1_outer_N, :lag2_outer_N, :lag1_outer_S, :lag2_outer_S
        ]
    )
    
    DataFrame(
        Parameter    = param_names,
        Winter_Mean  = [round(mean(valid_betas[winter_mask, j]), digits = 4) for j in 1:length(param_names)],
        Summer_Mean  = [round(mean(valid_betas[summer_mask, j]), digits = 4) for j in 1:length(param_names)],
        Seasonal_Δ   = [round(mean(valid_betas[winter_mask, j]) - mean(valid_betas[summer_mask, j]), digits = 4) for j in 1:length(param_names)]
    )
end

# ╔═╡ f0f55d3a-7a07-4e84-bf6a-178150a2b71c
# model_comparison_summary
let
    y_true = holdout_df.target
    
    # 1. Predictions aligned to the exact holdout sample
    y_m0 = holdout_df.lag1_target
    y_m1 = predict(ols_m1, holdout_df)
    
    m2_holdout = filter(r -> r.date in holdout_df.date, model_df)
    y_m2 = m2_holdout.pred_m2
    
    # 2. Metric calculation helper
    calc_metrics(y_hat, label) = begin
        err = y_true .- y_hat
        mae = mean(abs.(err))
        rmse = sqrt(mean(err.^2))
        (Model = label, Holdout_MAE = round(mae, digits=3), Holdout_RMSE = round(rmse, digits=3))
    end
    
    m0 = calc_metrics(y_m0, "Baseline M0 (Persistence)")
    m1 = calc_metrics(y_m1, "Static OLS M1 (Inner Ring)")
    m2 = calc_metrics(y_m2, "Dynamic OLS M2 (Rolling 365d)")
    
    df_comp = DataFrame([m0, m1, m2])
    
    # 3. Add percentage gains over persistence baseline
    base_mae  = df_comp.Holdout_MAE[1]
    base_rmse = df_comp.Holdout_RMSE[1]
    
    df_comp.MAE_Reduction = [
        "-",
        "$(round((1 - df_comp.Holdout_MAE[2] / base_mae) * 100, digits=1))%",
        "$(round((1 - df_comp.Holdout_MAE[3] / base_mae) * 100, digits=1))%"
    ]
    df_comp.RMSE_Reduction = [
        "-",
        "$(round((1 - df_comp.Holdout_RMSE[2] / base_rmse) * 100, digits=1))%",
        "$(round((1 - df_comp.Holdout_RMSE[3] / base_rmse) * 100, digits=1))%"
    ]
    
    df_comp
end

# ╔═╡ da1115e1-76c3-4c35-a6f3-9714b4263dea
# fetch_station_locations
function fetch_station_metadata(station_ids::Vector{String})
    url = "https://bulk.meteostat.net/v2/stations/lite.json.gz"
    
    io = IOBuffer()
    Downloads.download(url, io)
    seekstart(io)
    
    decompressed = GzipDecompressorStream(io)
    raw_stations = JSON.parse(decompressed)
    close(decompressed)
    
    id_set = Set(station_ids)
    matched = filter(s -> get(s, "id", "") in id_set, raw_stations)
    
    rows = map(matched) do s
        loc = get(s, "location", Dict())
        
        # Robust multilingual name parser (handles "en", "fi", or any available key)
        raw_name = get(s, "name", "")
        clean_name = if raw_name isa AbstractDict
            haskey(raw_name, "en") ? raw_name["en"] :
            haskey(raw_name, "fi") ? raw_name["fi"] :
            (isempty(raw_name) ? "Unknown" : first(values(raw_name)))
        else
            string(raw_name)
        end
        
        (
            id        = string(s["id"]),
            name      = clean_name,
            country   = string(get(s, "country", "")),
            lat       = Float64(loc["latitude"]),
            lon       = Float64(loc["longitude"]),
            elevation = haskey(loc, "elevation") && !isnothing(loc["elevation"]) ? 
                        Float64(loc["elevation"]) : missing
        )
    end
    
    return DataFrame(rows)
end

# ╔═╡ b0f27193-5c75-48b5-9bac-044089da7443
df_coords = begin
    target_ids = [
        "02787", # Target: Alajärvi Möksy
        "02725", # Inner N: Halsua Purola
        "02915", # Inner E: Viitasaari Haapaniemi
        "02924", # Inner S: Ähtäri
        "02913", # Inner W: Kauhava
        "02875", # Outer N: Oulu
        "02917", # Outer E: Kuopio
        "02974", # Outer S: Helsinki-Vantaa
        "02286"  # Outer W: Umeå (Sweden)
    ]
    
    coords = fetch_station_metadata(target_ids)
    coords.role = map(coords.id) do id
        id == "02787" ? :target :
        id in ["02725", "02915", "02924", "02913"] ? :inner : :outer
    end
    sort!(coords, :id)
end

# ╔═╡ fea4b109-3036-4819-94b4-a0a90626b3fb
# plot_figure1_executive_basemap
begin
    # =========================================================================
    # 1. BASEMAP THEME SELECTOR
    # Options: :light_gray (Recommended for papers), :topo, :streets, :terrain, :satellite, :osm
    # =========================================================================
    basemap_choice = :light_gray

    basemap_endpoints = Dict(
        :light_gray => "https://services.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/export",
        :topo       => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Topo_Map/MapServer/export",
        :streets    => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Street_Map/MapServer/export",
        :terrain    => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Terrain_Base/MapServer/export",
        :satellite  => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/export",
        :osm        => "https://services.arcgisonline.com/arcgis/rest/services/OpenStreetMap/MapServer/export"
    )

    # =========================================================================
    # 2. BOUNDING BOX & SQUARE GEOMETRY CALIBRATION
    # lon_max extended to 29.6 to provide full margin for Kuopio (27.63°E)
    # Coordinates calibrated so that ΔX (Mercator) ≈ ΔY (Mercator) -> 1:1 Square
    # =========================================================================
    lon_min, lon_max = 17.6, 29.6
    lat_min, lat_max = 59.7, 65.7

    lon2x(lon) = lon * 20037508.34 / 180.0
    lat2y(lat) = log(tan((90.0 + lat) * pi / 360.0)) * 20037508.34 / pi

    xmin, xmax = lon2x(lon_min), lon2x(lon_max)
    ymin, ymax = lat2y(lat_min), lat2y(lat_max)

    # Square dimension layout
    fig_w = 520
    fig_h = 520
    export_scale = 3 # 3x supersampling for publication-grade vector sharpness
    srv_w = fig_w * export_scale
    srv_h = fig_h * export_scale

    base_url = basemap_endpoints[basemap_choice]
    map_url = "$(base_url)?bbox=$(xmin),$(ymin),$(xmax),$(ymax)&bboxSR=3857&imageSR=3857&size=$(srv_w),$(srv_h)&f=image"

    io_map = IOBuffer()
    Downloads.download(map_url, io_map)
    seekstart(io_map)
    basemap_img = rotr90(FileIO.load(io_map))
    close(io_map)

    st_x = lon2x.(df_coords.lon)
    st_y = lat2y.(df_coords.lat)

    # =========================================================================
    # 3. FIGURE & AXIS SETUP
    # =========================================================================
    fig1 = Figure(
        size = (fig_w, fig_h),
        fonts = (; 
            regular = "Inter", 
            bold = "Inter Bold", 
            semibold = "Inter SemiBold"
        ),
        fontsize = 9.5
    )

    ax = Axis(
        fig1[1, 1],
        # title = "Ilmatar Alajärvi Wind Farm: Regional Telemetry & Synoptic Arrays",
        # titlesize = 11.0,
        titlefont = :bold,
        aspect = DataAspect()
    )

    hidedecorations!(ax)
    hidespines!(ax)

    # Render Basemap Layer
    image!(ax, xmin..xmax, ymin..ymax, basemap_img)

    # Coordinates of Target Station (Alajärvi Möksy)
    target_idx = findfirst(==( "02787"), df_coords.id)
    tx, ty = st_x[target_idx], st_y[target_idx]

    # =========================================================================
    # 4. SYNOPTIC TELEMETRY VECTORS (SPATIAL ARCS)
    # =========================================================================
    for i in 1:nrow(df_coords)
        if i != target_idx
            is_inner = df_coords.role[i] == :inner
            # Inner: Cobalt Blue dashed vector | Outer: Deep Rust dashed vector
            lines!(
                ax, [st_x[i], tx], [st_y[i], ty];
                color = is_inner ? (:royalblue, 0.85) : (colorant"#b45309", 0.75),
                linestyle = is_inner ? :dash : :dot,
                linewidth = is_inner ? 1.4 : 1.2
            )
        end
    end

    inner_mask = df_coords.role .== :inner
    outer_mask = df_coords.role .== :outer

    # =========================================================================
    # 5. STATION MARKERS WITH CONTRAST HALOS
    # =========================================================================
    # Outer Synoptic Array: Deep Tangerine/Burnt Amber with white outline
    scatter!(
        ax, st_x[outer_mask], st_y[outer_mask];
        color = "#ea580c", 
        markersize = 9.5, 
        strokewidth = 1.4, 
        strokecolor = :white,
        label = "Outer Synoptic Array (~200–400 km)"
    )

    # Inner Ring Array: Deep Royal Blue with white outline
    scatter!(
        ax, st_x[inner_mask], st_y[inner_mask];
        color = "#1d4ed8", 
        markersize = 9.5, 
        strokewidth = 1.4, 
        strokecolor = :white,
        label = "Inner Ring Array (~30–80 km)"
    )

    # Target Asset Marker: Layered Executive Concentric Reticle (Bullseye)
    # 1. Outer accent ring
    scatter!(
        ax, [tx], [ty];
        color = (:crimson, 0.20),
        markersize = 22,
        strokewidth = 0
    )
    # 2. Main target ring with sharp white border
    scatter!(
        ax, [tx], [ty];
        color = :crimson,
        marker = :circle,
        markersize = 13,
        strokewidth = 1.8,
        strokecolor = :white,
        label = "Target Asset: Alajärvi Möksy"
    )
    # 3. Inner core center dot
    scatter!(
        ax, [tx], [ty];
        color = :white,
        marker = :circle,
        markersize = 4.5,
        strokewidth = 0
    )

    # =========================================================================
    # 6. CARTOGRAPHIC LABELS (Optimized for square extent)
    # =========================================================================
    for i in 1:nrow(df_coords)
        id = df_coords.id[i]
        name = df_coords.name[i]

        offset = offset = if id == "02787"
            (7, 7)
        elseif id == "02286" # Umeå (Swedish coast)
            (-74, -2)
        elseif id == "02913" # Kauhava
            (-54, 4)
        elseif id == "02725" # Halsua Purola
            (8, 6)
        elseif id == "02924" # Ähtäri
            (8, -4)
        elseif id == "02915" # Viitasaari
            (10, -6)
        elseif id == "02917" # Kuopio (Safely inside map margin)
            (8, 2)
        elseif id == "02875" # Oulu
            (8, 5)
        elseif id == "02974" # Helsinki-Vantaa
            (8, 2)
        else
            (6, 4)
        end

        lbl = id == "02787" ? "Alajärvi Möksy" : name

        text!(
            ax, st_x[i], st_y[i];
            text = lbl,
            fontsize = id == "02787" ? 9.5 : 8.0,
            font = id == "02787" ? :bold : :semibold,
            color = "#0f172a",
            offset = offset
        )
    end

    # Clean executive legend
    axislegend(
        ax, 
        position = :lt, 
        framevisible = true, 
        backgroundcolor = (:white, 0.92),
        framecolor = "#94a3b8",
        framewidth = 0.8,
        padding = (6, 6, 5, 5),
        labelsize = 8.0,
        patchsize = (12.0, 8.0)
    )

    # Save vector PDF directly
    save("Figure1_Station_Network.pdf", fig1; pt_per_unit = 1.0)
    fig1
end

# ╔═╡ 3c3ed324-b766-4061-98dc-548a4b71694d
let
    slice_mask = Date(2025, 11, 1) .<= model_df.date .<= Date(2025, 12, 5)
    df_slice = filter(r -> r.date in model_df.date[slice_mask], model_df)

    holdout_payload = Dict(
        "dates"   => string.(df_slice.date),
        "actual"  => round.(df_slice.target, digits = 2),
        "m0_pred" => round.(df_slice.lag1_target, digits = 2),
        "m1_pred" => round.(predict(ols_m1, df_slice), digits = 2), # Evaluated dynamically from fitted OLS M1
        "m2_pred" => round.(df_slice.pred_m2, digits = 2)           # Dynamic 365-day rolling walk-forward
    )

    open("holdout_series.json", "w") do f
        JSON.print(f, holdout_payload)
    end
end

# ╔═╡ 9cbf0393-130e-4e6e-a1fc-f0566feec3e3
# 2. Dynamic Seasonal Parameter Drift (Computed directly from beta_history)
let
    valid_mask = .!isnan.(beta_history[:, 1])
    valid_dates = model_df.date[valid_mask]
    valid_betas = beta_history[valid_mask, :]

    months = Dates.month.(valid_dates)
    winter_mask = (months .== 12) .| (months .== 1) .| (months .== 2)
    summer_mask = (months .== 6)  .| (months .== 7) .| (months .== 8)

    # Ties directly to feature_symbols to guarantee 1:1 column index alignment with X_mat
    param_names = vcat(:Intercept, feature_symbols)

    seasonal_payload = [
        Dict(
            "param"        => string(param_names[j]),
            "winter"       => round(mean(valid_betas[winter_mask, j]), digits = 4),
            "summer"       => round(mean(valid_betas[summer_mask, j]), digits = 4),
            "seasonal_del" => round(mean(valid_betas[winter_mask, j]) - mean(valid_betas[summer_mask, j]), digits = 4)
        )
        for j in 1:length(param_names)
        if param_names[j] != :Intercept # Drops Intercept to focus exclusively on lagged features
    ]

    open("seasonal_betas.json", "w") do f
        JSON.print(f, seasonal_payload)
    end
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
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CairoMakie = "=0.15.15"
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DataFrames = "~1.8.2"
FileIO = "~1.20.0"
GLM = "~1.9.5"
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ImageIO = "~0.6.10"
JSON = "~1.11.0"
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# ╔═╡ 00000000-0000-0000-0000-000000000002
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[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "0716e01c3b40413de5dedbc9c5c69f27cddfddfc"
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[[deps.WebP]]
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[[deps.WoodburyMatrices]]
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[[deps.XZ_jll]]
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[[deps.Xorg_libX11_jll]]
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git-tree-sha1 = "ce91b54a5f90c4f27df6950706234b34e49cf79e"
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[[deps.Xorg_libXau_jll]]
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[[deps.Xorg_libXext_jll]]
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git-tree-sha1 = "75e00946e43621e09d431d9b95818ee751e6b2ef"
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[[deps.Xorg_libXrender_jll]]
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git-tree-sha1 = "bfcaf7ec088eaba362093393fe11aa141fa15422"
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version = "1.5.7+1"

[[deps.dlfcn_win32_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e141d67ffe550eadfb5af1bdbdaf138031e4805f"
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[[deps.isoband_jll]]
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git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
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[[deps.libaom_jll]]
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[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "cb007192783c56d8249db4cf0e3495001edfe414"
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[[deps.libdrm_jll]]
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git-tree-sha1 = "28e57478e8a160d346a19c28b3fffb9273bcc9c2"
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[[deps.libpng_jll]]
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git-tree-sha1 = "e067c8bae65bb40866552296a2d5b4dc65b8928f"
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[[deps.libva_jll]]
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git-tree-sha1 = "7dbf96baae3310fe2fa0df0ccbb3c6288d5816c9"
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[[deps.libvorbis_jll]]
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[[deps.libwebp_jll]]
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git-tree-sha1 = "52d3b9475133c3bc8c0a7f90f18bfc8cdb5a443a"
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[[deps.nghttp2_jll]]
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[[deps.p7zip_jll]]
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[[deps.x264_jll]]
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registries = "General"
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version = "10164.0.1+0"

[[deps.x265_jll]]
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git-tree-sha1 = "e7b67590c14d487e734dcb925924c5dc43ec85f3"
registries = "General"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
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[registries.General]
url = "https://github.com/JuliaRegistries/General.git"
uuid = "23338594-aafe-5451-b93e-139f81909106"
"""

# ╔═╡ Cell order:
# ╠═f6602dd0-292c-4a63-8247-94d626d87f39
# ╠═82290cf0-cc7e-11f1-afb5-679eb5954384
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# ╠═88ef8628-e883-4113-abd3-080e2b2376d8
# ╠═5c21db41-78f6-4db7-ad1e-ff1900afdca3
# ╠═d61dc3ca-fccc-4770-af6d-aa5082c55369
# ╠═fc5e12f2-3978-4979-935c-0d9c83d8d2e0
# ╠═faaa0eb8-be12-42a4-abf8-c9ffbb07df9a
# ╠═02acee4c-f198-43f5-ab21-939f30969361
# ╠═4e23c813-6e54-4f47-9345-40d6a698913e
# ╠═2f2ad881-4177-462c-b2e3-6c2ebc9d8fa1
# ╠═7a3ad16b-4d08-434e-82dd-23f186aa1d9b
# ╠═76184222-dd79-4e3b-877a-8eced64fe6ae
# ╠═c9c823ad-9875-4efa-82b4-d6189a141b12
# ╠═3b777682-5faa-47c6-bd70-98a06f368d1a
# ╠═c126fdcc-e149-4834-af5c-503465ffdede
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