"""Align US inflation, German inflation and ECB exchange rates on a monthly calendar before comparing their relationship.

Research example. Live observations can change.
"""

from datetime import UTC, datetime
from decimal import Decimal
from textwrap import fill

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

AS_OF = datetime.now(UTC).date()
NAVY, BLUE, PINK = "#132244", "#8fb9ff", "#e7a2cc"


def fetch(qj):
    return {
        "oecd_cpi": qj.domains.call(
            "macro.series.get_cpi_data",
            provider="oecd",
            countries=["USA", "DEU"],
            start_date="2020-01-01",
            end_date="2025-12-31",
        ),
        "bls_cpi": qj.domains.call(
            "macro.us.get_cpi", provider="bls", start_year=2020, end_year=2025, category="all", seasonally_adjusted=True
        ),
        "ecb_fx": qj.domains.call(
            "macro.eu.get_ecb_series",
            provider="ecb",
            series_id="EXR.D.USD.EUR.SP00.A",
            start_date="2020-01-01",
            end_date="2025-12-31",
        ),
    }


def analyze(raw):
    oecd = frame(raw["oecd_cpi"])
    germany = oecd.loc[oecd["country"] == "DEU"].set_index("date")["cpi"]
    germany = pd.to_numeric(germany, errors="raise").resample(pd.offsets.MonthEnd()).last()
    us = bls_cpi(raw["bls_cpi"]).resample(pd.offsets.MonthEnd()).last()
    rates = frame(raw["ecb_fx"]).set_index("date")["value"].map(decimal).resample(pd.offsets.MonthEnd()).last()
    data = pd.DataFrame(
        {
            "US CPI YoY %": 100 * (us / us.shift(12) - 1),
            "German CPI YoY %": 100 * (germany / germany.shift(12) - 1),
            "EUR/USD return %": 100 * change(rates),
        }
    ).dropna()
    if len(data) < 24:
        raise ValueError("Need at least 24 matched inflation and FX observations")
    data["Inflation gap pp"] = data["German CPI YoY %"] - data["US CPI YoY %"]
    fig, (top, bottom) = canvas("Macro and FX · prices across two economies", "CPI YoY %", panels=2)
    top.plot(data.index, data["US CPI YoY %"], label="US · BLS")
    top.plot(data.index, data["German CPI YoY %"], label="Germany · OECD compilation")
    legend(top)
    bottom.scatter(data["Inflation gap pp"], data["EUR/USD return %"], color=BLUE, s=26, alpha=0.8)
    bottom.set_xlabel("German minus US inflation · percentage points")
    bottom.set_ylabel("Monthly EUR/USD return %")
    bottom.axhline(0, color="#afc1dc", lw=0.8)
    return data.reset_index(), fig


def payload(response):
    if hasattr(response, "to_dict"):
        response = response.to_dict()
    while isinstance(response, dict):
        if response.get("error") or response.get("error_code") or response.get("success") is False:
            raise ValueError("Provider request failed")
        if "data" in response:
            response = response["data"]
        elif "value" in response:
            response = response["value"]
        else:
            break
    return response


def frame(response, date="date"):
    rows = payload(response)
    if not isinstance(rows, list) or not rows:
        raise ValueError("No provider observations returned")
    result = pd.DataFrame(rows)
    if date:
        result[date] = pd.to_datetime(result[date], utc=True)
        result = result.sort_values(date)
    return result


def decimal(value):
    if pd.isna(value):
        raise ValueError("Required monetary value or quantity is missing")
    return Decimal(str(value))


def ratio(numerator, denominator):
    denominator = decimal(denominator)
    if denominator == 0:
        raise ValueError("Required denominator is zero")
    return float(decimal(numerator) / denominator)


def change(values, periods=1):
    previous = values.shift(periods)
    return pd.Series(
        [ratio(x, y) - 1 if pd.notna(x) and pd.notna(y) else np.nan for x, y in zip(values, previous, strict=True)],
        index=values.index,
    )


def macro(response, column):
    values = frame(response).set_index("date")[column]
    values = pd.to_numeric(values, errors="coerce").dropna()
    if values.empty:
        raise ValueError(f"No numeric observations for {column}")
    return values


def canvas(title, ylabel, panels=1):
    plt.rcParams.update(
        {
            "figure.facecolor": NAVY,
            "axes.facecolor": NAVY,
            "savefig.facecolor": NAVY,
            "text.color": "#e9eef8",
            "axes.labelcolor": "#afc1dc",
            "xtick.color": "#afc1dc",
            "ytick.color": "#afc1dc",
            "axes.edgecolor": "#34486b",
            "grid.color": "#34486b",
            "font.size": 10,
            "axes.prop_cycle": plt.cycler(color=[BLUE, PINK, "#ba9dea"]),
        }
    )
    fig, axes = plt.subplots(panels, 1, figsize=(9, 6.1), squeeze=False, layout="constrained")
    fig.suptitle(fill(title, width=58), fontsize=17, x=0.04, ha="left", weight="medium")
    for ax in axes.flat:
        ax.grid(axis="y", alpha=0.45)
        ax.set_axisbelow(True)
        ax.spines[["top", "right"]].set_visible(False)
    axes.flat[0].set_ylabel(ylabel)
    return fig, list(axes.flat)


def legend(ax):
    ax.legend(frameon=False, labelcolor="#e9eef8", fontsize=9)


def run():
    from quantjourney.sdk import QuantJourneyAPI

    qj = QuantJourneyAPI.from_env()
    raw = fetch(qj)
    table, fig = analyze(raw)
    print(table.to_string(index=False))
    fig.savefig(OUTPUT, dpi=180)
    plt.show()


def bls_cpi(response):
    return macro(response, "cpi_all")


OUTPUT = "sdk_macro_fx.png"

if __name__ == "__main__":
    run()