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


def plotVariables(x,y):
    
    # scatter plot (matplotlib, single plot, no explicit colors)
    #plt.ylim(0, 1)
    plt.figure()
    plt.scatter(x, y)
    plt.axhline(0, linestyle="--")  # reference line
    plt.xlabel(x.name)
    plt.ylabel(y.name)
    plt.title("diff_price vs. confidence")
    plt.grid(True)
    plt.show()

def plotPriceConfidence(condensed):
    # pick the right confidence column
    conf_col = "confidence" if "confidence" in condensed.columns else (
        "resp_confidence" if "resp_confidence" in condensed.columns else None
    )
    if conf_col is None:
        raise KeyError("No 'confidence' or 'resp_confidence' column found in condensed.")
    
    # keep only the needed columns and coerce to numeric
    dfp = condensed[[conf_col, "diff_price"]].copy()
    dfp[conf_col] = pd.to_numeric(dfp[conf_col], errors="coerce")
    dfp["diff_price"] = pd.to_numeric(dfp["diff_price"], errors="coerce")
    dfp = dfp.dropna(subset=[conf_col, "diff_price"])
    
    # scatter plot (matplotlib, single plot, no explicit colors)
    #plt.ylim(0, 1)
    plt.figure()
    plt.scatter(dfp[conf_col], dfp["diff_price"])
    plt.axhline(0, linestyle="--")  # reference line
    plt.xlabel(conf_col)
    plt.ylabel("diff_price")
    plt.title("diff_price vs. confidence")
    plt.grid(True)
    plt.show()

def histPriceDiff(condensed):
    conf_col = (
        "confidence" if "confidence" in condensed.columns
        else "resp_confidence" if "resp_confidence" in condensed.columns
        else None
    )
    if conf_col is None:
        raise KeyError("No 'confidence' or 'resp_confidence' column in condensed.")
    
    # --- prepare data ---
    df = condensed[[conf_col, "diff_price"]].copy()
    df[conf_col] = pd.to_numeric(df[conf_col], errors="coerce")
    df["diff_price"] = pd.to_numeric(df["diff_price"], errors="coerce")
    df = df.dropna(subset=[conf_col, "diff_price"])
    
    # scale confidence to 0–100 if it looks like 0–1
    if df[conf_col].max() <= 1.01:
        df[conf_col] = df[conf_col] * 100
    
    # --- define bands ---
    bands = [
        ("confidence == 100",        df[ df[conf_col] == 100 ]),
        ("100 > confidence ≥ 90",    df[(df[conf_col] < 100) & (df[conf_col] >= 90)]),
        ("90 > confidence ≥ 80",     df[(df[conf_col] <  90) & (df[conf_col] >= 80)]),
        ("80 > confidence ≥ 50",     df[(df[conf_col] <  80) & (df[conf_col] >= 50)]),
        ("50 > confidence",          df[ df[conf_col] <  50 ]),
    ]
    
    # --- common bins across all groups for fair comparison ---
    all_vals = df["diff_price"].values
    if all_vals.size == 0:
        raise ValueError("No numeric diff_price values to plot.")
    xmin, xmax = np.nanmin(all_vals), np.nanmax(all_vals)
    if xmin == xmax:
        # degenerate case: make a tiny range around the single value
        xmin, xmax = xmin - 0.5, xmax + 0.5
    bins = np.linspace(xmin, xmax, 31)  # 30 bins
    
    # --- plot each histogram in its own figure (no subplots, no explicit colors) ---
    for title, d in bands:
        if d.empty:
            print(f"[skip] {title}: no rows")
            continue
        plt.figure()
        plt.hist(d["diff_price"].values, bins=bins)
        plt.title(f"diff_price for {title}")
        plt.xlabel("diff_price")
        plt.ylabel("count")
        plt.grid(True)
        plt.show()
    
    # (optional) quick counts per band
    for title, d in bands:
        print(f"{title}: {len(d)} rows")