import numpy as np
from scipy import optimize

metrics = GC.activityMetrics(compare=True)
n_activities = len(metrics)


def piecewise_linear(x, x0, y0, k1, k2):
    def fun0(x):
        k1 * x + y0 - k1 * x0

    def fun1(x):
        k2 * x + y0 - k2 * x0

    return np.piecewise(x, [x < x0], [fun0, fun1])


GC.setChart(
    title="Lactate-Ramp",
    type=GC.CHART_LINE,
    animate=False,
    legpos=GC_ALIGN_TOP,
    stack=False,
)

for i, metric in enumerate(metrics):
    date = metric[0]["date"]
    color = metric[1]
    print(color)
    x = list(GC.xdataSeries("Lactate-Ramp", "Power", compareindex=i))
    y = list(GC.xdataSeries("Lactate-Ramp", "Lactate", compareindex=i))
    if not (x and y):
        continue
    GC.addCurve(
        name=str(date),
        x=x,
        y=y,
        xaxis="Power",
        yaxis="Lactate",
        color=color,
        line=GC_LINE_SOLID,
        symbol=GC_SYMBOL_CIRCLE,
        size=3,
        opacity=100,
        opengl=False,
    )
    coeffs = np.polyfit(x, y, 3)
    poly = np.poly1d(coeffs)
    x_poly = np.linspace(x[0], x[-1], 51)
    y_poly = poly(x_poly)
    GC.addCurve(
        name="Fit",
        x=x_poly,
        y=y_poly,
        xaxis="Power",
        yaxis="Lactate",
        color=color,
        line=GC_LINE_DASH,
        symbol=GC_SYMBOL_NONE,
        size=3,
        opacity=100,
        opengl=False,
    )
    x_poly = np.linspace(x[0], x[-2], 51)
    x_poly = np.array(x)
    y_poly = poly(x_poly)
    p, e = optimize.curve_fit(piecewise_linear, np.log(x_poly), np.log(y_poly), p0=[np.mean(x_poly), np.mean(y_poly), 0, 0])
    # p, e = optimize.curve_fit(piecewise_linear, x_poly, y_poly)
    print(p)
    GC.addCurve(
        name="log-log",
        x=x_poly,
        y=piecewise_linear(x_poly, *p),
        xaxis="Power",
        yaxis="Lactate",
        line=GC_LINE_DASH,
        symbol=GC_SYMBOL_NONE,
        size=1,
        opacity=100,
        opengl=False,
    )
    print(piecewise_linear(x_poly, *p))


GC.setAxis("Power", min=90, max=350, type=GC.AXIS_CONTINUOUS)
GC.setAxis("Lactate", min=0.0, max=10.0, type=GC.AXIS_CONTINUOUS)