from typing import Any
from numpy import array, diff, mean, median
from numpy.linalg import norm
from rich.console import Console
from pathlib import Path
from matplotlib.pyplot import figure, show

from utils import diff_matrix, Directions, Axis, Vector

from framel import frgetvect

console = Console()


def frgetvectN(
    filename: str | list[str],
    channels: list[str],
    start: int = -1,
    span: int = -1,
    verbose: bool = False,
) -> list[Any]:
    return [frgetvect(filename, channel, start, span, verbose) for channel in channels]


def main(
    file: Path,
    start: int = 15,
    duration: int = 2 * 5 * 49,
    factors: list = [1.00, 0.90, 1.95, 1.19],
    step: int = 50,
    _show: bool = False,
):
    factors = array(factors)
    indexes = diff_matrix(3)
    steps = step * factors

    channels_name = ["FitPosY", "FitPosX"]
    channels = [
        "V1:Camera_Scatter_{}".format(channel_name) for channel_name in channels_name
    ]

    data = frgetvectN(str(file), channels)

    if _show:
        Figure = figure()
        for i in range(len(data)):
            Figure.gca().plot(
                data[i][0][start : start + duration], label=channels_name[i]
            )
        Figure.gca().legend()
        show()

    medians = [
        median(datum[0][start : start + duration].reshape((-1, 10)), axis=1)
        for datum in data
    ]
    positions = [
        [diff(median)[index] for index in indexes] for median in medians
    ]  # liste de mouvements associé à un axe pour une direction

    means: Any = list()

    for i in range(len(positions[0])):
        if i < 2:
            means.append(mean(positions[0][i] / steps[i]))
        else:
            means.append(mean(positions[1][i] / steps[i]))

    means = array(means)

    temps = list()
    for i in range(len(positions[0])):
        temp = list()
        for j in range(len(positions)):
            temp.append(mean(positions[j][i]) / steps[i])
        temps.append(Vector(x=temp[0], y=temp[1]))

    directions = Directions(
        x=Axis(pos=temps[0], neg=temps[1]),
        y=Axis(pos=temps[2], neg=temps[3]),
    )

    i, k = 1, norm(directions.x.pos) / norm(directions.y.pos)
    j = (
        directions.y.neg.x * (-k * directions.y.pos.y - i * directions.x.pos.y)
        + k * directions.y.neg.y * directions.y.pos.x
        + i * directions.x.pos.x * directions.y.neg.y
    ) / (
        directions.x.neg.y * directions.y.neg.x
        - directions.x.neg.x * directions.y.neg.y
    )
    l = -(
        directions.x.neg.x * (-k * directions.y.pos.y - i * directions.x.pos.y)
        + k * directions.x.neg.y * directions.y.pos.x
        + i * directions.x.neg.y * directions.x.pos.x
    ) / (
        directions.x.neg.y * directions.y.neg.x
        - directions.x.neg.x * directions.y.neg.y
    )

    if _show:
        Figure = figure()
        namess = ["X", "Y"]
        names = ["X+", "X-", "Y+", "Y-"]
        for i in range(len(positions)):
            position = positions[i]
            name = namess[i]
            for j in range(len(position)):
                name_ = names[j]
                Figure.gca().plot(position[j], label="{} {}".format(name, name_))
        Figure.gca().legend()
        show()

    new_factors = array([i, j, k, l])

    console.print("movement for one step:")
    console.print(directions)
    console.print("factors are: {}".format(new_factors))

    console.print("-------------------")
    console.print(new_factors * means)
    console.print(factors * means)


main(
    Path(
        "/home/demagny/data/backscattermeter/camera/snail_N3_SXp50_old_1-1429443262-102.gwf"
    ),
)

main(
    Path(
        "/home/demagny/data/backscattermeter/camera/snail_N3_SXp50_3-1429442495-124.gwf"
    ),
    96,
    2 * 5 * 49,
    [1.00, 0.90, 1.95, 1.404],
)