import sys

sys.path.append("D:/uni/daniel-tomografia")

from isttok import myLib, baseline
from isttok import signal
from isttok.ISTTOKSignal import ISTTOKSignal
import matplotlib

matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
from scipy.ndimage import shift
from scipy.optimize import curve_fit
import numpy as np
from calibrationShots import shots, keys

plt.close("all")

plt.rcParams.update({'font.size': 18})

channels = [
    "cal_top_01",
    "cal_top_02",
    "cal_top_03",
    "cal_top_04",
    "cal_top_05",
    "cal_top_06",
    "cal_top_07",
    "cal_top_08",
    "cal_top_09",
    "cal_top_10",
    "cal_top_11",
    "cal_top_12",
    "cal_top_13",
    "cal_top_14",
    "cal_top_15",
    "cal_top_16",

    "cal_out_01",
    "cal_out_02",
    "cal_out_03",
    "cal_out_04",
    "cal_out_05",
    "cal_out_06",
    "cal_out_07",
    "cal_out_08",
    "cal_out_09",
    "cal_out_10",
    "cal_out_11",
    "cal_out_12",
    "cal_out_13",
    "cal_out_14",
    "cal_out_15",
    "cal_out_16",
]


def export_signals(shot_id, plot=False):
    """Prepare tomography signals for reconstruction. Work only with data from the discharge lamp experiments.

    .. deprecated::
        `export_signals` will be removed
        `prepare_signals` should be used instead

    The reconstruction API relies on signals stored in .npy files. The signal preparation pipe-line follows.
    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Save the signals to the target files "signals_time.npy" and "signals_data.npy"
    This files serve as input to the reconstruction algorithm

    Parameters
    ----------
    shot_id: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: list of ND arrays
        List of the time arrays for the data of each tomography sensor
    signals_data: list of ND arrays
        List of value arrays containing the data from each tomography sensor

    """

    signals_data = []
    signals_time = []

    shot = shots[shot_id]

    if plot: plt.figure(figsize=(20, 12))

    for tag in channels:

        x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                         cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')
        detrend = baseline.baseline(x, deg=0)
        data = x.values - detrend
        time = x.times

        n = 10
        data = np.cumsum(data, axis=0)
        data = (data[n:] - data[:-n]) / n
        data = data[::n]
        data = np.clip(data, 0., None)
        time = time[n // 2::n]
        time = time[:data.shape[0]]
        signals_data.append(data)
        signals_time.append(time)
        if plot:
            plt.plot(time, data, label=tag)
            if tag == channels[15]:
                plt.title('signals (top camera)')
                plt.xlabel('t (s)')
                plt.legend()
                plt.figure(figsize=(20, 12))
            if tag == channels[31]:
                plt.title('signals (front camera)')
                plt.xlabel('t (s)')
                plt.legend()
            plt.show()

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32)
    signals_time = np.array(signals_time, dtype=np.float32)

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    fname = 'signals_data.npy'
    print(('Writing:', fname))
    np.save(fname, signals_data)

    fname = 'signals_time.npy'
    print(('Writing:', fname))
    np.save(fname, signals_time)

    return signals_time, signals_data


def prepare_signals(shot_id, plot=False):
    """Prepare tomography signals for reconstruction. Works only with data from the discharge lamp experiments.

    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Return a time array with length T and a data array T x 32.
    The data array can be directly served to the reconstruction classes

    Parameters
    ----------
    shot_id: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: 1D array
        Time array for the data of each tomography sensor
    signals_data: N x 32 ND-array
        Data from the sensors. Every row corresponds to a time value in `signals_time`.
        Each column corresponds to one sensor.
    """

    signals_data = []

    shot = shots[shot_id]

    if plot:
        plt.figure(figsize=(17, 8))

    for tag in channels:

        x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                         cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')
        detrend = baseline.baseline(x, deg=1)
        data = x.values - detrend
        time = np.array(x.times, dtype=np.float32)

        n = 1
        data = np.cumsum(data, axis=0)
        data = (data[n:] - data[:-n]) / n
        data = data[::n]
        data = np.clip(data, 0., None)
        time = time[n // 2::n]
        time = time[:data.shape[0]]
        signals_data.append(data)

        try:
            if not np.allclose(signals_time, time, 0.0, 1.e-8):
                raise ValueError("tomography signals have different time axis")
        except NameError:
            signals_time = time

        if plot:
            plt.plot(time, data, label=tag)
            if tag == channels[15]:
                plt.title('signals (top camera)')
                plt.xlabel('t (s)')
                plt.legend()
                plt.figure(figsize=(17, 8))
            if tag == channels[31]:
                plt.title('signals (front camera)')
                plt.xlabel('t (s)')
                plt.legend()
            plt.show()

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32).T

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    return signals_time, signals_data


def prepare_plasma_signals(shot, plot=False):
    """Prepare tomography signals for reconstruction. Works only on actual data starting from shot 44880

    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Return a time array with length T and a data array T x 32.
    The data array can be directly served to the reconstruction classes

    Parameters
    ----------
    shot: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: 1D array
        Time array for the data of each tomography sensor
    signals_data: N x 32 ND-array
        Data from the sensors. Every row corresponds to a time value in `signals_time`.
        Each column corresponds to one sensor.
    """

    tags = [
        'top_04',
        'top_05',
        'top_06',
        'top_07',
        'top_08',
        'top_09',
        'top_10',
        'top_11',
        'top_12',
        'top_13',
        'top_14',
        'top_15',
        'top_16',
        'top_17',
        'top_18',
        'top_19',

        'out_04',
        'out_05',
        'out_06',
        'out_07',
        'out_08',
        'out_09',
        'out_10',
        'out_11',
        'out_12',
        'out_13',
        'out_14',
        'out_15',
        'out_16',
        'out_17',
        'out_18',
        'out_19',

        'bot_04',
        'bot_05',
        'bot_06',
        'bot_07',
        'bot_08',
        'bot_09',
        'bot_10',
        'bot_11',
        'bot_12',
        'bot_13',
        'bot_14',
        'bot_15',
        'bot_16',
        'bot_17',
        'bot_18',
        'bot_19',
    ]

    signals_data = []

    for camera, title in zip(np.reshape(tags, (3, 16)), ['top', 'out', 'bot']):

        if plot:
            fig, axes = plt.subplots(nrows=4, ncols=4, sharex=True, sharey=True)
            axes = axes.flatten()
            plt.suptitle(title)
            plt_index = 0

        for tag in camera:

            x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                             cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')

            detrend = baseline.baseline(x, deg=1)
            data = x.values - detrend
            time = np.array(x.times, dtype=np.float32)

            n = 1
            data = np.cumsum(data, axis=0)
            data = (data[n:] - data[:-n]) / n
            data = data[::n]
            data = np.clip(data, 0., None)
            time = time[n // 2::n]
            time = time[:data.shape[0]]
            signals_data.append(data)

            try:
                if not np.allclose(signals_time, time, 0.0, 1.e-8):
                    raise ValueError("tomography signals have different time axis")
            except NameError:
                signals_time = time

            if plot:
                axes[plt_index].plot(time, data, label=tag + ' detrended')
                axes[plt_index].plot(x.times, x.values, label=tag)
                plt_index += 1

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32).T

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    return signals_time, signals_data


import sys

sys.path.append("D:/uni/daniel-tomografia")

from isttok import myLib, baseline
from isttok import signal
from isttok.ISTTOKSignal import ISTTOKSignal
import matplotlib

matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
from scipy.ndimage import shift
from scipy.optimize import curve_fit
import numpy as np
from calibrationShots import shots, keys

plt.close("all")

plt.rcParams.update({'font.size': 18})

channels = [
    "cal_top_01",
    "cal_top_02",
    "cal_top_03",
    "cal_top_04",
    "cal_top_05",
    "cal_top_06",
    "cal_top_07",
    "cal_top_08",
    "cal_top_09",
    "cal_top_10",
    "cal_top_11",
    "cal_top_12",
    "cal_top_13",
    "cal_top_14",
    "cal_top_15",
    "cal_top_16",

    "cal_out_01",
    "cal_out_02",
    "cal_out_03",
    "cal_out_04",
    "cal_out_05",
    "cal_out_06",
    "cal_out_07",
    "cal_out_08",
    "cal_out_09",
    "cal_out_10",
    "cal_out_11",
    "cal_out_12",
    "cal_out_13",
    "cal_out_14",
    "cal_out_15",
    "cal_out_16",
]


def export_signals(shot_id, plot=False):
    """Prepare tomography signals for reconstruction. Work only with data from the discharge lamp experiments.

    .. deprecated::
        `export_signals` will be removed
        `prepare_signals` should be used instead

    The reconstruction API relies on signals stored in .npy files. The signal preparation pipe-line follows.
    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Save the signals to the target files "signals_time.npy" and "signals_data.npy"
    This files serve as input to the reconstruction algorithm

    Parameters
    ----------
    shot_id: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: list of ND arrays
        List of the time arrays for the data of each tomography sensor
    signals_data: list of ND arrays
        List of value arrays containing the data from each tomography sensor

    """

    signals_data = []
    signals_time = []

    shot = shots[shot_id]

    if plot: plt.figure(figsize=(20, 12))

    for tag in channels:

        x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                         cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')
        detrend = baseline.baseline(x, deg=0)
        data = x.values - detrend
        time = x.times

        n = 10
        data = np.cumsum(data, axis=0)
        data = (data[n:] - data[:-n]) / n
        data = data[::n]
        data = np.clip(data, 0., None)
        time = time[n // 2::n]
        time = time[:data.shape[0]]
        signals_data.append(data)
        signals_time.append(time)
        if plot:
            plt.plot(time, data, label=tag)
            if tag == channels[15]:
                plt.title('signals (top camera)')
                plt.xlabel('t (s)')
                plt.legend()
                plt.figure(figsize=(20, 12))
            if tag == channels[31]:
                plt.title('signals (front camera)')
                plt.xlabel('t (s)')
                plt.legend()
            plt.show()

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32)
    signals_time = np.array(signals_time, dtype=np.float32)

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    fname = 'signals_data.npy'
    print(('Writing:', fname))
    np.save(fname, signals_data)

    fname = 'signals_time.npy'
    print(('Writing:', fname))
    np.save(fname, signals_time)

    return signals_time, signals_data


def prepare_signals(shot_id, plot=False):
    """Prepare tomography signals for reconstruction. Works only with data from the discharge lamp experiments.

    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Return a time array with length T and a data array T x 32.
    The data array can be directly served to the reconstruction classes

    Parameters
    ----------
    shot_id: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: 1D array
        Time array for the data of each tomography sensor
    signals_data: N x 32 ND-array
        Data from the sensors. Every row corresponds to a time value in `signals_time`.
        Each column corresponds to one sensor.
    """

    signals_data = []

    shot = shots[shot_id]

    if plot:
        plt.figure(figsize=(17, 8))

    for tag in channels:

        x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                         cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')
        detrend = baseline.baseline(x, deg=1)
        data = x.values - detrend
        time = np.array(x.times, dtype=np.float32)

        n = 1
        data = np.cumsum(data, axis=0)
        data = (data[n:] - data[:-n]) / n
        data = data[::n]
        data = np.clip(data, 0., None)
        time = time[n // 2::n]
        time = time[:data.shape[0]]
        signals_data.append(data)

        try:
            if not np.allclose(signals_time, time, 0.0, 1.e-8):
                raise ValueError("tomography signals have different time axis")
        except NameError:
            signals_time = time

        if plot:
            plt.plot(time, data, label=tag)
            if tag == channels[15]:
                plt.title('signals (top camera)')
                plt.xlabel('t (s)')
                plt.legend()
                plt.figure(figsize=(17, 8))
            if tag == channels[31]:
                plt.title('signals (front camera)')
                plt.xlabel('t (s)')
                plt.legend()
            plt.show()

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32).T

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    return signals_time, signals_data


def prepare_plasma_signals(shot, plot=False):
    """Prepare tomography signals for reconstruction. Works only on actual data starting from shot 44880

    - Load the data from the ISTTOK data base or from the cache folder
    - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)
    - Return a time array with length T and a data array T x 32.
    The data array can be directly served to the reconstruction classes

    Parameters
    ----------
    shot: int
        Shot number.
    plot: Bool, optional
        Show plots of the loaded data after processing

    Returns
    -------
    signals_time: 1D array
        Time array for the data of each tomography sensor
    signals_data: N x 32 ND-array
        Data from the sensors. Every row corresponds to a time value in `signals_time`.
        Each column corresponds to one sensor.
    """

    tags = [
        'top_04',
        'top_05',
        'top_06',
        'top_07',
        'top_08',
        'top_09',
        'top_10',
        'top_11',
        'top_12',
        'top_13',
        'top_14',
        'top_15',
        'top_16',
        'top_17',
        'top_18',
        'top_19',

        'out_04',
        'out_05',
        'out_06',
        'out_07',
        'out_08',
        'out_09',
        'out_10',
        'out_11',
        'out_12',
        'out_13',
        'out_14',
        'out_15',
        'out_16',
        'out_17',
        'out_18',
        'out_19',

        'bot_04',
        'bot_05',
        'bot_06',
        'bot_07',
        'bot_08',
        'bot_09',
        'bot_10',
        'bot_11',
        'bot_12',
        'bot_13',
        'bot_14',
        'bot_15',
        'bot_16',
        'bot_17',
        'bot_18',
        'bot_19',
    ]

    signals_data = []

    for camera, title in zip(np.reshape(tags, (3, 16)), ['top', 'out', 'bot']):

        if plot:
            fig, axes = plt.subplots(nrows=4, ncols=4, sharex=True, sharey=True)
            axes = axes.flatten()
            plt.suptitle(title)
            plt_index = 0

        for tag in camera:

            x = ISTTOKSignal(shot, tag=tag, uid=False, time_scale=1e-6,
                             cache_dir='D:/uni/daniel-tomografia/local/shot-cache/')

            detrend = baseline.baseline(x, deg=1)
            data = x.values - detrend
            time = np.array(x.times, dtype=np.float32)

            n = 1
            data = np.cumsum(data, axis=0)
            data = (data[n:] - data[:-n]) / n
            data = data[::n]
            data = np.clip(data, 0., None)
            time = time[n // 2::n]
            time = time[:data.shape[0]]
            signals_data.append(data)

            try:
                if not np.allclose(signals_time, time, 0.0, 1.e-8):
                    raise ValueError("tomography signals have different time axis")
            except NameError:
                signals_time = time

            if plot:
                axes[plt_index].plot(time, data, label=tag + ' detrended')
                axes[plt_index].plot(x.times, x.values, label=tag)
                plt_index += 1

    # -------------------------------------------------------------------------

    signals_data = np.array(signals_data, dtype=np.float32).T

    print(('signals_data:', signals_data.shape, signals_data.dtype))
    print(('signals_time:', signals_time.shape, signals_time.dtype))

    # -------------------------------------------------------------------------

    return signals_time, signals_data


Prepare tomography signals for reconstruction. Work only with data from the discharge lamp experiments.      .. deprecated::         `export_signals` will be removed         `prepare_signals` should be used instead      The reconstruction API relies on signals stored in .npy files. The signal preparation pipe-line follows.     - Load the data from the ISTTOK data base or from the cache folder     - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)     - Save the signals to the target files "signals_time.npy" and "signals_data.npy"     This files serve as input to the reconstruction algorithm      Parameters     ----------     shot_id: int         Shot number.     plot: Bool, optional         Show plots of the loaded data after processing      Returns     -------     signals_time: list of ND arrays         List of the time arrays for the data of each tomography sensor     signals_data: list of ND arrays         List of value arrays containing the data from each tomography sensor # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- Prepare tomography signals for reconstruction. Works only with data from the discharge lamp experiments.      - Load the data from the ISTTOK data base or from the cache folder     - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)     - Return a time array with length T and a data array T x 32.     The data array can be directly served to the reconstruction classes      Parameters     ----------     shot_id: int         Shot number.     plot: Bool, optional         Show plots of the loaded data after processing      Returns     -------     signals_time: 1D array         Time array for the data of each tomography sensor     signals_data: N x 32 ND-array         Data from the sensors. Every row corresponds to a time value in `signals_time`.         Each column corresponds to one sensor. # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- Prepare tomography signals for reconstruction. Works only on actual data starting from shot 44880      - Load the data from the ISTTOK data base or from the cache folder     - Apply Signal processing techniques to the loaded data e.g. (detrending, baseline removal, filtering)     - Return a time array with length T and a data array T x 32.     The data array can be directly served to the reconstruction classes      Parameters     ----------     shot: int         Shot number.     plot: Bool, optional         Show plots of the loaded data after processing      Returns     -------     signals_time: 1D array         Time array for the data of each tomography sensor     signals_data: N x 32 ND-array         Data from the sensors. Every row corresponds to a time value in `signals_time`.         Each column corresponds to one sensor. # ------------------------------------------------------------------------- # -------------------------------------------------------------------------