import math
from time import perf_counter
import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
import sigfig
from streamlit_ace import st_ace
from .show_echart import plot_interact_abs_from_f
from .data_parsing_utils import is_float, read_data, check_line_comments, count_columns, prepare_snp


def circle(ax, x, y, radius, color='#1946BA'):
    from matplotlib.patches import Ellipse
    drawn_circle = Ellipse((x, y),
                           radius * 2,
                           radius * 2,
                           clip_on=True,
                           zorder=2,
                           linewidth=2,
                           edgecolor=color,
                           facecolor=(0, 0, 0, .0))
    ax.add_artist(drawn_circle)


def plot_smith(r, i, g, r_cut, i_cut):
    from matplotlib.image import AxesImage
    show_excluded_points = True
    show_Abs_S_scale = False
    show_Re_Z_scale = False
    show_Im_Z_scale = False
    show_grid = True
    with st.expander("Smith chart options"):
        show_excluded_points = st.checkbox("Show excluded points",
                                           value=show_excluded_points)
        show_grid = st.checkbox("Show grid", value=show_grid)
        
        show_Abs_S_scale = st.checkbox("Show |S| gridlines",
                                       value=show_Abs_S_scale)
        show_Re_Z_scale = st.checkbox("Show Re(Z) gridlines",
                                      value=show_Re_Z_scale)
        show_Im_Z_scale = st.checkbox("Show Im(Z) gridlines",
                                      value=show_Im_Z_scale)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot()

    # major_ticks = np.arange(-1.0, 1.1, 0.25)
    minor_ticks = np.arange(-1.1, 1.1, 0.05)
    # ax.set_xticks(major_ticks)
    ax.set_xticks(minor_ticks, minor=True)
    # ax.set_yticks(major_ticks)
    ax.set_yticks(minor_ticks, minor=True)
    ax.grid(which='major', color='grey', linewidth=1.5)
    ax.grid(which='minor', color='grey', linewidth=0.5, linestyle=':')
    plt.xlabel('$Re(S)$', color='gray', fontsize=16, fontname="Cambria")
    plt.ylabel('$Im(S)$', color='gray', fontsize=16, fontname="Cambria")
    plt.title('Smith chart', fontsize=24, fontname="Cambria")

    # unit circle
    circle(ax, 0, 0, 1)

    if not show_grid:
        ax.axis('off')

    background_img_x = -1.981
    background_img_y = -1.949
    background_img_box = [
        background_img_x,
        background_img_x + 3.87,
        background_img_y,
        background_img_y + 3.87
    ]
    if show_Abs_S_scale:
        # imshow is extremely slow
        # TODO draw primitives in place
        background = plt.imread("./source/frontend/images/s.png")
        background = ax.imshow(background, extent=background_img_box, interpolation= 'none')


    if show_Re_Z_scale:
        background = plt.imread("./source/frontend/images/re(z).png")
        background = ax.imshow(background, extent=background_img_box, interpolation= 'none')

    if show_Im_Z_scale:
        background = plt.imread("./source/frontend/images/im(z).png")
        background = ax.imshow(background, extent=background_img_box, interpolation= 'none')

    # input data points
    if show_excluded_points:
        ax.plot(r, i, '+', ms=8, mew=2, color='#b6c7f4')

    # choosen data points
    ax.plot(r_cut, i_cut, '+', ms=8, mew=2, color='#1946BA')

    # circle approximation by calc
    radius = abs(g[1] - g[0] / g[2]) / 2
    x = ((g[1] + g[0] / g[2]) / 2).real
    y = ((g[1] + g[0] / g[2]) / 2).imag
    circle(ax, x, y, radius, color='#FF8400')

    XLIM = [-1.3, 1.3]
    YLIM = [-1.3, 1.3]
    ax.set_xlim(XLIM)
    ax.set_ylim(YLIM)
    st.pyplot(fig)


# plot abs(S) vs f chart with pyplot
def plot_abs_vs_f(f, r, i, fitted_mag_s):
    fig = plt.figure(figsize=(10, 10))
    s = np.abs(np.array(r) + 1j * np.array(i))
    if st.session_state.legendselection == '|S| (dB)':
        m = np.min(np.where(s==0, np.inf, s))
        s = list(20*np.where(s==0, np.log10(m), np.log10(s)))
        m = np.min(np.where(s==0, np.inf, fitted_mag_s))
        fitted_mag_s = list(20*np.where(s==0, np.log10(m), np.log10(fitted_mag_s)))
    s = list(s)
    min_f = min(f)
    max_f = max(f)
    xlim = [
        min_f - abs(max_f - min_f) * 0.1,
        max_f + abs(max_f - min_f) * 0.1
    ]
    min_s = min(s)
    max_s = max(s)
    print(min_s,max_s)
    ylim = [
        min_s - abs(max_s - min_s) * 0.5,
        max_s + abs(max_s - min_s) * 0.5
    ]
    ax = fig.add_subplot()
    ax.set_xlim(xlim)
    ax.set_ylim(ylim)
    ax.grid(which='major', color='k', linewidth=1)
    ax.grid(which='minor', color='grey', linestyle=':', linewidth=0.5)
    plt.xlabel(r'$f,\; 1/c$', color='gray', fontsize=16, fontname="Cambria")
    if st.session_state.legendselection == '|S| (dB)':
        plt.ylabel('$|S|$ (dB)', color='gray', fontsize=16, fontname="Cambria")
        plt.title('|S| (dB) vs frequency', fontsize=24, fontname="Cambria")
    else:
        plt.ylabel('$|S|$', color='gray', fontsize=16, fontname="Cambria")
        plt.title('|S| vs frequency', fontsize=24, fontname="Cambria")

    ax.plot(f, s, '+', ms=8, mew=2, color='#1946BA')

    ax.plot(f, fitted_mag_s, '-', linewidth=3, color='#FF8400')

    st.pyplot(fig)


def run(calc_function):
    # for Touchstone .snp format
    def parse_heading(data):
        nonlocal data_format_snp
        if data_format_snp:
            for x in range(len(data)):
                if data[x].lstrip()[0] == '#':
                    line = data[x].split()
                    if len(line) == 6:
                        repr_map = {"ri": 0, "ma": 1, "db": 2}
                        para_map = {"s": 0, "z": 1}
                        hz_map = {
                            "ghz": 10**9,
                            "mhz": 10**6,
                            "khz": 10**3,
                            "hz": 1
                        }
                        hz, measurement_parameter, data_representation, _r, ref_resistance = (
                            x.lower() for x in line[1:])
                        try:
                            return hz_map[hz], para_map[
                                measurement_parameter], repr_map[
                                    data_representation], int(
                                        float(ref_resistance))
                        except:
                            break
                    break
        return 1, 0, 0, 50

    def unpack_data(data, first_column, column_count, ref_resistance,
                    ace_preview_markers):
        nonlocal select_measurement_parameter
        nonlocal select_data_representation
        f, r, i = [], [], []
        return_status = 'data parsed'
        for x in range(len(data)):
            line = check_line_comments(data[x])
            if line is None:
                continue

            line = line.split()

            if column_count != len(line):
                return_status = "Wrong number of parameters on line № " + str(
                    x)
                break

            # 1: process according to data_placement
            a, b, c = None, None, None
            try:
                a = line[0]
                b = line[first_column]
                c = line[first_column + 1]
            except:
                return_status = 'Can\'t parse line №: ' + \
                    str(x) + ',\n not enough arguments'
                break
            if not ((is_float(a)) or (is_float(b)) or (is_float(c))):
                return_status = 'Wrong data type, expected number. Error on line: ' + \
                    str(x)
                break

            # mark as processed
            # for y in (a,b,c):
            #     ace_preview_markers.append(
            #         {"startRow": x,"startCol": 0,
            #         "endRow": x,"endCol": data[x].find(y)+len(y),
            #         "className": "ace_stack","type": "text"})

            a, b, c = (float(x) for x in (a, b, c))
            f.append(a)  # frequency

            # 2: process according to data_representation
            if select_data_representation == 'Frequency, real, imaginary':
                # std format
                r.append(b)  # Re
                i.append(c)  # Im
            elif select_data_representation == 'Frequency, magnitude, angle':
                r.append(b * np.cos(np.deg2rad(c)))
                i.append(b * np.sin(np.deg2rad(c)))
            elif select_data_representation == 'Frequency, db, angle':
                b = 10**(b / 20)
                r.append(b * np.cos(np.deg2rad(c)))
                i.append(b * np.sin(np.deg2rad(c)))
            else:
                return_status = 'Wrong data format'
                break

            # 3: process according to measurement_parameter
            if select_measurement_parameter == 'Z':
                # normalization
                r[-1] = r[-1] / ref_resistance
                i[-1] = i[-1] / ref_resistance

                # translate to S
                try:
                    # center_x + 1j*center_y, radius
                    p1, r1 = r[-1] / (1 + r[-1]) + 0j, 1 / (1 + r[-1])  #real
                    p2, r2 = 1 + 1j * (1 / i[-1]), 1 / i[-1]  #imag

                    d = abs(p2 - p1)
                    q = (r1**2 - r2**2 + d**2) / (2 * d)

                    h = (r1**2 - q**2)**0.5

                    p = p1 + q * (p2 - p1) / d

                    intersect = [(p.real + h * (p2.imag - p1.imag) / d,
                                  p.imag - h * (p2.real - p1.real) / d),
                                 (p.real - h * (p2.imag - p1.imag) / d,
                                  p.imag + h * (p2.real - p1.real) / d)]

                    intersect = [x + 1j * y for x, y in intersect]
                    intersect_shift = [p - (1 + 0j) for p in intersect]
                    intersect_shift = abs(np.array(intersect_shift))
                    p = intersect[0]
                    if intersect_shift[0] < intersect_shift[1]:
                        p = intersect[1]
                    r[-1] = p.real
                    i[-1] = p.imag
                except:
                    r.pop()
                    i.pop()
                    f.pop()

        if return_status == 'data parsed':
            if len(f) < 3 or len(f) != len(r) or len(f) != len(i):
                return_status = 'Choosen data range is too small, add more points'
            elif max(abs(np.array(r) + 1j * np.array(i))) > 2:
                return_status = 'Your data points have an abnormality:\
                            they are too far outside the unit cirlce.\
                            Make sure the format is correct'

        return f, r, i, return_status


    # info
    with st.expander("Info"):
        # streamlit.markdown does not support footnotes
        try:
            with open('./source/frontend/info.md') as f:
                st.markdown(f.read())
        except:
            st.write('Wrong start directory, see readme')

    # file upload button
    uploaded_file = st.file_uploader(
        'Upload a file from your vector analizer. \
        Make sure the file format is .snp or it has a similar inner structure.'
    )

    # check .snp
    data_format_snp = False
    data_format_snp_number = 0
    if uploaded_file is None:
        st.write("DEMO: ")
        # display DEMO
        data_format_snp = True
        try:
            with open('./resource/data/8_default_demo.s1p') as f:
                data = f.readlines()
        except:
            # 'streamlit run' call in the wrong directory. Display smaller demo:
            data = [
                '# Hz S MA R 50\n\
                11415403125 0.37010744 92.47802\n\
                11416090625 0.33831283 92.906929\n\
                11416778125 0.3069371 94.03318'
            ]
    else:
        data = uploaded_file.readlines()
        if uploaded_file.name[-4:-2] == '.s' and uploaded_file.name[-1] == 'p':
            data_format_snp = True
            data_format_snp_number = int(uploaded_file.name[-2])

    validator_status = '...'
    ace_preview_markers = []
    column_count = 0

    # data loaded
    circle_params = []
    if len(data) > 0:

        validator_status = read_data(data)
        if validator_status == 'data read, but not parsed':
            hz, select_measurement_parameter, select_data_representation, input_ref_resistance = parse_heading(
                data)

            col1, col2 = st.columns([1, 2])

            ace_text_value = ''.join(data).strip()
            with col1.expander("Processing options"):
                select_measurement_parameter = st.selectbox(
                    'Measurement parameter', ['S', 'Z'],
                    select_measurement_parameter)
                select_data_representation = st.selectbox(
                    'Data representation', [
                        'Frequency, real, imaginary',
                        'Frequency, magnitude, angle', 'Frequency, db, angle'
                    ], select_data_representation)
                if select_measurement_parameter == 'Z':
                    input_ref_resistance = st.number_input(
                        "Reference resistance:",
                        min_value=0,
                        value=input_ref_resistance)
                if not data_format_snp:
                    input_hz = st.selectbox('Unit of frequency',
                                            ['Hz', 'KHz', 'MHz', 'GHz'], 0)
                    hz_map = {
                        "ghz": 10**9,
                        "mhz": 10**6,
                        "khz": 10**3,
                        "hz": 1
                    }
                    hz = hz_map[input_hz.lower()]
                input_start_line = int(
                    st.number_input("First line for processing:",
                                    min_value=1,
                                    max_value=len(data)))
                input_end_line = int(
                    st.number_input("Last line for processing:",
                                    min_value=1,
                                    max_value=len(data),
                                    value=len(data)))
                data = data[input_start_line - 1:input_end_line]

            # Ace editor to show choosen data columns and rows
            with col2.expander("File preview"):
                # So little 'official' functionality in libs and lack of documentation
                # therefore beware: css hacks

                # yellow ~ ace_step
                # light yellow ~ ace_highlight-marker
                # green ~ ace_stack
                # red ~ ace_error-marker

                # no more good colors included in streamlit_ace for marking

                # st.markdown('''<style>
                # .choosen_option_1
                # {
                # color: rgb(49, 51, 63);
                # }</style>''', unsafe_allow_html=True)

                # markdown injection does not seems to work, 
                # since ace is in a different .html accessible via iframe

                # markers format:
                #[{"startRow": 2,"startCol": 0,"endRow": 2,"endCol": 3,"className": "ace_error-marker","type": "text"}]

                # add marking for choosen data lines?
                # todo or not todo?
                ace_preview_markers.append({
                    "startRow": input_start_line - 1,
                    "startCol": 0,
                    "endRow": input_end_line,
                    "endCol": 0,
                    "className": "ace_highlight-marker",
                    "type": "text"
                })

                st_ace(value=ace_text_value,
                       readonly=True,
                       auto_update=True,
                       placeholder="Your file is empty",
                       markers=ace_preview_markers,
                       height="300px")

            if data_format_snp and data_format_snp_number >= 3:
                data, validator_status = prepare_snp(data,
                                                     data_format_snp_number)

    if validator_status == "data read, but not parsed":
        column_count, validator_status = count_columns(data)

    f, r, i = [], [], []
    if validator_status == "data parsed":
        input_ports_pair = 1
        if column_count > 3:
            pair_count = (column_count - 1) // 2
            input_ports_pair = st.number_input(
                "Choose pair of ports with network parameters:",
                min_value=1,
                max_value=pair_count,
                value=1)
            input_ports_pair_id = input_ports_pair - 1
            ports_count = round(pair_count**0.5)
            st.write('Choosen ports: ' + select_measurement_parameter +
                     str(input_ports_pair_id // ports_count + 1) +
                     str(input_ports_pair_id % ports_count + 1))
        f, r, i, validator_status = unpack_data(data,
                                                (input_ports_pair - 1) * 2 + 1,
                                                column_count,
                                                input_ref_resistance,
                                                ace_preview_markers)
        f = [x * hz for x in f]  # to hz

    st.write("Use range slider to choose best suitable data interval")

    # make accessible a specific range of numerical data choosen with interactive plot
    # line id, line id
    interval_start, interval_end = plot_interact_abs_from_f(f,r,i)

    # plot_interact_abs_from_f( f, r, i, interval_range)

    f_cut, r_cut, i_cut = [], [], []
    if validator_status == "data parsed":
        f_cut, r_cut, i_cut = (x[interval_start:interval_end]
                               for x in (f, r, i))

        with st.expander("Selected data interval as .s1p"):
            st_ace(value="# Hz S RI R 50\n" +
                   ''.join(f'{f_cut[x]} {r_cut[x]} {i_cut[x]}\n'
                           for x in range(len(f_cut))),
                   readonly=True,
                   auto_update=True,
                   placeholder="Selection is empty",
                   height="150px")

        if len(f_cut) < 3:
            validator_status = "Choosen interval is too small, add more points"

    st.write("Status: " + validator_status)

    if validator_status == "data parsed":
        col1, col2 = st.columns(2)

        check_coupling_loss = col1.checkbox(
            'Apply correction for coupling losses')

        if check_coupling_loss:
            col1.write("Option: Lossy coupling")
        else:
            col1.write("Option: Cable attenuation")

        select_autoformat = col2.checkbox("Autoformat output", value=True)
        precision = '0.0f'
        if not select_autoformat:
            precision = col2.slider("Precision",
                                    min_value=0,
                                    max_value=7,
                                    value=4)
            precision = '0.' + str(precision) + 'f'

        Q0, sigmaQ0, QL, sigmaQL, k, ks, circle_params, fl, fitted_mag_s = calc_function(
            f_cut, r_cut, i_cut, check_coupling_loss)

        if Q0 <= 0 or QL <= 0:
            st.write("Negative Q detected, fitting may be inaccurate!")

        def show_result_in_latex(name, value, uncertainty=None):
            nonlocal select_autoformat
            if uncertainty is not None:
                if select_autoformat:
                    st.latex(
                        name + ' =' +
                        f'{sigfig.round(value, uncertainty=uncertainty, style="PDG")},  '
                        + r'\;\;\varepsilon_{' + name + '} =' +
                        f'{sigfig.round(uncertainty / value, sigfigs=1, style="PDG")}'
                    )
                else:
                    st.latex(name + ' =' + f'{format(value, precision)} \pm ' +
                             f'{format(uncertainty, precision)},  ' +
                             r'\;\;\varepsilon_{' + name + '} =' +
                             f'{format(uncertainty / value, precision)}')
            else:
                if select_autoformat:
                    st.latex(name + ' =' +
                             f'{sigfig.round(value, sigfigs=5, style="PDG")}')
                else:
                    st.latex(name + ' =' + f'{format(value, precision)}')

        show_result_in_latex('Q_0', Q0, sigmaQ0)
        show_result_in_latex('Q_L', QL, sigmaQL)
        show_result_in_latex(r'\kappa', k)
        if check_coupling_loss:
            show_result_in_latex(r'\kappa_s', ks)

        st.latex('f_L =' + f'{format(fl, precision)}' + r'\text{ }Hz')

        with st.expander("Show static abs(S) plot"):
            plot_abs_vs_f(f_cut, r_cut, i_cut, fitted_mag_s)

        t1= perf_counter()
        plot_smith(r, i, circle_params, r_cut, i_cut)
        print(perf_counter()-t1)