calc-q-factor-for-vna/source/frontend/front.py

import math
import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
import sigfig
from streamlit_ace import st_ace
from streamlit_echarts import st_echarts, JsCode


# So that you can choose an interval of points on which we apply q-calc algorithm
def plot_interact_abs_from_f(f, r, i, interval_range):
    if interval_range is None:
        interval_range = (0, 100)

    abs_S = list(abs(np.array(r) + 1j * np.array(i)))
    # echarts for datazoom https://discuss.streamlit.io/t/streamlit-echarts/3655
    # datazoom https://echarts.apache.org/examples/en/editor.html?c=line-draggable&lang=ts
    # axis pointer values https://echarts.apache.org/en/option.html#axisPointer
    options = {
        "xAxis": {
            "type": "category",
            "data": f,
            "name": "Hz",
            "nameTextStyle": {"fontSize": 16},
            "axisLabel": {"fontSize": 16},
        },
        "yAxis": {
            "type": "value",
            "name": "abs(S)",
            "nameTextStyle": {"fontSize": 16},
            "axisLabel": {"fontSize": 16},
            # "axisPointer": {
            #     "type": 'cross',
            #     "label": {
            #     "show":"true",
            #     "formatter": JsCode(
            #     "function(info){console.log(info);return 'line ' ;};"
            #     ).js_code
            #     }
            # }
        },
        "series": [{"data": abs_S, "type": "line", "name": "abs(S)"}],
        "height": 300,
        "dataZoom": [{"type": "slider", "start": interval_range[0], "end": interval_range[1], "height": 100, "bottom": 10}],
        "tooltip": {
                "trigger": "axis",
                "axisPointer": {
                    "type": 'cross',
                    # "label": {
                    # "show":"true",
                    # "formatter": JsCode(
                    # "function(info){console.log(info);return 'line ' ;};"
                    # ).js_code
                    # }
                }
        },
        "toolbox": {
            "feature": {
                # "dataView": { "show": "true", "readOnly": "true" },
                "restore": {"show": "true"},
            }
        },
    }
    # DataZoom event is not fired on new file upload. There are no default event to fix it.
    events = {
        "dataZoom": "function(params) { return ['dataZoom', params.start, params.end] }",
        "restore": "function() { return ['restore'] }",
    }

    # show echart with dataZoom and update intervals based on output

    get_event = st_echarts(
        options=options, events=events, height="500px", key="render_basic_bar_events"
    )

    if not get_event is None and get_event[0] == 'dataZoom':
        interval_range = get_event[1:]

    n = len(f)
    interval_start, interval_end = (
        int(n*interval_range[id]*0.01) for id in (0, 1))
    return interval_range, interval_start, interval_end



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, .0125))
    ax.add_artist(drawn_circle)


def plot_smith(r, i, g, r_cut, i_cut, show_excluded):
    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('$Re(\Gamma)$', color='gray', fontsize=16, fontname="Cambria")
    plt.ylabel('$Im(\Gamma)$', color='gray', fontsize=16, fontname="Cambria")
    plt.title('Smith chart', fontsize=24, fontname="Cambria")

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

    # input data points
    if show_excluded:
        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 = [-1.1, 1.1]
    YLIM = [-1.1, 1.1]
    ax.set_xlim(XLIM)
    ax.set_ylim(YLIM)
    st.pyplot(fig)


# plot (abs(S))(f) chart with pyplot
def plot_abs_vs_f(f, r, i):
    fig = plt.figure(figsize=(10, 10))
    abs_S = list((r[n] ** 2 + i[n] ** 2)**0.5 for n in range(len(r)))
    xlim = [min(f) - abs(max(f) - min(f)) * 0.1,
            max(f) + abs(max(f) - min(f)) * 0.1]
    ylim = [min(abs_S) - abs(max(abs_S) - min(abs_S)) * 0.5,
            max(abs_S) + abs(max(abs_S) - min(abs_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")
    plt.ylabel('$|S|$', color='gray', fontsize=16, fontname="Cambria")
    plt.title('Abs(S) vs frequency',
              fontsize=24, fontname="Cambria")

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

    # 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
    st.pyplot(fig)


def run(calc_function):

    def is_float(element) -> bool:
        try:
            float(element)
            val = float(element)
            if math.isnan(val) or math.isinf(val):
                raise ValueError
            return True
        except ValueError:
            return False

    # to utf-8
    def read_data(data):
        for x in range(len(data)):
            if type(data[x]) == bytes:
                try:
                    data[x] = data[x].decode('utf-8-sig', 'ignore')
                except:
                    return 'Not an utf-8-sig line №: ' + str(x)
        return 'data read, but not parsed'

    # 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][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 = line[1:]
                        try:
                            return hz_map[hz], para_map[measurement_parameter], repr_map[data_representation], int(ref_resistance)
                        except:
                            break
                    break
        return 1, 0, 0, 50

    # check if line has comments
    # first is a comment line according to .snp documentation,
    # others detects comments in various languages
    def check_line_comments(line):
        if len(line) < 2 or line[0] == '!' or line[0] == '#' or line[0] == '%' or line[0] == '/':
            return None
        else:
            # generally we expect these chars as separators
            line = line.replace(';', ' ').replace(',', ' ')
            if '!' in line:
                line = line[:line.find('!')]
            return line

    # unpack a few first lines of the file to get number of ports
    def count_columns(data):
        return_status = 'data parsed'
        column_count = 0
        for x in range(len(data)):
            line = check_line_comments(data[x])
            if line is None:
                continue
            line = line.split()
            # always at least 3 values for single data point
            if len(line) < 3:
                return_status = 'Can\'t parse line № ' + \
                    str(x) + ',\n not enough arguments (less than 3)'
                break
            column_count = len(line)
            break
        return column_count, return_status

    def prepare_snp(data, number):
        prepared_data = []
        return_status = 'data read, but not parsed'
        for x in range(len(data)):
            line = check_line_comments(data[x])
            if line is None:
                continue

            splitted_line = line.split()
            if number * 2 + 1 == len(splitted_line):
                prepared_data.append(line)
            elif number * 2 == len(splitted_line):
                prepared_data[-1] += line
            else:
                return_status = "Parsing error for .snp format on line №" + str(x)

        return prepared_data, return_status

    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 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

    # make accessible a specific range of numerical data choosen with interactive plot
    # percent, line id, line id
    interval_range, interval_start, interval_end = None, None, None

    # 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])

            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)
                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"):
                # st.button(copy selection)

                # 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
                ace_preview_markers.append({
                    "startRow": input_start_line - 1,
                    "startCol": 0,
                    "endRow": input_end_line,
                    "endCol": 0,
                    "className": "ace_highlight-marker",
                    "type": "text"
                })

                ace_text_value = ''.join(data).strip()
                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_id = st.number_input(
                "Choosen pair of ports with network parameters:",
                min_value = 1,
                max_value = pair_count,
                value = 1) - 1
            ports_count = round(pair_count **0.5)
            st.write(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")
    interval_range, interval_start, interval_end = 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 loss')

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

        select_autoformat = col2.checkbox("Autoformat output", value=True)
        precision = None
        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, circle_params = 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!")

        if select_autoformat:
            st.latex(
                r'Q_0 =' +
                f'{sigfig.round(Q0, uncertainty=sigmaQ0, style="PDG")},  '
                + r'\;\;\varepsilon_{Q_0} =' +
                f'{sigfig.round(sigmaQ0 / Q0, sigfigs=1, style="PDG")}')
            st.latex(
                r'Q_L =' +
                f'{sigfig.round(QL, uncertainty=sigmaQL, style="PDG")},  '
                + r'\;\;\varepsilon_{Q_L} =' +
                f'{sigfig.round(sigmaQL / QL, sigfigs=1, style="PDG")}')
        else:
            st.latex(
                r'Q_0 =' +
                f'{format(Q0, precision)} \pm ' + f'{format(sigmaQ0, precision)},  '
                + r'\;\;\varepsilon_{Q_0} =' +
                 f'{format(sigmaQ0 / Q0, precision)}')
            st.latex(
                r'Q_L =' +
                f'{format(QL, precision)} \pm ' + f'{format(sigmaQL, precision)},  '
                + r'\;\;\varepsilon_{Q_L} =' +
                 f'{format(sigmaQL / QL, precision)}')

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

        plot_smith(r, i, circle_params, r_cut, i_cut, st.checkbox("Show excluded points", value=True))