import streamlit as st import matplotlib.pyplot as plt import numpy as np import sigfig from streamlit_ace import st_ace from .draw_smith_utils import draw_smith_circle, plot_abs_s_gridlines, plot_im_z_gridlines, plot_re_z_gridlines from .show_amplitude_echart import plot_interact_abs_from_f from .data_parsing_utils import parse_snp_header, read_data, count_columns, prepare_snp, unpack_data def plot_smith(r, i, g, r_cut, i_cut): # maintaining state again (remember options for this session) if 'smith_options' not in st.session_state: st.session_state.smith_options = (True, True, False, False, False) with st.expander("Smith chart options"): smith_options_input = (st.checkbox( "Show excluded points", value=st.session_state.smith_options[0]), st.checkbox("Show grid", st.session_state.smith_options[1]), st.checkbox( "Show |S| gridlines", value=st.session_state.smith_options[2], ), st.checkbox( "Show Re(Z) gridlines", value=st.session_state.smith_options[3], ), st.checkbox( "Show Im(Z) gridlines", value=st.session_state.smith_options[4], )) if st.session_state.smith_options != smith_options_input: st.session_state.smith_options = smith_options_input st.experimental_rerun() (show_excluded_points, show_grid, show_Abs_S_gridlines, show_Re_Z_gridlines, show_Im_Z_gridlines) = st.session_state.smith_options fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot() ax.axis('equal') minor_ticks = np.arange(-1.1, 1.1, 0.05) ax.set_xticks(minor_ticks, minor=True) 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 draw_smith_circle(ax, 0, 0, 1, '#1946BA') if not show_grid: ax.axis('off') if show_Abs_S_gridlines: # imshow is extremely slow, so draw it in place plot_abs_s_gridlines(ax) if show_Re_Z_gridlines: plot_re_z_gridlines(ax) if show_Im_Z_gridlines: plot_im_z_gridlines(ax) # 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') # S-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 draw_smith_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) 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): # 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 is_data_format_snp = False data_format_snp_number = 0 if uploaded_file is None: st.write("DEMO: ") # display DEMO is_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': is_data_format_snp = True data_format_snp_number = int(uploaded_file.name[-2]) validator_status = '...' 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_snp_header( data, is_data_format_snp) 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 is_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('''''', 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 =[{ "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 is_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, select_measurement_parameter, select_data_representation) 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) 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', value = False) 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) plot_smith(r, i, circle_params, r_cut, i_cut)