import math import streamlit as st import matplotlib.pyplot as plt import numpy as np import sigfig import pyperclip 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: success' # 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 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] 2: return_status = 'Your data points have an abnormality:\ they are too far outlise 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 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 =[line.strip()+'\n' for line in '''# Hz S MA R 50 11415403125 0.37010744 92.47802 11416090625 0.33831283 92.906929 11416778125 0.3069371 94.03318 '''.split('\n')] else: data = uploaded_file.readlines() if uploaded_file.name[-4:-2]=='.s' and uploaded_file.name[-1]== 'p': data_format_snp = True 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: success': 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('''''', 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") column_count, validator_status = count_columns(data) if validator_status == "data parsed": input_ports_pair = 1 if column_count > 3: input_ports_pair = st.number_input( "Pair of data columns (pair of ports)\n with network parameters:", min_value=1, max_value=(column_count - 1) // 2, value=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)) def copy_to_clip_s_single(): pyperclip.copy("# 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)))) st.button("Copy selected data interval to clipboard as .s1p", on_click=copy_to_clip_s_single) 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) select_show_excluded_points = st.checkbox("Show excluded points", value=True) plot_smith(r, i, circle_params, r_cut, i_cut, select_show_excluded_points)