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@@ -5,7 +5,7 @@ import streamlit as st
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import matplotlib.pyplot as plt
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import numpy as np
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-XLIM = [-1.1, 1.1]
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+XLIM = [-1.1, 1.1] # why global...
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YLIM = [-1.1, 1.1]
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def circle(ax, x, y, radius, color='#1946BA'):
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@@ -21,9 +21,9 @@ def plot_data(r, i, g):
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major_ticks = np.arange(-1.0, 1.1, 0.25)
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minor_ticks = np.arange(-1.1, 1.1, 0.05)
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- ax.set_xticks(major_ticks)
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+ # ax.set_xticks(major_ticks)
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ax.set_xticks(minor_ticks, minor=True)
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- ax.set_yticks(major_ticks)
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+ # ax.set_yticks(major_ticks)
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ax.set_yticks(minor_ticks, minor=True)
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ax.grid(which='major', color='grey', linewidth=1.5)
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ax.grid(which='minor', color='grey', linewidth=0.5, linestyle=':')
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@@ -44,13 +44,14 @@ def plot_data(r, i, g):
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#
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ax.set_xlim(XLIM)
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ax.set_ylim(YLIM)
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+ # sometimes throws exception
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try:
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st.pyplot(fig)
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except:
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st.write("Plot size is too big. There are some problems with fitting. Check your input")
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-interval_range = (0, 100)
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+interval_range = (0, 100) # in percents
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interval_start, interval_end = 0, 0
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@@ -100,9 +101,11 @@ def plot_interact_abs_from_f(f, r, i):
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"dataZoom": "function(params) { return [params.start, params.end] }",
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}
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+ # show echart with dataZoom and update intervals based on output
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interval_range = st_echarts(
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options=options, events=events, height="500px", key="render_basic_bar_events"
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)
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+
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if interval_range is None:
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interval_range = (0, 100)
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@@ -110,7 +113,7 @@ def plot_interact_abs_from_f(f, r, i):
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interval_start, interval_end = (
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int(n*interval_range[id]*0.01) for id in (0, 1))
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-
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+# plot (abs(S))(f) chart with pyplot
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def plot_ref_from_f(f, r, i):
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fig = plt.figure(figsize=(10, 10))
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abs_S = list((r[n] ** 2 + i[n] ** 2)**0.5 for n in range(len(r)))
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@@ -184,12 +187,15 @@ def run(calc_function):
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# first is a comment line according to .snp documentation,
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# others detects comments in various languages
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continue
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- data[x] = data[x].replace(';', ' ').replace(',', ' ')
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+ data[x] = data[x].replace(';', ' ').replace(',', ' ') # generally we expect these chars as seperators
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line = data[x].split()
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+
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# always at least 3 values for single data point
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if len(line) < 3:
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return_status = 'Can\'t parse line №: ' + str(x) + ',\n not enough arguments (less than 3)'
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break
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+
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+ # 1: process according to data_placement
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a,b,c=[],[],[]
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try:
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a, b, c = (line[y] for y in range(min(len(line),3)))
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@@ -207,6 +213,8 @@ def run(calc_function):
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break
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a,b,c=(float(x) for x in (a,b,c))
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f.append(a) # frequency
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+
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+ # 2: process according to data_representation
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if select_data_representation == 'Frequency, real, imaginary':
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r.append(b) # Re
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i.append(c) # Im
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@@ -221,12 +229,14 @@ def run(calc_function):
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return_status = 'Wrong data format'
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break
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+ # 3: process according to measurement_parameter
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return f, r, i, return_status
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# make accessible specific range of numerical data choosen with interactive plot
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global interval_range, interval_start, interval_end
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+ # file upload button
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data = []
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uploaded_file = st.file_uploader('Upload a csv')
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if uploaded_file is not None:
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@@ -234,7 +244,7 @@ def run(calc_function):
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data = uploaded_file.readlines()
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if uploaded_file.name[-4:-2]=='.s' and uploaded_file.name[-1]== 'p':
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data_format_snp = True
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-
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+
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validator_status = '...'
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ace_preview_markers = []
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@@ -268,26 +278,32 @@ def run(calc_function):
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# Ace editor to show choosen data columns and rows
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with col2.expander("File preview"):
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- # web development is fundamentally imposible without such hacks
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- # if we have so little 'official' functionality in libs and this lack of documentation
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+ # So little 'official' functionality in libs and lack of documentation
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+ # therefore beware: css hacks
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# yellow ~ ace_step
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# light yellow ~ ace_highlight-marker
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# green ~ ace_stack
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# red ~ ace_error-marker
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+ # no more good colors included in streamlit_ace for marking
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+
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# st.markdown('''<style>
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# .choosen_option_1
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# {
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# color: rgb(49, 51, 63);
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# }</style>''', unsafe_allow_html=True)
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- # markdown injection does not work, since ace is in a different .html accessible via iframe
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+ # markdown injection does not seems to work, since ace is in a different .html accessible via iframe
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+
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# markers format:
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#[{"startRow": 2,"startCol": 0,"endRow": 2,"endCol": 3,"className": "ace_error-marker","type": "text"}]
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+
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+ # add general marking for choosen data lines? better just mark the problematic lines?
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ace_preview_markers.append(
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{"startRow": input_start_line,"startCol": 0,
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"endRow": input_end_line+1,"endCol": 0,"className": "ace_highlight-marker","type": "text"})
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+
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text_value = "Frequency,Hz | Re(S11) | Im(S11)\n" + \
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''.join(data).strip()
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st_ace(value=text_value,
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@@ -309,10 +325,6 @@ def run(calc_function):
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if validator_status == 'data parsed':
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Q0, sigmaQ0, QL, sigmaQl, circle_params = calc_function(
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f_cut, r_cut, i_cut, select_coupling_losses)
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- # Q0 = round_up(Q0)
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- # sigmaQ0 = round_up(sigmaQ0)
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- # QL = round_up(QL)
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- # sigmaQl = round_up(sigmaQl)
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if select_coupling_losses:
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st.write("Lossy coupling")
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else:
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ricet8ur