Merge pull request #21 from ricet8ur/docked

Docked

.gitignore

@@ -1,2 +1,3 @@
 **/__pycache__
-.idea/
+.idea/
+**/.ipynb_checkpoints

Dockerfile

@@ -5,6 +5,6 @@ RUN pip install -r requirements.txt
 EXPOSE 8501
 COPY . /app
 ENTRYPOINT ["streamlit", "run"]
-CMD ["./source/main.py"]
+CMD ["./source/main.py", "--browser.serverAddress=0.0.0.0", "--browser.gatherUsageStats=false", "--server.maxUploadSize=20"]
 # docker build -t calc-q-factor:latest .
-# docker run -p 8501:8501 calc-q-factor:latest
+# docker run -p 8501:8501 calc-q-factor:latest

README.md

@@ -1,15 +1,44 @@
-#Вычисление добротности и ее случайной погрешности с помощью автоматического векторного сетевого анализатора
+# Вычисление добротности и ее случайной погрешности с помощью автоматического векторного сетевого анализатора
 
-Данная программа принимает файл с измерениями сетевого анализатора в формате csv с возможностью выбора разделителя,
-среди которых мы указали " ", "," и ";". Из данных, загруженных пользователем, программа вычисляет добротность
-нагруженного (Q_l) и ненагруженного (Q_0) резонаторов. К самому значению прилагаются оцененная случайная погрешность,
-и относительная случайная погрешность. Кроме того программа строит диаграмму Смита и график зависимости модуля
-коэффициента отражения от частоты для наглядности. 
+## Описание
 
-Диаграмма Смита представляет собой математическое преобразование двумерной декартовой комплексной плоскости. 
-Комплексные числа с положительными вещественными частями отображаются внутри круга. Те, у кого отрицательные 
-вещественные части, отображаются за пределами круга. Если мы имеем дело только с импедансами с неотрицательными 
-резистивными компонентами, наш интерес сосредоточен на области внутри круга.
+Программа помогает вычислять добротность и её случайную погрешность в электрической цепи по измерениям векторного анализатора.
+
+Программа принимает на вход файл с измерениями сетевого анализатора в формате .snp или csv.
+По этому файлу вычисляется добротность нагруженного (Q_l) и ненагруженного (Q_0) резонатора.
+Для этих величин также вычисляется случайная погрешность.
+Кроме того строится диаграмма Смита и график зависимости модуля коэффициента отражения от частоты.
+
+Диаграмма Смита представляет собой математическое преобразование двумерной декартовой комплексной плоскости.
+Она помогает удобно представить поведение резонатора при изменении частоты сигнала.
+
+Интерфейс программы:
+
+![Program interface1](./resource/repository/readme_img1.png?raw=true)
+![Program interface2](./resource/repository/readme_img2.png?raw=true)
 
 Запуск:
 streamlit run source/main.py
+
+## Usage
+
+App calculates the quality factor and its random error in the electric circuit
+for the set of [vector analyzer](https://en.wikipedia.org/wiki/Network_analyzer_(electrical)) measuremets.
+
+How to start:
+streamlit run source/main.py
+
+## Input data
+
+The measurements made by a vector network analyzer (set of frequencies and corresponding network parameters).
+Main supported file format is .snp, but similar formats are accepted too. Noise data is not supported.
+
+* Supported network parameters: S, Z
+* Supported parameters representations:
+real and imaginary; magnitude and angle; db and angle
+
+## Result
+
+* Loaded and unloaded quality factor (q-factor) with random errors
+* plot amplitude vs frequency
+* [Smith chart](https://en.wikipedia.org/wiki/Smith_chart)

TODO.md

@@ -1,35 +1,34 @@
 # Todos sorted by importance
 
-1. Add a description for our program.
-    * What does it do?
+1. [x] Add a description for frontend.
+    * What is calculated? The format of the result
     * What is Q circle?
     * What data formats are supported?
-2. [x] Add validation of separator + convertor to std backend input
-3. Should we apply corrections for coupling losses? - yes, please add this option.
-4. [x] Draw continuous Q circle on a Smith chart using coefficients a[0..2]
-5. [x] Add axes labels to a Smith chart
-6. Pretty-print results and errors (7 digits after dot). Try latex output.
-7. Advanced output options (only frontend):
+2. [x] Demo
+3. Support .snp
+    * .s3p and more
+4. [x] Add button to copy choosen fragment to compare with other programs.
+5. [x] Advanced output options (only frontend):
     * Option to choose output values precision
-8. Add approximation for second chart (abs(S11) from f)
-9. Add impedance input option (Z instread of S11)
-    * additional field for omega
-    * convertion to reflection coeffitient (thats how: <https://radioprog.ru/post/195>)
-10. Codestyle fix:
+6. Add approximation for second chart (abs(S) from f)
+7. [x] Add impedance input option (Z)
+    * additional field for omega | from .snp
+    * convertion to reflection coeffitient (explanation: <https://radioprog.ru/post/195>)
+8. Codestyle fix:
     * Format all code files
     * Make a good code structure... If you can.
-11. Make all computations extra precise:
+9. Make all computations extra precise:
     * import sympy.mpmath as mp ..?
     * from sympy.mpmath import *  ..?
     * mp.dps = ~50 or something else.
     * make sure that result' ~ result
     * increase mp.dps while 7 digits after dot are not stable
-
-12. Advanced file loading:
+10. [x] Advanced file loading:
     * Show file preview
     * Options to skip first and last lines
-
-13. Do we need to calculate systematic errors? - yes, if its not too hard.
-14. Add direct support for output files from different vna models
-15. Make charts more interactive
-16. Make an option to pass the whole program to .html site as a iframe
+11. Advanced file preview: highlight choosen data fragments
+12. [x] Make charts more interactive
+13. Make an option to pass the whole program to .html site via iframe? - It works, but where to host?
+14. Add support lines for smith chart?
+<!-- Add direct support for output files from different vna models? Supported formats: .snp, .csv or similar -->
+<!-- Do we need to calculate systematic errors? - yes, if its not too hard. After some considerations... Rather not -->

requirements.txt

@@ -2,4 +2,7 @@
 streamlit
 matplotlib
 numpy
-# sympy?
+
+streamlit_echarts
+streamlit-ace
+sigfig

resource/data/10_check_4port.s4p

@@ -0,0 +1,13 @@
+# GHZ S MA R 50
+5.00000 0.60 161.24 0.40 -42.20 0.42 -66.58 0.53 -79.34 !row 1
+0.40 -42.20 0.60 161.20 0.53 -79.34 0.42 -66.58 !row 2
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+0.53 -79.34 0.42 -66.58 0.40 -42.20 0.60 161.24 !row 4
+6.00000 0.57 150.37 0.40 -44.34 0.41 -81.24 0.57 -95.77 !row 1
+0.40 -44.34 0.57 150.37 0.57 -95.77 0.41 -81.24 !row 2
+0.41 -81.24 0.57 -95.77 0.57 150.37 0.40 -44.34 !row 3
+0.57 -95.77 0.41 -81.24 0.40 -44.34 0.57 150.37 !row 4
+7.00000 0.50 136.69 0.45 -46.41 0.37 -99.09 0.62 -114.19 !row 1
+0.45 -46.41 0.50 136.69 0.62 -114.19 0.37 -99.09 !row 2
+0.37 -99.09 0.62 -114.19 0.50 136.69 0.45 -46.41 !row 3
+0.62 -114.19 0.37 -99.09 0.45 -46.41 0.50 136.69 !row 4

resource/data/6_s11.csv

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resource/data/7_snp-data-s11-1.s2p

@@ -0,0 +1,32010 @@
+!Agilent Technologies,E8362C,MY49020356,A.09.42.23
+!Agilent E8362C: A.09.42.23
+!Date: Saturday, September 11, 2021 04:16:11
+!Correction: S11(Full 2 Port(1,2)) 
+!S21(Full 2 Port(1,2)) 
+!S12(Full 2 Port(1,2)) 
+!S22(Full 2 Port(1,2)) 
+!S2P File: Measurements: S11, S21, S12, S22:
+# Hz S  MA   R 50
+10950000000 0.74965447 -166.38557 1.8760875e-005 58.91571 2.6291598e-005 -31.245991 1.0521692 -36.781563
+10950034375 0.75087458 -166.41307 1.8656898e-005 90.983665 1.5506361e-006 -142.72493 1.0513554 -36.748863
+10950068750 0.75000423 -166.41154 3.6543348e-005 -121.00239 2.1925991e-005 -60.887646 1.0512079 -36.74049
+10950103125 0.75002855 -166.42642 1.896779e-005 -172.31935 1.2844287e-005 -117.76297 1.0523971 -36.752674
+10950137500 0.75001824 -166.39752 2.9607079e-005 -157.73926 7.0293058e-006 133.74098 1.0529058 -36.665581
+10950171875 0.74974817 -166.52223 1.7779228e-005 -64.574913 1.6522179e-005 -23.027924 1.0530852 -36.748466
+10950206250 0.75010014 -166.52695 2.7386341e-005 111.49077 2.7993243e-005 -71.852234 1.0520016 -36.66925
+10950240625 0.75048745 -166.52328 0 90 8.3089544e-006 -179.888 1.0518347 -36.745335
+10950275000 0.75128311 -166.53815 3.2294523e-005 -18.383905 1.0503959e-005 76.651169 1.0526415 -36.727257
+10950309375 0.75067914 -166.52087 3.8837112e-005 -144.55904 4.0813815e-005 -34.49036 1.0517323 -36.66095
+10950343750 0.75073671 -166.3519 4.522482e-005 39.003826 2.7483782e-005 -5.9774041 1.0508236 -36.658405
+10950378125 0.75025481 -166.45699 3.1533891e-005 -69.16494 2.2231985e-005 91.413261 1.0515118 -36.710075
+10950412500 0.7487759 -166.46121 4.3516502e-005 41.106915 3.5443125e-005 39.823479 1.0528659 -36.686047
+10950446875 0.74898767 -166.42677 3.4372621e-005 -61.298401 1.3640401e-005 97.093712 1.0531536 -36.692776
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+10950825000 0.74951839 -166.49754 2.9867691e-005 83.604027 1.4185235e-005 -57.456535 1.0527 -36.753979
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+12049690625 0.7598598 78.528419 0.00018432856 -174.65974 0.00022162171 -166.83594 0.97256351 -39.258385
+12049725000 0.76056749 78.519165 0.00017062647 175.76266 0.00020304479 -179.07799 0.97244829 -39.229088
+12049759375 0.76006281 78.549591 0.00021323469 -174.69133 0.0002277012 -162.9496 0.97326642 -39.211243
+12049793750 0.76015943 78.498497 0.000210995 -179.94861 0.00016901226 177.98315 0.97317952 -39.217396
+12049828125 0.75990784 78.523552 0.0001874836 -178.44019 0.00020966902 -162.81371 0.97302085 -39.213177
+12049862500 0.76051527 78.488541 0.00016336855 -178.71722 0.00025320781 -174.65248 0.97478259 -39.160511
+12049896875 0.76020879 78.551018 0.00020224204 -177.2243 0.00020345581 -175.38354 0.97352844 -39.212719
+12049931250 0.76034641 78.478325 0.00015137669 179.27339 0.00019905437 -165.77748 0.9745484 -39.253559
+12049965625 0.76004183 78.457001 0.00022811149 -173.36923 0.0002296977 -166.55972 0.97527581 -39.279434
+12050000000 0.76021802 78.474815 0.00020628898 -178.8222 0.00023749837 -178.91029 0.97507316 -39.155201

resource/data/8_default_demo.s1p

@@ -0,0 +1,26 @@
+# Hz S MA R 50
+11414028125 0.4301177 93.203987
+11414715625 0.40031707 92.644608
+11415403125 0.37010744 92.47802
+11416090625 0.33831283 92.906929
+11416778125 0.3069371 94.03318
+11417465625 0.27716747 96.86544
+11418153125 0.25166345 100.84904
+11418840625 0.22895016 106.31691
+11419528125 0.21294561 113.55509
+11420215625 0.20525002 121.82249
+11420903125 0.20573099 130.26067
+11421590625 0.21451311 137.88812
+11422278125 0.22832666 144.17075
+11422965625 0.24686199 149.12198
+11423653125 0.26650944 152.45326
+11424340625 0.28751916 154.89363
+11425028125 0.30911326 156.45076
+11425715625 0.33000126 157.51169
+11426403125 0.35029748 158.05258
+11427090625 0.36924407 158.25481
+11427778125 0.38721535 158.16319
+11428465625 0.40406558 158.07317
+11429153125 0.42126176 157.74857
+11429840625 0.43520793 157.26349
+11430528125 0.44878739 156.78369

resource/data/9_Z_test.s1p

@@ -0,0 +1,7 @@
+# MHz Z MA R 75
+!freq magZ11 angZ11
+100 0.99 -4
+200 0.80 -22
+300 0.707 -45
+400 0.40 -62
+500 0.01 -89

source/backend/calc.py

@@ -52,7 +52,7 @@ def solution(data):
         c3 = np.vdot(data[i], data[4] * data[2])
         c.append([c1, c2, c3])
         b.append(np.vdot(data[i], data[4] * data[3]))
-    c = np.array(c)
+    # c = np.array(c)
     a = np.linalg.solve(c, b)
     d = np.linalg.inv(c)  # inverse of matrix c
     return a, c, d
@@ -95,7 +95,7 @@ def recalculating(data, a, c, d, n, printing=False):
         sigmaQ0, sigmaQl = random_deviation(a, sigma2A, diam, k, Ql)
         if printing:
             print(f"Q = {Q} +- {sigmaQ0}, if i == {i}")
-        return a, c, d, Ql, diam, k, Q, sigma2A, sigmaQ0, sigmaQl, data
+    return a, c, d, Ql, diam, k, Q, sigma2A, sigmaQ0, sigmaQl, data
 
 
 def random_deviation(a, sigma2A, diam, k, Ql):
@@ -114,7 +114,7 @@ def apply(filename):
     a, c, d, Ql, diam, k, Q, sigma2A, sigmaQ0, sigmaQl, data = recalculating(data, a, c, d, 10, printing=True)
 
 
-def fl_fitting(freq, re, im):
+def fl_fitting(freq, re, im, correction):
     """providing an option to find actual fl"""
 
     data, fl = prepare_data(freq, re, im)
@@ -137,17 +137,17 @@ def fl_fitting(freq, re, im):
 
     # taking into account coupling losses on page 69 of Qfactor Matlab
     # to get results similar to example program 
-    # if False:
-    #     phi1=np.arctan(np.double(g_d.imag/g_d.real)) # 1.239
-    #     phi2=np.arctan(np.double((g_c.imag-g_d.imag)/(g_c.real-g_d.real)))
-    #     phi=-phi1+phi2
-    #     d_s=(1-np.abs(g_d)**2)/(1-np.abs(g_d)*np.cos(phi))
-    #     diam = abs(a[1] - a[0] / a[2])
-    #     qk=1/(d_s/diam-1)
-    #
-    #     sigma2A = recalculation_of_data(data, a, c, d, error=True)
-    #     sigmaQ0 = random_deviation(a, sigma2A, diam, k, Ql)
-    #     Q = Ql * (1 + qk)  # Q-factor = result
-    #     print(f"Q0 = {Q} +- {sigmaQ0}")
+    if correction:
+        phi1=np.arctan(np.double(g_d.imag/g_d.real)) # 1.239
+        phi2=np.arctan(np.double((g_c.imag-g_d.imag)/(g_c.real-g_d.real)))
+        phi=-phi1+phi2
+        d_s=(1-np.abs(g_d)**2)/(1-np.abs(g_d)*np.cos(phi))
+        diam = abs(a[1] - a[0] / a[2])
+        qk=1/(d_s/diam-1)
+    
+        sigma2A = recalculation_of_data(data, a, c, d, error=True)
+        sigmaQ0, sigmaQl = random_deviation(a, sigma2A, diam, k, Ql)
+        Q = Ql * (1 + qk)  # Q-factor = result
+        # print(f"Q0 = {Q} +- {sigmaQ0}")
 
     return Q, sigmaQ0, Ql, sigmaQl, a

source/frontend/front.py

@@ -2,87 +2,160 @@ 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
 
-XLIM = [-1.1, 1.1]
-YLIM = [-1.1, 1.1]
 
+# 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 round_up(x, n=7):
-    if x == 0:
-        return 0
-    deg = math.floor(math.log(abs(x), 10))
-    return (10 ** deg) * round(x / (10 ** deg), n - 1)
 
 
 def circle(ax, x, y, radius, color='#1946BA'):
     from matplotlib.patches import Ellipse
-    drawn_circle = Ellipse((x, y), radius * 2, radius * 2, clip_on=False,
+    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_data(r, i, g):
+def plot_smith(r, i, g, r_cut, i_cut, show_excluded):
     fig = plt.figure(figsize=(10, 10))
     ax = fig.add_subplot()
-    ax.set_xlim(XLIM)
-    ax.set_ylim(YLIM)
-    major_ticks = np.arange(-1.0, 1.1, 0.25)
+
+    # 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(major_ticks)
     ax.set_xticks(minor_ticks, minor=True)
-    ax.set_yticks(major_ticks)
+    # 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(r'$Re(\Gamma)$', color='gray', fontsize=16, fontname="Cambria")
+    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")
-    # circle approximation
+
+    # 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')
-    #
-    # unit circle
-    circle(ax, 0, 0, 1)
-    #
-    # data
-    ax.plot(r, i, '+', ms=10, mew=2, color='#1946BA')
-    #
 
+    XLIM = [-1.1, 1.1]
+    YLIM = [-1.1, 1.1]
+    ax.set_xlim(XLIM)
+    ax.set_ylim(YLIM)
     st.pyplot(fig)
 
 
-def plot_ref_from_f(r, i, f):
+# plot (abs(S))(f) chart with pyplot
+def plot_abs_vs_f(f, r, i):
     fig = plt.figure(figsize=(10, 10))
-    abs_S = list(math.sqrt(r[n] ** 2 + i[n] ** 2) 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]
+    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('$|\Gamma|$', color='gray', fontsize=16, fontname="Cambria")
-    plt.title('Modulus of reflection coefficient from frequency', fontsize=24, fontname="Cambria")
-    ax.plot(f, abs_S, '+', ms=10, mew=2, color='#1946BA')
-    st.pyplot(fig)
-
+    plt.ylabel('$|S|$', color='gray', fontsize=16, fontname="Cambria")
+    plt.title('Abs(S) vs frequency',
+              fontsize=24, fontname="Cambria")
 
-def run(calc_function):
-    data = []
-    uploaded_file = st.file_uploader('Upload a csv')
-    if uploaded_file is not None:
-        data = uploaded_file.readlines()
+    ax.plot(f, abs_S, '+', ms=8, mew=2, color='#1946BA')
 
-    col1, col2 = st.columns(2)
+    # 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)
 
-    select_data_format = col1.selectbox('Choose data format from a list',
-                                        ['Frequency, Re(S11), Im(S11)', 'Frequency, Re(Zin), Im(Zin)'])
 
-    select_separator = col2.selectbox('Choose separator', ['" "', '","', '";"'])
-    select_coupling_losses = st.checkbox('Apply corrections for coupling losses (lossy coupling)')
+def run(calc_function):
 
     def is_float(element) -> bool:
         try:
@@ -94,54 +167,397 @@ def run(calc_function):
         except ValueError:
             return False
 
-    def unpack_data(data):
-        nonlocal select_separator
-        nonlocal select_data_format
-        f, r, i = [], [], []
-        if select_data_format == 'Frequency, Re(S11), Im(S11)':
+    # 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)):
-                # print(select_separator)
-                select_separator = select_separator.replace('\"', '')
-                if type(data[x])==bytes:
-                    data[x]=data[x].decode()
-                if select_separator == " ":
-                    tru = data[x].split()
-                else:
-                    data[x] = data[x].replace(select_separator, ' ')
-                    tru = data[x].split()
-
-                if len(tru) != 3:
-                    return f, r, i, 'Bad line in your file. №:' + str(x)
-                a, b, c = (y for y in tru)
-                if not ((is_float(a)) or (is_float(b)) or (is_float(c))):
-                    return f, r, i, 'Bad data. Your data isn\'t numerical type. Number of bad line:' + str(x)
-                f.append(float(a))  # frequency
-                r.append(float(b))  # Re of S11
-                i.append(float(c))  # Im of S11
+                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:
-            return f, r, i, 'Bad data format'
-        return f, r, i, 'very nice'
+            # 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
 
-    validator_status = 'nice'
-    # calculate
+                    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:
-        f, r, i, validator_status = unpack_data(data)
-
-        if validator_status == 'very nice':
-            Q0, sigmaQ0, QL, sigmaQl, circle_params = calc_function(f, r, i)
-            Q0 = round_up(Q0)
-            sigmaQ0 = round_up(sigmaQ0)
-            QL = round_up(QL)
-            sigmaQl = round_up(sigmaQl)
-            st.write("Cable attenuation")
-            st.latex(r'Q_0 =' + f'{Q0} \pm {sigmaQ0},  ' + r'\;\;\varepsilon_{Q_0} =' + f'{round_up(sigmaQ0 / Q0)}')
-            st.latex(r'Q_L =' + f'{QL} \pm {sigmaQl},  ' + r'\;\;\varepsilon_{Q_L} =' + f'{round_up(sigmaQl / QL)}')
+
+        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 len(data) > 0:
-        f, r, i, validator_status = unpack_data(data)
-        if validator_status == 'very nice':
-            plot_data(r, i, circle_params)
-            plot_ref_from_f(r, i, f)
+    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))

source/frontend/info.md

@@ -0,0 +1,44 @@
+##### Usage:
+
+App calculates the quality factor and its random error in the electric circuit
+for the set of [vector analyzer](https://en.wikipedia.org/wiki/Network_analyzer_(electrical)) measuremets.
+
+##### Input data
+
+The measurements made by a vector network analyzer (set of frequencies and corresponding network parameters).
+Main supported file format is .snp, but similar formats are accepted too. Noise data is not supported.
+
+* Supported network parameters: S, Z
+* Supported parameters representations:
+real and imaginary; magnitude and angle; db and angle
+
+##### Result
+
+* Loaded and unloaded quality factor (q-factor) with random errors
+* plot amplitude vs frequency
+* [Smith chart](https://en.wikipedia.org/wiki/Smith_chart)
+
+##### Calculation method
+
+Calculation of Q-factor and random errors is based on the algorithm, described in [1-2].
+Correction for coupling loss is implemented according to [3].
+
+##### References
+
+[1]: Random and Systematic Uncertainties of Reflection-Type Q-Factor Measurement with Network Analyzer (2003).
+    DOI: 10.1109/TMTT.2002.807831
+
+[2]: Data Processing For Q Factor Measurement (1994).
+    DOI: 10.1109/ARFTG.1994.327064
+
+[3]: Q Factor Measurements Using MATLAB (2011).
+    ISBN: 978-1-60807-161-6
+
+##### External links
+
+[Github](https://github.com/ricet8ur/calc-factor-of-vna)
+
+##### Alternatives
+
+* http://scikit-rf.org/
+* https://people.engineering.olemiss.edu/darko-kajfez/software/