Add selection of arbitrary data interval with the echart;

Turn on coupling losses correction;
Improve result formatting

requirements.txt

@@ -2,4 +2,5 @@
 streamlit
 matplotlib
 numpy
-# sympy?
+# sympy?
+streamlit_echarts

resource/data/3_trace0.csv

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resource/data/5_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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+10950756250 0.75009632 -166.41826 7.0724918e-006 93.975212 2.2766484e-005 69.697235 1.0513283 -36.702213
+10950790625 0.75024229 -166.40442 1.8506422e-005 -87.693192 1.7477156e-005 12.113461 1.051747 -36.724255
+10950825000 0.74951839 -166.49754 2.9867691e-005 83.604027 1.4185235e-005 -57.456535 1.0527 -36.753979
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+10950893750 0.75049984 -166.45558 2.7733702e-006 105.44815 4.5608626e-006 -20.01466 1.0521021 -36.731655
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+12049003125 0.76077229 78.764122 0.00016499328 -160.57004 0.00024217364 -170.5141 0.97309482 -39.176693
+12049037500 0.76036245 78.68679 0.00020070201 -173.16104 0.00024069287 -159.72375 0.97286701 -39.2547
+12049071875 0.76140815 78.708031 0.00019028531 -165.80403 0.00023363146 -173.29089 0.97398436 -39.166988
+12049106250 0.76097494 78.719704 0.00016340484 -158.41948 0.00023411105 -159.46565 0.97263521 -39.176586
+12049140625 0.7606988 78.732094 0.00017525931 -162.19124 0.00020996227 -164.31989 0.97333831 -39.268833
+12049175000 0.76058972 78.719933 0.00019797852 -170.105 0.00029129299 -165.7433 0.97411579 -39.278629
+12049209375 0.75935912 78.732903 0.00017185831 -173.33031 0.00023143149 -154.75945 0.97355586 -39.282475
+12049243750 0.76015335 78.742455 0.00021771659 -165.02711 0.00024436973 -166.62642 0.97356844 -39.25502
+12049278125 0.76118332 78.708229 0.00019994048 -164.09314 0.00022806325 175.4032 0.97296512 -39.189739
+12049312500 0.76079547 78.745285 0.00018232752 -169.60953 0.00021044587 -171.70633 0.97291005 -39.185043
+12049346875 0.76052624 78.656197 0.00016545728 -167.81717 0.00020711514 -169.73123 0.97377968 -39.234699
+12049381250 0.75997543 78.660751 0.00019846206 -163.09872 0.00026156806 -171.72806 0.97445023 -39.201572
+12049415625 0.76108634 78.565369 0.00016501987 -173.66028 0.00020924531 -153.32542 0.97473806 -39.140007
+12049450000 0.76042223 78.5037 0.00021210498 -175.47809 0.00021588731 -177.1515 0.97485131 -39.150589
+12049484375 0.76019377 78.582352 0.00020323726 -175.98524 0.00022197852 -170.29625 0.97473162 -39.088112
+12049518750 0.7599076 78.576828 0.00020101182 -167.70354 0.00025780141 -169.08905 0.97483349 -39.145214
+12049553125 0.7603116 78.572792 0.00020674035 179.56355 0.00020358359 -177.08922 0.97380364 -39.117702
+12049587500 0.76014364 78.556183 0.0001922095 -168.77605 0.0002303901 -175.11649 0.97418588 -39.181965
+12049621875 0.76065415 78.501625 0.00019147819 -176.62943 0.00018673218 -172.15009 0.9738813 -39.172295
+12049656250 0.76004612 78.511726 0.00021611166 -173.13324 0.00023170967 172.00484 0.97464591 -39.284771
+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

source/backend/calc.py

@@ -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

@@ -24,8 +24,7 @@ def circle(ax, x, y, radius, color='#1946BA'):
 def plot_data(r, i, g):
     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)
     minor_ticks = np.arange(-1.1, 1.1, 0.05)
     ax.set_xticks(major_ticks)
@@ -49,13 +48,66 @@ def plot_data(r, i, g):
     # data
     ax.plot(r, i, '+', ms=10, mew=2, color='#1946BA')
     #
-
+    ax.set_xlim(XLIM)
+    ax.set_ylim(YLIM)
     st.pyplot(fig)
 
 
-def plot_ref_from_f(r, i, f):
+from streamlit_echarts import st_echarts, JsCode
+interval_range = (0,100)
+interval_start,interval_end=0,0
+def plot_interact_abs_from_f(f,r,i):
+    abs_S = list((r[n] ** 2 + i[n] ** 2)**0.5 for n in range(len(r)))
+    global interval_range,interval_start,interval_end
+    # 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,
+        },
+        "yAxis": {
+            "type": "value",
+            "name":"abs(S)",
+        },
+        "series": [{"data": abs_S, "type": "line", "name":"abs(S)"}],
+        "dataZoom": [{"type": "slider", "start": 0, "end": 100}],
+        "tooltip": {
+            "trigger":"axis",
+            "axisPointer": {
+                "type": 'cross',
+                # "label": {
+                    # "show":"true",
+                # "formatter": JsCode(
+            # "function(info){return info.value;};"
+        # ).js_code
+                # }
+            }
+        },
+        "toolbox":{
+            "feature": {
+                "dataView": { "show": "true", "readOnly": "true" },
+                "restore": { "show": "true" },
+            }
+        },
+    }
+    events = {
+        "dataZoom": "function(params) { return [params.start, params.end] }",
+    }
+
+    interval_range = st_echarts(
+        options=options, events=events, height="500px", key="render_basic_bar_events"
+    )
+    if interval_range is None:
+        interval_range = (0, 100)
+
+    n=len(f)
+    interval_start,interval_end=(int(n*interval_range[id]*0.01) for id in (0,1))
+
+def plot_ref_from_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)))
+    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()
@@ -66,11 +118,14 @@ def plot_ref_from_f(r, i, f):
     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)
 
 
 def run(calc_function):
+    global interval_range,interval_start,   interval_end
+
     data = []
     uploaded_file = st.file_uploader('Upload a csv')
     if uploaded_file is not None:
@@ -83,7 +138,7 @@ def run(calc_function):
 
     select_separator = col2.selectbox('Choose separator', ['" "', '","', '";"'])
     select_coupling_losses = st.checkbox('Apply corrections for coupling losses (lossy coupling)')
-
+    
     def is_float(element) -> bool:
         try:
             float(element)
@@ -103,7 +158,12 @@ def run(calc_function):
                 # print(select_separator)
                 select_separator = select_separator.replace('\"', '')
                 if type(data[x])==bytes:
-                    data[x]=data[x].decode()
+                    # print('f')
+                    try: 
+                        data[x]=data[x].decode('utf-8-sig', 'ignore')
+                    except:
+                        return f, r, i, 'Not an utf-8-sig line №: ' + str(x)
+
                 if select_separator == " ":
                     tru = data[x].split()
                 else:
@@ -111,37 +171,49 @@ def run(calc_function):
                     tru = data[x].split()
 
                 if len(tru) != 3:
-                    return f, r, i, 'Bad line in your file. №:' + str(x)
+                    return f, r, i, 'Can\'t parse line №: ' + 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)
+                    return f, r, i, 'Your data isn\'t numerical type. Error on line: ' + str(x)
                 f.append(float(a))  # frequency
                 r.append(float(b))  # Re of S11
                 i.append(float(c))  # Im of S11
         else:
-            return f, r, i, 'Bad data format'
-        return f, r, i, 'very nice'
+            return f, r, i, 'Wrong data format'
+        return f, r, i, 'data parsed'
 
-    validator_status = 'nice'
+    validator_status = '...'
     # calculate
     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)}')
+        plot_interact_abs_from_f(f, r, i)
+
+        f_cut=f[interval_start:interval_end]
+        r_cut=r[interval_start:interval_end]
+        i_cut=i[interval_start:interval_end]
+
+        if validator_status == 'data parsed':
+            Q0, sigmaQ0, QL, sigmaQl, circle_params = calc_function(f_cut, r_cut, i_cut, select_coupling_losses)
+            # Q0 = round_up(Q0)
+            # sigmaQ0 = round_up(sigmaQ0)
+            # QL = round_up(QL)
+            # sigmaQl = round_up(sigmaQl)
+            if select_coupling_losses:
+                st.write("Lossy coupling")
+            else:
+                st.write("Cable attenuation")
+
+            out_precision='0.7f'
+            st.latex(r'Q_0 =' + f'{format(Q0, out_precision)} \pm {format(sigmaQ0, out_precision)},  ' + 
+                r'\;\;\varepsilon_{Q_0} =' + f'{format(sigmaQ0 / Q0, out_precision)}')
+            st.latex(r'Q_L =' + f'{format(QL, out_precision)} \pm {format(sigmaQl, out_precision)},  ' + 
+                r'\;\;\varepsilon_{Q_L} =' + f'{format(sigmaQl / QL, out_precision)}')
 
     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':
+            plot_ref_from_f(f_cut, r_cut, i_cut)
+            plot_data(r_cut, i_cut, circle_params)