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Add TODO
+ minor corrections

ricet8ur 2 年之前
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e95be751c0
共有 3 个文件被更改,包括 29 次插入4 次删除
  1. 24 0
      TODO.md
  2. 3 2
      source/backend/calc.py
  3. 2 2
      source/frontend/front.py

+ 24 - 0
TODO.md

@@ -0,0 +1,24 @@
+# Todos sorted by importance
+
+1. Do we need to calculate systematic errors?
+2. Should we apply corrections for coupling losses?
+3. Add validation of separator + convertor to std backend input
+4. Add impedance data format:
+    * additional field for omega
+    * convertion to reflection coeffitient (how?)
+
+5. Draw continuous Q circle on a Smith chart using coefficients a[0..2]
+6. Add axes labels to a Smith chart
+7. Pretty-print results and errors (7 digits after dot)
+8. Plot another chart: abs(S11) from f
+9. Make all computations extra precise:
+    * import sympy.mpmath as mp ..?
+    * from sympy.mpmath import *  ..?
+    * mp.dps = ~50
+    * make sure that result' ~ result
+    * increase mp.dps while 7 digits after dot are not stable
+10. Advanced file loading:
+    * Show file preview
+    * Options to skip first and last lines
+11. Add direct support for output files from different vna models
+12. Make charts more interactive

+ 3 - 2
source/backend/calc.py

@@ -19,7 +19,7 @@ def open_file(path):
 
 def prepare_data(freq, re, im, fl=[]):
     """the function takes raw data and gives vectors of eq (8)"""
-    # finding fl from the point with smaller magnitude if argument not provided
+    # finding fl from the point with smallest magnitude if argument not provided
     if type(fl) is list and len(fl)==0:
         s = abs(np.array(re) + np.array(im)*1j)
         # frequency of loaded resonance
@@ -113,7 +113,8 @@ def fl_fitting(freq, re, im):
     data, fl = prepare_data(freq, re, im)
     a, c, d = solution(data)
 
-    # 1.189 of Qfactor Matlab
+    # Repeated curve fitting
+    # 1.189 of Qfactor Matlab 
     fl2 = 0
     g_d=0
     g_c=0

+ 2 - 2
source/frontend/front.py

@@ -80,8 +80,8 @@ def unpack_data(data):
     for x in data:
         a, b, c = (float(y) for y in x.split())
         f.append(a)  # frequency
-        r.append(b)  # Re of something
-        i.append(c)  # Im of something
+        r.append(b)  # Re of S11
+        i.append(c)  # Im of S11
     return f, r, i, 'very nice'
 
 validator_status = 'nice'