Refactor temp table optimization for any algorithm

This should be squashed but is separated here for comparison.

configtool/thermistortablefile.py

@@ -100,46 +100,9 @@ def BetaTable(ofp, params, names, settings, finalTable):
   hiadc = thrm.setting(0)[0]
   N = int(settings.numTemps)
 
-  # This is a variation of the Ramer-Douglas-Peucker algorithm, see
-  # https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
-  #
-  # It works like this:
-  #
-  #   - Calculate all (1024) ideal values.
-  #   - Keep only the ones in the interesting range (0..500C).
-  #   - Insert the two extremes into our sample list.
-  #   - Calculate the linear approximation of the remaining values.
-  #   - Insert the correct value for the "most-wrong" estimation into our
-  #     sample list.
-  #   - Repeat until "N" values are chosen as requested.
+  samples = optimizeTempTable(thrm, N, hiadc)
 
-  # Calculate actual temps for all ADC values.
-  actual = dict([(x, thrm.temp(1.0 * x)) for x in range(1, int(hiadc + 1))])
-
-  # Limit ADC range to 0C to 500C.
-  MIN_TEMP = 0
-  MAX_TEMP = 500
-  actual = dict([(adc, actual[adc]) for adc in actual
-                if actual[adc] <= MAX_TEMP and actual[adc] >= MIN_TEMP])
-
-  # Build a lookup table starting with the extremes.
-  A = min(actual)
-  B = max(actual)
-  lookup = dict([(x, actual[x]) for x in [A,B]])
-  error = dict({})
-  while len(lookup) < N:
-    error.update(dict([(x, abs(actual[x] - LinearTableEstimate(lookup, x)))
-                       for x in range(A + 1, B)]))
-
-    # Correct the most-wrong lookup value.
-    next = max(error, key = error.get)
-    lookup[next] = actual[next]
-
-    # Prepare to update the error range.
-    A = before(lookup, next)
-    B = after(lookup, next)
-
-  for i in sorted(lookup.keys()):
+  for i in samples:
     t = int(thrm.temp(i))
     if t is None:
       ofp.output("// ERROR CALCULATING THERMISTOR VALUES AT ADC %d" % i)
@@ -150,7 +113,7 @@ def BetaTable(ofp, params, names, settings, finalTable):
 
     vTherm = i * vadc / 1024
     ptherm = vTherm * vTherm / r
-    if i == max(lookup):
+    if i == max(samples):
       c = " "
     else:
       c = ","
@@ -179,9 +142,10 @@ def SteinhartHartTable(ofp, params, names, settings, finalTable):
 
   hiadc = thrm.setting(0)[0]
   N = int(settings.numTemps)
-  step = int(hiadc / (N - 1))
 
-  for i in range(1, int(hiadc), step):
+  samples = optimizeTempTable(thrm, N, hiadc)
+
+  for i in samples:
     t = int(thrm.temp(i))
     if t is None:
       ofp.output("// ERROR CALCULATING THERMISTOR VALUES AT ADC %d" % i)
@@ -189,7 +153,7 @@ def SteinhartHartTable(ofp, params, names, settings, finalTable):
 
     r = int(thrm.adcInv(i))
 
-    if i + step >= int(hiadc):
+    if i == max(samples):
       c = " "
     else:
       c = ","
@@ -201,6 +165,49 @@ def SteinhartHartTable(ofp, params, names, settings, finalTable):
   else:
     ofp.output("  },")
 
+def optimizeTempTable(thrm, length, hiadc):
+
+  # This is a variation of the Ramer-Douglas-Peucker algorithm, see
+  # https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
+  #
+  # It works like this:
+  #
+  #   - Calculate all (1024) ideal values.
+  #   - Keep only the ones in the interesting range (0..500C).
+  #   - Insert the two extremes into our sample list.
+  #   - Calculate the linear approximation of the remaining values.
+  #   - Insert the correct value for the "most-wrong" estimation into our
+  #     sample list.
+  #   - Repeat until "N" values are chosen as requested.
+
+  # Calculate actual temps for all ADC values.
+  actual = dict([(x, thrm.temp(1.0 * x)) for x in range(1, int(hiadc + 1))])
+
+  # Limit ADC range to 0C to 500C.
+  MIN_TEMP = 0
+  MAX_TEMP = 500
+  actual = dict([(adc, actual[adc]) for adc in actual
+                 if actual[adc] <= MAX_TEMP and actual[adc] >= MIN_TEMP])
+
+  # Build a lookup table starting with the extremes.
+  A = min(actual)
+  B = max(actual)
+  lookup = dict([(x, actual[x]) for x in [A, B]])
+  error = dict({})
+  while len(lookup) < length:
+    error.update(dict([(x, abs(actual[x] - LinearTableEstimate(lookup, x)))
+                       for x in range(A + 1, B)]))
+
+    # Correct the most-wrong lookup value.
+    next = max(error, key = error.get)
+    lookup[next] = actual[next]
+
+    # Prepare to update the error range.
+    A = before(lookup, next)
+    B = after(lookup, next)
+
+  return sorted(lookup)
+
 def after(lookup, value):
   return min([x for x in lookup.keys() if x > value])