usage: heatmap.py [-h] [-w WINDOW] [-l LOAD_FILTER] [-r RPM_FILTER] [-m MIN_SAMPLES] [-f] [-u] [-v] [filename ...]

Create trim heatmap for KFKHFM based on fr/frm datalog

positional arguments:
  filename              csv files(s) to parse (log.csv)

options:
  -h, --help            show this help message and exit
  -w WINDOW, --window WINDOW
                        number of sequential rows to detect constant rpm/load (5)
  -l LOAD_FILTER, --load-filter LOAD_FILTER
                        change in load which is still "constant" load (10)
  -r RPM_FILTER, --rpm-filter RPM_FILTER
                        change in RPM which is still "constant" RPM (100)
  -m MIN_SAMPLES, --min-samples MIN_SAMPLES
                        minimum number of samples required to generate a cell (10)
  -f, --use-fr          use "fr" instead of "frm" (the default)
  -u, --use-unweighted-mean
                        use unweighted mean instead of weighted average (the default)
  -v, --verbose

diff -ur trim-heatmap-0.0.5/src/heatmap.py trim-heatmap-0.0.5-testing/src/heatmap.py
--- trim-heatmap-0.0.5/src/heatmap.py	2024-11-06 02:39:02.000000000 -0700
+++ trim-heatmap-0.0.5-testing/src/heatmap.py	2025-04-29 23:49:57.097326680 -0600
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 # internal modules
 import os
@@ -78,6 +78,7 @@
 		frdata[lk]={}
 		for rk,rv in rpms.items():
 			query = f"{rv[0]} <= @lc.rpm <= {rv[1]} & {lv[0]} <= @lc.load <= {lv[1]}"
+			print(query)
 			res = lc.query(query).copy()
 			if len(res.index) >= (args.min_samples, 1)[args.no_filter]: # ternary is (false, true)
 				# size of cell, center to corner

diff -ur trim-heatmap-0.0.5/src/heatmap.py trim-heatmap-fkkvs-mod/src/heatmap.py
--- trim-heatmap-0.0.5/src/heatmap.py	2024-11-06 02:39:02.000000000 -0700
+++ trim-heatmap-fkkvs-mod/src/heatmap.py	2025-06-29 21:06:28.834621188 -0600
@@ -44,23 +44,34 @@
 	except subprocess.CalledProcessError:
 		return True
 
+def calc_median(num1, num2):
+  median_value = (num1 + num2) / 2
+  return median_value
+
 def calc_ranges(ary, max):
-	buckets = {}
-	for i,v in enumerate(ary):
-		if i == 0:
-			# very start
-			buckets[v] = [0, (ary[i]+ary[i+1])/2-1]
-		elif i != len(ary)-1:
-			buckets[v] = [(ary[i-1]+ary[i])/2, (ary[i]+ary[i+1])/2-1]
-		else:
-			# very end
-			buckets[v] = [(ary[i-1]+ary[i])/2, max]
-	return buckets
+    buckets = {}
+    for i,v in enumerate(ary):
+        if i == 0:
+            # very start
+            #buckets[v] = [0, (ary[i]+ary[i+1])/2-1]
+            if i > 30:
+                buckets[v] = [0, calc_median(ary[i],ary[i+1])-1]
+            else:
+                buckets[v] = [0, calc_median(ary[i],ary[i+1])-0.001]
+        elif i != len(ary)-1:
+            if i > 30:
+                buckets[v] = [calc_median(ary[i-1],ary[i]), calc_median(ary[i],ary[i+1])-1]
+            else:
+                buckets[v] = [calc_median(ary[i-1],ary[i]), calc_median(ary[i],ary[i+1])-0.001]
+        else:
+            # very end
+            buckets[v] = [(ary[i-1]+ary[i])/2, max]
+    return buckets
 
 def continuous_heatmap(lc):
-	lc['load'] = lc['load'].round(0)
+	lc['iot'] = lc['iot'].round(0)
 	lc['rpm'] = (lc['rpm']/10).round(0) * 10
-	heatmap = lc.pivot_table(index='load', columns='rpm', values='fr')
+	heatmap = lc.pivot_table(index='iot', columns='rpm', values='fr')
 	return heatmap.dropna(how='all', axis=0).dropna(how='all', axis=1)
 
 def distance(x0, y0, x1, y1):
@@ -68,23 +79,23 @@
 
 def quantized_heatmap(lc, args):
 	# create bucket ranges
-	rpms = calc_ranges([720, 1000, 1240, 1520, 2000, 2520, 3000, 3520, 4000, 4520, 5000, 5520, 6000, 6520], 10000)
-	loads = calc_ranges([9.75, 20.25, 30, 39.75, 50.25, 60, 69.75, 80.25, 90, 99.75, 110.25, 120, 140.25, 159.75], 300)
-	#loads = calc_ranges([9.75, 20.25, 30, 39.75, 50.25, 60, 69.75, 80.25, 90, 99.75, 110.25, 140.25, 150, 168])
+    # 1.8T
+	rpms = calc_ranges([600, 800, 1000, 1240, 1520, 1720, 2000, 2520, 3000, 3520, 4000, 4520, 5000, 5520, 6000, 6520], 10000)
+	iots = calc_ranges([0.997, 1.299, 1.6, 2, 2.499, 3.003, 4, 5.003, 6.001, 7.004, 8.001, 9.004, 10.001, 13.004, 16.002, 19.005], 30)
 
 	# sort lambda control (fr/frm) into buckets
 	frdata={}
-	for lk,lv in loads.items():
+	for lk,lv in iots.items():
 		frdata[lk]={}
 		for rk,rv in rpms.items():
-			query = f"{rv[0]} <= @lc.rpm <= {rv[1]} & {lv[0]} <= @lc.load <= {lv[1]}"
+			query = f"{rv[0]} <= @lc.rpm <= {rv[1]} & {lv[0]} <= @lc.iot <= {lv[1]}"
 			res = lc.query(query).copy()
 			if len(res.index) >= (args.min_samples, 1)[args.no_filter]: # ternary is (false, true)
 				# size of cell, center to corner
 				cellradius = distance(lv[0], rv[0], lv[1], rv[1])/2
 				# find distance to cell center
 				# FIXME: this is not right for cells at edges of map, but we have to weight data off the map somehow
-				res['distance'] = res.apply(lambda row: distance(row.load, row.rpm, lk, rk), axis=1)
+				res['distance'] = res.apply(lambda row: distance(row.iot, row.rpm, lk, rk), axis=1)
 				# Weight is proportional to closeness to center: distance = 0 has highest weight
 				# Don't let weights get negative due to weird radius calcs
 				res['weight'] = np.maximum((cellradius-res.distance)/cellradius, 0.1)
@@ -109,11 +120,10 @@
 def main():
 	parser = argparse.ArgumentParser(description='Create trim heatmap for KFKHFM based on fr/frm datalog')
 	parser.add_argument('filename', default=['log.csv'], nargs='*', help='csv files(s) to parse (log.csv)')
-	parser.add_argument('-w', '--window', type=int, default=10, help='number of sequential rows to detect constant rpm/load (10)')
+	parser.add_argument('-w', '--window', type=int, default=10, help='number of sequential rows to detect constant rpm/iot (10)')
 
-	parser.add_argument('-l', '--load-filter', type=float, default=10, help='change in load which is still "constant" load (10)')
+	parser.add_argument('-i', '--iot-filter', type=float, default=0.001, help='change in iot which is still "constant" iot (0.001)')
 	parser.add_argument('-r', '--rpm-filter', type=int, default=100, help='change in RPM which is still "constant" RPM (100)')
-	parser.add_argument('-m', '--maf-filter', type=float, default=10, help='change in MAF which is still "constant" MAF (10)')
 
 	parser.add_argument('-n', '--no-filter', action='store_true', help='disable filter (default is enabled)')
 
@@ -171,13 +181,13 @@
 	df.rename(str.strip, axis='columns', inplace=True)
 
 	# pick rl or frm_w
-	whichrl = ('rl', 'rl_w')['rl_w' in df]
+	whichrl = ('FIXME', 'ti_b1')['ti_b1' in df]
 	# pick nmot or nmot_w
 	whichnmot = ('nmot', 'nmot_w')['nmot_w' in df]
 	# pick frm or fr
 	whichfr = ('frm', 'fr')[args.use_fr or 'frm_w' not in df]
 
-	rows = [whichnmot, whichrl, whichfr + '_w', 'mshfm_w']
+	rows = [whichnmot, whichrl, whichfr + '_w']
 
 	if whichfr + '2_w' in df:
 		rows.append(whichfr + '2_w')
@@ -185,11 +195,10 @@
 	if args.verbose:
 		print(f"using {rows} from log")
 
-	# grab only the things we need, rename rl/nmot/mshfm to load/rpm/maf
+	# grab only the things we need, rename rl/nmot to iot/rpm
 	lc = df[rows]. \
-		rename(columns={whichrl:'load'}). \
-		rename(columns={whichnmot:'rpm'}). \
-		rename(columns={'mshfm_w':'maf'})
+		rename(columns={whichrl:'iot'}). \
+		rename(columns={whichnmot:'rpm'})
 
 	# flatten index
 	lc.columns = lc.columns.get_level_values(0)
@@ -204,15 +213,12 @@
 
 	# set up filter source data
 	lc['rpm_delta'] = lc.rpm.rolling(window=args.window).apply(lambda x: x.max() - x.min())
-	lc['load_delta'] = lc.load.rolling(window=args.window).apply(lambda x: x.max() - x.min())
-	lc['maf_delta'] = lc.maf.rolling(window=args.window).apply(lambda x: x.max() - x.min())
+	lc['iot_delta'] = lc.iot.rolling(window=args.window).apply(lambda x: x.max() - x.min())
 
 	if not args.no_filter:
 		# tag rows we want to use based on source data
 		lc['use'] = \
-			lc.rpm_delta.notnull()  & (lc.rpm_delta  <= args.rpm_filter)  & \
-			lc.load_delta.notnull() & (lc.load_delta <= args.load_filter) & \
-			lc.maf_delta.notnull()  & (lc.maf_delta  <= args.maf_filter)
+			lc.rpm_delta.notnull()  & (lc.rpm_delta  <= args.rpm_filter)
 
 		#print(lc[(abs(lc.fr)>5) & (lc.rpm_delta > 0)].to_string())
 
@@ -245,7 +251,7 @@
 	sbs.heatmap(heatmap, annot=(not args.continuous), center=0, cmap='PiYG', cbar_kws={'label': '% trim'}).invert_yaxis()
 
 	plt.xlabel('RPM')
-	plt.ylabel('Load')
+	plt.ylabel('IOT')
 
 	signal.signal(signal.SIGINT, signal_handler)
 	plt.show()