From 9cba6dfac186b2cb4d558084fcd7bc5688002745 Mon Sep 17 00:00:00 2001
From: VulcanixFR <vulcanix.gamingfr@gmail.com>
Date: Wed, 7 Aug 2024 15:06:30 +0200
Subject: [PATCH] Tweak database
---
tools/database.py | 2 +-
tools/predictive_indicators.py | 34 +++++++++-------------------------
2 files changed, 10 insertions(+), 26 deletions(-)
diff --git a/tools/database.py b/tools/database.py
index dd44de8..93adf88 100644
--- a/tools/database.py
+++ b/tools/database.py
@@ -347,7 +347,7 @@ class Database:
# Format columns
print(prefix, "Formatting file", f.file_name, end=" ... ")
- Extractor.auto_fit_trace_positions()
+ # Extractor.auto_fit_trace_positions()
Extractor.apply_correction()
# Get the output
diff --git a/tools/predictive_indicators.py b/tools/predictive_indicators.py
index e90c197..b8c78f4 100644
--- a/tools/predictive_indicators.py
+++ b/tools/predictive_indicators.py
@@ -25,7 +25,8 @@ COMPUTED_COLUMS = [
"Class",
"Date",
"Axis",
- "RMS", "KMeans", "PeakFactor", "MeanTemperature"
+ "RMS", "KMeans", "PeakFactor", "MeanTemperature",
+ "Health"
]
# for m in range(1,7):
# for v in [ "RMS", "KMeans", "PeakFactor", "MeanTemperature" ]:
@@ -44,26 +45,7 @@ def compute_run (DB: Database, robot: int, run: int, t0: int, date: str, next: C
data = DB.robot(robot).by_run(run).run()
- # Compute sub-arrays
- subs: List[pd.DataFrame] = [
- data[data["MovingMotor"] == m] for m in range(1,7)
- ]
-
runTime = t0 + mktime(datetime.strptime(date, "%Y-%m-%d").timetuple())
- # Compute for whole experiment
- # line = [ t0, 0 ]
- for m in range(1,7):
- sub = subs[m - 1]
- c = sub[f"Current_A{m}"].to_numpy()
- temp = sub[f"Temperature_A{m}"].to_numpy()
- line = [
- # *line,
- runTime, runTime, 0, data["Class"][0], date,
- f"A{m}", RMS(c), kmeans(c), peak_factor(c), np.mean(temp)
- ]
- out.append(line)
-
- # out.append(line)
# Compute for each speed
speeds = data['Speed'].unique()
@@ -72,21 +54,23 @@ def compute_run (DB: Database, robot: int, run: int, t0: int, date: str, next: C
]
for i, s in enumerate(speeds):
d = speed_data[i]
- # time = d["Sample_time"].to_numpy()[0] + t0
- # line = [ time, s ]
for m in range(1,7):
sub = d[d["MovingMotor"] == m]
time = d["Sample_time"].to_numpy()[0] + runTime
c = sub[f"Current_A{m}"].to_numpy()
temp = sub[f"Temperature_A{m}"].to_numpy()
+ rms, km, pf = RMS(c), kmeans(c), peak_factor(c)
+
+ # THE HEALTH INDEX IS COMPUTED HERE
+ health = rms - km - pf
+
line = [
- # *line,
time, runTime, s, data["Class"][0], date,
- f"A{m}", RMS(c), kmeans(c), peak_factor(c), np.mean(temp)
+ f"A{m}", rms, km, pf, np.mean(temp),
+ health
]
out.append(line)
- # out.append(line)
return out
--
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