50 steering and yaw rate reconiciliation

Data/emeterDecode.py

@@ -1,5 +1,6 @@
 from nptdms import TdmsFile
 import polars as pl
+import numpy as np
 import matplotlib.pyplot as plt
 from Data.FSLib.IntegralsAndDerivatives import *
 from Data.FSLib.AnalysisFunctions import *
@@ -9,10 +10,10 @@
 autoxDaniel12File = "FS-3/compEmeterData/autoxDaniel12.tdms"
 accel1 = "FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250620-203307_ ACCEL-EV.tdms"
 accel2 = "FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250620-205609_ ACCEL-EV.tdms"
-endur1 = "FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250621-154731_ ENDUR-EV.tdms"
-endur2 = "FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250621-160530_ ENDUR-EV.tdms"
+endur1 = "../fs-data/FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250621-154731_ ENDUR-EV.tdms"
+endur2 = "../fs-data/FS-3/compEmeterData/216_univ-of-calif---santa-cruz-_250621-160530_ ENDUR-EV.tdms"
 
-dfLaptimes = pl.read_csv("FS-3/compLapTimes.csv")
+dfLaptimes = pl.read_csv("../fs-data/FS-3/compLapTimes.csv")
 firstHalf = dfLaptimes.filter(pl.col("Lap") < 12)["Time"].sum()
 secondHalf = dfLaptimes.filter(pl.col("Lap") > 11)["Time"].sum()
 
@@ -93,7 +94,37 @@ def fileTodf(path):
     pl.Series(arr).cast(pl.Int64).alias("Lap")
 )
 
+l = []
 
+for i in np.unique(dfendur1["Lap"]):
+    # plt.plot(dfendur1.filter(pl.col("Lap") == i)[I])
+    l.append(dfendur1.filter(pl.col("Lap") == i)[I])
+plt.show()
+
+shortest = min([len(x) for x in l])
+l2 = [x[:shortest].alias(f"Current_Lap_{i}") for i, x in enumerate(l)]
+df2 = pl.DataFrame(l2)
+plt.plot(df2.mean_horizontal())
+plt.show()
+
+from scipy.fft import fft, ifft
+
+f = fft(df2.mean_horizontal().to_numpy())
+# freq = np.fft.fftfreq(len(df2.mean_horizontal()), d=0.01)
+
+plt.plot(np.append(np.log(f[-len(f)//2:]), np.log(f[:len(f)//2])))
+plt.show()
+
+fFiltered = np.where(np.log(f) > 10, f, 0)
+invF = ifft(fFiltered)
+plt.plot(invF)
+plt.plot(df2.mean_horizontal())
+plt.show()
+
+dfTableCurrOut = pl.DataFrame({"Current": df2.mean_horizontal().gather_every(100), "Time": np.arange(0, df2.height/100)})
+dfTableCurrOut.write_csv("endur1Curr.csv")
+
+df2.mean_horizontal()
 
 dfendur2 = fileTodf(endur2).filter(pl.col(t) > endur2_StartTime).filter(pl.col(t) < endur2_EndTime)
 

Data/temp.py

@@ -3,8 +3,8 @@
 import cantools.database as db
 
 from Data.DataDecoding_N_CorrectionScripts.dataDecodingFunctions import *
-from Data.AnalysisFunctions import *
-from Data.integralsAndDerivatives import *
+from Data.FSLib.AnalysisFunctions import *
+from Data.FSLib.IntegralsAndDerivatives import *
 from scipy.interpolate import CubicSpline
 
 dbcPath = "../fs-3/CANbus.dbc"
@@ -70,38 +70,7 @@
 etcRTDButton = "ETC_STATUS_RTD_BUTTON"
 etcBrakeVoltage = "ETC_STATUS_BRAKE_SENSE_VOLTAGE"
 
-df = read("C:/Projects/FormulaSlug/fs-data/FS-3/10112025/firstDriveMCError30.parquet")
-df = df.with_columns(
-    df["timestamp"].alias("Time")
-)
+df = read("C:/Projects/FormulaSlug/fs-data/FS-3/10082025/fixed_wheels_nathaniel_inv_test_w_fault.parquet")
 
-df = read("C:/Projects/FormulaSlug/fs-data/FS-3/10112025/firstDriveMCError30-filled-null.parquet")
-df = df.with_columns(
-    simpleTimeCol(df)
-)
-
-fig = plt.figure()
-ax = fig.add_subplot(111)
-
-ax.plot(df[t], df[frT], label=frT, c="blue")
-ax.plot(df[t], df[flT], label=flT, c="red")
-ax.plot(df[t], df[brT], label=brT, c="orange")
-ax.plot(df[t], df[blT], label=blT, c="cyan")
-ax.set_title("Suspension Travel during First Drive with MC Fault")
-ax.set_xlabel("Time")
-ax.set_ylabel("Suspension Travel (mm)")
-ax.legend()
-plt.show()
-
-
-dfNullless = df.drop_nulls(subset=[frT, flT, brT, blT])
-
-cs = CubicSpline(dfNullless[t], dfNullless[frT])
-
-fig = plt.figure()
-ax = fig.add_subplot(111)
-
-ax.scatter(dfNullless[t], cs(dfNullless[t]), label=frT, s=0.5)
-ax.scatter(dfNullless[t], in_place_derive(cs(dfNullless[t])), label=f"Derived {frT}", s=0.5)
-ax.legend()
+plt.plot(df[busV])
 plt.show()

Docs/SimulationTodo.md

@@ -0,0 +1,82 @@
+# Simulation Todo
+
+1. Better drag model that takes into account aeropackage (FS-3)
+1. Individual wheel models
+    1. Suspension
+        1. Travel (x)
+        1. Velocity (v)
+        1. How will this react under acceleration in any direction (steering causes lateral acceleration) (throttle/brakes causes longitudinal acceleration)
+    1. Wheel
+        1. Brake temp (more complex soon)
+        1. Wheel temp (more complex soon)
+        1. Wheel rpm/speed
+1. Differential + Drivetrain losses
+    1. Energy loss due to chain, tripods, axle, hub/upright rubbing
+    1. Model rolling resistance better
+    1. Model limited slip differential
+    1. Log losses so we have an idea of energy loss in the drivetrain
+1. Motor Efficiency + Heating
+    1. Function of temp and current draw
+    1. Keep track of losses
+    1. Efficiency loss goes into heat of motor. Need an estimate of its thermal mass and then change in temp. (Motor temp new var)
+1. Cleaner logging
+    1. Log everything without having to add more rows constantly
+    1. Keep efficiency in mind
+1. Tractive system heat generation (Not acc)
+    1. Estimate how much heat is generated in the accumulator
+    1. Not high priority unless we can get more data.
+1. Steering model
+1. Suspension Model
+
+
+# New simulation architecture idea
+
+
+```python
+# Dynamic Vars
+posX = 0
+posY = 1
+velX = 2
+velY = 3
+accelX = 4
+accelY = 5
+
+arr = np.array((simSteps, 6+1))
+arr[0] = step0
+
+def step():
+    newPosX = step[posX] + step[velX] * t
+
+for i in range(simSteps):
+    arr[i+1] = step(arr[i])
+```
+
+```python
+# Dictionary Idea #1
+# Array of dictionaries where each time step gets its own dictionary
+# Trivial to access a specific thing from any row
+
+arr = np.array((simSteps))
+arr[0] = step0
+
+def step():
+    newPosX = arr[posX] + arr[velX] * t
+
+for i in range(simSteps):
+    arr[i+1] = step(arr[i])
+```
+
+```python
+# Dictionary Idea #2
+# Dictionary of arrays. Each key is a column and each array is the length of the simulation
+# Trivial to access columns which is typically how we access data
+
+arr = np.array((simSteps))
+arr[0] = step0
+
+def step():
+    newPosX = arr[posX] + arr[velX] * t
+
+for i in range(simSteps):
+    arr[i+1] = step(arr[i])
+```

FullVehicleSim/Electrical/HisteresisCellModel/batteryModelTraining2.py

@@ -4,11 +4,11 @@
 import numpy as np
 import polars as pl
 import matplotlib.pyplot as plt
-from Data.FSLib.IntegralsAndDerivatives import integrate_with_Scipy_tCol
-from Data.FSLib.AnalysisFunctions import simpleTimeCol
+# from Data.FSLib.IntegralsAndDerivatives import integrate_with_Scipy_tCol
+# from Data.FSLib.AnalysisFunctions import simpleTimeCol
 
 df = pl.read_csv("C:/Projects/FormulaSlug/fs-data/FS-3/voltageTableVTC5A.csv")
-dfLowCurr = df.filter(pl.col("Current") < 3).filter(pl.col("Voltage") > 2.5)
+dfLowCurr = df.filter(pl.col("Current") < 1).filter(pl.col("Voltage") > 2.5)
 
 df.head
 
@@ -88,7 +88,7 @@ def voltage_model(x, a1, a2, a3, a4, a5, a6, a7, a8, a9):
 plt.legend()
 plt.show()
 
-dfLowCurr1 = df.filter(pl.col("Current") < 3).filter(pl.col("Voltage") > 2.5)
+dfLowCurr1 = df.filter(pl.col("Current") < 1).filter(pl.col("Voltage") > 2.5)
 # dfLowCurr2 = df.filter(pl.col("Current") < 3)