Moved stuff over from FS github

fftTools.py

@@ -0,0 +1,14 @@
+import numpy as np
+import polars as pl
+import scipy.fftpack as fftpack
+
+def low_pass_filter(array, fraction):
+    transform = fftpack.fft(array)
+    length = array.shape[0]
+    end_length = ((1 - fraction)/2)
+    start = round(length*end_length)
+    end = round(length*(1-end_length))
+    transform[start:end] = np.zeros_like(transform[start:end])
+    output = fftpack.ifft(transform)
+    # output.real
+    return output.real

gpsGraph.py

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+## This file contains the code originally used to create a plot using gps coordinates to create
+## a heat map. Currently has updated functioning graphs for rpm, torque, and braking. 2025-01-20 (last update by Nathaniel)
+## Made and commented by Nathaniel Platt
+import polars as pl
+import matplotlib.pyplot as plt
+from heatGraph import colored_line
+
+df = pl.read_parquet("Parquet/2024-12-02-Part1-100Hz.pq")
+# df = pl.read_csv("Temp/2024-12-02-Part1-100Hz.csv",infer_schema_length=0).with_columns(pl.all().cast(pl.Float32, strict=False))
+# df1 = pl.read_csv("Temp/2024-12-02-Part2-100Hz.csv",infer_schema_length=0).with_columns(pl.all().cast(pl.Float32, strict=False))
+df1 = pl.read_parquet("Parquet/2024-12-02-Part2-100Hz.pq")
+
+df.columns
+
+time1 = 1400
+# time1 = 1000
+time2 = 1650
+# time2 = 1900
+lat = "VDM_GPS_Latitude"
+long = "VDM_GPS_Longitude"
+speed = "SME_TRQSPD_Speed"
+busCurrent = "SME_TEMP_BusCurrent"
+tsCurrent = "TS_Current"
+torque = "SME_THROTL_TorqueDemand"
+brakes = "Brakes"
+df.columns
+short = pl.DataFrame(df.filter(pl.col("Seconds") >= time1).filter(pl.col("Seconds") <= time2)).filter(pl.col("VDM_GPS_Latitude") != 0).filter(pl.col("VDM_GPS_Longitude") != 0)
+
+
+# df.drop_nulls().select(lat).mean()
+
+# df.select(lat).filter(pl.col("VDM_GPS_Latitude") != 0)
+# df.filter(pl.col("Seconds") == 498.199).select([lat,long])\
+# fig = plt.figure()
+# fig.add_subplot(1,1,1)
+# ax = plt.figure().add_subplot(1,1,1)
+# ax.pcolorfast(-1*short[long],-1*short[lat],a)
+# ax.plot(-1*short[long],-1*short[lat])
+# ax.axis('scaled')
+# plt.show()
+# df.columns
+
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from matplotlib.collections import LineCollection
+
+fig1 = plt.figure()
+ax1 = fig1.add_subplot(1,1,1)
+lines = colored_line(short[lat], short[long], short[busCurrent], ax1, linewidth=1, cmap="plasma")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Bus Current (A)")
+plt.show()
+
+# Create a figure and plot the line on it
+fig1 = plt.figure()
+ax1 = fig1.add_subplot(1,3,1)
+lines = colored_line(short[lat], short[long], short[speed]/7500*109, ax1, linewidth=1, cmap="plasma")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("RPM (motor)")
+
+ax1 = fig1.add_subplot(1,3,2)
+lines = colored_line(short[lat], short[long], short[torque]/30000*7500, ax1, linewidth=1, cmap="viridis")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Torque (Nm)")
+
+ax1 = fig1.add_subplot(1,3,3)
+lines = colored_line(short[lat], short[long], (short[brakes]-0.1)*2000, ax1, linewidth=1, cmap="inferno")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Braking (psi)")
+
+plt.show()
+
+fig1 = plt.figure()
+ax1 = fig1.add_subplot(1,3,1)
+lines = colored_line(short[lat], short[long], short[busCurrent], ax1, linewidth=1, cmap="plasma")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Motor Controller Current (A)")
+
+ax1 = fig1.add_subplot(1,3,2)
+lines = colored_line(short[lat], short[long], short[tsCurrent], ax1, linewidth=1, cmap="viridis")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Accumulator Current (A)")
+
+ax1 = fig1.add_subplot(1,3,3)
+lines = colored_line(short[lat], short[long], (short[brakes]-0.1)*2000, ax1, linewidth=1, cmap="inferno")
+fig1.colorbar(lines)  # add a color legend
+ax1.axis('scaled')
+ax1.set_title("Braking (psi)")
+
+plt.show()

graphData.py

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+## This file is for visualizing the tire data we bought fall 2024.
+## All code should still be functional - Nathaniel 1/11/25
+
+import polars as pl
+from matplotlib import pyplot as plt
+import os
+import re
+
+# folder = r""
+# files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]
+# files = [folder+"\\"+f for f in files if f[-4:] == ".dat"]
+# files.sort(key = lambda f:int(re.match(r".+?([0-9]+)\.dat", f).group(1)))
+
+files = [r"TireData\B1965raw2.dat"]
+
+timeKey = r"ET"
+size = 1000
+
+# see = df.columns
+see = ["FY", "FX", "P"]
+
+t1 = 0
+t2 = float("inf")
+
+def readfile(filename):
+    if filename[-3:] == ".pq":
+        df = pl.read_parquet(filename)
+    elif filename[-4:] == ".csv":
+        df = pl.read_csv(filename, infer_schema_length=10000, ignore_errors=True)
+    elif filename[-4:] == ".dat":
+        with open(filename, "r") as file:
+            text = file.readlines()
+        text.pop(0)
+        text.pop(1)
+        text = [row.strip() for row in text]
+        rows = [row.split("\t") for row in text]
+        header = rows[0]
+        rows = [[float(i) for i in row] for row in rows[1:]]
+        # frame = {h:[for row in text] for n, h in enumerate(rows[0])}
+        df = pl.DataFrame(rows, schema=header, orient="row")
+    else:
+        print("can't read")
+        return None
+
+    return df
+
+def getTime(df):
+    print(df.columns)
+    t = df[timeKey]
+    print(t, len(t))
+    return t
+
+def plot(x, y, lab = ""):
+    skip = max(1, int(len(x)/size))
+    plt.plot(x[::skip], y[::skip], linestyle="", marker="o")
+    plt.xlabel(lab)
+    plt.show()
+
+def getIndexes(t1, t2, t):
+    t2 = min(t[-1], t2)
+    i1 = int((t1/t[-1])*len(t))
+    i2 = int((t2/t[-1])*len(t))
+    return i1, i2
+
+for f in files:
+    df = readfile(f)
+    if isinstance(df, type(None)):
+        continue
+    # df.write_csv(f.replace(folder, "TireDataCSV").replace(".dat",".csv"))
+    t = getTime(df)
+    i1, i2 = getIndexes(t1, t2, t)
+    for c in see:
+        plot(t[i1:i2], df[c][i1:i2], f + " " + c)
+

heatGraph.py

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+## Contains the function we use for generating heat plots.
+## Code placed, edited and commented by Nathaniel Platt. Sourced online (don't remember where :(
+## Use like this:
+
+# fig1 = plt.figure()
+# ax1 = fig1.add_subplot(1,3,2)
+# lines = colored_line(short[lat], short[long], short[torque], ax1, linewidth=1, cmap="viridis")
+# fig1.colorbar(lines)  # add a color legend
+# ax1.axis('scaled')
+# ax1.set_title("Torque")
+
+import numpy as np
+import warnings
+from matplotlib.collections import LineCollection
+
+# -------------- Create and show plot --------------
+# Some arbitrary function that gives x, y, and color values
+def colored_line(x, y, c, ax, **lc_kwargs):
+    """
+    Plot a line with a color specified along the line by a third value.
+
+    It does this by creating a collection of line segments. Each line segment is
+    made up of two straight lines each connecting the current (x, y) point to the
+    midpoints of the lines connecting the current point with its two neighbors.
+    This creates a smooth line with no gaps between the line segments.
+
+    Parameters
+    ----------
+    x, y : array-like
+        The horizontal and vertical coordinates of the data points.
+    c : array-like
+        The color values, which should be the same size as x and y.
+    ax : Axes
+        Axis object on which to plot the colored line.
+    **lc_kwargs
+        Any additional arguments to pass to matplotlib.collections.LineCollection
+        constructor. This should not include the array keyword argument because
+        that is set to the color argument. If provided, it will be overridden.
+
+    Returns
+    -------
+    matplotlib.collections.LineCollection
+        The generated line collection representing the colored line.
+    """
+    if "array" in lc_kwargs:
+        warnings.warn('The provided "array" keyword argument will be overridden')
+
+    # Default the capstyle to butt so that the line segments smoothly line up
+    default_kwargs = {"capstyle": "butt"}
+    default_kwargs.update(lc_kwargs)
+
+    # Compute the midpoints of the line segments. Include the first and last points
+    # twice so we don't need any special syntax later to handle them.
+    x = np.asarray(x)
+    y = np.asarray(y)
+    x_midpts = np.hstack((x[0], 0.5 * (x[1:] + x[:-1]), x[-1]))
+    y_midpts = np.hstack((y[0], 0.5 * (y[1:] + y[:-1]), y[-1]))
+
+    # Determine the start, middle, and end coordinate pair of each line segment.
+    # Use the reshape to add an extra dimension so each pair of points is in its
+    # own list. Then concatenate them to create:
+    # [
+    #   [(x1_start, y1_start), (x1_mid, y1_mid), (x1_end, y1_end)],
+    #   [(x2_start, y2_start), (x2_mid, y2_mid), (x2_end, y2_end)],
+    #   ...
+    # ]
+    coord_start = np.column_stack((x_midpts[:-1], y_midpts[:-1]))[:, np.newaxis, :]
+    coord_mid = np.column_stack((x, y))[:, np.newaxis, :]
+    coord_end = np.column_stack((x_midpts[1:], y_midpts[1:]))[:, np.newaxis, :]
+    segments = np.concatenate((coord_start, coord_mid, coord_end), axis=1)
+
+    lc = LineCollection(segments, **default_kwargs)
+    lc.set_array(c)  # set the colors of each segment
+
+    return ax.add_collection(lc)

imuBasicGraphs.py

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+## This file was created to generate IMU data but was too simple to function properly.
+## Does not work in its current state - Nathaniel 1/11/25
+
+from matplotlib.axes import Axes
+from matplotlib.text import Text
+from matplotlib.transforms import Bbox
+import polars as pl
+import matplotlib.pyplot as plt
+from matplotlib import animation
+import numpy as np
+
+df = pl.read_parquet("./Parquet/2024-12-02-Part1-100Hz.pq")
+# df = pl.read_parquet("./Parquet/2024-12-02-Part2-100Hz.pq")
+
+time1 = 100
+# time1 = 1530.4
+# time1 = 1000
+time2 = 9999999999
+# time2 = 1710
+# time2 = 1900
+lat = "VDM_GPS_Latitude"
+long = "VDM_GPS_Longitude"
+
+df = pl.DataFrame(df.filter(pl.col("Seconds") >= time1).filter(pl.col("Seconds") <= time2))[::100]
+
+print(df.columns)
+
+fig, ax = plt.subplots(1,1)
+fig.set_label("Gyroscope (deg/s)")
+
+ax.plot(df.select("Seconds"), df.select("VDM_Z_AXIS_YAW_RATE"), label="Z Axis")
+ax.legend(loc="best")
+
+fig.tight_layout()
+plt.show()
+
+