Note
Go to the end to download the full example code
Annotate a session and perform a Ferraris Calibration#
The following example demonstrates the main workflow we will use when working with imucal.
We assume that you recorded a Ferraris session with you IMU unit following our tutorial.
We further assume that you recorded all calibration steps in a single recording and still need to annotate the various
sections.
If you have already separated them, you can create a instance of FerrarisSignalRegions or use
ferraris_regions_from_df.
But let’s start with the tutorial
Loading the session#
First we need to load the recorded session.
This will depend on you sensor unit.
In this example we have exported the sensor data as csv first, so that we can easily import it.
Note, that the sensor data already has the units m/s^2 and deg/s and not some raw bit values, which you might
encounter when just streaming raw sensor values from a custom IMU board.
We highly recommend to perform this conversion using the equations provided in the IMU chip documentation before
applying a Ferraris calibration.
This will ensure that you calibration is independent of the selected sensor ranges and you do not need to record a
new calibration, when you change these settings.
from pathlib import Path
import pandas as pd
from example_data import EXAMPLE_PATH
data = pd.read_csv(EXAMPLE_PATH / "example_ferraris_session.csv", header=0, index_col=0)
data.head()
Annotating the data#
Now we need to annotate the different sections of the ferraris calibration in the interactive GUI. Note, that this will only work, if your Python process runs on your machine and not some kind of sever. Otherwise, the GUI will not open.
To start the annotation, run:
>>> from imucal import ferraris_regions_from_interactive_plot
>>> regions, section_list = ferraris_regions_from_interactive_plot(data)
You can see in the gif below, how you can annotate the session.
Some Notes:
If your data has other column names than
acc_x, ..., gyr_z, you can provide them in the function callIf you have recorded the section in the correct order, you can just jump to the next section by pressing Enter
If you need to annotate/correct a specific section, click on it in the sidebar
If you use the zoom or pan tool of matplotlib, you need to deselect it again, before you can make annotations
Once you annotated all sections, simply close the plot
In the video you can see that the gyro rotations are split up in 4 sections of 90 deg instead of one 360 deg rotation. This was simply how the operator performed the rotation in this case. But usually you would expect a single large spike there.
Instead of performing the annotation in this example, we will load the section list from a previous annotation of the data. In general it is advisable to save the annotated sections, so that you can rerun the calibration in the future.
from imucal import ferraris_regions_from_section_list
section_list = pd.read_json(EXAMPLE_PATH / "example_ferraris_session_list.json").T
section_list
This section list can then be used to recreate the regions
FerrarisSignalRegions(acc_x_p=array([[10.31774414, -0.55564453, 0.49816406],
[10.30337402, -0.57001465, 0.5269043 ],
[10.28900391, -0.56522461, 0.5269043 ],
...,
[10.32732422, -0.54606445, 0.51253418],
[10.31774414, -0.54606445, 0.49816406],
[10.30816406, -0.54606445, 0.48379395]]), acc_x_a=array([[-9.23998535, -0.73287598, 0.23950195],
[-9.23998535, -0.71850586, 0.27303223],
[-9.25435547, -0.71371582, 0.23471191],
...,
[-9.25914551, -0.71850586, 0.24908203],
[-9.24477539, -0.70892578, 0.24908203],
[-9.24477539, -0.68976563, 0.26824219]]), acc_y_p=array([[0.37841309, 9.22561523, 0.38799316],
[0.38799316, 9.20166504, 0.37841309],
[0.38320312, 9.20645508, 0.38320312],
...,
[0.38320312, 9.21124512, 0.36883301],
[0.4167334 , 9.23519531, 0.3927832 ],
[0.40715332, 9.21124512, 0.41194336]]), acc_y_a=array([[ 0.68497559, -10.4470752 , 0.32572266],
[ 0.69455566, -10.45665527, 0.34009277],
[ 0.67060547, -10.44228516, 0.38320312],
...,
[ 0.69455566, -10.45665527, 0.34967285],
[ 0.68976563, -10.44228516, 0.36883301],
[ 0.66102539, -10.43270508, 0.38799316]]), acc_z_p=array([[ 0.50774414, -0.57959473, 10.43270508],
[ 0.50774414, -0.60354492, 10.42791504],
[ 0.51253418, -0.60833496, 10.4470752 ],
...,
[ 0.50774414, -0.58438477, 10.42791504],
[ 0.49816406, -0.5891748 , 10.423125 ],
[ 0.5029541 , -0.5891748 , 10.42791504]]), acc_z_a=array([[ 0.65623535, -0.64186523, -9.64234863],
[ 0.68018555, -0.63228516, -9.65671875],
[ 0.65623535, -0.613125 , -9.63276855],
...,
[ 0.65623535, -0.61791504, -9.64713867],
[ 0.67060547, -0.62749512, -9.65192871],
[ 0.64665527, -0.64665527, -9.63755859]]), gyr_x_p=array([[-0.61035156, -0.36621094, 0.06103516],
[-0.54931641, -0.36621094, 0.12207031],
[-0.61035156, -0.36621094, 0.06103516],
...,
[-0.61035156, -0.36621094, 0.06103516],
[-0.54931641, -0.30517578, 0.12207031],
[-0.67138672, -0.42724609, 0.06103516]]), gyr_x_a=array([[-0.54931641, -0.42724609, 0.06103516],
[-0.61035156, -0.42724609, 0.06103516],
[-0.67138672, -0.36621094, 0.06103516],
...,
[-0.61035156, -0.36621094, 0. ],
[-0.54931641, -0.42724609, 0. ],
[-0.48828125, -0.36621094, 0.06103516]]), gyr_y_p=array([[-0.54931641, -0.36621094, 0.12207031],
[-0.67138672, -0.42724609, 0.06103516],
[-0.67138672, -0.42724609, 0.06103516],
...,
[-0.61035156, -0.30517578, 0.06103516],
[-0.67138672, -0.36621094, 0.06103516],
[-0.67138672, -0.42724609, 0.12207031]]), gyr_y_a=array([[-0.67138672, -0.36621094, 0.06103516],
[-0.67138672, -0.30517578, 0. ],
[-0.67138672, -0.36621094, 0. ],
...,
[-0.61035156, -0.42724609, 0.06103516],
[-0.61035156, -0.36621094, 0.06103516],
[-0.61035156, -0.36621094, 0.06103516]]), gyr_z_p=array([[-0.61035156, -0.48828125, 0. ],
[-0.54931641, -0.30517578, -0.06103516],
[-0.67138672, -0.24414062, 0. ],
...,
[-0.61035156, -0.36621094, 0.06103516],
[-0.61035156, -0.42724609, 0. ],
[-0.54931641, -0.36621094, 0. ]]), gyr_z_a=array([[-0.67138672, -0.42724609, 0.12207031],
[-0.61035156, -0.36621094, -0.06103516],
[-0.61035156, -0.36621094, 0.06103516],
...,
[-0.67138672, -0.42724609, 0. ],
[-0.61035156, -0.48828125, 0.06103516],
[-0.54931641, -0.30517578, 0.12207031]]), acc_x_rot=array([[10.3129541 , -0.55085449, 0.52211426],
[10.29858398, -0.56043457, 0.52211426],
[10.27942383, -0.57480469, 0.57480469],
[10.24589355, -0.55564453, 0.68018555],
[10.28900391, -0.47900391, 0.70413574],
[10.34169434, -0.63228516, 0.61791504],
[10.34648437, -0.57480469, 0.8095166 ],
[10.29858398, -0.49337402, 0.47421387],
[10.29858398, -0.53169434, 0.5891748 ],
[10.30816406, -0.33530273, 1.27894043],
[10.29858398, -0.34488281, 1.21187988],
[10.26984375, -0.45505371, 1.40348145],
[10.29858398, -0.68018555, 1.33163086],
[10.30816406, -0.79514648, 1.15918945],
[10.29379395, -0.6370752 , 0.45505371],
[10.30337402, -0.55564453, 0.06227051],
[10.28900391, -0.24908203, 0.86699707],
[10.29858398, -0.19160156, 1.09691895],
[10.30816406, -0.25866211, 0.9436377 ],
[10.34169434, -0.57480469, 1.54239258],
[10.26026367, -0.60833496, 2.00223633],
[10.31774414, -0.42152344, 2.02139648],
[10.27463379, 0.30177246, 1.88248535],
[10.31774414, 0.3927832 , 0.35925293],
[10.29858398, 0.0862207 , -0.14849121],
[10.30337402, -0.03832031, -0.14849121],
[10.30337402, -0.62270508, -0.32093262],
[10.37522461, -1.27415039, 0. ],
[10.32732422, -1.35079102, 0.83825684],
[10.32732422, -1.12086914, 0.70892578],
[10.27942383, -0.76640625, 0.43589355],
[10.27942383, -0.55564453, 0.34967285],
[10.38001465, 0.07185059, 0.98674805],
[10.24589355, 0.71371582, 0.93405762],
[10.33211426, 0.38799316, 1.01548828],
[10.30816406, -0.04311035, 0.03353027],
[10.32732422, -0.6370752 , -1.3124707 ],
[10.32732422, -1.01069824, -1.82500488],
[10.32253418, -0.15807129, -1.21666992],
[10.26984375, 0.53648438, 0.1963916 ],
[10.26984375, 0.81430664, 0.56522461],
[10.32732422, 0.29219238, 0.64186523],
[10.26026367, -0.38799316, 0.75682617],
[10.26026367, -0.68497559, 0.56522461],
[10.34648437, -0.68497559, 0.5269043 ],
[10.36564453, -0.49337402, 0.1963916 ],
[10.32253418, -0.50774414, -0.23950195],
[10.31774414, -0.82388672, -0.67539551],
[10.28421387, -0.49337402, -0.86699707],
[10.30816406, 0.37841309, 0.2203418 ],
[10.24589355, 0.37362305, 1.78668457],
[10.32732422, -0.7232959 , 0.86699707],
[10.30816406, -0.54606445, 0.37841309],
[10.30337402, -0.49337402, 0.52211426],
[10.30337402, -0.49816406, 0.55085449],
[10.27463379, -0.5029541 , 0.51253418],
[10.29858398, -0.49816406, 0.53169434],
[10.29858398, -0.50774414, 0.55564453],
[10.29379395, -0.50774414, 0.50774414],
[10.29379395, -0.49816406, 0.51732422],
[10.3129541 , -0.49816406, 0.51253418],
[10.32732422, -0.49337402, 0.53169434],
[10.31774414, -0.5029541 , 0.52211426],
[10.28900391, -0.5029541 , 0.54127441],
[10.30337402, -0.48858398, 0.54127441],
[10.30816406, -0.49816406, 0.54606445],
[10.31774414, -0.49816406, 0.55564453],
[10.31774414, -0.51253418, 0.51732422],
[10.28900391, -0.53169434, 0.51732422],
[10.29858398, -0.51732422, 0.5269043 ],
[10.29858398, -0.49816406, 0.53648438],
[10.29858398, -0.50774414, 0.51732422],
[10.29858398, -0.50774414, 0.52211426],
[10.29379395, -0.48858398, 0.53169434],
[10.30816406, -0.49816406, 0.56043457],
[10.31774414, -0.51732422, 0.53169434],
[10.3129541 , -0.49816406, 0.5269043 ],
[10.31774414, -0.5029541 , 0.53169434],
[10.30337402, -0.48858398, 0.49816406],
[10.30816406, -0.48858398, 0.51253418],
[10.27942383, -0.51732422, 0.53648438],
[10.32253418, -0.51253418, 0.50774414],
[10.33211426, -0.49816406, 0.51732422],
[10.29858398, -0.48858398, 0.54127441],
[10.28421387, -0.49337402, 0.51253418],
[10.3129541 , -0.49337402, 0.50774414],
[10.3129541 , -0.49337402, 0.53648438],
[10.29858398, -0.48858398, 0.5269043 ],
[10.29858398, -0.52211426, 0.49816406],
[10.32253418, -0.54127441, 0.50774414],
[10.30816406, -2.39501953, 0.56043457],
[10.30337402, -4.08111328, 0.87178711],
[10.32253418, -3.57815918, 1.06817871],
[10.23152344, -2.70637207, 1.29331055],
[10.18362305, 5.04391113, 1.48012207],
[10.27463379, 3.71228027, 1.70525391],
[10.30337402, -0.45026367, 0.60354492],
[10.32253418, -1.08254883, -0.97237793],
[10.35127441, -0.5891748 , -1.39869141],
[10.25068359, -0.06706055, -0.77119629],
[10.21236328, -0.10059082, -0.04311035],
[10.32253418, -0.47421387, 0.8334668 ],
[10.28421387, -0.21076172, 1.47533203],
[10.34648437, -0.10538086, 1.76273438],
[10.27463379, -0.31135254, 1.48491211],
[10.34648437, -0.0862207 , 1.17355957],
[10.27463379, -0.16765137, 0.91489746],
[10.36564453, 0.02874023, 0.56043457],
[10.31774414, 0.40236328, 0.48858398],
[10.23631348, 0.66581543, -0.09580078],
[10.37522461, -0.10059082, -0.47421387],
[10.30816406, -0.74724609, -1.18792969],
[10.30337402, -0.24429199, -0.80472656],
[10.34648437, -0.17723145, 0.03832031],
[10.34648437, 0.91489746, 0.45026367],
[10.30337402, 1.0059082 , 0.24429199],
[10.3369043 , 0.20118164, -0.60354492],
[10.30337402, -1.29810059, -0.71371582],
[10.25068359, -2.54351074, 0.34488281],
[10.32732422, -2.96982422, 0.76161621],
[10.3369043 , -2.57225098, 1.05859863],
[10.28421387, -1.79147461, 1.76752441],
[10.27942383, -0.74245605, 0.54606445],
[10.32253418, 0.06706055, -1.20708984],
[10.23631348, 0.34009277, -1.36037109],
[10.25547363, 0.39757324, 0.5891748 ],
[10.3369043 , -0.59875488, 0.71850586],
[10.29379395, -0.97716797, 0.43110352],
[10.32732422, -0.82867676, 0.72808594],
[10.29379395, -0.85741699, 1.2502002 ],
[10.32253418, -0.85262695, 1.60945313],
[10.27463379, -0.91010742, 1.79626465],
[10.26505371, -1.41785156, 1.68130371],
[10.37043457, -0.56522461, 0.51253418],
[10.3369043 , -0.45505371, 0.53169434],
[10.28421387, -0.54606445, 0.60833496],
[10.29858398, -0.54606445, 0.60833496],
[10.31774414, -0.5269043 , 0.59875488],
[10.32732422, -0.51732422, 0.61791504],
[10.32732422, -0.51253418, 0.57480469],
[10.29379395, -0.52211426, 0.60833496],
[10.29858398, -0.51732422, 0.5891748 ],
[10.30337402, -0.54127441, 0.5891748 ],
[10.32253418, -0.52211426, 0.57480469],
[10.32253418, -0.54127441, 0.59396484],
[10.30816406, -0.51732422, 0.59875488],
[10.30816406, -0.51732422, 0.59396484],
[10.27942383, -0.5269043 , 0.62270508],
[10.32732422, -0.51732422, 0.613125 ],
[10.3129541 , -0.52211426, 0.60354492],
[10.30816406, -0.51253418, 0.5891748 ],
[10.32253418, -0.5269043 , 0.5269043 ],
[10.24589355, -0.32572266, 0.21555176],
[10.32732422, 0.35925293, -0.23471191],
[10.31774414, 1.02506836, -0.5269043 ],
[10.29858398, 0.34488281, -0.43589355],
[10.15009277, -1.56155273, 1.90643555],
[10.41833496, -2.94587402, 3.97094238],
[10.30816406, -0.59396484, 0.59396484],
[10.26026367, -0.22992187, 0.87178711],
[10.28421387, -0.48858398, 0.7232959 ],
[10.32732422, -0.48379395, 0.78556641],
[10.29858398, -0.42152344, 1.05380859],
[10.29379395, -0.38320312, 1.61424316],
[10.27463379, 0.0239502 , 1.76273438],
[10.28900391, 0.35446289, 1.71004395],
[10.33211426, 0.51253418, 1.58071289],
[10.29379395, 0.64665527, 0.74245605],
[10.25068359, 0.47421387, 0.57001465],
[10.3369043 , 0.36883301, 0.39757324],
[10.29858398, -0.27782227, -0.15807129],
[10.36564453, -0.93405762, -1.10170898],
[10.34169434, -0.95321777, -1.44180176],
[10.32732422, -0.95800781, -1.24062012],
[10.34169434, -0.97237793, -0.62749512],
[10.34169434, -0.9196875 , 0.20597168],
[10.37043457, -0.72808594, 1.16397949],
[10.26505371, -0.51253418, 1.24541016],
[10.27942383, -0.29219238, 1.43222168],
[10.3129541 , 0.0239502 , 1.39869141],
[10.27463379, 0.1101709 , 0.98674805],
[10.30816406, 0.16765137, 0.57959473],
[10.26505371, 0.2826123 , 0.5029541 ],
[10.28421387, 0.57001465, -0.04790039],
[10.30337402, 0.64186523, -0.13412109],
[10.2267334 , 0.77119629, 0.34967285],
[10.28421387, 0.76161621, 0.48379395],
[10.35606445, 0.22513184, 0.4167334 ],
[10.28900391, -0.3305127 , 0.64186523],
[10.28900391, -0.32093262, 1.24062012],
[10.25068359, -0.13412109, 1.65256348],
[10.29379395, 0.12933105, 2.33753906],
[10.35606445, 0.03353027, 2.73032227],
[10.30337402, 1.13044922, 1.6190332 ],
[10.15009277, 1.26936035, -0.45984375],
[10.32253418, -0.68018555, -1.54718262],
[10.38959473, -2.24173828, -1.42743164],
[10.36085449, -2.17946777, -1.49449219],
[10.3129541 , -1.37474121, -1.45138184],
[10.33211426, -1.23104004, -1.0059082 ],
[10.37522461, -1.03943848, -0.9196875 ],
[10.29379395, -0.75203613, -0.96279785],
[10.31774414, -0.93884766, -1.12086914],
[10.3369043 , -1.12565918, -1.06817871],
[10.27942383, -0.6370752 , 0.38320312],
[10.26984375, -0.55564453, 0.75203613],
[10.30337402, -0.5891748 , 0.57959473],
[10.33211426, -0.613125 , 0.56522461],
[10.30816406, -0.59396484, 0.55085449],
[10.30337402, -0.58438477, 0.52211426],
[10.30816406, -0.5891748 , 0.53169434],
[10.30816406, -0.59396484, 0.53648438],
[10.3129541 , -0.59875488, 0.5269043 ],
[10.29858398, -0.59396484, 0.54127441],
[10.28900391, -0.57001465, 0.57959473],
[10.29379395, -0.58438477, 0.58438477],
[10.32732422, -0.58438477, 0.55564453],
[10.28900391, -0.57001465, 0.53648438],
[10.29858398, -0.59396484, 0.56043457],
[10.30337402, -0.60354492, 0.55085449],
[10.32253418, -0.58438477, 0.58438477],
[10.28900391, -0.58438477, 0.57001465],
[10.30337402, -0.59875488, 0.54606445],
[10.29379395, -0.60354492, 0.57001465],
[10.32253418, -0.5891748 , 0.56043457],
[10.31774414, -0.60354492, 0.56043457],
[10.3129541 , -0.57001465, 0.56522461],
[10.32253418, -0.56043457, 0.56522461],
[10.28421387, -0.59875488, 0.56043457],
[10.30337402, -0.59396484, 0.56522461],
[10.32253418, -0.5891748 , 0.57001465],
[10.3129541 , -0.5891748 , 0.55564453],
[10.3129541 , -0.57959473, 0.54606445],
[10.27463379, -0.58438477, 0.56043457],
[10.28421387, -0.57959473, 0.59875488],
[10.32732422, -0.57959473, 0.57959473],
[10.30337402, -0.58438477, 0.57480469],
[10.32253418, -0.58438477, 0.56043457],
[10.31774414, -0.59875488, 0.51732422],
[10.29858398, -0.59875488, 0.57480469],
[10.27942383, -0.58438477, 0.58438477],
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[ 0.00000000e+00, 2.44140625e-01, -1.44165039e+02],
[ 4.27246094e-01, 2.44140625e-01, -1.87316895e+02],
[ 9.76562500e-01, 3.66210938e-01, -2.23144531e+02],
[ 1.83105469e+00, -2.44140625e-01, -2.29309082e+02],
[ 8.54492188e-01, -6.10351562e-02, -2.21984863e+02],
[ 4.27246094e-01, -6.10351562e-02, -1.89392090e+02],
[ 4.88281250e-01, -2.44140625e-01, -1.30004883e+02],
[-1.83105469e-01, 0.00000000e+00, -7.79418945e+01],
[-5.49316406e-01, 3.05175781e-01, -5.72509766e+01],
[-1.22070312e-01, -5.49316406e-01, -6.95190430e+01],
[ 0.00000000e+00, 0.00000000e+00, -1.02722168e+02],
[ 3.05175781e-01, 1.22070312e-01, -1.41540527e+02],
[ 6.10351562e-01, -6.71386719e-01, -1.38244629e+02],
[-4.88281250e-01, 3.05175781e-01, -9.88769531e+01],
[-1.22070312e-01, -6.10351562e-02, -5.27954102e+01],
[-2.44140625e-01, -3.66210938e-01, -3.43017578e+01],
[-1.83105469e-01, -3.05175781e-01, -3.88183594e+01],
[-1.83105469e-01, -1.22070312e-01, -6.89086914e+01],
[ 2.44140625e-01, -2.44140625e-01, -9.13085938e+01],
[ 0.00000000e+00, -1.22070312e-01, -8.86230469e+01],
[-2.44140625e-01, -1.22070312e-01, -7.24487305e+01],
[-5.49316406e-01, -1.22070312e-01, -5.38330078e+01],
[-4.27246094e-01, -4.27246094e-01, -3.07006836e+01],
[-3.05175781e-01, -3.66210938e-01, -2.40478516e+01],
[-3.05175781e-01, -1.83105469e-01, -3.69262695e+01],
[-6.71386719e-01, 1.83105469e-01, -4.57153320e+01],
[-6.71386719e-01, 6.10351562e-02, -4.93774414e+01],
[-2.44140625e-01, -3.05175781e-01, -4.98657227e+01],
[-3.05175781e-01, -3.66210938e-01, -4.01000977e+01],
[-2.44140625e-01, -1.22070312e-01, -4.85229492e+01],
[-4.88281250e-01, -1.83105469e-01, -5.98754883e+01],
[-6.10351562e-01, -2.44140625e-01, -4.44946289e+01],
[ 6.10351562e-02, -4.27246094e-01, -4.15649414e+01],
[-4.27246094e-01, -2.44140625e-01, -4.62036133e+01],
[-2.44140625e-01, -3.05175781e-01, -4.39453125e+01],
[ 0.00000000e+00, -6.10351562e-01, -3.90014648e+01],
[-4.27246094e-01, -4.27246094e-01, -3.24096680e+01],
[ 6.10351562e-02, -5.49316406e-01, -2.82592773e+01],
[-1.83105469e-01, -4.88281250e-01, -2.78320312e+01],
[-7.32421875e-01, 6.10351562e-02, -2.47802734e+01],
[-9.15527344e-01, 1.22070312e-01, -2.70385742e+01],
[ 0.00000000e+00, -4.88281250e-01, -3.93066406e+01],
[-3.05175781e-01, -3.05175781e-01, -4.27856445e+01],
[-5.49316406e-01, -4.27246094e-01, -7.93457031e-01],
[-7.32421875e-01, -3.05175781e-01, 3.17382812e+00],
[-6.10351562e-01, -3.66210938e-01, -9.15527344e-01],
[-6.10351562e-01, -3.66210938e-01, -1.83105469e-01]]))
Now we can calculate the actual calibration parameters.
For this we will create a instance of FerrarisCalibration with the desired settings and then call compute with
the regions we have extracted.
Note that we need to specify the units of the input data. This information is stored with the calibration as documentation to check in the future that data is suitable for a given calibration. We can further add a comment if we want.
from imucal import FerrarisCalibration
cal = FerrarisCalibration()
cal_info = cal.compute(
regions, sampling_rate_hz=204.8, from_acc_unit="m/s^2", from_gyr_unit="deg/s", comment="My comment"
)
print(cal_info.to_json())
{
"acc_unit": "m/s^2",
"gyr_unit": "deg/s",
"from_acc_unit": "m/s^2",
"from_gyr_unit": "deg/s",
"comment": "My comment",
"K_a": [
[
0.9967458650887003,
0.0,
0.0
],
[
0.0,
1.0024376185894108,
0.0
],
[
0.0,
0.0,
1.0233953475452096
]
],
"R_a": [
[
0.9998620291746435,
-0.014830571004926794,
-0.007481763056406514
],
[
0.008576740442555632,
0.9999615197419686,
0.0018435180263559792
],
[
0.01333118773067902,
0.002003617949970859,
0.9999091283455711
]
],
"b_a": [
0.5371174244458201,
-0.6162029930860964,
0.3988673423902682
],
"K_g": [
[
0.5139621632144018,
0.0,
0.0
],
[
0.0,
0.4912138869234458,
0.0
],
[
0.0,
0.0,
0.4991552437225757
]
],
"R_g": [
[
0.999979342528037,
-0.00045421361799804057,
-0.006411568231274507
],
[
-0.0002222054063646413,
0.9999961009525417,
-0.002783649487862618
],
[
0.009714004498496572,
0.0076410932929403316,
0.9999236229882218
]
],
"K_ga": [
[
0.00038961109153281214,
-0.00045815544407725904,
-2.9844819440802578e-05
],
[
0.0004320123104123373,
0.0004871099695542217,
0.0006178601683550832
],
[
9.966554956457076e-05,
-9.110691206112818e-05,
0.0002356473632041452
]
],
"b_g": [
-0.5996686296674446,
-0.36984313466306884,
0.0587739354671091
],
"cal_type": "Ferraris",
"_format_version": "2.0.0"
}
The final cal_info now contains all information to calibrate future data recordings from the same sensor.
For now we will save it to disk and then see how to load it again.
Note, that we will use a temporary folder here. In reality you would chose some folder where you can keep the calibration files save until eternity.
import tempfile
d = tempfile.TemporaryDirectory()
d.name
'/tmp/tmpso_cz1by'
You can either use the to_json_file or to_hdf5 methods of FerrarisCalibration directly …
cal_info.to_json_file(Path(d.name) / "my_sensor_cal.json")
… or use the provided management tools to save the file in a predefined folder structure. Read more about this in the our guide on that topic.
from imucal.management import save_calibration_info
from datetime import datetime
file_path = save_calibration_info(
cal_info, sensor_id="imu1", cal_time=datetime(2020, 8, 12, 13, 21), folder=Path(d.name)
)
file_path
PosixPath('/tmp/tmpso_cz1by/imu1/Ferraris/imu1_2020-08-12_13-21.json')
In the latter case, we can use the helper functions find_calibration_info_for_sensor
and find_closest_calibration_info_to_date to find the calibration again.
from imucal.management import find_calibration_info_for_sensor
cals = find_calibration_info_for_sensor("imu1", Path(d.name))
cals
[PosixPath('/tmp/tmpso_cz1by/imu1/Ferraris/imu1_2020-08-12_13-21.json')]
In any case, we can use load_calibration_info to load the calibration if we know the file
path.
from imucal.management import load_calibration_info
loaded_cal_info = load_calibration_info(cals[0])
print(loaded_cal_info.to_json())
{
"acc_unit": "m/s^2",
"gyr_unit": "deg/s",
"from_acc_unit": "m/s^2",
"from_gyr_unit": "deg/s",
"comment": "My comment",
"K_a": [
[
0.9967458650887003,
0.0,
0.0
],
[
0.0,
1.0024376185894108,
0.0
],
[
0.0,
0.0,
1.0233953475452096
]
],
"R_a": [
[
0.9998620291746435,
-0.014830571004926794,
-0.007481763056406514
],
[
0.008576740442555632,
0.9999615197419686,
0.0018435180263559792
],
[
0.01333118773067902,
0.002003617949970859,
0.9999091283455711
]
],
"b_a": [
0.5371174244458201,
-0.6162029930860964,
0.3988673423902682
],
"K_g": [
[
0.5139621632144018,
0.0,
0.0
],
[
0.0,
0.4912138869234458,
0.0
],
[
0.0,
0.0,
0.4991552437225757
]
],
"R_g": [
[
0.999979342528037,
-0.00045421361799804057,
-0.006411568231274507
],
[
-0.0002222054063646413,
0.9999961009525417,
-0.002783649487862618
],
[
0.009714004498496572,
0.0076410932929403316,
0.9999236229882218
]
],
"K_ga": [
[
0.00038961109153281214,
-0.00045815544407725904,
-2.9844819440802578e-05
],
[
0.0004320123104123373,
0.0004871099695542217,
0.0006178601683550832
],
[
9.966554956457076e-05,
-9.110691206112818e-05,
0.0002356473632041452
]
],
"b_g": [
-0.5996686296674446,
-0.36984313466306884,
0.0587739354671091
],
"cal_type": "Ferraris",
"_format_version": "2.0.0"
}
After loading the calibration file, we will apply it to a “new” recording (we will just use the calibration session as example here).
calibrated_data = loaded_cal_info.calibrate_df(data, "m/s^2", "deg/s")
We can see the effect of the calibration, when we plot the acc norm in the beginning of the recording. The calibrated values are now much closer to 9.81 m/s^2 compared to before the calibration.
import matplotlib.pyplot as plt
from numpy.linalg import norm
plt.figure()
plt.plot(norm(data.filter(like="acc"), axis=1)[500:1000], label="before cal")
plt.plot(norm(calibrated_data.filter(like="acc")[500:1000], axis=1), label="after cal")
plt.legend()
plt.ylabel("acc norm [m/s^2]")
plt.show()
Finally, remove temp directory.
d.cleanup()
Total running time of the script: ( 0 minutes 1.409 seconds)
Estimated memory usage: 12 MB