import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Mission Milestones

This is the 4th notebook in the series. Earlier parts:

• ./010-video-export.html - for exporting still frames and timecodes for the videos
• ./020-video-analysis.html - for analyzing the video frames to match mission time with key events
• ./030-flight-data.html - for analyzing the flight data

Here we will compare the flight data with the mission timeline

df = pd.read_csv("mission-timeline.csv")
df

	Unnamed: 0	Frame	Time (s)	Description	∆T (s)
0	0	25	18.145000	First frame showing rocket plume	0.000000
1	1	31	18.245000	Rocket has cleared the viewport	0.100000
2	2	91	19.245000	Bottle re-acquired	1.100000
3	3	176	20.663333	Rocket at apogee, starting to descend	2.518333
4	4	208	21.196667	Rocket clearly descending and leaning right	3.051667
5	5	245	21.813333	Rocket starts executing flip to nose down	3.668333
6	6	265	22.146667	Rocket pointing downwards, precessing around z...	4.001667
7	7	388	24.198333	Possible rocket crash into building	6.053333

We also found that the launch moment according to the onboard clock was 442461 ms.

LAUNCH_TIME = 442461

Let's look how the video compares with the data

We can load in the sensor data and plot the key events from the video

baro = pd.read_csv('data/BarometerData-20250405-162645.csv')
baro = baro.iloc[11:]
baro = baro[baro['timestamp'] > 440000]
baro['mission_time'] = (baro.timestamp - LAUNCH_TIME) / 1000
baro = baro.set_index('mission_time')
baro.head()

	time	timestamp	pressure	altitude	average_altitude	temperature
mission_time
-2.428	16:20:54.830000	440033	102139.593750	0.210183	0.046781	28.620001
-2.175	16:20:55.083000	440286	102143.968750	-0.150846	0.055706	28.629999
-1.923	16:20:55.335000	440538	102141.726562	0.034192	0.055847	28.650000
-1.669	16:20:55.589000	440792	102138.843750	0.272032	0.047770	28.629999
-1.419	16:20:55.839000	441042	102140.531250	0.132747	0.044345	28.639999

imu = pd.read_csv('data/AccelData-20250405-162645.csv')
imu = imu.iloc[1000:]
imu = imu[imu['timestamp'] > 440000]
imu['mission_time'] = (imu.timestamp - LAUNCH_TIME) / 1000
imu = imu.set_index('mission_time')
imu['total_accel'] = np.sqrt(imu['accelX']**2 + imu['accelY']**2 + imu['accelZ']**2)
imu.head()

	time	timestamp	accelX	accelY	accelZ	gyroX	gyroY	gyroZ	temperature	total_accel
mission_time
-2.459	16:20:54.799000	440002	-0.081984	-0.155672	-1.013576	-1.40	-2.24	2.24	42.7500	1.028733
-2.451	16:20:54.807000	440010	-0.083448	-0.093208	-0.195688	-1.19	-2.59	1.82	42.7500	0.232261
-2.443	16:20:54.815000	440018	-0.122976	-0.087352	-1.002840	-1.82	-2.73	-8.12	42.6875	1.014121
-2.435	16:20:54.823000	440026	-0.131272	-0.179584	-0.770064	-2.80	-0.91	-4.20	42.6875	0.801549
-2.420	16:20:54.838000	440041	0.058072	-0.064904	-1.726544	0.49	-1.96	12.88	42.6875	1.728739

Let's create a function to annotate the plot with the mission milestones

def annotate_plot(fig, y):

    for _, row in df.iterrows():
        fig.add_vline(
            x=row['∆T (s)'],
            line_dash="dash",
            line_color="red",
        )
        fig.add_annotation(
            x=row['∆T (s)'],
            y=y,
            text=row['Description'],
            showarrow=False,
            textangle=-90,
            font=dict(color="grey", size=10),
            xanchor="left",
            yanchor="top"
        )

Flight profile

Let's first examine how our "video" milestones compare to the real flight data

filtered_data = baro['altitude']

fig = px.scatter(x=filtered_data.index, y=filtered_data.values, 
                 title="Rocket Altitude Measurements",
                 labels={"x": "Time (s)", "y": "Altitude (m)"})

fig.update_traces(mode='lines+markers')

fig.update_layout(height=1000)
fig.update_xaxes(range=[-0.2, 7])
fig.update_yaxes(range=[-1, 100])

annotate_plot(fig, 90)

fig.show()