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Automatic Flight Test Report

import matplotlib.pyplot as plt
# Insert your logo into the generated images folder and re-write the directory below with your image name.
# This cell might need to be run twice to successfully load the image.
image = plt.imread("..\images\Your_logo_file_name_here.png")
_ = plt.figure(figsize=(20,8))
img = plt.imshow(image)
_ = plt.axis('off')

Flight No 1, 10/01/2019

Project description here.

Below is the Required code, please run this code to allow other code to function

# GRAPH_DATA_IMPORT
import sys
import pickle as pk
import warnings

# Hides warnings
warnings.filterwarnings('ignore')

# Creates a link to where the code is stored.
sys.path.append("C:\\Users\\aw6g15\\University of Southampton\\Weishaeupl - Documents\\PhD\\2019\\AutoFLpy\\AutoFLpy\\autoflpy")
from autoflpy.util.plotting import *
from autoflpy.util.metar_processing import *
#Path to the compressed data.
data_file_path = "C:\\Users\\aw6g15\\University of Southampton\\Weishaeupl - Documents\\PhD\\2019\\AutoFLpy\\AutoFLpy\\autoflpy\\user_files\\excel_file_path\\20190110_Flight01.pkl"
# Uncompressing the data.
values_list = pk.load(open(data_file_path, "rb"))
print("Data imported")

PROJ: proj_create_from_database: Cannot find proj.db

Data imported

Table of Contents

• Weather information
• METAR information
• Runway information
• Graphs
  • GNSS altitude, signal strength and speed plotted against time
  • GNSS latitude and longitude
  • Flight computer battery voltage and current
  • Control inputs plotted against time
  • Air data aginst time
  • Flight attitude
  • Flight computer vibration data
  • CSV temprature data
  • CSV battery data
  • Autopilot data
  • Take-off data

Weather Information

Flight	Action time (hh:mm)	Pressure (Pa)	Temperature (C)	Wind direction (degrees)	Wind speed (mps)
Flight 1 20190110	15:15	103000	10.2	20	2

METAR Information

The METARs for EGHE were:

type: routine report, cycle 16 (automatic report) (manually corrected report)

time: Thu Jan 10 15:50:00 2019

temperature: 9.0 C

dew point: 5.0 C

wind: NW to NNE at 4 knots

visibility: greater than 10000 meters

pressure: 1032.0 mb

sky: a few clouds at 800 feet

 broken clouds at 2000 feet

METAR: COR EGHE 101550Z 02004KT 320V030 9999 FEW008 BKN020 09/05 Q1032



type: routine report, cycle 15 (automatic report)

time: Thu Jan 10 15:20:00 2019

temperature: 9.0 C

dew point: 5.0 C

wind: NNE at 3 knots

visibility: greater than 10000 meters

pressure: 1032.0 mb

sky: broken clouds at 2200 feet

METAR: EGHE 101520Z 03003KT 9999 BKN022 09/05 Q1032



This METAR data was from:https://www.ogimet.com/display_metars2.php?lang=en&lugar=EGHE&tipo=ALL&ord=REV&nil=SI&fmt=html&ano=2019&mes=01&day=10&hora=15&anof=2019&mesf=01&dayf=10&horaf=15&minf=59&send=send

Runway Information

Flight	Runway surface	Surface condition	Take off direction
Flight 1 20190110	Grass	Dry	000

Graphs

Below are the Graphs of the data recorded from the flight. For more information on how to manipulate these graphs and customise them, please visit https://autoflpy.readthedocs.io/en/latest/examples.html.

marker_list = []

GNSS altitude, signal strength and speed plotted against time

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "altitude", "gps"], ["x", "time", "gps"]], values_list, x_limits, y_limits, marker_list)

Altitude - from the GPS data set. This is an altitude above ground level and is using the GNSS.

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "hdop", "gps"]], [["y", "number of satellites", "gps"], ["x", "time", "gps"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

HDop - horizontal dilution of precision. HDop is a factor in determining the relative accuracy of a horizontal position. The smaller the HDop number, the better the data accuracy.

Number of Satellites - The number of satellites whose signal is being received. The type of satellite network being used depends on you sensor.

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "airspeed", "arsp"], ["x", "time", "arsp"]], [["y", "groundspeed", "gps"], ["x", "time", "gps"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

Airspeed - the type of airspeed (IAS, TAS, CAS, EAS) depends on the sensor you are using.

Groundspeed - from the GPS data set. This is the speed travelled along the ground and is derived from GNSS measurements.

GNSS latitude and longitude

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "latitude", "gps"], ["x", "longitude", "gps"]], values_list, x_limits, y_limits, marker_list)

Location does not have high resolution Stamen terrain map data. Defaulting to OpenStreetMap data.

Position data - from the GPS data set.

This is a completely fictitious, illustrative virtual flight and this example should in no way be seen as a suggestion of the suitability of the location for flight operations. All flights in UK airspace should be conducted in the framework set out by the Civil Aviation Authority.

Flight computer battery voltage and current

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "current", "bat"], ["x", "time", "bat"]], [["y", "voltage", "bat"], ["x", "time", "bat"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

Battery voltage - as read by the power sensor.

Current - as read by the power sensor.

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "current", "bat"], ["x", "time", "bat"]], [["y", "ch3", "rcou"], ["x", "time", "rcou"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

Control outputs plotted against time

The following variables are represented in micro seconds (us). This is a Pulse Width Modulation (PWM) signal which is a way of representing an analogue signal digitally. It is used in many applications such as servo motors.

NOTE: Channels need to be mapped to your system. To change the channel mapping and names, access the Name_converter_list.txt file and change the following names.

Example:

RCOU, C5, CH5, us

to

RCOU, C5, throttle_CH5, us

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["x", "time", "rcou"], ["y", "ch5", "rcou"], ["y", "ch6", "rcou"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["x", "time", "rcou"], ["y", "ch3", "rcou"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["x", "time", "rcou"], ["y", "ch1", "rcou"], ["y", "ch2", "rcou"], ["y", "ch3", "rcou"]], values_list, x_limits, y_limits, marker_list)

Air data against time

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "airspeed", "arsp"], ["x", "time", "arsp"]], [["y", "altitude", "baro"], ["x", "time", "baro"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

Altitude - from the barometer data set. This is the altitude derived from the air pressure and is relative to the arming altitude.

Airspeed - the type of airspeed (IAS, TAS, CAS, EAS) depends on the sensor you are using.

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "temp", "arsp"], ["x", "time", "arsp"]], values_list, x_limits, y_limits, marker_list)

Temperature - measured by the airspeed sensor.

Flight attitude

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "pitch", "ctun"], ["x", "time", "ctun"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "roll", "ctun"], ["x", "time", "ctun"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "aoa", "aoa"], ["x", "time", "aoa"]], values_list, x_limits, y_limits, marker_list)

AOA - Angle Of Attack. This is the angle between the incoming air and the aircraft's horizontal plane. It is a calculated measurement.

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "ssa", "aoa"], ["x", "time", "aoa"]], values_list, x_limits, y_limits, marker_list)

SSA - Side Slip Angle. The angle between the incoming air and the aircraft's vertical plane. It is a calculated measurement.

Flight computer vibration data

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["x", "time", "vibe"], ["y", "vibex", "vibe"], ["y", "vibey", "vibe"], ["y", "vibez", "vibe"]], values_list, x_limits, y_limits, marker_list)

CSV temprature data

The CSV data section serves to show the user how to add additional, non-flight computer related data to the report. In this example, the data is recorded by an arduino.

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["x", "time", "arduinomicro"], ["y", "temp0", "arduinomicro"], ["y", "temp1", "arduinomicro"], ["y", "temp2", "arduinomicro"], ["y", "temp3", "arduinomicro"], ["y", "temp4", "arduinomicro"], ["y", "temp5", "arduinomicro"]], values_list, x_limits, y_limits, marker_list)

The above graph shows some mock data of 6 additional temperature sensors plotted. These were recorded by an external arduino.

CSV battery data

x_limits=["x_min", "x_max"]
y_limits_left=["y_min", "y_max"]
y_limits_right=["y_min", "y_max"]
legend_location=1
multiaxis_graph_plotter([["y", "battvoltage", "arduinomicro"], ["x", "time", "arduinomicro"]], [["y", "battcharging", "arduinomicro"], ["x", "time", "arduinomicro"]], values_list, x_limits, y_limits_left, y_limits_right, marker_list, legend_location)

The above data shows additional mock battery data recorded by an arduino. In this case, battvoltage represents the voltage of the external battery and battcharging is a binary representation of whether or not the battery is currently being charged.

Autopilot data

The following section represents data about the autopilot's operation. The data can be understood through the following example:

desired_roll - the roll that the autopilot is instructing the aircraft to do.

roll - the roll that the aircraft is actually doing.

This data is useful for troubleshooting flight computer problems.

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "desired roll", "att"], ["y", "roll", "att"], ["x", "time", "att"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "desired pitch", "att"], ["y", "pitch", "att"], ["x", "time", "att"]], values_list, x_limits, y_limits, marker_list)

x_limits=["x_min", "x_max"]
y_limits=["y_min", "y_max"]
graph_plotter([["y", "desired yaw", "att"], ["y", "yaw", "att"], ["x", "time", "att"]], values_list, x_limits, y_limits, marker_list)

Take-off data

take_off_graph(values_list)

Detected take-off time:  34.01
Groundspeed at detected take-off:  24.2
Detected ground run start time:  31.21