* Run the program with main.py¶
For the first example we will showcase how you can use the full features of the package with main.py. Simply executing the main.py by giving the name of .csv file that contains the positional data of the satellite, as an argument in the function process(data_file):
def process(data_file, error_apriori): ''' Given a .csv data file in the format of (time, x, y, z) applies both filters, generates a filtered.csv data file, prints out the final keplerian elements computed from both Lamberts and Interpolation and finally plots the initial, filtered data set and the final orbit. Args: data_file (string): The name of the .csv file containing the positional data error_apriori (float): apriori estimation of the measurements error in km Returns: Runs the whole process of the program '''
Simply input the name of the .csv file in the format of (time, x, y, z) and tab delimiter like the orbit.csv that is located in the src folder and the process will run. You also need to input a apriori estimation of the measurements errors, which in the example case is 20km per point (points every 1 second). In the case you are using your own positional data set you need to estimate this value and input it because it is critical for the filtering process:
run = process("orbit.csv")
If the format of you data is (time, azimuth, elevation, distance) you can use the input_transf function first and be sure that the delimiter for the data file is tab delimiter since this is the one read_data supports.
The process that will run with the use of the process function is, first the program reads your data from the .csv file then, applies both filters (Triple moving average and Savintzky - Golay), generates a .csv file called filtered, that included the filtered data set, computes the keplerian elements of the orbit with both methods (Lamberts - Kalman and Spline Interpolation) and finally prints and plots some results. More specifically, the results printed by this process will be first the sum and mean value of the residuals (difference between filtered and initial data), the computed keplerian elements in format of (a - semi major axis, e - eccentricity, i - inclination, ω - argument of perigee, Ω - right ascension of the ascending node, v - true anomaly) and a 3d matplotlib graph that plots the initial, filtered data set and the final computed orbit described by the keplerian elements (via the interpolation method).
- Reads the data
- Uses both filters on them (Triple moving average and Savintzky - Golay )
- Generates a .csv file called filtered that includes the filtered data set
- Computes keplerian elements with both methods (Lamberts - Kalman and Spline Interpolation)
- Prints results and plot a 3d matplotlib graph
- Sum and mean of the residuals (differences between filtered and initial data set)
- Final keplerian elements from both methods (first column : Lamberts - Kalman, second column : Spline Interpolation)
- 3d matplotlib graph with the initial, filtered data set and the final orbit described by the keplerian elements from Spline Interpolation
Measurement unit for distance is kilometer and for angle degrees
The output should look like the following image.
* Run the program with automated.py¶
automated.py is another flavour of main.py that is supposed to run on a server. It keeps listening for new files in a particular directory and processes them when they arrive.
All the processing invloved in this module is identical to that of main.py.
For testing purpose some files have already put in a folder named src. These are raw unprocessed files. There is another folder named dst which contains processed files along with a graph saved in the form of svg.
To execute this script, change the directory to the script’s directory:
and run the code using python3:
and thats it. This will keep listening for new files and process them as they arrive.
- Initialize an empty git repository in src folder
- Read the untracked files of that folder and put them in a list
- Process the files in this list and save the results(processed data and graph) to dst folder
- Stage the processed file in the src folder in order to avoid processing the same files multiple times.
- Check for any untracked files in src and apply steps 2-4 again.
* Using certain modules¶
In this example we are not going to use the main.py, but some of the main modules provided. First of all lets clear the path we are going to follow which is fairly straightforward. Note that we are going to use the same orbit.csv that is located inside the src folder and has tab delimeter (read_data.py reads with this delimiter).
- Read the data
- Filter the data
- Compute keplerian elements for the final orbit
So first we read the data using the util/read_data.load_data function. Just input the .csv file name into the function and it will create a numpy array with the positional data ready to be processed:
data = read_data.load_data("orbit.csv")
If the format of you data is (time, azimuth, elevation, distance) you can use the util/input_transf.spher_to_cart function first. And it is critical for the x, y, z to be in kilometers.
We continue by applying the Triple moving average filter:
data_after_filter = triple_moving_average.generate_filtered_data(data, 3)
We suggest using 3 as the window size of the filter. Came to this conclusion after a lot of testing. Next we apply the second filter to the data set which will be of a larger window size so that we can smooth the data set in a larger scale. The optimal window size for the Savintzky - Golay filter is being computed by the function golay_window.c(error_apriori) in which we only have to input the apriori error estimation for the initial data set (or the measurements error):
error_apriori = 20.0 c = golay_window.c(error_apriori) window = len(data) / c window = int(window)
The other 2 lines after the use of the golay_window.c(error_apriori) are needed to compute the window size for the Savintzky - Golay filter and again for the polynomial parameter of the filter we suggest using 3:
data_after_filter = sav_golay.golay(data_after_filter, window, 3)
At this point we have the filtered positional data set ready to be inputed into the Lamberts - Kalman and Spline interpolation algorithms so that the final keplerian elements can be computed:
kep_lamb = lamberts_kalman.create_kep(data_after_filter) kep_final_lamb = lamberts_kalman.kalman(kep_lamb, 0.01 ** 2) kep_inter = interpolation.main(data_after_filter) kep_final_inter = lamberts_kalman.kalman(kep_inter, 0.01 ** 2)
With the above 4 lines of code the final set of 6 keplerian elements is computed by the two methods. The output format is (semi major axis (a), eccentricity (e), inclination (i), argument of perigee (ω), right ascension of the ascending node (Ω), true anomaly (v)). So finally, in the variables kep_final_lamb and kep_final_inter a numpy array 1x6 has the final computed keplerian elements.
If the orbit you want to compute is polar (i = 90) then we suggest you to use only the interpolation method.