Post-processing Results in Python

Content of this page

Using NumPy Arrays
- Converting simulator results to numpy.ndarray
- Subset observations from a numpy.ndarray
Using pandas DataFrames

The tudat C++ libraries were build around the use of a std::map<double, Eigen::vectorXd> as the data type of the state and dependent variable histories. In Python, this is returned as a dict[float, np.ndarray].

# ...

# Create simulation object and propagate dynamics.
dynamics_simulator = numerical_simulation.create_dynamics_simulator(
    bodies,
    propagator_settings )
state_history = dynamics_simulator.propagation_results.state_history

The returned data type from the simulator is returned as (for a single translational popagation):

state_history = {
    t[0]: np.array([pos_x[0], pos_y[0], pos_z[0], vel_x[0], vel_y[0], vel_z[0]]),
    t[1]: np.array([pos_x[1], pos_y[1], pos_z[1], vel_x[1], vel_y[1], vel_z[1]]),
    t[2]: np.array([pos_x[2], pos_y[2], pos_z[2], vel_x[2], vel_y[2], vel_z[2]]),
    # ...
    t[-1]: np.array([pos_x[-1], pos_y[-1], pos_z[-1], vel_x[-1], vel_y[-1], vel_z[-1]]),
}

Note

vel is a shortening of “velocity” & pos is a shortening of “position”.

Using NumPy Arrays 

Converting simulator results to `numpy.ndarray`

The provided utility result2array converts the dict[float, numpy.ndarray] to a numpy.ndarray as:

# Required: Import for result conversion.
# from tudatpy.util import result2array

# the use of the utility function:
states_array = result2array(state_history)

This returns the equivalent expanded expression:

states_array = np.array([
    [t[0], pos_x[0], pos_y[0], pos_z[0], vel_x[0], vel_y[0], vel_z[0]],
    [t[1], pos_x[1], pos_y[1], pos_z[1], vel_x[1], vel_y[1], vel_z[1]],
    [t[2], pos_x[2], pos_y[2], pos_z[2], vel_x[2], vel_y[2], vel_z[2]],
    # ...
    [t[-1], pos_x[-1], pos_y[-1], pos_z[-1], vel_x[-1], vel_y[-1], vel_z[-1]],
])

Subset observations from a `numpy.ndarray`

The following examples deal with specific cases that may arise during post-processing of simulator results. In some cases, you may wish to combine multiple examples for your desired processing step.

Extract state/dependent variables at a given time point:

t_0_state = states_array[states_array[:, 0] == 0] # returns a 2d array with one row

t_0_state = t_0_state[0] # extracts a 1d array from the 2d array

t_0_state = t_0_state[1:] # slices the array, ignoring the time value

As a one-liner:

t_0_state = states_array[states_array[:, 0] == 0][0][1:0]

Extract state/dependent variables within a time period:

t_start = 100
t_end = 1000
t_period_array = states_array[(states_array[:, 0] > t_start) & (states_array[:, 0] < t_end)]

Using pandas DataFrames 

Tip

Download the pandas cheatsheet! pdf

Converting simulator results to `pandas.DataFrame`

The following examples demonstrate how pandas can be used in post-processing of simulation results from tudatpy. The examples assume tutorial 1 of tudatpy is being used. In this case, a single satellite translational state is propagated.

from tudatpy.util import result2array
import pandas as pd

# the use of the utility function:
df = pd.DataFrame(data=result2array(state_history),
                  columns="t pos_x pos_y pos_x vel_x vel_y vel_z".split())

	t	pos_x	pos_y	pos_z	vel_x	vel_y	vel_z
0	0	7.03748e+06	3.23806e+06	2.15072e+06	-1465.66	-40.9584	6622.8
1	10	7.02256e+06	3.23753e+06	2.21687e+06	-1519.53	-65.7719	6606.06
2	20	7.00709e+06	3.23674e+06	2.28284e+06	-1573.2	-90.5372	6588.85
…	…	…	…	…	…	…	…
8638	86380	-4.46865e+06	-1.38981e+06	6.07194e+06	-4630.32	-2437.64	-4843.76
8639	86390	-4.51475e+06	-1.41413e+06	6.02323e+06	-4590.52	-2425.21	-4897.34
8640	86400	-4.56045e+06	-1.43832e+06	5.97399e+06	-4550.21	-2412.54	-4950.63

Printing the DataFrame will output:

>> print(df)

            t         pos_x  ...        vel_y        vel_z
       0.0  7.037484e+06  ...   -40.958395  6622.797609
      10.0  7.022558e+06  ...   -65.771910  6606.061690
      20.0  7.007094e+06  ...   -90.537171  6588.849542
      30.0  6.991095e+06  ...  -115.252840  6571.163857
      40.0  6.974562e+06  ...  -139.917596  6553.007326
...       ...           ...  ...          ...          ...
86360.0 -4.375255e+06  ... -2461.730180 -4735.762481
86370.0 -4.422146e+06  ... -2449.807206 -4789.899972
86380.0 -4.468646e+06  ... -2437.635419 -4843.761214
86390.0 -4.514750e+06  ... -2425.214496 -4897.340123
86400.0 -4.560454e+06  ... -2412.544139 -4950.630569

Adjusting the print options of a `pandas.DataFrame`

The print of the DataFrame can be adjusted using the following (adjust the values as needed):

import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

>> print(df)

            t         pos_x         pos_y         pos_x        vel_x        vel_y        vel_z
       0.0  7.037484e+06  3.238059e+06  2.150724e+06 -1465.657627   -40.958395  6622.797609
      10.0  7.022558e+06  3.237525e+06  2.216869e+06 -1519.533718   -65.771910  6606.061690
      20.0  7.007094e+06  3.236744e+06  2.282844e+06 -1573.199711   -90.537171  6588.849542
      30.0  6.991095e+06  3.235715e+06  2.348644e+06 -1626.653078  -115.252840  6571.163857
      40.0  6.974562e+06  3.234439e+06  2.414266e+06 -1679.891319  -139.917596  6553.007326
...       ...           ...           ...           ...          ...          ...          ...
86360.0 -4.375255e+06 -1.340820e+06  6.167734e+06 -4708.356140 -2461.730180 -4735.762481
86370.0 -4.422146e+06 -1.365378e+06  6.120105e+06 -4669.596584 -2449.807206 -4789.899972
86380.0 -4.468646e+06 -1.389815e+06  6.071937e+06 -4630.319239 -2437.635419 -4843.761214
86390.0 -4.514750e+06 -1.414129e+06  6.023231e+06 -4590.524624 -2425.214496 -4897.340123
86400.0 -4.560454e+06 -1.438318e+06  5.973991e+06 -4550.213316 -2412.544139 -4950.630569

Subset observations from a `pandas.DataFrame`

Adding dependent columns to a `pandas.DataFrame`

# the use of the utility function:
df["r"] = np.sqrt(np.square(df[["pos_x", "pos_y", "pos_z"]]).sum(axis=1))

	t	pos_x	pos_y	pos_z	vel_x	vel_y	vel_z	r
0	0	7.03748e+06	3.23806e+06	2.15072e+06	-1465.66	-40.9584	6622.8	8.0397e+06
1	10	7.02256e+06	3.23753e+06	2.21687e+06	-1519.53	-65.7719	6606.06	8.0444e+06
2	20	7.00709e+06	3.23674e+06	2.28284e+06	-1573.2	-90.5372	6588.85	8.04905e+06
…	…	…	…	…	…	…	…	…
8638	86380	-4.46865e+06	-1.38981e+06	6.07194e+06	-4630.32	-2437.64	-4843.76	7.66608e+06
8639	86390	-4.51475e+06	-1.41413e+06	6.02323e+06	-4590.52	-2425.21	-4897.34	7.65912e+06
8640	86400	-4.56045e+06	-1.43832e+06	5.97399e+06	-4550.21	-2412.54	-4950.63	7.65213e+06

Outputting a LaTeX table from a `pandas.DataFrame`

print(pd.concat([df.head(3), df.tail(3)]).to_latex())

\begin{tabular}{lrrrrrrrr}
\toprule
{} &        t &         pos_x &         pos_y &         pos_z &        vel_x &        vel_y &        vel_z &             r \\
\midrule
0    &      0.0 &  7.037484e+06 &  3.238059e+06 &  2.150724e+06 & -1465.657627 &   -40.958395 &  6622.797609 &  8.039703e+06 \\
1    &     10.0 &  7.022558e+06 &  3.237525e+06 &  2.216869e+06 & -1519.533718 &   -65.771910 &  6606.061690 &  8.044402e+06 \\
2    &     20.0 &  7.007094e+06 &  3.236744e+06 &  2.282844e+06 & -1573.199711 &   -90.537171 &  6588.849542 &  8.049053e+06 \\
8638 &  86380.0 & -4.468646e+06 & -1.389815e+06 &  6.071937e+06 & -4630.319239 & -2437.635419 & -4843.761214 &  7.666081e+06 \\
8639 &  86390.0 & -4.514750e+06 & -1.414129e+06 &  6.023231e+06 & -4590.524624 & -2425.214496 & -4897.340123 &  7.659115e+06 \\
8640 &  86400.0 & -4.560454e+06 & -1.438318e+06 &  5.973991e+06 & -4550.213316 & -2412.544139 & -4950.630569 &  7.652129e+06 \\
\bottomrule
\end{tabular}