Plot model kinematics

In this example, we use PyThea’s utilities to plot the kinematics of the geometrical model from a fitting file.

Import Required Modules

from datetime import datetime

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from IPython.display import display

from PyThea.data.sample_data import json_fitting_file_sample_data
from PyThea.utils import model_fittings, plot_fitting_model

Import a sample JSON fitting file using json_fitting_file_sample_data.fetch() method. This sample data contains a series of fitted ellipsoids for a selected event. Then use the model_fittings.load_from_json(json_fitting_file) to load the model parameters.

json_fitting_file = json_fitting_file_sample_data.fetch('FLX1p0D20211028T153500MEllipsoid.json')
model_fittings_class = model_fittings.load_from_json(json_fitting_file)

Print the information about the selected event and geometrical model using print() statements and the parameters of the model fittings using display(model_fittings_class.parameters)

print(f'Selected Event: {model_fittings_class.event_selected}')
print(f'Geometrical Model: {model_fittings_class.geometrical_model}')
display(model_fittings_class.parameters)

Selected Event: FLX1.0|2021-10-28T15:35:00
Geometrical Model: Ellipsoid
                         hgln   hglt        crln  ...  alpha  imager  fits_file
2021-10-28 15:48:18.842  3.62 -25.07  274.118677  ...   0.86     LC2
2021-10-28 16:00:17.986  3.62 -23.56  274.008914  ...   0.78     LC2
2021-10-28 16:06:14.730  3.62 -22.35  273.954464  ...   0.78     LC3
2021-10-28 16:12:18.325  3.62 -21.25  273.898969  ...   0.77     LC2
2021-10-28 16:18:14.323  3.62 -21.00  273.844633  ...   0.77     LC3
2021-10-28 16:30:14.364  3.62 -20.34  273.734733  ...   0.77     LC3

[6 rows x 15 columns]

Use the stored curve fitting parameters

This part uses the curve fitting method parameters that are stored in the json fitting file. The curve fitting parameters are selected by the user during the fitting process in the application. View the curve fitting method parameters. These consist of the method (type) of the curve fitting (‘polynomial’ in this example) and the order (2nd order in this example).

print(f'Fitting Parameters: {model_fittings_class.kinematics["fit_method"]}')

Fitting Parameters: {'type': 'polynomial', 'order': 2}

Use the utility function plot_fitting_model to plot the kinematic figures. Select for plt_type the HeightT option to plot the height-time profile of the geometrical fitting and the SpeedT to plot the speed-time profile.

fig, axis = plot_fitting_model(model_fittings_class,
                               fit_args=model_fittings_class.kinematics['fit_method'],
                               plt_type='HeightT')
axis.set_xlim([datetime(2021, 10, 28, 15, 40, 0), datetime(2021, 10, 28, 16, 40, 0)])
axis.xaxis.set_minor_locator(mdates.MinuteLocator(byminute=range(60), interval=1))
plt.show()

fig, axis = plot_fitting_model(model_fittings_class,
                               fit_args=model_fittings_class.kinematics['fit_method'],
                               plt_type='SpeedT')
axis.set_xlim([datetime(2021, 10, 28, 15, 40, 0), datetime(2021, 10, 28, 16, 40, 0)])
axis.xaxis.set_minor_locator(mdates.MinuteLocator(byminute=range(60), interval=1))
axis.set_ylim([1000, 2200])
plt.show()

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Manual fitting parameters

To use other curve fitting parameters parse different ‘type’ and ‘order’. In the example bellow we plot the height time profile using a linear fit.

fit_method = {'type': 'polynomial', 'order': 1}

fig, axis = plot_fitting_model(model_fittings_class,
                               fit_args=fit_method,
                               plt_type='HeightT')
axis.set_xlim([datetime(2021, 10, 28, 15, 40, 0), datetime(2021, 10, 28, 16, 40, 0)])
axis.xaxis.set_minor_locator(mdates.MinuteLocator(byminute=range(60), interval=1))
plt.show()