SessionPlotter

class Data_Reduction.SLAPlotter.SessionPlotter(project=None, dss=None, year=None, DOY=None)

Bases: Data_Reduction.SLATool.SessionAnalyzer

Methods Summary

plot_bmsw_diff([figtitle, savefig])	Plot the difference between the two beams for each pol
plot_elev_and_Tsys([figtitle, weather_data, …])	Plots Tsys vs time and vs elevation.
plot_passband([figtitle])	Plots the passbands as dynamic spectra for the whole session.
plot_possw_diff([figtitle, savefig])	Plots the difference between a SIG=True scan and the next SIG=False scan.
plot_weather([figtitle, weather_data, …])	Plots temperature, humidity and pressure from get_good_weather_data()
plot_wind([figtitle, weather_data, …])	Plots wind velocity and direction

Methods Documentation

plot_bmsw_diff(figtitle=None, savefig=True)

Plot the difference between the two beams for each pol

This is an indicator of receiver gain stability.

plot_elev_and_Tsys(figtitle=None, weather_data=None, examiner_keys=None, savepath=None)

Plots Tsys vs time and vs elevation.

The data asociated with each key of ‘weather_data’ is a dict with numpy array for (SIG state) True and for False. The ‘TSYS’ array has four axes representing:

time index   - 0-based sequence in order of matplotlib datenum
subchannel   - CYCLE value
beam         - 1-based number sequence
IF           - 1-based number sequence, usually representing pol

The other keys have only a time axis.

:param figtitle : figure title :type figtitle : str

:param weather_data : consolidated environmental data :type weather_data : dict

:param examiner_keys : keys of files from this date to be included :type examiner_keys : list of int

:param savepath : path to directory to save figure :type savepath : str

plot_passband(figtitle=None)

Plots the passbands as dynamic spectra for the whole session.

This plots the passbands for each SINGLE DISH table in the session. Generally, there is only one.

plot_possw_diff(figtitle=None, savefig=True)

Plots the difference between a SIG=True scan and the next SIG=False scan.

This eliminates receiver systematics.

plot_weather(figtitle=None, weather_data=None, examiner_keys=None, savepath=None)

Plots temperature, humidity and pressure from get_good_weather_data()

plot_wind(figtitle=None, weather_data=None, examiner_keys=None, savepath=None)

Plots wind velocity and direction