SessionAnalyzer

class Data_Reduction.SLATool.SessionAnalyzer(project=None, dss=None, year=None, DOY=None)

Bases: object

Tool for reducing multiple data reduction sessions

Attributes::

datapath DOY DSS examiners logger project projectdatapath projworkpath year

Example::

In [1]: from Data_Reduction.SLATool import SessionAnalyzer In [2]: sa = SessionAnalyzer(project=’67P’, year=2015, DOY=204) In [3]: x, sum_y, sum_Tsys, sum_intgr = sa.get_average()

Methods Summary

Tsys_scaling([weather_data, examiner_keys, Tatm])	factors to rescale the system powers or temperatures to K based on FE noise
fit_Tsys_to_airmass([weather_data, …])	Fit tipping curve data implicit in sessions elev and Tsys data
get_average([source])	Computes average spectrum for a source in the session
get_good_weather_data([examiner_keys])	Get data from specific datasets for analyzing environmental conditions.
get_sources()	Gets a list of sources from all the datasets in the session
make_session_names()	creates the directory path to the data from the session
open_datafiles(datafiles)	Opens the files for the session in /usr/local/RA_data/FITS
rescale_Tsys(gain, siggain, Kpam, sigKpam)	rescale system temperatures from tipping curve fits

Methods Documentation

Tsys_scaling(weather_data=None, examiner_keys=None, Tatm=250)

factors to rescale the system powers or temperatures to K based on FE noise

: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 Tatm : air temperature along line of sight :type Tatm : float

fit_Tsys_to_airmass(weather_data=None, examiner_keys=None, Tatm=250, linear=True)

Fit tipping curve data implicit in sessions elev and Tsys data

Returns numpy arrays with indices for sig/ref state, subchannel, beam, IF. The first returned value is the zero airmass intercept and the second its standard deviation. The second returned value is K/airmass

: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 Tatm : air temperature along line of sight :type Tatm : float

:param linear : use the linear (low tau) approximation :type linear : True

get_average(source='67P_CG_201')

Computes average spectrum for a source in the session

Prints r.m.s. noise for each dataset and all datasets together

:param source : source for which averaging is done

get_good_weather_data(examiner_keys=None)

Get data from specific datasets for analyzing environmental conditions.

Returns a dict with keys ‘TAMBIENT’, ‘WINDDIRE’, ‘UNIXtime’, ‘TSYS’, ‘HUMIDITY’, ‘PRESSURE’, ‘ELEVATIO’, ‘WINDSPEE’. The data asociated with each key 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.

get_sources()

Gets a list of sources from all the datasets in the session

make_session_names()

creates the directory path to the data from the session

It is crafted from the name of the first FITS file

open_datafiles(datafiles)

Opens the files for the session in /usr/local/RA_data/FITS

rescale_Tsys(gain, siggain, Kpam, sigKpam)

rescale system temperatures from tipping curve fits