CalibrateFM([ws, parfile]) | Description: |
Task([ws, parfile]) | Base class from which all tasks should be derived. |
getMultiFreqDelay(lines, freqs, ...) | Get multi-frequency delay. |
getOneSampleShifts(timeDiff, ...) | Scan the array of time differences. |
vectorAverage(directions) | Takes a list of (az, el) tuples |
Task:
Calibrate delays between antennas, and especially inter-station delays, using narrow-band radio transmitters. Especially transmitters with a known location are useful, e.g. the FM tower in Smilde (GPS long = 6.403565 E, lat = 52.902671 N).
Uses the phases (average and rms) obtained in the FindRFI Task; or will re-run that task if no phases are given as input.
Module author: Arthur Corstanje <a.corstanje@astro.ru.nl>
Description:
Calibrate antenna delays, especially inter-station delays, by using FM transmitter RFI in the data.
Usage:
See also:
plotfootprint
Example:
filefilter="/Volumes/Data/sandertv/VHECR/LORAtriggered/results/VHECR_LORA-20110716T094509.665Z/" crfootprint=cr.trun("plotfootprint",filefilter=filefilter,pol=polarization)
Input parameters
Output parameters
Get multi-frequency delay.
Properties
Parameter | Description |
---|---|
lines | – |
freqs | – |
phase_average | – |
median_phase_spreads | – |
modelTimes | – |
Scan the array of time differences.
Properties
Parameter | Description |
---|---|
timeDiff | – |
stationStartIndex | – |
interStationDelays | – |
Description:
Scan the array of time differences (between measured phases and the modeled incoming wave) for presence of offsets that are a multiple of 5 ns. Note: due to the modulo-2pi from the phases, only small multiples (effectively +/- 1) can be reliably corrected.
Input stationStartIndex contains the starting points in the array for each station. interStationDelays are subtracted here.
Returns array oneSampleShifts, containing integer # samples by which the data is shifted. So when it returns [0, 0, 1, 0, -1, ...] a positive number means that the data has to be advanced 1 sample i.e. sample 0 <– sample 1 Negative number the other way round.
(NB. check!)
Takes a list of (az, el) tuples Converts to cartesian vectors, sums them up to average Converts average back to (az, el) returns average (az, el)