Open jrs65 opened 5 years ago
@mondana anything else we want on here?
@jrs65 I substituted the percentage of bad feeds metric with a num_feeds_above_threshold metric which has labels frequency, source and threshold. I chose threshold levels 2.0, 3.0, 4.0, 5.0, 7.0, 10.0. The unit is a foot (one feed separation)
Feeds that are flagged by flagging broker are excluded when calculating the number of bad feed positions above these thresholds. This should make the analyzer immune against flagged bad feeds.
If lets say eigenvalue on versus off source is greater than some threshold, then the analyzer will flag the data for that specific frequency. This was intended to serve as a test for RFI issues in a frequency bin.
Since it's using the chemical
dataset - I think the only thing that needs to have worked is the eigen decomposition of the visibility matrix at source transit. If understand that remark correctly, the feed position does not rely on derived gains/ or calibration.
As for rain jumps and decorrelated cylinders ... I have no idea at this point.
A brief summary of changes that would be good to have for the feed positions analyser:
Grafana:
Analyzer:
num_feeds_above_threshold{threshold="1.0",freq_id="14"}
, i.e. we pick a few (maybe ~10) distance thresholds and just return the number above each.Analysis:
There's a few questions we need to address from the analysis: