BST Data
Beamlet statictics (BSTs) are effectively dyanmic spectra produced by the station at a regular, relatively low cadence, interval.
Generating BSTs
BSTs are generated using the rspctl command in conjunction with a running beamctl command (as a result, the station must be in at least swlevel 3). It will write an output summary of the last Nsec (int) of antenna correlations to disk in a given folder_location. The overall synctax to call a rspctl command is
user1@lcu$ rspctl --statistics=beamlet --duration=n_observation_sec --integration=n_sec --directory=folder_location/The rspctl command will generate data for Nobservation_sec or until the process is killed. As a result, the process must be kept active in a screen or by trailing the execution with an ampersand to send it to the background.
Enabling BST generation with the rspctl command will disable the CEP packet data stream, which can be re-enabled afterwards by calling $ rspctl --datastream=1 . You can verify this worked by calling $ rspctl --datastream to get the current status of the datastream.
BST Data Format
BSTs are frequency-majour files that are written to disk every integration period. They do not come with any metadata outside of the stating time, output ring (only 0 available to international stations) and antenna polarization which are visible within the file name.
Each beamlet controlled by a beamctl command will generate a single output sample per intergration. So the output array dimensions will depend on your observation, and may be up to 244 in 16-bit mode, 488 in 8-bit mode and 976 in 4-bit mode.
The output data is a float, 4 times the size of your input.
| Bitmode | Output (float) |
| 4 | float32 (verify) |
| 8 | float64 |
| 16 | float128 |
BST Plotting
A fast way to plot BSTs in Python can be achieved with the numpy and matplotlib libraries. If you want to test this out for yourself, there are a large number of BSTs available on the data.lofar.ie archive which contain 488 subband observations from our Solar monitoring campaign.
import numpy as np
import matplotlib.pyplot as plt
bstLocation = "/path/to/bst.dat"
#bstDtype = np.float32 # 4-bit obs
#bstDtype = np.float64 # 8-bit obs
#bstDtype = np.float128 # 16-bit obs
rawData = np.fromfile(bstLocation, dtype = bstDtype)
#numBeamlets = 976 # 4-bit obs
#numBeamlets = 488 # 8-bit obs
#numBeamlets = 244 # 16-bit obs
rawData = rawData.reshape(-1, numBeamlets)
plt.figure(figsize=(24,12))
plt.imshow(np.log10(rawData.T), aspect = "auto")
plt.xlabel("Time Samples"); plt.ylabel("Subband"); plt.title("BST Data ({})".format(bstLocation))
plt.show()