This package ports the original antabfs program (see VLBI-utilities repository) to python3 and implements several other modifications. The main rationale behind this package is to enable gathering information provided by users in order to help building training sets that will be useful in machine learning approach to generating antab files automatically. The program also:
- unifies tabs/spaces convention of the original program,
- improves modularization/re-usability of the code,
- provides support for configuration files and command line parser,
- supports various fitting functions for Tcal,
- allows for semi-automatic outliers rejection,
- improves and extends implementations of linear regression by robust models
- adds new methods to define confidence ranges based on (per-BBC) noise levels,
- provides configuration and command line level options to control Tsys rejection ranges,
- helps extracting data from log/antabfs files (e.g from past EVN sessions),
- provides basic plotting options for the gathered outputs and
- introduces experimental, Makefile-based, antab files processing pipeline.
This version of the program is developed at Toruń VLBI station, but the core functionality related to reading logs and generating antabs is largely unchanged and compatible with antabfs program version '20201123'.
The name of the program "antabTr" is a variation of the original name and indicates that the program collects and stores users input as training data useful in machine learning.
The user has a choice to decide when or if the collected data should be shared and made visible to other contributors.
git clone https://github.com/bslew/antabTr.git
Download the package from git repository
Change directory to newly downloaded antabTr repository, create and activate virtual environment, update it and install required packages.
cd antabTr
python3 -m venv venv
. venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
Make sure the venv is located in antabTr subdirectory.
Execute
make install
Now edit ~/.config/antabfs/antabfs.ini config file to match your preferences. The config file currently defines the location of the .rxg files required to generate .antab files. This is the only user-side option that needs to be specified, although several other options are also available.
Use antabTr.py from virtual environment, i.e activate the environment unless you already have done so:
cd antabTr
. venv/bin/activate
"(venv)" prompt will appear to indicate you are in the virtual environment.
antabTr.py --help
Any pre-processing of the log files that were necessary before (e.g. fix-tsys.py)
should also be done with antabTr.py program. These steps are included in the simple
Makefile pipeline.
Go to your logs directory and use the antabTr.py program instead of antabfs.py:
antabTr.py ea065btr.log
The standard way of operation of the program is to detect strong outliers via linear regression of linear model fitted per scan basis. The Tsys outliers are considered of points stand out by more than 10% from the Ordinary Least Squares best fit. The outliers candidates are plotted in red and it is up to user to mark them and remove them. This behavior is enabled via --clean ols option (default)
This version of the program allows also for modifications to this approach by implementing Robust Linear Model of the statsmodels that penalizes outliers. This is done by adding --clean rlm option at the command line. Furthermore, although the best fit models are still done per scan basis the the detection of the outliers is done by considering median (or mean depending on configuration settings) 1-sigma noise levels in all scans in a given BBC. The outliers are marked in red as before based on gaussian confidence regions defined by alpha value specified in the configuration file. By default alpha=0.1 which detects points that lie outside 90% confidence region. Finally, the user doesn't need to remove the outliers manually as it is done automatically. At increased verbosity, the points that are to be removed are plotted along with the best fit model and confidence contours. Closing the plot window will save wisdom and the points will be automatically marked as outliers to be cleaned.
For the data points that are classified as outliers (red) the best fit model Tsys values are used instead. Data that are not classified as outliers (green) are retained. This is also the case for the original version of the program.
antabTr.py --clean rlm -v ea065btr.log
At even higher verbosity
antabTr.py --clean rlm -v -v ea065btr.log
the user is presented with the plots showing the coverage of the Tcal from RXG files for the observation BBC frequency (vertical line) as well as with the Tcal model to be used for interpolation. This is to verify that reasonable model is being used and that RXG actually covers the needed frequency range.
In this example the rolling average was set as fitting function and compared with 4-th order polynomial fit (green). The exact details of which fitting function should be used are defined in the antabfs.ini config file in section [CALIB]. For this example, two Tcal observation sessions were merged into RXG file (hence the zig-zag shape of Tcal black line indicating the uncertainty levels). The fitting function used for the calibration is always plotted in red. Typically 4-th order polynomial does a good job. For other options see antabfs.ini config file. The original approach from python2 version of the program was linear interpolation between data points nearest to the observation frequency. (This behavior is still available by setting smooth_rxg=raw in the config file.)
If Tcal is not measured for the required frequency extrapolations are possible.
In the config file it is possible to specify data rejection thresholds globally or per observation frequency basis. E.g. for Toruń station at C-band the typical Tsys would be around 30-40K. However, for certain observations (e.g. very strong sources such as Crab nebula) Tsys might be significantly higher in which case the config file setting may be inappropriate. The setting can be overridden from command line by explicitly providing --minTsys and --maxTsys values for a given logfile or for a given BBC in that log. This may be useful with --clean rlm options where the user need not remove outliers manually, but merely can view whether the detected points are reasonable eg:
antabTr.py --clean rlm -v ea065btr.log --maxTsys 150 --minTsys 20
With this setting all Tsys points outside [20,150] range will be removed in at pre-processing stage.
antabTr.py program can be used in the same way as the original antabfs.py program for
generating antab files but
this version will automatically store the information about how the user cleans the data.
That information is stored in wisdom files.
Wisdom files are meant to simplify I/O operations in ML approach to automatic generation of antabs.
Any pre-processing steps on logs that are possibly performed prior to using antabfs.py should also be
performed when using antabTr.py. The Makefile scripts makes some of that steps easier, but using
the Makefile pipeline is currently in experimental stage (type make help for more information)
Since typically the Tcal information from RXG files is not sent to VLBeer server, it is not directly possible to extract the wisdom from .antab nor from .log files, or from the combination of the two. This is one of the reasons why wisdom is collected.
If you cancel execution of the antabTr.py program
before saving your final .antabfs file and then restart processing the same log file,
the program will continue from the point your left off using the wisdom data stored in the local
directory. The wisdom files are stored by default in the 'wisdom' sub-directory
in .awpkl files.
The wisdom files contain Tsys data from both the log file and from the generated antab file.
To enlarge training, under certain conditions set such information can also be extracted for past EVN sessions from analyses
of both .log and .antab files.
In order to plot wisdom from awpkl file use eg:
antabTr.py --plot_wisdom wisdom/example.awpkl
Example wisdom data
awpkl files are typically located in "wisdom" subdirectory.
If you wish to reprocess your log file all over again without loading the wisdom from the previous run, simply go to the wisdom directory and remove the file[s] corresponding to that log file.
It will be useful if you decide to share the wisdom gathered by this program after your antab files are prepared and ready to export to VLBeer. After all, this is one of the reasons for writing this variation of the original program. In order to share the wisdom execute:
share_wisdom.py
script in the directory where you started antabTr.py program. The wisdom files will be sent to a remote server in order to improve machine learning training process that will eventually remove the need for manual preparation of antab files.
The server will ask you for a password, which is the same as the one used for uploading antab files to VLBeer. Alternatively, if you do not want to enter the password every time you run this script, please send your public ssh-key to the author.
In time, the wisdom will be made publicly available via antab-wisdom repository.
Do not share wisdom files in case when you decide to upload noisy (i.e. not cleaned) data to VLBeer, unless the data doesn't really contain any RFI that should be tagged for removal in the antabTr.py program.
Only wisdom files are being shared. Wisdom files are python3 pickled dictionaries containing the name of log file,
the input Tsys (X key in the pickled dictionary) data,
cleaned Tsys data (Y key) and perhaps what is most interesting
the indexes of the points that were marked as noise by the user. Those, if present are
found under ridx key in the pickled dictionary.
Note that the data stored under key Y is generated by the antabTr.py program
based on the information provided by the user and processing of the input (X)
data.
The naming of the wisdom files follows convention:
yyyy-mm.logfile_prefix.user_name.bbc.awpkl
where:
- yyyy-mm are year and month at saving time
- logfile_prefix is the log file name without extension (VLBI experiment with station code)
- user_name is the value of the $USER environment variable
- bbc - an integer
- awpkl - wisdom files extension
Hence, sharing wisdom is equivalent with sharing fraction of the information stored in .antabfs files, .log files and .rxg files.
If you shared the wisdom once and then decided to correct and regenerate the antab files you can share_wisdom.py again. The files will be uploaded again and their previous version will be overwritten if the updated version of the antab files is generated the same month as the previous one which results from the naming convention of the wisdom files.
Although, there is not enough information in log and antabfs files (on VLBeer) to recreate information about which data the user has removed during antab preparation, at least some information can be extracted, as many years of user experience ha already been uploaded to VLBeer server.
The provided extract_wisdom.py script along with antabTr.py can be
used to extract
part of that information stored in antabfs and log files.
See
extract_wisdom.py --help
This is done by e.g.
antabTr.py --extract_wisdom --antabfs clean/vlbeer-mar18-n18c1ys.antabfs clean/vlbeer-mar18-n18c1ys.log
The program extracts Tsys from the log file even if the calibration is not available and stores it as (noisy) inputs. The target data is taken from the corresponding antab file. The calibration of the input is inferred from the most likely (most frequent) target/input ratio, although several other check are performed before the extracted wisdom enters the training set. Extracting wisdom also automatically rejects parts of the data from the beginning and end of the session (controlled by parameters in the configuration files) as it is not easy to clean those regions manually with the original (and current) version of the the antabfs program.
Unfortunately, there is no way to restore information about which particular data points the user have manually marked as noisy during the antab preparation, and hence training sets generated this way, although larger, will necessarily be noisier.
In examples/validate directory there are some examplary data that can be used to validate compatibility of the modifications of the current version of the program with the original 20201123 version from python2. The provided log file, 'ec071mtr.log', is a typical log from a 5GHz experiment.
The antabfs file, 'ec071mtr.antabfs.orig', is the antab file generated by the original 20201123 version of the antab program using the calibration files from the local directory.
python2 antabfs.py ec071mtr.log
For the demonstration purpose only, while generating this file, only Tsys<10K data were removed and only for the bbc01. All other channels were not cleaned.
The antabfs file 'ec071mtr.antabfs' was generated using this version of the program (antabTr.py) in the analogous way.
antabTr.py ec071mtr.log --rxgDir rxg_files/
The program created wisdom subdirectory and stored wisdom files in it.
A comparison the two files confirms they are identical.
diff ec071mtr.antabfs ec071mtr.antabfs.orig
Bartosz Lew [email protected]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent