The rail_scripts directory contains the main python scripts that call RAIL's methods to run photo-z algorithms.
The rail-estimate program is the main standalone script used to
compute the PZ tables. The script accepts an input file containing raw
data and outputs the estimated photo-z values as a probability
distribution. Running rail-estimate depends on having a "pre-trained"
configuration file generated by rail-train (or some other procedure to
train the algorithms).
The rail-train program is a script to prepare a "pre-trained" file for
certain algorithms used to compute PZ tables (for example, the
calculated weights for a neural network). rail-train receives an input
file containing both the raw input data and reference output redshift
results, considered to be correct and accurate. The output is a Python
pickle file with the configuration data relevant to the estimator
algorithms. Training is slow, but can be done only once and used later
with rail-estimate for any production data that is expected to have
the same behavior as the reference file.
The install-pz-rail is a trivial script to install RAIL in an environment with the conda package
manager. The script should be used by having an installed conda with a
preconfigured and active environment and then calling it to install all
the dependencies and RAIL.
The rail-preprocess-parquet script may be used to read input data
in parquet format containing either flux or magnitude data and transform
it to a standard format. It does the following preprocessing steps:
- Limit number of rows per file to a maximum, splitting the file if necessary.
- Generate files only with necessary columns (magnitudes and errors).
- Convert infinite values to NaN.
- Convert flux to magnitude.
Note: dereddening is not implemented yet (TDB).
Preprocessing is done as efficiently as possible and memory usage is limited even with arbitrarily large files. The resulting output files are a set of parquet files, which are expected to be much smaller than the original file and suitable to use with the pz-rail algorithms.
The scheduler_examples directory contains examples showing how to execute independent parallel tasks with the help of some scheduler. Currently, there are examples for 2 schedulers: HTCondor and Slurm.
The utils directory contains simple tools to help using the cluster, the RAIL libraries and relevant data formats. The following utilities are available:
condor-dispatch: a trivial script for HTCondor that allows executing an arbitrary command in the cluster, possibly on multiple slots.condor-slot-run: a trivial script for HTCondor to run a single command on a specific condor slot.parquet-count-rows: count the total number of rows in a set of parquet files without loading all data in memory.
The performance directory contains performance analysis scripts for the schedulers and cluster. The available scripts are the following:
condor-analyze-host-performance: parses the HTCondor log file and outputs the minimum and maximum execution time of each host in the cluster.condor-analyze-host-allocation: plots a time chart showing allocated time and dead time caused by the HTCondor scheduler.rail-sum-execution-time: calculates the total live time (excluding the scheduler dead time) of the rail scripts executed on some cluster.benchmark-write-speed: a set of scripts for HTCondor to test the cluster's I/O write speed by writing random data from each condor slot.
The directory LNCC contains information and configuration necessary or useful when connecting to the LNCC supercomputer Santos Dumont. Currently there are sample configuration files for configuring the VPN routes required when connecting to the Cisco VPN server using the vpnc client program. The manually specified routes can be used to avoid configuring the default route through the LNCC VPN, which would route all Internet traffic to it.