# AstroCatR

**Repository Path**: AstroTJU/AstroCatR

## Basic Information

- **Project Name**: AstroCatR
- **Description**: A Tool for Time Series Reconstruction of Large-Scale Astronomical Catalogs

- **Primary Language**: Unknown
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-03-23
- **Last Updated**: 2022-07-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# AstroCatR

### Description

A Tool for Time Series Reconstruction of Large-Scale Astronomical Catalogues

This is the main source code of AstroCatR, which can reconstruct all celestial objects' time series data for astronomical catalogues.

AstroCatR is a command-line opensource program running on the Linux platform, which is implemented in C and Python. Its capabilities are based on specialized sky partitioning and MPI parallel programming.

### Software Architecture

AstroCatR contains three parts, ETL (extract-transform-load) preprocessing, TS-matching calculation and time series data retrieval. You need to have original catalogs, and small data samples are provided in the **Data** directory. Then run program **ETL preprocessing** to generate sky zoning file. Next, run program **TS-matching** to mark celestial objects. Finally, run program **Query** to search the celestial objects from time series datasets which matched with the target. 


**CMP** directory is the comparative experiments for in-memory reference table, which uses MySQL's memory table and PostgreSQL's unlogged table to store the reference table for matching calculation.


### Prerequisites

This program has the following dependencies, which can be found in main directory.
- mpich
- Python
- Gnuplot
- Cfitsio

Cfitsio is used to parse catalogue FITS files information, MPI is used to accelerate TS-matching calculations and Python is used to manage user queries. Gnuplot creates scatter plots according to the time series data.

**Installation steps of Cfitsio**

1. Decompress and extract the contents of the distribution file in a source directory. You can use the following commands：

```shell
tar zxvf cfitsio_latest.tar.gz
```

Generate the make file:

```shell
./configure --prefix=/usr
```

Note that I have included the option --prefix=/usr in order to control where the library will be installed. Otherwise, by default CFITSIO will be installed under the source directory, which normally is a bad idea because that directory will not be included in the default search path for auto-tools.

Compile the source files:

```shell
make
make install
```

The different versions of the CFITSIO library (libcfitsio.*) are installed under: /usr/lib. The auxiliary files longnam.h, fitsio.h, fitsio2.h, and drvrsmem.h are placed under: /usr/include.

### Installation

**1.  Folder: ETL Preprocessing**

Under the xingbiao folder, use the next command to compile

```shell
make
```

It will generate an executable file：xingbiao1, then the ETL preprocessing can be executed by running a shell script.

**2.  Folder: Matching Calculation**

Use the next command to compile

```shell
make
```
It will generate an executable file：Matching

**3. Folder：Query**

It is written in Python, so it needs to install Python and the Python version must be greater than 2.7.


### Operating guide

**1. The ETL Preprocessing**

Set up your own configuration file and create a new folder.

Setting Paths according to actual requirements and execute corresponding shell scripts.

```shell
time sh test.sh
```
To generate sky zoning files

**2. TS-matching Calculation**

Set the number of processes and level of partitions according to the actual situation, and achieve the desired performance based on partition function.

Use the next command to run mactching operation

```shell
mpirun -np $procs ./Matching $input $output $procs
```

**3. Query**

Set position parameters and use next command to query service

```shell
python Query.py $ra $dec $input $output
```
Use next command to produce scatter plots

```shell
time sh Draw.sh
```

Use the following command to realize the transfer of time series data

```shell
python TScat.py $input $output
```