ALMA Science Archive Notebooks
Introduction
This page contains Jupyter Notebooks to programatically access the ALMA Science Archive. The notebooks interact through Virtual Observatory standards with ALMA's ObsCore Table Access Protocol (TAP) service.
Queries in TAP are written in the SQL-like Astronomical Data Query Language (ADQL). ADQL queries include spatial queries as well as operations on other properties/columns of the database. This also allows the user detailed control over the returned columns. In these Jupyter notebook we will exemplify some of the most common queries. For this we will be using the astropy affiliated PyVO client, which is interoperable with other valid TAP services from other observatories.
Installation
Install the required software with
pip3 install jupyterlab matplotlib pandas sklearn
pip3 install --upgrade --pre astroquery
download the Jupyter Notebooks
wget https://almascience.org/alma-data/archive/archive-notebooks/alma-science-archive-notebooks.tgz -O - | tar -xz
cd alma-science-archive-notebooks
and start the notebook server
jupyter-lab
You can modify the code in the cells and run each cell with "Shift+Enter". An introduction to Jupyter Notebooks can be found here
Troubleshooting
Python 3.6 users, run: pip3 --upgrade astropy==4.1
If you experience problems with these notebooks, please contact us through the ALMA Helpdesk.
Individual notebooks
In addition to downloading all notebooks, you can also access an html version as well as each notebook individually:
| nb0_ALMA_notebook_introduction | html | ipynb |
| nb1_ALMA_Query_one_source | html | ipynb |
| nb2_ALMA_Query_public_catalogue | html | ipynb |
| nb3_ALMA_Query_by_proposal_or_publisher_id | html | ipynb |
| nb4_ALMA_Query_by_science_keyword | html | ipynb |
| nb5_ALMA_Query_by_spatial_resolution | html | ipynb |
| nb6_ALMA_Query_by_frequency | html | ipynb |
| nb7_ALMA_Query_by_sensitivity | html | ipynb |
| nb8_ALMA_Query_using_astroquery | html | ipynb |
| nb9_ALMA_Download_data | html | ipynb |