Available Software

SIVACOR only allows you to run certain well-defined software stacks, packaged as Docker images, from known and trusted sources. The table below shows all available software images with their tags and links to their Docker Hub repositories.

import yaml
import pandas as pd
import requests
from IPython.display import HTML, display
from itables import init_notebook_mode, show
import warnings

# Suppress warnings
warnings.filterwarnings('ignore')

# Initialize interactive tables
init_notebook_mode(all_interactive=True)

# Fetch the YAML data
url = "https://raw.githubusercontent.com/SIVACOR/sivacor-repo-choice/main/allowed_repos.yaml"
response = requests.get(url)
data = yaml.safe_load(response.text)

# Process the data into a pandas DataFrame
rows = []
for image_name, tags in data.items():
    # Extract software name from image name
    if 'stata' in image_name.lower():
        software = 'Stata'
    elif 'dynare' in image_name.lower():
        software = 'MATLAB'
    elif image_name.startswith('rocker/'):
        software = 'R'
    else:
        # Capitalize first letter of each part
        parts = image_name.split('/')[-1].split('-')
        software = ' '.join([part.capitalize() for part in parts])

    # Create Docker Hub URL and link
    docker_hub_url = f"https://hub.docker.com/r/{image_name}"
    docker_hub_link = f'<a href="{docker_hub_url}" target="_blank">More info</a>'

    # Add a row for each tag
    for tag in tags:
        rows.append({
            'Software': software,
            'Container': image_name,
            'Tag': str(tag),
            'Link to Docker Hub': docker_hub_link
        })

# Create DataFrame
df = pd.DataFrame(rows)

# Sort by software name for better organization
df = df.sort_values('Software')

# Store software data for use in MyST tabs below
software_groups = df.groupby('Software')
software_data = {}
for software, group_df in software_groups:
    software_data[software] = group_df

# Export data to global namespace for use in MyST tabs
globals().update({f'df_{software.lower().replace(" ", "_")}': group_df[['Container', 'Tag']].copy().rename(columns={'Container': 'Image'}) 
                  for software, group_df in software_data.items()})

Stata

MATLAB

Stata images are built by the AEA Data Editor, with permission from StataCorp. See the dataeditors repositories for more information. Stata containers define Stata versions, with tags identifying the within-version regular updates. Usually, you can use the latest tag for a specific version.

if 'df_stata' in globals():
    show(df_stata, lengthMenu=[10, 25, 50, -1], classes="display compact", showIndex=False,
         columnDefs=[{"width": "400px", "targets": 0, "className": "dt-left"}, {"width": "150px", "targets": 1, "className": "dt-left"}],
         autoWidth=False)

We use a subset of images from the rocker project for R. See the rocker repositories for more information. An image name is defined by a combination of pre-installed packages. Tags identify different versions of R.

if 'df_r' in globals():
    show(df_r, lengthMenu=[10, 25, 50, -1], classes="display compact", showIndex=False,
         columnDefs=[{"width": "400px", "targets": 0, "className": "dt-left"}, {"width": "150px", "targets": 1, "className": "dt-left"}],
         autoWidth=False)

We use images from dynare/dynare for MATLAB, because they contain most toolboxes. Only certain versions of MATLAB are supported. You should use these even if you do not use Dynare. Tags identify a particular combination of Dynare and MATLAB versions.

if 'df_matlab' in globals():
    show(df_matlab, lengthMenu=[10, 25, 50, -1], classes="display compact", showIndex=False,
         columnDefs=[{"width": "400px", "targets": 0, "className": "dt-left"}, {"width": "150px", "targets": 1, "className": "dt-left"}],
         autoWidth=False)