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‎.github/workflows/update.yml‎

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name: Update statistics
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on:
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schedule:
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- cron: '50 23 * * *' # Run at 23:50 everyday
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workflow_dispatch:
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jobs:
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update:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Install requirements
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run: pip3 install -r requirements.txt
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- name: Extract data
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run: python3 extract.py
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- name: Create plots
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run: |
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python3 plot.py
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python3 plot_OS.py
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python3 plot_file_type.py
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- name: Create badges
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run: |
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python3 create_badges.py
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- name: Git commit and push
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run: |
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git config --global user.email "github-actions[bot]@users.noreply.github.com"
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git config --global user.name "github-actions[bot]"
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git add data/*.json
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git add data/last_updated
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git add plots/*.svg
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git add badges/*.svg
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git commit -m "Update"
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git push https://x-access-token:${{ secrets.GH_TOKEN }}@github.com/${{ github.repository }}.git HEAD:${{ github.ref_name }}

‎LICENSE.md‎

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MIT License
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Copyright (c) 2025 SINTEF and Norwegian University of Science and Technology (NTNU)
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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FAST GitHub Statistics
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=========================
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Repository for updating FAST GitHub statistics automatically using [PyGitHub](https://github.com/PyGithub/PyGithub).

‎badges/.gitkeep‎

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‎create_badges.py‎

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import json
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import requests
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import os
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# Get data for all years
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data = {}
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for filename in os.listdir('data/'):
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if '-' not in filename and '.json' in filename:
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print(filename)
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with open(f'data/{filename}', 'r') as file:
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data.update(json.load(file))
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sum = 0
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for date, value in data.items():
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sum += value
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sum = round(sum / 1000, 1)
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url = f'https://img.shields.io/badge/GitHub_Downloads-{sum}k-green?logo=github'
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response = requests.get(url)
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filename = 'badges/total.svg'
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if response.status_code == 200:
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with open(filename, 'wb') as file:
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for chunk in response.iter_content(chunk_size=1024):
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file.write(chunk)
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print(f"Image downloaded successfully to {filename}")
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else:
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print('Failed')

‎data/.gitkeep‎

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‎extract.py‎

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import json
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import os
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from datetime import datetime
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from github import Github
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year = str(datetime.now().year)
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date_format = '%Y-%m-%d'
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date_today = datetime.now().strftime(date_format)
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# Check if today is already processed
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if os.path.exists('data/last_updated'):
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with open('data/last_updated', 'r') as file:
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last_updated = file.readline().strip()
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if last_updated == date_today:
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print('Today has already been processed!')
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exit(0)
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# =====================> Get downloads
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github = Github()
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repo = github.get_repo('FAST-Imaging/FAST')
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releases = repo.get_releases()
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downloads_total = 0
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downloads_per_OS = {
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'Windows': 0,
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'Linux': 0,
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'macOS x86_64': 0,
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'macOS arm64': 0,
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}
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downloads_per_type = {
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'Archive': 0,
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'Python Wheel': 0,
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'Installer': 0,
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}
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for release in releases:
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assets = release.get_assets()
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for asset in assets:
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downloads_total += asset.download_count
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if 'win' in asset.name:
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downloads_per_OS['Windows'] += asset.download_count
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elif 'ubuntu' in asset.name or 'linux' in asset.name:
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downloads_per_OS['Linux'] += asset.download_count
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elif 'mac' in asset.name:
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if 'arm64' in asset.name:
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downloads_per_OS['macOS arm64'] += asset.download_count
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else:
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downloads_per_OS['macOS x86_64'] += asset.download_count
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else:
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print(f'Error parsing OS for {asset.name}')
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continue
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if asset.name.endswith('.tar.xz') or asset.name.endswith('.zip') or asset.name.endswith('.tar.gz'):
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downloads_per_type['Archive'] += asset.download_count
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elif asset.name.endswith('.exe') or asset.name.endswith('.deb'):
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downloads_per_type['Installer'] += asset.download_count
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elif asset.name.endswith('.whl'):
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downloads_per_type['Python Wheel'] += asset.download_count
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else:
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print(f'Error parsing file type for {asset.name}')
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continue
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# ========================> Total downloads
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if os.path.exists(f'data/{year}.json'):
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with open(f'data/{year}.json', 'r') as file:
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data = json.load(file)
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# Get sum of all
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sum = 0
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for date, value in data:
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sum += value
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data[date_today] = downloads_total - sum
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else:
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data = {date_today: downloads_total}
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with open(f'data/{year}.json', 'w') as file:
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json.dump(data, file, indent=4)
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# ========================> Per OS
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if os.path.exists(f'data/{year}-OS.json'):
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with open(f'data/{year}-OS.json', 'r') as file:
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data = json.load(file)
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for OS in data.keys():
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# Find sum for OS
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sum = 0
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for date, value in data[OS]:
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sum += value
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data[OS][date_today] = downloads_per_OS[OS] - sum
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else:
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data = {}
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for OS in downloads_per_OS.keys():
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data[OS] = {date_today: downloads_per_OS[OS]}
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with open(f'data/{year}-OS.json', 'w') as file:
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json.dump(data, file, indent=4)
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# ========================> Per file type
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if os.path.exists(f'data/{year}-file-type.json'):
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with open(f'data/{year}-file-type.json', 'r') as file:
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data = json.load(file)
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for type in data.keys():
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# Find sum for OS
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sum = 0
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for date, value in data[type]:
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sum += value
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data[type][date_today] = downloads_per_type[type] - sum
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else:
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data = {}
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for type in downloads_per_type.keys():
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data[type] = {date_today: downloads_per_type[type]}
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with open(f'data/{year}-file-type.json', 'w') as file:
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json.dump(data, file, indent=4)
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# =====================> Update last_updated file
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with open('data/last_updated', 'w') as file:
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file.write(date_today)

‎plot.py‎

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import matplotlib.pyplot as plt
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import json
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import pandas as pd
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import calendar
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from datetime import datetime
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from style import set_style
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set_style(plt)
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year = datetime.now().year
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with open(f'data/{year}.json', 'r') as file:
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data = json.load(file)
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# Skip first date
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if year == 2025:
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first_date = sorted(data.keys())[0]
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del data[first_date]
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# Per month
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df = pd.DataFrame({'date': pd.to_datetime(list(data.keys())),
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'downloads': list(data.values())
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})
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# Fill missing days with 0
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df = df.set_index('date')
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full_date_range = pd.date_range(start=pd.to_datetime(f'{year}-01-01'), end=pd.Timestamp.now(), freq='D')
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df = df.reindex(full_date_range, fill_value=0)
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df['month'] = df.index.to_period('M')
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df['month_name'] = df.index.strftime('%b')
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monthly_data = df.groupby('month')['downloads'].sum().reset_index()
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monthly_data = monthly_data.sort_values(by='month')
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monthly_data['month_name'] = monthly_data['month'].apply(lambda x: calendar.month_abbr[x.month])
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fig, ax = plt.subplots(figsize=(10, 6))
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bars = ax.bar(monthly_data['month_name'], monthly_data['downloads'], color='skyblue')
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ax.bar_label(bars, label_type='edge', fmt='{:,.0f}') # Customize format as needed
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plt.title(f'Monthly downloads of FAST GitHub releases {year}')
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plt.tight_layout()
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fig.savefig(f'plots/{year}.svg')
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#plt.show()

‎plot_OS.py‎

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import matplotlib.pyplot as plt
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import json
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import pandas as pd
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import calendar
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from datetime import datetime
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from style import set_style
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set_style(plt)
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year = datetime.now().year
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with open(f'data/{year}-OS.json', 'r') as file:
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all_data = json.load(file)
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fig, ax = plt.subplots(figsize=(10, 6))
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previous = None
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for OS in all_data.keys():
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data = all_data[OS]
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# Skip first date
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if year == 2025:
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first_date = sorted(data.keys())[0]
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del data[first_date]
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if len(data) == 0:
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continue
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# Per month
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df = pd.DataFrame({'date': pd.to_datetime(list(data.keys())),
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'downloads': list(data.values())
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})
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# Fill missing days with 0
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df = df.set_index('date')
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full_date_range = pd.date_range(start=pd.to_datetime(f'{year}-01-01'), end=pd.Timestamp.now(), freq='D')
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df = df.reindex(full_date_range, fill_value=0)
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df['month'] = df.index.to_period('M')
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df['month_name'] = df.index.strftime('%b')
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monthly_data = df.groupby('month')['downloads'].sum().reset_index()
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monthly_data = monthly_data.sort_values(by='month')
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monthly_data['month_name'] = monthly_data['month'].apply(lambda x: calendar.month_abbr[x.month])
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bars = ax.bar(monthly_data['month_name'], monthly_data['downloads'], bottom=previous, label=OS)
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if previous is None:
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previous = monthly_data['downloads']
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else:
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previous += monthly_data['downloads']
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plt.title(f'Monthly downloads of FAST GitHub releases per operating system {year}')
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plt.legend()
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plt.tight_layout()
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fig.savefig(f'plots/{year}-OS.svg')
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#plt.show()

‎plot_file_type.py‎

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import matplotlib.pyplot as plt
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import json
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import pandas as pd
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import calendar
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from datetime import datetime
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from style import set_style
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set_style(plt)
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year = datetime.now().year
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with open(f'data/{year}-file-type.json', 'r') as file:
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all_data = json.load(file)
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fig, ax = plt.subplots(figsize=(10, 6))
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previous = None
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for type in all_data.keys():
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data = all_data[type]
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# Skip first date
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if year == 2025:
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first_date = sorted(data.keys())[0]
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del data[first_date]
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if len(data) == 0:
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continue
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# Per month
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df = pd.DataFrame({'date': pd.to_datetime(list(data.keys())),
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'downloads': list(data.values())
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})
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# Fill missing days with 0
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df = df.set_index('date')
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full_date_range = pd.date_range(start=pd.to_datetime(f'{year}-01-01'), end=pd.Timestamp.now(), freq='D')
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df = df.reindex(full_date_range, fill_value=0)
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df['month'] = df.index.to_period('M')
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df['month_name'] = df.index.strftime('%b')
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monthly_data = df.groupby('month')['downloads'].sum().reset_index()
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monthly_data = monthly_data.sort_values(by='month')
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monthly_data['month_name'] = monthly_data['month'].apply(lambda x: calendar.month_abbr[x.month])
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bars = ax.bar(monthly_data['month_name'], monthly_data['downloads'], bottom=previous, label=type)
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if previous is None:
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previous = monthly_data['downloads']
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else:
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previous += monthly_data['downloads']
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plt.title(f'Monthly downloads of FAST GitHub releases per file type {year}')
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plt.legend()
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plt.tight_layout()
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fig.savefig(f'plots/{year}-file-type.svg')
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#plt.show()

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