Improving short-term prandial blood glucose outcomes for people with type 1 diabetes, a complex disease that affects nearly 10 million people worldwide. We aim to leverage semi-supervised learning to identify unlabelled meals in time-series blood glucose data, develop meal-scoring functions, and explore causal machine-learning techniques.
This pull request introduces custom_pip_install.py, a Python script designed to simplify the process of installing Python packages and updating Conda environment YAML files (environment.yml and environment-ci.yml). The script automates package installation and ensures that environment configuration files remain consistent and well-structured, enhancing workflow efficiency for developers.
Key Features:
Multiple Package Installation: Install one or more Python packages in a single command, mirroring pip's native behavior.
Automated YAML Updates: Automatically adds installed packages under the pip section in specified YAML files. If the pip section doesn't exist, the script creates it.
Order Preservation: Maintains the order of keys in YAML files, ensuring the name field stays at the top for better readability and consistency.
Backup Mechanism: Creates .bak backups of YAML files before making any modifications, allowing easy restoration if needed.
Adding requests and pandas to Default Environment Files:
./custom_pip_install.py requests pandas
This command installs both requests and pandas and updates environment.yml and environment-ci.yml accordingly.
Adding flask to a Custom Environment File:
./custom_pip_install.py flask -f custom_env.yml
This command installs flask and updates only custom_env.yml.
Benefits:
Efficiency: Reduces manual steps by automating package installation and environment file updates.
Consistency: Ensures environment YAML files remain properly formatted and up-to-date with installed packages.
Safety: Provides backups of environment files before making changes, preventing accidental loss of configurations.
Flexibility: Allows specifying multiple packages and targeting various YAML files based on project needs.
Additional Notes:
Duplicate Handling: The script checks if a package already exists in the pip section to avoid duplicate entries.
Graceful Handling of Missing Files: If a specified YAML file does not exist, the script warns the user and continues processing other files without interruption.
Interrupt Safety: If the installation process is interrupted (e.g., via Ctrl+C), the script exits gracefully without modifying any environment files.
Conclusion:
By integrating custom_pip_install.py into your development workflow, you can streamline the management of Python packages and Conda environments, ensuring that your project dependencies are consistently and accurately maintained. This enhancement promotes a more efficient and error-free development process.
Description:
This pull request introduces
custom_pip_install.py
, a Python script designed to simplify the process of installing Python packages and updating Conda environment YAML files (environment.yml
andenvironment-ci.yml
). The script automates package installation and ensures that environment configuration files remain consistent and well-structured, enhancing workflow efficiency for developers.Key Features:
Multiple Package Installation: Install one or more Python packages in a single command, mirroring
pip
's native behavior.Automated YAML Updates: Automatically adds installed packages under the
pip
section in specified YAML files. If thepip
section doesn't exist, the script creates it.Order Preservation: Maintains the order of keys in YAML files, ensuring the
name
field stays at the top for better readability and consistency.Backup Mechanism: Creates
.bak
backups of YAML files before making any modifications, allowing easy restoration if needed.Flexible File Targets: Supports updating multiple YAML files, customizable via command-line arguments.
Usage Instructions:
Install Dependencies:
Ensure
ruamel.yaml
is installed, as it's required for YAML parsing and writing:Make the Script Executable (Optional):
On Unix-like systems (e.g., Linux, macOS), you can make the script executable:
Run the Script:
Install a Single Package:
Install Multiple Packages:
Specify Custom YAML Files:
Examples:
Adding
requests
andpandas
to Default Environment Files:This command installs both
requests
andpandas
and updatesenvironment.yml
andenvironment-ci.yml
accordingly.Adding
flask
to a Custom Environment File:This command installs
flask
and updates onlycustom_env.yml
.Benefits:
Efficiency: Reduces manual steps by automating package installation and environment file updates.
Consistency: Ensures environment YAML files remain properly formatted and up-to-date with installed packages.
Safety: Provides backups of environment files before making changes, preventing accidental loss of configurations.
Flexibility: Allows specifying multiple packages and targeting various YAML files based on project needs.
Additional Notes:
Duplicate Handling: The script checks if a package already exists in the
pip
section to avoid duplicate entries.Graceful Handling of Missing Files: If a specified YAML file does not exist, the script warns the user and continues processing other files without interruption.
Interrupt Safety: If the installation process is interrupted (e.g., via
Ctrl+C
), the script exits gracefully without modifying any environment files.Conclusion:
By integrating
custom_pip_install.py
into your development workflow, you can streamline the management of Python packages and Conda environments, ensuring that your project dependencies are consistently and accurately maintained. This enhancement promotes a more efficient and error-free development process.Credit: ChatGPT