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.github/workflows/cgatcore_python.yml

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- name: Install MkDocs and Dependencies
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run: |
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pip install mkdocs mkdocs-material mkdocstrings[python]
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pip install mkdocs \
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mkdocs-material \
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mkdocstrings[python] \
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pymdown-extensions \
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mkdocs-material[imaging]
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- name: Build and Deploy MkDocs Site
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run: mkdocs gh-deploy --force --clean
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run: |
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mkdocs build
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mkdocs gh-deploy --force --clean
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env:
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GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

docs/defining_workflow/run_parameter.md

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docs/getting_started/examples.md

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- **Logs**: Check the log files generated during the pipeline run for detailed error messages.
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- **Support**: For further assistance, refer to the [CGAT-core documentation](https://cgat-developers.github.io/cgat-core/) or raise an issue on our [GitHub repository](https://github.com/cgat-developers/cgat-core/issues).
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## CGAT-core Examples
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## CGAT-core Examples {#cgat-core-examples}
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This guide provides practical examples of CGAT-core pipelines for various use cases, from basic file processing to complex genomics workflows.
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## Quick Start Examples
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## Quick Start Examples {#quick-start-examples}
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### Hello World Pipeline
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### Hello World Pipeline {#hello-world-pipeline}
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```python
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"""hello_world.py - Simple CGAT pipeline example
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sys.exit(P.main(sys.argv))
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```
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### Configuration Example
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### Configuration Example {#configuration-example}
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```yaml
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# pipeline.yml
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memory_default: 1G
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```
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## Real-World Examples
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## Real-World Examples {#real-world-examples}
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### 1. Genomics Pipeline
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### 1. Genomics Pipeline {#genomics-pipeline}
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This example demonstrates a typical RNA-seq analysis pipeline:
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sys.exit(P.main(sys.argv))
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```
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### 2. Data Processing Pipeline
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### 2. Data Processing Pipeline {#data-processing-pipeline}
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Example of a data processing pipeline with S3 integration:
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sys.exit(P.main(sys.argv))
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```
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### 3. Image Processing Pipeline
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### 3. Image Processing Pipeline {#image-processing-pipeline}
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Example of an image processing pipeline:
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sys.exit(P.main(sys.argv))
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```
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## Best Practices
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## Best Practices {#best-practices}
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### 1. Resource Management
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### 1. Resource Management {#resource-management}
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```python
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@transform("*.bam", suffix(".bam"), ".sorted.bam")
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P.run(statement)
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```
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### 2. Error Handling
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### 2. Error Handling {#error-handling}
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```python
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@transform("*.txt", suffix(".txt"), ".processed")
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### 3. Configuration Management
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### 3. Configuration Management {#configuration-management}
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```yaml
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# pipeline.yml - Example configuration
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## Running the Examples
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## Running the Examples {#running-the-examples}
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1. **Setup Configuration**
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```bash

docs/getting_started/installation.md

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The following sections describe how to install the `cgatcore` framework.
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## Conda installation
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## Conda installation {#conda-installation}
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The preferred method of installation is using Conda. If you do not have Conda installed, you can install it using [Miniconda](https://conda.io/miniconda.html) or [Anaconda](https://www.anaconda.com/download/#macos).
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conda install -c conda-forge -c bioconda cgatcore
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```
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### Prerequisites
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### Prerequisites {#prerequisites}
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Before installing `cgatcore`, ensure that you have the following prerequisites:
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- **Operating System**: Linux or macOS
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- **Python**: Version 3.6 or higher
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- **Conda**: Recommended for dependency management
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### Troubleshooting
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### Troubleshooting {#troubleshooting}
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- **Conda Issues**: If you encounter issues with Conda, ensure that the Bioconda and Conda-Forge channels are added and prioritized correctly.
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- **Pip Dependencies**: When using pip, manually install any missing dependencies listed in the error messages.
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- **Script Errors**: If the installation script fails, check the script's output for error messages and ensure all prerequisites are met.
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### Verification
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### Verification {#verification}
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## Pip installation
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## Pip installation {#pip-installation}
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We recommend installation through Conda because it manages dependencies automatically. However, `cgatcore` is generally lightweight and can also be installed using the `pip` package manager. Note that you may need to manually install other dependencies as needed:
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```bash
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## Automated installation
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## Automated installation {#automated-installation}
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The preferred method to install `cgatcore` is using Conda. However, we have also created a Bash installation script, which uses [Conda](https://conda.io/docs/) under the hood.
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The installation script will place everything under the specified location. The aim of the script is to provide a portable installation that does not interfere with existing software environments. As a result, you will have a dedicated Conda environment that can be activated as needed to work with `cgatcore`.
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## Manual installation
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## Manual installation {#manual-installation}
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To obtain the latest code, check it out from the public Git repository and activate it:
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```
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## Installing additional software
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## Installing additional software {#installing-additional-software}
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When building your own workflows, we recommend using Conda to install software into your environment where possible. This ensures compatibility and ease of installation.
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## Accessing the libdrmaa shared library
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## Accessing the libdrmaa shared library {#accessing-libdrmaa}
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You may also need access to the `libdrmaa.so.1.0` C library, which can often be installed as part of the `libdrmaa-dev` package on most Unix systems. Once installed, you may need to specify the location of the DRMAA library if it is not in a default library path. Set the `DRMAA_LIBRARY_PATH` environment variable to point to the library location.
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