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Merge pull request #293 from PNNL-CompBio/291-update-documentation
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‎docs/assets/css/style.css

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box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.05);
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}
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code {
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/* inline code */
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code.highlighter-rouge{
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background: white;
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border: 1px solid #ccc;
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padding: 2.5px;
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}
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/* code block */
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div.highlighter-rouge {
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background-color: white;
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width: 60%;
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border: 1px solid #ccc;
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padding: 2.5px;
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padding: 2.5px;
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overflow-x: auto;
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}
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/* pre > code.language-bash {
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/*
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pre > code.language-bash {
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background-color: white;
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width: 60%;
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border: 1px solid #ccc;
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display: block;
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overflow-x: auto;
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border: 1px solid #ccc;
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} */
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}
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*/
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/*
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.hamburger {
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*/
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.code-box {
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.code-box {
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background-color: white;
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font-family: monospace; /* Gives the text a code-like appearance */
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.flex-container {
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display: flex;
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justify-content: space-around; /* Or use 'center' if you prefer */
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justify-content: space-around; /* Or use 'center' if you ffer */
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align-items: center;
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flex-wrap: wrap; /* Allows items to wrap onto the next line on smaller screens */
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width: 100%;
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font-size: 16px;
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}
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}
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}
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/* Github stlye syntax highlighting*/
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‎docs/index.md

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<!-- # Cancer Omics and Drug Experiment Response Data (`coderdata`) Python Package -->
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### Introduction
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## Introduction
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CoderData is a cancer benchmark data package developed in Python and R.
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There are two aspects of this package, the backend build section and the user facing python package.
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The build section is a github workflow that generates four cancer datasets in a format that is easy for users and algorithms to ingest.
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The python package allows users to easily download the data, load it into python and reformat it as desired.
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### Installation and Usage
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##### Bash / Command Line
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## Installation and Usage
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### Installation
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To install coderdata, simply run the following command in your terminal:
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Assuming `python>=3.9` is installed on the system, simply run the following command in the terminal to install the most recent release of the coderdata API:
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```bash
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pip install coderdata
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$ pip install coderdata
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```
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##### Bash / Command line
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To download datasets, simply run the following command in your terminal. Remove the prefix argument if you'd like to install all datasets.
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### Bash / Command line
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A full list of available datasets can be retrieved via:
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```sh
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$ coderdata --list
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```
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To download datasets, simply run the following command in your terminal substituting `<DATASET>` with the desired dataset (e.g. `beataml`). To download all datasets use `--name all`.
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```bash
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coderdata download --prefix hcmi
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$ coderdata download --name <DATASET>
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```
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##### Python
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To download, load, and call datasets in python, simply run the following commands.
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<div class="code-box">
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<p>import coderdata as cd </p>
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<p>cd.download_data_by_prefix('hcmi')</p>
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<p>hcmi_data = cd.DatasetLoader('hcmi')</p>
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<p>hcmi_data.transcriptomics</p>
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</div>
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### Python
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To download, load, and call datasets in python, simply run the following commands.
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```python
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>>> import coderdata as cd
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>>> cd.download(name='beataml')
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>>> beataml = cd.load('beataml')
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>>> beataml.experiments
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source improve_sample_id improve_drug_id study time time_unit dose_response_metric dose_response_value
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0 synapse 3907 SMI_11123 BeatAML 72 hrs fit_auc 0.0564
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1 synapse 3907 SMI_11211 BeatAML 72 hrs fit_auc 0.9621
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2 synapse 3907 SMI_12192 BeatAML 72 hrs fit_auc 0.1691
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3 synapse 3907 SMI_12254 BeatAML 72 hrs fit_auc 0.4245
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4 synapse 3907 SMI_12469 BeatAML 72 hrs fit_auc 0.7397
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... ... ... ... ... ... ... ... ...
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233775 synapse 3626 SMI_7110 BeatAML 72 hrs dss 0.0000
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233776 synapse 3626 SMI_7590 BeatAML 72 hrs dss 0.0000
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233777 synapse 3626 SMI_8159 BeatAML 72 hrs dss 0.1946
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233778 synapse 3626 SMI_8724 BeatAML 72 hrs dss 0.0000
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233779 synapse 3626 SMI_987 BeatAML 72 hrs dss 0.7165
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[233780 rows x 8 columns]
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```
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View our [Usage](pages/usage.md) page for full instructions.
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For more indepth instructions view our [Usage](pages/usage.md) page.
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### Datasets
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## Datasets
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</div> -->
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### Data Overview
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## Data Overview
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<div class="flex-container">
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<div class="flex-item">

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