Add docstrings and docs

README.md

@@ -5,7 +5,7 @@
 Python package to detect AKI within time series data.
 
 The goal of this package is to establish well tested, comprehensive functions for the detection of Acute Kidney Injury (AKI) in time series data, according to the Kidney Disease Improving Global Outcomes (KDIGO) Criteria, established in 2012 [^kdigo].
-![](img/kdigo_criteria.png)
+![](doc/doc/img/kdigo_criteria.png)
 
 ## Installation
 

docs/docs/getting_started/basic_usage.md

@@ -0,0 +1,22 @@
+# Basic Usage
+
+An example of the basic usage is shown below. depending on your data you can specify the different datasets with the corresponding types. For the required columns please refer to the api reference documentation. After creating the analyser, you can analyse the data by calling the `process_stays` to process all patients. to process a single patient, you can use the `process_stay` method. please specify the patient id (`stay_id`) when analysing for a single patient.
+
+the result will be a dataset containing several columns with the suffix `stage` containing the stages corresponding to the different probes. the overall `stage` is found in the stage column.
+
+```python
+import pandas as pd
+
+from pyaki.probes import Dataset, DatasetType
+from pyaki.kdigo import Analyser
+
+data = [
+    Dataset(DatasetType.URINEOUTPUT, pd.DataFrame()),
+    Dataset(DatasetType.CREATININE, pd.DataFrame()),
+    Dataset(DatasetType.DEMOGRAPHICS, pd.DataFrame()),
+    Dataset(DatasetType.RRT, pd.DataFrame()),
+]
+
+analyser = Analyser(data)
+results: pd.Dataframe =  analyser.process_stays()
+```

docs/docs/getting_started/contributing.md

@@ -0,0 +1,37 @@
+# Contributing
+
+Here is how you can contribute to this project.
+
+## Setup
+
+Use the included dev container to automatically install all the necessary dev tools and dependencies.
+
+> **Prerequisite**: To use this you first need to install docker under Linux or WSL2 under windows.
+
+1. **Clone the repository:**
+    ```bash
+    git clone git+https://github.com/aidh-ms/pyAKI
+    cd pyAKI
+    ```
+
+2. **Open the project in Visual Studio Code:**
+    ```bash
+    code .
+    ```
+
+3. **Reopen in Container:**
+    - Press `F1` to open the command palette.
+    - Type `Remote-Containers: Reopen in Container` and select it.
+    - VS Code will build the Docker container defined in the `.devcontainer` folder and open the project inside the container.
+
+## Development
+
+
+
+## Testing
+
+To test your contribution, you can use the testing tap in the VS code or utilise the following command to run the unit tests for this project:
+
+```shell
+poetry run pytest .
+```

docs/docs/getting_started/installation.md

@@ -3,11 +3,41 @@
 
 ## Requirements
 
+The supported Python versions for this Python library are the following:
+* 3.12
+* 3.13
 
 ## Installing from PyPI
 
+The pyAKI library can be installed from PyPI using various package managers.
+
+**With pip**
+```bash
+pip install pyAKI
+```
+
+**With poetry**
+```bash
+poetry add pyAKI
+```
 
 ## Installing from Source
 
+The pyAKI library can be installed from source (GitHub).
+
+**With pip**
+```bash
+pip install git+https://github.com/aidh-ms/pyAKI
+```
+
+**With poetry**
+```bash
+poetry add git+https://github.com/aidh-ms/pyAKI
+```
 
 ## Dependencies
+
+This library has the following dependencies:
+* [Pandas](https://pandas.pydata.org/)
+* [SciPy](https://scipy.org/)
+* [Typer](https://typer.tiangolo.com/)

docs/docs/getting_started/overview.md

@@ -1,7 +1,9 @@
 # Package overview
 
+Python package to detect AKI within time series data.
 
-
+The goal of this package is to establish well tested, comprehensive functions for the detection of Acute Kidney Injury (AKI) in time series data, according to the Kidney Disease Improving Global Outcomes (KDIGO) Criteria, established in 2012 [^kdigo].
+![](../img/kdigo_criteria.png)
 
 # License
 
@@ -28,3 +30,5 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 ```
+
+[^kdigo]: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney inter., Suppl. 2012; 2: 1–138.

docs/docs/index.md

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+# pyAKI
+
+<p style="text-align:center;" markdown="1">
+_A Python package to detect AKI within time series data._
+[![Coverage Status](https://coveralls.io/repos/github/aidh-ms/pyAKI/badge.svg?branch=main)](https://coveralls.io/github/aidh-ms/pyAKI?branch=main)
+</p>
+
+---
+
+**Source Code:** [https://github.com/aidh-ms/pyAKI](https://github.com/aidh-ms/pyAKI)
+**Paper:** [https://doi.org/10.1371/journal.pone.0315325](https://doi.org/10.1371/journal.pone.0315325)
+
+---
+
+The goal of this package is to establish well tested, comprehensive functions for the detection of Acute Kidney Injury (AKI) in time series data, according to the Kidney Disease Improving Global Outcomes (KDIGO) Criteria, established in 2012 [^kdigo].
+![](img/kdigo_criteria.png)
+
+
+
+[^kdigo]: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney inter., Suppl. 2012; 2: 1–138.

mkdocs.yml

@@ -5,6 +5,7 @@ nav:
   - Package Overview: 'getting_started/overview.md'
   - Installation: 'getting_started/installation.md'
   - Basic Usage: 'getting_started/basic_usage.md'
+  - Contributing: 'getting_started/contributing.md'
   - Citing: 'getting_started/citing.md'
 - User Guide:
   - Tutorial: 'guide/tutorial.md'
@@ -27,10 +28,12 @@ extra:
 copyright: Copyright © 2023 AIDH-MS
 
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