adds analysis plugin guide in readme

README.md

@@ -93,6 +93,129 @@ flask run
 
 2. Open your web browser and navigate to `http://127.0.0.1:5000/`.
 
+## Adding an Analysis Module
+
+This guide explains how to add a new analysis module using the provided base classes: `BasePlotlyAnalysis` and `BasePlotAnalysis`. These base classes ensure a structured workflow for data preparation, transformation, and visualization.
+
+### 1. Choosing the Right Base Class
+Before implementing an analysis module, decide on the appropriate base class:
+- **`BasePlotlyAnalysis`**: Use this for interactive plots with **Plotly** that generate **HTML** outputs.
+- **`BasePlotAnalysis`**: Use this for static plots with **Matplotlib/Seaborn** that generate **PNG** image files.
+- **`BaseAnalysis`**: Use this for any other type of analysis with **text** or **HTML** output for max flexibility.
+
+### 2. Naming Convention
+Follow a structured naming convention for consistency:
+- **File name:** `plotly_<analysis_name>.py` for Plotly analyses, `plot_<analysis_name>.py` for Matplotlib-based analyses.
+- **Class name:** Use PascalCase and a descriptive suffix:
+  - Example for Plotly: `PlotlyActivityHeatmap`
+  - Example for Matplotlib: `PlotUserSessionDuration`
+
+### 3. Data Structure
+The following DataFrame structure is passed to analysis classes:
+
+| user_id  | name       | last_action          | status | timestamp                   | prev_timestamp | was_active | hour |
+|----------|-----------|----------------------|--------|-----------------------------|----------------|------------|------|
+| XXXXXXX  | UserA     | 2025-02-08 17:58:11  | Okay   | 2025-02-08 18:09:41.867984056 | NaT            | False      | 18   |
+| XXXXXXX  | UserB     | 2025-02-08 17:00:10  | Okay   | 2025-02-08 18:09:42.427846909 | NaT            | False      | 18   |
+| XXXXXXX  | UserC     | 2025-02-08 16:31:52  | Okay   | 2025-02-08 18:09:42.823201895 | NaT            | False      | 18   |
+| XXXXXXX  | UserD     | 2025-02-06 23:57:24  | Okay   | 2025-02-08 18:09:43.179914951 | NaT            | False      | 18   |
+| XXXXXXX  | UserE     | 2025-02-06 06:33:40  | Okay   | 2025-02-08 18:09:43.434650898 | NaT            | False      | 18   |
+
+Note that the first X rows, depending on the number of the members, will always contain empty values in prev_timestamp as there has to be a previous timestamp ....
+
+### 4. Implementing an Analysis Module
+Each analysis module should define two key methods:
+- `transform_data(self, df: pd.DataFrame) -> pd.DataFrame`: Processes the input data for plotting.
+- `plot_data(self, df: pd.DataFrame)`: Generates and saves the plot.
+
+#### Example: Adding a Plotly Heatmap
+Below is an example of how to create a new analysis module using `BasePlotlyAnalysis`.
+
+```python
+import pandas as pd
+import plotly.graph_objects as go
+from .basePlotlyAnalysis import BasePlotlyAnalysis
+
+class PlotlyActivityHeatmap(BasePlotlyAnalysis):
+    """
+    Displays user activity trends over multiple days using an interactive heatmap.
+    """
+    name = "Activity Heatmap (Interactive)"
+    description = "Displays user activity trends over multiple days."
+    plot_filename = "activity_heatmap.html"
+
+    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        df['hour'] = df['timestamp'].dt.hour
+        active_counts = df[df['was_active']].pivot_table(
+            index='name', 
+            columns='hour', 
+            values='was_active', 
+            aggfunc='sum', 
+            fill_value=0
+        ).reset_index()
+        return active_counts.melt(id_vars='name', var_name='hour', value_name='activity_count')
+
+    def plot_data(self, df: pd.DataFrame):
+        df = df.pivot(index='name', columns='hour', values='activity_count').fillna(0)
+        self.fig = go.Figure(data=go.Heatmap(
+            z=df.values, x=df.columns, y=df.index, colorscale='Viridis',
+            colorbar=dict(title='Activity Count')
+        ))
+        self.fig.update_layout(title='User Activity Heatmap', xaxis_title='Hour', yaxis_title='User')
+```
+
+#### Example: Adding a Static Matplotlib Plot
+Below is an example of a Matplotlib-based analysis module using `BasePlotAnalysis`.
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+from .basePlotAnalysis import BasePlotAnalysis
+
+class PlotUserSessionDuration(BasePlotAnalysis):
+    """
+    Displays a histogram of user session durations.
+    """
+    name = "User Session Duration Histogram"
+    description = "Histogram of session durations."
+    plot_filename = "session_duration.png"
+
+    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        df['session_duration'] = (df['last_action'] - df['timestamp']).dt.total_seconds()
+        return df
+
+    def plot_data(self, df: pd.DataFrame):
+        plt.figure(figsize=(10, 6))
+        plt.hist(df['session_duration'].dropna(), bins=30, edgecolor='black')
+        plt.xlabel('Session Duration (seconds)')
+        plt.ylabel('Frequency')
+        plt.title('User Session Duration Histogram')
+```
+
+### 5. Registering the Module
+Once you have created your analysis module, it will be automatically discovered by `load_analysis_modules()`, provided it is placed in the correct directory.
+
+### 6. Running the Analysis
+To execute the analysis, pass a Pandas DataFrame to its `execute` method:
+```python
+from app.analysis.plotly_activity_heatmap import PlotlyActivityHeatmap
+analysis = PlotlyActivityHeatmap()
+result_html = analysis.execute(df)
+print(result_html)  # Returns the HTML for embedding the plot
+```
+
+### Summary
+- Choose the appropriate base class (`BasePlotlyAnalysis` or `BasePlotAnalysis`).
+- Follow the naming convention (`plotly_<name>.py` for Plotly, `plot_<name>.py` for Matplotlib).
+- Implement `transform_data()` and `plot_data()` methods.
+- The module will be auto-registered if placed in the correct directory.
+- Execute the analysis by calling `.execute(df)`.
+
+This structure ensures that new analyses can be easily integrated and maintained.
+
+
+
+
 ## License
 
 All assets and code are under the  [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/) LICENSE and in the public domain unless specified otherwise.