Refeactoring and bug fix of wrongly named module. Add plotting to documentation.

Author: patr1ck-m

README.md

@@ -17,6 +17,7 @@ This library provides tools for aggregating sensor data over time windows, detec
 - [Architecture Overview](#architecture-overview)
 - [Running Tests](#running-tests)
 - [Project Structure](#project-structure)
+- [Plotting Results](#plotting-results)
 - [Implementation Status](#implementation-status)
 
 ---
@@ -41,25 +42,25 @@ This library provides tools for aggregating sensor data over time windows, detec
 ### Option 1: Using Effekt directly
 
 1. Clone this repository:
-   ```sh
-   git clone <repo-url>
-   cd Real-Time-Stream-Processing-Library
-   ```
+  ```sh
+  git clone <repo-url>
+  cd Real-Time-Stream-Processing-Library
+  ```
 
 2. Make sure Effekt is installed at version `0.59.0` and available in your PATH. You can verify with:
-   ```sh
-   effekt --version
-   ```
+  ```sh
+  effekt --version
+  ```
 
 3. Run the main file:
-   ```sh
-   effekt src/main.effekt
-   ```
+  ```sh
+  effekt src/main.effekt
+  ```
 
 4. Run the tests:
-   ```sh
-   effekt src/test.effekt
-   ```
+  ```sh
+  effekt src/test.effekt
+  ```
 
 ### Option 2: Using Nix
 
@@ -264,7 +265,6 @@ Tests use the `test` module from Effekt's standard library and return exit code
 │   ├── main.effekt           # Main entry point
 │   ├── test.effekt           # Test runner
 │   ├── examples.effekt       # Usage examples
-│   ├── lib.effekt            # Library re-exports
 │   ├── lib/                  # Library modules
 │   │   ├── aggregation.effekt
 │   │   ├── anomaly_detection.effekt
@@ -284,16 +284,50 @@ Tests use the `test` module from Effekt's standard library and return exit code
 │       ├── lib.effekt
 │       ├── testdata.csv
 │       └── testdata_with_timestamp.csv
-├── flake.nix                 # Nix flake configuration
-├── flake.lock                # Nix lockfile
-├── PROJECT_SPEC.md           # Project specification
-├── ARCHITECTURE.md           # Developer/architecture documentation
+├── plot_data.py               # Visualization script for results
+├── flake.nix                  # Nix flake configuration
+├── flake.lock                 # Nix lockfile
+├── PROJECT_SPEC.md            # Project specification
+├── ARCHITECTURE.md            # Developer/architecture documentation
 ├── LICENSE
 └── README.md
 ```
 
 ---
 
+## Plotting Results
+
+The `plot_data.py` script provides visualization tools for analyzing output from the stream processing library. It generates plots from CSV files produced by the logging handlers.
+
+### Usage
+
+```sh
+python plot_data.py --raw <csv_file> --value-name "<Name>" [--out <output.png>]
+python plot_data.py --anomalies <csv_file> --value-name "<Name>" [--show-anomaly-values] [--out <output.png>]
+```
+
+### Features
+
+- **Raw Data Plotting**: Visualize time-series data with timestamps converted from Unix milliseconds
+- **Anomaly Visualization**: Plot data with anomalies highlighted in red, including both point markers and background shading for anomaly regions
+- **Anomaly Scores**: Optional secondary y-axis displaying anomaly scores for detailed analysis
+- **Export**: Save plots to PNG files or display interactively
+
+### Examples
+
+```sh
+# Plot raw CPU usage data
+python plot_data.py --raw cpu_usage_raw.csv --value-name "CPU Usage" --out raw_plot.png
+
+# Plot with anomalies and scores
+python plot_data.py --anomalies cpu_anomalies.csv --value-name "CPU Usage" --show-anomaly-values --out anomaly_plot.png
+```
+
+### Requirements
+
+- `pandas`: Data loading and processing
+- `matplotlib`: Plotting and visualization
+
 ## Implementation Status
 
 All requirements from the project specification have been implemented:

plot_data.py

@@ -36,8 +36,6 @@ def plot_raw_data(csv_file, value_name='Value', out_file=None):
     plt.grid(True, alpha=0.3)
     plt.legend()
 
-    # Rotate x-axis labels by 45 degrees for better readability
-    plt.xticks(rotation=45, ha='right')
     plt.tight_layout()
 
     if out_file:
@@ -123,8 +121,6 @@ def plot_anomalies(csv_file, value_name='Value', show_anomaly_values=False, out_
     if not show_anomaly_values:
         ax.legend()
 
-    # Rotate x-axis by 45 degrees
-    plt.xticks(rotation=45, ha='right')
     plt.tight_layout()
 
     if out_file:

src/examples.effekt

@@ -15,7 +15,7 @@ def cpuUsageAnomalyDetectionExample(): Unit = {
   with boundary()
   with anomaly_logger::logToConsole();
   with anomaly_logger::logToFileCsvRethrow("cpu_anomalies.csv");
-  with minMaxAnomalyDetector(0.0, 0.25);
+  with minMaxAnomalyDetector(0.0, 0.90);
   with event_logger::logToFileCsvRethrow("cpu_usage_aggregated.csv"); // Just for convenience, information is already in cpu_anomalies.csv
 
   with cpuUsagePullStream(100);

src/lib.effekt

@@ -1,4 +1,4 @@
-module src/lib/resource_utilization_input
+module src/lib/cpu_utilization_input
 
 import src/lib/event
 import src/lib/timestamp

src/lib/cpu_utilization_input.effekt

@@ -4,5 +4,5 @@ import src/examples
 
 
 def main(): Unit = {
-  examples::exponentialDistributionWithZScoreAnomalyDetectionExample()
+  examples::cpuUsageAnomalyDetectionExample()
 }

src/main.effekt

@@ -6,16 +6,6 @@ import src/lib/event
 import src/test/lib
 import process
 
-def eventsFromList(values: List[Double]): List[Event] = {
-  def loop(vals: List[Double], ts: Int): List[Event] = {
-    vals match {
-      case Nil() => Nil()
-      case Cons(h, t) => Cons(Event(ts, h), loop(t, ts + 1))
-    }
-  }
-  loop(values, 0)
-}
-
 def sameEvent(a: Event, b: Event): Bool = {
   a.timestamp == b.timestamp && a.value == b.value
 }

src/test/aggregation_test.effekt

@@ -6,16 +6,6 @@ import src/lib/event
 import src/test/lib
 import process
 
-def eventsFromList(values: List[Double]): List[Event] = {
-	def loop(vals: List[Double], ts: Int): List[Event] = {
-		vals match {
-			case Nil() => Nil()
-			case Cons(h, t) => Cons(Event(ts, h), loop(t, ts + 1))
-		}
-	}
-	loop(values, 0)
-}
-
 def approx(a: Double, b: Double): Bool = {
 	abs(a - b) <= 0.000001
 }

src/test/anomaly_detection_test.effekt

@@ -2,6 +2,7 @@ module src/test/lib
 
 import stream
 import test
+import src/lib/event
 
 def assertEmits[T](expected: List[T]) { equals: (T, T) => Bool } { program: () => Unit / emit[T] } : Unit / { Assertion } = {
   var lst = expected
@@ -69,4 +70,14 @@ def assertDoNotRaise[E] {block: () => Unit / Exception[E]} = {
       assertTrue(false, "Expected exception not to raise but was raised: " ++ msg)
     }
   }
+}
+
+def eventsFromList(values: List[Double]): List[Event] = {
+  def loop(vals: List[Double], ts: Int): List[Event] = {
+    vals match {
+      case Nil() => Nil()
+      case Cons(h, t) => Cons(Event(ts, h), loop(t, ts + 1))
+    }
+  }
+  loop(values, 0)
 }