Merge pull request #103 from amosproj/ad_visualization

Ad visualization

Author: Perimora

amos_team_resources/anomaly_detection/mad/anomaly_detection_plot.html

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5682a007",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imports complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Add SDK to path\n",
+    "SDK_PATH = Path().resolve().parents[2] / \"src\" / \"sdk\" / \"python\"\n",
+    "sys.path.insert(0, str(SDK_PATH))\n",
+    "\n",
+    "from rtdip_sdk.pipelines.visualization.plotly.anomaly_detection import AnomalyDetectionPlotly\n",
+    "from rtdip_sdk.pipelines.visualization.matplotlib.anomaly_detection import AnomalyDetectionPlot\n",
+    "\n",
+    "from rtdip_sdk.pipelines.anomaly_detection.spark.mad.mad_anomaly_detection import MadAnomalyDetection\n",
+    "\n",
+    "print(\"Imports complete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9a3db7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "session = SparkSession.builder.appName(\"PlotlyDemoAD\").getOrCreate()\n",
+    "\n",
+    "\n",
+    "base = datetime(2024, 1, 1)\n",
+    "\n",
+    "data = [\n",
+    "    (base + timedelta(seconds=i), v)\n",
+    "    for i, v in enumerate(\n",
+    "        [10.0, 12.0, 10.5, 11.0, 30.0, 10.2, 9.8, 10.1, 10.3, 10.0]\n",
+    "    )\n",
+    "]\n",
+    "columns = [\"timestamp\", \"value\"]\n",
+    "\n",
+    "df = session.createDataFrame(data, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f290695a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad_scorer = MadAnomalyDetection()\n",
+    "\n",
+    "anoms = ad_scorer.detect(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "abca5439",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('anomaly_detection_plot.html')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plotly_visualizer = AnomalyDetectionPlotly(ts_data=df, ad_data=anoms, sensor_id=\"Plotly_Demo\")\n",
+    "plotly_visualizer.plot()\n",
+    "plotly_visualizer.save(\"anomaly_detection_plot.html\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rtdip-sdk",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

amos_team_resources/anomaly_detection/mad/visualize_test.ipynb

@@ -0,0 +1,25 @@
+## Time Series Anomaly Visualization – Guideline
+
+This visualization is used consistently across the project to answer the
+following standard questions for time series anomaly analysis.
+
+### Standard Questions
+
+1. Where in the time series do unusual deviations from typical signal behavior occur?
+
+2. Do highlighted deviations appear as isolated events or as contiguous time segments?
+
+3. How do highlighted deviations relate to the local signal shape
+   (e.g., extremes, transitions, plateaus, or abrupt level changes)?
+
+4. What are the exact timestamp and value of a detected anomaly when closer inspection is required?
+
+### Visualization Constraints
+
+- A single continuous line plot is used to show the full time series context.
+- Highlighted markers are overlaid directly on the signal.
+- No additional subplots, thresholds, or secondary axes are introduced.
+- The same visual encoding is used across different detection methods.
+- Interactive features (e.g., hover tooltips) are optional and may be used
+  to support precise inspection of individual anomaly points without altering
+  the overall visual structure.

amos_team_resources/anomaly_detection/visualization.md

@@ -0,0 +1,13 @@
+# Copyright 2025 RTDIP
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/spark/mad/__init__.py

@@ -17,7 +17,7 @@
 from pyspark.sql import DataFrame
 from typing import Optional, List, Union
 
-from src.sdk.python.rtdip_sdk.pipelines._pipeline_utils.models import (
+from ...._pipeline_utils.models import (
     Libraries,
     SystemType,
 )

src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/spark/mad/mad_anomaly_detection.py

@@ -32,6 +32,8 @@
     ModelsOverlayPlot: Overlay multiple model forecasts
     ForecastDistributionPlot: Box plots of forecast distributions
     ComparisonDashboard: Model comparison dashboard
+
+    AnomalyDetectionPlot: Static plot of time series with anomalies
 """
 
 from .forecasting import (
@@ -51,3 +53,6 @@
     ForecastDistributionPlot,
     ComparisonDashboard,
 )
+from .anomaly_detection import (
+    AnomalyDetectionPlot
+)