Differences

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--- ai_life_connection [2025/05/28 01:21] – [Modular Workflow] eagleeyenebula
+++ ai_life_connection [2025/05/28 01:30] (current) – [Example 3: Visualizing Rhythm Data] eagleeyenebula
@@ Line 103: / Line 103: @@
 The following examples demonstrate how to utilize and extend the **LifeConnection** class to analyze biological data and detect patterns.
----
 ==== Example 1: Simple Heartbeat Analysis ====
 This example demonstrates the standard usage of `LifeConnection` to analyze heartbeat data.
-```python
+<code>
+python
 from ai_life_connection import LifeConnection
+</code>
-# Sample heartbeat intervals (in milliseconds)
+**Sample heartbeat intervals (in milliseconds)**
+<code>
 data = [800, 810, 795, 803, 802]
+</code>
-# Initialize the LifeConnection system
+**Initialize the LifeConnection system**
+<code>
 life_connector = LifeConnection()
+</code>
-# Analyze the heartbeat data
+**Analyze the heartbeat data**
+<code>
 insight = life_connector.describe_living_element(data)
 print(insight)
+</code>
-# Output:
+**Output:**
-# This organism exhibits a balanced and steady rhythm of life.
+<code>
-```
+This organism exhibits a balanced and steady rhythm of life.
+</code>
 **Explanation**:
-- Computes the average rate and variability to determine whether the heartbeat rhythm is stable or indicates distress.
+   * Computes the average rate and variability to determine whether the heartbeat rhythm is stable or indicates distress.
----
 ==== Example 2: Threshold Customization ====
 Modify the threshold for stability detection to analyze different datasets.
-```python
+<code>
+python
 class CustomLifeConnection(LifeConnection):
     """
@@ Line 156: / Line 155: @@
         }
+</code>
-# Usage
+**Usage**
+<code>
 data = [815, 820, 810, 807, 812]
 custom_life = CustomLifeConnection(stability_threshold=15)
@@ Line 163: / Line 163: @@
 print(result)
-```
+</code>
 **Explanation**:
-- Dynamically adjusts the variability threshold to accommodate different use-case requirements or datasets.
+   * Dynamically adjusts the variability threshold to accommodate different use-case requirements or datasets.
----
 ==== Example 3: Visualizing Rhythm Data ====
@@ Line 174: / Line 172: @@
 Leverage matplotlib to visualize the rhythms and provide deeper insights.
-```python
+<code>
+python
 import matplotlib.pyplot as plt
 from ai_life_connection import LifeConnection
+</code>
-# Sample heartbeat intervals
+**Sample heartbeat intervals**
+<code>
 data = [800, 810, 795, 803, 802]
+</code>
-# Analyze the data
+**Analyze the data**
+<code>
 life_connector = LifeConnection()
 analysis = life_connector.read_heartbeat_pattern(data)
@@ Line 193: / Line 194: @@
 plt.legend()
 plt.show()
-```
+</code>
 **Explanation**:
-- Provides a visual representation of the heartbeat pattern alongside the computed average rate to assist in better understanding.
+    * Provides a visual representation of the heartbeat pattern alongside the computed average rate to assist in better understanding.
----
 ==== Example 4: Extending to Respiratory Patterns ====
 Extend the system to process respiratory data (breathing rate intervals).
-```python
+<code>
+python
 class RespiratoryLifeConnection(LifeConnection):
     """
@@ Line 220: / Line 219: @@
             return "Unstable breathing cycles detected—possible respiratory distress."
+</code>
-# Usage
+**Usage**
+<code>
 respiratory_data = [12, 13, 11, 12, 12]  # Breaths per minute intervals
 resp_connector = RespiratoryLifeConnection()
 respiratory_insight = resp_connector.describe_respiratory_element(respiratory_data)
 print(respiratory_insight)
+</code>
-# Output:
+**Output:**
+<code>
 # This organism exhibits steady and balanced respiratory cycles.
-```
+</code>
 **Explanation**:
-- Adapts the system from heartbeat analysis to monitor respiration, demonstrating flexibility for other physiological metrics.
+    * Adapts the system from heartbeat analysis to monitor respiration, demonstrating flexibility for other physiological metrics.
----
 ==== Example 5: Persistent Health Monitoring ====
 Store and monitor long-term analysis data using a persistent data structure.
-```python
+<code>
+python
 class PersistentLifeConnection(LifeConnection):
     """
@@ Line 258: / Line 256: @@
         return result
+</code>
-# Usage
+**Usage**
+<code>
 data_streams = [
     [800, 810, 795, 803, 802],
@@ Line 271: / Line 270: @@
 print(persistent_connector.history)
-```
+</code>
 **Explanation**:
-- Creates a monitoring framework to track health changes over time by logging multiple analyses.
+    * Creates a monitoring framework to track health changes over time by logging multiple analyses.
----
 ===== Best Practices =====
 . **Validate Input Data**:
-   Ensure biological data is pre-processed and validated before analysis.
+     * Ensure biological data is pre-processed and validated before analysis.
 . **Tune Stability Thresholds**:
-   Adjust variability thresholds based on the physiological metric being analyzed.
+     * Adjust variability thresholds based on the physiological metric being analyzed.
 . **Leverage Visualization**:
-   Use tools like matplotlib for visualizing patterns to support human understanding.
+     * Use tools like matplotlib for visualizing patterns to support human understanding.
 . **Extend for Variety**:
-   Adapt the system for analyzing other biological metrics, such as ECG or brainwave data.
+     * Adapt the system for analyzing other biological metrics, such as ECG or brainwave data.
 . **Monitor Trends Over Time**:
-   Introduce historical logging to analyze trends and detect long-term abnormalities.
+     * Introduce historical logging to analyze trends and detect long-term abnormalities.
----
 ===== Conclusion =====