Update version to 0.0.4, remove Python 3.8 and 3.9 from tox configuration, and update GitHub Actions to support Python 3.10, 3.11, 3.12, and 3.13. Enhance documentation to include HDRHistogram and improve examples with updated methods.

Author: DhyeyMavani2003

.github/workflows/test.yaml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - uses: actions/checkout@v4

QuantileFlow/__init__.py

@@ -22,7 +22,7 @@
 from QuantileFlow.hdrhistogram.core import HDRHistogram
 from QuantileFlow.ddsketch.core import DDSketch
 
-__version__ = "0.0.3"
+__version__ = "0.0.4"
 __all__ = [
     "MomentSketch",
     "HDRHistogram",

QuantileFlow/pytest.ini

@@ -0,0 +1,87 @@
+# HDRHistogram
+
+HDRHistogram (High Dynamic Range Histogram) is a logarithmic-bucketed histogram implementation designed for efficient tracking of values across multiple orders of magnitude.
+
+## Overview
+
+HDRHistogram offers the following features:
+
+* **Wide Value Range**: Efficiently tracks values across multiple orders of magnitude
+* **Configurable Precision**: Adjustable number of buckets for different accuracy needs
+* **Memory Efficient**: Uses logarithmic bucketing to minimize memory usage
+* **Summary Statistics**: Provides comprehensive statistics including min, max, quartiles, and count
+* **Visualization**: Built-in support for plotting distributions with logarithmic scales
+* **Serialization**: Supports converting histograms to/from dictionaries for storage and transmission
+* **Value Range Control**: Configurable minimum and maximum trackable values
+
+## Main Class
+
+```{eval-rst}
+.. autoclass:: QuantileFlow.HDRHistogram
+   :members:
+   :undoc-members:
+   :special-members: __init__
+   :exclude-members: __dict__, __weakref__
+   :noindex:
+   :show-inheritance:
+```
+
+## Mathematical Background
+
+HDRHistogram works by maintaining a fixed number of logarithmic buckets to efficiently track values across a wide range. The key aspects of the implementation are:
+
+1. **Logarithmic Bucketing**: Values are assigned to buckets based on their logarithm, allowing efficient tracking of values across multiple orders of magnitude
+2. **Value Range Control**: Configurable minimum and maximum values ensure memory efficiency
+3. **Linear Interpolation**: Within each bucket, linear interpolation is used to estimate quantiles
+
+### Bucket Calculation
+
+The bucket index for a value $x$ is calculated as:
+
+$$
+\text{bucket_index} = \lfloor \log_2(x) \rfloor
+$$
+
+This ensures that:
+- Values in the same order of magnitude are grouped together
+- The number of buckets grows logarithmically with the value range
+- Memory usage remains constant regardless of the number of values inserted
+
+### Quantile Estimation
+
+Quantiles are estimated by:
+1. Finding the bucket containing the target quantile
+2. Using linear interpolation within the bucket to estimate the exact value
+
+For a target quantile $q$ and total count $N$, the estimated value is:
+
+$$
+\text{value} = \text{lower_bound} + (\text{upper_bound} - \text{lower_bound}) \times \frac{qN - \text{cumulative_count}}{\text{bucket_count}}
+$$
+
+Where:
+- $\text{lower_bound}$ and $\text{upper_bound}$ are the bucket boundaries
+- $\text{cumulative_count}$ is the count of values in previous buckets
+- $\text{bucket_count}$ is the count of values in the current bucket
+
+## Performance Characteristics
+
+- **Memory Usage**: O(num_buckets)
+- **Insertion Time**: O(1) per value
+- **Query Time**: O(num_buckets) for quantile queries
+- **Merge Time**: O(num_buckets) for merging histograms
+
+## Use Cases
+
+HDRHistogram is particularly useful for:
+- Tracking metrics with wide value ranges (e.g., response times, request sizes)
+- Monitoring systems with multiple orders of magnitude in measurements
+- Applications requiring configurable precision and value range control
+- Distributed systems where histograms need to be merged
+- Real-time monitoring and alerting systems
+
+## Usage Examples
+
+For basic usage, see the [Getting Started](../getting-started.md) guide.
+
+For more advanced examples, see the [Examples](../examples.md) page. 

docs/api/hdrhistogram.md

@@ -4,10 +4,11 @@ This page provides links to the detailed API documentation for all components of
 
 ## Sketch Algorithms
 
-QuantileFlow provides two main algorithms for quantile estimation:
+QuantileFlow provides three main algorithms for quantile estimation:
 
 - [DDSketch](api/ddsketch.md) - Distributed and Deterministic Sketch with configurable relative error
 - [MomentSketch](api/momentsketch.md) - Moment-based quantile sketch using maximum entropy optimization
+- [HDRHistogram](api/hdrhistogram.md) - High Dynamic Range Histogram for tracking values across multiple orders of magnitude
 
 ## API Reference