doc(readme): more conscise readme

Author: ElouenGinat

README.md

@@ -10,28 +10,7 @@
 
 Khisto is a Python library for creating histograms using the **Khiops optimal binning algorithm**. Unlike standard histograms that use fixed-width bins or simple heuristics, Khisto automatically determines the optimal number of bins and their variable widths to best represent the underlying data distribution.
 
-## Features
-
-- **Optimal Binning**: Uses the MODL (Minimum Description Length) principle to find the best discretization.
-- **Variable-Width Bins**: Captures dense regions with fine bins and sparse regions with wider bins.
-- **NumPy Compatible**: Drop-in replacement for `numpy.histogram`.
-- **Matplotlib Integration**: `khisto.matplotlib.hist` works like `plt.hist`.
-- **Core Histogram API**: Inspect every available granularity with `khisto.core.compute_histograms` and `HistogramResult`.
-- **Minimal Dependencies**: Only requires NumPy (matplotlib optional for plotting).
-
-| Standard Gaussian | Heavy-tailed Pareto |
-| --- | --- |
-| ![Adaptive Gaussian histogram](docs/images/gaussian-quick-start.png) | ![Adaptive Pareto histogram](docs/images/pareto-quick-start.png) |
-
-## Reproducing The Example Distributions
-
-The complete runnable script is available in `scripts/generate_distribution_examples.py`.
-
-Run it from the repository root to regenerate both example distributions and the figure files used in this README:
-
-```bash
-python scripts/generate_distribution_examples.py
-```
+Documentation is available at **[khiops.github.io/khisto-python](https://khiopsml.github.io/khisto-python/)**.
 
 ## Installation
 
@@ -58,74 +37,8 @@ data = np.random.normal(0, 1, 10000)
 
 # Compute optimal histogram (drop-in replacement for np.histogram)
 hist, bin_edges = histogram(data)
-
-# With density normalization
-density, bin_edges = histogram(data, density=True)
-
-# Limit maximum number of bins
-hist, bin_edges = histogram(data, max_bins=10)
-
-# Specify range
-hist, bin_edges = histogram(data, range=(-2, 2))
-```
-
-Using 10,000 samples keeps the adaptive refinement visible while remaining fast to compute.
-
-Heavy-tailed example:
-
-```python
-import numpy as np
-import matplotlib.pyplot as plt
-from khisto.matplotlib import hist
-
-# Generate 10,000 samples from a Pareto distribution, shifted to start at 1 for better log-log visualization
-shape = 3
-long_tail_data = np.random.pareto(shape, size=10000) + 1
-
-# Plot an adaptive histogram on logarithmic axes.
-n, bins, patches = hist(long_tail_data, density=True)
-plt.xscale("log")
-plt.yscale("log")
-plt.show()
-```
-
-### Matplotlib Integration
-
-```python
-import numpy as np
-import matplotlib.pyplot as plt
-from khisto.matplotlib import hist
-
-# Generate 10,000 samples from a standard Gaussian distribution.
-data = np.random.normal(0, 1, 10000)
-
-# Density is usually the most interpretable view with variable-width bins.
-n, bins, patches = hist(data, density=True)
-plt.xlabel('Value')
-plt.ylabel('Density')
-plt.show()
-
-# Cumulative density follows matplotlib semantics.
-n, bins, patches = hist(data, density=True, cumulative=True)
-plt.ylabel('Cumulative probability')
-plt.show()
 ```
 
-## How It Works
-
-Khisto uses the Khiops optimal binning algorithm based on the MODL (Minimum Optimal Description Length) principle. Instead of using fixed-width bins like traditional histograms, it:
-
-1. Analyzes the data distribution
-2. Finds bin boundaries that minimize information loss
-3. Creates variable-width bins that adapt to data density
-
-This results in histograms that better represent the underlying distribution, with finer bins in dense regions and wider bins in sparse regions.
-
-The method implemented in Khiops is comprehensively detailed in [2] and further extended in [1].
-
-- [1] M. Boullé. Floating-point histograms for exploratory analysis of large scale real-world data sets. Intelligent Data Analysis, 28(5):1347-1394, 2024
-- [2] V. Zelaya Mendizábal, M. Boullé, F. Rossi. Fast and fully-automated histograms for large-scale data sets. Computational Statistics & Data Analysis, 180:0-0, 2023
-
 ## Development
 
 ```bash
@@ -140,16 +53,6 @@ uv sync --group dev --extra all
 uv run pytest
 ```
 
-## Documentation
-
-Full documentation is hosted at **[khiops.github.io/khisto-python](https://khiops.github.io/khisto-python/)**.
-
-- [API Reference](https://khiops.github.io/khisto-python/array/histogram/index.html) — NumPy-like histogram API
-- [Matplotlib Integration](https://khiops.github.io/khisto-python/matplotlib/index.html) — `hist` plotting function
-- [Core API](https://khiops.github.io/khisto-python/core/index.html) — full access to histogram granularity levels
-- [API Comparison](https://khiops.github.io/khisto-python/api_comparison.html) — side-by-side with NumPy and Matplotlib
-- [Demo Notebook](https://khiops.github.io/khisto-python/demo.html) — interactive walkthrough
-
 ## License
 
 [BSD 3-Clause Clear License](LICENSE)