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Author: virgesmith

README.md

@@ -61,34 +61,7 @@ When working with `Itr`, keep these points in mind:
 
 `Itr` provides a comprehensive set of methods for various iterable operations. For a complete list of methods and their detailed descriptions, please refer to the [full documentation](autodoc.md).
 
-*Note: The detailed list of methods in `autodoc.md` is automatically generated by [introspect.py], where `Itr` introspects itself.*
-
-```py introspect.py
-from operator import add
-
-from itr import Itr
-
-# use Itr to introspect itself for methods and their docstrings
-methods = (
-    Itr(dir(Itr))
-    .filter(lambda m: m in ("__init__", "__iter__", "__next__") or not m.startswith("_"))
-    .map(lambda m: (m, getattr(Itr, m).__doc__))
-    .collect(dict)
-)
-
-# use Itr to make some documentation
-method_template = """
-### `{method_name}`
-
-{method_doc}
-"""
-
-with open("doc/autodoc.md", "w") as fd:
-    fd.write("# `Itr` class documentation\n")
-    fd.write(Itr.__doc__ or "")
-    fd.write("## Public methods\n")
-    fd.write(Itr(methods.items()).map(lambda m: method_template.format(method_name=m[0], method_doc=m[1])).reduce(add))
-```
+*Note: The detailed list of methods in `autodoc.md` is automatically generated by [introspect.py](src/scripts/introspect.py), where `Itr` introspects itself.*
 
 ## Examples
 
@@ -163,52 +136,4 @@ For reference, an equivalent using `itertools` directly:
 
 ### More examples
 
-More examples can be found [here](./doc/examples.md).
-
-
-
-
-
-Beyond basic transformations, `Itr` enables powerful composition of operations, making complex data processing more manageable. This example demonstrates numerical differentiation and integration using `Itr`.
-
-```python
-from itr import Itr
-import numpy as np
-from operator import add
-
-x = Itr(np.linspace(0, 1, 11))
-# Need a copy of 'x' as the original 'x' is consumed in the zip method.
-xy = x.copy().zip(x.map(lambda xi: xi * xi / 2))
-
-# Numerical differentiation (central difference)
-def diff1central(xy_segment):
-    # Unpack the 3-element rolling window: (x0, y0), (x1, y1_ignored), (x2, y2)
-    (x0, y0), (x1, _y1), (x2, y2) = xy_segment
-    return x1, (y2 - y0) / (x2 - x0)
-
-print("Numerical Derivatives:")
-print(
-    xy.copy() # Need a copy as xy will be reused below for integration
-    .rolling(3)
-    .map(diff1central)  # Produces (x, dy/dx) pairs
-    .collect()
-)
-
-# Numerical integration (trapezoidal rule)
-# Integrates x**2 / 2, resulting in x**3 / 6. Over 0,1 this is 1/6.
-def trapz_accumulate(current_sum, xy_pair):
-    # Unpack (x0, y0) and (x1, y1) from the pairwise iteration
-    (x0, y0), (x1, y1) = xy_pair
-    return current_sum + 0.5 * (y0 + y1) * (x1 - x0)
-
-print("\nNumerical Integral (Trapezoidal Rule - Fold):")
-print(xy.pairwise().fold(0, trapz_accumulate))
-
-# ...or an alternative integration implementation using starmap and flattening
-def trapz2(x0, y0, x1, y1):  # type: ignore[no-untyped-def]
-    """Calculates trapezoidal area for two points."""
-    return (y0 + y1) * (x1 - x0) / 2
-
-print("\nNumerical Integral (Trapezoidal Rule - Starmap):")
-print(xy.copy().pairwise().flatten().flatten().batched(4).starmap(trapz2).reduce(add))
-```
+More examples can be found [here](./doc/examples.ipynb).