chore: lowercase for filenames

docs/user/tutorial/indexing.md

@@ -0,0 +1,103 @@
+# Indexing
+
+## Constant Indexing
+
+Constant indexing refers to the index being static (i.e., known during compilation).
+
+Here are some examples of constant indexing:
+
+### Extracting a single element
+
+```python
+import concrete.numpy as hnp
+import numpy as np
+
+def f(x):
+    return x[1]
+
+inputset = [np.random.randint(0, 2 ** 3, size=(3,), dtype=np.uint8) for _ in range(10)]
+
+compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
+compiler.eval_on_inputset(inputset)
+circuit = compiler.get_compiled_fhe_circuit()
+
+test_input = np.array([4, 2, 6], dtype=np.uint8)
+expected_output = 2
+
+assert np.array_equal(circuit.run(test_input), expected_output)
+```
+
+You can use negative indexing.
+
+```python
+import concrete.numpy as hnp
+import numpy as np
+
+def f(x):
+    return x[-1]
+
+inputset = [np.random.randint(0, 2 ** 3, size=(3,), dtype=np.uint8) for _ in range(10)]
+
+compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
+compiler.eval_on_inputset(inputset)
+circuit = compiler.get_compiled_fhe_circuit()
+
+test_input = np.array([4, 2, 6], dtype=np.uint8)
+expected_output = 6
+
+assert np.array_equal(circuit.run(test_input), expected_output)
+```
+
+You can use multidimensional indexing as well.
+
+```python
+import concrete.numpy as hnp
+import numpy as np
+
+def f(x):
+    return x[-1, 1]
+
+inputset = [np.random.randint(0, 2 ** 3, size=(3, 2), dtype=np.uint8) for _ in range(10)]
+
+compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
+compiler.eval_on_inputset(inputset)
+circuit = compiler.get_compiled_fhe_circuit()
+
+test_input = np.array([[4, 2], [1, 5], [7, 6]], dtype=np.uint8)
+expected_output = 6
+
+assert np.array_equal(circuit.run(test_input), expected_output)
+```
+
+### Extracting a slice
+
+```python
+import concrete.numpy as hnp
+import numpy as np
+
+def f(x):
+    return x[1:4]
+
+inputset = [np.random.randint(0, 2 ** 3, size=(5,), dtype=np.uint8) for _ in range(10)]
+
+compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
+compiler.eval_on_inputset(inputset)
+circuit = compiler.get_compiled_fhe_circuit()
+
+test_input = np.array([4, 2, 6, 1, 7], dtype=np.uint8)
+expected_output = np.array([2, 6, 1], dtype=np.uint8)
+
+assert np.array_equal(circuit.run(test_input), expected_output)
+```
+
+You can use multidimensional slicing as well.
+
+#### Note
+
+There are certain limitations of slicing due to MLIR. So if you stumple into `RuntimeError: Compilation failed: Failed to lower to LLVM dialect`, know that we are aware of it, and we are trying to make such cases compilable.
+
+## Dynamic Indexing
+
+Dynamic indexing refers to the index being dynamic (i.e., can change during runtime).
+Such indexing is especially useful for things like decision trees.
+Unfortunately, we don't support dynamic indexing for the time being.