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+---
+sidebar_position: 5
+---
+
+# Input types (Python / NumPy) and numerical precision
+
+`pyamtrack` is a Python interface to the C/C++ **libamtrack** library. Many functions accept either a **single number** or a **set of numbers** (a Python list or a NumPy array). Internally, most continuous physical quantities are computed using C/C++ **double precision** (`double`).
+
+This page explains what you can pass to `pyamtrack` functions and how to avoid the most common numerical pitfalls—especially when using `numpy.float32`.
+
+---
+
+## Quick recommendations (safe defaults)
+
+If you only read one section, read this:
+
+- For **single values** (energy, LET, etc.): use Python `float`  
+  (example: `150.0`, not `np.float32(150)`).
+- For **arrays of values**: use NumPy arrays with `dtype=np.float64`.
+- For **IDs** (material IDs, model IDs): use Python `int` or integer NumPy arrays (`np.int32` / `np.int64`).
+- Avoid `numpy.float32` / `dtype=np.float32` for inputs to physics calculations unless you really know you can tolerate reduced precision.
+
+---
+
+## 1) What inputs are accepted?
+
+Most numeric functions accept these input forms:
+
+### A. A single number
+Use when you want one result.
+
+```python
+import pyamtrack
+pyamtrack.converters.beta_from_energy(150.0)
+```
+
+### B. A list of numbers
+Use when you want results for multiple values.
+
+```python
+import pyamtrack
+energies = [50.0, 100.0, 150.0]
+pyamtrack.stopping.electron_range(energies, material=1, model="tabata")
+```
+
+### C. A NumPy array (`ndarray`)
+Recommended for performance and clarity, especially for longer vectors.
+
+```python
+import numpy as np
+import pyamtrack
+
+energies = np.asarray([50.0, 100.0, 150.0], dtype=np.float64)
+pyamtrack.stopping.electron_range(energies, material=1, model="tabata")
+```
+
+**Note:** Some functions require NumPy input arrays to be **1‑dimensional** (a simple vector).
+
+---
+
+## 2) Continuous values vs IDs (different “kinds” of inputs)
+
+In `pyamtrack`, it helps to think of inputs in two categories:
+
+### A. Continuous physical quantities (use float64)
+Examples: energies, ranges, stopping power values, etc.
+
+- Recommended dtype for arrays: **`np.float64`**
+- Recommended type for scalars: Python **`float`**
+
+### B. Integer identifiers (use integers)
+Examples: `material` ID, sometimes model ID.
+
+- Recommended dtype for arrays: **`np.int32`** or **`np.int64`**
+- Recommended type for scalars: Python **`int`**
+
+Example (material IDs as an integer array):
+
+```python
+import numpy as np
+import pyamtrack
+
+energies  = np.asarray([50.0, 100.0, 150.0], dtype=np.float64)
+materials = np.asarray([1, 1, 1], dtype=np.int32)
+
+pyamtrack.stopping.electron_range(energies, material=materials, model="tabata")
+```
+
+---
+
+## 3) Why `numpy.float32` is not recommended
+
+You will often see NumPy arrays created with `dtype=np.float32` to save memory. However, for `pyamtrack` this can be a bad default.
+
+### What happens
+Even though the C/C++ code uses `double`, if you store inputs as `float32`:
+
+- the values are already rounded to about **7 significant digits**,
+- and converting that float32 value to C/C++ `double` **cannot recover the lost precision**.
+
+So you can get slightly different results compared to float64 inputs.
+
+### What to do instead
+Prefer `float64`:
+
+```python
+import numpy as np
+
+# Not recommended for numerically sensitive inputs
+x32 = np.asarray([0.1, 0.2, 0.3], dtype=np.float32)
+
+# Recommended
+x64 = np.asarray([0.1, 0.2, 0.3], dtype=np.float64)
+
+# If you receive float32 from elsewhere, convert early:
+x = np.asarray(x32, dtype=np.float64)
+```
+
+---
+
+## 4) NumPy dtype vs C type (quick reference)
+
+This is a short summary based on the NumPy documentation section
+“Relationship Between NumPy Data Types and C Data Types”.  
+([numpy.org](https://numpy.org/doc/stable/user/basics.types.html?utm_source=openai))
+
+| Recommended NumPy dtype | NumPy “C-like” name | Rough C type | Notes |
+|---|---|---|---|
+| `np.int32` | *(no single alias on every platform)* | usually `int32_t` | fixed width (portable) |
+| `np.int64` | `np.longlong` | `long long` | fixed width (portable) |
+| `np.float32` | `np.single` | `float` | ~7 significant digits |
+| `np.float64` | `np.double` | `double` | ~15–16 significant digits |
+
+---
+
+## 5) “One value” vs “many values”: how results are returned
+
+Many functions behave like this:
+
+- If you pass a **single value**, you get a **single value** back (Python `float`).
+- If you pass a **list** or **NumPy array**, you get a **vector of results** back (often a NumPy array).
+
+Example:
+
+```python
+import numpy as np
+import pyamtrack
+
+print(pyamtrack.converters.beta_from_energy(150.0))  # scalar -> scalar
+
+arr = np.asarray([50.0, 100.0, 150.0], dtype=np.float64)
+print(pyamtrack.converters.beta_from_energy(arr))    # array -> array
+```
+
+---
+
+## 6) Practical checklist for users
+
+Before running calculations, quickly check:
+
+1. Are my **continuous values** stored as `float64`?
+   - `np.asarray(x, dtype=np.float64)`
+2. Are my **ID-like inputs** (materials/models) integers?
+   - `np.asarray(ids, dtype=np.int32)` or plain `int`
+3. Am I accidentally using `float32` because of upstream data loading?
+   - If yes: upcast early to `float64`.
+
+---