dtmf-table 1.1.1

A zero-heap, Rust backed, const-first DTMF keypad frequency table with runtime tolerance helpers.

DTMF Table (Python)

A lightweight, dependency-free Python implementation of the standard DTMF (Dual-Tone Multi-Frequency) keypad used in telephony systems.

This library provides safe mappings between keypad keys and their canonical low/high frequencies, together with practical runtime helpers for resolving noisy frequency estimates produced by FFT-based audio pipelines.

The API is intentionally small, predictable, and suitable for both batch analysis and real-time signal processing.

---

Features

• Canonical forward and reverse mappings between DTMF keys and frequencies

• Closed key representation Invalid keys cannot be constructed; all validation happens at creation time

• Zero external dependencies

• Runtime helpers:

  • Tolerance-based reverse lookup (e.g. from FFT peak estimates)
  • Nearest snapping for noisy frequency measurements
  • Iteration over all keys and tones

---

Installation

pip install dtmf-table

---

Quick Example

from dtmf_table import DtmfTable, DtmfKey

# Construct a table instance
table = DtmfTable()

# Forward lookup from key to canonical frequencies
key = DtmfKey.from_char('8')
low, high = key.freqs()
assert (low, high) == (852, 1336)

# Reverse lookup with tolerance (e.g. from FFT bin centres)
key = table.from_pair_tol_f64(770.2, 1335.6, 6.0)
assert key.to_char() == '5'

# Nearest snapping for noisy estimates
key, snapped_low, snapped_high = table.nearest_u32(768, 1342)
assert key.to_char() == '5'
assert (snapped_low, snapped_high) == (770, 1336)

---

Design Rationale

DTMF tone mappings are fixed, tiny (a 4×4 keypad), and stable. Rather than exposing a mutable lookup table or relying on dynamic configuration, the mapping is encoded directly in the library and validated eagerly.

This yields:

• Deterministic behaviour with no hidden state
• Early detection of invalid keys and invalid frequency pairs
• Predictable performance characteristics for tight audio loops

While Python cannot provide compile-time guarantees, the API enforces the same invariants at runtime.

---

API Overview

Core Types

DtmfKey

Represents a valid DTMF keypad key.

• DtmfKey.from_char(char) -> DtmfKey Create a key from a character. Raises if invalid.

• key.to_char() -> str Convert a key back to its character representation.

• key.freqs() -> (int, int) Return the canonical (low_freq, high_freq) pair.

---

DtmfTable

Provides lookup and matching utilities.

• DtmfTable.lookup_key(key) -> (int, int) Forward lookup: key → frequencies.

• DtmfTable.from_pair_exact(low, high) -> Optional[DtmfKey] Reverse lookup requiring an exact frequency match.

• DtmfTable.from_pair_normalised(f1, f2) -> Optional[DtmfKey] Reverse lookup ignoring frequency order.

• DtmfTable.from_pair_tol_f64(f1, f2, tol) -> Optional[DtmfKey] Reverse lookup allowing tolerance in Hz.

• DtmfTable.nearest_u32(f1, f2) -> (DtmfKey, int, int) Snap noisy integer frequency estimates to the nearest canonical pair.

• DtmfTable.nearest_f64(f1, f2) -> (DtmfKey, float, float) Float variant of nearest snapping.

• DtmfTable.all_keys() -> list[DtmfKey] Return all valid keys.

• DtmfTable.all_tones() -> list[(DtmfKey, int, int)] Return all keys with their canonical frequencies.

---

Integration Example

A typical audio workflow looks like:

• Extract an audio segment
• Compute an FFT magnitude spectrum
• Identify two dominant frequency peaks
• Resolve the DTMF key using tolerance or snapping

# freq1 and freq2 are the peak frequencies extracted from your FFT
key = table.from_pair_tol_f64(freq1, freq2, 5.0)
if key is not None:
    print(f"Detected key: {key.to_char()}")

This keeps the signal-processing logic decoupled from keypad semantics while remaining simple to integrate.

---

Documentation

• Python API: https://jmg049.github.io/dtmf_tones/

---

License

This project is licensed under the MIT License.