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# librosa.segment.recurrence_to_lag

librosa.segment.recurrence_to_lag(rec, pad=True, axis=-1)[source]

Convert a recurrence matrix into a lag matrix.

`lag[i, j] == rec[i+j, j]`

This transformation turns diagonal structures in the recurrence matrix into horizontal structures in the lag matrix. These horizontal structures can be used to infer changes in the repetition structure of a piece, e.g., the beginning of a new section as done in [1].

Parameters:
recnp.ndarray, or scipy.sparse.spmatrix [shape=(n, n)]

A (binary) recurrence matrix, as returned by `recurrence_matrix`

padbool

If False, `lag` matrix is square, which is equivalent to assuming that the signal repeats itself indefinitely.

If True, `lag` is padded with `n` zeros, which eliminates the assumption of repetition.

axisint

The axis to keep as the `time` axis. The alternate axis will be converted to lag coordinates.

Returns:
lagnp.ndarray

The recurrence matrix in (lag, time) (if `axis=1`) or (time, lag) (if `axis=0`) coordinates

Raises:
ParameterErrorif `rec` is non-square

Examples

```>>> y, sr = librosa.load(librosa.ex('nutcracker'))
>>> hop_length = 1024
>>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length)
>>> chroma_stack = librosa.feature.stack_memory(chroma, n_steps=10, delay=3)
>>> recurrence = librosa.segment.recurrence_matrix(chroma_stack)
>>> lag_pad = librosa.segment.recurrence_to_lag(recurrence, pad=True)
>>> lag_nopad = librosa.segment.recurrence_to_lag(recurrence, pad=False)
```
```>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> librosa.display.specshow(lag_pad, x_axis='time', y_axis='lag',
...                          hop_length=hop_length, ax=ax[0])
>>> ax[0].set(title='Lag (zero-padded)')
>>> ax[0].label_outer()
>>> librosa.display.specshow(lag_nopad, x_axis='time', y_axis='lag',
...                          hop_length=hop_length, ax=ax[1])
>>> ax[1].set(title='Lag (no padding)')
```