Module music_df.sync_df

Functions

def get_unique_from_array_by_df(a: numpy.ndarray,
music_df: pandas.DataFrame,
unique_col_name_or_names: list[str] | str,
sync_col_name_or_names: None | str | list[str] = None,
transform: str | Callable = 'mean',
return_indices: bool = False) ‑> numpy.ndarray | tuple[numpy.ndarray, numpy.ndarray]

Expand source code

def get_unique_from_array_by_df(
    a: np.ndarray,
    music_df: pd.DataFrame,
    unique_col_name_or_names: str | list[str],
    sync_col_name_or_names: None | str | list[str] = None,
    transform: str | Callable = "mean",
    return_indices: bool = False,
) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
    """
    >>> df = pd.DataFrame(
    ...     {
    ...         "pitch": [60, 61, 62, 63],
    ...         "onset": [0.0, 0.0, 0.0, 1.0],
    ...         "release": [1.0, 1.0, 2.0, 2.0],
    ...     }
    ... )
    >>> a = np.array([[0.0, 1.0], [1.0, 0.0], [2.0, -1.0], [3.0, -2.0]])

    Get the items of `a` corresponding to unique onsets in df:
    >>> get_unique_from_array_by_df(a, df, "onset")
    array([[ 0.,  1.],
           [ 3., -2.]])

    Include the indices too:
    >>> get_unique_from_array_by_df(a, df, "onset", return_indices=True)
    (array([[ 0.,  1.],
           [ 3., -2.]]), array([0, 3]))

    Get the items of `a` corresponding to unique releases in df:
    >>> get_unique_from_array_by_df(a, df, "release")
    array([[ 0.,  1.],
           [ 2., -1.]])

    Get the items of `a` corresponding to unique combinations of onset/release in df:
    >>> get_unique_from_array_by_df(a, df, ["onset", "release"])
    array([[ 0.,  1.],
           [ 2., -1.],
           [ 3., -2.]])


    >>> df_with_irregular_index = df.copy()
    >>> df_with_irregular_index.index = [3, 2, 7, 1]
    >>> get_unique_from_array_by_df(a, df_with_irregular_index, "onset")
    array([[ 0.,  1.],
           [ 3., -2.]])
    """
    if sync_col_name_or_names is not None:
        a = sync_array_by_df(a, music_df, sync_col_name_or_names, transform)
    if not has_range_index(music_df):
        music_df = music_df.reset_index(drop=True)
    grouped = music_df.groupby(unique_col_name_or_names)
    first_index = grouped.apply(lambda x: x.index[0])
    if return_indices:
        return a[first_index], first_index.values  # type:ignore
    return a[first_index]

>>> df = pd.DataFrame(
...     {
...         "pitch": [60, 61, 62, 63],
...         "onset": [0.0, 0.0, 0.0, 1.0],
...         "release": [1.0, 1.0, 2.0, 2.0],
...     }
... )
>>> a = np.array([[0.0, 1.0], [1.0, 0.0], [2.0, -1.0], [3.0, -2.0]])

Get the items of a corresponding to unique onsets in df:

>>> get_unique_from_array_by_df(a, df, "onset")
array([[ 0.,  1.],
       [ 3., -2.]])

Include the indices too:

>>> get_unique_from_array_by_df(a, df, "onset", return_indices=True)
(array([[ 0.,  1.],
       [ 3., -2.]]), array([0, 3]))

Get the items of a corresponding to unique releases in df:

>>> get_unique_from_array_by_df(a, df, "release")
array([[ 0.,  1.],
       [ 2., -1.]])

Get the items of a corresponding to unique combinations of onset/release in df:

>>> get_unique_from_array_by_df(a, df, ["onset", "release"])
array([[ 0.,  1.],
       [ 2., -1.],
       [ 3., -2.]])

>>> df_with_irregular_index = df.copy()
>>> df_with_irregular_index.index = [3, 2, 7, 1]
>>> get_unique_from_array_by_df(a, df_with_irregular_index, "onset")
array([[ 0.,  1.],
       [ 3., -2.]])

def get_unique_note_onset_indices(music_df: pandas.DataFrame) ‑> numpy.ndarray

Expand source code

def get_unique_note_onset_indices(music_df: pd.DataFrame) -> np.ndarray:
    """
    >>> df = pd.DataFrame(
    ...     {
    ...         "type": ["bar"] + ["note"] * 4,
    ...         "pitch": [0, 60, 61, 62, 63],
    ...         "onset": [0.0, 0.0, 0.0, 0.0, 1.0],
    ...         "release": [4.0, 1.0, 1.0, 2.0, 2.0],
    ...     }
    ... )
    >>> get_unique_note_onset_indices(df)
    array([1, 4])
    """
    grouped = music_df[music_df.type == "note"].groupby("onset")
    first_index = grouped.apply(lambda x: x.index[0])
    return first_index.values  # type:ignore

>>> df = pd.DataFrame(
...     {
...         "type": ["bar"] + ["note"] * 4,
...         "pitch": [0, 60, 61, 62, 63],
...         "onset": [0.0, 0.0, 0.0, 0.0, 1.0],
...         "release": [4.0, 1.0, 1.0, 2.0, 2.0],
...     }
... )
>>> get_unique_note_onset_indices(df)
array([1, 4])

def has_range_index(df: pandas.DataFrame) ‑> bool

Expand source code

def has_range_index(df: pd.DataFrame) -> bool:
    return (
        isinstance(df.index, pd.RangeIndex)
        and df.index.start == 0
        and df.index.stop == len(df)
        and df.index.step == 1
    )

def sync_array_by_df(a: numpy.ndarray,
music_df: pandas.DataFrame,
sync_col_name_or_names: list[str] | str,
transform: str | Callable = 'mean') ‑> numpy.ndarray

Expand source code

def sync_array_by_df(
    a: np.ndarray,
    music_df: pd.DataFrame,
    sync_col_name_or_names: str | list[str],
    transform: str | Callable = "mean",
) -> np.ndarray:
    """
    >>> df = pd.DataFrame(
    ...     {
    ...         "pitch": [60, 61, 62, 63],
    ...         "onset": [0.0, 0.0, 1.0, 1.0],
    ...         "release": [0.5, 1.0, 2.0, 2.0],
    ...     }
    ... )
    >>> a = np.array([[0.0, 1.0], [1.0, 0.0], [2.0, -1.0], [3.0, -2.0]])
    >>> sync_array_by_df(a, df, "onset")
    array([[ 0.5,  0.5],
           [ 0.5,  0.5],
           [ 2.5, -1.5],
           [ 2.5, -1.5]])
    >>> sync_array_by_df(a, df, ["onset", "release"])
    array([[ 0. ,  1. ],
           [ 1. ,  0. ],
           [ 2.5, -1.5],
           [ 2.5, -1.5]])
    """
    output = np.empty_like(a)
    grouped = music_df.groupby(sync_col_name_or_names).groups
    if isinstance(transform, str):
        transform = getattr(np, transform)
    assert not isinstance(transform, str)
    for group_indices in grouped.values():
        output[group_indices] = transform(a[group_indices], axis=0)  # type:ignore
    return output

>>> df = pd.DataFrame(
...     {
...         "pitch": [60, 61, 62, 63],
...         "onset": [0.0, 0.0, 1.0, 1.0],
...         "release": [0.5, 1.0, 2.0, 2.0],
...     }
... )
>>> a = np.array([[0.0, 1.0], [1.0, 0.0], [2.0, -1.0], [3.0, -2.0]])
>>> sync_array_by_df(a, df, "onset")
array([[ 0.5,  0.5],
       [ 0.5,  0.5],
       [ 2.5, -1.5],
       [ 2.5, -1.5]])
>>> sync_array_by_df(a, df, ["onset", "release"])
array([[ 0. ,  1. ],
       [ 1. ,  0. ],
       [ 2.5, -1.5],
       [ 2.5, -1.5]])

def sync_by_onset(music_df: pandas.DataFrame, col_name: str, transform: str | Callable = 'mean') ‑> pandas.DataFrame

Expand source code

def sync_by_onset(
    music_df: pd.DataFrame, col_name: str, transform: str | Callable = "mean"
) -> pd.DataFrame:
    """
    >>> df = pd.DataFrame(
    ...     {
    ...         "pitch": [60, 61, 62, 63],
    ...         "onset": [0.0, 0.0, 1.0, 1.0],
    ...         "release": [0.5, 1.0, 2.0, 3.0],
    ...         "val": [1.0, 2.0, 3.0, 4.0],
    ...     }
    ... )
    >>> sync_by_onset(df, "val")
       pitch  onset  release  val
    0     60    0.0      0.5  1.5
    1     61    0.0      1.0  1.5
    2     62    1.0      2.0  3.5
    3     63    1.0      3.0  3.5
    """
    return sync_df(music_df, "onset", col_name, transform)

>>> df = pd.DataFrame(
...     {
...         "pitch": [60, 61, 62, 63],
...         "onset": [0.0, 0.0, 1.0, 1.0],
...         "release": [0.5, 1.0, 2.0, 3.0],
...         "val": [1.0, 2.0, 3.0, 4.0],
...     }
... )
>>> sync_by_onset(df, "val")
   pitch  onset  release  val
0     60    0.0      0.5  1.5
1     61    0.0      1.0  1.5
2     62    1.0      2.0  3.5
3     63    1.0      3.0  3.5

def sync_df(music_df: pandas.DataFrame,
sync_col_name_or_names: list[str] | str,
val_col_name: str,
transform: str | Callable = 'mean') ‑> pandas.DataFrame

Expand source code

def sync_df(
    music_df: pd.DataFrame,
    sync_col_name_or_names: str | list[str],
    val_col_name: str,
    transform: str | Callable = "mean",
) -> pd.DataFrame:
    """
    >>> df = pd.DataFrame(
    ...     {
    ...         "pitch": [60, 61, 62, 63],
    ...         "onset": [0.0, 0.0, 1.0, 1.0],
    ...         "release": [0.5, 1.0, 2.0, 2.0],
    ...         "val": [1.0, 2.0, 3.0, 4.0],
    ...     }
    ... )
    >>> sync_df(df, "onset", "val")
       pitch  onset  release  val
    0     60    0.0      0.5  1.5
    1     61    0.0      1.0  1.5
    2     62    1.0      2.0  3.5
    3     63    1.0      2.0  3.5
    >>> sync_df(df, ["onset", "release"], "val")
       pitch  onset  release  val
    0     60    0.0      0.5  1.0
    1     61    0.0      1.0  2.0
    2     62    1.0      2.0  3.5
    3     63    1.0      2.0  3.5
    """
    music_df = music_df.copy()
    music_df[val_col_name] = music_df.groupby(sync_col_name_or_names)[
        val_col_name
    ].transform(transform)
    return music_df

>>> df = pd.DataFrame(
...     {
...         "pitch": [60, 61, 62, 63],
...         "onset": [0.0, 0.0, 1.0, 1.0],
...         "release": [0.5, 1.0, 2.0, 2.0],
...         "val": [1.0, 2.0, 3.0, 4.0],
...     }
... )
>>> sync_df(df, "onset", "val")
   pitch  onset  release  val
0     60    0.0      0.5  1.5
1     61    0.0      1.0  1.5
2     62    1.0      2.0  3.5
3     63    1.0      2.0  3.5
>>> sync_df(df, ["onset", "release"], "val")
   pitch  onset  release  val
0     60    0.0      0.5  1.0
1     61    0.0      1.0  2.0
2     62    1.0      2.0  3.5
3     63    1.0      2.0  3.5