mattress package

Submodules

mattress.TatamiNumericPointer module

class mattress.TatamiNumericPointer.TatamiNumericPointer(ptr, obj)[source]

Bases: object

Pointer to a tatami numeric matrix allocated by C++ code. Instances of this class should only be created by developers and used within package functions; this is done by fetching the ptr attribute and casting it to a Mattress* in C++ code. Pointers are expected to be transient within a Python session; they should not be serialized, nor should they be visible to end users. Each instance will automatically free the C++-allocated memory upon its own destruction.

column(c)[source]

Access a column from the tatami matrix. This method is primarily intended for troubleshooting and should not be used to iterate over the matrix in production code. (Do that in C++ instead.)

Parameters:

c (int) – Column to access.

Return type:

ndarray

Returns:

Column from the matrix. This is always in double-precisino, regardless of the underlying representation.

column_maxs(num_threads=1)[source]

Convenience method to compute column maxima.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column maxs.

column_medians(num_threads=1)[source]

Convenience method to compute column medians.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column medians.

column_medians_by_group(group, num_threads=1)[source]

Convenience method to compute the column-wise median for each group of row.

Parameters:

  • group (Sequence) – Sequence of length equal to the number of row of the matrix, containing the group assignment for each row.

  • num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, list]

Returns:

Tuple containing a 2-dimensional array where each row represents a group and contains the column-wise medians for that group, across all columns of the matrix; and a list containing the unique levels of group represented by each row.

column_mins(num_threads=1)[source]

Convenience method to compute column minima.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column mins.

column_nan_counts(num_threads=1)[source]

Convenience method to count the number of NaNs on each column.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column NaN counts.

column_ranges(num_threads=1)[source]

Convenience method to compute column ranges.

Parameters:

num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, ndarray]

Returns:

Tuple containing the column minima and maxima.

column_sums(num_threads=1)[source]

Convenience method to compute column sums.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column sums.

column_sums_by_group(group, num_threads=1)[source]

Convenience method to compute the column-wise median for each group of row.

Parameters:

  • group (Sequence) – Sequence of length equal to the number of row of the matrix, containing the group assignment for each row.

  • num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, list]

Returns:

Tuple containing a 2-dimensional array where each row represents a group and contains the column-wise sums for that group, across all columns of the matrix; and a list containing the unique levels of group represented by each row.

column_variances(num_threads=1)[source]

Convenience method to compute column variances.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of column variances.

property dtype: dtype

Type of the matrix, to masquerade as a NumPy-like object.

ncol()[source]

Get number of columns.

Return type:

int

Returns:

Number of columns.

nrow()[source]

Get number of rows.

Return type:

int

Returns:

Number of rows.

row(r)[source]

Access a row from the tatami matrix. This method is primarily intended for troubleshooting and should not be used to iterate over the matrix in production code. (Do that in C++ instead.)

Parameters:

r (int) – Row to access.

Return type:

ndarray

Returns:

Row from the matrix. This is always in double-precision, regardless of the underlying representation.

row_maxs(num_threads=1)[source]

Convenience method to compute row maxima.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row maxima.

row_medians(num_threads=1)[source]

Convenience method to compute row medians.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row medians.

row_medians_by_group(group, num_threads=1)[source]

Convenience method to compute the row-wise median for each group of columns.

Parameters:

  • group (Sequence) – Sequence of length equal to the number of columns of the matrix, containing the group assignment for each column.

  • num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, list]

Returns:

Tuple containing a 2-dimensional array where each column represents a group and contains the row-wise medians for that group, across all rows of the matrix; and a list containing the unique levels of group represented by each column.

row_mins(num_threads=1)[source]

Convenience method to compute row minima.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row minima.

row_nan_counts(num_threads=1)[source]

Convenience method to count the number of NaNs on each row.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row NaN counts.

row_ranges(num_threads=1)[source]

Convenience method to compute row ranges.

Parameters:

num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, ndarray]

Returns:

Tuple containing the row minima and maxima.

row_sums(num_threads=1)[source]

Convenience method to compute row sums.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row sums.

row_sums_by_group(group, num_threads=1)[source]

Convenience method to compute the row-wise median for each group of columns.

Parameters:

  • group (Sequence) – Sequence of length equal to the number of columns of the matrix, containing the group assignment for each column.

  • num_threads (int) – Number of threads.

Return type:

Tuple[ndarray, list]

Returns:

Tuple containing a 2-dimensional array where each column represents a group and contains the row-wise sums for that group, across all rows of the matrix; and a list containing the unique levels of group represented by each column.

row_variances(num_threads=1)[source]

Convenience method to compute row variances.

Parameters:

num_threads (int) – Number of threads.

Return type:

ndarray

Returns:

Array of row variances.

property shape: Tuple[int, int]

Shape of the matrix, to masquerade as a NumPy-like object.

sparse()[source]

Is the matrix sparse?

Return type:

bool

Returns:

True if matrix is sparse.

mattress.TatamiNumericPointer.chunk_grid_tatami(x)[source]

See chunk_grid().

Return type:

bool

mattress.TatamiNumericPointer.extract_dense_array_tatami(x, subset)[source]

See extract_dense_array().

Return type:

ndarray

mattress.TatamiNumericPointer.extract_sparse_array_tatami(x, subset)[source]

See extract_sparse_array().

Return type:

SparseNdarray

mattress.TatamiNumericPointer.is_masked_tatami(x)[source]

See is_masked().

Return type:

bool

mattress.TatamiNumericPointer.is_sparse_tatami(x)[source]

mattress.interface module

mattress.interface.tatamize(x)[source]

Converts Python matrix representations to a tatami pointer.

Parameters:

x (Any) – Any matrix-like object.

Raises:

NotImplementedError – if x is not supported.

Return type:

TatamiNumericPointer

Returns:

A pointer to tatami object.

mattress.utils module

mattress.utils.includes()[source]

Provides access to C++ headers (including tatami) for downstream packages.

Return type:

List[str]

Returns:

List of paths to the header files.

Module contents