Common CPHD-Type objects (sarpy.io.phase_history.base)

Base structures for phase history readers and usage

class sarpy.io.phase_history.base.CPHDTypeReader(data_segment: None | DataSegment | Sequence[DataSegment], cphd_meta: None | CPHDType | CPHDType, close_segments: bool = True, delete_files: None | str | Sequence[str] = None)

Bases: BaseReader

A class for common CPHD reading functionality.

Updated in version 1.3.0

property cphd_meta: None | CPHDType | CPHDType

the cphd meta_data.

Type:

None|CPHDType1_0|CPHDType0_3

read_support_array(index: int | str, *ranges: Sequence[None | int | Tuple[int, ...] | slice]) → ndarray

Read the support array.

Parameters:

  • index (int|str) – The support array integer index.

  • ranges (Sequence[None|int|Tuple[int, ...]|slice]) – The slice definition appropriate for support array usage.

Return type:

numpy.ndarray

Raises:

TypeError – If called on a reader which doesn’t support this.

read_support_block() → Dict[str, ndarray]

Reads the entirety of support block(s).

Returns:

Dictionary of numpy.ndarray containing the support arrays.

Return type:

Dict[str, numpy.ndarray]

read_pvp_variable(variable: str, index: int | str, the_range: None | int | Tuple[int, ...] | slice = None) → ndarray | None

Read the vector parameter for the given variable and CPHD channel.

Parameters:

  • variable (str) –

  • index (int|str) – The channel index or identifier.

  • the_range (None|int|Tuple[int, ...]|slice) – The indices for the vector parameter. None returns all, a integer returns the single value at that location, otherwise the input determines a slice.

Returns:

This will return None if there is no such variable, otherwise the data.

Return type:

None|numpy.ndarray

read_pvp_array(index: int | str, the_range: None | int | Tuple[int, ...] | slice = None) → ndarray

Read the PVP array from the requested channel.

Parameters:

  • index (int|str) – The support array integer index (of cphd.Data.Channels list) or identifier.

  • the_range (None|int|Tuple[int, ...]|slice) – The indices for the vector parameter. None returns all, a integer returns the single value at that location, otherwise the input determines a slice.

Returns:

pvp_array

Return type:

numpy.ndarray

read_pvp_block() → Dict[int | str, ndarray]

Reads the entirety of the PVP block(s).

Returns:

Dictionary containing the PVP arrays.

Return type:

Dict[Union[int, str], numpy.ndarray]

read_signal_block() → Dict[int | str, ndarray]

Reads the entirety of signal block(s), with data formatted as complex64 (after accounting for AmpSF).

Returns:

Dictionary of numpy.ndarray containing the signal arrays.

Return type:

Dict[Union[int, str], numpy.ndarray]

close() → None

This should perform any necessary clean-up operations, like closing open file handles, deleting any temp files, etc.

property closed: bool

Is the reader closed? Reading will result in a ValueError

Type:

bool

property data_segment: DataSegment | Tuple[DataSegment, ...]

The data segment collection.

Type:

DataSegment|Tuple[DataSegment, …]

property data_size: Tuple[int, ...] | Tuple[Tuple[int, ...]]

the output/formatted data size(s) of the data segment(s). If there is a single data segment, then this will be Tuple[int, …], otherwise it will be Tuple[Tuple, int, …], …].

Type:

Tuple[int, …]|Tuple[Tuple[int, …], …]

property file_name: str | None

Defined as a convenience property.

Type:

None|str

property files_to_delete_on_close: List[str]

A collection of files to delete on the close operation.

Type:

List[str]

get_data_segment_as_tuple() → Tuple[DataSegment, ...]

Get the data segment collection as a tuple, to avoid the need for redundant checking issues.

Return type:

Tuple[DataSegment, …]

get_data_size_as_tuple() → Tuple[Tuple[int, ...], ...]

Get the data size collection as a tuple of tuples, to avoid the need for redundant checking issues.

Return type:

Tuple[Tuple[int, …], …]

get_raw_data_size_as_tuple() → Tuple[Tuple[int, ...], ...]

Get the raw data size collection as a tuple of tuples, to avoid the need for redundant checking issues.

Return type:

Tuple[Tuple[int, …], …]

property image_count: int

The number of images/data segments from which to read.

Type:

int

property raw_data_size: Tuple[int, ...] | Tuple[Tuple[int, ...]]

the raw data size(s) of the data segment(s). If there is a single data segment, then this will be Tuple[int, …], otherwise it will be Tuple[Tuple, int, …], …].

Type:

Tuple[int, …]|Tuple[Tuple[int, …], …]

read(*ranges: None | int | Tuple[int, ...] | slice, index: int = 0, squeeze: bool = True) → ndarray

Read formatted data from the given data segment. Note this is an alias to the __call__() called as reader(*ranges, index=index, raw=False, squeeze=squeeze).

Parameters:

  • ranges (Sequence[Union[None, int, Tuple[int, ...], slice]]) – The slice definition appropriate for data_segment[index].read() usage.

  • index (int) – The data_segment index. This is ignored if image_count== 1.

  • squeeze (bool) – Squeeze length 1 dimensions out of the shape of the return array?

Return type:

numpy.ndarray

See also

See

meth:sarpy.io.general.data_segment.DataSegment.read.

read_chip(*ranges: Sequence[None | int | Tuple[int, ...] | slice], index: int = 0, squeeze: bool = True) → ndarray

This is identical to read(), and presented for backwards compatibility.

Parameters:

  • ranges (Sequence[Union[None, int, Tuple[int, ...], slice]]) –

  • index (int) –

  • squeeze (bool) –

Return type:

numpy.ndarray

See also

read()

read_raw(*ranges: None | int | Tuple[int, ...] | slice, index: int = 0, squeeze: bool = True) → ndarray

Read raw data from the given data segment. Note this is an alias to the __call__() called as reader(*ranges, index=index, raw=True, squeeze=squeeze).

Parameters:

  • ranges (Sequence[Union[None, int, Tuple[int, ...], slice]]) – The slice definition appropriate for data_segment[index].read() usage.

  • index (int) – The data_segment index. This is ignored if image_count== 1.

  • squeeze (bool) – Squeeze length 1 dimensions out of the shape of the return array?

Return type:

numpy.ndarray

See also

See

meth:sarpy.io.general.data_segment.DataSegment.read_raw.

read_signal_block_raw() → Dict[int | str, ndarray]

Reads the entirety of signal block(s), with data formatted in file storage format (no converting to complex, no consideration of AmpSF).

Returns:

Dictionary of numpy.ndarray containing the signal arrays.

Return type:

Dict[Union[int, str], numpy.ndarray]

property reader_type: str

A descriptive string for the type of reader

Type:

str