nitypes.waveform.NumericWaveform

class nitypes.waveform.NumericWaveform(sample_count: SupportsIndex | None = None, dtype: numpy.typing.DTypeLike = None, *, raw_data: numpy.typing.NDArray[_TRaw] | None = None, start_index: SupportsIndex | None = None, capacity: SupportsIndex | None = None, extended_properties: collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None = None, copy_extended_properties: bool = True, timing: nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None = None, scale_mode: nitypes.waveform.ScaleMode | None = None)

Bases: abc.ABC, Generic[_TRaw, _TScaled]

A numeric waveform, which encapsulates numeric data and timing information.

This is an abstract base class. To create a numeric waveform, use AnalogWaveform or ComplexWaveform.

Parameters:

  • sample_count (SupportsIndex | None)

  • dtype (numpy.typing.DTypeLike)

  • raw_data (numpy.typing.NDArray[_TRaw] | None)

  • start_index (SupportsIndex | None)

  • capacity (SupportsIndex | None)

  • extended_properties (collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None)

  • copy_extended_properties (bool)

  • timing (nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None)

  • scale_mode (nitypes.waveform.ScaleMode | None)

classmethod from_array_1d(array: numpy.typing.NDArray[Any] | collections.abc.Sequence[Any], dtype: numpy.typing.DTypeLike = None, *, copy: bool = True, start_index: SupportsIndex | None = 0, sample_count: SupportsIndex | None = None, extended_properties: collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None = None, timing: nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None = None, scale_mode: nitypes.waveform.ScaleMode | None = None) → typing_extensions.Self

Construct a waveform from a one-dimensional array or sequence.

Parameters:

  • array (numpy.typing.NDArray[Any] | collections.abc.Sequence[Any]) – The waveform data as a one-dimensional array or a sequence.

  • dtype (numpy.typing.DTypeLike) – The NumPy data type for the waveform data. This argument is required when array is a sequence.

  • copy (bool) – Specifies whether to copy the array or save a reference to it.

  • start_index (SupportsIndex | None) – The sample index at which the waveform data begins.

  • sample_count (SupportsIndex | None) – The number of samples in the waveform.

  • extended_properties (collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None) – The extended properties of the waveform.

  • timing (nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None) – The timing information of the waveform.

  • scale_mode (nitypes.waveform.ScaleMode | None) – The scale mode of the waveform.

Returns:

A waveform containing the specified data.

Return type:

typing_extensions.Self

classmethod from_array_2d(array: numpy.typing.NDArray[Any] | collections.abc.Sequence[collections.abc.Sequence[Any]], dtype: numpy.typing.DTypeLike = None, *, copy: bool = True, start_index: SupportsIndex | None = 0, sample_count: SupportsIndex | None = None, extended_properties: collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None = None, timing: nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None = None, scale_mode: nitypes.waveform.ScaleMode | None = None) → collections.abc.Sequence[typing_extensions.Self]

Construct multiple waveforms from a two-dimensional array or nested sequence.

Parameters:

  • array (numpy.typing.NDArray[Any] | collections.abc.Sequence[collections.abc.Sequence[Any]]) – The waveform data as a two-dimensional array or a nested sequence.

  • dtype (numpy.typing.DTypeLike) – The NumPy data type for the waveform data. This argument is required when array is a sequence.

  • copy (bool) – Specifies whether to copy the array or save a reference to it.

  • start_index (SupportsIndex | None) – The sample index at which the waveform data begins.

  • sample_count (SupportsIndex | None) – The number of samples in the waveform.

  • extended_properties (collections.abc.Mapping[str, nitypes.waveform.typing.ExtendedPropertyValue] | None) – The extended properties of the waveform.

  • timing (nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta] | None) – The timing information of the waveform.

  • scale_mode (nitypes.waveform.ScaleMode | None) – The scale mode of the waveform.

Returns:

A sequence containing a waveform for each row of the specified data.

Return type:

collections.abc.Sequence[typing_extensions.Self]

When constructing multiple waveforms, the same extended properties, timing information, and scale mode are applied to all waveforms. Consider assigning these properties after construction.

__slots__ = ['_data', '_start_index', '_sample_count', '_extended_properties', '_timing', '_scale_mode',...
property raw_data: numpy.typing.NDArray[_TRaw]

The raw waveform data.

Return type:

numpy.typing.NDArray[_TRaw]

get_raw_data(start_index: SupportsIndex | None = 0, sample_count: SupportsIndex | None = None) → numpy.typing.NDArray[_TRaw]

Get a subset of the raw waveform data.

Parameters:

  • start_index (SupportsIndex | None) – The sample index at which the data begins.

  • sample_count (SupportsIndex | None) – The number of samples to return.

Returns:

A subset of the raw waveform data.

Return type:

numpy.typing.NDArray[_TRaw]

property scaled_data: numpy.typing.NDArray[_TScaled]

The scaled waveform data.

This property converts all of the waveform samples from the raw data type to the scaled data type and scales them using scale_mode. To scale a subset of the waveform or scale to single-precision floating point, use the get_scaled_data() method instead.

Return type:

numpy.typing.NDArray[_TScaled]

get_scaled_data(dtype: None = ..., *, start_index: SupportsIndex | None = ..., sample_count: SupportsIndex | None = ...) → numpy.typing.NDArray[_TScaled]
get_scaled_data(dtype: type[_TOtherScaled] | numpy.dtype[_TOtherScaled], *, start_index: SupportsIndex | None = ..., sample_count: SupportsIndex | None = ...) → numpy.typing.NDArray[_TOtherScaled]
get_scaled_data(dtype: numpy.typing.DTypeLike = ..., *, start_index: SupportsIndex | None = ..., sample_count: SupportsIndex | None = ...) → numpy.typing.NDArray[Any]

Get a subset of the scaled waveform data with the specified dtype.

Parameters:

  • dtype – The NumPy data type to use for scaled data.

  • start_index – The sample index at which to start scaling.

  • sample_count – The number of samples to scale.

Returns:

A subset of the scaled waveform data.

property sample_count: int

The number of samples in the waveform.

Return type:

int

property start_index: int

The sample index of the underlying array at which the waveform data begins.

Return type:

int

property capacity: int

The total capacity available for waveform data.

Setting the capacity resizes the underlying NumPy array in-place.

  • Other Python objects with references to the array will see the array size change.

  • If the array has a reference to an external buffer (such as an array.array), attempting to resize it raises ValueError.

Return type:

int

property dtype: numpy.dtype[_TRaw]

The NumPy dtype for the waveform data.

Return type:

numpy.dtype[_TRaw]

property extended_properties: nitypes.waveform.ExtendedPropertyDictionary

The extended properties for the waveform.

Note

Data stored in the extended properties dictionary may not be encrypted when you send it over the network or write it to a TDMS file.

Return type:

nitypes.waveform.ExtendedPropertyDictionary

property channel_name: str

The name of the device channel from which the waveform was acquired.

Return type:

str

property units: str

The unit of measurement, such as volts, of the waveform.

Return type:

str

property timing: nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta]

The timing information of the waveform.

The default value is Timing.empty.

Return type:

nitypes.waveform.Timing[nitypes.time.typing.AnyDateTime, nitypes.time.typing.AnyTimeDelta, nitypes.time.typing.AnyTimeDelta]

property scale_mode: nitypes.waveform.ScaleMode

The scale mode of the waveform.

Return type:

nitypes.waveform.ScaleMode

append(other: numpy.typing.NDArray[_TRaw] | NumericWaveform[_TRaw, _TScaled] | collections.abc.Sequence[NumericWaveform[_TRaw, _TScaled]], /, timestamps: collections.abc.Sequence[datetime.datetime] | collections.abc.Sequence[hightime.datetime] | None = None) → None

Append data to the waveform.

Parameters:
Raises:

  • TimingMismatchError – The current and other waveforms have incompatible timing.

  • TimingMismatchWarning – The sample intervals of the waveform(s) do not match.

  • ScalingMismatchWarning – The scale modes of the waveform(s) do not match.

  • ValueError – The other array has the wrong number of dimensions or the length of the timestamps argument does not match the length of the other array.

  • TypeError – The data types of the current waveform and other array or waveform(s) do not match, or an argument has the wrong data type.

Return type:

None

When appending waveforms:

  • Timing information is merged based on the sample interval mode of the current waveform:

    • SampleIntervalMode.NONE or SampleIntervalMode.REGULAR: The other waveform(s) must also have SampleIntervalMode.NONE or SampleIntervalMode.REGULAR. If the sample interval does not match, a TimingMismatchWarning is generated. Otherwise, the timing information of the other waveform(s) is discarded.

    • SampleIntervalMode.IRREGULAR: The other waveforms(s) must also have SampleIntervalMode.IRREGULAR. The timestamps of the other waveforms(s) are appended to the current waveform’s timing information.

  • Extended properties of the other waveform(s) are merged into the current waveform if they are not already set in the current waveform.

  • If the scale mode of other waveform(s) does not match the scale mode of the current waveform, a ScalingMismatchWarning is generated. Otherwise, the scaling information of the other waveform(s) is discarded.

load_data(array: numpy.typing.NDArray[_TRaw], *, copy: bool = True, start_index: SupportsIndex | None = 0, sample_count: SupportsIndex | None = None) → None

Load new data into an existing waveform.

Parameters:

  • array (numpy.typing.NDArray[_TRaw]) – A NumPy array containing the data to load.

  • copy (bool) – Specifies whether to copy the array or save a reference to it.

  • start_index (SupportsIndex | None) – The sample index at which the waveform data begins.

  • sample_count (SupportsIndex | None) – The number of samples in the waveform.

Return type:

None

__eq__(value: object, /) → bool

Return self==value.

Parameters:

value (object)

Return type:

bool

__reduce__() → tuple[Any, Ellipsis]

Return object state for pickling.

Return type:

tuple[Any, Ellipsis]

__repr__() → str

Return repr(self).

Return type:

str