111 lines
4.3 KiB
Plaintext
111 lines
4.3 KiB
Plaintext
---
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title: SliceSpan (latest version)
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description: API reference for qiskit_ibm_runtime.execution_span.SliceSpan in the latest version of qiskit-ibm-runtime
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit_ibm_runtime.execution_span.SliceSpan
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---
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# SliceSpan
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<Class id="qiskit_ibm_runtime.execution_span.SliceSpan" isDedicatedPage={true} github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.36/qiskit_ibm_runtime/execution_span/slice_span.py#L27-L74" signature="SliceSpan(start, stop, data_slices)" modifiers="class">
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Bases: [`ExecutionSpan`](execution-span-execution-span "qiskit_ibm_runtime.execution_span.execution_span.ExecutionSpan")
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An [`ExecutionSpan`](execution-span-execution-span "qiskit_ibm_runtime.execution_span.ExecutionSpan") for data stored in a sliceable format.
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This type of execution span references pub result data by assuming that it is a sliceable portion of the (row major) flattened data. Therefore, for each pub dependent on this span, the constructor accepts a single `slice` object, along with the corresponding shape of the data to be sliced.
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**Parameters**
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* **start** (*datetime*) – The start time of the span, in UTC.
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* **stop** (*datetime*) – The stop time of the span, in UTC.
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* **data\_slices** (*dict\[int, tuple\[ShapeType, slice]]*) – A map from pub indices to pairs `(shape_tuple, slice)`.
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## Attributes
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### duration
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<Attribute id="qiskit_ibm_runtime.execution_span.SliceSpan.duration">
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The duration of this span, in seconds.
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</Attribute>
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### pub\_idxs
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<Attribute id="qiskit_ibm_runtime.execution_span.SliceSpan.pub_idxs" />
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### size
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<Attribute id="qiskit_ibm_runtime.execution_span.SliceSpan.size" />
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### start
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<Attribute id="qiskit_ibm_runtime.execution_span.SliceSpan.start">
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The start time of the span, in UTC.
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</Attribute>
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### stop
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<Attribute id="qiskit_ibm_runtime.execution_span.SliceSpan.stop">
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The stop time of the span, in UTC.
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</Attribute>
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## Methods
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### contains\_pub
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<Function id="qiskit_ibm_runtime.execution_span.SliceSpan.contains_pub" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.36/qiskit_ibm_runtime/execution_span/execution_span.py#L112-L122" signature="contains_pub(pub_idx)">
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Return whether the pub with the given index has data with dependence on this span.
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**Parameters**
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**pub\_idx** (*int | Iterable\[int]*) – One or more pub indices from the original primitive call.
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**Returns**
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Whether there is dependence on this span.
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**Return type**
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bool
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</Function>
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### filter\_by\_pub
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<Function id="qiskit_ibm_runtime.execution_span.SliceSpan.filter_by_pub" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.36/qiskit_ibm_runtime/execution_span/slice_span.py#L71-L74" signature="filter_by_pub(pub_idx)">
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Return a new span whose slices are filtered to the provided pub indices.
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For example, if this span contains slice information for pubs with indices 1, 3, 4 and `[1, 4]` is provided, then the span returned by this method will contain slice information for only those two indices, but be identical otherwise.
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**Parameters**
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**pub\_idx** (*int | Iterable\[int]*) – One or more pub indices from the original primitive call.
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**Returns**
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A new filtered span.
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**Return type**
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[SliceSpan](#qiskit_ibm_runtime.execution_span.SliceSpan "qiskit_ibm_runtime.execution_span.SliceSpan")
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</Function>
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### mask
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<Function id="qiskit_ibm_runtime.execution_span.SliceSpan.mask" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.36/qiskit_ibm_runtime/execution_span/slice_span.py#L65-L69" signature="mask(pub_idx)">
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Return an array-valued mask specifying which parts of a pub result depend on this span.
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**Parameters**
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**pub\_idx** (*int*) – The index of the pub to return a mask for.
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**Returns**
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An array with the same shape as the pub data.
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**Return type**
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[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.2)")\[*Any*, [*dtype*](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype "(in NumPy v2.2)")\[[*bool*](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool "(in NumPy v2.2)")]]
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</Function>
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</Class>
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