Source code for zhinst.toolkit.driver.devices.uhfqa
"""UHFQA Instrument Driver."""
import typing as t
import numpy as np
from zhinst.toolkit.driver.devices import UHFLI
from zhinst.toolkit.nodetree import Node, NodeTree
from zhinst.toolkit.nodetree.helper import (
create_or_append_set_transaction,
lazy_property,
)
from zhinst.toolkit.nodetree.node import NodeList
from zhinst.toolkit.waveform import Waveforms
Numpy2DArray = t.TypeVar("Numpy2DArray")
[docs]class Integration(Node):
"""Integration part for the UHFQA.
Args:
root: Underlying node tree.
tree: tree (node path as tuple) of the corresponding node.
.. versionadded:: 0.3.2
"""
def __init__(
self,
root: NodeTree,
tree: tuple,
):
super().__init__(root, tree)
[docs] def write_integration_weights(self, weights: t.Union[Waveforms, dict]) -> None:
"""Upload complex integration weights.
The weight functions are applied to the real and imaginary part of
the input signal. In the hardware the weights are implemented
as 17-bit integers.
Args:
weights: Dictionary containing the weight functions, where
keys correspond to the indices of the integration weights to be
configured.
Note:
Does not raise an error when sample limit is exceeded, but applies only
the maximum number of samples. Please refer to LabOne node documentation
for the number of maximum integration weight samples.
Note:
This function calls both `/qas/n/integration/weights/n/real` and
`/qas/n/integration/weights/n/imag` nodes.
If only real or imaginary part is defined, the number of defined samples
from the other one is zeroed.
"""
waveform_dict = {}
if isinstance(weights, Waveforms):
for slot in weights.keys():
waveform_dict[slot] = weights.get_raw_vector(slot, complex_output=True)
else:
waveform_dict = weights
with create_or_append_set_transaction(self._root):
for key, waveform in waveform_dict.items():
self.weights[key].real(np.copy(waveform.real))
self.weights[key].imag(np.copy(waveform.imag))
[docs]class QAS(Node):
"""Quantum Analyzer Channel for the UHFQA.
Args:
root: Underlying node tree.
tree: tree (node path as tuple) of the corresponding node.
"""
def __init__(
self,
root: NodeTree,
tree: tuple,
):
super().__init__(root, tree)
[docs] def crosstalk_matrix(self, matrix: Numpy2DArray = None) -> t.Optional[Numpy2DArray]:
"""Sets or gets the crosstalk matrix of the UHFQA as a 2D array.
Args:
matrix: The 2D matrix used in the digital signal
processing path to compensate for crosstalk between the
different channels. The given matrix can also be a part
of the entire 10 x 10 matrix. Its maximum dimensions
are 10 x 10 (default: None).
Returns:
If no argument is given the method returns the current
crosstalk matrix as a 2D numpy array.
Raises:
ValueError: If the matrix size exceeds the maximum size of
10 x 10
"""
if matrix is None:
m = np.zeros((10, 10))
for r in range(10):
for c in range(10):
m[r, c] = self.crosstalk.rows[r].cols[c]()
return m
else:
rows, cols = matrix.shape # type: ignore[attr-defined]
if rows > 10 or cols > 10:
raise ValueError(
f"The shape of the given matrix is {rows} x {cols}. "
"The maximum size is 10 x 10."
)
for r in range(rows):
for c in range(cols):
self.crosstalk.rows[r].cols[c](matrix[r, c]) # type: ignore[index]
return None
[docs] def adjusted_delay(self, value: int = None) -> int:
"""Set or get the adjustment in the quantum analyzer delay.
Adjusts the delay that defines the time at which the integration starts
in relation to the trigger signal of the weighted integration units.
Depending if the deskew matrix is bypassed there exists a different
default delay. This function can be used to add an additional delay to
the default delay.
Args:
value: Number of additional samples to adjust the delay. If not
specified this function will just return the additional delay
currently set.
Returns:
The adjustment in delay in units of samples.
Raises:
ValueError: If the adjusted quantum analyzer delay is outside the
allowed range of 1021 samples.
"""
# The default delay value is defined by wether the deskew matrix is
# bypassed or not.
default_delay = 184 if self.bypass.deskew() else 200
if value is None:
return self.delay() - default_delay
# Round down to greatest multiple of 4, as in LabOne.
qa_delay_user = int(value // 4) * 4
# Calculate final value of adjusted QA delay.
qa_delay_adjusted = qa_delay_user + default_delay
# Check if final delay is between 0 and 1020.
if qa_delay_adjusted not in range(0, 1021):
raise ValueError(
"The quantum analyzer delay is out of range (0 <= "
f"{qa_delay_user} + {default_delay} <= 1021)"
)
# Write the adjusted delay value to the node.
self.delay(qa_delay_adjusted)
return qa_delay_user
@lazy_property
def integration(self) -> Integration:
"""Integration.
.. versionadded:: 0.3.2
"""
return Integration(self.root, self._tree + ("integration",))
[docs]class UHFQA(UHFLI):
"""High-level driver for the Zurich Instruments UHFQA."""
[docs] def enable_qccs_mode(self) -> None:
"""Configure the instrument to work with PQSC.
This method sets the reference clock source and DIO settings
correctly to connect the instrument to the PQSC.
Info:
Use ``factory_reset`` to reset the changes if necessary
"""
with create_or_append_set_transaction(self._root):
# Use external 10 MHz clock as reference
self.system.extclk("external")
# Configure DIO to be used in a QCCS
# Clock DIO internally with a frequency of 50 MHz
self.dios[0].extclk("internal")
# Set DIO output values to QA results compatible with QCCS
self.dios[0].mode("qa_result_qccs")
# Drive the two least significant bytes of the DIO port
self.dios[0].drive(0b0011)
# Set correct DIO triggering in the AWG sequencer
self.awgs[0].dio.strobe.slope("off")
self.awgs[0].dio.valid.index(16)
self.awgs[0].dio.valid.polarity("high")
@lazy_property
def qas(self) -> t.Sequence[QAS]:
"""A Sequence of QAS."""
return NodeList(
[
QAS(self.root, self._tree + ("qas", str(i)))
for i in range(len(self["qas"]))
],
self._root,
self._tree + ("qas",),
)