qat.backend.qblox.config.specification module

class AttConfig(**data)

Bases: BaseModel

Configuration for output/input attenuation.

Parameters:

  • out0 – Attenuation (in dB) for output 0.

  • out1 – Attenuation (in dB) for output 1.

  • out2 – Attenuation (in dB) for output 2.

  • out3 – Attenuation (in dB) for output 3.

  • out4 – Attenuation (in dB) for output 4.

  • out5 – Attenuation (in dB) for output 5.

  • in0 – Attenuation (in dB) for input 0.

  • in1 – Attenuation (in dB) for input 1.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

in0: float | None
in1: float | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: float | None
out1: float | None
out2: float | None
out3: float | None
out4: float | None
out5: float | None
class AwgConfig(**data)

Bases: BaseModel

Configuration components related to the sequencer’s AWG.

Parameters:

  • cont_mode_en_path0 – Flag to enable/disable continuous waveform mode enable path 0 (I).

  • cont_mode_en_path1 – Flag to enable/disable continuous waveform mode enable path 1 (Q).

  • cont_mode_waveform_idx_path0 – Waveform index to play continuously on AWG path 0 (if enabled, see :param:`cont_mode_en_path0`)

  • cont_mode_waveform_idx_path1 – Waveform index to play continuously on AWG path 1 (if enabled, see :param:`cont_mode_en_path1`)

  • upsample_rate_path0 – Upsample rate for AWG path 0.

  • upsample_rate_path1 – Upsample rate for AWG path 1.

  • gain_path0 – Gain for AWG path 0.

  • gain_path1 – Gain for AWG path 1.

  • offset_path0 – Offset for AWG path 0.

  • offset_path1 – Offset for AWG path 1.

  • mod_en – Flag to enable/disable modulation for AWG.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

cont_mode_en_path0: bool | None
cont_mode_en_path1: bool | None
cont_mode_waveform_idx_path0: int | None
cont_mode_waveform_idx_path1: int | None
gain_path0: float | None
gain_path1: float | None
mod_en: bool | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

offset_path0: float | None
offset_path1: float | None
upsample_rate_path0: int | None
upsample_rate_path1: int | None
class ConnectionConfig(**data)

Bases: BaseModel

Configuration for the sequencer’s connection to the analogue input/output paths.

Parameters:

  • bulk_value – A list of strings in the format <direction><channel> or <direction><I-channel>_<Q-channel>: <direction> must be ‘in’ to make a connection between an input and the acquisition path, ‘out’ to make a connection from the waveform generator to an output, or ‘io’ to do both. <channel> must be integer channel indices. If only one channel is specified,the sequencer operates in real mode; if two channels are specified, it operates in complex mode.

  • out0 – Component config of a sequencer’s connection to output 0, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • out1 – Component config of a sequencer’s connection to output 1, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • out2 – Component config of a sequencer’s connection to output 2, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • out3 – Component config of a sequencer’s connection to output 3, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • out4 – Component config of a sequencer’s connection to output 4, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • out5 – Component config of a sequencer’s connection to output 5, if any. Possible values are ‘I’, ‘Q’, ‘IQ’, or ‘off’

  • acq_I – Input config for the ‘I’ input of the acquisition path of this sequencer is connected to, if any. Possible values are ‘in0’, ‘in1’, or ‘off’

  • acq_Q – Input config for the ‘Q’ input of the acquisition path of this sequencer is connected to, if any. Possible values are ‘in0’, ‘in1’, or ‘off’

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

acq_I: str | None
acq_Q: str | None
bulk_value: list[str]
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class ExpOvershoot0Config(**data)

Bases: BaseModel

Configuration of exponential overshoot filter 0. Possible values for the outputs/markers are ‘bypassed’ where the filter is disabled, or ‘delay_comp’ where the filter is bypassed and the output is delayed as if it were applied.

Parameters:

  • out0 – Configuration of exponential overshoot filter 0 for output 0.

  • out1 – Configuration of exponential overshoot filter 0 for output 1.

  • out2 – Configuration of exponential overshoot filter 0 for output 2.

  • out3 – Configuration of exponential overshoot filter 0 for output 3.

  • out4 – Configuration of exponential overshoot filter 0 for output 4.

  • out5 – Configuration of exponential overshoot filter 0 for output 5.

  • marker0 – Configuration of exponential overshoot filter 0 for marker 0.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker0: str | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class ExpOvershoot1Config(**data)

Bases: BaseModel

Configuration of exponential overshoot filter 1. Possible values for the outputs/markers are ‘bypassed’ where the filter is disabled, or ‘delay_comp’ where the filter is bypassed and the output is delayed as if it were applied.

Parameters:

  • out0 – Configuration of exponential overshoot filter 1 for output 0.

  • out1 – Configuration of exponential overshoot filter 1 for output 1.

  • out2 – Configuration of exponential overshoot filter 1 for output 2.

  • out3 – Configuration of exponential overshoot filter 1 for output 3.

  • out4 – Configuration of exponential overshoot filter 1 for output 4.

  • out5 – Configuration of exponential overshoot filter 1 for output 5.

  • marker0 – Configuration of exponential overshoot filter 1 for marker 0.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker0: str | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class ExpOvershoot2Config(**data)

Bases: BaseModel

Configuration of exponential overshoot filter 2. Possible values for the outputs/markers are ‘bypassed’ where the filter is disabled, or ‘delay_comp’ where the filter is bypassed and the output is delayed as if it were applied.

Parameters:

  • out0 – Configuration of exponential overshoot filter 2 for output 0.

  • out1 – Configuration of exponential overshoot filter 2 for output 1.

  • out2 – Configuration of exponential overshoot filter 2 for output 2.

  • out3 – Configuration of exponential overshoot filter 2 for output 3.

  • out4 – Configuration of exponential overshoot filter 2 for output 4.

  • out5 – Configuration of exponential overshoot filter 2 for output 5.

  • marker0 – Configuration of exponential overshoot filter 2 for marker 0.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker0: str | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class ExpOvershoot3Config(**data)

Bases: BaseModel

Configuration of exponential overshoot filter 3. Possible values for the outputs/markers are ‘bypassed’ where the filter is disabled, or ‘delay_comp’ where the filter is bypassed and the output is delayed as if it were applied.

Parameters:

  • out0 – Configuration of exponential overshoot filter 3 for output 0.

  • out1 – Configuration of exponential overshoot filter 3 for output 1.

  • out2 – Configuration of exponential overshoot filter 3 for output 2.

  • out3 – Configuration of exponential overshoot filter 3 for output 3.

  • out4 – Configuration of exponential overshoot filter 3 for output 4.

  • out5 – Configuration of exponential overshoot filter 3 for output 5.

  • marker0 – Configuration of exponential overshoot filter 3 for marker 0.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker0: str | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class FirConfig(**data)

Bases: BaseModel

Configuration of the Finite Impulse Response filter. Possible values for the outputs/markers are ‘bypassed’ where the filter is disabled, or ‘delay_comp’ where the filter is bypassed and the output is delayed as if it were applied.

Parameters:

  • out0 – Configuration for the FIR filter for output 0.

  • out1 – Configuration for the FIR filter for output 1.

  • out2 – Configuration for the FIR filter for output 2.

  • out3 – Configuration for the FIR filter for output 3.

  • out4 – Configuration for the FIR filter for output 4.

  • out5 – Configuration for the FIR filter for output 5.

  • marker0 – Configuration for the FIR filter for marker 0.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker0: str | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: str | None
out1: str | None
out2: str | None
out3: str | None
out4: str | None
out5: str | None
class GainConfig(**data)

Bases: BaseModel

Configuration for input gain relevant in the QRM.

Parameters:

  • in0 – Gain (in dB) for input 0.

  • in1 – Gain (in dB) for input 1.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

in0: int | None
in1: int | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class LoConfig(**data)

Bases: BaseModel

Configuration for the local oscillator in QCM-RF, QRM-RF, and QRC.

Parameters:

  • out0_en – Flag to enable/diable the LO on output 0. Relevant in QCM-RF.

  • out0_freq – Frequency (in Hz) for the LO attached to output 0. Relevant in QCM-RF.

  • out1_en – Flag to enable/diable the LO on output 1.Relevant in QCM-RF.

  • out1_freq – Frequency (in Hz) for the LO attached to output 1. Relevant in QCM-RF.

  • out2_freq – Frequency (in Hz) for the LO attached to output 2. Relevant in QRC.

  • out3_freq – Frequency (in Hz) for the LO attached to output 3. Relevant in QRC.

  • out4_freq – Frequency (in Hz) for the LO attached to output 4. Relevant in QRC.

  • out5_freq – Frequency (in Hz) for the LO attached to output 5. Relevant in QRC.

  • out0_in0_en – Flag to enable/diable the LO common to output 0 and input 0. Relevant in QRM-RF.

  • out0_in0_freq – Frequency (in Hz) for the LO common to output 0 and input 0. Relevant in QCM-RF/QRC.

  • out1_in1_freq – Frequency (in Hz) for the LO common to output 1 and input 1. Relevant in QRC.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0_en: bool | None
out0_freq: float | None
out0_in0_en: bool | None
out0_in0_freq: float | None
out1_en: bool | None
out1_freq: float | None
out1_in1_freq: float | None
out2_freq: float | None
out3_freq: float | None
out4_freq: float | None
out5_freq: float | None
class MixerConfig(**data)

Bases: BaseModel

Configuration related to the sequencer’s mixer correction component.

Parameters:

  • phase_offset – Mixer phase imbalance correction for AWG.

  • gain_ratio – Mixer gain imbalance correction for AWG.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

gain_ratio: float | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

phase_offset: float | None
class ModuleConfig(**data)

Bases: BaseModel

Configuration specification for module (the analogue side of the RF chain).

Parameters:

  • marker_inverts – Dictionary mapping marker indices to a flag whether to enable/disable marker inversion.

  • offset – Offset configuration, see OffsetConfig.

  • lo – Local Oscillator configuration, see LoConfig.

  • attenuation – Attenuation configuration, see AttenuationConfig.

  • gain – Gain configuration, see GainConfig.

  • scope_acq – Scope acquisition configuration, see ScopeAcqConfig.

  • fir – FIR filter configuration, see FirConfig.

  • exp0 – Exponential overshoot 0 configuration, see ExpOvershoot0Config.

  • exp1 – Exponential overshoot 1 configuration, see ExpOvershoot1Config.

  • exp2 – Exponential overshoot 2 configuration, see ExpOvershot2Config.

  • exp3 – Exponential overshoot 3 configuration, see ExpOvershoot3Config.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

attenuation: AttConfig
exp0: ExpOvershoot0Config
exp1: ExpOvershoot1Config
exp2: ExpOvershoot2Config
exp3: ExpOvershoot3Config
fir: FirConfig
gain: GainConfig
lo: LoConfig
marker_inverts: dict[int, bool]
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

offset: OffsetConfig
scope_acq: ScopeAcqConfig
class NcoConfig(**data)

Bases: BaseModel

Configuration components related to the sequencer’s NCO.

Parameters:

  • freq – NCO frequency in Hz.

  • phase_offs – Phase offset of the NCO in degrees with a resolution of 3.6e-7 degrees.

  • prop_delay_comp – Delay that compensates the NCO phase to the input path with respect to the instrument’s combined output and input propagation delay. This delays the frequency update as well.

  • prop_delay_comp_en – Flag to enable/disable compensation of propagation delay.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

freq: float | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

phase_offs: float | None
prop_delay_comp: int | None
prop_delay_comp_en: bool | None
class OffsetConfig(**data)

Bases: BaseModel

Configuration components to apply on the input/output the signal. They are DC voltage levels can be used to shift the baseline the waveforms or to calibrate out hardware imperfections such as mixer leakage.

Parameters:

  • out0 – Offset (in V) for output 0 (I) in QCM/QRM.

  • out1 – Offset (in V) for output 1 (Q) in QCM/QRM.

  • out2 – Offset (in V) for output 2 (I) in QCM.

  • out3 – Offset (in V) for output 3 (Q) in QCM.

  • in0 – Offset (in V) for input 0 (I) in QRM.

  • in1 – Offset (in V) for input 1 (Q) in QRM.

  • out0_path0 – Offset (in mV) for output 0 path 0 (I) in QCM-RF/QRM-RF.

  • out0_path1 – Offset (in mV) for output 0 path 1 (Q) in QCM-RF/QRM-RF.

  • out1_path0 – Offset (in mV) for output 1 path 0 (I) in QCM-RF.

  • out1_path1 – Offset (in mV) for output 1 path 1 (Q) in QCM-RF.

  • in0_path0 – Offset (in V) for input 0 path (I) in QRM-RF.

  • in0_path1 – Offset (in V) for input 1 path (Q) in QRM-RF.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

in0: float | None
in0_path0: float | None
in0_path1: float | None
in1: float | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

out0: float | None
out0_path0: float | None
out0_path1: float | None
out1: float | None
out1_path0: float | None
out1_path1: float | None
out2: float | None
out3: float | None
class QbloxConfig(**data)

Bases: BaseModel

Object grouping configuration of the analogue side and the digital side of the RF chain. For a given output/input analogue path, :param:`module` describes the necessary QCodes configuration to set it up completely and :param:`sequencers` is a collection of sequencer indices that are allowed down/up the said output/input channel. Such mechanism allows us to restrict where the code generator is allowed to pick the next available sequencer.

Parameters:

  • slot_idx – The slot index on the Qblox chassis (a.k.a. Cluster) where the module is installed.

  • module – Module configuration, see ModuleConfig.

  • sequencers – Statically mapped sequencers and their configuration, see SequencerConfig.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

module: ModuleConfig
sequencers: dict[int, SequencerConfig]
slot_idx: int | None
class ScopeAcqConfig(**data)

Bases: BaseModel

Scope acquisition configuration relevant in QRM/QRM-RF/QRC. Possible values For the trigger mode are ‘sequencer’ to trigger by sequencer, ‘level’ to trigger by input level.

Parameters:

  • sequencer_select – Sequencer that specifies which sequencer triggers the scope acquisition when using sequencer trigger mode. It is a sequencer id, or a list of sequencer ids for each scope IQ pair.

  • trigger_mode_path0 – Trigger mode for input path 0.

  • trigger_mode_path1 – Trigger mode for input path 1.

  • trigger_mode_path2 – Trigger mode for input path 2.

  • trigger_mode_path3 – Trigger mode for input path 3.

  • trigger_level_path0 – Trigger level when using input level trigger mode for input path 0.

  • trigger_level_path1 – Trigger level when using input level trigger mode for input path 1.

  • trigger_level_path2 – Trigger level when using input level trigger mode for input path 2.

  • trigger_level_path3 – Trigger level when using input level trigger mode for input path 3.

  • avg_mode_en_path0 – Flag to enable/disable scope acquisition averaging mode for input path 0.

  • avg_mode_en_path1 – Flag to enable/disable scope acquisition averaging mode for input path 1.

  • avg_mode_en_path2 – Flag to enable/disable scope acquisition averaging mode for input path 2.

  • avg_mode_en_path3 – Flag to enable/disable scope acquisition averaging mode for input path 3.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

avg_mode_en_path0: bool | None
avg_mode_en_path1: bool | None
avg_mode_en_path2: bool | None
avg_mode_en_path3: bool | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sequencer_select: int | None
trigger_level_path0: float | None
trigger_level_path1: float | None
trigger_level_path2: float | None
trigger_level_path3: float | None
trigger_mode_path0: str | None
trigger_mode_path1: str | None
trigger_mode_path2: str | None
trigger_mode_path3: str | None
class SequencerConfig(**data)

Bases: BaseModel

Configuration specification for sequencer (the digital side of the RF chain).

Parameters:

  • sync_en – Flag to enable/disable party-line synchronization. If enabled, the sequencer is “registered” in the SYNC protocol to coordinate the timeline with other sequencers using the wait_sync instruction

  • marker_ovr_en – Flag to enable/disable marker overriding feature. It has priority and will overwrite set_mrk instruction.

  • marker_ovr_value – Marker override value. Its binary representation codifies On/Off flags for marker channels. It has priority and will overwrite set_mrk instruction.

  • trigger_count_thresholds – Threshold map for counters on trigger addresses 0-15. Thresholding condition used: greater than or equal.

  • trigger_threshold_inverts – Comparison result inversion for trigger addresses 0-15.

  • connection – Sequencer connection config, see ConnectionConfig.

  • nco – NCO config, see NcoConfig.

  • awg – AWG config, see AwgConfig.

  • mixer – Mixer config, see MixerConfig.

  • demod_en_acq – Flag to enable/disable demodulation on the acquisition path.

  • square_weight_acq – Unweighed acquisition config, see SquareWeightAcq.

  • thresholded_acq – Thresholded acquisition config, see ThresholdedAcqConfig.

  • ttl_acq – TTL acquisition config, see TtlAcqConfig.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

awg: AwgConfig
connection: ConnectionConfig
demod_en_acq: bool | None
marker_ovr_en: bool | None
marker_ovr_value: int | None
mixer: MixerConfig
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

nco: NcoConfig
square_weight_acq: SquareWeightAcq
sync_en: bool | None
thresholded_acq: ThresholdedAcqConfig
trigger_count_thresholds: dict[int, float]
trigger_threshold_inverts: dict[int, bool]
ttl_acq: TtlAcqConfig
class SquareWeightAcq(**data)

Bases: BaseModel

Configuration components for non-weighed acquisition.

Parameters:

integration_length – Integration length in number of samples for non-weighed acquisitions on paths 0 and 1. Must be a multiple of 4. Default value is 1024.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

integration_length: int | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ThresholdedAcqConfig(**data)

Bases: BaseModel

Configuration components for thresholded acquisition.

Parameters:

  • rotation – Phase rotation (in degrees) for the integration result.

  • threshold – Threshold for discretizing the phase-rotated result (see :param:`rotation`). Discretization is done by comparing the threshold to the rotated integration result of path 0. This comparison is applied before normalization (i.e. division) of the rotated value with the integration length and therefore the threshold needs to be compensated (i.e. multiplied) with this length for the discretization to function properly.

  • marker_en – Flag to enable/disable mapping of thresholded acquisition result to markers.

  • marker_address – Marker mask which maps the thresholded acquisition result to the markers (M1 to M4).

  • marker_invert – Inversion of the thresholded acquisition result before it is masked onto the markers.

  • trigger_en – Flag to enable/disable mapping of the thresholded acquisition result to trigger network.

  • trigger_address – Trigger address to which the thresholded acquisition result is mapped to the trigger network (T1 to T15)

  • trigger_invert – Inversion of the thresholded acquisition result before it is mapped to the trigger network.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

marker_address: int | None
marker_en: bool | None
marker_invert: bool | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rotation: float | None
threshold: float | None
trigger_address: int | None
trigger_en: bool | None
trigger_invert: bool | None
class TtlAcqConfig(**data)

Bases: BaseModel

Configuration components for Transistor-Transistor-Logic acquisition.

Parameters:

  • auto_bin_incr_en – Flag to enable/disable whether the bin index is automatically incremented when acquiring multiple triggers. Disabling the TTL trigger acquisition path resets the bin index.

  • threshold – Threshold value with which to compare the input ADC values of the selected input path.

  • input_select – The input used to compare against the threshold value in the TTL trigger acquisition path.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

auto_bin_incr_en: bool | None
input_select: int | None
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

threshold: float | None