auspex.filters package¶

Submodules¶

auspex.filters.average module¶

auspex.filters.channelizer module¶

class auspex.filters.channelizer.Channelizer(frequency=None, bandwidth=None, decimation_factor=None, follow_axis=None, follow_freq_offset=None, **kwargs)¶

Digital demodulation and filtering to select a particular frequency multiplexed channel. If an axis name is supplied to follow_axis then the filter will demodulate at the freqency axis_frequency_value - follow_freq_offset otherwise it will demodulate at frequency. Note that the filter coefficients are still calculated with respect to the frequency paramter, so it should be chosen accordingly when follow_axis is defined.

bandwidth = <FloatParameter(name='bandwidth',value=5000000.0)>¶

decimation_factor = <IntParameter(name='decimation_factor',value=4)>¶

final_init()¶

follow_axis = <Parameter(name='follow_axis',value='')>¶

follow_freq_offset = <FloatParameter(name='follow_freq_offset',value=0.0)>¶

frequency = <FloatParameter(name='frequency',value=10000000.0)>¶

init_filters(frequency, bandwidth)¶

process_data(data)¶

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

update_references(frequency)¶

auspex.filters.correlator module¶

class auspex.filters.correlator.Correlator(filter_name=None, **kwargs)¶

filter_name = 'Correlator'¶

operation()¶: Must be overridden with the desired mathematical function

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

unit(base_unit)¶: Must be overridden accoriding the desired mathematical function e.g. return base_unit + “^{}”.format(len(self.sink.input_streams))

auspex.filters.debug module¶

class auspex.filters.debug.Print(*args, **kwargs)¶

Debug printer that prints data comming through filter

process_data(data)¶

sink = <InputConnector(name=)>¶

class auspex.filters.debug.Passthrough(*args, **kwargs)¶

process_data(data)¶

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

auspex.filters.elementwise module¶

class auspex.filters.elementwise.ElementwiseFilter(filter_name=None, **kwargs)¶

Perform elementwise operations on multiple streams: e.g. multiply or add all streams element-by-element

filter_name = 'GenericElementwise'¶

main()¶: Generic run method which waits on a single stream and calls process_data on any new_data

operation()¶: Must be overridden with the desired mathematical function

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

unit(base_unit)¶: Must be overridden accoriding the desired mathematical function e.g. return base_unit + “^{}”.format(len(self.sink.input_streams))

update_descriptors()¶: Must be overridden depending on the desired mathematical function

auspex.filters.filter module¶

class auspex.filters.filter.Filter(name=None, **kwargs)¶

Any node on the graph that takes input streams with optional output streams

checkin()¶: For any filter-specific loop needs

configure_with_proxy(proxy_obj)¶: For use with bbndb, sets this filter’s properties using a FilterProxy object taken from the filter database.

descriptor_map(input_descriptors)¶: Return a dict of the output descriptors.

execute_on_run()¶

main()¶: Generic run method which waits on a single stream and calls process_data on any new_data

on_done()¶: To be run when the done signal is received, in case additional steps are needed (such as flushing a plot or data).

process_message(msg)¶: To be overridden for interesting default behavior

push_resource_usage()¶

push_to_all(message)¶

run()¶: Method to be run in sub-process; can be overridden in sub-class

shutdown()¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

auspex.filters.framer module¶

class auspex.filters.framer.Framer(axis=None, **kwargs)¶

Mete out data in increments defined by the specified axis.

axis = <Parameter(name='axis',value=None)>¶

final_init()¶

process_data(data)¶

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

auspex.filters.integrator module¶

class auspex.filters.integrator.KernelIntegrator(**kwargs)¶

bias = <FloatParameter(name='bias',value=0.0)>¶

box_car_start = <FloatParameter(name='box_car_start',value=0.0)>¶

box_car_stop = <FloatParameter(name='box_car_stop',value=1e-07)>¶

demod_frequency = <FloatParameter(name='demod_frequency',value=0.0)>¶: Integrate with a given kernel. Kernel will be padded/truncated to match record length

kernel = <Parameter(name='kernel',value=None)>¶

process_data(data)¶

simple_kernel = <BoolParameter(name='simple_kernel',value=True)>¶

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

auspex.filters.io module¶

class auspex.filters.io.WriteToFile(filename=None, groupname=None, datasetname=None, **kwargs)¶

Writes data to file using the Auspex container type, which is a simple directory structure with subdirectories, binary datafiles, and json meta files that store the axis descriptors and other information.

datasetname = <Parameter(name='datasetname',value='data')>¶

filename = <Parameter(name='filename',value=None)>¶

final_init()¶

get_data()¶

get_data_while_running(return_queue)¶: Return data to the main thread or user as requested. Use a MP queue to transmit.

groupname = <Parameter(name='groupname',value='main')>¶

process_data(data)¶

sink = <InputConnector(name=)>¶

class auspex.filters.io.DataBuffer(**kwargs)¶

Writes data to IO.

checkin()¶: For any filter-specific loop needs

final_init()¶

get_data()¶

main()¶: Generic run method which waits on a single stream and calls process_data on any new_data

process_data(data)¶

sink = <InputConnector(name=)>¶

auspex.filters.plot module¶

class auspex.filters.plot.Plotter(*args, name='', plot_dims=None, plot_mode=None, **plot_args)¶

axis_label(index)¶

desc()¶

execute_on_run()¶

final_init()¶

get_final_plot(quad_funcs=[<ufunc 'absolute'>, <function angle>])¶

on_done()¶: To be run when the done signal is received, in case additional steps are needed (such as flushing a plot or data).

plot_dims = <IntParameter(name='plot_dims',value=0)>¶

plot_mode = <Parameter(name='plot_mode',value='quad')>¶

process_data(data)¶

send(message)¶

set_done()¶

set_quit()¶

sink = <InputConnector(name=)>¶

update()¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

class auspex.filters.plot.ManualPlotter(name='', x_label=['X'], y_label=['y'], y_lim=None, numplots=1)¶

Establish a figure, then give the user complete control over plot creation and data. There isn’t any reason to run this as a process, but we provide the same interface for convenience.

add_data_trace(name, custom_mpl_kwargs={}, subplot_num=0)¶

add_fit_trace(name, custom_mpl_kwargs={}, subplot_num=0)¶

add_trace(name, matplotlib_kwargs={}, subplot_num=0)¶

desc()¶

execute_on_run()¶

send(message)¶

set_data(trace_name, xdata, ydata)¶

set_done()¶

set_quit()¶

start()¶

stop()¶

class auspex.filters.plot.MeshPlotter(*args, name='', plot_mode=None, x_label='', y_label='', **plot_args)¶

desc()¶

execute_on_run()¶

on_done()¶: To be run when the done signal is received, in case additional steps are needed (such as flushing a plot or data).

plot_mode = <Parameter(name='plot_mode',value='quad')>¶

process_direct(data)¶

send(message)¶

sink = <InputConnector(name=)>¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

SingleShotMeasurement auspex.filters.singleshot module —————————-

class auspex.filters.singleshot.SingleShotMeasurement(save_kernel=False, optimal_integration_time=False, zero_mean=False, set_threshold=False, logistic_regression=False, **kwargs)¶

TOLERANCE = 0.001¶

compute_filter()¶

Compute the single shot kernel and obtain single-shot measurement fidelity.

Expects that the data will be in self.ground_data and self.excited_data, which are (T, N)-shaped numpy arrays, with T the time axis and N the number of shots.

final_init()¶

logistic_fidelity()¶

logistic_regression = <BoolParameter(name='logistic_regression',value=False)>¶

optimal_integration_time = <BoolParameter(name='optimal_integration_time',value=False)>¶

process_data(data)¶: Fill the ground and excited data bins

save_kernel = <BoolParameter(name='save_kernel',value=False)>¶

set_threshold = <BoolParameter(name='set_threshold',value=False)>¶

sink = <InputConnector(name=)>¶

source = <OutputConnector(name=)>¶

update_descriptors()¶: This method is called whenever the connectivity of the graph changes. This may have implications for the internal functioning of the filter, in which case update_descriptors should be overloaded. Any simple changes to the axes within the StreamDescriptors should take place via the class method descriptor_map.

zero_mean = <BoolParameter(name='zero_mean',value=False)>¶