pipeline.hifv.tasks.syspower package

Submodules

pipeline.hifv.tasks.syspower.display module

class pipeline.hifv.tasks.syspower.display.syspowerBarChart(context, result)

Bases: object

create_plot(prefix)

get_figfile(prefix)

get_plot_wrapper(prefix)

plot()

class pipeline.hifv.tasks.syspower.display.syspowerBoxChart(context, result)

Bases: object

create_plot(prefix)

get_figfile(prefix)

get_plot_wrapper(prefix)

plot()

class pipeline.hifv.tasks.syspower.display.syspowerPerAntennaChart(context, result, yaxis, caltable, fileprefix, tabletype)

Bases: object

plot()

pipeline.hifv.tasks.syspower.renderer module

Created on 24 Oct 2014

@author: brk

class pipeline.hifv.tasks.syspower.renderer.T2_4MDetailssyspowerRenderer(uri='syspower.mako', description='Syspower (modified rq gains)', always_rerender=False)

Bases: pipeline.infrastructure.renderer.basetemplates.T2_4MDetailsDefaultRenderer

get_display_context(context, results)

class pipeline.hifv.tasks.syspower.renderer.VLASubPlotRenderer(context, result, plots, json_path, template, filename_prefix)

Bases: object

property dirname

property filename

get_file()

property path

render()

pipeline.hifv.tasks.syspower.syspower module

class pipeline.hifv.tasks.syspower.syspower.Syspower(inputs)

Bases: pipeline.infrastructure.basetask.StandardTaskTemplate

Inputs

alias of SyspowerInputs

analyse(results)

Determine the best parameters by analysing the given jobs before returning any final jobs to execute.

Parameters

jobs (a list ofJobRequest) – the job requests generated by prepare()

Return type

Result

flag_with_medfilt(x, temp, k=21, threshold=6, do_shift=False, **kwargs)

interp_with_medfilt(x, k=21, threshold=99, max_interp=10)

medfilt(x, k, threshold=6.0, flag_rms=False, flag_median=False, flag_only=False, fill_gaps=False)

prepare()

Prepare job requests for execution.

Parameters

parameters – the parameters to pass through to the subclass. Refer to the implementing subclass for specific information on what these parameters are.

Return type

a class implementing Result

class pipeline.hifv.tasks.syspower.syspower.SyspowerInputs(context, vis=None, clip_sp_template=None, antexclude=None, usemedian=None, templatevalue=None)

Bases: pipeline.infrastructure.vdp.StandardInputs

antexclude

VisDependentProperty is a Python data descriptor that standardises the behaviour of pipeline Inputs properties and lets them create default values more easily.

On reading a VisDependentProperty (ie. using the dot prefix: inputs.solint), one of two things happens:

1. If a NullMarker is found - signifying that no user input has been provided - and a ‘getter’ function has been defined, the getter function will be called to provide a default value for that measurement set.

2. If a user has overridden the value (eg. inputs.solint = 123), that value will be retrieved.

3. The value, either the default from step 1 or user-provided from step 2, is run through the optional postprocess function, which gives a final opportunity to change the value depending on the state/value of other properties.

A VisDependentProperty can be made read-only by specifying ‘readonly=True’ when creating the instance.

A VisDependentProperty can be hidden from the containing Inputs string representation by setting ‘hidden=True’ when creating the instance. This will hide the property from the web log and CLI getInputs calls.

Each VisDependentProperty has a set of values it considers equivalent to null. When the user sets the VDP value to one of these null values, the VDP machinery converts this to a private NullObject marker that signifies the property is now unset, resulting in the default value being returned next time the property is read. Developers can specify which values should be converted to NullObject by specifying null_input at creation time, e.g.,

solint = @VisDependentProperty(default=5, null_input=[None, ‘’, ‘RESET’, -1])

clip_sp_template

VisDependentProperty is a Python data descriptor that standardises the behaviour of pipeline Inputs properties and lets them create default values more easily.

On reading a VisDependentProperty (ie. using the dot prefix: inputs.solint), one of two things happens:

1. If a NullMarker is found - signifying that no user input has been provided - and a ‘getter’ function has been defined, the getter function will be called to provide a default value for that measurement set.

2. If a user has overridden the value (eg. inputs.solint = 123), that value will be retrieved.

3. The value, either the default from step 1 or user-provided from step 2, is run through the optional postprocess function, which gives a final opportunity to change the value depending on the state/value of other properties.

A VisDependentProperty can be made read-only by specifying ‘readonly=True’ when creating the instance.

A VisDependentProperty can be hidden from the containing Inputs string representation by setting ‘hidden=True’ when creating the instance. This will hide the property from the web log and CLI getInputs calls.

Each VisDependentProperty has a set of values it considers equivalent to null. When the user sets the VDP value to one of these null values, the VDP machinery converts this to a private NullObject marker that signifies the property is now unset, resulting in the default value being returned next time the property is read. Developers can specify which values should be converted to NullObject by specifying null_input at creation time, e.g.,

solint = @VisDependentProperty(default=5, null_input=[None, ‘’, ‘RESET’, -1])

usemedian

VisDependentProperty is a Python data descriptor that standardises the behaviour of pipeline Inputs properties and lets them create default values more easily.

On reading a VisDependentProperty (ie. using the dot prefix: inputs.solint), one of two things happens:

1. If a NullMarker is found - signifying that no user input has been provided - and a ‘getter’ function has been defined, the getter function will be called to provide a default value for that measurement set.

2. If a user has overridden the value (eg. inputs.solint = 123), that value will be retrieved.

3. The value, either the default from step 1 or user-provided from step 2, is run through the optional postprocess function, which gives a final opportunity to change the value depending on the state/value of other properties.

A VisDependentProperty can be made read-only by specifying ‘readonly=True’ when creating the instance.

A VisDependentProperty can be hidden from the containing Inputs string representation by setting ‘hidden=True’ when creating the instance. This will hide the property from the web log and CLI getInputs calls.

Each VisDependentProperty has a set of values it considers equivalent to null. When the user sets the VDP value to one of these null values, the VDP machinery converts this to a private NullObject marker that signifies the property is now unset, resulting in the default value being returned next time the property is read. Developers can specify which values should be converted to NullObject by specifying null_input at creation time, e.g.,

solint = @VisDependentProperty(default=5, null_input=[None, ‘’, ‘RESET’, -1])

class pipeline.hifv.tasks.syspower.syspower.SyspowerResults(gaintable=None, spowerdict=None, dat_common=None, clip_sp_template=None, template_table=None)

Bases: pipeline.infrastructure.basetask.Results

merge_with_context(context)

See :method:`~pipeline.infrastructure.api.Results.merge_with_context`

pipeline.hifv.tasks.syspower.syspower.savitzky_golay(y, window_size, order, deriv=0, rate=1)

Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques. :Parameters: * y (array_like, shape (N,)) – the values of the time history of the signal.

• window_size (int) – the length of the window. Must be an odd integer number.

• order (int) – the order of the polynomial used in the filtering. Must be less then window_size - 1.

• deriv (int) – the order of the derivative to compute (default = 0 means only smoothing)

Returns

ys – the smoothed signal (or it’s n-th derivative).

Return type

ndarray, shape (N)

Notes

The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

Examples

t = np.linspace(-4, 4, 500) y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape) ysg = savitzky_golay(y, window_size=31, order=4) import matplotlib.pyplot as plt plt.plot(t, y, label=’Noisy signal’) plt.plot(t, np.exp(-t**2), ‘k’, lw=1.5, label=’Original signal’) plt.plot(t, ysg, ‘r’, label=’Filtered signal’) plt.legend() plt.show()

References

1

A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.

2

Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688

Module contents