qtt.utilities package

Submodules

qtt.utilities.debug module

qtt.utilities.debug.dumpstring(txt, tag='dump', showfile=False)

Dump a text string to temperary file on disk

qtt.utilities.debug.functioncalldecorator(f, name=None)

Decorate a function to log input and output arguments

qtt.utilities.debug.logInstrument(instrument)

Decorate all parameters of an instrument with logging methods

qtt.utilities.imagetools module

qtt.utilities.imagetools.Vtrace(cdata, param, fig=None)

Calculate position of next V-trace from fitted model .

Parameters:

• cdata – TODO

• param – TODO

• fit (None or integer) – figure handle.

qtt.utilities.imagetools.cleanSensingImage(im, dy=0, sigma=None, order=3, fixreversal=True, removeoutliers=False, verbose=0)

Clean up image from sensing dot.

Parameters:

• im (numpy array) –

• dy (int or str) – direction for differentiation.

• order (int) – TODO.

• fixreversal (bool) – TODO.

• removeoutliers (bool) –

Returns:

processed image.

Return type:

ww (image)

qtt.utilities.imagetools.createCross(param, samplesize, l=20, w=2.5, lsegment=10, H=100, scale=None, lines=range(0, 4), istep=1, centermodel=True, linesegment=True, addX=True, verbose=0)

Create a cross model. The parameters are [x, y, width, alpha_1, …, alpha_4, [rotation of polarization line] ] With the optional parameters psi (angle of transition line). The x, y, width are in mV. Angles in radians.

Parameters:

• param (array) – parameters of the model.

• samplesize (int) – size of image patch in pixels.

• l (float) – parameters of the model in mV?. lsegment is the length of the 4 addition lines w is width of lines in the model. l is not used by default.

• w (float) – parameters of the model in mV?. lsegment is the length of the 4 addition lines w is width of lines in the model. l is not used by default.

• lsegment (float) – parameters of the model in mV?. lsegment is the length of the 4 addition lines w is width of lines in the model. l is not used by default.

• istep (float) – scan resolution in pixel/mV.

• scale (None) – parameter not used any more.

• addX (bool) – if True add polarization line to model.

• H (float) – intensity of cross.

• linesegment (bool) – if True create line segments instead of full lines.

Returns:

return data.

Return type:

modelpatch, (cc, lp, hp, ip, opr, w, H, lsegment)

qtt.utilities.imagetools.evaluateCross(param, im, verbose=0, fig=None, istep=1, istepmodel=1, linewidth=2, usemask=False, use_abs=False, w=2.5)

Calculate cross matching score.

Parameters:

• param (array or list) – used by createCross to create image template.

• im (numpy array) – TODO.

Returns:

cost, patch, cdata, tuple.

See also

createCross.

qtt.utilities.imagetools.fitBackground(im, smooth=True, fig=None, order=3, verbose=1, removeoutliers=False, returndict=None)

Fit smooth background to 1D or 2D image.

Parameters:

im (array) – input image.

Returns

vv (array): estimated background.

qtt.utilities.imagetools.fitModel(param0, imx, verbose=1, cfig=None, ksizemv=41, istep=None, istepmodel=0.5, cb=None, use_abs=False, model_line_width=2.5)

Fit model of an anti-crossing .

This is a wrapper around evaluateCross and the scipy optimization routines.

Parameters:

• param0 (array) – parameters for the anti-crossing model.

• imx (array) – input image.

qtt.utilities.imagetools.lineSegment(im, x0, x1=None, theta=None, w=2, l=12, H=200, ml=0)

Plot half-line into image .

>>> lineSegment(np.zeros( (160,240)), [60,40], [70,40], w=10, l=60)
>>> lineSegment(np.zeros( (160,240)), [60,40], theta=np.deg2rad(20), w=10, l=60)

qtt.utilities.imagetools.semiLine(im, x0, theta, w, l, H=200, dohalf=True)

Plot half-line into image .

Parameters:

• im (array) –

• x0 (array) – starting point of semi-line.

• theta (float) – angle in radians.

• w (float) – width.

• l (float) – length of line segment.

• H (float) – intensity of line segment.

• dohalf (bool) – add smoothing?

>>> im=semiLine(np.zeros( (160,240)), [60,40], theta=np.deg2rad(20), w=10, l=60)
>>> plt.imshow(im)

qtt.utilities.json_serializer module

class qtt.utilities.json_serializer.QttSerializer

Bases: Serializer

qtt.utilities.json_serializer.decode_json(json_string: str) → Any

Decode Python object to JSON

Parameters:

json_string – data to be decoded

Returns

Python object

qtt.utilities.json_serializer.decode_numpy_array(item)

Decode a numpy array from JSON

qtt.utilities.json_serializer.decode_numpy_number(item)

Decode a numpy scalar from JSON

qtt.utilities.json_serializer.decode_qcodes_dataset(item)

qtt.utilities.json_serializer.decode_qcodes_instrument(item)

qtt.utilities.json_serializer.encode_json(data: object) → str

Encode Python object to JSON

Parameters:

data – data to be encoded

Returns

String with formatted JSON

qtt.utilities.json_serializer.encode_numpy_array(item)

Encode a numpy array to JSON

qtt.utilities.json_serializer.encode_numpy_number(item)

Encode a numpy scalar to JSON

qtt.utilities.json_serializer.encode_qcodes_dataset(item)

qtt.utilities.json_serializer.encode_qcodes_instrument(item)

qtt.utilities.json_serializer.load_json(filename: str) → object

Write a Python object from a JSON file

Parameters:

filename (str) – filename to write data to

Returns:

object loaded from JSON file

Return type:

object

qtt.utilities.json_serializer.save_json(data: Any, filename: str)

Write a Python object to a JSON file

Parameters:

• data (object) – object to be serialized

• filename (str) – filename to write data to

qtt.utilities.optimization module

class qtt.utilities.optimization.AverageDecreaseTermination(N: int, tolerance: float = 0.0)

Bases: object

property parameters

reset()

Reset the data

property values

class qtt.utilities.optimization.OptimizerCallback(show_progress: bool = False, store_data: bool = True, residual_fitting: bool = True)

Bases: object

clear()

Clear the data from this instance

property data: DataFrame

Return data gathered by callback

data_callback(iteration: int, parameters: Any, residual: float) → None

Callback used to store data

Parameters:

• iteration – Iteration on the optimization procedure

• parameters – Current values of the parameters to be optimized

• residual – Current residual (value of the objective function)

lmfit_callback(parameters, iteration, residual, *args, **kws)

Callback method for lmfit optimizers

number_of_evaluations() → int

Return the number of callback evaluations

Note: this can differ from the number of objective evaluations

optimization_time() → float

Return time difference between the first and the last invocation of the callback

Returns:

Time in seconds

property parameters: List[Any]

Returns list of parameters that have been used in evaluations

Returns:

The list of parameters

plot(ax: Axes | None = None, **kwargs) → None

Plot optimization results

qiskit_callback(number_evaluations, parameters, value, stepsize, accepted)

Callback method for Qiskit optimizers

scipy_callback(parameters)

Callback method for scipy optimizers

qtt.utilities.tools module

qtt.utilities.tools.addPPT_dataset(dataset, title=None, notes=None, show=False, verbose=1, paramname='measured', printformat='fancy', customfig=None, extranotes=None, **kwargs)

Add slide based on dataset to current active Powerpoint presentation.

Dummy implementation.

qtt.utilities.tools.addPPTslide(title: str | None = None, fig: int | ndarray | Figure | Any | None = None, txt: str | None = None, notes: str | Station | None = None, figsize: Tuple[int, int] | None = None, subtitle: str | None = None, maintext: str | None = None, show: bool = False, verbose: int = 1, activate_slide: bool = True, ppLayout: int | None = None, extranotes: str | None = None, background_color: Tuple | None = None, maximum_notes_size: int = 10000) → Tuple[Any, Any]

Add slide to current active Powerpoint presentation.

Dummy implementation.

qtt.utilities.tools.cfigure(*args, **kwargs)

Create Matplotlib figure with copy to clipboard functionality.

By pressing the ‘c’ key figure is copied to the clipboard.

qtt.utilities.tools.checkPickle(obj, verbose=0)

Check whether an object can be pickled.

Parameters:

• obj (object) – object to be checked.

• verbose (int) – verbosity (0 == silent).

Returns:

True of the object can be pickled.

Return type:

c (bool)

qtt.utilities.tools.clickGatevals(plot, drawmode='ro')

Get gate values for all gates at clicked point in a heatmap.

Parameters:

• plot (qcodes MatPlot object) – plot of measurement data.

• drawmode (string) – plotting style.

Returns:

values of the gates at clicked point.

Return type:

gatevals (dict)

qtt.utilities.tools.code_version(repository_names=None, package_names=None, get_dirty_status=False, verbose=0)

Returns the python version, module version for; numpy, scipy, qupulse

and the git guid and dirty status for; qcodes, qtt, spin-projects and pycqed, if present on the machine. NOTE: currently the dirty status is not working correctly due to a bug in dulwich…

Parameters:

• repository_names ([str]) – a list with repositories, e.g. [‘qtt’, ‘qcodes’].

• package_names ([str]) – a list with pip packages, e.g. [‘numpy’, ‘scipy’].

• get_dirty_status (bool) – selects whether the local code has changed for the repositories.

• verbose (int) – output level.

Returns:

python, modules and git repos status.

Return type:

status (dict)

qtt.utilities.tools.connect_slot(target)

Create a slot by dropping signal arguments.

qtt.utilities.tools.create_figure_ppt_callback(fig: int | Figure | None = None, title: str | None = None, notes: str | DataSet | None = None, position: Tuple[float, float, float, float] = (0.9, 0.925, 0.075, 0.05)) → None

Create a button on a matplotlib figure to copy data to PowerPoint slide.

The figure is copied to PowerPoint using @ref addPPTslide.

Parameters:

• fig – Handle to matplotlib window. If None, then use the current figure

• title – title for the slide.

• notes – notes to add to the slide.

• position – position specified as fraction left, right, width, height.

Example

>>> plt.figure(10)
>>> plt.plot(np.arange(100), np.random.rand(100), 'o', label='input data')
>>> create_figure_ppt_callback(fig=10, title='test')
>>> plt.show()

qtt.utilities.tools.cutoffFilter(x, thr, omega)

Smooth cutoff filter.

Filter definition from: http://paulbourke.net/miscellaneous/imagefilter/

Example

>>> plt.clf()
>>> x=np.arange(0, 4, .01)
>>> _=plt.plot(x, cutoffFilter(x, 2, .25), '-r')

qtt.utilities.tools.deprecated(func)

This is a decorator which can be used to mark functions as deprecated. It will result in a warning being emitted when the function is used.

qtt.utilities.tools.diffImage(im, dy, size=None)

Simple differentiation of an image.

Parameters:

• im (numpy array) – input image.

• dy (integer or string) – method of differentiation. For an integer it is the axis of differentiation. Allowed strings are ‘x’, ‘y’, ‘xy’.

• size (str) – describes the size e.g. ‘same’.

qtt.utilities.tools.diffImageSmooth(im, dy='x', sigma=2)

Simple differentiation of an image.

Parameters:

• im (array) – input image.

• dy (string or integer) – direction of differentiation. can be ‘x’ (0) or ‘y’ (1) or ‘xy’ (2) or ‘g’ (3).

• sigma (float) – parameter for gaussian filter kernel.

qtt.utilities.tools.flatten(lst)

Flatten a list.

Parameters:

lst (list) – list to be flattened.

Returns:

flattened list.

Return type:

list

Example

>>> flatten([ [1,2], [3,4], [10] ])
[1, 2, 3, 4, 10]

qtt.utilities.tools.fourierHighPass(imx, nc=40, omega=4, fs=1024, fig=None)

Implement simple high pass filter using the Fourier transform.

qtt.utilities.tools.freezeclass(cls)

Decorator to freeze a class.

qtt.utilities.tools.get_git_versions(repos, get_dirty_status=False, verbose=0)

Returns the repository head guid and dirty status and package version number if installed via pip.

The version is only returned if the repo is installed as pip package without edit mode. NOTE: currently the dirty status is not working correctly due to a bug in dulwich…

Parameters:

• repos ([str]) – a list with repositories, e.g. [‘qtt’, ‘qcodes’].

• get_dirty_status (bool) – selects whether to use the dulwich package and collect the local code changes for the repositories.

• verbose (int) – verbosity (0 == silent).

Retuns:

r (dict): dictionary with repo names, head guid and (optionally) dirty status for each given repository.

qtt.utilities.tools.get_module_versions(modules, verbose=0)

Returns the module version of the given pip packages.

Parameters:

• modules ([str]) – a list with pip packages, e.g. [‘numpy’, ‘scipy’].

• verbose (int) – verbosity (0 == silent).

Returns:

dictionary with package names and version number for each given module.

Return type:

r (dict)

qtt.utilities.tools.get_python_version(verbose=0)

Returns the python version.

qtt.utilities.tools.in_ipynb()

qtt.utilities.tools.is_spyder_environment()

Return True if the process is running in a Spyder environment

qtt.utilities.tools.logging_context(level: int = 20, logger: Logger | None = None)

A context manager that changes the logging level

Parameters:

• level – Logging level to set in the context

• logger – Logger to update, if None then update the default logger

class qtt.utilities.tools.measure_time(message: str | None = 'dt: ')

Bases: object

Create context manager that measures execution time and prints to stdout

property current_delta_time: float

Return time since start of the context

Returns:

Time in seconds

property delta_time: float

Return time spend in the context

If still in the context, return -1. :returns: Time in seconds

qtt.utilities.tools.monitorSizes(verbose=0)

Return monitor sizes.

qtt.utilities.tools.negfloat(x) → float

Helper function

qtt.utilities.tools.pythonVersion()

qtt.utilities.tools.rdeprecated(txt=None, expire=None)

This is a decorator which can be used to mark functions as deprecated.

It will result in a warning being emitted when the function is used. After the expiration data the decorator will generate an Exception.

Parameters:

• txt (str) – reason for deprecation.

• expire (str) – date of expiration.

qtt.utilities.tools.resampleImage(im)

Resample the image so it has the similar sample rates (samples/mV) in both axis.

Parameters:

im (DataArray) – input image.

Returns:

resampled image. setpoints (list of 2 numpy arrays): setpoint arrays from resampled image.

Return type:

imr (numpy array)

qtt.utilities.tools.reshape_metadata(dataset, printformat='dict', add_scanjob=True, add_gates=True, add_analysis_results=True, verbose=0) → str

Reshape the metadata of a DataSet.

Parameters:

• dataset (DataSet or qcodes.Station) – a dataset of which the metadata will be reshaped.

• printformat (str) – can be ‘dict’ or ‘txt’,’fancy’ (text format).

• add_scanjob (bool) – If True, then add the scanjob at the beginning of the notes.

• add_analysis_results (bool) – If True, then add the analysis_results at the beginning of the notes.

• add_gates (bool) – If True, then add the scanjob at the beginning of the notes.

• verbose (int) – verbosity (0 == silent).

Returns:

The reshaped metadata.

qtt.utilities.tools.scanTime(dd)

Return date a scan was performed.

qtt.utilities.tools.set_ppt_slide_background(slide, color, verbose=0)

Sets the background color of PPT slide.

Parameters:

• slide (object) – PowerPoint COM object for slide.

• color (tuple) – tuple with RGB color specification.

• verbose (int) – verbosity (0 == silent).

qtt.utilities.tools.setupMeasurementWindows(*args, **kwargs)

qtt.utilities.tools.showImage(im, extent=None, fig=None, title=None)

Show image in figure window.

Parameters:

• im (array) – TODO.

• extent (list) – matplotlib style image extent.

• fig (None or int) – figure window to show image.

• title (None or str) – figure title.

qtt.utilities.tools.slopeClick(drawmode='r--', **kwargs)

Calculate slope for line piece of two points clicked by user. Works with matplotlib but not with pyqtgraph. Uses the currently active figure.

Parameters:

drawmode (string) – plotting style.

Returns:

coordinates of the two clicked points. signedslope (float): slope of linepiece connecting the two points.

Return type:

coords (2 x 2 array)

qtt.utilities.tools.smoothFourierFilter(fs=100, thr=6, omega=2, fig=None)

Create smooth ND filter for Fourier high or low-pass filtering.

Example

>>> F=smoothFourierFilter([24,24], thr=6, omega=2)
>>> _=plt.figure(10); plt.clf(); _=plt.imshow(F, interpolation='nearest')

qtt.utilities.tools.static_var(varname, value)

Helper function to create a static variable.

qtt.utilities.tools.stripDataset(dataset)

Make sure a dataset can be pickled .

Parameters:

dataset (qcodes DataSet) – TODO.

Returns:

the dataset from the function argument.

Return type:

dataset (qcodes DataSet)

qtt.utilities.tools.updatePlotTitle(qplot, basetxt='Live plot')

Update the plot title of a QtPlot window.

qtt.utilities.tools.update_dictionary(alldata: dict, **kwargs: dict)

Update elements of a dictionary.

Parameters:

• alldata (dict) – dictionary to be updated.

• kwargs (dict) – keyword arguments.

qtt.utilities.visualization module

qtt.utilities.visualization.combine_legends(axis_list: List[Axes], target_ax: Axes | None = None)

Combine legends of a list of matplotlib axis objects into a single legend

Parameters:

• axis_list – List of matplotlib axis containing legends

• target_ax – Axis to add the combined legend to. If None, use the first axis from the axis_list

Example

import matplotlib.pyplot as plt ax1=plt.gca() plt.plot([1,2,3], [.1,.2,.3], ‘.b’, label=’X’) plt.legend() ax2=ax1.twinx() ax2.plot([1,2,3], [1, 2, 3], ‘-r’, label=’miliX’ ) plt.legend() combine_legends([ax1, ax2])

qtt.utilities.visualization.get_axis(handle: int | Axes | Figure | None) → Axes

Create or return matplotlib axis object

Parameters:

handle – Specification of how to obtain the axis object. For an integer, generate a new figure. For an Axis object, return the handle. For a Figure, return the default axis of the figure. For None, use the matplotlib current axis.

Returns:

Axis object

qtt.utilities.visualization.plot_double_gaussian_fit(result_dict: dict, xdata: ndarray) → None

Plot a two Gaussians from a double Gaussian fit

Parameters:

• result_dict – Result of the double Gaussian fitting

• xdata – Independent data

qtt.utilities.visualization.plot_horizontal_line(x: float, color: str = 'c', alpha: float = 0.5, label: str | None = None, ax: Axes | None = None) → Any

Plot vertical alignment line

Parameters:

• x – Position on horizontal axis to plot the line

• color – Color specification of the line

• alpha – Value to use for the transparency of the line

• label – Label for the line

• ax – Matplotlib axis handle to plot to. If None, select the default handle

Returns:

Handle to the plotted line

qtt.utilities.visualization.plot_single_traces(traces: ndarray, time: ndarray | None = None, trace_color: ndarray | None = None, offset: None | bool | float = None, fig: int = 1, maximum_number_of_traces: int = 40)

Plot single traces with offset for separation

Parameters:

• traces – Array with single traces in the rows

• time – Option array for time axis

• trace_color – Specification of trace color

• offset – Offset to use between traces. For None automatically determine the offset

• fig – Specification of Matplotlib window

• maximum_number_of_traces – Maximum number of traces to plot

qtt.utilities.visualization.plot_vertical_line(x: float, color: str = 'c', alpha: float = 0.5, label: str | None = None, ax: Axes | None = None) → Any

Plot vertical alignment line

Parameters:

• x – Position on horizontal axis to plot the line

• color – Color specification of the line

• alpha – Value to use for the transparency of the line

• label – Label for the line

• ax – Matplotlib axis handle to plot to. If None, select the default handle

Returns:

Handle to the plotted line