Welcome to qutil’s documentation!#
qutil / qutech_util#
Long term goal is to gather utility functions here.
The original name was qutil but we included the additional alias qutech_util so you can install it via pipy.
It is not meant as a lightweight package but some heavy dependencies like qcodes are feature gated.
If you don’t have a reason for a lightweight install you should install all features i.e. qutil[complete].
If you just want to use it you can install the latest “released” version via
python -m pip install qutech-util[complete]
However, this package profits from everybody’s work and the releases are infrequent. Please make a development install and contribute your changes. You can do this via
python -m pip install -e git+https://git.rwth-aachen.de/qutech/qutil.git#egg=qutech-util[complete]
This will download the source code (i.e. clone the git repository) into a subdirectory of the ./src argument and link the files into your environment instead of copying them. If you are on windows you can use SourceTree which is a nice GUI for git.
You can specify the source code directory with the --src argument (which needs to be BEFORE -e):
python -m pip install --src some_directory/my_python_source -e git+https://git.rwth-aachen.de/qutech/qutil.git#egg=qutech-util[complete]
If you have already downloaded/cloned the package yourself you can use python -m pip install -e .[complete].
Please file an issue if any of these instructions does not work.
Tests#
There is no plan for writing extensive tests for the code in this package but please try to write proper docstrings for
your functions and include examples in them which can be checked via doctest.
Follow the link for an example for an example :)
You can run the tests either via
python -m pytest --doctest-modules
or to check if everything works for a clean install (requires hatch to be installed)
python -m hatch run test:run
Documentation#
The auto-generated documentation can be found at the Gitlab Pages.
To build the documentation locally, navigate to doc/ and run either
.\make.bat html
(on Windows),
make html
(on Unix), or
sphinx-build -b html source build
Make sure the dependencies are installed via
python -m pip install -e .[doc]
in the top-level directory.
qutil.plotting#
This module contains useful classes and functions surrounding maptlotlib plots.
cycle_plots helps you cycling through many plots with the arrow keys (there are probably much better functions for this out there)
plot_2d_dataframe helps you plot 2d data frames with numeric indices
BlitManager can be used to significantly speed up plotting when certain parts of the plot, such as the artists on the canvas (lines, annotations, etc.) change but others do not (axes, labels, etc.) does not.
CoordClicker and LineClicker allow some interactive selection of data.
get_rwth_color_cycle and the predefined rwth_color_cycle are cycler instances with the official RWTH corporate design colors:
qutil.matlab#
In this module there are functions that are helpful for reading .mat files, especially those created with special measure.
It depends on the optional matlab feature which is included in the complete install.
If you simply want to open a random .mat file you can use hdf5storage.loadmat.
Some functionality requires the matlab engine python interface to work, i.e. python will use a MATLAB instance to open files.
However, the matlab engine interface is not installed by default because the install process depends on the version and fails if MATLAB is not installed.
For older MATLAB versions navigate to $MATLAB_INSTALL_FOLDER/extern/engines/python and execute python setup.py install.
For newer MATLAB versions you can install the engine interface via python -m pip install matlabengine.
Loading matlab files with “newer” MATLAB classes like table requires connecting (and starting) MATLAB instance.
The function load_special_measure_with_matlab_engine can load most special measure scans by utilizing the MATLAB engine interface. To use it you require a “sufficiently new” version of MATLAB and then navigate to C:\Program Files\MATLAB\$VERSION\extern\engines\python and call python setup.py install.
Recommended: There are dataclasses like SimpleScan or VirtualScan that are a python representation of certain common scan
types and have a convenience to_xarray method. Use load_simple_scan_with_matlab_engine or load_virtual_scan_with_matlab_engine to load them.
There are the dataclasses FigureData, AxesData and PlotData that represent matlab figure data. They help inspecting saved matlab figures with the help of a matlab engine.
qutil.const#
This module defines all the constants you could wish for as well as functions to convert temperatures (convert_temperature) or between wavelengths and frequencies (lambda2nu, nu2lambda). For an overview, see the module docstring.
qutil.linalg#
This module provides several handy linear algebra functions. While some are implemented elsewhere, the implementation here is typically speedier for large arrays. For example, pauli_expm exploits the fact that a matrix exponential of Pauli matrices can be written as a cosine times the identity matrix plus a sine times the Paulis to speed up the calculation.
For an overview of the included functions, see the module docstring.
qutil.ui#
This module collects UI helpers, such as a progress bar for loops that can be used like so:
for i in qutil.ui.progressbar(range(n)):
do_something()
qutil.qi#
In this module there are some quantities and functions related to quantum information, like the Pauli matrices in different data types.
qutil.random#
Here we collect functions for random numbers like random_hermitian to generate random Hermitian matrices.
qutil.itertools#
This module contains everything from itertools, more_itertools and custom functions.
qutil.caching#
Here you find decorators, functions and classes that help you implement caching like file_cache and lru_cache. This is helpful if you need to call computationally expensive functions with the same arguments repeatedly.
qutil.io#
User input related functions like query_yes_no or a CsvLogger interface (for reading use pandas.read_csv).
to_global_path resolves all network drive mappings (such as Z:\) as well as domain names
(such as \\janeway) to their global address (\\janeway.physik.rwth-aachen.de in this case).
qutil.parallel#
Functions and classes related to parallel execution i.e. multi-threading, multi-processing and asyncio.
There is a class for periodic callbacks from another thread ThreadedPeriodicCallback.
qutil.hardware#
This package contains little scripts to talk to various hardware devices. For example reading the leak tester via serial interface.
qutil.electronics#
lumped_elements#
Exposes the contents of fastz, a package for simple lumped-elements calculations. Overloads + and // to implement series and parallel connections, respectively.
See the fastz documentation for more information.
qutil.qcodes#
Functions to convert from and to qcodes data sets. Currently only
from pandas.DataFrame to qcodes.data.data_set.DataSet
qutil.measurement#
This package is supposed to contain measurement-related functionality. It is currently empty besides some backward compatibility imports.
spectrometer#
Moved to https://git.rwth-aachen.de/qutech/python-spectrometer.
qutil.typecheck#
Functions and decorators to help with runtime typechecking. Notably the @check_literals decorator to ensure that arguments match an annotated literal.
Imports the typeguard which provides the powerful @typechecked decorator.
from typing import Literal, Sequence
from qutil.typecheck import check_literals
@check_literals
def my_function(a: Sequence[int], b: Literal['forward', 'backward']):
pass # do something
# works
my_function([1, 2, 3], 'backward')
# works because the first arguement is not checked at runtime
my_function({'no': 'sequence'}, 'backward')
# runtime error because of typo in 'backward'
my_function('wrong', 'backwardd')
qutil.pandas_tools#
Pandas utility functions for common code patterns. consecutive_groupby is like pandas.DataFrame.groupby but only
groups consecutive rows.
qutil.image#
Image and video processing tools. convert_tiff converts a multipage .tif image to a video with a format of choice using moviepy.