Welcome to pyIMD’s documentation!¶

Evolution of mass over time and the corresponding microscopy images are shown for a time span of 20min. The mass data was acquired every 10 ms (data shown in black), overlaid in red is the rolling mean with a window of 1000. Images taken every 3 min over the observed times span. The mammalian cell increases mass steadily..

The total mass of single cells can be accurately monitored in real time under physiological conditions with our recently developed picobalance. It is a powerful tool to investigate crucial processes in biophysics, cell biology or medicine, such as cell mass regulation. However, processing of the raw data can be challenging, as computation is needed to extract the mass and long-term measurements can generate large amounts of data. Here, we introduce the software package pyIMD that automates raw data processing, particularly when investigating non-migrating cells. pyIMD stands for Python inertial mass determination and is implemented using Python >=3.5 and can be used as a command line tool or as a stand-alone version including a graphical user interface.

This documentation of pyIMD describes the API and provides sample data sets as well as sample scripts to run pyIMD from Jupyter or the Python console. It also contains a tutorial about how pyIMD is used with the user interface.

Installation¶

Stable release¶

As module¶

To install pyIMD, just run this command in your terminal:

$ pip install pyIMD

Installing pyIMD this way ensures that you get always the latest release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

As stand alone executable¶

If you want to install pyIMD on your system without installing Python yourself just download the pre-compiled executable matching your operating system:

Download the latest in one portable for WINDOWS, UNIX, OSX x64 systems

pyIMD can then be used trough its graphical user interface (GUI) directly.

From sources¶

The latest sources for pyIMD can be downloaded from the Github repo.

You can clone the public repository:

$ git clone git://git.gitlab.com/csb.ethz/pyIMD.git

Once you have a copy of the source, navigate into the directory and run:

$ python setup.py install .

Use and Examples¶

The examples show the basic usage of pyIMD to calculate the mass

pyIMD example script¶

This example script demonstrates the simplest interaction with pyIMD:

# /********************************************************************************
# * Copyright © 2018-2019, ETH Zurich, D-BSSE, Andreas P. Cuny & Gotthold Fläschner
# * All rights reserved. This program and the accompanying materials
# * are made available under the terms of the GNU Public License v3.0
# * which accompanies this distribution, and is available at
# * http://www.gnu.org/licenses/gpl
# *
# * Contributors:
# *     Andreas P. Cuny - initial API and implementation
# *******************************************************************************/

from pyIMD.imd import InertialMassDetermination

# Create the inertial mass determination object
imd = InertialMassDetermination()

# Create a config file for the project / experiment to analyze using default values. Note non default parameters can be
# added as optional arguments for e.g. spring_constant = 5.
file_path1 = "/pyIMD/examples/data/pll/20170712_RSN_3_B"
file_path2 = "/pyIMD/examples/data/pll/20170712_RSN_3_A"
file_path3 = "/pyIMD/examples/data/pll/20170712_RSN_3_A_long_term.tdms"
imd.create_pyimd_project(file_path1, file_path2, file_path3, '\t', 23, 'PLL', figure_width=5.4, figure_height=9.35,
                         initial_parameter_guess=[73.0, 5.2, 0.0, 0.0], upper_parameter_bounds=[100.0, 7.0, 3.0, 3.0],
                         spring_constant=8.0, cell_position=9.5, cantilever_length=100.0, figure_format='pdf')

# Print the config file to the console to check if all parameters are set correctly before starting the calculation.
imd.print_pyimd_project()

# If one needs to change a parameter on the fly just type: imd.settings.<parameter_key> = value as eg.
# imd.settings.figure_resolution_dpi = 300. Note: Just hit imd.settings. + TAB to get automatically a list of all
# available <parameter_keys>

# To enter all the parameters one can also start the settings user interface and enter all the parameter values there.
# imd.show_settings_dialog()

# Run the inertial mass determination
imd.run_inertial_mass_determination()

# Save the config file for the project / experiment for documentation purpose or to re-run with different /
# same parameter later
imd.save_pyimd_project("/pyIMD/examples/data/show_case/pyIMDProjectName.xml")

# To load an existing project type
imd.load_pyimd_project("/pyIMD/examples/data/show_case/pyIMDProjectName.xml")
# change a parameter i.e
imd.settings.figure_format = 'png'
# and run again
imd.run_inertial_mass_determination()

Download example pyIMD script.

pyIMD example IPython/Jupyter notebook¶

[11]:

from pyIMD.imd import InertialMassDetermination

[12]:

imd = InertialMassDetermination()

2019-03-24 22:05:11 - pyIMD.imd - Object constructed successfully

[13]:

file_path1 = "../data/pll/20170712_RSN_3_B"
file_path2 = "../data/pll/20170712_RSN_3_A"
file_path3 = "../data/pll/20170712_RSN_3_A_long_term.tdms"

[14]:

imd.create_pyimd_project(file_path1, file_path2, file_path3, '\t', 23, 'PLL', figure_height=40,
                         figure_unit='cm',initial_parameter_guess=[70.0, 2, 0.0, 0.0],
                         upper_parameter_bounds=[100.0, 5, 3.0, 3.0], spring_constant=1.05,
                         cell_position=3.23, cantilever_length=59.84, figure_format='png',
                         correct_for_frequency_offset=True, frequency_offset_n_measurements_used=15)

[15]:

imd.run_inertial_mass_determination()

2019-03-24 22:05:12 - pyIMD.imd - Start reading all files
2019-03-24 22:06:32 - pyIMD.imd - Done reading all files
2019-03-24 22:06:32 - pyIMD.imd - Done converting units
2019-03-24 22:06:33 - pyIMD.imd - Done with pre start no cell resonance frequency calculation
2019-03-24 22:06:34 - pyIMD.imd - Done with pre start with cell resonance frequency calculation
2019-03-24 22:06:36 - pyIMD.imd - Done with pre start frequency shift figure generation
2019-03-24 22:06:36 - pyIMD.imd - Offset calculation result: -0.10173519457150339
100%|██████████| 692625/692625 [00:21<00:00, 32255.79it/s]
2019-03-24 22:06:57 - pyIMD.imd - Start writing figure to disk
2019-03-24 22:08:50 - pyIMD.imd - Done writing figure to disk
2019-03-24 22:08:50 - pyIMD.imd - Start writing data to disk
2019-03-24 22:08:54 - pyIMD.imd - Done writing data to disk
2019-03-24 22:08:54 - pyIMD.imd - Done with all calculations

The run_intertial_mass_determination() method generates automatically figures of the curve fitting for the per pre experiment data with and without a cell attached to the cantilever. A combined figure illustrating the shift in the phase response and the function fits and the resulting calculated cell mass of the long term measurement data.

Show resulting plots¶

Shift in the phase response with and without cell attached to the cantilever and the corresponding function fit
Final result of the mass calculation for the long term data set

[ ]:

pyIMD tutorial with user interface¶

Before starting, make sure pyIMD is installed

This tutorial provides a simple example with a test dataset, teaching step by step how to:

create a pyIMD project

calculate the mass form the measured data

The layout of the following windows and the paths are set for windows and might differ for Mac or Unix. First, lets have a look at the input data. The typical data set consists of 3 files: 1) a sweep file of the cantilever WITHOUT cell (text file with multi-line header) 2) a sweep file of the cantilever WITH cell (text file with multi-line header) and 3) the actual (long-term) measurement file, which is either a text file or TDMS file (lab-view specific file type). A typical time resolution is 10 ms for the data acquisition so these files can be quite large. Fig. 1 visualizes the data input which can be found as example data set for download and testing.

Figure 1: Data format for pyIMD. pyIMD supports data from picobalance device controllers. (Cytomass and Nanonis)

The example pyIMD script section demonstrates how a pyIMD project is created on the console:

from pyIMD.imd import InertialMassDetermination

# Create the inertial mass determination object
imd = InertialMassDetermination()

# Create a config file for the project / experiment to analyze using default values. Note non default parameters can be
# added as optional arguments for e.g. cell_position = 9.5.
file_path1 = "/pyIMD/examples/data/show_case/0190110_ShowCase_PLL_B.txt"
file_path2 = "/pyIMD/examples/data/show_case/20190110_ShowCase_PLL_A.txt"
file_path3 = "/pyIMD/examples/data/show_case/20190110_ShowCase_PLL_LongTerm.txt"
imd.create_pyimd_project(file_path1, file_path2, file_path3, '\t', 23, 'PLL', figure_width=16.5, figure_height=20,
                         initial_parameter_guess=[60.0, 2.0, 0.0, 0.0], cell_position=9.5, figure_format='pdf')

When using pyIMD through its user interface (UI) in the stand alone mode, the pyIMD project is created in exact the same way in the background. Yet, the user does not need to take care to type the paths or arguments correctly as all the input entered trough the UI will be validated automatically. Fig. 2 shows the main window and the settings window of the pyIMD application. A new pyIMD project is created by selecting the three data files required for the calculation from a directory (3). Next, it needs to be declared which measurement each file contains and what the measurement mode is (5). Using the menu (1), opens the settings dialog and lets you determine all project related parameters such as the names of the output figures. After all settings are set, the mass calculation is started with (6).

The tools menu in Fig. 2 (7) allows for data concatenation from multiple files into a single one, in case the data was acquired with the Nanonis data logger. The resulting file can then be loaded as mentioned above along with the before and after cell attachment file.

# Run the inertial mass determination
imd.run_inertial_mass_determination()

The console (8) logs all actions performed with the UI and indicates when all calculations are done. The results can be viewed in the results tab (2), where as all the output figures are listed as well as the data can be inspected.

Figure 2: . Through the menu bar (1) the pyIMD project can be loaded, saved, and the settings and parameter dialog opened (shown at the right-hand side). The help menu contains the software documentation, the quick help (also shown during startup), change log and information about the software dependencies and authors. The tabs (2) allow to switch between single calculation, batch calculation, and results. After all calculations are done the results tab is enabled and shows the latest result figures and data table in (7). (3) Creates a new pyIMD project while selecting at least three data files required for the calculation. After the files have been selected, it needs to be declared which type of data they contain, i.e. whether it is the single reference measurement of the cantilever without cell or the reference measurement with cell or the time resolved data (4). (5) Sets the acquisition mode that was used to collect the experimental long-term data. (6) Starts the mass calculation. If the batch processing is selected in (2) one or multiple pyIMD project files can be loaded, which will be run sequentially in different threads. With the settings dialog on the right, all the required parameters needed for the calculation as well as the output file formats or file names are set. The user input is validated live and if a parameter of a wrong type is entered, the input field turns yellow to notify the user of the mistake. When the user has inserted all necessary parameters correctly and started the calculation, a process is reported in the info window (8), and finally the result is shown in the main window.

The first output created by pyIMD are control figures visualizing the fit of the cantilevers phase response is shown for the case with and without cell (Fig. 3). The shift towards lower frequencies can be clearly seen, when the cell is attached. Moreover, the Q-factor changes and therefore the slope of the response curve. If the fits are not fitting the raw data the parameter ‘initial_parameter_guess’, ‘lower_parameter_bounds’, ‘upper_parameter_bounds’ need to be adjusted in the settings dialog.

Figure 3: Frequency vs cantilever phase response

The analysis output by the software is shown in Fig. 4, the exemplary data for a mammalian cell is provided for download. The evolution of mass vs time is shown for a time span of 20 min. The mass data was acquired every 10 ms (data shown in black), overlaid in red is the rolling mean with a window of 1000 (adjustable parameter ‘rolling_window_size’). Images taken every 3 min over the observed time span, we see on average a steady increase of the cell mass, the spring constant is 8 N/m (adjustable parameter ‘sprint_constant’). The position of the cell projected along the long axis of the cantilever was 9.5 um (adjustable parameter, ‘cell_position’) and did not change, which is of importance for the current use of the software.

Figure 4: Evolution of mass over time

The project can either be re-run with different parameters, to i.e. improve the function fits or be saved using the menu (Fig. 2, (1)).

# save a pyIMD project
imd.save_pyimd_project("/pyIMD/examples/data/show_case/pyIMDShowCaseProject.xml")

A previously saved project can be loaded again at a later time from the menu (Fig. 2, (1))or also from the command line without the user interface:

# load a pyIMD project
imd.load_pyimd_project("/pyIMD/examples/data/show_case/pyIMDShowCaseProject.xml")

pyIMD example script Nanonis long term¶

This example script demonstrates the command line interface use with pyIMD and Nanonis long term type of data:

# /********************************************************************************
# * Copyright © 2018-2019, ETH Zurich, D-BSSE, Andreas P. Cuny & Gotthold Fläschner
# * All rights reserved. This program and the accompanying materials
# * are made available under the terms of the GNU Public License v3.0
# * which accompanies this distribution, and is available at
# * http://www.gnu.org/licenses/gpl
# *
# * Contributors:
# *     Andreas P. Cuny - initial API and implementation
# *******************************************************************************/

from pyIMD.imd import InertialMassDetermination

# Create the inertial mass determination object
imd = InertialMassDetermination()

# Create a config file for the project / experiment to analyze using default values. Note non default parameters can be
# added as optional arguments for e.g. spring_constant = 5.
file_path1 = "/pyIMD/examples/data/nanonis_long_term/20190510_LC_05_B001.dat"
file_path2 = "/pyIMD/examples/data/nanonis_long_term/20190510_LC_05_A001.dat"
file_path3 = "/pyIMD/examples/data/nanonis_long_term/20190510_LC_05_Longterm001.dat"
imd.create_pyimd_project(file_path1, file_path2, file_path3, '\t', 23, 'PLL', figure_width=5.4, figure_height=9.35,
                         initial_parameter_guess=[73.0, 5.2, 0.0, 0.0], upper_parameter_bounds=[100.0, 8.0, 3.0, 3.0],
                         spring_constant=8.0, cell_position=10, cantilever_length=100.0)

# Print the config file to the console to check if all parameters are set correctly before starting the calculation.
imd.print_pyimd_project()

# Save the config file for the project / experiment for documentation purpose or to re-run with different /
# same parameter later
imd.save_pyimd_project("/pyIMD/examples/data/nanonis_long_term/pyIMDProjectName.xml")

# Run the inertial mass determination
imd.run_inertial_mass_determination()

Download example script Nanonis long term.

API Reference¶

analysis¶

pyIMD.analysis.curve_fit.fit_function(x, fn, q, a, b)¶

Defines the phase response of a damped harmonic oscillator (i.e. the cantilever with or without cell). It is called from calculate_resonance_frequencies, to be fitted to the data primarily to extract the natural resonance frequency.

Parameters:	x (float) – Frequency (the independent variable of that function) fn (float) – Natural resonance frequency q (float) – Q factor (losses) a (float) – Linear factor accounting for a linear background b (float) – Constant Phase-Offset
Returns:	Returns the phase.
Return type:	phase (float)

pyIMD.analysis.calculations.calculate_center_of_mass(polygon_vertices)¶

Calculates the center of mass for a polygon defined by a list of vertices. Based on formulas taken from https://en.wikipedia.org/wiki/Centroid

Parameters:	polygon_vertices ('int') – List of polygon vertices where as each vertex contains an X and Y coordinate.
Returns:	X and Y coordinate of the polygon center of mass
Return type:	center_of_mass (‘int’)

pyIMD.analysis.calculations.calculate_mass(spring_constant, res_freq_after_cell_load, res_freq_before_cell_load)¶

Calculates the mass given the spring constant of the cantilever and the resonance frequency without and with cell attached to the cantilever.

Args:

spring_constant (float): Stiffness of the cantilever [in N/m] res_freq_after_cell_load (float): Resonance frequency of the cantilever AFTER the cell is picked up, at time point t [in kHz] res_freq_before_cell_load (float): Resonance frequency of the cantilever BEFORE the cell is picked up [in kHz]

Returns: mass (float): Returns data as float, which is the mass at time point t.

pyIMD.analysis.calculations.calculate_position_correction(cell_position, cantilever_length)¶

Calculates the correction factor with which the measured mass needs to be multiplied to get all the mass present on the cantilever. This is needed as the cantilever is differently sensitive to mass, depending on the location where this mass is attached. The measurements are performed with the first mode of vibration, which is described by the factor kL = 1.875. For higher modes, different would be used (4.694 for the second , 7.855 for the third etc.)

Parameters:	cell_position (float) – Cell position from the free end of the cantilever [in micrometer] cantilever_length (float) – Cantilever length [in micrometer]
Returns:	Returns a double which is the correction factor.
Return type:	correction_factor (float)

pyIMD.analysis.calculations.calculate_resonance_frequencies(frequency_array, phase_array, initial_param_guess, lower_param_bounds, upper_param_bounds)¶

Calculate_resonance_frequencies calculates the resonance frequency from input frequency and phase array. It does so via fitting the phase response of a harmonic oscillator (defined in pyIMD.analysis.curve_fit). The first fit parameter of the fit parameter array is the resonance frequency.

Parameters:	frequency_array (float array) – Array of frequencies [in kHz] phase_array (float array) – Array of phase [in Rad] initial_param_guess (float) – Initial parameter guess (1x4 array) lower_param_bounds (float) – Lower bounds (1x4 array) upper_param_bounds (float) – Upper bounds (1x4 array)
Returns:	Resonance frequency [in kHz]
Return type:	resonance_frequency (float)
Returns:	Curve fit parameters curve_fit_parameter[0] := Q factor (losses) curve_fit_parameter[1] := Linear factor accounting for a linear background curve_fit_parameter[2] := Offset of the background
Return type:	curve_fit_parameter (float array)

configuration¶

io¶

pyIMD.io.read_from_disk.read_from_dat(file, delimiter)¶

Method to read data from dat files (i.e from Nanonis software).

Parameters:	file (str) – File path + file name. delimiter (str) – Delimiter used in the data file to separate columns
Returns:	Returns data structured in a pandas data frame.
Return type:	data (pandas data frame)

pyIMD.io.read_from_disk.read_from_file(file, delimiter, header=0)¶

Method to read data from a file.

Parameters:	file (str) – File path + file name to a .TDMS or .txt file. delimiter (str) – Delimiter used in the data file to separate columns header (int) – True if file has a header. False otherwise
Returns:	Returns data structured in a pandas data frame.
Return type:	data (pandas data frame)

pyIMD.io.read_from_disk.read_from_tdms(file)¶

Method to read data from National Instruments technical data management streaming files (TDMS).

Parameters:	file (str) – File path + file name string.
Returns:	Returns data structured in a pandas data frame.
Return type:	data (pandas data frame)

pyIMD.io.read_from_disk.read_from_text(file, delimiter, read_from_row, header=0)¶

Method to read data from text files.

Parameters:	file (str) – File path + file name. delimiter (str) – Delimiter used in the data file to separate columns read_from_row (int, None) – Row number from where to start reading data to be able to skip heading text rows. Make sure that you keep the Frequency, Amplitude and Phase headers. header (bool) – True if file has a header. False otherwise
Returns:	Returns data structured in a pandas data frame.
Return type:	data (pandas data frame)

pyIMD.io.read_from_disk.read_tdms_metadata(file)¶

Method to read metadata from National Instruments technical data management streaming files (TDMS).

Parameters:	file (str) – File path + file name string.
Returns:	Returns metadata structured in groups.
Return type:	data (pandas data frame)

pyIMD.io.write_to_disk.write_concat_data(directory, delimiter, time_interval)¶

Method to write concatenate data from single dat files (i.e data logger from Nanonis software).

Parameters:	directory (str) – Directory containing files to concatenate. delimiter (str) – Delimiter to be used in the data file to separate columns. time_interval (int) – Measurement time interval in milliseconds.
Returns:	Writes concatenated data to single .csv file.
Return type:	file (void)

pyIMD.io.write_to_disk.write_to_disk_as(file_format, plot_object, file, **kwargs)¶

Method to write figures in various file formats

Keyword Arguments:
Parameters:	file_format (str) – File format identifier i.e. png or pdf plot_object (ggplot object) – ggplot object file (str) – File path + file name of the figure to save
	width (int) – Figure width (optional) height (int) – Figure height (optional) units ('str`) – Figure units (optional) ‘in’, ‘mm’ or ‘cm’ resolution (int) – Figure resolution in dots per inch [dpi] (optional)
Returns:	Writes figure to disk in the respective file format
Return type:	file (void)

pyIMD.io.write_to_disk.write_to_pdf(plot_object, file, **kwargs)¶

Method to write figures in pdf format to current directory

Keyword Arguments:
Parameters:	plot_object (ggplot object) – ggplot object file (str) – File path + file name of figure to save
	width (int) – Figure width (optional) height (int) – Figure height (optional) units ('str`) – Figure units (optional) ‘in’, ‘mm’ or ‘cm’ resolution (int) – Figure resolution in dots per inch [dpi] (optional)
Returns:	Writes figure to disk as pdf
Return type:	pdf file (void)

pyIMD.io.write_to_disk.write_to_png(plot_object, file, **kwargs)¶

Method to write figures in png format to current directory

Keyword Arguments:
Parameters:	plot_object (ggplot obj) – ggplot object file (str) – File path + file name of the figure to save
	width (int) – Figure width (optional) height (int) – Figure height (optional) units (str) – Figure units (optional) ‘in’, ‘mm’ or ‘cm’ resolution (int) – Figure resolution in dots per inch [dpi] (optional)
Returns:	Writes figure to disk as png
Return type:	png file (void)

plotting¶

pyIMD.plotting.figures.create_montage_array(img_stack, size)¶

Creates an image montage of a 3D numpy array with the shape [image frames, image row, image col] for the specified size.

Parameters:	img_stack (3D numpy array) – 3D numpy image array [image row, image col, image frames]. size (numpy array) – Array specifying the amount of images displayed in the montage per row and column. If one argument is replaced with np.nan, the needed amount of rows or columns is calculated automatically. E. g. [5, np.nan]
Returns:	2D numpy array with the image montage
Return type:	montage (2D numpy array)

pyIMD.plotting.figures.get_montage_array_size(size, image_row_count, image_col_count, frame_count)¶

Calculates the final size of a numpy array needed to hold a the number of specified image frames given the row and column count of the final array.

Parameters:	size (numpy array) – Array specifying the amount of images displayed in the montage per row and column. If one argument is replaced with np.nan, the needed amount of rows or columns is calculated automatically. E. g. [5, np.nan] image_row_count (int) – Number of rows per image image_col_count (int) – Number of columns per image frame_count (int) – Number of image frames in the stack
Returns:	Array with the number of rows and columns needed in the montage array for the images
Return type:	montage_size (numpy array)

pyIMD.plotting.figures.plot_fitting(x, y, resonance_frequency, parameter)¶

Plots the phase response and the corresponding fit of the harmonic damped oscillator.

Parameters:	x (float array) – X coordinates (frequency in kHz) y (float array) – Y coordinates (phase in radians) resonance_frequency (float array) – Resonance frequency given by the fit of x and y parameter (float array) – Others parameters of function fit (Q factor, offset, linear background)
Returns:	Returns a ggplot object
Return type:	p (ggplot object)

pyIMD.plotting.figures.plot_mass(calculated_cell_mass, plot_every_nth_point)¶

Plots the resulting mass

Parameters:	calculated_cell_mass (pandas data frame) – Pandas data frame [Nx3] with time and calculated cell mass and rolling mean averaged cell mass plot_every_nth_point (int) – If 1 all data points are plotted. Otherwise every nth data point is used for plotting.
Returns:	Returns a ggplot plot object
Return type:	p (ggplot object)

pyIMD.plotting.figures.plot_response_shift(x, y, resonance_frequency_without, parameter_without, xx, yy, resonance_frequency_with, parameter)¶

Plots the phase response of pre start data without and with cell attached to cantilever with the respective function fit.

Parameters:	x (float array) – X coordinates w/o cell (frequency in kHz) y (float array) – Y coordinates w/o cell (phase in radians) xx (float array) – X coordinates w/ cell(frequency in kHz) yy (float array) – Y coordinates w/ cell (phase in radians) resonance_frequency_without (float array) – Resonance frequency given by the fit of x and y w/o cell resonance_frequency_with (float array) – Resonance frequency given by the fit of x and y w/ cell parameter (float array) – Others parameters of function fit (Q factor, offset, linear background) w/o cell parameter_without (float array) – Others parameters of function fit (Q factor, offset, linear background) w/ cell
Returns:	Returns a ggplot object
Return type:	p (ggplot object)

ui¶

class pyIMD.ui.settings.SettingsDialog(settings_dictionary)¶

Bases: PyQt5.QtWidgets.QDialog

Settings QDialog user interface implementation.

check_state()¶

Live validation if parameters entered by user are valid.

Returns:	Returns color formatter validator state.
Return type:	sender (obj)

close_settings_dialog()¶

Close the settings UI dialog without saving changes made on parameters

Returns:	None.
Return type:	Null (void)

commit_parameters()¶

Saves changes on parameters.

Returns:	Returns the changed parameters as dictionary.
Return type:	Parameters (dict)

find_checked_radiobutton()¶

Find the checked radiobutton

Returns:	Returns the name of the selected radio button.
Return type:	selected radio (str)

on_frequency_offset_mode_auto(checked)¶

Enables the auto offset mode fields

Parameters:	checked (bool) – Boolean enabling or disabling the frequency offset spin
Returns:	None
Return type:	Null (void)

on_frequency_offset_mode_manual(checked)¶

Enables the manual offset mode fields

Parameters:	checked (bool) – Boolean enabling or disabling the frequency offset field
Returns:	None
Return type:	Null (void)

on_toggle_frequency_offset(state)¶

Enables or disables the frequency offset optional parameters

Parameters:	state (int) – State enabling or disabling the frequency offset correction
Returns:	None
Return type:	Null (void)

print_to_console(text)¶

Print changes to console

Parameters:	text (str) – Text to print to the console
Returns:	Prints message to console.
Return type:	Message (str)

send_to_console_signal¶

pyqtSignal sends message to console

Returns:	Status message to be send to console.
Return type:	message (str)

set_defaults()¶

Set parameters default values to user interface.

Returns:	None
Return type:	Null (void)

set_values()¶

Set parameter values to user interface.

Returns:	None
Return type:	Null (void)

settings_has_changed_signal¶

pyqtSignal sends dictionary with all settings

Returns:	Dictionary with settings.
Return type:	settings (dict)

imd¶

Authors¶

Andreas P. Cuny <andreas.cuny@bsse.ethz.ch>
Gotthold Fläschner <gotthold.flaeschner@bsse.ethz.ch>

How to cite¶

If you use pyIMD in your academic work we would appreciate if you cite us. To do so please use:

@article{Cuny2019,
    title = {pyIMD: Automated analysis of inertial mass measurements of single cells},
    journal = {SoftwareX},
    volume = {10},
    pages = {100303},
    year = {2019},
    issn = {2352-7110},
    doi = {https://doi.org/10.1016/j.softx.2019.100303},
    url = {https://www.sciencedirect.com/science/article/pii/S2352711019300871},
    author = {Andreas P. Cuny and David Martínez-Martín and Gotthold Fläschner},
    keywords = {Single cell, Mass, Picobalance, Oscillators},
    abstract = {The total mass of single cells can be accurately monitored in real time under physiological conditions with our recently developed picobalance. It is a powerful tool to investigate crucial processes in biophysics, cell biology or medicine, such as cell growth or hydration dynamics. However, processing of the raw data can be challenging, as computation is needed to extract the mass and long-term measurements can generate large amounts of data. Here, we introduce the software package pyIMD that automates raw data processing, particularly when investigating non-migrating cells. pyIMD is implemented in Python and can be used as a command line tool or as a stand-alone version including a graphical user interface.}
    }

License¶

pyIMD is released under the GPL v3 license:

GNU GENERAL PUBLIC LICENSE¶

Version 3, 29 June 2007

Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

### Preamble

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### TERMS AND CONDITIONS

#### 0. Definitions.

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#### 2. Basic Permissions.

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#### 4. Conveying Verbatim Copies.

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#### 5. Conveying Modified Source Versions.

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#### 6. Conveying Non-Source Forms.

You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:

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#### 7. Additional Terms.

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#### 8. Termination.

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#### 9. Acceptance Not Required for Having Copies.

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#### 10. Automatic Licensing of Downstream Recipients.

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#### 11. Patents.

A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor’s “contributor version”.

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A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.

Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

#### 12. No Surrender of Others’ Freedom.

If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

#### 13. Use with the GNU Affero General Public License.

Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

#### 14. Revised Versions of this License.

The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.

Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.

If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.

Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.

#### 15. Disclaimer of Warranty.

THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

#### 16. Limitation of Liability.

IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

#### 17. Interpretation of Sections 15 and 16.

If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.

END OF TERMS AND CONDITIONS

### How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

<one line to give the program’s name and a brief idea of what it does.> Copyright (C) <year> <name of author>

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

<program> Copyright (C) <year> <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type `show w’. This is free software, and you are welcome to redistribute it under certain conditions; type `show c’ for details.

The hypothetical commands `show w’ and `show c’ should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.

You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <https://www.gnu.org/licenses/>.

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <https://www.gnu.org/licenses/why-not-lgpl.html>.

References¶

[1]	Martinez-Martin D., et al., Inertial picobalance reveals fast mass fluctuations in mammalian cells. Nature, 2017. 550(7677): p. 500+. https://doi.org/10.1038/nature24288.

[2]	Martinez-Martin D., Alsteens D., Müller D.J., Martin S. and Fläschner G., Top-cover for a controlled environmental system, top-cover set and controlled environmental system compatible with probe based techniques and procedure to control the environment for a sample. WO/2017/012708.

[3]	Martinez-Martin D., Müller D.J., Martin S., Gerber C. and Bircher B., Measuring device and method for determining mass and/or mechanical properties of a biological system. WO/2015/120992.

[4]	Sarid D., Scanning Force Microscopy: With Applications to Electric, Magnetic, and Atomic Forces. Oxford University Press, 1991.

[5]	Sader J.E., et al., Method for the Calibration of Atomic-Force Microscope Cantilevers. Review of Scientific Instruments, 1995. 66(7): p. 3789-3798. https://doi.org/10.1063/1.1145439.

[6]	Hutter, J.L. and Bechhoefer J., Calibration of Atomic-Force Microscope Tips. Review of Scientific Instruments, 1993. 64(11): p. 3342-3342, https://doi.org/10.1063/1.1143970.

[7]	Bircher B.A., et al., Real-Time Viscosity and Mass Density Sensors Requiring Microliter Sample Volume Based on Nanomechanical Resonators. Analytical Chemistry, 2013. 85(18): p. 8676-8683. https://doi.org/10.1021/ac4014918.

[8]	Martinez-Martin D., Müller D.J. and Fläschner G., Microcantilever. US16038250A1.

[9]	Dohn S., et al., Mass and position determination of attached particles on cantilever based mass sensors. Review of Scientific Instruments, 2007. 78(10). https://doi.org/10.1063/1.2804074.