experiments.vis_pump_ir_probe module

Providing the “UV/VIS Pump IR Probe” experiment. In this experiment a UV/VIS pump pulse is used to excite the sample. An IR probe pulse is used to scan the samples’ response. The UV/VIS stage is used to move to different delay times between each collection of data. Phase cycling is not necessary for UV/VIS pump experiments because the scattered light cannot be observed on the MCT detector. The high intensity and frequency of the pump light can cause the sample to burn or create gas bubbles in the sample. An unnecessarily high exposure of the sample to pump light must thus be avoided. This is achieved by closing the pump shutter when the delay stage is moving. To distribute the exposure to pump light a Lissajous scanner or a different translation device can be used. For this experiment the UV/VIS chopper must be running.

Step by Step Algorithm:

Acquisition:

  1. Preallocate dictionary (data container) which will contain data and information about scan index, delay index etc.

  2. Set the number of samples to acquire to account for weights specified for the current delay as specified in the delay file

  3. Close Shutter

  4. Move to delay

  5. Open Shutter

  6. Read the data from the ADC

  7. Place data into dictionary and hand it over to primary processing

Primary Processing:

  1. Preallocate arrays for data, counts, weights, chopper. Here there are 2 states. On (chopper high) and off (chopper low).

  2. Subtract background from raw data (dark noise)

  3. Linearize response of pixels

  4. Calculate transmission, or more precisely, relative intensity (probe intensity / reference intensity) for each laser shot for each pixel pair

  5. Identify the chopper states for all laser shots using the corresponding channel(s) in the ADCs’ data

  6. Sort the data (transmissions) for each state and calculate statistics

  7. Average the data by weighting equally

  8. Calculate shot to shot difference signal and its statistical properties

  9. Put this information into data container and hand it over to secondary processing

  10. Save data (and raw data) including counts, shot to shot signal, weights and s2s_std if respective checkboxes on GUI were checked.

Secondary Processing:

  1. Calculate the absorption (-log10) of the sorted data

  2. Calculate the pump probe difference signal (chopper high - chopper low)

  3. Calculate the average intensities and standard deviation of the intensities for each pixel

  4. Put this information into data container and hand it over to Pyqtplotting thread

  5. Calculate the numbers which are displayed in the “statistics box” on the GUI

Pyqt Plotting:

  1. Remove old plots

  2. Setup the plot that displays:

    • Pump probe signal of current scan for current delay

    • Average pump probe signal for current delay

    • Time-signal heatmap of current scan

    • Average time-signal heatmap

    • Intensities and their standard deviation (multiplied by 5)

    • Standard deviation of shot to shot signal

  3. Plot the plots for the first time

  4. Update plots

Saving:

programming data dimension:
[(2 ([0] is current average scan, [1] is last single scan), n_delays, n_probe_pixels, n_chopper_states)]

saving dimension:
[(n_probe_pixels, n_chopper_states)]

raw data dimension:
[(n_channels, samples_to_acquire)]

Folder Structure

In scans/dXXX the first file of 5 contains the data in the saving dimensions. The counts file contains the number of times a given state was observed. This should be used as weights when averaging equally weighted. The weights file contains the inverse variances of a given state for all pixels, etc. The dimensions of these files are as the saving dimension suggests. These files can be used to average in different ways to obtain the complete resulting spectrum. Check the actual code of the corresponding experiment to understand how to calculate the desired end result (difference spectrum). The s2s_std file contains the standard deviation of the shot to shot difference spectrum (this was used in the old software as weights to average different scans. However, this method of averaging has ambiguity and it has been proven difficult to reason why it should work when averaging transmissions. It is saved so that the option to revert to this averaging method exists). Note that the dimensionality of the s2s_std file is not as “saving dimension” suggests since it results from difference spectra. This means that except for the probe pixel dimension every other dimension collapses. In addition to the s2s_std, the shot to shot signal is saved now, too. This was implemented because Jan Loeffler discovered that the shot to shot signal has better quality.

The data in /averaged_data is averaged equally weighted (using counts). Likewise to the scan data the difference spectrum still needs to be calculated from the transmissions for every state. Because the array contains the transmissions averaged with counts it is only intended to be used as a first indicator for the measurement.

The /figure folder is currently empty. It is possible to implement plotting of figures which are saved here while the experiment is running. This is however not implemented yet.

The /hardware config folder holds every file which contains hardware configuration parameters. This folder is a compressed copy of the hardware configuration folder in the software’s directory. Here it is possible to look up the ADC configuration to obtain what channel was connected to which hardware element (e.g. chopper - ai78). It is also possible to obtain the R-2R values, the FPAS configuration, the linearization parameters, etc.

The /raw data folder is in the experiments directory only if the “save raw data” checkbox on the GUI was checked. It contains the raw (unedited… as raw as it can get) data. Basically, the voltages which the ADC measured for each channel. The dimensions are as “raw data dimensions” suggests.

The background.npy file is a copy of the most recently collected dark noise background of the MCT detector array. It corrects for dark noise. If no new background was collected before the experiment was started the code will use the latest available background and display a warning in the log.

setupinfo.txt is a ReadMe file that contains the most relevant experimental parameters at first glance. Additionally, the user can decide to write a comment in the readme editor of the GUI. The content of this is written to the notes.txt file.

probe_wn_axis.npy contains the wavenumber axis which is generated by the spectrometer triax.py class.

delays.npy contains the delays including weights.

username/
├── date1_experimentname1_000/
│   ├── averaged_data
│   │   ├──  d000_date1_experimentname1_000.npy
│   │   ├──  ...
│   │   └──  d999_date1_experimentname1_000.npy
│   ├── figures
│   ├── hardware config
│   ├── raw data
│   │   ├──  delay000
│   │   │   ├──  s000000_d000_date1_experimentname1_000_raw.npy
│   │   │   ├──  ...
│   │   │   └──  s000099_d000_date1_experimentname1_000_raw.npy
│   │   ├──  ...
│   │   └──  delay999
│   ├── scans
│   │   ├──  delay000
│   │   │   ├──  s000000_d000_date1_experimentname1_000.npy
│   │   │   ├──  s000000_d000_counts_date1_experimentname1_000.npy
│   │   │   ├──  s000000_d000_s2s_std_date1_experimentname1_000.npy
│   │   │   ├──  s000000_d000_weights_date1_experimentname1_000.npy
│   │   │   ├──  ...
│   │   │   └──  s000099_d000_date1_experimentname1_000.npy
│   │   ├──  ...
│   │   └──  delay999
│   ├── probe_wn_axis_date1_experimentname1_000.npy
│   ├── delays_date1_experimentname1_000.npy
│   ├── setupinfo_date1_experimentname1_000.txt
│   ├── notes_date1_experimentname1_000.txt
│   └── background_date1_experimentname1_000.npy
├── date1_experimentname1_001/
├── date2_experimentname1_000/
└── date2_experimentname2_000/
class Acquisition(delays: numpy.ndarray, adc: analog_digital_converter.AnalogDigitalConverter, delay_stage: pi_control.PiStage, spectrometer: triax.Triax, shutter: shutter.Shutter, acq_queue: multiprocessing.context.BaseContext.Queue)[source]

Bases: threading.Thread

Acquisition class (python multithreaded). This is where the actual experiment is conducted.

This class is used to control the hardware devices required for the experiment. The sole purpose of it is to collect the data according to the parameters specified for the experiment by moving the delay stages, opening and closing shutters etc.

A dictionary which will contain data and other information (scan index etc.) is instantiated here. The acquisition class passes the collected information (raw data, scan index etc.) to the primary processing class.

Note

No data processing beyond what is required to conduct the experiment should be implemented in this class. The rationale behind this is to minimize down time/ maximize laser time. Data processing costs computation time and will, generally speaking, slow down the measurement process because the computer is busy while the rest of the hardware is idle. If implemented correctly the data processing could be carried out while the data acquisition is waiting for all data to become available. But even in this scenario the problem that the data processing takes longer than the acquisition time can occur and is thus best avoided through parallelisation.

The reason why this class is a child of the threading module instead of the multiprocessing module is that to use multiprocessing all objects passed to the function must be picklable. This is not the case for some of the objects interfacing with the hardware (e.g. ADC). In an ideal scenario the acquisition too, would run in its own process seperated from the GUI thread but this would only be possible with major restructuring of the software.

Parameters

  • delays (ndarray) –

    Array containing the delays in fs that are supposed to be measured in the 0th column and their corresponding weights in the 1st column. I.e.: loaded from a delay file which can be generated via the delay file editor.

    • shape: 2D

    • E.g.: (number of delays, 2)

  • adc (ADC) – Analog to digital converter hardware object which is used to communicate with and read data from the ADC.

  • delay_stage (PiStage) – PiStage hardware control object which provides the interface to the delay stage needed for this experiment.

  • spectrometer (Spectrometer) – Spectrometer/Triax hardware class which grants functionality to control the triax spectrometer. Needed to obtain e.g. wavenumber axis etc.

  • shutter (Shutter) – Shutter object that can be used to open and close the UV/VIS (pump) shutter

  • acq_queue (Queue) – Multiprocessing queue object that the acquisition thread uses to pass data to the primary processing process.

run()[source]

Method representing the thread’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.

shutdown()[source]
class PrimaryProcessing(acq_queue: multiprocessing.context.BaseContext.Queue, processing_queue: multiprocessing.context.BaseContext.Queue, delays: numpy.ndarray, pixel_idx: numpy.ndarray, probe_pixel_idx: numpy.ndarray, reference_pixel_idx: numpy.ndarray, index_dict: dict, prl: data_processing.PixelResponseLinearization, chopper_info: dict, background_handler: save_data.Background, saver: Optional[save_data.SaveData] = None)[source]

Bases: multiprocessing.context.Process

Primary processing class (python multiprocess). This class’ purpose is to process the raw data to a state where it can be saved onto the hard drive as npy (binary) files.

This step generally includes linearization, normalisation, sorting and averaging. Besides the actual data additional information that is required for post processing purposes is calculated and saved. I.e. counts and weights. The last step of primary processing should always be to pass the data to secondary processing and save it to the hard disk.

Note

The actual signal(s) are generally not intended to be calculated here. Signals and other information that is supposed to be displayed on the GUI should be calculated in secondary processing. The main reason for this is minimizing the risk of an error leading to a crash of the software which then in turn ruins the measurement. The more code that has to run the more likely a crash becomes. Others reasons mostly imply open questions regarding averaging. Generally, shot to shot normalized intensities that are sorted and averaged by their state are saved (m2 method). From this - for a simple experiment at least - the signal can be easily calculated while offering different choices of averaging in post processing. For more complex experiments e.g. VIPER or time domain experiments the argument of saving sorted transmissions instead of signals is even more compelling. In VIPER experiments more than one signal of interest is present in the different states. Saving each signal separately would actually increase the amount of data that has to be saved. For time domain experiments we want to save the data in the time domain for post processing reasons like zeropadding and apodization.

The goal of primary processing is to make the data as compact as possible while keeping as much information and flexibility as possible. Even if the processes later on crash, the data is secured and can be analysed in post processing.

Parameters

  • acq_queue (Queue) – Multiprocessing queue object that the acquisition thread uses to pass data to the primary processing process.

  • processing_queue (Queue) – Multiprocessing queue object that the primary processing process uses to pass data to the secondary processing process.

  • delays (ndarray) –

    Array containing the delays in fs that are supposed to be measured in the 0th column and their corresponding weights in the 1st column. I.e.: loaded from a delay file which can be generated via the delay file editor.

    • shape: 2D

    • E.g.: (number of delays, 2)

  • pixel_idx (ndarray) –

    Array that contains the indices of the rows in the ADCs’ data that correspond to pixel input channels. These are specified in the “analog input configuration.json” for each laboratory and can be easily accessed with the attribute “pixel_idx” of the ADC.

    • shape: 1D

    • E.g.: (64) or (128)

  • probe_pixel_idx (ndarray) –

    Array that contains the indices of the rows in the ADCs’ data that correspond to probe pixel input channels. These are specified in the “analog input configuration.json” for each laboratory and can be easily accessed with the attribute “probe_pixel_idx” of the ADC. It is highly relevant that the order the pixels are listed in this array match the order of the array in the reference_pixel_idx argument. This means that if reference pixel 3 is listed first in the other array here probe pixel 3 needs to be listed first as well and so on. Otherwise the intensities on the probe array are not going to be normalized correctly. For the plotting to work correctly the pixels also need to be listed in the order of the wavenumber axis array of the spectrometer.

    • shape: 1D

    • E.g.: (32) or (64)

  • reference_pixel_idx (ndarray) –

    Array that contains the indices of the rows in the ADCs’ data that correspond to reference pixel input channels. These are specified in the “analog input configuration.json” for each laboratory and can be easily accessed with the attribute “reference_pixel_idx” of the ADC. It is highly relevant that the order the pixels are listed in this array match the order of the array in the probe_pixel_idx argument. This means that if probe pixel 10 is listed first in the other array here reference pixel 10 needs to be listed first as well and so on. Otherwise the intensities on the probe array are not going to be normalized correctly. For the plotting to work correctly the pixels also need to be listed in the order of the wavenumber axis array of the spectrometer.

    • shape: 1D

    • E.g.: (32) or (64)

  • index_dict (dict) – Dictionary that maps the names of the input channels to their corresponding row in the ADCs’ data as they are specified in the “analog input configuration.json” for each laboratory. I.e.: It contains the information which entries of the ADCs’ data array belong choppers, wobblers etc. This dictionary can be easily accessed with the attribute “index_dict” of the ADC.

  • prl (PRL) – Pixel response linearization object which grants the functionality to linearize raw data according to the linearization parameters specified in the corresponding pixel_linearization_fit_parameters.json file (for each lab).

  • chopper_info (dict) – Contains the information that is necessary to identify the different chopper states of the chopper that chops the pump pulse. It contains the keys “high voltage level” and “name”. “high voltage level” is the voltage read by the ADC when the chopper reference output is high. It is needed as a reference for the digitization function that is used. The “name” key is required to determine to which channel of the adc the chopper is connected and its value needs to match the corresponding key in index_dict.

  • background_handler (Background) – Instance of Background class which can access the most recently collected background. This background is later subtracted from the raw data as dark noise correction.

  • saver (SaveData) – Object that manages saving of data (including counts, weights, probe wavenumber axis etc.) into their respective directories. If None is passed, no data is going to be saved. If the raw data checkbox was checked on the GUI the savers raw data attribute is set to True and the raw data is saved automatically. Defaults to None.

run()[source]

Method to be run in sub-process; can be overridden in sub-class

class PyqtPlotting(widget_pyqtgraph, adc: analog_digital_converter.AnalogDigitalConverter, plot_queue, delays: numpy.ndarray)[source]

Bases: object

Pyqt Plotting class (Qt multithreaded). This class is necessary for displaying plots on the GUI. PyQtGraph is used as the plotting engine. Generally the plots are set up first (type of plot, layout, title etc.). On the first run, the plots are drawn for the first time. Then the plots are updated every iteration. We update the same plot references every time to make it more efficient.

Parameters

  • widget_pyqtgraph (QWidget) – WidgetPyqtgraph object on which the plots are going to be displayed. Has methods for plot manipulation (i.e. removal of plots, autoscale).

  • adc (ADC) – Analog to digital converter hardware object which is used to communicate with and read data from the ADC.

  • plot_queue (Queue) – Multiprocessing queue object that the secondary processing process uses to pass data to the plot thread.

  • delays (ndarray) –

    Array containing the delays in fs that are supposed to be measured in the 0th column and their corresponding weights in the 1st column. I.e.: loaded from a delay file which can be generated via the delay file editor.

    • shape: 2D

    • E.g.: (number of delays, 2)

class Signals[source]

Bases: PyQt5.QtCore.QObject

new_data
run()[source]
update_plot(data_container)[source]
class SecondaryProcessing(processing_queue: multiprocessing.context.BaseContext.Queue, plot_queue: multiprocessing.context.BaseContext.Queue, info_queue: multiprocessing.context.BaseContext.Queue, delays: numpy.ndarray, probe_pixel_idx: numpy.ndarray, saver: Optional[save_data.SaveData] = None)[source]

Bases: multiprocessing.context.Process

Secondary processing class (python multiprocess). This class is used to process the data from the primary processing class such that it can be displayed on the GUI within plots and lineEdits. This generally implies (if applicable):

  • calculation of signals

  • calculation of statistics like standard deviation of intensities and shot to shot standard deviation of signal

  • interpolation for 2D / heatmap plots (see comments in code why this is necessary)

  • Fourier transform and phasing for time domain data

This data is handed over to the plotting thread.

Note

The feature of saving figures/plots to the hard drive should be implemented here if it is needed.

Parameters

  • processing_queue (Queue) – Multiprocessing queue object that the primary processing process uses to pass data to the secondary processing process.

  • plot_queue (Queue) – Multiprocessing queue object that the secondary processing process uses to pass data to the plot thread.

  • info_queue (Queue) – Multiprocessing queue object which is used to transfer/hand over information to lineEdits on GUI. Contains (if applicable) scan index, delay index, interleave index, values for statistics groupBox etc.

  • delays (ndarray) –

    Array containing the delays in fs that are supposed to be measured in the 0th column and their corresponding weights in the 1st column. I.e.: loaded from a delay file which can be generated via the delay file editor.

    • shape: 2D

    • E.g.: (number of delays, 2)

  • probe_pixel_idx (ndarray) –

    Array that contains the indices of the rows in the ADCs’ data that correspond to probe pixel input channels. These are specified in the “analog input configuration.json” for each laboratory and can be easily accessed with the attribute “probe_pixel_idx” of the ADC. It is highly relevant that the order the pixels are listed in this array match the order of the array in the reference_pixel_idx argument. This means that if reference pixel 3 is listed first in the other array here probe pixel 3 needs to be listed first as well and so on. Otherwise the intensities on the probe array are not going to be normalized correctly. For the plotting to work correctly the pixels also need to be listed in the order of the wavenumber axis array of the spectrometer.

    • shape: 1D

    • E.g.: (32) or (64)

  • saver (SaveData) – Object that manages saving of data (including counts, weights, probe wavenumber axis etc.) into their respective directories. If None is passed, no data is going to be saved. If the raw data checkbox was checked on the GUI the savers raw data attribute is set to True and the raw data is saved automatically. Defaults to None.

run()[source]

Method to be run in sub-process; can be overridden in sub-class

class VisPumpIrProbe(widget_pyqtgraph, delays: numpy.ndarray, adc: analog_digital_converter.AnalogDigitalConverter, delay_stage: pi_control.PiStage, shutter: shutter.Shutter, prl: data_processing.PixelResponseLinearization, chopper_info: dict, background_handler: save_data.Background, spectrometer: triax.Triax, info_queue: multiprocessing.context.BaseContext.Queue, saver: Optional[save_data.SaveData] = None)[source]

Bases: object

Parameters

  • widget_pyqtgraph (QWidget) – WidgetPyqtgraph object on which the plots are going to be displayed. Has methods for plot manipulation (i.e. removal of plots, autoscale).

  • delays (ndarray) –

    Array containing the delays in fs that are supposed to be measured in the 0th column and their corresponding weights in the 1st column. I.e.: loaded from a delay file which can be generated via the delay file editor.

    • shape: 2D

    • E.g.: (number of delays, 2)

  • adc (ADC) – Analog to digital converter hardware object which is used to communicate with and read data from the ADC.

  • delay_stage (PiStage) – PiStage hardware control object which provides the interface to the delay stage needed for this experiment.

  • shutter (Shutter) – Shutter object that can be used to open and close the UV/VIS (pump) shutter.

  • prl (PRL) – Pixel response linearization object which grants the functionality to linearize raw data according to the linearization parameters specified in the corresponding pixel_linearization_fit_parameters.json file (for each lab).

  • chopper_info (dict) – Contains the information that is necessary to identify the different chopper states of the chopper that chops the pump pulse. It contains the keys “high voltage level” and “name”. “high voltage level” is the voltage read by the ADC when the chopper reference output is high. It is needed as a reference for the digitization function that is used. The “name” key is required to determine to which channel of the adc the chopper is connected and its value needs to match the corresponding key in index_dict.

  • background_handler (Background) – Instance of Background class which can access the most recently collected background. This background is later subtracted from the raw data as dark noise correction.

  • spectrometer (Spectrometer) – Spectrometer/Triax hardware class which grants functionality to control the triax spectrometer. Needed to obtain e.g. wavenumber axis etc.

  • info_queue (Queue) – Multiprocessing queue object which is used to transfer/hand over information to lineEdits on GUI. Contains (if applicable) scan index, delay index, interleave index, values for statistics groupBox etc.

  • saver (SaveData) – Object that manages saving of data (including counts, weights, probe wavenumber axis etc.) into their respective directories. If None is passed, no data is going to be saved. If the raw data checkbox was checked on the GUI the savers raw data attribute is set to True and the raw data is saved automatically. Defaults to None.

start()[source]