"""
Providing the "Show VIPER" experiment. This experiment is used to adjust
the setup for a VIPER measurement. It displays the VIPER signal as well
as the four additional types of signals that can be calculated from this
type of experiment. For this experiment, the UV/VIS and IR chopper must
be running at different frequencies.
VIPER stands for Vibrationally Promoted Electronic Resonance. Frequency
domain VIPER experiments use 2 choppers. One chopper chops the IR Pump,
the second one chops the UV/VIS Pump. The IR pump excites the molecules
to a higher vibrational state. The UV/VIS pump pulse then excites the
molecules from that higher vibrational state to an excited electronic
state that is also vibrationally excited. An IR probe pulse is used to
measure the changes induced in the molecule.
VIPER measurements typically contain 4 different types of signals. These
are listed according to their chopper states:
* (IR off, UV off): Background
* (IR off, UV on): TRIR + Background
* (IR on, UV off): IR pump/IR probe + Background
* (IR on, UV on): VIPER + TRIR + IR pump/IR probe + Background
TRIR stands for transient IR signal.
Note:
**Chopper:**
It is necessary to adjust the chopper phases. The light pulse
travels through a different point of the chopper blade than the
reference of the chopper itself. This results in a phase shift
between the laser and the position on the chopper where the
light passes through. To adjust for that it is necessary to
carefully set the phase in such a way that the pulse or part of
it is not cut of by the chopper blade. (Also make sure that the
diameter of the pulse physically fits through the holes in the
chopper blade.) It is also necessary to make sure that a HIGH
signal is sent to the ADC when the laser pulse was able to pass
through the chopper and a LOW signal is sent to the ADC when
when the chopper blade was blocking the light. This adjustment
is done via external electronics. For this a circuit that delays
the TTL output of the chopper is used. For some kind of choppers
additional electronics are required to make the chopper run at
the desired frequency (frequency divider). For this experiment
the IR Chopper typically runs at 1/4 and the UV/VIS Chopper on
1/2 of the laser frequency.
**Choppers at low frequencies:**
Sometimes when turning the chopper on and off, the choppers'
phase moves relative to the laser. In the case where the
frequency is 1/2 of the laser frequency, this might cause the
sign of the observed signal to be flipped. When reducing the
frequency further, which is necessary for VIPER measurements,
the phase can be out of sync in more than just steps of 180°.
Additionally, the duty cycle of the TTL that the delay
electronics outputs need to be adapted to the correct length
(50%). The two facts can be illustrated with the following
examples:
Incorrect duty cycle:
.. code-block::
Pump light: 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0
Chopper output: H H H L L L L L H H H L L L L L
Incorrect phase:
.. code-block::
Pump light: 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0
Chopper output: L H H H H L L L L H H H H L L L
If chopper output is not set correctly the signal amplitude that
is displayed on the GUI is going to be lower than it actually
is. Always correct the duty cycle first and then adjust the
phase. The duty cycle is adjusted the most easily by connecting
the delayed TTL to an oscilloscope and measuring the duty cycle.
The phase is set correctly when the signal has the correct sign
and the maximum amplitude. We recommend doing this with the
respective show signal for each of the choppers, as it displays
only what is currently being adjusted instead of this
experiment.
The same problem but with an extra layer of complexity arises
when using the wobbler.
Note that alternatively this could be remedied by a different
method for referencing the chopper. E.g.: Using a photodiode
plus additional electronics to reference the laser light
directly or using a light barrier to reference the position on
the chopper where the laser passes through.
#######################
Step by Step Algorithm:
#######################
**Acquisition:**
1. Preallocate dictionary (data container) which will contain data
and information about scan index, delay index etc.
2. Read the data from the ADC
3. 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 for each chopper
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 for both choppers
6. Sort the data (transmissions) for each state and calculate
statistics
7. Put this information into data container and hand it over to
secondary processing
8. Average the data by weighting equally
9. Save data (and raw data) including counts and weights
if respective checkboxes on GUI were checked
**Secondary Processing:**
1. Calculate the absorption (-log10) of the sorted data
2. Calculate all difference signals:
* (IR off, UV off): Background
* (IR off, UV on): TRIR + Background
* (IR on, UV off): IR pump/IR probe + Background
* (IR on, UV on): VIPER + TRIR + IR pump/IR probe + Background
subtract them in the proper way to obtain:
* TRIR: (IR off, UV on) - (IR off, UV off)
* Pseudo TRIR: (IR on, UV on) - (IR on, UV off)
* IR Pump: (IR on, UV off) - (IR off, UV off)
* Pseudo IR Pump:(IR on, UV on) - (IR off, UV on)
* VIPER signal: (IR on, UV on) - (IR on, UV off)
- (IR off, UV on) + (IR off, UV off)
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:
* All four additional signals
* VIPER signal
* Standard deviation of shot to shot signal
* Intensities and their standard deviation (multiplied by 5)
3. Plot the plots for the first time
4. Update plots.
**Saving:**
.. code-block::
programming data dimension:
[(2 ([0] is current average scan, [1] is last single scan), n_probe_pixels, n_ir_chopper_states, n_vis_chopper_states)]
saving dimension:
[(n_probe_pixels, n_ir_chopper_states, n_vis_chopper_states)]
raw data dimension:
[(n_channels, samples_to_acquire)]
################
Folder Structure
################
In **scans/** the first file of 3 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 in order to obtain the complete resulting spectrum. Check
the actual code of the corresponding experiment to understand how to
calculate the desired end result (VIPER spectrum).
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.
.. code-block::
username/
├── date1_experimentname1_000/
│ ├── averaged_data
│ │ └── date1_experimentname1_000.npy
│ ├── figures
│ ├── hardware config
│ ├── raw data
│ │ ├── s000000_date1_experimentname1_000_raw.npy
│ │ ├── ...
│ │ └── s000099_date1_experimentname1_000_raw.npy
│ ├── scans
│ │ ├── s000000_date1_experimentname1_000.npy
│ │ ├── s000000_counts_date1_experimentname1_000.npy
│ │ ├── s000000_weights_date1_experimentname1_000.npy
│ │ ├── ...
│ │ └── s000099_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/
"""
if __name__ == "__main__":
# Add directories to path for imports
import os, sys, inspect
currentdir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))
)
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
sys.path.insert(0, os.path.join(parentdir, "hardware_interfaces"))
sys.path.insert(0, os.path.join(parentdir, "gui"))
import multiprocessing
from multiprocessing import Process, Queue
import sys
import threading
import numpy as np
from numpy import ndarray
# Matplotlib
import matplotlib
from matplotlib.backends.backend_qt5agg import (
FigureCanvasQTAgg,
NavigationToolbar2QT as NavigationToolbar,
)
from matplotlib.figure import Figure
# PyQTGraph
from pyqtgraph import PlotWidget, plot
import pyqtgraph as pg
from PyQt5 import QtWidgets, QtCore # , QtGui
from PyQt5.QtCore import QRunnable, QThreadPool, pyqtSignal, QObject
from qt_multithreading_wrapper import Worker
# Data processing
import data_processing as dp
from data_processing import PixelResponseLinearization as PRL
from data_processing import ChopperStateFinder as CSF
from analog_digital_converter import AnalogDigitalConverter as ADC
from triax import Triax as Spectrometer
from save_data import SaveData, Background
[docs]class ShowViper:
"""
Args:
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.
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).
ir_chopper_info (dict): Contains the information that is
necessary to identify the different chopper states of the
chopper that chops the IR 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.
vis_chopper_info (dict): Contains the information that is
necessary to identify the different chopper states of the
chopper that chops the UV/VIS 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.
"""
def __init__(
self,
widget_pyqtgraph,
adc: ADC,
prl: PRL,
ir_chopper_info: dict,
vis_chopper_info: dict,
background_handler: Background,
spectrometer: Spectrometer,
info_queue: Queue,
saver: SaveData = None,
):
# Initialise Queues
self.acq_queue = Queue()
self.processing_queue = Queue()
self.plot_queue = Queue()
if saver:
saver.save_other(background_handler.load_background(), "background")
self.acquisition = Acquisition(adc, spectrometer, self.acq_queue)
self.primary_processing = PrimaryProcessing(
self.acq_queue,
self.processing_queue,
adc.pixel_idx,
adc.probe_pixel_idx,
adc.reference_pixel_idx,
adc.index_dict,
prl,
ir_chopper_info,
vis_chopper_info,
background_handler,
saver,
)
self.secondary_processing = SecondaryProcessing(
self.processing_queue, self.plot_queue, info_queue
)
# self.plotting = Plotting(mpl_widget, adc, self.plot_queue)
self.plotting = PyqtPlotting(widget_pyqtgraph, adc, self.plot_queue)
[docs] def start(self):
self.plotting.threadpool.start(self.plotting.work)
self.secondary_processing.start()
self.primary_processing.start()
self.acquisition.start()
[docs]class Acquisition(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.
Args:
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.
acq_queue (Queue): Multiprocessing queue object that the
acquisition thread uses to pass data to the primary
processing process.
"""
def __init__(
self, adc: ADC, spectrometer: Spectrometer, acq_queue: Queue,
):
threading.Thread.__init__(self)
self.adc = adc
self.spectrometer = spectrometer
# Multiprocessing Queue
self.acq_queue = acq_queue
# Initialize scan index
self.scan_idx = 0
# Create dictionary that will hold data that is passed
# to other queues
self.data_container = {}
# Create dictionary that holds information
# in which scan we are (etc.)
self.info = {}
# Multiprocessing event to stop experiment
self.exit = threading.Event()
[docs] def run(self):
# * You might wonder why this acquisition
# * is structured differently from the acquisition
# * of the other (older/simpler) experiments
# * [Or you might not wonder -
# * Lets hope that someone fixed this discrepancy
# * already]
# * The way it is implemented here (the straight
# * forward way) was chosen because it simplifies
# * the indices counting a lot, and within multiprocessing
# * should not lead to any loss of laser time.
# * Before the 0th data set was acquired outside of
# * the loop and was processed while the second
# * acquisition was running
while not self.exit.is_set():
# Acquire data
self.adc.read()
# Put relevant information into data container
self.data_container["data"] = self.adc.data
self.data_container["scan index"] = self.scan_idx
self.data_container["probe axis"] = self.spectrometer.wn_axis
# Give data of acquisition to queue
# Hand over raw data because standard
# deviations needs to be calculated in
# the other process
self.acq_queue.put(self.data_container.copy())
# Update scan idx
self.scan_idx += 1
self.acq_queue.put("stop")
[docs] def shutdown(self):
# Setting exit will stop the loop
# within run
self.exit.set()
[docs]class PrimaryProcessing(multiprocessing.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.
Args:
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.
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).
ir_chopper_info (dict): Contains the information that is
necessary to identify the different chopper states of the
chopper that chops the IR 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.
vis_chopper_info (dict): Contains the information that is
necessary to identify the different chopper states of the
chopper that chops the UV/VIS 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.
"""
def __init__(
self,
acq_queue: Queue,
processing_queue: Queue,
pixel_idx: ndarray,
probe_pixel_idx: ndarray,
reference_pixel_idx: ndarray,
index_dict: dict,
prl: PRL,
ir_chopper_info: dict,
vis_chopper_info: dict,
background_handler: Background,
saver: SaveData = None,
):
super(multiprocessing.Process, self).__init__()
# Multiprocessing Queue
# Gets data from acquisition class
self.acq_queue = acq_queue
# Gives data to secondary processing class
self.processing_queue = processing_queue
self.prl = prl
self.saver = saver
# Pixel index is an array which tells us which
# entries in our adc data are pixels
# Append probe and reference pixel indices
self.pixel_idx = pixel_idx
self.probe_pixel_idx = probe_pixel_idx
self.ref_pixel_idx = reference_pixel_idx
self.index_dict = index_dict
# Load background data from file
self.background = background_handler.load_background()
# We need an array, which describes the number of
# all possible states for the sort data function
# for each device in this example
# the ir chopper as well as the vis chopper
# both have two possible states
# We have two different chopper states
# for each different chopper because we decided
# not to use the convolution electronics box built by
# victor and connect both choppers to separate inputs
self.n_possible_states = np.array([2, 2])
# Save chopper high voltage level divided by 2 as list
# We need this for the digitize function that identifies
# the High and Low Chopper
# We select half of the high voltage as the limit
# at which the distinction between states is done
# -IR chopper-
self.ir_chopper_voltage_level = [ir_chopper_info["high voltage level"][0] / 2]
# The chopper name is needed to retrieve the index (row)
# within the adc raw data that corresponds to this
# chopper
self.ir_chopper_name = ir_chopper_info["name"][0]
# - UV/VIS chopper -
self.vis_chopper_voltage_level = [vis_chopper_info["high voltage level"][0] / 2]
# The chopper name is needed to retrieve the index (row)
# within the adc raw data that corresponds to this
# chopper
self.vis_chopper_name = vis_chopper_info["name"][0]
# Initialize array that holds both
# temporary and averaged data
# In this case the data is average
# relative intensity
# (transmission (probe/ref))
# for each pixel.
# By convention the averaged is the
# 0 th entry of the 0th axis of the array
# and the temp data is the 1st entry of the 0th
# axis of the array.
# dimensions: 2 = (current average scan, last single scan)
# dimensions: (2, n_probe_pixels, (n_ir_chopper_states, n_vis_chopper_states))
self.data = np.zeros((2, self.probe_pixel_idx.size, *self.n_possible_states))
self.counts = np.zeros(self.data.shape)
self.weights = np.zeros(self.data.shape)
[docs] def run(self):
while True:
# Get data / information from acquisition process
data_container = self.acq_queue.get()
if type(data_container) == str:
if data_container == "stop":
break
# Write data in dictionary into variable
raw_data = data_container["data"]
scan_idx = data_container["scan index"]
# Subtract background from raw data
# of pixels and pixels only
background_corrected_data = (
raw_data[self.pixel_idx] - self.background[self.pixel_idx, np.newaxis]
)
# Linearize response of Pixels (and pixels only)
intensities = self.prl.linearize(background_corrected_data)
# Calculate transmission/ relative intensity
# (probe intensity / reference intensity)
transmission = (
intensities[self.probe_pixel_idx] / intensities[self.ref_pixel_idx]
)
# Get the corresponding chopper state for each shot
# -IR Chopper-
ir_chopper_states = np.digitize(
raw_data[self.index_dict[self.ir_chopper_name]],
self.ir_chopper_voltage_level,
)
# -UV/VIS Chopper-
vis_chopper_states = np.digitize(
raw_data[self.index_dict[self.vis_chopper_name]],
self.vis_chopper_voltage_level,
)
# Stack chopper states into one array
# for sort data function
# This implicitly decides upon the
# order of dimensionality of the data array:
# The last axis of data is the vis chopper
# distinction while the second to
# last axis corresponds to the
# ir chopper
# (Always make sure that the n_possible_states
# array lists its values in the same order!)
states = np.vstack((ir_chopper_states, vis_chopper_states))
# Sort and average data
self.data[1], self.weights[1], self.counts[1], statistics = dp.sort_data(
transmission, states, self.n_possible_states
)
# Add everything to data container
# and give it to processing queue
data_container["sorted data"] = self.data[1]
data_container["intensities"] = intensities
data_container["transmission"] = transmission
data_container["statistics"] = statistics
data_container["states"] = states
self.processing_queue.put(data_container.copy())
# ----------------------------------------
# Save data (if specified)
if self.saver:
# Create / Average "averaged" data by using weighted average
# between the temp data (weight = 1) and the already
# existing average data (weight = scan_idx)
self.data[0] = np.average(self.data, axis=0, weights=self.counts)
# Use saver class to save data
self.saver.save_scan(self.data[1], scan_idx)
self.saver.save_avg(self.data[0])
self.saver.save_counts(self.counts[1], scan_idx)
self.saver.save_weights(self.weights[1], scan_idx)
if self.saver.raw_data:
self.saver.save_raw_data(raw_data, scan_idx)
# Update counts
self.counts[0] += self.counts[1]
# Once stop signal was received the function breaks out of the loop.
# Now we need to tell the secondary processing to stop.
self.processing_queue.put("stop")
[docs]class SecondaryProcessing(multiprocessing.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.
Args:
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.
"""
def __init__(
self, processing_queue: Queue, plot_queue: Queue, info_queue: Queue,
):
super(multiprocessing.Process, self).__init__()
# Multiprocessing Queue
# Gets data from acquisition class
self.processing_queue = processing_queue
# Gives data to secondary processing class
self.plot_queue = plot_queue
# Give experimental status and statistics to
# GUI
self.info_queue = info_queue
[docs] def run(self):
while True:
# Get data / information from acquisition process
data_container = self.processing_queue.get()
if type(data_container) == str:
if data_container == "stop":
break
# ----------------------------------------
# Calculate information for plotting and GUI
# We first calculate everything that is required
# for plotting and pass it to the plot queue because
# plotting can also cost time. Only then we
# calculate the values that we want to display on
# line edits on the GUI
# Calculate the signals from the sorted data
sorted_data = data_container["sorted data"]
absorption = -np.log10(sorted_data)
# Now calculate all the different difference signals
# Last axis UV/VIS pump on-off
# Second to last axis IR pump on-off
# Beginners notes:
# (IR off, UV off): Background = [:, 0, 0]
# (IR off, UV on): TRIR + Background = [:, 0, 1]
# (IR on, UV off): IR pump/IR probe + Background = [:, 1, 0]
# (IR on, UV on): VIPER + TRIR + IR pump/IR probe + Background = [:, 1, 1]
# To calculate the transient IR signal
# we calculate difference between UV pump on
# and UV pump off (while IR pump is off)
trir_signal = absorption[:, 0, 1] - absorption[:, 0, 0]
# Analogously we can calculate this difference
# when the IR pump is on - which is not
# technically the transient IR signal but
# TRIR + VIPER. Because VIPER is an order of
# magnitude smaller than TRIR it should yield
# more or less the same signal. This can be
# used to check if the phases of the choppers are correct.
pseudo_trir_signal = absorption[:, 1, 1] - absorption[:, 1, 0]
# To calculate the IR pump IR probe signal
# we calculate difference between IR pump on
# and IR pump off (while UV/VIS pump is off)
irpump_signal = absorption[:, 1, 0] - absorption[:, 0, 0]
# Analogously to pseudo TRIR we calculate
# a pseudo IR Pump - IR Probe signal
pseudo_irpump_signal = absorption[:, 1, 1] - absorption[:, 0, 1]
# We calculate the VIPER signal by subtracting the
# TRIR Signal (UV on, IR off) and IR Pump - IR Probe
# Signal (UV off, IR on) from (UV off)
# This implies that we removed the background twice
# so we additionally add the background
viper_signal = (
absorption[:, 1, 1]
- absorption[:, 1, 0]
- absorption[:, 0, 1]
+ absorption[:, 0, 0]
)
# Calculate the average intensity for each pixel
avg_intensities = np.average(data_container["intensities"], axis=1)
# Calculate the standard deviation of the
# intensities
std_intensities = np.std(data_container["intensities"], axis=1, ddof=1)
# Calculate the s2s (shot to shot (technically every 4 shots))
# VIPER difference signal and its statistical information
(
_,
s2s_amp_signal,
s2s_std_signal,
s2s_avg_std_signal,
) = dp.shot_to_shot_viper(
data_container["transmission"],
data_container["states"][1, 0], # 0th sample of UV/VIS Chopper
data_container["states"][0, :2], # 0th and 1st sample of IR Chopper
)
# Add everything to data container
# and give to plot queue
data_container["TRIR signal"] = trir_signal
data_container["pseudo trir signal"] = pseudo_trir_signal
data_container["IR pump signal"] = irpump_signal
data_container["pseudo IR pump signal"] = pseudo_irpump_signal
data_container["VIPER signal"] = viper_signal
data_container["average intensity"] = avg_intensities
data_container["std intensity"] = std_intensities
data_container["std s2s signal"] = s2s_std_signal # s2s: shot to shot
# # Average standard deviation of signal over all wavenumbers
data_container[
"s2s signal average std"
] = s2s_avg_std_signal # s2s: shot to shot
self.plot_queue.put(data_container.copy())
# ----------------------------------------
# Calculate everything that is needed for statistical information
# on GUI and add it to data container and give it to info queue
# Hand over the mean state information for every pixel
data_container["mean state intensity"] = np.average(
data_container["transmission"], axis=1
)
data_container["mean state std"] = data_container["statistics"][1]
# Average s2s signal amplitude
data_container["s2s signal amplitude"] = s2s_amp_signal # s2s: shot to shot
self.info_queue.put(data_container.copy())
# Once stop signal was received the function breaks out of the loop.
# Now we need to tell the plotting and updating of info on GUI to stop.
self.plot_queue.put("stop")
self.info_queue.put("stop")
[docs]class PyqtPlotting:
"""
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.
Args:
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.
"""
[docs] class Signals(QObject):
new_data = pyqtSignal(dict)
def __init__(self, widget_pyqtgraph, adc: ADC, plot_queue):
# Assign attributes
self.adc = adc
self.widget_pyqtgraph = widget_pyqtgraph
self.graphics_layout = widget_pyqtgraph.graphics_layout
self.plot_queue = plot_queue
# Setup signals and threadpool
self.threadpool = QThreadPool()
self.signals = self.Signals()
# Clear old plots
self.widget_pyqtgraph.remove_plots()
# Setup plot that displays different signals
self.widget_pyqtgraph.plots["signal"] = self.graphics_layout.addPlot(
row=0, col=0
)
self.widget_pyqtgraph.plots["signal"].setTitle("Average signals")
self.widget_pyqtgraph.plots["signal"].setLabel(
"bottom", "wavenumber [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["signal"].setLabel("left", "difference signal[OD]")
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["signal"])
# Setup plot that will display VIPER signal
self.widget_pyqtgraph.plots["viper signal"] = self.graphics_layout.addPlot(
row=0, col=1
)
self.widget_pyqtgraph.plots["viper signal"].setTitle("VIPER signal")
self.widget_pyqtgraph.plots["viper signal"].setLabel(
"bottom", "wavenumber [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["viper signal"].setLabel(
"left", "difference signal [OD]"
)
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["viper signal"])
# Setup plot that displays intensities
self.widget_pyqtgraph.plots["intensities"] = self.graphics_layout.addPlot(
row=2, col=0
)
self.widget_pyqtgraph.plots["intensities"].setTitle("Average intensities")
self.widget_pyqtgraph.plots["intensities"].setLabel(
"bottom", "wavenumber [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["intensities"].setLabel("left", "intensity [a.u.]")
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["intensities"])
# Setup plot that displays standard deviation of signal
# Make the title of std signal so that the Average standard deviation
# of signal over all wavenumbers is displayed. HTML is
# used because setTitle works with it.
# self.std_s2s_signal_title= """
# <span style="color: #000000; font-size: 12pt;">
# Standard deviation of VIPER signal</span>
# <span style="color: #E93610 ; font-size: 30pt;">
# {:0.3e} OD</span></div>
# """
self.widget_pyqtgraph.plots["std s2s signal"] = self.graphics_layout.addPlot(
row=2, col=1
)
self.widget_pyqtgraph.plots["std s2s signal"].setTitle(
"Standard deviation of VIPER signal"
)
self.widget_pyqtgraph.plots["std s2s signal"].setLabel(
"bottom", "wavenumber [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["std s2s signal"].setLabel(
"left", "difference signal [OD]"
)
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["std s2s signal"])
# Create dictionary that holds reference to lines etc.
self.plot_ref = {}
# Connect signal that data has arrived to update the plot
self.signals.new_data.connect(
lambda data_container: self.update_plot(data_container)
)
# Start loop that gets data from queue in Qt Thread
self.work = Worker(self.run)
[docs] def run(self):
while True:
data_container = self.plot_queue.get()
if type(data_container) == str:
if data_container == "stop":
break
self.signals.new_data.emit(data_container)
[docs] def update_plot(self, data_container):
trir_signal = data_container["TRIR signal"]
pseudo_trir_signal = data_container["pseudo trir signal"]
irpump_signal = data_container["IR pump signal"]
pseudo_irpump_signal = data_container["pseudo IR pump signal"]
viper_signal = data_container["VIPER signal"]
std_signal = data_container["std s2s signal"]
s2s_avg_std_signal = data_container["s2s signal average std"]
probe_axis = data_container["probe axis"]
avg_intensities = data_container["average intensity"]
std_intensities = data_container["std intensity"]
# # Update the title of the std_signal plot to display the
# # Average standard deviation of signal over all wavenumbers
# self.widget_pyqtgraph.plots["std s2s signal"].setTitle(self.std_s2s_signal_title.format(s2s_avg_std_signal))
if self.plot_ref:
# Update different signal plots
self.plot_ref["trir"].setData(x=probe_axis, y=trir_signal)
self.plot_ref["pseudo trir"].setData(x=probe_axis, y=pseudo_trir_signal)
self.plot_ref["ir pump"].setData(x=probe_axis, y=irpump_signal)
self.plot_ref["pseudo ir pump"].setData(
x=probe_axis, y=pseudo_irpump_signal
)
# Plot viper signal
self.plot_ref["viper signal"].setData(x=probe_axis, y=viper_signal)
# Update intensity error bars for probe array
self.plot_ref["probe error bars"].setData(
x=probe_axis,
y=avg_intensities[self.adc.probe_pixel_idx],
height=5 * std_intensities[self.adc.probe_pixel_idx],
)
# Update intensity error bars for reference array
self.plot_ref["ref error bars"].setData(
x=probe_axis,
y=avg_intensities[self.adc.reference_pixel_idx],
height=5 * std_intensities[self.adc.reference_pixel_idx],
)
# Update intensities
self.plot_ref["probe intensities"].setData(
x=probe_axis, y=avg_intensities[self.adc.probe_pixel_idx]
)
self.plot_ref["ref intensities"].setData(
x=probe_axis, y=avg_intensities[self.adc.reference_pixel_idx]
)
# Plot standard deviation of s2s VIPER signal
self.plot_ref["std s2s signal"].setData(x=probe_axis, y=std_signal)
else:
# First time plotting
trir_pen = pg.mkPen(color="#1f77b4", width=2.5, style=QtCore.Qt.DashLine)
pseudo_trir_pen = pg.mkPen(
color="#ff7f0e", width=2.5, style=QtCore.Qt.DotLine
)
irpump_pen = pg.mkPen(
color="#2ca02c", width=2.5, style=QtCore.Qt.DashDotLine
)
pseudo_irpump_pen = pg.mkPen(
color="#7f7f7f", width=2.5, style=QtCore.Qt.DashDotDotLine
)
viper_pen = pg.mkPen(color="#d62728", width=2.5, style=QtCore.Qt.SolidLine)
probe_pen = pg.mkPen(color="#1f77b4", width=2.5, style=QtCore.Qt.SolidLine)
reference_pen = pg.mkPen(
color="#ff7f0e", width=2.5, style=QtCore.Qt.SolidLine
)
std_intensity_pen = pg.mkPen(
color="#7f7f7f", width=1.5, style=QtCore.Qt.SolidLine
) # ? dashed lines?
std_signal_pen = pg.mkPen(
color="#2ca02c", width=2.5, style=QtCore.Qt.SolidLine
)
# Plot signal
self.plot_ref["trir"] = self.widget_pyqtgraph.plots["signal"].plot(
x=probe_axis, y=trir_signal, name="TR-IR signal", pen=trir_pen
)
self.plot_ref["pseudo trir"] = self.widget_pyqtgraph.plots["signal"].plot(
x=probe_axis,
y=pseudo_trir_signal,
name="TR-IR + VIPER signal",
pen=pseudo_trir_pen,
)
self.plot_ref["ir pump"] = self.widget_pyqtgraph.plots["signal"].plot(
x=probe_axis,
y=irpump_signal,
name="IR pump/IR probe signal",
pen=irpump_pen,
)
self.plot_ref["pseudo ir pump"] = self.widget_pyqtgraph.plots[
"signal"
].plot(
x=probe_axis,
y=pseudo_irpump_signal,
name="IR pump/IR probe + VIPER signal",
pen=pseudo_irpump_pen,
)
# Plot viper signal signal
self.plot_ref["viper signal"] = self.widget_pyqtgraph.plots[
"viper signal"
].plot(x=probe_axis, y=viper_signal, name="VIPER signal", pen=viper_pen)
# Create intensity error bars for probe array
self.plot_ref["probe error bars"] = pg.ErrorBarItem(
x=probe_axis,
y=avg_intensities[self.adc.probe_pixel_idx],
height=5 * std_intensities[self.adc.probe_pixel_idx],
beam=0.3,
pen=std_intensity_pen,
)
self.widget_pyqtgraph.plots["intensities"].addItem(
self.plot_ref["probe error bars"]
)
# Create intensity error bars for reference array
self.plot_ref["ref error bars"] = pg.ErrorBarItem(
x=probe_axis,
y=avg_intensities[self.adc.reference_pixel_idx],
height=5 * std_intensities[self.adc.reference_pixel_idx],
beam=0.3,
pen=std_intensity_pen,
)
self.widget_pyqtgraph.plots["intensities"].addItem(
self.plot_ref["ref error bars"]
)
# Plot intensities
self.plot_ref["probe intensities"] = self.widget_pyqtgraph.plots[
"intensities"
].plot(
x=probe_axis,
y=avg_intensities[self.adc.probe_pixel_idx],
name="Average intensities on probe array",
pen=probe_pen,
)
self.plot_ref["ref intensities"] = self.widget_pyqtgraph.plots[
"intensities"
].plot(
x=probe_axis,
y=avg_intensities[self.adc.reference_pixel_idx],
name="Average intensities on reference array",
pen=reference_pen,
)
# Plot standard deviation of pseudo signal
self.plot_ref["std s2s signal"] = self.widget_pyqtgraph.plots[
"std s2s signal"
].plot(
x=probe_axis,
y=std_signal,
name="Standard deviation of VIPER signal",
pen=std_signal_pen,
)
self.widget_pyqtgraph.disable_autoscale()