"""
Providing the "Fabry Perot 2D IR (Interleaves)" experiment. In this
experiment, a narrow IR pump pulse, created with the Fabry Perot, is
used to excite the sample. An IR probe pulse is used to scan the
samples' response. The IR delay stage is used to move to different delay
times between each collection of data. The Fabry Perot is tuned to
different pump pixels (frequencies) specified in the pump pixel delay
file. For this experiment, the chopper must be running. To reduce pump
light scattering on the detector, the IR delay stage is used to phase
cycle the pump pulse. This is called "interleaves" and can be achieved
because the delay stage can move distances that are fractions of the
pump wavelength. This effectively only changes the phase of the pump
light incident on the sample while the effect of the slightly altered
delay is negligible.
Note:
**Fabry Perot Algorithm:**
The algorithm to tune the wavelength of the Fabry Perot is not
particularly stable. Especially when tuning the Fabry Perot to
the borders of the detector it might crash. I.e.: The algorithm
wants to tune to a negative voltage or tunes to a "satellite"
peak. It is advisable to check your pump pixel "delays" by hand
before starting a measurement.
In the case where the tuning algorithm fails it might be
necessary to reset its voltage via the voltage lineEdit. I.e.:
If the central wavelength of the spectrometer corresponds to a
voltage of 3.65 V on the Fabry Perot, then it needs to be reset
to 3.65 V.
When setting up the Fabry Perot the controller needs to be
offset corrected to avoid the algorithm moving out of the
voltage range (0-10 V) under otherwise normal circumstances.
E.g.: If the central wavelength of the spectrometer is at 0.5 V
the lower frequencies might only be accessible with negative
voltages. We noticed that setting the central wavelength to
about 3-4 V is a good starting point for further adjustments.
#######################
Step by Step Algorithm:
#######################
**Acquisition:**
1. Preallocate dictionary (data container) which will contain data
and information about scan index, delay index etc.
2. Tune the Fabry Perot to pump pixel specified in the pump
pixel "delay" file
3. Set the number samples to acquire to account for pump pixel
weight
4. Open Shutter
5. Collect pump spectrum (read data from ADC)
6. Close Shutter
7. Calculate interleave positions for the current delay and pump
frequency
8. Set the number of samples to acquire to account for weights
specified for the current delay as specified in the delay file
9. Move to the interleaves (the 0th interleave is the actual delay)
10. Read the data from the ADC
11. 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). Here two new index dimensions, namely the different pump
pixels and interleaves are added in addition to the delay index.
Note that the pump pixels and interleaves are not "sorted" into
separate states because we already know beforehand what pump
pixel and interleaves represent (analogously to how it is done
for the delays)
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. Calculate shot to shot difference signal and its statistical
properties
8. Average the data by weighting equally
9. Calculate the average pump spectrum of the Fabry Perot
10. Put this information into data container and hand it over to
secondary processing
11. Save data (and raw data) including counts, weights and s2s_std
if respective checkboxes on GUI were checked. Also
save pump spectrum of Fabry Perot.
12. Hand over the data container to secondary processing
process
**Secondary Processing:**
1. Calculate the absorption (-log10) of the sorted data
2. Calculate the pump probe difference signal (chopper high - chopper low)
3. Calculate phase cycled signal (this should be moved to phase
cycle transmissions instead of the difference signal)
4. Calculate the average intensities and standard deviation of
the intensities for each pixel
5. Put this information into data container and hand it over to
Pyqtplotting thread
6. 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:
* Single and average signal current delay
* Single and average 2D-signal heatmap
* Intensities and their standard deviation (multiplied by 5)
* Standard deviation of shot to shot signal
* Pump spectrum of Fabry Perot
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_pump_pixels, n_delays, n_interleaves, n_probe_pixels, n_chopper_states)]
saving dimension:
[(n_interleaves, n_probe_pixels, n_chopper_states)]
raw data dimension:
[(n_channels, samples_to_acquire)]
################
Folder Structure
################
In **scans/pXXX/dXXX** the first file of 4 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 the probe pixel dimension
every other dimension collapses.
Additionally, the **pump spectrum** of the Fabry Perot for every
pump pixel for every scan is saved in the scans directory.
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.
**pump_pixel.npy** contains the pump pixels including weights.
.. code-block::
username/
├── date1_experimentname1_000/
│ ├── averaged_data
│ │ ├── p000_d000_date1_experimentname1_000.npy
│ │ ├── ...
│ │ ├── p000_d999_date1_experimentname1_000.npy
│ │ ├── p001_d000_date1_experimentname1_000.npy
│ │ ├── ...
│ │ └── p001_d999_date1_experimentname1_000.npy
│ ├── figures
│ ├── hardware config
│ ├── raw data
│ │ ├── pump_pixel000
│ │ │ ├── delay000
│ │ │ │ ├── s000000_p000_d000_intl000_date1_experimentname1_000_raw.npy
│ │ │ │ ├── s000000_p000_d000_intl001_date1_experimentname1_000_raw.npy
│ │ │ │ ├── ...
│ │ │ │ └── s000099_p000_d000_intl016_date1_experimentname1_000_raw.npy
│ │ │ ├── ...
│ │ │ └── pump_pixel999
│ ├── scans
│ │ ├── pump_pixel000
│ │ │ ├── delay000
│ │ │ │ ├── s000000_p000_d000_date1_experimentname1_000.npy
│ │ │ │ ├── s000000_p000_d000_counts_date1_experimentname1_000.npy
│ │ │ │ ├── s000000_p000_d000_s2s_std_date1_experimentname1_000.npy
│ │ │ │ ├── s000000_p000_d000_weights_date1_experimentname1_000.npy
│ │ │ │ ├── ...
│ │ │ │ └── s000099_p000_d000_date1_experimentname1_000_raw.npy
│ │ │ ├── ...
│ │ │ ├── delay999
│ │ │ ├── s000000_p000_pump_spectrum_date1_experimentname1_000.npy
│ │ │ ├── ...
│ │ │ └── s000099_p000_pump_spectrum_date1_experimentname1_000.npy
│ │ ├── ...
│ │ └── pump_pixel999
│ ├── probe_wn_axis_date1_experimentname1_000.npy
│ ├── pump_pixels_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/
"""
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 import pyplot as plt
from matplotlib.backends.backend_qt5agg import (
FigureCanvasQTAgg,
NavigationToolbar2QT as NavigationToolbar,
)
from matplotlib.figure import Figure
from matplotlib import cm
# 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 save_data import SaveData, Background
# Hardware modules
from analog_digital_converter import AnalogDigitalConverter as ADC
from triax import Triax as Spectrometer
from pi_control import PiStage
from fabry_perot import FabryPerot
from shutter import Shutter
[docs]class FabryPerot2dIrInterleaves:
"""
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).
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)
pump_pixels (ndarray): Array containing the pump pixels to which
the Fabry Perot is supposed be tuned in the 0th column and
their corresponding weights in the 1st column.
* shape: 2D
* E.g.: (number of pump pixels, 2)
interleaves (int): Number of interleaves that should be scanned.
Number should be even and a power of 2. An interleave is a
small step of the delay stage (in addition to the normal
delay) used for phase cycling and removing scattering.
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.
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.
fabry_perot (FabryPerot): Object that provides the functionality
of tuning the Fabry Perot to a pixel on the MCT array.
shutter (Shutter): Shutter object that can be used to open and
close the IR pump shutter.
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,
delays: ndarray,
pump_pixels: ndarray,
interleaves: int,
adc: ADC,
delay_stage: PiStage,
prl: PRL,
chopper_info: dict,
background_handler: Background,
spectrometer: Spectrometer,
fabry_perot: FabryPerot,
shutter: Shutter,
info_queue: Queue,
saver: SaveData = None,
):
# Initialise Queues
self.acq_queue = Queue()
self.processing_queue = Queue()
self.plot_queue = Queue()
# Save wavenumber axis, delay files etc. to file system
if saver:
saver.save_other(spectrometer.wn_axis, "probe_wn_axis")
saver.save_other(delays, "delay_file")
saver.save_other(pump_pixels, "pump_pixels")
saver.save_other(background_handler.load_background(), "background")
self.acquisition = Acquisition(
delays,
pump_pixels,
interleaves,
adc,
delay_stage,
spectrometer,
fabry_perot,
shutter,
self.acq_queue,
)
self.primary_processing = PrimaryProcessing(
self.acq_queue,
self.processing_queue,
delays,
pump_pixels,
interleaves,
adc.pixel_idx,
adc.probe_pixel_idx,
adc.reference_pixel_idx,
adc.index_dict,
prl,
chopper_info,
background_handler,
saver,
)
self.secondary_processing = SecondaryProcessing(
self.processing_queue,
self.plot_queue,
info_queue,
delays,
pump_pixels,
interleaves,
adc.probe_pixel_idx,
saver,
)
self.plotting = PyqtPlotting(
widget_pyqtgraph, adc, self.plot_queue, delays, pump_pixels, interleaves
)
[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:
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)
pump_pixels (ndarray): Array containing the pump pixels to which
the Fabry Perot is supposed be tuned in the 0th column and
their corresponding weights in the 1st column.
* shape: 2D
* E.g.: (number of pump pixels, 2)
interleaves (int): Number of interleaves that should be scanned.
Number should be even and a power of 2. An interleave is a
small step of the delay stage (in addition to the normal
delay) used for phase cycling and removing scattering.
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.
fabry_perot (FabryPerot): Object that provides the functionality
of tuning the Fabry Perot to a pixel on the MCT array.
shutter (Shutter): Shutter object that can be used to open and
close the IR pump shutter.
acq_queue (Queue): Multiprocessing queue object that the
acquisition thread uses to pass data to the primary
processing process.
"""
def __init__(
self,
delays: ndarray,
pump_pixels: ndarray,
interleaves: int,
adc: ADC,
delay_stage: PiStage,
spectrometer: Spectrometer,
fabry_perot: FabryPerot,
shutter: Shutter,
acq_queue: Queue,
):
threading.Thread.__init__(self)
# Assign attributes
self.delays = delays
self.pump_pixels = pump_pixels
self.interleaves = interleaves
self.adc = adc
self.delay_stage = delay_stage
self.spectrometer = spectrometer
self.fabry_perot = fabry_perot
self.shutter = shutter
# Multiprocessing Queue
self.acq_queue = acq_queue
# Save the base amount of samples
# to acquire
# This is needed to reset the weighting
# for each delay
self.base_samples = self.adc.samples_to_acquire
# Initialize scan index
self.scan_idx = 0
# Create dictionary that will hold data that is passed
# to other queues
self.data_container = {}
# Multithreading 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():
# Iterate over pump frequencies
for p_idx, pump_pixel in enumerate(self.pump_pixels):
# Tune fabry perot to the specified pump frequency (pixel)
self.fabry_perot.set_pixel(int(pump_pixel[0]))
# Set samples to acquire according to weight
# for this pump pixel
samples_to_acquire = int(round(self.base_samples * pump_pixel[1]))
self.adc.set_samples_to_acquire(samples_to_acquire)
# Measure Pump Spectrum
self.shutter.open()
self.adc.read()
pump_spectrum = self.adc.data[self.adc.probe_pixel_idx].copy()
self.shutter.close()
# Add pump (frequency) index to data container
self.data_container["pump index"] = p_idx
# Add pump spectrum to data container
self.data_container["pump spectrum"] = pump_spectrum
# Iterate over all delays
for d_idx, delay in enumerate(self.delays):
# Generate interleave positions of
# IR delay stage for a given pump frequency
interleave_pos = dp.calculate_interleave_array(
self.interleaves,
self.spectrometer.wl_axis[int(pump_pixel[0])],
delay[0],
)
# Set samples to acquire according to weight
# for this delay
samples_to_acquire = int(
round(self.adc.samples_to_acquire * delay[1])
)
self.adc.set_samples_to_acquire(samples_to_acquire)
# Add delay index to data container
self.data_container["delay index"] = d_idx
# Iterate over all interleaves
for i_idx, interleave in enumerate(interleave_pos):
# Move to next delay/interleave
self.delay_stage.move(interleave)
# Read data with given parameters
self.adc.read()
# Put data in acquisition queue
self.data_container["data"] = self.adc.data.copy()
self.data_container["scan index"] = self.scan_idx
self.data_container["interleave index"] = i_idx
self.data_container[
"probe axis"
] = self.spectrometer.wn_axis.copy()
# Add pump axis this is inefficient and should be moved to init
# (like some other stuff too)
self.data_container["pump axis"] = self.spectrometer.wn_axis[
self.pump_pixels[:, 0].astype(int)
]
# Give data of prior acquisition to queue
self.acq_queue.put(self.data_container.copy())
self.scan_idx += 1
# Tell processes to stop after last data
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.
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)
pump_pixels (ndarray): Array containing the pump pixels to which
the Fabry Perot is supposed be tuned in the 0th column and
their corresponding weights in the 1st column.
* shape: 2D
* E.g.: (number of pump pixels, 2)
interleaves (int): Number of interleaves that should be scanned.
Number should be even and a power of 2. An interleave is a
small step of the delay stage (in addition to the normal
delay) used for phase cycling and removing scattering.
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.
"""
def __init__(
self,
acq_queue: Queue,
processing_queue: Queue,
delays: ndarray,
pump_pixels: ndarray,
interleaves: int,
pixel_idx: ndarray,
probe_pixel_idx: ndarray,
reference_pixel_idx: ndarray,
index_dict: dict,
prl: PRL,
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
# Number of interleaves
self.interleaves = interleaves
# Pixel response linearisation
self.prl = prl
# Data saving instance
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()
# In this experiment we have two different chopper states
self.n_chopper_states = np.array(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
self.chopper_voltage_level = [chopper_info["high voltage level"][0] / 2]
self.chopper_name = 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_pump_pixels, n_delays, n_interleaves, n_probe_pixels, n_chopper_states)
self.data = np.zeros(
(
2,
pump_pixels.shape[0],
delays.shape[0],
self.interleaves,
self.probe_pixel_idx.size,
self.n_chopper_states,
)
)
self.counts = np.zeros(self.data.shape)
self.weights = np.zeros(self.data.shape)
# * The following code should be moved to
# * secondary processing once it is clear
# * whether or not it is actually needed
# * for the averaging of the scans in post
# * processing
#! Insert array that holds data to s2s
#! signal variance for each interleave.
self.s2s_std_signal = np.zeros((self.interleaves, self.probe_pixel_idx.size))
self.s2s_avg_std_signal = np.zeros(self.interleaves)
self.s2s_amp_signal = np.zeros(self.interleaves)
[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"]
pump_idx = data_container["pump index"]
scan_idx = data_container["scan index"]
delay_idx = data_container["delay index"]
intl_idx = data_container["interleave 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
chopper_states = np.digitize(
raw_data[self.index_dict[self.chopper_name]], self.chopper_voltage_level
)
# Sort and average data
idx = (1, pump_idx, delay_idx, intl_idx)
(
self.data[idx],
self.weights[idx],
self.counts[idx],
statistics,
) = dp.sort_data(transmission, chopper_states, self.n_chopper_states)
# * The following code should be moved to
# * secondary processing once it is clear
# * whether or not it is actually needed
# * for the averaging of the scans in post
# * processing
# Calculate the s2s (shot to shot) difference signal
# and its statistical information
# We are not interested in the 0th element of the tuple
# which represents the shot to shot averaged difference
# signal - we calculated it correctly with the transmissions
# above
(
_,
self.s2s_amp_signal[intl_idx],
self.s2s_std_signal[intl_idx],
self.s2s_avg_std_signal[intl_idx],
) = dp.shot_to_shot_signal(transmission, chopper_state=chopper_states[0])
# Create / Average "averaged" data by using weighted average
# between the temp data (weight = 1) and the already
# existing average data (weight = scan_idx) (technically
# it is the number of total samples that were acquired in a
# given state)
# * Data that was averaged this way should
# * probably yield worse results than
# * phase cycling directly for each scan.
# * this has probably to do with the
# * imprecision of the delay stage
# * when measuring two time the same delay
# * it will move to slightly different
# * locations yielding slightly different
# * phases. Averaging these my yield
# * artifacts.
# * For this type of weighting
# * (essentially equal weights) it should
# * not make a difference as the two
# * sums (one for averaging the interleaves
# * within one scan and one for averaging scans)
# * can be swapped.
self.data[0, pump_idx, delay_idx, intl_idx] = np.average(
self.data[:, pump_idx, delay_idx, intl_idx],
axis=0,
weights=self.counts[:, pump_idx, delay_idx, intl_idx],
)
# Calculate average pump spectrum and corresponding standard deviation
#! (placing it here is inefficient, because the pump spectrum information
#! is only updated once all delays were measured, but is effectively calculated
#! for every delay - but nvm for now)
avg_pump_spectrum = np.average(data_container["pump spectrum"], axis=1)
std_pump_spectrum = np.std(data_container["pump spectrum"], axis=1, ddof=1)
#! In VB6 the interleaves are averaged
#! once the difference spectra are calculated
#! this (below) first phase cycles and then
#! offers data for signal calculating
#!! Update: We tested/compared the difference
#!! for one data set and the absolute difference
#!! was consistently smaller 10e-3 smaller
#!! than the signal
#!! We (Jens and us) agree that it conceptually
#!! makes more sense to average the relative
#!! intensities.
#!! Also do NOT use the code below to implement
#!! averaging of interleaves!!!
#!! Instead do it in secondary processing.
#!! If you uncomment this code it will mess
#!! with the data saving.
# # If last interleave was measured
# # average up interleaves - then average
# # this scan with all other already
# # acquired scans
# if intl_idx + 1 == self.interleaves:
# # Sum counts for each chopper state
# # over all interelaves
# # save these counts into the 0th entry
# # of the interleave dimension
# self.counts[1, delay_idx, 0] = self.counts[1, delay_idx].sum(axis=0)
# # Average the interleaves into the 0th
# # entry of the interleave dimension
# self.data[1, delay_idx, 0] = np.average(self.data[1, delay_idx], axis=0)
# # Create / Average "averaged" data by using weighted average
# # between the temp data (weight = 1) and the already
# # existing average data (weight = scan_idx) (technically
# # it is the number of total samples that were acquired in a
# # given state)
# self.data[0, delay_idx , 0] = np.average(
# self.data[:, delay_idx, 0],
# axis=0,
# weights=self.counts[:, delay_idx, 0]
# )
# # Update counts
# self.counts[0, delay_idx] += self.counts[1, delay_idx]
# #* The following code should be moved to
# #* secondary processing once it is clear
# #* whether or not it is actually needed
# #* for the averaging of the scans in post
# #* processing
# # Calculate the s2s (shot to shot) difference signal
# # and its statistical information
# # We are not interested in the 0th element of the tuple
# # which represents the shot to shot averaged difference
# # signal - we calculated it correctly with the transmissions
# # above
# _, s2s_amp_signal, s2s_std_signal, s2s_avg_std_signal = dp.shot_to_shot_signal(
# data_container["transmission"],
# chopper_state=data_container["chopper states"][0])
# data_container["std s2s signal"] = s2s_std_signal # s2s: shot to shot
# # Average s2s signal amplitude
# data_container["s2s signal amplitude"] = s2s_amp_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
# #*-------------
# Add everything to data container
# and give it to processing queue
data_container["sorted data"] = self.data.copy()
data_container["intensities"] = intensities.copy()
data_container["transmission"] = transmission.copy()
data_container["statistics"] = statistics
data_container["chopper states"] = chopper_states
data_container["std s2s signal"] = self.s2s_std_signal[intl_idx]
data_container["s2s signal amplitude"] = self.s2s_amp_signal[intl_idx]
data_container["s2s signal average std"] = self.s2s_avg_std_signal[intl_idx]
data_container["average pump spectrum"] = avg_pump_spectrum
data_container["std pump spectrum"] = std_pump_spectrum
self.processing_queue.put(data_container.copy())
# ----------------------------------------
# Save data (if specified)
if self.saver:
# Use saver class to save data
# Save data once last interleave was measured
# (this does not apply to raw data)
if intl_idx == self.interleaves - 1:
self.saver.save_scan(
self.data[1, pump_idx, delay_idx],
scan_idx,
delay_idx=delay_idx,
pump_idx=pump_idx,
)
self.saver.save_avg(
self.data[0, pump_idx, delay_idx],
delay_idx=delay_idx,
pump_idx=pump_idx,
)
self.saver.save_counts(
self.counts[1, pump_idx, delay_idx],
scan_idx,
delay_idx=delay_idx,
pump_idx=pump_idx,
)
self.saver.save_weights(
self.weights[1, pump_idx, delay_idx],
scan_idx,
delay_idx=delay_idx,
pump_idx=pump_idx,
)
self.saver.save_pump_spectrum(
avg_pump_spectrum, scan_idx, pump_idx
) #! This inefficient because pump spectrum will be overwritten for each delay with the same data
# *-------------
self.saver.save_s2s_std(
self.s2s_std_signal,
scan_idx,
delay_idx=delay_idx,
pump_idx=pump_idx,
)
# *-------------
if self.saver.raw_data:
self.saver.save_raw_data(
raw_data,
scan_idx,
delay_idx=delay_idx,
pump_idx=pump_idx,
intlv=intl_idx,
)
# Update counts
self.counts[0, pump_idx, delay_idx, intl_idx] += self.counts[
1, pump_idx, delay_idx, intl_idx
]
# 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.
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)
pump_pixels (ndarray): Array containing the pump pixels to which
the Fabry Perot is supposed be tuned in the 0th column and
their corresponding weights in the 1st column.
* shape: 2D
* E.g.: (number of pump pixels, 2)
interleaves (int): Number of interleaves that should be scanned.
Number should be even and a power of 2. An interleave is a
small step of the delay stage (in addition to the normal
delay) used for phase cycling and removing scattering.
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.
"""
def __init__(
self,
processing_queue: Queue,
plot_queue: Queue,
info_queue: Queue,
delays: ndarray,
pump_pixels: ndarray,
interleaves: int,
probe_pixel_idx: ndarray,
saver: SaveData = None,
):
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
# Number of interleaves
self.interleaves = interleaves
# The file saver is required the save the resulting plot
self.saver = saver
# Pixel index is an array which tells us which
# entries in our adc data are pixels
# In this case we only need this to
# preallocate our signal array
self.probe_pixel_idx = probe_pixel_idx
# Preallocate array that will hold signal information
# 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.
self.signal = np.zeros(
(
2,
pump_pixels.shape[0],
delays.shape[0],
self.interleaves,
self.probe_pixel_idx.size,
)
)
self.phase_cycled_signal = np.zeros(
(2, pump_pixels.shape[0], delays.shape[0], self.probe_pixel_idx.size)
)
# Needed for 2D heatmap /contour plot
# intp: interpolated
self.intp_phase_cycled_signal = [None, None]
[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
# Get pump index
pump_idx = data_container["pump index"]
# Get delay index
delay_idx = data_container["delay index"]
# Get interleave index
intl_idx = data_container["interleave index"]
# Calculate the pump probe signal from the sorted data
sorted_data = data_container["sorted data"]
absorption = -np.log10(
sorted_data[:, pump_idx, delay_idx, intl_idx, :, :]
) #! Comments
self.signal[:, pump_idx, delay_idx, intl_idx, :] = (
absorption[:, :, 1] - absorption[:, :, 0]
)
if intl_idx == self.interleaves - 1:
# Calculate phase cycled signal
# phase cycled signal = scatter free signal
self.phase_cycled_signal[:, pump_idx, delay_idx] = np.average(
self.signal[:, pump_idx, delay_idx], axis=1
)
# Computing this outside the if statement is slow and
# unnecessary but at least the plotting works in all cases
self.intp_phase_cycled_signal[1] = dp.generate_img_data(
data_container["probe axis"],
data_container["pump axis"],
self.phase_cycled_signal[1, :, delay_idx],
)
self.intp_phase_cycled_signal[0] = dp.generate_img_data(
data_container["probe axis"],
data_container["pump axis"],
self.phase_cycled_signal[0, :, delay_idx],
)
# 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)
# Add everything to data container
# and give to plot queue
data_container["signal"] = self.signal.copy()
data_container["phase cycled signal"] = self.phase_cycled_signal.copy()
data_container[
"phase cycled signal (interpol)"
] = self.intp_phase_cycled_signal
data_container["average intensity"] = avg_intensities
data_container["std intensity"] = std_intensities
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
# Calculate the average intensities over all pixels
data_container["mean state intensity"] = np.average(
data_container["transmission"], axis=1
)
data_container["mean state std"] = data_container["statistics"][1]
self.info_queue.put(data_container.copy())
# if self.saver:
# scan_idx = data_container["scan index"]
# [ax.clear() for ax in self.axes[delay_idx+1,:]]
# self.axes[delay_idx+1,0].plot(data_container["probe axis"], self.signal[1, delay_idx])
# self.axes[delay_idx+1,1].plot(data_container["probe axis"], self.signal[0, delay_idx])
# self.axes[delay_idx+1,2].plot(data_container["probe axis"], data_container["average intensity"][self.probe_pixel_idx])
# # Save figure once last delay was processed
# if delay_idx +1 == self.delays.shape[0]:
# self.axes[0,0].clear()
# self.axes[0,1].clear()
# self.fig.suptitle("UV/VIS Pump - IR Probe: Scan {}".format(scan_idx))
# X, Y = np.meshgrid(self.delays[:,0], data_container["probe axis"]) # Inefficient
# self.axes[0,0].contour(X, Y, self.signal[1].T)
# self.axes[0,1].contour(X, Y, self.signal[0].T)
# save_path = self.saver.save_figures(scan_idx)
# plt.savefig(save_path+".pdf")
# 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.
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)
pump_pixels (ndarray): Array containing the pump pixels to which
the Fabry Perot is supposed be tuned in the 0th column and
their corresponding weights in the 1st column.
* shape: 2D
* E.g.: (number of pump pixels, 2)
interleaves (int): Number of interleaves that should be scanned.
Number should be even and a power of 2. An interleave is a
small step of the delay stage (in addition to the normal
delay) used for phase cycling and removing scattering.
"""
[docs] class Signals(QObject):
new_data = pyqtSignal(dict)
def __init__(
self,
widget_pyqtgraph,
adc: ADC,
plot_queue,
delays: ndarray,
pump_pixels: ndarray,
interleaves: int,
):
# Assign attributes
self.adc = adc
self.widget_pyqtgraph = widget_pyqtgraph
self.graphics_layout = widget_pyqtgraph.graphics_layout
self.plot_queue = plot_queue
self.delays = delays
self.pump_pixels = pump_pixels
self.interleaves = interleaves
# Setup signals and threadpool
self.threadpool = QThreadPool()
self.signals = self.Signals()
# Clear old plots
self.widget_pyqtgraph.remove_plots()
# Create dictionary that holds reference to lines etc.
self.plot_ref = {}
# Add a sub-layout to hold the first 2 plots in the first row
self.widget_pyqtgraph.plots["upper layout"] = self.graphics_layout.addLayout(
colspan=4
)
# Setup plot that holds plot for single scan signal for current delay
self.widget_pyqtgraph.plots[
"single signal current delay"
] = self.widget_pyqtgraph.plots["upper layout"].addPlot(colspan=2)
self.widget_pyqtgraph.plots["single signal current delay"].setTitle(
"Signal for delay time {} fs and pump frequency ω<sub>3</sub> {} cm<sup>-1</sup>"
)
self.widget_pyqtgraph.plots["single signal current delay"].setLabel(
"bottom", "probe frequency ω<sub>1</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["single signal current delay"].setLabel(
"left", "difference signal [OD]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["single signal current delay"]
)
# Setup plot that holds plot for average signal for current delay
self.widget_pyqtgraph.plots[
"avg signal current delay"
] = self.widget_pyqtgraph.plots["upper layout"].addPlot(colspan=2)
self.widget_pyqtgraph.plots["avg signal current delay"].setTitle(
"Scan averaged signal for delay time {} fs and pump frequency ω<sub>3</sub> {} cm<sup>-1</sup>"
)
self.widget_pyqtgraph.plots["avg signal current delay"].setLabel(
"bottom", "probe frequency ω<sub>1</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["avg signal current delay"].setLabel(
"left", "difference signal [OD]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["avg signal current delay"]
)
# Skip to next row
self.graphics_layout.nextRow()
# Add a sub-layout to hold the 2D plots and the histograms in the second row
self.widget_pyqtgraph.plots["middle layout"] = self.graphics_layout.addLayout(
colspan=4
)
# Setup plot that holds heatmap plot y-axis: probe wavenumber, x-axis: delay
self.widget_pyqtgraph.plots[
"single 2d-signal heatmap"
] = self.widget_pyqtgraph.plots["middle layout"].addPlot()
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].setTitle(
"2D-signal for delay time {} fs"
)
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].setLabel(
"bottom", "probe frequency ω<sub>1</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].setLabel(
"left", "pump frequency ω<sub>3</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["single 2d-signal heatmap"]
)
# Add the HistogramLUTItem to the plotlayout directly after 2D plot
# Also add the item. With that it will be directly drawn at the correct position
self.plot_ref["single 2d-signal heatmap histogram"] = pg.HistogramLUTItem()
self.widget_pyqtgraph.plots["middle layout"].addItem(
self.plot_ref["single 2d-signal heatmap histogram"]
)
self.widget_pyqtgraph.plots[
"avg 2d-signal heatmap"
] = self.widget_pyqtgraph.plots["middle layout"].addPlot()
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].setTitle(
"Scan averaged 2D-signal for delay time {} fs"
)
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].setLabel(
"bottom", "probe frequency ω<sub>1</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].setLabel(
"left", "pump frequency ω<sub>3</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"]
)
self.plot_ref["avg 2d-signal heatmap histogram"] = pg.HistogramLUTItem()
self.widget_pyqtgraph.plots["middle layout"].addItem(
self.plot_ref["avg 2d-signal heatmap histogram"]
)
# Setup colormap for heatmaps
# Credit: https://github.com/pyqtgraph/pyqtgraph/issues/561
colormap = cm.get_cmap("seismic") # cm.get_cmap("CMRmap")
colormap._init()
# [:-3,:] because the last values of the colormap are fringe
# cases which are matplotlib specific and do not define our
# colormap
self.lut = (colormap._lut * 255).view(np.ndarray)[
:-3, :
] # Convert matplotlib colormap from 0-1 to 0 -255 for Qt
# Add next row for the last row of plots. here there are 2
self.graphics_layout.nextRow()
# 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", "probe frequency ω<sub>1</sub> [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.widget_pyqtgraph.plots["std signal"] = self.graphics_layout.addPlot(
row=2, col=1
)
self.widget_pyqtgraph.plots["std signal"].setTitle(
"Standard deviation of pump-probe signal"
)
self.widget_pyqtgraph.plots["std signal"].setLabel(
"bottom", "probe frequency ω<sub>1</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["std signal"].setLabel(
"left", "difference signal [OD]"
)
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["std signal"])
# Setup plot that displays pump spectrum for current fabry perot settings
self.widget_pyqtgraph.plots["pump spectrum"] = self.graphics_layout.addPlot(
row=2, col=2
)
self.widget_pyqtgraph.plots["pump spectrum"].setTitle(
"Current pump spectrum of Fabry Perot"
)
self.widget_pyqtgraph.plots["pump spectrum"].setLabel(
"bottom", "pump frequency ω<sub>3</sub> [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.plots["pump spectrum"].setLabel(
"left", "intensity [a.u.]"
)
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["pump spectrum"])
# 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):
delay_idx = data_container["delay index"]
intl_idx = data_container["interleave index"]
pump_idx = data_container["pump index"]
phase_cycled_signal = data_container["phase cycled signal"]
intp_phase_cycled_signal = data_container["phase cycled signal (interpol)"]
signal = data_container["signal"]
probe_axis = data_container["probe axis"]
pump_axis = data_container["pump axis"]
avg_intensities = data_container["average intensity"]
std_intensities = data_container["std intensity"]
std_signal = data_container["std s2s signal"]
avg_pump_spectrum = data_container["average pump spectrum"]
std_pump_spectrum = data_container["std pump spectrum"]
# Update titles with current delay and pump frequency
self.widget_pyqtgraph.plots["single signal current delay"].setTitle(
"Signal for delay time {} fs and pump frequency ω<sub>3</sub> {:.0f} cm<sup>-1</sup>".format(
self.delays[delay_idx][0], pump_axis[pump_idx]
)
)
self.widget_pyqtgraph.plots["avg signal current delay"].setTitle(
"Scan averaged signal for delay time {} fs and pump frequency ω<sub>3</sub> {:.0f} cm<sup>-1</sup>".format(
self.delays[delay_idx][0], pump_axis[pump_idx]
)
)
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].setTitle(
"2D-signal for delay time {} fs".format(self.delays[delay_idx][0])
)
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].setTitle(
"Scan averaged 2D-signal for delay time {} fs".format(
self.delays[delay_idx][0]
)
)
# Needs to be bigger than two because
# we define two colorbar/histogram items
# in init which are already in plot ref
if len(self.plot_ref) > 2:
# Update signal plots
# Single scan
self.plot_ref["single signal current delay"].setData(
x=probe_axis, y=phase_cycled_signal[1, pump_idx, delay_idx]
)
# Scan averaged
self.plot_ref["avg signal current delay"].setData(
x=probe_axis, y=phase_cycled_signal[0, pump_idx, delay_idx]
)
for i in range(self.interleaves):
self.plot_ref["single interleave {} current delay".format(i)].setData(
x=probe_axis, y=signal[1, pump_idx, delay_idx, i],
)
self.plot_ref["avg interleave {} current delay".format(i)].setData(
x=probe_axis, y=signal[0, pump_idx, delay_idx, i],
)
# -----------Start 2D Plots---------------
# Update 2d image: probe frequency vs. pump frequency
# Single Scan
# Get old levels so it does not rescale the colors everytime (see below)
levels = self.plot_ref["single 2d-signal heatmap histogram"].getLevels()
self.plot_ref["single 2d-signal heatmap"].setImage(
intp_phase_cycled_signal[1]
)
# "Disable" autoscale after 0th scan has run.
if data_container["scan index"] > 0:
# Set to old levels of the histogram
self.plot_ref["single 2d-signal heatmap histogram"].setLevels(*levels)
# Update contour lines
dp.update_contour_lines(
intp_phase_cycled_signal[1],
self.plot_ref["single 2d-signal heatmap contours"],
)
# Scan averaged
# Get old levels so it does not rescale the colors everytime (see below)
levels = self.plot_ref["avg 2d-signal heatmap histogram"].getLevels()
self.plot_ref["avg 2d-signal heatmap"].setImage(intp_phase_cycled_signal[0])
# "Disable" autoscale after 0th scan has run.
if data_container["scan index"] > 0:
# Set to old levels of the histogram
self.plot_ref["avg 2d-signal heatmap histogram"].setLevels(*levels)
# Update contour lines
dp.update_contour_lines(
intp_phase_cycled_signal[0],
self.plot_ref["avg 2d-signal heatmap contours"],
)
# ----------End 2D Plots----------------
# 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 pseudo signal
self.plot_ref["std signal"].setData(x=probe_axis, y=std_signal)
# Update pump spectrum of fabry perot
self.plot_ref["pump spectrum"].setData(x=probe_axis, y=avg_pump_spectrum)
# Update intensity error bars for pump spectrum
self.plot_ref["pump spectrum error bars"].setData(
x=probe_axis, y=avg_pump_spectrum, height=5 * std_pump_spectrum
)
else:
# First time plotting
signal_pen = pg.mkPen(color="#d62728", width=2.5, style=QtCore.Qt.SolidLine)
avg_signal_pen = pg.mkPen(
color="#17becf", width=2.5, style=QtCore.Qt.SolidLine
)
intl_signal_pen = pg.mkPen(
color="#7f7f7f", width=0.7, 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
)
pump_spectrum_pen = pg.mkPen(
color="#8c564b", width=2.5, style=QtCore.Qt.SolidLine
)
bisector_pen = pg.mkPen(
color="#7f7f7f", width=1.5, style=QtCore.Qt.SolidLine
)
# Histogram colormap
seismic = pg.graphicsItems.GradientEditorItem.Gradients["seismic"]
# Plot signal
# Single scan
self.plot_ref["single signal current delay"] = self.widget_pyqtgraph.plots[
"single signal current delay"
].plot(
x=probe_axis,
y=phase_cycled_signal[1, pump_idx, delay_idx],
name="single scan pump-probe signal",
pen=signal_pen,
)
# Scan averaged
self.plot_ref["avg signal current delay"] = self.widget_pyqtgraph.plots[
"avg signal current delay"
].plot(
x=probe_axis,
y=phase_cycled_signal[0, pump_idx, delay_idx],
name="scan averaged pump-probe signal",
pen=avg_signal_pen,
)
for i in range(self.interleaves):
self.plot_ref[
"single interleave {} current delay".format(i)
] = self.widget_pyqtgraph.plots["single signal current delay"].plot(
x=probe_axis,
y=signal[1, pump_idx, delay_idx, i],
name="interleave {}".format(i),
pen=intl_signal_pen,
)
self.plot_ref[
"avg interleave {} current delay".format(i)
] = self.widget_pyqtgraph.plots["avg signal current delay"].plot(
x=probe_axis,
y=signal[0, pump_idx, delay_idx, i],
name="interleave {}".format(i),
pen=intl_signal_pen,
)
# Plot 2d image: probe vs pump frequency (wavenumber)
# Single scan
self.plot_ref["single 2d-signal heatmap"] = pg.ImageItem(
intp_phase_cycled_signal[1]
)
# Generate a scrollable colorbar
# This generates a histogram with
# which it is possible to scale the
# Data which will be displayed in
# the heatmaps
# First create a reference for the histogram
# which containes the image item "single time-signal heatmap"
# This basically means, that HistogramLUTItem contains the data
# from the heatmap
self.plot_ref["single 2d-signal heatmap histogram"].setImageItem(
self.plot_ref["single 2d-signal heatmap"]
)
# Set the color levels of the histogram
self.plot_ref["single 2d-signal heatmap histogram"].gradient.restoreState(
seismic
)
# Scale image to match axes
dp.scale_img(
probe_axis,
pump_axis,
intp_phase_cycled_signal[1],
self.plot_ref["single 2d-signal heatmap"],
)
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].addItem(
self.plot_ref["single 2d-signal heatmap"]
)
self.plot_ref["single 2d-signal heatmap"].setLookupTable(self.lut)
# Generate contour lines
self.plot_ref[
"single 2d-signal heatmap contours"
] = dp.generate_contour_lines(
intp_phase_cycled_signal[1], self.plot_ref["single 2d-signal heatmap"]
)
# Add bisector to plot (we do not add it to plot_ref because we are not going to change it, ever)
bisector = pg.InfiniteLine(angle=45, pen=bisector_pen)
bisector.setZValue(10)
self.widget_pyqtgraph.plots["single 2d-signal heatmap"].addItem(bisector)
# Plot 2d image: probe vs pump frequency (wavenumber)
# Scan averaged
self.plot_ref["avg 2d-signal heatmap"] = pg.ImageItem(
intp_phase_cycled_signal[0]
)
self.plot_ref["avg 2d-signal heatmap histogram"].setImageItem(
self.plot_ref["avg 2d-signal heatmap"]
)
# Set the color levels of the histogram
self.plot_ref["avg 2d-signal heatmap histogram"].gradient.restoreState(
seismic
)
dp.scale_img(
probe_axis,
pump_axis,
intp_phase_cycled_signal[0],
self.plot_ref["avg 2d-signal heatmap"],
)
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].addItem(
self.plot_ref["avg 2d-signal heatmap"]
)
self.plot_ref["avg 2d-signal heatmap"].setLookupTable(self.lut)
# Generate contour lines
self.plot_ref["avg 2d-signal heatmap contours"] = dp.generate_contour_lines(
intp_phase_cycled_signal[1], self.plot_ref["avg 2d-signal heatmap"]
)
# Add bisector to plot (we do not add it to plot_ref because we are not going to change it, ever)
bisector = pg.InfiniteLine(angle=45, pen=bisector_pen)
bisector.setZValue(10)
self.widget_pyqtgraph.plots["avg 2d-signal heatmap"].addItem(bisector)
# 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 signal"] = self.widget_pyqtgraph.plots[
"std signal"
].plot(
x=probe_axis,
y=std_signal,
name="Standard deviation of pump-probe signal",
pen=std_signal_pen,
)
# Plot pump spectrum of fabry perot
self.plot_ref["pump spectrum"] = self.widget_pyqtgraph.plots[
"pump spectrum"
].plot(
x=probe_axis,
y=avg_pump_spectrum,
name="Pump spectrum resulting from Fabry Perot",
pen=pump_spectrum_pen,
)
# Create intensity error bars for pump spectrum
self.plot_ref["pump spectrum error bars"] = pg.ErrorBarItem(
x=probe_axis,
y=avg_pump_spectrum,
height=5 * std_pump_spectrum,
beam=0.3,
pen=std_intensity_pen,
)
self.widget_pyqtgraph.plots["pump spectrum"].addItem(
self.plot_ref["pump spectrum error bars"]
)