"""
Providing the "Scan t zero (Interleaves)" experiment. This experiment is
used to find the position on the stage (e.g. UV/VIS stage or IR stage)
where the pump pulse and the probe pulse temporally overlap perfectly
(so called t zero). The experiment displays the first and second
derivative of the signal with respect to time for the central pixel.
This can be used to identify the t zero position. Usually the maximum of
the first derivative is the position of maximum temporal overlap. This
provides estimates which the user can choose to use as the new t zero
position. For this experiment the chopper has to be running. If set,
interleaves are used to phase cycle the signal. Please note, for UV/VIS
pump pulses phase cycling is not necessary. In such case, set
interleaves equal to 1 (or do not specify it) on the GUI for UV/VIS
experiments.
Note:
**Interferometer:**
It is also possible to use the scan t zero experiment to obtain
the t zero of the interferometer stage. This t zero is the
position of maximum temporal overlap of the static and movable
path of the Michelson interferometer (temporal overlap of the
two pump pulses in this case). Block the moving path of the
interferometer. Then scan t zero for the **IR pump-probe** delay
stage. Reset the t zero of the **IR pump-probe** delay stage
accordingly. Now unblock the moving path and block the static
interferometer path and execute the scan t zero experiment for
the **IR pump-probe** delay stage again. The now obtained t zero
value is the temporal distance between the static and the
movable path of the interferometer for the current position of
the interferometer stage. Adding this value to the current
position of the interferometer stage results in the t zero of
the interferometer stage. Move the interferometer stage
accordingly and reset its t zero.
#######################
Step by Step Algorithm:
#######################
**Acquisition:**
1. Preallocate dictionary (data container) which will contain data
and information about scan index, delay index etc.
2. Calculate interleave positions for the current delay
3. Set the number of samples to acquire to account for weights
specified for the current delay as specified in the delay file
4. Move to the interleaves (the 0th interleave is the actual delay)
5. Read the data from the ADC
6. 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 a new axis, namely the
different interleaves are added in addition
to the delay axis. Note that the interleaves
are not "sorted" into separate states because we already
know beforehand which interleave was measured
(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. Average the data by weighting equally
8. Put this information into data container and hand it over to
secondary processing
9. Save data (and raw data) including counts and weights
if respective checkboxes on GUI were checked
**Secondary Processing:**
1. Calculate the phase cycled transmissions by averaging
2. Calculate the phase cycled absorption (-log10)
3. Calculate the non phase cycled absorption (-log10)
of the sorted data
4. Calculate the pump probe difference signal (chopper high - chopper low)
5. Calculate the first and second derivative of the signal of the
central pixel with respect to time
6. Find maximum of first derivative of averaged signal
because this should be the t zero value
7. Calculate the average intensities and standard deviation of
the intensities for each pixel
8. Calculate the shot to shot difference signal and its statistical
properties
9. Put this information into data container and hand it over to
Pyqtplotting thread
10. 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:
* Time-signal heatmap with histogram
* Signal (for current interleave)
* Intensities and their standard deviation (multiplied by 5)
* Standard deviation of shot to shot signal
* Time-signal (phase cycled and non phase cycled)
(central pixel)
* Time-signal 1st derivative (central pixel)
* Time-signal 2nd derivative (central pixel)
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_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/dXXX** 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 (difference 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.
**probe_wn_axis.npy** contains the wavenumber axis which is generated by
the spectrometer triax.py class.
**delays.npy** contains the delays including weights.
.. code-block::
username/
├── date1_experimentname1_000/
│ ├── averaged_data
│ │ ├── d000_date1_experimentname1_000.npy
│ │ ├── ...
│ │ └── d999_date1_experimentname1_000.npy
│ ├── figures
│ ├── hardware config
│ ├── raw data
│ │ ├── delay000
│ │ │ ├── s000000_d000_intl000_date1_experimentname1_000_raw.npy
│ │ │ ├── s000000_d000_intl001_date1_experimentname1_000_raw.npy
│ │ │ ├── ...
│ │ │ └── s000099_d000_intl016_date1_experimentname1_000_raw.npy
│ │ ├── ...
│ │ └── delay999
│ ├── scans
│ │ ├── delay000
│ │ │ ├── s000000_d000_date1_experimentname1_000.npy
│ │ │ ├── s000000_d000_counts_date1_experimentname1_000.npy
│ │ │ ├── s000000_d000_weights_date1_experimentname1_000.npy
│ │ │ ├── ...
│ │ │ └── s000099_d000_date1_experimentname1_000.npy
│ │ ├── ...
│ │ └── delay999
│ ├── probe_wn_axis_date1_experimentname1_000.npy
│ ├── delays_date1_experimentname1_000.npy
│ ├── setupinfo_date1_experimentname1_000.txt
│ ├── notes_date1_experimentname1_000.txt
│ └── background_date1_experimentname1_000.npy
├── date1_experimentname1_001/
├── date2_experimentname1_000/
└── date2_experimentname2_000/
"""
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
# this is important! Otherwise the axis order is reversed w.r.t. numpy arrays!
# pg.setConfigOptions(imageAxisOrder='row-major')
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 analog_digital_converter import AnalogDigitalConverter as ADC
from triax import Triax as Spectrometer
from pi_control import PiStage
from scipy.signal import find_peaks
from save_data import SaveData, Background
[docs]class ScanTZeroInterleaves:
"""
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)
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.
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,
interleaves: int,
adc: ADC,
delay_stage: PiStage,
prl: PRL,
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()
# 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(background_handler.load_background(), "background")
# Get index of central pixel in MCT array
# ? Not sure if this works for all analog input
# ? configurations
self.central_pixel = adc.probe_pixel_idx.size // 2
self.acquisition = Acquisition(
delays, interleaves, adc, delay_stage, spectrometer, self.acq_queue
)
self.primary_processing = PrimaryProcessing(
self.acq_queue,
self.processing_queue,
delays,
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,
interleaves,
adc.probe_pixel_idx,
self.central_pixel,
saver,
)
self.plotting = PyqtPlotting(
widget_pyqtgraph,
adc,
self.plot_queue,
delays,
interleaves,
self.central_pixel,
)
[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)
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.
acq_queue (Queue): Multiprocessing queue object that the
acquisition thread uses to pass data to the primary
processing process.
"""
def __init__(
self,
delays: ndarray,
interleaves: int,
adc: ADC,
delay_stage,
spectrometer: Spectrometer,
acq_queue,
):
threading.Thread.__init__(self)
self.delays = delays
self.interleaves = interleaves
self.adc = adc
self.delay_stage = delay_stage
self.spectrometer = spectrometer
# Save the base amount of samples
# to acquire
self.base_samples = self.adc.samples_to_acquire
# 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():
for d_idx, delay in enumerate(self.delays):
# Generate interleave positions of
# IR delay stage
interleave_pos = dp.calculate_interleave_array(
self.interleaves, self.spectrometer.wavelength, delay[0]
)
# Set samples to acquire according to weight
# for this delay
samples_to_acquire = int(round(self.base_samples * 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()
# Give data of acquisition to queue
self.acq_queue.put(self.data_container.copy())
# Update scan idx
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)
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,
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 linearization
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
# intensity 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_delays, n_interleaves, n_probe_pixels, n_chopper_states)
self.data = np.zeros(
(
2,
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)
[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"]
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, delay_idx, intl_idx)
(
self.data[idx],
self.weights[idx],
self.counts[idx],
statistics,
) = dp.sort_data(transmission, chopper_states, self.n_chopper_states)
# 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 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, delay_idx, intl_idx] = np.average(
self.data[:, delay_idx, intl_idx],
axis=0,
weights=self.counts[:, delay_idx, intl_idx],
)
# 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
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, delay_idx], scan_idx, delay_idx=delay_idx
)
self.saver.save_avg(self.data[0, delay_idx], delay_idx=delay_idx)
self.saver.save_counts(
self.counts[1, delay_idx], scan_idx, delay_idx=delay_idx
)
self.saver.save_weights(
self.weights[1, delay_idx], scan_idx, delay_idx=delay_idx
)
if self.saver.raw_data:
self.saver.save_raw_data(
raw_data, scan_idx, delay_idx=delay_idx, intlv=intl_idx
)
# Update counts
self.counts[0, delay_idx, intl_idx] += self.counts[1, 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)
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)
central_pixel (int): Index of the central pixel of the detector.
Used to display the signal on the central pixel.
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,
interleaves: int,
probe_pixel_idx: ndarray,
central_pixel: int,
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
# The explicit delays are needed for the calculation of the
# finite difference derivatives
self.delays = delays
# 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
self.central_pixel = central_pixel
# 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, delays.shape[0], self.interleaves, self.probe_pixel_idx.size)
)
self.phase_cycled_signal = np.zeros(
(2, delays.shape[0], self.probe_pixel_idx.size)
)
[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 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"]
# Phase cycle transmissions if last interleave was
# reached
# * 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.
# phase cycled = scatter free
if intl_idx == self.interleaves - 1:
phase_cycled_transmission = np.average(
sorted_data[:, delay_idx], axis=1
)
# Calculate absorption of phase cycled transmissions
# pc_abs = phase cycled absorption
pc_abs = -np.log10(phase_cycled_transmission)
# Calculate phase cycled signal
self.phase_cycled_signal[:, delay_idx] = (
pc_abs[:, :, 1] - pc_abs[:, :, 0]
)
# Calculate absorption of non phase cycled transmission
absorption = -np.log10(sorted_data[:, delay_idx, intl_idx])
self.signal[:, delay_idx, intl_idx] = (
absorption[:, :, 1] - absorption[:, :, 0]
)
#!!!!! Move to data processing
# Calculate 1st and 2nd derivate of
# the phase cycled signal
# using central finite differences
# of the central probe pixel
# (Computing this outside the if statement is slow and
# unnecessary but at least the plotting works in all cases)
# d signal(t) / dt. t: pump probe delay time
self.signal_dt = np.gradient(
self.phase_cycled_signal[:, :, self.central_pixel],
self.delays[:, 0],
axis=1,
)
# d^2 signal(t) / dt^2
self.signal_dt2 = np.gradient(self.signal_dt, self.delays[:, 0], axis=1)
# Find maximum of first derivative of averaged data
# because this should be t zero
t_zero_guess_idx, _ = find_peaks(
self.signal_dt[0], height=0.8 * self.signal_dt.max()
)
#!!!!!
# Generate "smooth" array that is going to be used for
# 2D Heatmap plot using the phase cycled signal
# Computing this outside the if statement is slow and
# unnecessary but at least the plotting works in all cases
signal_intp_single = dp.generate_img_data(
self.delays[:, 0],
data_container["probe axis"],
self.phase_cycled_signal[1].T,
)
# 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) difference signal
# and its statistical information
(
_,
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],
)
# Add everything to data container
# and give to plot queue
data_container["signal"] = self.signal
data_container["phase cycled signal"] = self.phase_cycled_signal
data_container[
"single signal (interpol)"
] = signal_intp_single # This is also phase cycled
data_container[
"d signal(t) / dt"
] = self.signal_dt # This is also phase cycled
data_container[
"d^2 signal(t) / dt^2"
] = self.signal_dt2 # This is also phase cycled
data_container["t zero guess"] = self.delays[t_zero_guess_idx, 0]
data_container["central pixel"] = self.central_pixel
data_container["average intensity"] = avg_intensities
data_container["std intensity"] = std_intensities
data_container["std s2s signal"] = s2s_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
# 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]
# 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
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")
# ToDO
# if self.saver:
# Create (matplotlib) figure that will be saved
# self.fig, self.ax = plt.subplots(nrows=4)
# ToDO : CREATE PLOTTTT
[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)
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.
central_pixel (int): Index of the central pixel of the detector.
Used to display the signal on the central pixel.
"""
[docs] class Signals(QObject):
new_data = pyqtSignal(dict)
def __init__(
self,
widget_pyqtgraph,
adc: ADC,
plot_queue,
delays: ndarray,
interleaves: int,
central_pixel: 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.interleaves = interleaves
self.central_pixel = central_pixel
# 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
# Here we have to do a row-style layouting because the
# three plots on the right have to be spaced equally
self.widget_pyqtgraph.plots["left layout"] = self.graphics_layout.addLayout()
# Setup plot that holds heatmap plot y-axis: probe wavenumber, x-axis: delay
self.widget_pyqtgraph.plots[
"time-signal heatmap"
] = self.widget_pyqtgraph.plots["left layout"].addPlot()
self.widget_pyqtgraph.plots["time-signal heatmap"].setTitle(
"Signal with respect to delay time"
)
self.widget_pyqtgraph.plots["time-signal heatmap"].setLabel(
"bottom", "pump-probe delay [fs]"
)
self.widget_pyqtgraph.plots["time-signal heatmap"].setLabel(
"left", "wavenumber [cm<sup>-1</sup>]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["time-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["time-signal heatmap histogram"] = pg.HistogramLUTItem()
self.widget_pyqtgraph.plots["left layout"].addItem(
self.plot_ref["time-signal heatmap histogram"]
)
# Setup colormap for heatmap
# 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
# Skip to next row of the left layout
# to place the next plot underneath the
# top one
self.widget_pyqtgraph.plots["left layout"].nextRow()
# Setup plot that displays pump probe signal
self.widget_pyqtgraph.plots["signal"] = self.widget_pyqtgraph.plots[
"left layout"
].addPlot(colspan=2)
self.widget_pyqtgraph.plots["signal"].setTitle("Average signal")
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"])
self.widget_pyqtgraph.plots["left layout"].nextRow()
# Create another layout containing the two
# plots in the last row of the left layout
# This is needed as they will be spaced weiredly
# if we don't do it
self.widget_pyqtgraph.plots["bottom left layout"] = self.widget_pyqtgraph.plots[
"left layout"
].addLayout(colspan=2)
# Setup plot that displays intensities
self.widget_pyqtgraph.plots["intensities"] = self.widget_pyqtgraph.plots[
"bottom left layout"
].addPlot()
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
self.widget_pyqtgraph.plots["std signal"] = self.widget_pyqtgraph.plots[
"bottom left layout"
].addPlot()
self.widget_pyqtgraph.plots["std signal"].setTitle(
"Standard deviation of pump-probe signal"
)
self.widget_pyqtgraph.plots["std signal"].setLabel(
"bottom", "wavenumber [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"])
# Create a sub-layout for the right side of the window
self.widget_pyqtgraph.plots["right layout"] = self.graphics_layout.addLayout()
# Setup plot that holds plot for signal of central pixel for each delay
self.widget_pyqtgraph.plots["time-signal"] = self.widget_pyqtgraph.plots[
"right layout"
].addPlot()
self.widget_pyqtgraph.plots["time-signal"].setTitle(
"Signal of central pixels with respect to delay time"
)
self.widget_pyqtgraph.plots["time-signal"].setLabel(
"bottom", "pump-probe delay [fs]"
)
self.widget_pyqtgraph.plots["time-signal"].setLabel("left", "signal [OD]")
self.widget_pyqtgraph.set_style(self.widget_pyqtgraph.plots["time-signal"])
self.widget_pyqtgraph.plots["right layout"].nextRow()
# Setup plot that holds plot for 1st derivative signal of central pixel for each delay
self.widget_pyqtgraph.plots[
"time-signal 1st derivative"
] = self.widget_pyqtgraph.plots["right layout"].addPlot()
self.widget_pyqtgraph.plots["time-signal 1st derivative"].setTitle(
"1st derivative of signal of central pixels with respect to delay time"
)
self.widget_pyqtgraph.plots["time-signal 1st derivative"].setLabel(
"bottom", "pump-probe delay [fs]"
)
self.widget_pyqtgraph.plots["time-signal 1st derivative"].setLabel(
"left", "∂signal/∂t [OD]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["time-signal 1st derivative"]
)
self.widget_pyqtgraph.plots["right layout"].nextRow()
# Setup plot that holds plot for 2nd derivative signal of central pixel for each delay
self.widget_pyqtgraph.plots[
"time-signal 2nd derivative"
] = self.widget_pyqtgraph.plots["right layout"].addPlot()
self.widget_pyqtgraph.plots["time-signal 2nd derivative"].setTitle(
"2nd derivative of signal of central pixels with respect to delay time"
)
self.widget_pyqtgraph.plots["time-signal 2nd derivative"].setLabel(
"bottom", "pump-probe delay [fs]"
)
self.widget_pyqtgraph.plots["time-signal 2nd derivative"].setLabel(
"left", "∂<sup>2</sup>signal/∂t<sup>2</sup> [OD]"
)
self.widget_pyqtgraph.set_style(
self.widget_pyqtgraph.plots["time-signal 2nd derivative"]
)
# 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"]
# Non phase cycled data
signal = data_container["signal"]
# Phase cycled data
phase_cycled_signal = data_container["phase cycled signal"]
signal_intp_single = data_container["single signal (interpol)"]
signal_dt = data_container["d signal(t) / dt"]
signal_dt2 = data_container["d^2 signal(t) / dt^2"]
t_zero_guess = data_container["t zero guess"]
probe_axis = data_container["probe axis"]
avg_intensities = data_container["average intensity"]
std_intensities = data_container["std intensity"]
std_signal = data_container["std s2s signal"]
# Update title with current t_zero estimate
self.widget_pyqtgraph.plots["time-signal"].setTitle(
"Signal of central pixels with respect to delay time, t0 estimate: {} fs".format(
t_zero_guess
)
)
# Needs to be bigger than one because
# we define two colorbar/histogram items
# in init which are already in plot ref
if len(self.plot_ref) > 1:
# Update 2d image: time vs. signal(wavenumber)
# We only display the non averaged data
# Get old levels so it does not rescale the colors everytime (see below)
levels = self.plot_ref["time-signal heatmap histogram"].getLevels()
self.plot_ref["time-signal heatmap"].setImage(signal_intp_single)
# "Disable" autoscale after 0th scan has run.
if data_container["scan index"] > 0:
# Set to old levels of the histogram
self.plot_ref["time-signal heatmap histogram"].setLevels(*levels)
# Update contour lines
dp.update_contour_lines(
signal_intp_single, self.plot_ref["time-signal heatmap contours"]
)
# Update time vs signal plots
# Single scan interleaves
for i in range(self.interleaves):
self.plot_ref["time-signal single interleave {}".format(i)].setData(
x=self.delays[:, 0], y=signal[1, :, i, self.central_pixel]
)
# Scan averaged
self.plot_ref["time-signal avg"].setData(
x=self.delays[:, 0], y=phase_cycled_signal[0, :, self.central_pixel]
)
# Single scan
self.plot_ref["time-signal single"].setData(
x=self.delays[:, 0], y=phase_cycled_signal[1, :, self.central_pixel]
)
# Update time vs signal 1st derivative
self.plot_ref["time-signal 1st derivative avg"].setData(
x=self.delays[:, 0], y=signal_dt[0]
)
self.plot_ref["time-signal 1st derivative single"].setData(
x=self.delays[:, 0], y=signal_dt[1]
)
# Update time vs signal plots 2nd derivative
self.plot_ref["time-signal 2nd derivative avg"].setData(
x=self.delays[:, 0], y=signal_dt2[0]
)
self.plot_ref["time-signal 2nd derivative single"].setData(
x=self.delays[:, 0], y=signal_dt2[1]
)
# Update signal plot
self.plot_ref["signal"].setData(
x=probe_axis, y=signal[1, delay_idx, intl_idx]
)
# 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)
else:
# First time plotting
avg_time_signal_pen = pg.mkPen(
color="#17becf", width=2.5, style=QtCore.Qt.SolidLine
)
single_time_signal_pen = pg.mkPen(
color="#d62728", width=2.5, style=QtCore.Qt.SolidLine
)
intl_signal_pen = pg.mkPen(
color="#A9A9A9", width=1.2, style=QtCore.Qt.SolidLine
)
signal_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
)
# Histogram colormap
seismic = pg.graphicsItems.GradientEditorItem.Gradients["seismic"]
# Plot 2d image: time vs. signal(wavenumber)
# We only display the non averaged data
self.plot_ref["time-signal heatmap"] = pg.ImageItem(signal_intp_single)
# 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["time-signal heatmap histogram"].setImageItem(
self.plot_ref["time-signal heatmap"]
)
# Set the color levels of the histogram
self.plot_ref["time-signal heatmap histogram"].gradient.restoreState(
seismic
)
# Scale image to match axes
dp.scale_img(
self.delays[:, 0],
probe_axis,
signal_intp_single,
self.plot_ref["time-signal heatmap"],
)
self.widget_pyqtgraph.plots["time-signal heatmap"].addItem(
self.plot_ref["time-signal heatmap"]
)
self.plot_ref["time-signal heatmap"].setLookupTable(self.lut)
# Generate contour lines
self.plot_ref["time-signal heatmap contours"] = dp.generate_contour_lines(
signal_intp_single, self.plot_ref["time-signal heatmap"]
)
# Plot time vs signal
# Single scan interleaves
for i in range(self.interleaves):
self.plot_ref[
"time-signal single interleave {}".format(i)
] = self.widget_pyqtgraph.plots["time-signal"].plot(
x=self.delays[:, 0],
y=signal[1, :, i, self.central_pixel],
name="interleave {}".format(i),
pen=intl_signal_pen,
)
# Scan averaged (phase cycled)
self.plot_ref["time-signal avg"] = self.widget_pyqtgraph.plots[
"time-signal"
].plot(
x=self.delays[:, 0],
y=phase_cycled_signal[0, :, self.central_pixel],
name="average pump-probe signal",
pen=avg_time_signal_pen,
)
# Single scan (phase cycled)
self.plot_ref["time-signal single"] = self.widget_pyqtgraph.plots[
"time-signal"
].plot(
x=self.delays[:, 0],
y=phase_cycled_signal[1, :, self.central_pixel],
name="single pump-probe signal",
pen=single_time_signal_pen,
)
# Plot time vs signal 1st derivative
self.plot_ref[
"time-signal 1st derivative avg"
] = self.widget_pyqtgraph.plots["time-signal 1st derivative"].plot(
x=self.delays[:, 0],
y=signal_dt[0],
name="average 1st derivative pump-probe signal",
pen=avg_time_signal_pen,
)
self.plot_ref[
"time-signal 1st derivative single"
] = self.widget_pyqtgraph.plots["time-signal 1st derivative"].plot(
x=self.delays[:, 0],
y=signal_dt[1],
name="single 1st derivative pump-probe signal",
pen=single_time_signal_pen,
)
# Plot time vs signal 2nd derivative
self.plot_ref[
"time-signal 2nd derivative avg"
] = self.widget_pyqtgraph.plots["time-signal 2nd derivative"].plot(
x=self.delays[:, 0],
y=signal_dt2[0],
name="average 2nd derivative pump-probe signal",
pen=avg_time_signal_pen,
)
self.plot_ref[
"time-signal 2nd derivative single"
] = self.widget_pyqtgraph.plots["time-signal 2nd derivative"].plot(
x=self.delays[:, 0],
y=signal_dt2[1],
name="single 2nd derivative pump-probe signal",
pen=single_time_signal_pen,
)
# Plot signal
self.plot_ref["signal"] = self.widget_pyqtgraph.plots["signal"].plot(
x=probe_axis,
y=signal[1, delay_idx, intl_idx],
name="pump-probe signal (single scan current interleave)",
pen=signal_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 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,
)
# self.widget_pyqtgraph.disable_autoscale()