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Data Slicing using PyQtGraph

  • Last Updated : 12 Nov, 2020
Geek Week

In this article, we will see how we can perform data slicing using PyQtGraph module in Python. PyQtGraph is a graphics and user interface library for Python that provides functionality commonly required in designing and science applications. Its primary goals are to provide fast, interactive graphics for displaying data (plots, video, etc.) and second is to provide tools to aid in rapid application development (for example, property trees such as used in Qt Designer).

In order to install the PyQtGraph we use the command given below

pip install pyqtgraph

A slice in a multidimensional array is a column of data corresponding to a single value for one or more members of the dimension. Slicing is the act of divvying up the cube to extract this information for a given slice. It is important because it helps the user visualize and gather information specific to a dimension. When you think of slicing, think of it as a specialized filter for a particular value in a dimension. A simple data-slicing task would be for given 3D data selecting a 2D plane and interpolate data along that plane to generate a slice image.
 

In order to do this, we have to do the following 

  • Import the required libraries like pyqtgraph, pyqt5 and numpy 
  • Create a main window class using pyqt5 
  • Create a graph window to add the widgets required to show the slicing 
  • Create two image view object in the layout, first to show the whole 3d data and second to show the slice 
  • Create a roi object and add it to the first image view to select the slice 
  • Create a 3d data and add it to the image view 
  • Connect a update method to the roi object when the region is changed, inside the update method get the region and set it to the second image view 
  • Add this graph window to the main window layout with any additional widgets. 

Below is the implementation.



Python3




# importing Qt widgets
from PyQt5.QtWidgets import *
  
# importing system
import sys
  
# importing numpy as np
import numpy as np
  
# importing pyqtgraph as pg
import pyqtgraph as pg
from PyQt5.QtGui import *
from PyQt5.QtCore import *
  
  
class Window(QMainWindow):
  
    def __init__(self):
        super().__init__()
  
        # setting title
        self.setWindowTitle("PyQtGraph")
  
        # setting geometry
        self.setGeometry(100, 100, 700, 550)
  
        # icon
        icon = QIcon("skin.png")
  
        # setting icon to the window
        self.setWindowIcon(icon)
  
        # calling method
        self.UiComponents()
  
        # showing all the widgets
        self.show()
  
    # method for components
    def UiComponents(self):
  
        # creating a widget object
        widget = QWidget()
  
        # text
        text = "Data Slicing"
  
        # creating a label
        label = QLabel(text)
  
        # setting minimum width
        label.setMinimumWidth(130)
  
        # making label do word wrap
        label.setWordWrap(True)
  
        # creating a window for graphs
        win = QMainWindow()
  
        # creating a widget object
        cwid = QWidget()
  
        # setting central widget to the graph window
        win.setCentralWidget(cwid)
  
        # creating a grid layout
        lay = QGridLayout()
  
        # setitng grid layout to central widget of graphs
        cwid.setLayout(lay)
  
        # creating a image view objects
        imv1 = pg.ImageView()
        imv2 = pg.ImageView()
  
        # adding image view objects to the layout
        lay.addWidget(imv1, 0, 0)
        lay.addWidget(imv2, 1, 0)
  
        # creating a ROI object for selecting slice
        roi = pg.LineSegmentROI([[30, 64], [100, 64]], pen='r')
  
        # add roi to image view 1
        imv1.addItem(roi)
  
        # creating 3d data
        # x value using numpy
        x1 = np.linspace(-30, 10, 128)[:, np.newaxis, np.newaxis]
        x2 = np.linspace(-20, 20, 128)[:, np.newaxis, np.newaxis]
  
        # y value using numpy
        y = np.linspace(-30, 10, 128)[np.newaxis, :, np.newaxis]
        z = np.linspace(-20, 20, 128)[np.newaxis, np.newaxis, :]
  
        # dimension 1 values
        d1 = np.sqrt(x1 ** 2 + y ** 2 + z ** 2)
  
        # dimension 2 values
        d2 = 2 * np.sqrt(x1[::-1] ** 2 + y ** 2 + z ** 2)
  
        # dimension 3 value
        d3 = 4 * np.sqrt(x2 ** 2 + y[:, ::-1] ** 2 + z ** 2)
  
        # whole data ie all 3 dimensions
        data = (np.sin(d1) / d1 ** 2) + \
            (np.sin(d2) / d2 ** 2) + (np.sin(d3) / d3 ** 2)
  
        # method to update the image view 2
        def update():
  
            # get the roi selected data from image view 1
            d2 = roi.getArrayRegion(data, imv1.imageItem, axes=(1, 2))
  
            # update the image view 2 data
            imv2.setImage(d2)
  
        # adding update method to the roi
        # when region is changed this method get called
        roi.sigRegionChanged.connect(update)
  
        # Display the data in both image view
        imv1.setImage(data)
  
        # setting the range of image view
        imv1.setHistogramRange(-0.01, 0.01)
  
        # setting levels of the image view
        imv1.setLevels(-0.003, 0.003)
  
        # call the update method
        update()
  
        # Creating a grid layout
        layout = QGridLayout()
  
        # minimum width value of the label
        label.setMinimumWidth(130)
  
        # setting this layout to the widget
        widget.setLayout(layout)
  
        # adding label in the layout
        layout.addWidget(label, 1, 0)
  
        # plot window goes on right side, spanning 3 rows
        layout.addWidget(win, 0, 1, 3, 1)
  
        # setting this widget as central widget of the main widow
        self.setCentralWidget(widget)
  
  
# create pyqt5 app
App = QApplication(sys.argv)
  
# create the instance of our Window
window = Window()
  
# start the app
sys.exit(App.exec())

Output : 
 

 

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