![]() Exactly, exporting SVG in Veusz and editing in Inkscape is very useful. This is what I have been able to do so far and this is what I want to achieve (I painted in red what I was NOT able to do so far). Apparently this is impossible because the inter-graph space is 0, is that so? This means I need to see the X-axis information related to the graph at col1, row4 and at graph col2,row3. Problem 2) the labels of the X axis: I need them to appear in the last graphs of both columns. I kind of solved this with a dirty trick (and would add it using Inkscape as last resort), so the remaining problem now is the following: Problem 1) I would like to place the legend (veusz's "key") in the "empty" space (ie where the is one "missing" graph, see the images linked below). Obviously both X and Y axis are the same for all graphs, so I can use the Y axis label (title) from the graph at row1,col2. I need them to be as close as possible so the inter-graph space is close to 0. This means I have 4 graphs on the left column and 3 graphs in the right column. I need to fit 7 graphs in one figure, so I created a grid (2 columns, 4 rows). I am new to reddit so I apologies if I make mistakes. The PyQtGraph repository on Github also has complete set of more complex example plots in Plotting.py (shown below).Hi, I love this plotting software and I appreciate the authors for making it free software for all. For a complete overview of PyQtGraph methods and capabilities see the PyQtGraph Documentation & API Reference. In this tutorial we've discovered how to draw simple plots with PyQtGraph and customize lines, markers and labels. Self.data_tData(self.x, self.y) # Update the data. ![]() Self.y.append( randint(0,100)) # Add a new random value. Self.x.append(self.x + 1) # Add a new value 1 higher than the last. Self.x = self.x # Remove the first y element. We define this timer in the _init_ block so it is automatically started. We're going to update our data every 50ms, although PyQtGraph can plot data much more quickly than this it can get hard to watch! To do this we define a Qt timer, and set it to call a custom method update_plot_data where we'll change the data. Self.data_line = (self.x, self.y, pen=pen) Self.x = list(range(100)) # 100 time points Clearing the plotįinally, sometimes you might want to clear and refresh the plot periodically. Play around with this function, customising your markers, line widths, colours and other parameters. (x, y, name=plotname, pen=pen, symbol='+', symbolSize=30, symbolBrush=(color)) The code below will set the background to white, by passing in the string 'w'. setBackground on our PlotWidget instance (in aphWidget). Background Colourīeginning with the app skeleton above, we can change the background colour by calling. However, PyQtGraph provides an API for using these to draw plots and manage the plot canvas.īelow we'll go through the most common styling features you'll need to create and customize your own plots. This gives us access to all the standard Qt line and shape styling options for use in plots. PyQtGraph uses Qt's QGraphicsScene to render the graphs. In the next section we'll look at what options we have available to us in PyQtGraph to improve the appearance and usability of our plots. The default plot style of PyQtGraph is quite bare - a black background with a thin (barely visible) white line. The custom PyQtGraph widget showing dummy data. You an import and use it as import pyqtgraph if you prefer. This is a common convention in PyQtGraph examples to keep things tidy & reduce typing. In all our examples below we import PyQtGraph using import pyqtgraph as pg. Python from PySide2.QtWidgets import QApplication, QMainWindow To be able to use PyQtGraph with PySide you first need to install the package to your Python environment. In this tutorial we'll walk through the first steps of creating a plot widget with PyQtGraph and then demonstrate plot customization using line colours, line type, axis labels, background colour and plotting multiple lines. PyQtGraph is built on top of Qt's native QGraphicsScene giving better drawing performance, particularly for live data, as well as providing interactivity and the ability to easily customize plots with Qt graphics widgets. For simple and highly interactive plots you may want to consider using PyQtGraph instead. While it is possible to embed matplotlib plots in PySide the experience does not feel entirely native. Buiding GUI applications with PySide gives you access to all these Python tools directly from within your app, allowing you to build complex data-driven apps and interactive dashboards. One of the major strengths of Python is in exploratory data science and visualization, using tools such as Pandas, numpy, sklearn for data analysis and matplotlib plotting.
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