![]() ![]() Alter the aspect ratio of the plotting area using ax.set_aspect().This can be done in a for loop and is demonstrated in the example below. set_color() method of the spines (Matplotlib’s word for the axis lines). To colour the axis lines (eg if you want them to match your gridlines) you will need to use the.Use ax.set_axisbelow(True) after adding gridlines to move them behind your data points.If you want minor gridlines and axis ticks you will also need to use plt.minorticks_on().Gridlines: use the plt.grid() function in which you can set which gridlines to mark (major, minor or both) and the axis to apply the lines to (x, y or both), along with other keyword arguments related to line plots.Colour of the graph area: use the ax.set_facecolor((R, G, B)) function where R, G and B are the red, green and blue colour proportions on a scale of 0 to 1.Transparency of the line: use the alpha keyword argument within ax.plot().Some more options that can be tinkered with: It would also have been possible to produce this plot using functions directly from Matplotlib (and these would have started with plt instead of ax) although this page is specifically demonstrating how to produce plots using axes objects. Notice that the formatting options in the above example are all being changed by accessing the axes object, which is what the ax at the start of each line is indicating. Change the line colour and width using the c and lw keyword arguments in the ax.plot() call (see this page for all the colour and line options).Change the axis limits with ax.set_ylim() and ax.set_xlim().Set the axis labels with ax.set_ylabel() and ax.set_xlabel().The y-data was then created by taking the square of the x-data.Ĭhange the look of the plot with the following options: ![]() In this case the inputs were 0, 13 and 100, so 100 values were generated starting at 0 and ending at 13. This function creates an array of numbers evenly spaced between a given start point and a given end point with the number of values created being the third input to the function. ![]() So the jagged edges still exist but they are now too small to be noticeable! Note the function that was used to create the x-data: Numpy’s linspace(). ![]() The above example is plotting 100 points and connecting them with straight lines. While it isn’t technically possible to plot a continuous curve using this method, you can at least make the line appear smooth by simply increasing the number of points you plot: import matplotlib.pyplot as plt If you are plotting a continuous function like this it’s usually better to have it be a smooth curve. When you only plot a few points as in the previous example, the resulting curve will look jagged. hex2rgb ( f "# kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting for i, k in enumerate ( kpts ): color_k = [ int ( x ) for x in self. pose_palette (np.ndarray): A specific color palette array with dtype np.uint8. n (int): The number of colors in the palette. Attributes: palette (list of tuple): List of RGB color values. This class provides methods to work with the Ultralytics color palette, including converting hex color codes to RGB values. Class Colors : """ Ultralytics default color palette. ![]()
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