Python

# Contour Plot in Python

A contour plot is a method to represent a 3D apparent on a 2D plane. Plot two interpreters X and Y on the Y-axis and plot one variable Z utilizing a contour line. Sometimes, these contour lines are referred as iso-response values.

Contour diagrams are useful for seeing how the value of Z fluctuates in response to the input of these two variables, X and Y. These variables are frequently constrained to a systematic grid termed as meshgrid. Np.meshgrid generates an oblong grid from an array of values of x variable ​​and an array of values of y variable. The contour plots are created by the use of Matplotlib.

Civil engineering allows us to view the topography of a building in a contour map. In mechanical engineering, contour diagrams can demonstrate the stress gradient over the entire surface of a part. Let’s discuss different methods that are used for contour plots in Python.

## Plotting of Contour by the Use of contour() Function

To create a contour plot by using Matplotlib.py plot, we need to utilize the ax.contour() function. This method contains three arguments. The first two arguments x and y are two-dimensional arrays of points x and y, and the third argument Z is a two-dimensional array that decides the contour height, denoted by the colors of the two-dimensional plot.

For the execution of Python code, first, we install spyder5. The name of the new file is “temp44.py”. This example contains the NumPy method np.meshgrid(), which generates a two-dimensional array from a one-dimensional array. The ax.contourf() function is related to ax.contour(), excluding that the method ax.contourf() creates a “filled” contour graph. As an alternative to the lines in the plot created by the method ax.contour().

## Contour Plots Contain Colorbars

Colors denote the third magnitude on a two-dimensional 2D plot (such as “height”), so it’s suitable to scale the meaning of every color. The color scale is usually displayed next to the figure.

The colorbar is supplemental to the contour plot matplotlib by the use of the fig.colorbar() function. Colorbars are not a fragment of that contour plots, so colorbars should be functional to objects (frequently named fig).

We need to pass the contour plot to the fig.colorbar() function. Hence, when adding the color bar to any figure, the object of the plot must be existing. That object of the plot is the result of using the function ax.contourf(). The outcome of the ax.contourf() function has not been allocated to any variable. However, to insert a colorbar in any contour plot, we need to save the object of that plot to any variable so that we can assign the object of the plot to function fig.colorbar(). In this code, the ‘cf’ is an object of plot generated by the function ax.contourf(). The Axis of that object containing the contour diagram is passed by ax to the other function fig.colorbar() along with the object of plot ‘cf’. Here we utilize the ax.contourf (X, Y, Z) function. Where X parameter and Y parameter are 2D arrays of points x and y, and Z parameter is a 2D array that defines the color of the region of ​​the plot. In the output, we get the shaded contour plot. The shaded region is generated by the function ax.contourf ().

## Visualization of 3D Functions

We start representing the contour plot by the use of the method f (x, y). We do an exact selection of the function ‘f’. First, we import matplotlib.pyplot as a plot. Then, we decide the plot style by passing the parameter ‘seaborn white’ color. We import NumPy as np. After this, we define the function ‘f’. Contour plots are created by using the plot.contour method. This requires three parameters: an x-value grid, a y-value grid, and a z-value grid. The values of x and y ​​show locations on the plot, the value of z ​​is signified by contour lines. Maybe the easiest method to make such figures is to utilize the np.meshgrid method. This function creates a 2D grid from a 1D array. We pass the argument color=’red’ to the function plot.contour() so the resultant plot contains the red color of lines. When we use a single color, by default the negative numbers ​​are denoted by dashed lines and the solid lines signify the positive values.

## Color Maps of Contour Plot

We can change the default scheme of color for matplotlib contours and occupy the contour plots. A common method to change the color schemes is to call plot.get_cmap() method, which results in a Colormap thing. Various color maps are existing to contour the plots. The colormap thing is passed as a keyword parameter to the ax.contourf() function or ax.contour() function. In this section of the code, we will create two complete contour plots. Those contour plots have different colors of maps. ## Conclusion

We represent 3D data in 2D by the use of contour lines or color-coded areas. Some Matplotlib methods are used for the plotting of contour. The Matplotlib API includes methods Contourf() and Contour() that are used to design contour lines or complete contours. 