Contour plot in python how to#
Contour plot in python code#

We used the contourf() function of matplotlib to plot the figures. Next, we created the axes objected using the axes() function and specified the projection of the plot to “polar”. We specified the plot’s size using the function’s figsize attribute. Under the function, we first created the figure object using the plt.figure() function. The function is again a void function, and it takes 6 parameters, called theta1, r1, theta2, r2, values1, and values2. Next we created the contour_plot() function.

Contour plot in python code#

First, we imported the numpy library and the matplotlib.pyplot module in our code using the import statement of python.

We can use all the other attributes associated with the function.ĭef contour_polar(theta1, r1, theta2, r2, values1, values2): As we have seen for other plots, we need to call the contour() function multiple times to plot multiple plots in the figure. Plotting multiple contour plots in the exact figure is quite an easy task.

Contour plot in python how to#

Finally, we called the main() function with the help of the following lines of codes: if _name_ = “ main“: main()Ĭheck This: I have wrote a full guide on Matplotlib Contour Plot if you want to know more about contour plot in python matplotlib library (What it is and how to plot a contour plot using different functions and techniques).We called the contour_polar() function with appropriate arguments there, r, and values.We printed all the values of x, y, r, and theta to understand the plot better. We also created the values array and used the random.the random() function of the numpy library.This is a crucial step to map the data points correctly in the plot. Next, we created a mesh grid out of y and x. We took 0 as the first and 360 as the last elements to get the values in degree and converted them into radians values using the np.radians() function of numpy.The stop defines the number up to which the iteration should run, and the last parameter defines the total number of elements required. The start defines the number with which the iteration should start in the code. The linspace function takes three parameters: to start, stop, and the number of elements. We used the lispace function to generate the data points.

We used the radians() function of numpy to convert the degree values into radians values. Under this function, we created all the data points required for plotting the graph.

Next, we created the main() function, which is the driving code of the program.

Note that this is optional in Jupyter Notebook, and we can safely skip this step.

We used the plt.show() function to display the graph.

We passed the value of theta, r, and values to this function.

We plotted the contour plot using the contourf() function.

We used the attribute projection=” polar” to specify that we want to plot polar coordinates.

Under this function, we first created the figure, and the axes object using the subplots() function. The function is void and takes three parameters: theta, r, and values.

Next we created a user-defined function named contour_plot().

We used the technique of aliasing names to write the codes in our program conveniently.

First, we imported the necessary libraries and packages in our code using the import statement of python.

# Creating the mesh grid for plotting the contour plot Plt.title('Demonstrating polar contour plot') # Creating a user-defined function named contour_plot # Import all the necessary libraries and packages in the code.