The various points are updated in the excel sheet (or csv), shown in fig 2, and read using pandas. The regression plots are based on seaborn lmplot and the points with labels are annotated on the chart based on the individual x, and y values.īesides the seaborn, pandas, matplotlib and numpy, additional module adjustText is used to prevent overlapping of the text labels in the plotĭata_list = for BPI in BPI_tgt for ADC in ADC_tgt]ĪDC_df = pd.DataFrame(data_list, columns=) #ĪDC_df = ADC_df.astype('category')īAR_tgt =ĭata_list = for BPI in BPI_tgt for BAR in BAR_tgt]īAR_df = pd.DataFrame(data_list, columns=) #īAR_df = BAR_df.astype('category')Īdding the demo points with text from Excel The idea will be to use the regression plots for both the ADC and the BAR contours while the points and labels can be automatically added to the plots after reading from an Excel table (or csv file). Further requirements include having additional points (with labels) to be added in fairly easily and charts with different sets of data can be recreated rapidly. The two different contours shown in the plot are made up of ADC (BPI * TPI) and bit aspect ratio BAR (BPI/TPI).Ī way to create the plot might be to generate the contours based on Excel and manually added in the different points. The chart shows the areal density capacity (ADC) demo of certain samples on a bit density (BPI) by track density (TPI) chart. I was asked to create a customized contour plot based on a chart (Fig 1 ) found in IEEE Transactions on Magnetics journal with some variant in requirements.

To show the plot, use the show() function.Creating Customized Contour Plots with Labelled Points.

To set axes labels at x, y, and z axes use set_xlabel(), set_ylabel(), and set_zlabel() functions respectively.

Plot the contour, using the contour() function.

Set the projection to 3d by defining axes object = add_subplot().

Generate and set the size of the figure, using plt.figure() function and figsize() method.

Import from mpl_toolkits.mplot3d import Axes3D library.

We take x and y values in a one-dimensional array using the array method of numpy, then we need to convert them into a two-dimensional array, so we use the meshgrid function of numpy.

levels: The number and positions of the contour lines are determined.

z: Specifies the height over which contour is drawn.

x and y: Specifies 2D or 1D numpy array for plotting.

In matplotlib, matplotlib.pyplot includes a method contour to make it easy to construct contour plots due to its widespread use. When we plot X and Y as our variables, the response Z is shown as slices on the X-Y plane, which is why contours are sometimes referred to as Z-slices.Ĭontour plots are commonly used in meteorological departments to illustrate densities, elevations, or mountain heights.

We take x and y values in a one-dimensional array using the linspace method of numpy, then we need to convert them into a two-dimensional array, so we use meshgrid function of numpy.Īlso, check Matplotlib xlim – Complete Guide Matplotlib 2d contour plotĬontour plots, also known as level plots, are a multivariate analytic tool that allows you to visualize 3-D plots in 2-D space.

Then we use the z to plot on a map with axes x and y using the surface plot to get the 3D effects.

Here we need x and y values, and from x and y we compute the value of z called height.Here our main motive is to generate two-dimensional data using matplotlib and plot it with three-dimensional effects i.e Surface. The syntax to create the surface plot: ax.plot_surface(X, Y, Z)

To create a surface plot we import Matplotlib’s mpl_toolkits.mplot3d toolkit which has functions to create a 3D surface plot. Importing the mplot3d package enables the 3d plots.Ī surface plot is the representation of a three-dimensional dataset.

But after release 1.0, you can develop 3d utilities upon 2d utilities.

Before the release of the 1.0 version, matplotlib is used only used for two-dimensional plotting.

Contour plot in python