How to smoothen 2D color map in matplotlib

Question

My question is if there is any way to smoothen 2D color map using matplotlib? My code:

def map():
    # setup parameters
    j = 0
    N = 719
    N2 = 35
    x = np.linspace(190, 800, N) 
    y = np.linspace(10, 360, N2) # (1,2,3), 1 - start Temp, 2- end temp + 10K, 3 - how many steps to reach it
    z = []
    A = np.zeros([35,719]) # [1 2], 1 - number of spectras, 2 - delta wavelength
    # run
    for i in range(10,360,10):
            Z = []
            file_no = (str(0) + str(i))[-3:]
            data = np.genfromtxt('C:\\Users\\micha_000\\Desktop\\Measure\\' + '160317_LaPONd_g500_%s_radio.txt'%file_no,skip_header = 12)
            for line in data:
                Z.append(line[1]-6000)
            A[j,:] = Z
            j = j+1
    X, Y = np.meshgrid(x,y)
    fig, ax = plt.subplots()
    cs = ax.contourf(X, Y, A, cmap=cm.viridis)
    norm = colors.Normalize(vmin = 0, vmax = 1)
    plt.xlabel('wavelength [nm]')
    plt.ylabel('temperature [K]')
    plt.title('LaPONd_g500')
    cbar = fig.colorbar(cs, norm = norm)
    plt.savefig('C:\\Users\\micha_000\\Desktop\\Measure\\LaPONd_g500_radio_map.png')
    plt.show()
    plt.close()

And here is an example of what i receive:

Is there any way to make it look better by smoothening pixels transitions?

Answer 1

The problem is not the palette (which are all smooth in matplotlib), but that fact that you are using contourf(), which generates a finite set of countours, each with a single color, and is therefore not smooth. The default is something like 10 countours.

One quick solution:, increase the number of contour levels by specifying levels (you can also give an array of which levels to include):

cs = ax.contourf(X, Y, A, cmap=cm.viridis, levels=100)

Better yet, since it seems your data data is already on a grid (e.g. X,Y,Z values for each pixel), you should use pcolormesh(X,Y,A) instead of contour to plot it. That will plot with fully continuous values, rather than steps.