English: A screenshot showing matplotlib plots of a polar bar graph resembling the matplotlib logo (upper left), a 3D surface graph with the new default 'viridis' colormap (lower left), a graph of 2D random walk trajectories (lower right), and the python source code (of the logo part) opened in a text editor (upper right).
The matplotlib (mpl) version is the development branch 2.x, with Python 2.7.11 and numpy 1.11.1
############ Code for the mpl logo figure##########importmatplotlib.pyplotaspltimportnumpyasnpfrommatplotlib.cmimportjetascolormapfrommatplotlib.tickerimportNullFormatter,MultipleLocatort,w,r=zip((0.1,0.4,1),(0.9,0.3,5),(1.7,0.5,7),(2.7,0.6,6),(3.5,0.3,3),(4.5,0.4,4),(5.3,0.3,7))fig,ax=plt.subplots(subplot_kw={'polar':True})bars=ax.bar(t,r,width=w,bottom=0.0,lw=2,edgecolor='Black',zorder=2)forr,barinzip(r,bars):bar.set_facecolor(colormap(r/9.0))bar.set_alpha(0.7)ax.yaxis.set_major_locator(MultipleLocator(2))foraxisin(ax.xaxis,ax.yaxis):axis.set_major_formatter(NullFormatter())# no tick labelsax.set_ylim([0,8])ax.grid(True)plt.show()####################
############ Code for the 3D surface plot and the 2D random walk tajectories##########importmatplotlib.pyplotaspltimportnumpyasnpfrommpl_toolkits.mplot3dimportAxes3Dfrommatplotlib.cmimportviridisascolormap"""Figure 1: a 3D surface plot (from matplotlib gallery)"""step=0.04maxval=1.0fig1=plt.figure("Figure_1")ax1=fig1.add_subplot(111,projection='3d')# Create supporting points in polar coordinatesr=np.linspace(0,1.2,50)p=np.linspace(0,2*np.pi,50)R,P=np.meshgrid(r,p)# Transform them to cartesian systemX,Y=R*np.cos(P),R*np.sin(P)Z=((R**2-1)**2)ax1.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=colormap)ax1.set_zlim3d(0,1)ax1.set_xlabel(r'$\phi_\mathrm{real}$')ax1.set_ylabel(r'$\phi_\mathrm{im}$')ax1.set_zlabel(r'$V(\phi)$')"""Figure 2: a few examples of 2D random walk"""fig2,ax2=plt.subplots(num="Figure_2")prng=np.random.RandomState(123)x=np.linspace(0,10,101)defrandom_walk(xy0=(0.0,0.0),nsteps=100,std=1.0):xy=np.zeros((nsteps+1,2))xy[0,:]=xy0deltas=prng.normal(loc=0.0,scale=std,size=(nsteps,2))xy[1:,:]=xy[0,:]+np.cumsum(deltas,axis=0)returnxyforcntinrange(3):traj=random_walk()ax2.plot(traj[:,0],traj[:,1],label="Traj. {c}".format(c=cnt))ax2.legend(loc='best')plt.show()####################
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