%load_ext watermark

%watermark -u -v -d -p matplotlib,numpy

%matplotlib inline

importmatplotlib.pyplotaspltx=[1,2,3]y_1=[50,60,70]y_2=[20,30,40]plt.plot(x,y_1,marker='x')plt.plot(x,y_2,marker='^')plt.xlim([0,len(x)+1])plt.ylim([0,max(y_1+y_2)+10])plt.xlabel('x-axis label')plt.ylabel('y-axis label')plt.title('Simple line plot')plt.legend(['sample 1','sample2'],loc='upper left')plt.show()

importmatplotlib.pyplotaspltx=[1,2,3]y_1=[50,60,70]y_2=[20,30,40]y_1_err=[4.3,4.5,2.0]y_2_err=[2.3,6.9,2.1]x_labels=["x1","x2","x3"]plt.errorbar(x,y_1,yerr=y_1_err,fmt='-x')plt.errorbar(x,y_2,yerr=y_2_err,fmt='-^')plt.xticks(x,x_labels)plt.xlim([0,len(x)+1])plt.ylim([0,max(y_1+y_2)+10])plt.xlabel('x-axis label')plt.ylabel('y-axis label')plt.title('Line plot with error bars')plt.legend(['sample 1','sample2'],loc='upper left')plt.show()

importmatplotlib.pyplotaspltx=[1,2,3]y_1=[0.5,7.0,60.0]y_2=[0.3,6.0,30.0]x_labels=["x1","x2","x3"]plt.plot(x,y_1,marker='x')plt.plot(x,y_2,marker='^')plt.xticks(x,x_labels)plt.xlim([0,4])plt.xlabel('x-axis label')plt.ylabel('y-axis label')plt.yscale('log')plt.title('Line plot with x-axis labels and log-scale')plt.legend(['sample 1','sample2'],loc='upper left')plt.show()

importnumpyasnpfrommatplotlibimportpyplotaspltimportmathdefpdf(x,mu=0,sigma=1):"""    Calculates the normal distribution's probability density    function (PDF).    """term1=1.0/(math.sqrt(2*np.pi)*sigma)term2=np.exp(-0.5*((x-mu)/sigma)**2)returnterm1*term2x=np.arange(0,100,0.05)pdf1=pdf(x,mu=5,sigma=2.5**0.5)pdf2=pdf(x,mu=10,sigma=6**0.5)plt.plot(x,pdf1)plt.plot(x,pdf2)plt.title('Probability Density Functions')plt.ylabel('p(x)')plt.xlabel('random variable x')plt.legend(['pdf1 ~ N(5,2.5)','pdf2 ~ N(10,6)'],loc='upper right')plt.ylim([0,0.5])plt.xlim([0,20])plt.show()

importnumpyasnpimportmatplotlib.pyplotaspltA=np.arange(1,11)B=np.random.randn(10)# 10 rand. values from a std. norm. distr.C=B.cumsum()fig,(ax0,ax1)=plt.subplots(ncols=2,sharex=True,sharey=True,figsize=(10,5))## A) via plt.step()ax0.step(A,C,label='cumulative sum')# cumulative sum via numpy.cumsum()ax0.scatter(A,B,label='actual values')ax0.set_ylabel('Y value')ax0.legend(loc='upper right')## B) via plt.plot()ax1.plot(A,C,label='cumulative sum')# cumulative sum via numpy.cumsum()ax1.scatter(A,B,label='actual values')ax1.legend(loc='upper right')fig.text(0.5,0.04,'sample number',ha='center',va='center')fig.text(0.5,0.95,'Cumulative sum of 10 samples from a random normal distribution',ha='center',va='center')plt.show()

importnumpyasnpimportmatplotlib.pyplotaspltA=np.arange(1,11)B=np.random.randn(10)# 10 rand. values from a std. norm. distr.plt.figure(figsize=(10,5))plt.step(np.sort(B),A)plt.ylabel('sample count')plt.xlabel('x value')plt.title('Number of samples at a certain threshold')plt.show()

importnumpyasnpimportmatplotlib.pyplotaspltfig,(ax0,ax1)=plt.subplots(1,2,figsize=(14,7))samples=range(1,16)# Default Color Cycleforiinsamples:ax0.plot([0,10],[0,i],label=i,lw=3)# Colormapcolormap=plt.cm.Pairedplt.gca().set_color_cycle([colormap(i)foriinnp.linspace(0,0.9,len(samples))])foriinsamples:ax1.plot([0,10],[0,i],label=i,lw=3)# Annotationax0.set_title('Default color cycle')ax1.set_title('plt.cm.Paired colormap')ax0.legend(loc='upper left')ax1.legend(loc='upper left')plt.show()

importnumpyasnpimportmatplotlib.pyplotaspltmarkers=['.',# point',',# pixel'o',# circle'v',# triangle down'^',# triangle up'<',# triangle_left'>',# triangle_right'1',# tri_down'2',# tri_up'3',# tri_left'4',# tri_right'8',# octagon's',# square'p',# pentagon'*',# star'h',# hexagon1'H',# hexagon2'+',# plus'x',# x'D',# diamond'd',# thin_diamond'|',# vline]plt.figure(figsize=(13,10))samples=range(len(markers))foriinsamples:plt.plot([i-1,i,i+1],[i,i,i],label=markers[i],marker=markers[i],markersize=10)# Annotationplt.title('Matplotlib Marker styles',fontsize=20)plt.ylim([-1,len(markers)+1])plt.legend(loc='lower right')plt.show()

importnumpyasnpimportmatplotlib.pyplotaspltlinestyles=['-.','--','None','-',':']plt.figure(figsize=(8,5))samples=range(len(linestyles))foriinsamples:plt.plot([i-1,i,i+1],[i,i,i],label='"%s"'%linestyles[i],linestyle=linestyles[i],lw=4)# Annotationplt.title('Matplotlib line styles',fontsize=20)plt.ylim([-1,len(linestyles)+1])plt.legend(loc='lower right')plt.show()