1. 动图-animation
import matplotlib.animation as animation n = 100 x = np.random.randn(n) #************************** # create the function that will do the plotting, where curr is the current frame def update(curr): # check if animation is at the last frame, and if so, stop the animation a if curr == n: a.event_source.stop() plt.cla() bins = np.arange(-4, 4, 0.5) plt.hist(x[:curr], bins=bins) plt.axis([-4,4,0,30]) plt.gca().set_title('Sampling the Normal Distribution') plt.gca().set_ylabel('Frequency') plt.gca().set_xlabel('Value') plt.annotate('n = {}'.format(curr), [3,27]) #***************************** fig = plt.figure() a = animation.FuncAnimation(fig, update, interval=100)2. 交互性(interactivity)
Example1:
plt.figure() data = np.random.rand(10) plt.plot(data) def onclick(event): plt.cla() plt.plot(data) plt.gca().set_title('Event at pixels {},{} \nand data {},{}'.format(event.x, event.y, event.xdata, event.ydata)) # tell mpl_connect we want to pass a 'button_press_event' into onclick when the event is detected plt.gcf().canvas.mpl_connect('button_press_event', onclick)Example2 :
from random import shuffle origins = ['China', 'Brazil', 'India', 'USA', 'Canada', 'UK', 'Germany', 'Iraq', 'Chile', 'Mexico'] shuffle(origins) df = pd.DataFrame({'height': np.random.rand(10), 'weight': np.random.rand(10), 'origin': origins}) df作图:
plt.figure() # picker=5 means the mouse doesn't have to click directly on an event, but can be up to 5 pixels away plt.scatter(df['height'], df['weight'], picker=5) plt.gca().set_ylabel('Weight') plt.gca().set_xlabel('Height')def onpick(event): origin = df.iloc[event.ind[0]]['origin'] plt.gca().set_title('Selected item came from {}'.format(origin)) # tell mpl_connect we want to pass a 'pick_event' into onpick when the event is detected plt.gcf().canvas.mpl_connect('pick_event', onpick)
转载于:https://www.cnblogs.com/Shinered/p/9531102.html
