dayfirst=True)map_chart=multi_index_df.plot_animated(kind="bubble",filename="examples/example-bubble-chart.gif",x_data_label="Longitude",y_data_label="Latitude",size_data_label="Cases",color_data_label="Cases",
("%d/%m/%Y")plots = [bar_chart, race_chart, map_chart, line_chart]# Otherwise titles overlap and adjust_subplot does nothingfrom matplotlib import rcParamsfrom matplotlib.animation import FuncAnimationrcParams.update({"figure.autolayout": False})# make sure figures are `Figure()` instancesfigs...
1# Make animated gif 2imgFiles = [fn for fn in os.listdir('.') if fn.endswith('.png')] 3imgFiles.sort(key=lambda x:int(x[:-4])) 4print(imgFiles) 5images = [Image.open(fn) for fn in imgFiles] 6im = images[0] 7filename = 'test.gif' 8im.save(fp=filename, format='...
period_label=False, add_legend=False)animated_bar_chart = df_result.plot_animated(n_visible=10)pandas_alive.animate_multiple_plots('examples/yuhuanshui.gif', [animated_bar_chart, animated_line_chart
from mpl_toolkits.mplot3d import Axes3D import seaborn as sns import os from sklearn.decomposition import PCA import imageio df = sns.load_dataset('iris') my_dpi=96 plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi) # Keep the 'specie' column appart + make it numeric for ...
Make Kernel available to Jupyter IRkernel::installspec() OR IRkernel::installspec(user = FALSE) #install system-wide Open a notebook and open new R script. Further notes After getting Additional R library might be hard to install inside the Notebook. For workaround, install desired library ...
make_gif('./PCA/', './PCA/PCA.gif') 获得的结果应该和图1的结果相同。这种相同的机制可以应用于其他很多应用,就像:动画分布,轮廓和机器学习模型。 图1:PCA方差图 在Matplotlib中制作动画图形的另一种方法是使用Matplotlib动画API。这个API可以制作一些简单的动画和实时图标。一些案例可以在这里[2]找到。
x: os.path.getmtime(x)) episode_frames = [imageio.imread(file_path) for file_path in file_paths if file_path.endswith('.png')] episode_frames = np.array(episode_frames) imageio.mimsave(save_filepath, episode_frames, duration=time_per_step) make_gif('./PCA/', './PCA/PCA.gif')...
# We are going to do 20 plots, for 20 different angles for angle in range(70,210,2): # Make the plot fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.viridis, linewidth=0.2) ...
filename="examples/example-bubble-chart.gif", x_data_label="Longitude", y_data_label="Latitude", size_data_label="Cases", color_data_label="Cases", vmax=5, steps_per_period=3, interpolate_period=True, period_length=500, dpi=100 ...