# GIF source code (example 8)# https://mianbaoduo.com/o/bread/mbd-YpWVlppt
在我们科研、工作中，将数据完美展现出来尤为重要。
数据可视化是以数据为视角，探索世界。我们真正想要的是 — 数据视觉，以数据为工具，以可视化为手段，目的是描述真实，探索世界。
下面介绍一些数据可视化的作品（包含部分代码），主要是地学领域，可迁移至其他学科。
Example 1 ：散点图、密度图（Python）
import numpy as npimport matplotlib.pyplot as plt# 创建随机数n = 100000x = np.random.randn(n)y = (1.5 * x) + np.random.randn(n)fig1 = plt.figure()plt.plot(x,y,’.r’)plt.xlabel(‘x’)plt.ylabel(‘y’)plt.savefig(‘2D_1V1.png’,dpi=600)nbins = 200H, xedges, yedges = np.histogram2d(x,y,bins=nbins)# H needs to be rotated and flippedH = np.rot90(H)H = np.flipud(H)# 将zeros maskHmasked = np.ma.masked_where(H==0,H) # Plot 2D histogram using pcolorfig2 = plt.figure()plt.pcolormesh(xedges,yedges,Hmasked) plt.xlabel(‘x’)plt.ylabel(‘y’)cbar = plt.colorbar()cbar.ax.set_ylabel(‘Counts’)plt.savefig(‘2D_2V1.png’,dpi=600)plt.show()
Example 2 ：双Y轴（Python）
import csvimport pandas as pdimport matplotlib.pyplot as pltfrom datetime import datetimedata=pd.read_csv(‘LOBO0010-2020112014010.tsv’,sep=’\t’)time=data[‘date [AST]’]sal=data[‘salinity’]tem=data[‘temperature [C]’]print(sal)DAT = []for row in time:DAT.append(datetime.strptime(row,”%Y-%m-%d %H:%M:%S”))#create figurefig, ax =plt.subplots(1)# Plot y1 vs x in blue on the left vertical axis.plt.xlabel(“Date [AST]”)plt.ylabel(“Temperature [C]”, color=”b”)plt.tick_params(axis=”y”, labelcolor=”b”)plt.plot(DAT, tem, “b-“, linewidth=1)plt.title(“Temperature and Salinity from LOBO (Halifax, Canada)”)fig.autofmt_xdate(rotation=50) # Plot y2 vs x in red on the right vertical axis.plt.twinx()plt.ylabel(“Salinity”, color=”r”)plt.tick_params(axis=”y”, labelcolor=”r”)plt.plot(DAT, sal, “r-“, linewidth=1) #To save your graphplt.savefig(‘saltandtemp_V1.png’ ,bbox_inches=’tight’)plt.show()
Example 3：拟合曲线（Python）
import csvimport numpy as npimport pandas as pdfrom datetime import datetimeimport matplotlib.pyplot as pltimport scipy.signal as signaldata=pd.read_csv(‘LOBO0010-20201122130720.tsv’,sep=’\t’)time=data[‘date vps云服务器 [AST]’]temp=data[‘temperature [C]’]datestart = datetime.strptime(time[1],”%Y-%m-%d %H:%M:%S”)DATE,decday = [],[]for row in time: daterow = datetime.strptime(row,”%Y-%m-%d %H:%M:%S”) DATE.append(daterow) decday.append((daterow-datestart).total_seconds()/(3600*24))# First, design the Buterworth filterN = 2 # Filter orderWn = 0.01 # Cutoff frequencyB, A = signal.butter(N, Wn, output=’ba’)# Second, apply the filtertempf = signal.filtfilt(B,A, temp)# Make plotsfig = plt.figure()ax1 = fig.add_subplot(211)plt.plot(decday,temp, ‘b-‘)plt.plot(decday,tempf, ‘r-‘,linewidth=2)plt.ylabel(“Temperature (oC)”)plt.legend([‘Original’,’Filtered’])plt.title(“Temperature from LOBO (Halifax, Canada)”)ax1.axes.get_xaxis().set_visible(False) ax1 = fig.add_subplot(212)plt.plot(decday,temp-tempf, ‘b-‘)plt.ylabel(“Temperature (oC)”)plt.xlabel(“Date”)plt.legend([‘Residuals’])plt.savefig(‘tem_signal_filtering_plot.png’, bbox_inches=’tight’)plt.show()
Example 4：三维地形（Python）
# This import registers the 3D projectionfrom mpl_toolkits.mplot3d import Axes3D from matplotlib import cbookfrom matplotlib import cmfrom matplotlib.colors import LightSourceimport matplotlib.pyplot as pltimport numpy as npfilename = cbook.get_sample_data(‘jacksboro_fault_dem.npz’, asfileobj=False)with np.load(filename) as dem: z = dem[‘elevation’] nrows, ncols = z.shape x = np.linspace(dem[‘xmin’], dem[‘xmax’], ncols) y = np.linspace(dem[‘ymin’], dem[‘ymax’], nrows)x, y = np.meshgrid(x, y)region = np.s_[5:50, 5:50]x, y, z = x[region], y[region], z[region]fig, ax = plt.subplots(subplot_kw=dict(projection=’3d’))ls = LightSource(270, 45)rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode=’soft’)surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb, linewidth=0, antialiased=False, shade=False)plt.savefig(‘example4.png’,dpi=600, bbox_inches=’tight’)plt.show()
Example 5：三维地形，包含投影（Python）
Example 6：切片，多维数据同时展现（Python）
Example 7：SSH GIF 动图展现（Matlab）
Example 8：Glider GIF 动图展现（Python）
Example 9：涡度追踪 GIF 动图展现
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