Matlab、Python为工具解析数据可视化之美
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2022-06-17 22:59:37
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...
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 np import matplotlib.pyplot as plt # 创建随机数 n = 100000 x = 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 = 200 h, xedges, yedges = np.histogram2d(x,y,bins=nbins) # h needs to be rotated and flipped h = np.rot90(h) h = np.flipud(h) # 将zeros mask hmasked = np.ma.masked_where(h==0,h) # plot 2d histogram using pcolor fig2 = 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 csv import pandas as pd import matplotlib.pyplot as plt from datetime import datetime data=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 figure fig, 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 graph plt.savefig('saltandtemp_v1.png' ,bbox_inches='tight') plt.show()
example 3:拟合曲线(python)
import csv import numpy as np import pandas as pd from datetime import datetime import matplotlib.pyplot as plt import scipy.signal as signal data=pd.read_csv('lobo0010-20201122130720.tsv',sep='\t') time=data['date [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 filter n = 2 # filter order wn = 0.01 # cutoff frequency b, a = signal.butter(n, wn, output='ba') # second, apply the filter tempf = signal.filtfilt(b,a, temp) # make plots fig = 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 projection from mpl_toolkits.mplot3d import axes3d from matplotlib import cbook from matplotlib import cm from matplotlib.colors import lightsource import matplotlib.pyplot as plt import numpy as np filename = 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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