Matlab、Python为工具解析数据可视化之美

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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