本篇介绍了数据分析之数据可视化之美 -- 以Matlab、Python为工具，通过具体的内容展现，希望对大家数据分析数据可视化的学习有所帮助。

 

# 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 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()