数据可视化之美 -- 以Matlab、Python为工具

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在我们科研、工作中，将数据完美展现出来尤为重要。

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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。

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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。

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Example 1 ：散点图、密度图（Python）

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import numpy as np

import matplotlib.pyplot as plt

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# 创建随机数

n = 100000

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x = np.random.randn(n)

y = (1.5 * x) + np.random.randn(n)

fig1 = plt.figure()

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plt.plot(x,y,.r)

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plt.xlabel(x)

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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)

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H = np.flipud(H)

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# 将zeros mask

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Hmasked = np.ma.masked_where(H==0,H)

# Plot 2D histogram using pcolor

fig2 = plt.figure()

plt.pcolormesh(xedges,yedges,Hmasked)

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plt.xlabel(x)

plt.ylabel(y)

cbar = plt.colorbar()

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cbar.ax.set_ylabel(Counts)

plt.savefig(2D_2V1.png,dpi=600)

plt.show()

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Example 2 ：双Y轴（Python）

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import csv

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import pandas as pd

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import matplotlib.pyplot as plt

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from datetime import datetime

data=pd.read_csv(LOBO0010-2020112014010.tsv,sep=)

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time=data[date [AST]]

sal=data[salinity]

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tem=data[temperature [C]]

print(sal)

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DAT = []

for row in time:

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DAT.append(datetime.strptime(row,"%Y-%m-%d %H:%M:%S"))

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#create figure

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fig, ax =plt.subplots(1)

# Plot y1 vs x in blue on the left vertical axis.

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plt.xlabel("Date [AST]")

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plt.ylabel("Temperature [C]", color="b")

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plt.tick_params(axis="y", labelcolor="b")

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plt.plot(DAT, tem, "b-", linewidth=1)

plt.title("Temperature and Salinity from LOBO (Halifax, Canada)")

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fig.autofmt_xdate(rotation=50)

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# Plot y2 vs x in red on the right vertical axis.

plt.twinx()

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plt.ylabel("Salinity", color="r")

plt.tick_params(axis="y", labelcolor="r")

plt.plot(DAT, sal, "r-", linewidth=1)

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#To save your graph

plt.savefig(saltandtemp_V1.png ,bbox_inches=tight)

plt.show()

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Example 3：拟合曲线（Python）

import csv

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import numpy as np

import pandas as pd

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from datetime import datetime

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import matplotlib.pyplot as plt

import scipy.signal as signal

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data=pd.read_csv(LOBO0010-20201122130720.tsv,sep=)

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time=data[date [AST]]

temp=data[temperature [C]]

datestart = datetime.strptime(time[1],"%Y-%m-%d %H:%M:%S")

DATE,decday = [],[]

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for row in time:

daterow = datetime.strptime(row,"%Y-%m-%d %H:%M:%S")

DATE.append(daterow)

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decday.append((daterow-datestart).total_seconds()/(3600*24))

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# First, design the Buterworth filter

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N = 2 # Filter order

Wn = 0.01 # Cutoff frequency

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B, A = signal.butter(N, Wn, output=ba)

# Second, apply the filter

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tempf = signal.filtfilt(B,A, temp)

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# Make plots

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fig = plt.figure()

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ax1 = fig.add_subplot(211)

plt.plot(decday,temp, b-)

plt.plot(decday,tempf, r-,linewidth=2)

plt.ylabel("Temperature (oC)")

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plt.legend([Original,Filtered])

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plt.title("Temperature from LOBO (Halifax, Canada)")

ax1.axes.get_xaxis().set_visible(False)

ax1 = fig.add_subplot(212)

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plt.plot(decday,temp-tempf, b-)

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plt.ylabel("Temperature (oC)")

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plt.xlabel("Date")

plt.legend([Residuals])

plt.savefig(tem_signal_filtering_plot.png, bbox_inches=tight)

plt.show()

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Example 4：三维地形（Python）

# This import registers the 3D projection

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from mpl_toolkits.mplot3d import Axes3D

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from matplotlib import cbook

from matplotlib import cm

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from matplotlib.colors import LightSource

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import matplotlib.pyplot as plt

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import numpy as np

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filename = cbook.get_sample_data(jacksboro_fault_dem.npz, asfileobj=False)

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with np.load(filename) as dem:

z = dem[elevation]

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nrows, ncols = z.shape

x = np.linspace(dem[xmin], dem[xmax], ncols)

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y = np.linspace(dem[ymin], dem[ymax], nrows)

x, y = np.meshgrid(x, y)

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region = np.s_[5:50, 5:50]

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x, y, z = x[region], y[region], z[region]

fig, ax = plt.subplots(subplot_kw=dict(projection=3d))

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ls = LightSource(270, 45)

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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)

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plt.show()

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Example 5：三维地形，包含投影（Python）

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Example 6：切片，多维数据同时展现（Python）

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Example 7：SSH GIF 动图展现（Matlab）

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Example 8：Glider GIF 动图展现（Python）

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Example 9：涡度追踪 GIF 动图展现

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