数据可视化之美 -- 以Matlab、Python为工具

% N, e; g' R7 y9 X/ q

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。

0 @3 d( j! c# `! g

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

7 V' W+ i* G1 N

import matplotlib.pyplot as plt

# 创建随机数

' K8 Z1 S# m6 n" z. M8 Y

n = 100000

& y& N0 y# n% Z; e) V

x = np.random.randn(n)

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

4 ?) Z" `+ v+ v

fig1 = plt.figure()

plt.plot(x,y,.r)

! _" ^# u s3 V, ~6 _6 x2 |( x

plt.xlabel(x)

plt.ylabel(y)

! {0 c' g4 R" O' w% X

plt.savefig(2D_1V1.png,dpi=600)

' C1 s1 O0 e: C1 m, L9 x

nbins = 200

H, xedges, yedges = np.histogram2d(x,y,bins=nbins)

. @. a- ]) I' E( m% Q1 \

# 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)

- P/ _9 X# g' f& p7 L+ j

# Plot 2D histogram using pcolor

fig2 = plt.figure()

plt.pcolormesh(xedges,yedges,Hmasked)

plt.xlabel(x)

4 F5 ?; h k3 h- O: p: m

plt.ylabel(y)

/ K' n/ M" m9 B- l4 m s7 p9 X

cbar = plt.colorbar()

0 v! u! b) z2 \. O

cbar.ax.set_ylabel(Counts)

N+ q; Q0 h% B

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

plt.show()

3 z6 J% l! I+ e% b$ Q8 x# o0 Q

打开凤凰新闻，查看更多高清图片

* f6 `& L1 z4 `; T- f1 l/ G

Example 2 ：双Y轴（Python）

5 E8 @' r; d$ H B: g

import csv

import pandas as pd

0 l6 @) c/ k- z

import matplotlib.pyplot as plt

from datetime import datetime

7 w5 a" K1 I* L! Y5 A+ s

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

time=data[date [AST]]

& d. p: y( c5 b, G# G+ h

sal=data[salinity]

P5 X4 G$ w+ y4 y; F: b

tem=data[temperature [C]]

6 ^% h m8 { L! Q

print(sal)

DAT = []

" ^1 y4 k# x0 ~8 D- b0 V" P ^

for row in time:

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

#create figure

2 `9 h' F5 j+ g1 y9 ^% F

fig, ax =plt.subplots(1)

8 k+ k" b* T7 z t2 c3 L6 G/ z

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

9 a5 Y0 {) ~9 a" ]: x: j9 S

plt.xlabel("Date [AST]")

, e% g& I, ~$ Z( x( I! E

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

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

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

; z! y+ W8 i3 K- u8 @* f

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

# [2 U2 t# A( [

fig.autofmt_xdate(rotation=50)

6 A: B: H& K- \' j# _5 @

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

4 a( @7 m) K4 Y, a/ N

plt.twinx()

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

) }$ t$ c3 }1 s

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

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

#To save your graph

/ K8 ~5 Q! H5 B7 n$ i0 i6 C

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

3 T) a% \' A( Y$ M$ v) G

plt.show()

# Q( l E' ]; D# x$ E) Q* t

; |+ a: X0 l. N4 A: U/ E

Example 3：拟合曲线（Python）

import csv

import numpy as np

& \" u/ S( X' P+ {5 h9 n

import pandas as pd

from datetime import datetime

+ B3 o/ E" }* x# r' T/ H7 e( `- W4 S

import matplotlib.pyplot as plt

import scipy.signal as signal

6 Q; g. M- d! f6 b3 ~ q

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

time=data[date [AST]]

temp=data[temperature [C]]

2 t0 ^6 l! I; c% Q {( |. _ W

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

DATE,decday = [],[]

5 ^) O7 n9 q+ a h; l

for row in time:

4 \0 \% h3 Q% p

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

DATE.append(daterow)

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

% f$ g! I6 Y# i3 x G( P7 [

# First, design the Buterworth filter

5 R# F3 A. u; x! V1 A9 q4 I }

N = 2 # Filter order

Wn = 0.01 # Cutoff frequency

( z! v3 d- J0 C8 @& J/ y/ n

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

# Second, apply the filter

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

# Make plots

3 `4 \; O+ H7 m |

fig = plt.figure()

6 o( j+ c+ Y( x/ T

ax1 = fig.add_subplot(211)

% F- k3 y6 _! H; `6 G' \9 k' R

plt.plot(decday,temp, b-)

; I$ K$ G8 l) x* b

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

plt.ylabel("Temperature (oC)")

plt.legend([Original,Filtered])

6 n! z$ e9 h m) w& L5 l

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

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

1 W/ m6 b- c% j. r0 a! x- E7 e" R- t

ax1 = fig.add_subplot(212)

4 n3 I0 e9 |& n3 ?

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

plt.ylabel("Temperature (oC)")

plt.xlabel("Date")

$ ~$ B$ q u& x

plt.legend([Residuals])

0 O% b+ T. U2 w

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

plt.show()

% V& I' U: R, H+ t

Example 4：三维地形（Python）

# This import registers the 3D projection

from mpl_toolkits.mplot3d import Axes3D

6 ^* E3 J7 u) Q% _, H' ?( m- }

from matplotlib import cbook

' I. ~$ p9 L6 j9 R

from matplotlib import cm

: y) {$ s! d$ P* @

from matplotlib.colors import LightSource

import matplotlib.pyplot as plt

* m) S# T9 Q$ n" u

import numpy as np

, A( s( N9 [' U( W

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

with np.load(filename) as dem:

$ z7 J6 [2 c+ C, {+ R5 n3 y

z = dem[elevation]

7 o% N: [6 Z2 s7 Q

nrows, ncols = z.shape

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

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

+ N' @1 v8 E( U7 Y' N

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

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

c% I6 P7 V* j* O8 c

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

" _* Y+ C; l7 ^! Y& B0 t8 C" P

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

* X8 i+ I6 r. j$ Z* s4 d1 W5 v$ k

ls = LightSource(270, 45)

rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode=soft)

/ Y3 i* ~, N& o4 K1 _

surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,

) {! J- m( `; O( J2 E, G, v4 t4 b. [

linewidth=0, antialiased=False, shade=False)

7 F& Y3 ^* i9 A, H2 E, }

plt.savefig(example4.png,dpi=600, bbox_inches=tight)

2 v' g: S% ?* E

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）

0 E j2 X, D+ Z" y

Example 7：SSH GIF 动图展现（Matlab）

0 d z! N- r U* \! h1 A# u) l

Example 8：Glider GIF 动图展现（Python）

Example 9：涡度追踪 GIF 动图展现

7 W& P; X2 t/ y* K6 b. R

- n: f4 p2 n9 |4 h+ f ?$ ^

1 P' }! h6 X* C6 O5 @