日欧毛片,午夜aaaa,香蕉成人

在我們科研、工作中，將數據完美展現出來尤為重要。
數據可視化是以數據為視角，探索世界。我們真正想要的是 ― 數據視覺，以數據為工具，以可視化為手段，目的是描述真實，探索世界。
下面介紹一些數據可視化的作品（包含部分代碼），主要是地學領域，可遷移至其他學科。

Example 1 ：散點圖、密度圖（Python）

import numpy as np
import matplotlib.pyplot as plt

# 創建隨機數
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)
# 將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()

Matlab、Python為工具解析數據可視化之美

Example 2 ：雙Y軸（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()

Matlab、Python為工具解析數據可視化之美

Example 3：擬合曲線（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()

Matlab、Python為工具解析數據可視化之美

Example 4：三維地形（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()

Matlab、Python為工具解析數據可視化之美