1、前言
泰勒图可以全面直观地比较模拟的极端温度与观测的极端温度的一致性，它是由模拟场与观测场的空间相关系数、相对标准差及其中心化的均方根误差组成的极坐标图，中心化的均方根误差越接近0，空间相关系数和相对标准差越接近1，模式模拟能力越好。 需提前安装：xlsxwriter和SkillMetrics模块。
2、数据处理
导入模块
代码语言：javascript复制import matplotlib.pyplot as plt
from matplotlib import rcParams
import numpy as np
import pickle
import skill_metrics as sm
from sys import version_info
读取数据
代码语言：javascript复制def load_obj(name):
# Load object from file in pickle format
if version_info[0] == 2:
suffix = 'pkl'
else:
suffix = 'pkl3'
with open(name '.' suffix, 'rb') as f:
return pickle.load(f) # Python2 succeeds
class Container(object):
def __init__(self, pred1, pred2, pred3, ref):
self.pred1 = pred1
self.pred2 = pred2
self.pred3 = pred3
self.ref = ref
3、绘制泰勒图
泰勒图1：
代码语言：javascript复制if __name__ == '__main__':
# Set the figure properties (optional)
rcParams["figure.figsize"] = [8.0, 6.4]
rcParams['lines.linewidth'] = 1 # line width for plots
rcParams.update({'font.size': 12}) # font size of axes text
# Close any previously open graphics windows
# ToDo: fails to work within Eclipse
plt.close('all')
# Read data from pickle file
data = load_obj('/home/kesci/input/Talyor5298/taylor_data')
# Calculate statistics for Taylor diagram
# The first array element (e.g. taylor_stats1[0]) corresponds to the
# reference series while the second and subsequent elements
# (e.g. taylor_stats1[1:]) are those for the predicted series.
taylor_stats1 = sm.taylor_statistics(data.pred1,data.ref,'data')
taylor_stats2 = sm.taylor_statistics(data.pred2,data.ref,'data')
taylor_stats3 = sm.taylor_statistics(data.pred3,data.ref,'data')
# Store statistics in arrays
sdev = np.array([taylor_stats1['sdev'][0], taylor_stats1['sdev'][1],
taylor_stats2['sdev'][1], taylor_stats3['sdev'][1]])
crmsd = np.array([taylor_stats1['crmsd'][0], taylor_stats1['crmsd'][1],
taylor_stats2['crmsd'][1], taylor_stats3['crmsd'][1]])
ccoef = np.array([taylor_stats1['ccoef'][0], taylor_stats1['ccoef'][1],
taylor_stats2['ccoef'][1], taylor_stats3['ccoef'][1]])
'''
Produce the Taylor diagram
Note that the first index corresponds to the reference series for
the diagram. For example sdev[0] is the standard deviation of the
reference series and sdev[1:4] are the standard deviations of the
other 3 series. The value of sdev[0] is used to define the origin
of the RMSD contours. The other values are used to plot the points
(total of 3) that appear in the diagram.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> taylor_diagram
'''
sm.taylor_diagram(sdev,crmsd,ccoef)
# Show plot
plt.show()
泰勒图2：
代码语言：javascript复制if __name__ == '__main__':
# Close any previously open graphics windows
# ToDo: fails to work within Eclipse
plt.close('all')
# Read data from pickle file
data = load_obj('/home/kesci/input/Talyor5298/Farmington_River_data')
# Change number of data points to illustrate effect
# of changing number of columns
ncol = 2
if ncol == 1:
sdev = data.sdev[0:11]
crmsd = data.crmsd[0:11]
ccoef = data.ccoef[0:11]
gageID = data.gageID[0:11]
elif ncol == 2:
sdev = data.sdev
crmsd = data.crmsd
ccoef = data.ccoef
gageID = data.gageID
else:
sdev = data.sdev
crmsd = data.crmsd
ccoef = data.ccoef
gageID = data.gageID
sdev = np.append(sdev,data.sdev[1:11])
crmsd = np.append(crmsd,data.crmsd[1:11])
ccoef = np.append(ccoef,data.ccoef[1:11])
gageID = gageID data.gageID[1:11]
# Specify labels for points in a cell array using gage ID.
label = gageID
# Must set figure size here to prevent legend from being cut off
plt.figure(num=1, figsize=(8, 6))
'''
Produce the Taylor diagram
Label the points and change the axis options for SDEV, CRMSD, and CCOEF.
Increase the upper limit for the SDEV axis and rotate the CRMSD contour
labels (counter-clockwise from x-axis). Exchange color and line style
choices for SDEV, CRMSD, and CCOEFF variables to show effect. Increase
the line width of all lines. Suppress axes titles and add a legend.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> taylor_diagram
'''
sm.taylor_diagram(sdev,crmsd,ccoef, markerLabel = label, markerLabelColor = 'r',
markerLegend = 'on', markerColor = 'r',
styleOBS = '-', colOBS = 'r', markerobs = 'o',
markerSize = 6, tickRMS = [0.0, 1.0, 2.0, 3.0],
tickRMSangle = 115, showlabelsRMS = 'on',
titleRMS = 'on', titleOBS = 'Ref', checkstats = 'on')
# Show plot
plt.show()
