Python气象数据处理与绘图：泰勒图

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

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

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()

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()