# Specify labels for points in a list (M1 for model prediction # 1,
# etc.).
label = ['M1', 'M2', 'M3']
'''
Produce the target diagram
Label the points and change the axis options. Increase the upper
limit for the axes, change color and line style of circles. Increase
the line width of circles. Change color of labels and points. Add a
legend. Suppress x-axis label for uRMSD = 30 so the markers appear
more clearly.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> target_diagram
'''
sm.target_diagram(bias,crmsd,rmsd, MarkerDisplayed = 'colorBar', \
titleColorbar = 'RMSD', nonRMSDz = 'on', \
markerLabel = label, markerLabelColor = 'b', \
markerColor = 'b', markerLegend = 'on', \
ticks = np.arange(-50,60,10), axismax = 50, \
xtickLabelPos = [np.arange(-50,20,20), 40, 50], \
circles = [20, 40, 50], \
circleLineSpec = '-.b', circleLineWidth = 1.5)
# Write plot to file
plt.savefig('target5.png')
# Show plot
plt.show()
target_stats3['crmsd']])
rmsd = np.array([target_stats1['rmsd'], target_stats2['rmsd'],
target_stats3['rmsd']])
# Specify labels for points in a list (M1 for model prediction # 1,
# etc.).
label = ['M1', 'M2', 'M3']
'''
Produce the target diagram
Label the points and change the axis options. Increase the upper
limit for the axes, change color and line style of circles. Increase
the line width of circles.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> target_diagram
'''
sm.target_diagram(bias,crmsd,rmsd, markerLabel = label,
markerLabelColor = 'r',
ticks = np.arange(-50,60,10),
axismax = 50.0,
circleLineSpec = 'b-.', circleLineWidth = 1.5)
# Write plot to file
plt.savefig('target3.png')
# Show plot
plt.show()
# etc.).
label = ['M1', 'M2', 'M3']
'''
Produce the target diagram
Label the points and change the axis options. Increase the upper
limit for the axes, change color and line style of circles.
Increase the line width of circles.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> target_diagram
'''
sm.target_diagram(bias,crmsd,rmsd, \
markerLabel = label, markerLabelColor = 'r', \
ticks = np.arange(-60,70,10), axismax = 50, \
circleLineSpec = '-.b', circleLineWidth = 1.5)
# Calculate a negative uRMSD for one of the data values.
data.pred3['data'] = -data.pred3['data']
target_stats3 = sm.target_statistics(data.pred3,data.ref,'data')
bias = target_stats3['bias']
crmsd = target_stats3['crmsd']
rmsd = target_stats3['rmsd']
# Overlay new data point (blue) on existing diagram
label = ['M4']
sm.target_diagram(bias,crmsd,rmsd, \
overlay = 'on', \
markerLabel = label, markerLabelColor = 'b', \
markerColor = 'b')
rmsd = np.array([target_stats1['rmsd'], target_stats2['rmsd'], target_stats3['rmsd'], target_stats4['rmsd'], target_stats5['rmsd'], target_stats6['rmsd'],
target_stats7['rmsd'] , target_stats8['rmsd'], target_stats9['rmsd'], target_stats10['rmsd'] ] )
label1 = ['Non-Dimensional Observation', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'M10']
label2 = ['Non-Dimensional Observation', 'M1: NNLS', 'M2: X2-I', 'M3: X2-L1', 'M4: X2-L2','M5: L-curve-I', 'M6: L-curve-L1', 'M7: L-curve-L2', 'M8: GCV-I', 'M9: GCV-L1', 'M10: GCV-L2']
sm.taylor_diagram(sdev,crmsd,ccoef, MarkerDisplayed='marker', markerLabel = label1, markerColor='red', colRMS='green', colSTD='blue', colCOR='black', alpha=0.5, markerSize=13, titleOBS = 'observation', styleOBS = '-')
plt.savefig(path + 'taylor_SNR.png', dpi=300)
plt.close('all')
sm.taylor_diagram(sdev,crmsd,ccoef, MarkerDisplayed='marker', markerLabel = label2, colRMS='green', colSTD='blue', colCOR='black', alpha=0.5, markerLegend='on', markerSize=6)
plt.savefig(path + 'taylor_SNR_legend.png', dpi=300)
plt.close('all')
sm.target_diagram(100*bias, 100*crmsd2, 100*rmsd, markerLabel = label1[1:], markerLabelColor = 'r',
circleLineSpec = 'b-.', circleLineWidth = 1.5, alpha=0.5, markerSize=13)
plt.savefig(path + 'target_bias_SNR.png', dpi=300)
plt.close('all')
MAE_S = np.array([
metric_vector_nnls[11],
metric_vector_X2_I[11],
metric_vector_X2_L1[11],
metric_vector_X2_L2[11],
metric_vector_L_curve_I[11],
metric_vector_L_curve_L1[11],
metric_vector_L_curve_L2[11],
metric_vector_GCV_I[11],
metric_vector_GCV_L1[11],
metric_vector_GCV_L2[11],
# Read target statistics for ERA Interim (stats1) and TRMM (stats2)
# data with respect to APHRODITE observations for each of years 2001 to
# 2014 from pickle file
stats = load_obj('Mekong_Basin_data') # observations
# Specify labels for points in a dictionary because only desire labels
# for each data set.
label = {'ERA-5': 'r', 'TRMM': 'b'}
'''
Produce the target diagram for the first dataset
'''
sm.target_diagram(stats.target_stats1['bias'],
stats.target_stats1['crmsd'],
stats.target_stats1['rmsd'], markercolor ='r', alpha = 0.0,
ticks=np.arange(-2.0,2.5,0.5),
circles = [0.5, 1.0, 2.0],
circleLineSpec = 'k--', circleLineWidth = 1.0)
'''
Overlay the second dataset
'''
sm.target_diagram(stats.target_stats2['bias'],
stats.target_stats2['crmsd'],
stats.target_stats2['rmsd'], markercolor ='b', alpha = 0.0,
overlay = 'on', markerLabel = label)
# Write plot to file
plt.savefig('target8.png',dpi=150,facecolor='w')
# Show plot
# Read data from pickle file
data = load_obj('target_data')
# Calculate statistics for target diagram
target_stats1 = sm.target_statistics(data.pred1, data.ref, 'data')
target_stats2 = sm.target_statistics(data.pred2, data.ref, 'data')
target_stats3 = sm.target_statistics(data.pred3, data.ref, 'data')
# Store statistics in arrays
bias = np.array(
[target_stats1['bias'], target_stats2['bias'], target_stats3['bias']])
crmsd = np.array([
target_stats1['crmsd'], target_stats2['crmsd'], target_stats3['crmsd']
])
rmsd = np.array(
[target_stats1['rmsd'], target_stats2['rmsd'], target_stats3['rmsd']])
'''
Produce the target diagram
Reference circles are plotted at the maximum range of the axes and at 0.7
times the maximum range by default.
'''
sm.target_diagram(bias, crmsd, rmsd)
# Write plot to file
plt.savefig('target1.png')
# Show plot
plt.show()
# Store statistics in arrays
bias = np.array([target_stats1['bias'], target_stats2['bias'],
target_stats3['bias']])
crmsd = np.array([target_stats1['crmsd'], target_stats2['crmsd'],
target_stats3['crmsd']])
rmsd = np.array([target_stats1['rmsd'], target_stats2['rmsd'],
target_stats3['rmsd']])
# Specify labels for points in a list (M1 for model prediction # 1,
# etc.).
label = ['M1', 'M2', 'M3']
'''
Produce the target diagram
Label the points and change the axis options for BIAS and CRMSD.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> target_diagram
'''
sm.target_diagram(bias,crmsd,rmsd, markerLabel = label, \
ticks=np.arange(-50,60,10))
# Write plot to file
plt.savefig('target2.png')
# Show plot
plt.show()
# Specify labels for points in a list (M1 for model prediction # 1,
# etc.).
label = ['M1', 'M2', 'M3']
'''
Produce the target diagram
Label the points and change the axis options. Increase the upper limit
for the axes, change color and line style of circles. Increase
the line width of circles. Change color of labels and points. Add a
legend.
For an exhaustive list of options to customize your diagram,
please call the function at a Python command line:
>> target_diagram
'''
#ToDo: fix placement of legend
sm.target_diagram(bias,crmsd,rmsd, markerLabel = label, \
markerLabelColor = 'b', \
markerColor = 'b', markerLegend = 'on', \
ticks = np.arange(-50,60,10), \
axismax = 50.0, \
circles = [20, 40, 50], \
circleLineSpec = 'b-.', circleLineWidth = 1.5)
# Write plot to file
plt.savefig('target4.png')
# Show plot
plt.show()