def test___init__(self):
self.metrics = verify.ErrorMetrics(self.obs, self.est)
with pytest.raises(ValueError):
verify.ErrorMetrics(self.obs, self.est[:10])
def test_pprint(self):
metrics = verify.ErrorMetrics(self.obs, self.est)
metrics.pprint()
def test___init__warn(self):
with pytest.warns(UserWarning):
verify.ErrorMetrics(self.obs, self.non)
def test_all_metrics(self):
metrics = verify.ErrorMetrics(self.obs, self.est)
metrics.all()
def ex_adjust():
###########################################################################
# 1d Example ##############################################################
###########################################################################
# gage and radar coordinates
obs_coords = np.array([5, 10, 15, 20, 30, 45, 65, 70, 77, 90])
radar_coords = np.arange(0, 101)
# true rainfall
truth = np.abs(1.5 + np.sin(0.075 * radar_coords)) + np.random.uniform(
-0.1, 0.1, len(radar_coords))
# radar error
erroradd = 0.7 * np.sin(0.2 * radar_coords + 10.)
errormult = 0.75 + 0.015 * radar_coords
noise = np.random.uniform(-0.05, 0.05, len(radar_coords))
# radar observation
radar = errormult * truth + erroradd + noise
# gage observations are assumed to be perfect
obs = truth[obs_coords]
# add a missing value to observations (just for testing)
obs[1] = np.nan
# number of neighbours to be used
nnear_raws = 3
# adjust the radar observation by additive model
add_adjuster = adjust.AdjustAdd(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
add_adjusted = add_adjuster(obs, radar)
# adjust the radar observation by multiplicative model
mult_adjuster = adjust.AdjustMultiply(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mult_adjusted = mult_adjuster(obs, radar)
# adjust the radar observation by MFB
mfb_adjuster = adjust.AdjustMFB(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mfb_adjusted = mfb_adjuster(obs, radar)
# adjust the radar observation by AdjustMixed
mixed_adjuster = adjust.AdjustMixed(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mixed_adjusted = mixed_adjuster(obs, radar)
# plotting
pl.plot(radar_coords,
radar,
'k-',
label="Unadjusted radar",
linewidth=2.,
linestyle="dashed")
pl.xlabel("Distance (km)")
pl.ylabel("Rainfall intensity (mm/h)")
pl.plot(radar_coords, truth, 'k-', label="True rainfall", linewidth=2.)
pl.plot(obs_coords,
obs,
'o',
label="Gage observation",
markersize=10.0,
markerfacecolor="grey")
pl.plot(radar_coords,
add_adjusted,
'-',
color="red",
label="Additive adjustment")
pl.plot(radar_coords,
mult_adjusted,
'-',
color="green",
label="Multiplicative adjustment")
pl.plot(radar_coords,
mfb_adjusted,
'-',
color="orange",
label="Mean Field Bias adjustment")
pl.plot(radar_coords,
mixed_adjusted,
'-',
color="blue",
label="Mixed (mult./add.) adjustment")
pl.legend(prop={'size': 12})
pl.show()
# Verification for this example
rawerror = verify.ErrorMetrics(truth, radar)
mfberror = verify.ErrorMetrics(truth, mfb_adjusted)
adderror = verify.ErrorMetrics(truth, add_adjusted)
multerror = verify.ErrorMetrics(truth, mult_adjusted)
mixerror = verify.ErrorMetrics(truth, mixed_adjusted)
# Verification reports
maxval = 4.
fig = pl.figure(figsize=(14, 8))
ax = fig.add_subplot(231, aspect=1.)
rawerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Unadjusted radar")
ax = fig.add_subplot(232, aspect=1.)
adderror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Additive adjustment")
ax = fig.add_subplot(233, aspect=1.)
multerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Multiplicative adjustment")
ax = fig.add_subplot(234, aspect=1.)
mixerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Mixed (mult./add.) adjustment")
mixerror.report(ax=ax, unit="mm", maxval=maxval)
ax = fig.add_subplot(235, aspect=1.)
mfberror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Mean Field Bias adjustment")
pl.show()
###########################################################################
# 2d Example ##############################################################
###########################################################################
# a) CREATE SYNTHETIC DATA ------------------------------------------------
# grid axes
xgrid = np.arange(0, 10)
ygrid = np.arange(20, 30)
# number of observations
num_obs = 10
# create grid
gridshape = len(xgrid), len(ygrid)
grid_coords = util.gridaspoints(ygrid, xgrid)
# Synthetic true rainfall
truth = np.abs(10. * np.sin(0.1 * grid_coords).sum(axis=1))
# Creating radar data by perturbing truth with multiplicative and additive error
# YOU CAN EXPERIMENT WITH THE ERROR STRUCTURE
radar = 0.6 * truth + 1. * np.random.uniform(
low=-1., high=1, size=len(truth))
radar[radar < 0.] = 0.
# indices for creating obs from raw (random placement of gauges)
obs_ix = np.random.uniform(low=0, high=len(grid_coords),
size=num_obs).astype('i4')
# creating obs_coordinates
obs_coords = grid_coords[obs_ix]
# creating gauge observations from truth
obs = truth[obs_ix]
# b) GAUGE ADJUSTMENT -----------------------------------------------------
# Mean Field Bias Adjustment
mfbadjuster = adjust.AdjustMFB(obs_coords, grid_coords)
mfbadjusted = mfbadjuster(obs, radar)
# Additive Error Model
addadjuster = adjust.AdjustAdd(obs_coords, grid_coords)
addadjusted = addadjuster(obs, radar)
# Multiplicative Error Model
multadjuster = adjust.AdjustMultiply(obs_coords, grid_coords)
multadjusted = multadjuster(obs, radar)
# c) PLOTTING
# Maximum value (used for normalisation of colorscales)
maxval = np.max(np.concatenate((truth, radar, obs, addadjusted)).ravel())
# Helper functions for repeated plotting tasks
def scatterplot(x, y, title):
"""Quick and dirty helper function to produce scatter plots
"""
pl.scatter(x, y)
pl.plot([0, 1.2 * maxval], [0, 1.2 * maxval], '-', color='grey')
pl.xlabel("True rainfall (mm)")
pl.ylabel("Estimated rainfall (mm)")
pl.xlim(0, maxval + 0.1 * maxval)
pl.ylim(0, maxval + 0.1 * maxval)
pl.title(title)
def gridplot(data, title):
"""Quick and dirty helper function to produce a grid plot
"""
xplot = np.append(xgrid, xgrid[-1] + 1.) - 0.5
yplot = np.append(ygrid, ygrid[-1] + 1.) - 0.5
grd = ax.pcolormesh(xplot,
yplot,
data.reshape(gridshape),
vmin=0,
vmax=maxval)
ax.scatter(obs_coords[:, 0],
obs_coords[:, 1],
c=obs.ravel(),
marker='s',
s=50,
vmin=0,
vmax=maxval)
pl.colorbar(grd, shrink=0.7)
pl.title(title)
# open figure
fig = pl.figure(figsize=(10, 10))
# True rainfall
ax = fig.add_subplot(331, aspect='equal')
gridplot(truth, 'True rainfall')
# Unadjusted radar rainfall
ax = fig.add_subplot(332, aspect='equal')
gridplot(radar, 'Radar rainfall')
# Scatter plot radar vs. observations
ax = fig.add_subplot(333, aspect='equal')
scatterplot(truth, radar, 'Radar vs. Truth (red: Gauges)')
pl.plot(obs, radar[obs_ix], linestyle="None", marker="o", color="red")
# Adjusted radar rainfall (MFB)
ax = fig.add_subplot(334, aspect='equal')
gridplot(mfbadjusted, 'Adjusted (MFB)')
# Adjusted radar rainfall (additive)
ax = fig.add_subplot(335, aspect='equal')
gridplot(addadjusted, 'Adjusted (Add.)')
# Adjusted radar rainfall (multiplicative)
ax = fig.add_subplot(336, aspect='equal')
gridplot(multadjusted, 'Adjusted (Mult.)')
# Adjusted (MFB) vs. radar (for control purposes)
ax = fig.add_subplot(337, aspect='equal')
# scatterplot(obs, mfbadjusted[obs_ix], 'Adjusted (MFB) vs. Gauges\n(no x-validation!)')
scatterplot(truth, mfbadjusted, 'Adjusted (MFB) vs. Truth')
# Adjusted (Add) vs. radar (for control purposes)
ax = fig.add_subplot(338, aspect='equal')
# scatterplot(obs, addadjusted[obs_ix], 'Adjusted (Add.) vs. Gauges\n(no x-validation!)')
scatterplot(truth, addadjusted, 'Adjusted (Add.) vs. Truth')
# Adjusted (Mult.) vs. radar (for control purposes)
ax = fig.add_subplot(339, aspect='equal')
# scatterplot(obs, multadjusted[obs_ix], 'Adjusted (Mult.) vs. Gauges\n(no x-validation!)')
scatterplot(truth, multadjusted, 'Adjusted (Mult.) vs. Truth')
pl.tight_layout()
pl.show()
