#print reference_time
for k, name in enumerate(gauge_names):
sqsum = 0
denom = 0
model = []
if verbose:
print
print 'Validating ' + name
observed_timeseries = validation_data[name]
for i, t in enumerate(reference_time):
model.append(f(t, point_id=k)[0])
# Covariance measure
res = cov(observed_timeseries, model)
if verbose:
report_difference('Covariance', res, expected_covariance[name], rtol,
atol)
# Difference measures
res = sum(abs(observed_timeseries - model)) / len(model)
if verbose:
report_difference('Accumulated difference', res,
expected_difference[name], rtol, atol)
# Extrema
res = max(model)
if verbose:
report_difference('Maximum', res, expected_maximum[name], rtol, atol)
def compare_timeseries(timevector,
timeseries1,
timeseries2,
name='',
legend=('Time series 1', 'Time series 2'),
eps=1.0e-6):
"""Compare and plot two timeseries
"""
timeseries1 = num.array(timeseries1)
timeseries2 = num.array(timeseries2)
N = timevector.shape[0]
assert timeseries1.shape[0] == N
assert timeseries2.shape[0] == N
print 'Testing gauge "%s"' % name
print 'epsilon', eps
# Covariance measure
res = cov(timeseries1 - timeseries2)
print '2 norm diff', res
msg = '2-norm of timeseries difference was too large: %e' % res
assert res < eps, msg
# Maximum norm
nominator = max(abs(timeseries1 - timeseries2))
denominator = max(abs(timeseries1))
if denominator > 0:
# Relative measure
res = nominator / denominator
else:
# Absolute measure
res = nominator / N
print 'Max abs diff', res
msg = '%s: Difference between timeseries was too large: %e' % (name, res)
assert res < eps, msg
nominator = sum(abs(timeseries1 - timeseries2))
denominator = sum(abs(timeseries1))
if denominator > 0:
# Relative measure
res = nominator / denominator
else:
# Absolute measure
res = nominator / N
print 'Sum abs diff', res
msg = '%s: Difference between timeseries was too large: %e' % (name, res)
assert res < eps, msg
# Extrema
max1 = max(timeseries1)
max2 = max(timeseries2)
res = abs(max1 - max2)
print 'Max diff', res
msg = '%s: Difference between maxima was too large: %e' % (name, res)
assert res < eps, msg
min1 = min(timeseries1)
min2 = min(timeseries2)
res = abs(min1 - min2)
print 'Min diff', res
msg = '%s: Difference between minima was too large: %e' % (name, res)
assert res < eps, msg
# Locations of extrema
i1 = num.argmax(timeseries1)
i2 = num.argmax(timeseries2)
res = abs(timevector[i1] - timevector[i2])
print 'Max loc diff', res
msg = '%s: Difference between location of maxima was too large: %e' % (
name, res)
assert res < eps, msg
i1 = num.argmin(timeseries1)
i2 = num.argmin(timeseries2)
res = abs(timevector[i1] - timevector[i2])
print 'Min loc diff', res
msg = '%s: Difference between location of minima was too large: %e' % (
name, res)
assert res < eps, msg
if plotting:
# Generate plots
#pylab.ion() # No plotting on screen
pylab.hold(False)
pylab.plot(timevector, timeseries1, 'r-', timevector, timeseries2,
'k-')
pylab.title('Gauge %s' % name)
pylab.xlabel('time(s)')
pylab.ylabel('stage (m)')
pylab.legend(legend, shadow=True, loc='upper left')
pylab.savefig(name, dpi=300)
# Error vector
#pylab.ion() # No plotting on screen
pylab.hold(False)
pylab.plot(timevector, timeseries1 - timeseries2, 'b-')
pylab.title('Gauge %s (difference)' % name)
pylab.xlabel('time(s)')
pylab.ylabel('stage difference (m)')
pylab.savefig(name + '_diff', dpi=300)
print 'Gauge "%s" OK' % name
print
#print reference_time
for k, name in enumerate(gauge_names):
sqsum = 0
denom = 0
model = []
if verbose:
print
print 'Validating ' + name
observed_timeseries = validation_data[name]
for i, t in enumerate(reference_time):
model.append(f(t, point_id=k)[0])
# Covariance measure
res = cov(observed_timeseries, model)
if verbose:
report_difference('Covariance', res, expected_covariance[name], rtol, atol)
# Difference measures
res = sum(abs(observed_timeseries-model))/len(model)
if verbose:
report_difference('Accumulated difference', res,
expected_difference[name], rtol, atol)
# Extrema
res = max(model)
if verbose:
report_difference('Maximum', res, expected_maximum[name], rtol, atol)
res = min(model)
def compare_timeseries(timevector,
timeseries1,
timeseries2,
name='',
legend = ('Time series 1', 'Time series 2'),
eps=1.0e-6):
"""Compare and plot two timeseries
"""
timeseries1 = num.array(timeseries1)
timeseries2 = num.array(timeseries2)
N = timevector.shape[0]
assert timeseries1.shape[0] == N
assert timeseries2.shape[0] == N
print 'Testing gauge "%s"' % name
print 'epsilon', eps
# Covariance measure
res = cov(timeseries1-timeseries2)
print '2 norm diff', res
msg = '2-norm of timeseries difference was too large: %e' % res
assert res < eps, msg
# Maximum norm
nominator = max(abs(timeseries1-timeseries2))
denominator = max(abs(timeseries1))
if denominator > 0:
# Relative measure
res = nominator/denominator
else:
# Absolute measure
res = nominator/N
print 'Max abs diff', res
msg = '%s: Difference between timeseries was too large: %e' % (name, res)
assert res < eps, msg
nominator = sum(abs(timeseries1-timeseries2))
denominator = sum(abs(timeseries1))
if denominator > 0:
# Relative measure
res = nominator/denominator
else:
# Absolute measure
res = nominator/N
print 'Sum abs diff', res
msg = '%s: Difference between timeseries was too large: %e' % (name, res)
assert res < eps, msg
# Extrema
max1 = max(timeseries1)
max2 = max(timeseries2)
res = abs(max1-max2)
print 'Max diff', res
msg = '%s: Difference between maxima was too large: %e' % (name, res)
assert res < eps, msg
min1 = min(timeseries1)
min2 = min(timeseries2)
res = abs(min1-min2)
print 'Min diff', res
msg = '%s: Difference between minima was too large: %e' % (name, res)
assert res < eps, msg
# Locations of extrema
i1 = num.argmax(timeseries1)
i2 = num.argmax(timeseries2)
res = abs(timevector[i1]-timevector[i2])
print 'Max loc diff', res
msg = '%s: Difference between location of maxima was too large: %e' % (name, res)
assert res < eps, msg
i1 = num.argmin(timeseries1)
i2 = num.argmin(timeseries2)
res = abs(timevector[i1]-timevector[i2])
print 'Min loc diff', res
msg = '%s: Difference between location of minima was too large: %e' % (name, res)
assert res < eps, msg
if plotting:
# Generate plots
#pylab.ion() # No plotting on screen
pylab.hold(False)
pylab.plot(timevector, timeseries1, 'r-',
timevector, timeseries2, 'k-')
pylab.title('Gauge %s' % name)
pylab.xlabel('time(s)')
pylab.ylabel('stage (m)')
pylab.legend(legend, shadow=True, loc='upper left')
pylab.savefig(name, dpi = 300)
# Error vector
#pylab.ion() # No plotting on screen
pylab.hold(False)
pylab.plot(timevector, timeseries1-timeseries2, 'b-')
pylab.title('Gauge %s (difference)' % name)
pylab.xlabel('time(s)')
pylab.ylabel('stage difference (m)')
pylab.savefig(name + '_diff', dpi = 300)
print 'Gauge "%s" OK' % name
print
