def test_gaussian_smoother(plots=False):
"""Test a bare parameter set with the smoother"""
# 7 values, all set to 1.0
myparam = ScanVaryingParameterSet(1.0, 7)
# Adjust a couple of the values
myparam.value[3:4] = [2.0, 2.0]
# Make a smoother with x_range as an 'image range', between 1 and 100.
# This smoother needs 5 intervals (for 7 total values). The default
# smoother uses an averaging window of 3 values
smoother = GaussianSmoother((1, 100), 5)
assert smoother.num_values() == 7
assert smoother.num_samples() == 5
assert smoother.num_average() == 3
# The smoother positions depend on the number of intervals but not on
# the x_range
assert smoother.positions() == [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5]
# By contrast the spacing is in units of the original, unnormalised
# coordinate
assert smoother.spacing() == 19.8
# Use the single value smoother multiple times...
smooth_at = [e for e in range(1, 101)]
data = [smoother.value_weight(e, myparam) for e in smooth_at]
vals, weights, sumweights = zip(*data)
assert len(smooth_at) == len(vals)
# ...and the multi value smoother once
mvals, mweights, msumweights = smoother.multi_value_weight(
smooth_at, myparam)
# the results should be identical.
assert (flex.double(vals) == mvals).all_eq(True)
assert (flex.double(sumweights) == msumweights).all_eq(True)
for v1, v2 in zip(weights, mweights.transpose().cols()):
assert (v1.as_dense_vector() == v2.as_dense_vector()).all_eq(True)
# make scatterplots
if plots:
import matplotlib.pyplot as plt
plt.ion()
plt.scatter(smooth_at, vals)
plt.draw()
def __init__(self, phi_range_deg, deg_per_interval=5):
n_intervals = max(
int(abs(phi_range_deg[1] - phi_range_deg[0]) / deg_per_interval),
1)
# Set up the smoother
self._smoother = GaussianSmoother(phi_range_deg, n_intervals)
self._num_samples = self._smoother.num_values()
# initial value of scale factors is 1
value = 1
self._param = ScanVaryingParameterSet(value,
self._num_samples,
name="IncidentBeam")
def __init__(self, plots = False):
# make scatterplots
self.do_plots = plots
# 7 values, all set to 1.0
self.myparam = ScanVaryingParameterSet(1.0, 7)
# Adjust a couple of the values
self.myparam.value[3:4] = [2.0, 2.0]
# Make a smoother with x_range as an 'image range', between 1 and 100.
# This smoother needs 5 intervals (for 7 total values). The default
# smoother uses an averaging window of 3 values
self.smoother = GaussianSmoother((1, 100), 5)
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
def parameter_type(value, name):
return ScanVaryingParameterSet(value, nv, name=name)
