class SmootherTest(object):
"""Test a bare parameter set with the smoother"""
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 run(self):
assert self.smoother.num_values() == 7
assert self.smoother.num_samples() == 5
assert self.smoother.num_average() == 3
# The smoother positions depend on the number of intervals but not on
# the x_range
assert self.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 self.smoother.spacing() == 19.8
# Use the single value smoother multiple times...
smooth_at = [e for e in range(1, 101)]
data = [self.smoother.value_weight(e, self.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 = self.smoother.multi_value_weight(
smooth_at, self.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)
if self.do_plots:
try:
import matplotlib.pyplot as plt
plt.ion()
plt.scatter(smooth_at, vals)
plt.draw()
except ImportError as e:
print "pyplot not available", e
print "OK"
class SmootherTest(object):
"""Test a bare parameter set with the smoother"""
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 run(self):
assert self.smoother.num_values() == 7
assert self.smoother.num_samples() == 5
assert self.smoother.num_average() == 3
# The smoother positions depend on the number of intervals but not on
# the x_range
assert self.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 self.smoother.spacing() == 19.8
# Use the single value smoother multiple times...
smooth_at = [e for e in range(1, 101)]
data = [self.smoother.value_weight(e, self.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 = self.smoother.multi_value_weight(smooth_at, self.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)
if self.do_plots:
try:
import matplotlib.pyplot as plt
plt.ion()
plt.scatter(smooth_at, vals)
plt.draw()
except ImportError as e:
print "pyplot not available", e
print "OK"
class SmootherTest(object):
"""Test a bare parameter set with the smoother"""
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 run(self):
assert self.smoother.num_values() == 7
assert self.smoother.num_samples() == 5
assert self.smoother.num_average() == 3
# The smoother positions depend on the number of intervals but not on
# the x_range
assert self.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 self.smoother.spacing() == 19.8
smooth_at = [e for e in range(1, 101)]
data = [self.smoother.value_weight(e, self.myparam) for e in smooth_at]
vals = [v for v, w, sw in data]
assert len(smooth_at) == len(vals)
if self.do_plots:
try:
import matplotlib.pyplot as plt
plt.ion()
plt.scatter(smooth_at, vals)
plt.draw()
except ImportError as e:
print "pyplot not available", e
print "OK"
class SmootherTest(object):
"""Test a bare parameter set with the smoother"""
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 run(self):
assert self.smoother.num_values() == 7
assert self.smoother.num_samples() == 5
assert self.smoother.num_average() == 3
# The smoother positions depend on the number of intervals but not on
# the x_range
assert self.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 self.smoother.spacing() == 19.8
smooth_at = [e for e in range(1, 101)]
data = [self.smoother.value_weight(e, self.myparam) for e in smooth_at]
vals = [v for v, w, sw in data]
assert len(smooth_at) == len(vals)
if self.do_plots:
try:
import matplotlib.pyplot as plt
plt.ion()
plt.scatter(smooth_at, vals)
plt.draw()
except ImportError as e:
print "pyplot not available", e
print "OK"
