def windowed_smooth(measurements, size=1, population_size=512):
"""
Returns a new list of measurements with the same length as the source
measurements, where each value in the result is calculated as the median
and weighted MAD of the nearest +/- size measurements and the measurement
itself.
For example, if the resulting value for measurement X10 with a size of 2
would be median +/- the weighted MAD of the measurements
(X8, X9, X10, X11, X12)
Edge conditions are handled so that they are smoothed with partial windows
:param measurements:
:param size:
The extend of the smoothing window.
:param population_size:
:return:
"""
window = []
window_populations = []
while len(window) < size + 1:
m = measurements[len(window)]
window.append(m)
window_populations.append(
mdists.population(mdists.Distribution([m]), count=population_size))
out = []
for i in range(len(measurements)):
pop_combined = []
for p in window_populations:
pop_combined += p
out.append(
ValueUncertainty(
mdists.percentile(pop_combined),
mdists.weighted_median_average_deviation(pop_combined)))
append_index = i + size + 1
if append_index >= len(measurements):
window.pop(0)
window_populations.pop(0)
continue
m = measurements[append_index]
d = mdists.Distribution([m])
pop = mdists.population(d, population_size)
window.append(m)
window_populations.append(pop)
while len(window) > (2 * size + 1):
window.pop(0)
window_populations.pop(0)
return out
def get_weighted_median(quantities) -> mstats.ValueUncertainty:
dist = mstats.create_distribution(quantities)
pop = mdist.population(dist)
return mstats.ValueUncertainty(
mdist.percentile(pop, 0.5),
max(0.00001, mdist.weighted_median_average_deviation(pop))
)
def get_median(
quantities: typing.List[mstats.ValueUncertainty]
) -> mstats.ValueUncertainty:
dist = mstats.create_distribution(quantities)
pop = mdist.population(dist)
return mstats.ValueUncertainty(
mdist.percentile(pop, 0.5),
max(0.00001, mdist.weighted_median_average_deviation(pop))
)
def calculate_median(values, uncertainties, color_index=0, plot_name=None):
"""
:param values:
:param uncertainties:
:param color_index:
:param plot_name:
:return:
"""
try:
values = values.values
except:
pass
try:
uncertainties = uncertainties.values
except:
pass
measurements = [
(val, unc) for val, unc in zip(values, uncertainties)
if not np.isnan(val) and not np.isnan(unc)
]
values, uncertainties = zip(*measurements)
measurements = mstats.values.join(values, uncertainties)
dist = mstats.create_distribution(measurements)
pop = mdist.population(dist)
median = mstats.ValueUncertainty(
mdist.percentile(pop),
mdist.weighted_median_average_deviation(pop)
)
x = mdist.adaptive_range(dist, 4)
y = dist.probabilities_at(x)
trace = go.Scatter(
x=x,
y=y,
name=plot_name,
fill='tozeroy',
mode='lines',
line=dict(
color=plotting.get_color(color_index)
)
)
return dict(
measurements=measurements,
median=median,
trace=trace
)
def calculate_median_value(values):
if not values or not len(values):
return dict(values=[],
dist=None,
pop=None,
median=mstats.ValueUncertainty(0, 0.0001))
dist = mstats.create_distribution(values)
pop = mdist.population(dist)
median = mstats.ValueUncertainty(
mdist.percentile(pop), mdist.weighted_median_average_deviation(pop))
return dict(values=values, dist=dist, pop=pop, median=median)
def add_to_group(
segment: Segment,
groups: SegmentGroupList,
trackway_data: dict
) -> SegmentGroupList:
"""
:param segment:
:param groups:
:param trackway_data:
:return:
"""
deviations = [deviation_between(g, segment) for g in groups]
if not deviations or min(deviations) > 2:
return groups + [SegmentGroup(
index=len(groups),
segments=[segment],
median=segment.median,
normalized_median=segment.median / trackway_data['width_median']
)]
group_index = deviations.index(min(deviations))
group = groups[group_index]
cauldron.step.breathe()
dist = mstats.create_distribution([s.median for s in group.segments])
pop = mdist.population(dist)
median = mstats.ValueUncertainty(
mdist.percentile(pop, 0.5),
max(0.00001, mdist.weighted_median_average_deviation(pop))
)
normalized = median / trackway_data['width_median']
replacement_group = SegmentGroup(
index=group.index,
median=median,
segments=group.segments + [segment],
normalized_median=normalized
)
return (
groups[:group_index] +
[replacement_group] +
groups[(group_index + 1):]
)
def calculate_median(values, uncertainties, color_index=0, plot_name=None):
"""
:param values:
:param uncertainties:
:param color_index:
:param plot_name:
:return:
"""
try:
values = values.values
except:
pass
try:
uncertainties = uncertainties.values
except:
pass
measurements = [(val, unc) for val, unc in zip(values, uncertainties)
if not np.isnan(val) and not np.isnan(unc)]
values, uncertainties = zip(*measurements)
measurements = mstats.values.join(values, uncertainties)
dist = mstats.create_distribution(measurements)
pop = mdist.population(dist)
median = mstats.ValueUncertainty(
mdist.percentile(pop), mdist.weighted_median_average_deviation(pop))
x = mdist.adaptive_range(dist, 4)
y = dist.probabilities_at(x)
trace = go.Scatter(x=x,
y=y,
name=plot_name,
fill='tozeroy',
mode='lines',
line=dict(color=plotting.get_color(color_index)))
return dict(measurements=measurements, median=median, trace=trace)
def box_smooth(measurements, size=2, population_size=512):
"""
:param measurements:
:param size:
:param population_size:
:return:
"""
out = []
for i in range(0, len(measurements), size):
d = mdists.Distribution(measurements[i:(i + size)])
pop = mdists.population(d, count=population_size)
median = mdists.percentile(pop)
mad = mdists.weighted_median_average_deviation(pop)
while len(out) < (i + size):
out.append(ValueUncertainty(median, mad))
return out
def calculate_median_value(values):
if not values or not len(values):
return dict(
values=[],
dist=None,
pop=None,
median=mstats.ValueUncertainty(0, 0.0001)
)
dist = mstats.create_distribution(values)
pop = mdist.population(dist)
median = mstats.ValueUncertainty(
mdist.percentile(pop),
mdist.weighted_median_average_deviation(pop)
)
return dict(
values=values,
dist=dist,
pop=pop,
median=median
)
