# coding: utf-8
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"""This module provides mechanism relative to stats calculation within a
:class:`PlotWidget`.
It also include the implementation of the statistics themselves.
"""
__authors__ = ["H. Payno"]
__license__ = "MIT"
__date__ = "06/06/2018"
from collections import OrderedDict
from functools import lru_cache
import logging
import numpy
import numpy.ma
from .. import items
from ..CurvesROIWidget import ROI
from ..items.roi import RegionOfInterest
from ....math.combo import min_max
from silx.utils.proxy import docstring
from ....utils.deprecation import deprecated
logger = logging.getLogger(__name__)
[docs]class Stats(OrderedDict):
"""Class to define a set of statistic relative to a dataset
(image, curve...).
The goal of this class is to avoid multiple recalculation of some
basic operations such as filtering data area where the statistics has to
be apply.
Min and max are also stored because they can be used several time.
:param List statslist: List of the :class:`Stat` object to be computed.
"""
def __init__(self, statslist=None):
OrderedDict.__init__(self)
_statslist = statslist if not None else []
if statslist is not None:
for stat in _statslist:
self.add(stat)
[docs] def calculate(self, item, plot, onlimits, roi, data_changed=False,
roi_changed=False):
"""
Call all :class:`Stat` object registered and return the result of the
computation.
:param item: the item for which we want statistics
:param plot: plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: region of interest for statistic calculation. Incompatible
with the `onlimits` option.
:type roi: Union[None, :class:`~_RegionOfInterestBase`]
:param bool data_changed: did the data changed since last calculation.
:param bool roi_changed: did the associated roi (if any) has changed
since last calculation.
:return dict: dictionary with :class:`Stat` name as ket and result
of the calculation as value
"""
res = {}
context = self._getContext(item=item, plot=plot, onlimits=onlimits,
roi=roi)
for statName, stat in list(self.items()):
if context.kind not in stat.compatibleKinds:
logger.debug('kind %s not managed by statistic %s'
% (context.kind, stat.name))
res[statName] = None
else:
if roi_changed is True:
context.clear_mask()
if data_changed is True or roi_changed is True:
# if data changed or mask changed
context.clipData(item=item, plot=plot, onlimits=onlimits,
roi=roi)
# init roi and data
res[statName] = stat.calculate(context)
return res
def __setitem__(self, key, value):
assert isinstance(value, StatBase)
OrderedDict.__setitem__(self, key, value)
[docs] def add(self, stat):
"""Add a :class:`Stat` to the set
:param Stat stat: stat to add to the set
"""
self.__setitem__(key=stat.name, value=stat)
@staticmethod
@lru_cache(maxsize=50)
def _getContext(item, plot, onlimits, roi):
context = None
# Check for PlotWidget items
if isinstance(item, items.Curve):
context = _CurveContext(item, plot, onlimits, roi=roi)
elif isinstance(item, items.ImageData):
context = _ImageContext(item, plot, onlimits, roi=roi)
elif isinstance(item, items.Scatter):
context = _ScatterContext(item, plot, onlimits, roi=roi)
elif isinstance(item, items.Histogram):
context = _HistogramContext(item, plot, onlimits, roi=roi)
else:
# Check for SceneWidget items
from ...plot3d import items as items3d # Lazy import
if isinstance(item, (items3d.Scatter2D, items3d.Scatter3D)):
context = _plot3DScatterContext(item, plot, onlimits,
roi=roi)
elif isinstance(item,
(items3d.ImageData, items3d.ScalarField3D)):
context = _plot3DArrayContext(item, plot, onlimits,
roi=roi)
if context is None:
raise ValueError('Item type not managed')
return context
class _StatsContext(object):
"""
The context is designed to be a simple buffer and avoid repetition of
calculations that can appear during stats evaluation.
.. warning:: this class gives access to the data to be used for computation
. It deal with filtering data visible by the user on plot.
The filtering is a simple data sub-sampling. No interpolation
is made to fit data to boundaries.
:param item: the item for which we want to compute the context
:param str kind: the kind of the item
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlimits` calculation
:type roi: Union[None,:class:`_RegionOfInterestBase`]
"""
def __init__(self, item, kind, plot, onlimits, roi):
assert item
assert plot
assert type(onlimits) is bool
self.kind = kind
self.min = None
self.max = None
self.data = None
self.roi = None
self.onlimits = onlimits
self.values = None
"""The array of data with limit filtering if any. Is a numpy.ma.array,
meaning that it embed the mask applied by the roi if any"""
self.axes = None
"""A list of array of position on each axis.
If the signal is an array,
then each axis has the length of that dimension,
and the order is (z, y, x) (i.e., as the array shape).
If the signal is not an array,
then each axis has the same length as the signal,
and the order is (x, y, z).
"""
self.clipData(item, plot, onlimits, roi=roi)
def clear_mask(self):
"""
Remove the mask to force recomputation of it on next iteration
:return:
"""
raise NotImplementedError()
@property
def mask(self):
if self.values is not None:
assert isinstance(self.values, numpy.ma.MaskedArray)
return self.values.mask
else:
return None
@property
def is_mask_valid(self, **kwargs):
"""Return if the mask is valid for the data or need to be recomputed"""
raise NotImplementedError("Base class")
def _set_mask_validity(self, **kwargs):
"""User to set some values that allows to define the mask properties
and boundaries"""
raise NotImplementedError("Base class")
def clipData(self, item, plot, onlimits, roi):
"""Clip the data to the current mask to have accurate statistics
Function called before computing each statistics associated to this
context. It will insure the context for the (item, plot, onlimits, roi)
is created.
:param item: item for which we want statistics
:param plot: plot containing the statistics
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlimits` calculation
:type roi: Union[None,:class:`_RegionOfInterestBase`]
"""
raise NotImplementedError("Base class")
@deprecated(reason="context are now stored and keep during stats life."
"So this function will be called only once",
replacement="clipData", since_version="0.13.0")
def createContext(self, item, plot, onlimits, roi):
return self.clipData(item=item, plot=plot, onlimits=onlimits,
roi=roi)
def isStructuredData(self):
"""Returns True if data as an array-like structure.
:rtype: bool
"""
if self.values is None or self.axes is None:
return False
if numpy.prod([len(axis) for axis in self.axes]) == self.values.size:
return True
else:
# Make sure there is the right number of value in axes
for axis in self.axes:
assert len(axis) == self.values.size
return False
def isScalarData(self):
"""Returns True if data is a scalar.
:rtype: bool
"""
if self.values is None or self.axes is None:
return False
if self.isStructuredData():
return len(self.axes) == self.values.ndim
else:
return self.values.ndim == 1
def _checkContextInputs(self, item, plot, onlimits, roi):
if roi is not None and onlimits is True:
raise ValueError('Stats context is unable to manage both a ROI'
'and the `onlimits` option')
class _ScatterCurveHistoMixInContext(_StatsContext):
def __init__(self, kind, item, plot, onlimits, roi):
self.clear_mask()
_StatsContext.__init__(self, item=item, kind=kind,
plot=plot, onlimits=onlimits, roi=roi)
def _set_mask_validity(self, onlimits, from_, to_):
self._onlimits = onlimits
self._from_ = from_
self._to_ = to_
def clear_mask(self):
self._onlimits = None
self._from_ = None
self._to_ = None
def is_mask_valid(self, onlimits, from_, to_):
return (onlimits == self.onlimits and from_ == self._from_ and
to_ == self._to_)
class _CurveContext(_ScatterCurveHistoMixInContext):
"""
StatsContext for :class:`Curve`
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
_ScatterCurveHistoMixInContext.__init__(self, kind='curve', item=item,
plot=plot, onlimits=onlimits,
roi=roi)
@docstring(_StatsContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
self.roi = roi
self.onlimits = onlimits
xData, yData = item.getData(copy=True)[0:2]
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
if self.is_mask_valid(onlimits=onlimits, from_=minX, to_=maxX):
mask = self.mask
else:
mask = (minX <= xData) & (xData <= maxX)
mask = mask == 0
self._set_mask_validity(onlimits=onlimits, from_=minX, to_=maxX)
elif roi:
minX, maxX = roi.getFrom(), roi.getTo()
if self.is_mask_valid(onlimits=onlimits, from_=minX, to_=maxX):
mask = self.mask
else:
mask = (minX <= xData) & (xData <= maxX)
mask = mask == 0
self._set_mask_validity(onlimits=onlimits, from_=minX, to_=maxX)
else:
mask = numpy.zeros_like(yData)
mask = mask.astype(numpy.uint32)
self.xData = xData
self.yData = yData
self.values = numpy.ma.array(yData, mask=mask)
unmasked_data = self.values.compressed()
if len(unmasked_data) > 0:
self.min, self.max = min_max(unmasked_data)
else:
self.min, self.max = None, None
self.data = (xData, yData)
self.axes = (xData,)
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, ROI):
raise TypeError('curve `context` can ony manage 1D roi')
class _HistogramContext(_ScatterCurveHistoMixInContext):
"""
StatsContext for :class:`Histogram`
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
_ScatterCurveHistoMixInContext.__init__(self, kind='histogram',
item=item, plot=plot,
onlimits=onlimits, roi=roi)
@docstring(_StatsContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
yData, edges = item.getData(copy=True)[0:2]
xData = item._revertComputeEdges(x=edges, histogramType=item.getAlignment())
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
if self.is_mask_valid(onlimits=onlimits, from_=minX, to_=maxX):
mask = self.mask
else:
mask = (minX <= xData) & (xData <= maxX)
mask = mask == 0
self._set_mask_validity(onlimits=onlimits, from_=minX, to_=maxX)
elif roi:
if self.is_mask_valid(onlimits=onlimits, from_=roi._fromdata, to_=roi._todata):
mask = self.mask
else:
mask = (roi._fromdata <= xData) & (xData <= roi._todata)
mask = mask == 0
self._set_mask_validity(onlimits=onlimits, from_=roi._fromdata,
to_=roi._todata)
else:
mask = numpy.zeros_like(yData)
mask = mask.astype(numpy.uint32)
self.xData = xData
self.yData = yData
self.values = numpy.ma.array(yData, mask=(mask))
unmasked_data = self.values.compressed()
if len(unmasked_data) > 0:
self.min, self.max = min_max(unmasked_data)
else:
self.min, self.max = None, None
self.data = (self.xData, self.yData)
self.axes = (self.xData,)
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, ROI):
raise TypeError('curve `context` can ony manage 1D roi')
class _ScatterContext(_ScatterCurveHistoMixInContext):
"""StatsContext scatter plots.
It supports :class:`~silx.gui.plot.items.Scatter`.
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
_ScatterCurveHistoMixInContext.__init__(self, kind='scatter',
item=item, plot=plot,
onlimits=onlimits, roi=roi)
@docstring(_ScatterCurveHistoMixInContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
valueData = item.getValueData(copy=True)
xData = item.getXData(copy=True)
yData = item.getYData(copy=True)
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
minY, maxY = plot.getYAxis().getLimits()
# filter on X axis
valueData = valueData[(minX <= xData) & (xData <= maxX)]
yData = yData[(minX <= xData) & (xData <= maxX)]
xData = xData[(minX <= xData) & (xData <= maxX)]
# filter on Y axis
valueData = valueData[(minY <= yData) & (yData <= maxY)]
xData = xData[(minY <= yData) & (yData <= maxY)]
yData = yData[(minY <= yData) & (yData <= maxY)]
if roi:
if self.is_mask_valid(onlimits=onlimits, from_=roi.getFrom(),
to_=roi.getTo()):
mask = self.mask
else:
mask = (xData < roi.getFrom()) | (xData > roi.getTo())
else:
mask = numpy.zeros_like(xData)
self.data = (xData, yData, valueData)
self.values = numpy.ma.array(valueData, mask=mask)
self.axes = (xData, yData)
unmasked_values = self.values.compressed()
if len(unmasked_values) > 0:
self.min, self.max = min_max(unmasked_values)
else:
self.min, self.max = None, None
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, ROI):
raise TypeError('curve `context` can ony manage 1D roi')
class _ImageContext(_StatsContext):
"""StatsContext for images.
It supports :class:`~silx.gui.plot.items.ImageData`.
:warning: behaviour of scale images: now the statistics are computed on
the entire data array (there is no sampling in the array or
interpolation regarding the scale).
This also mean that the result can differ from what is displayed.
But I guess there is no perfect behaviour.
:warning: `isIn` functions for image context: for now have basically a
binary approach, the pixel is in a roi or not. To have a fully
'correct behaviour' we should add a weight on stats calculation
to moderate the pixel value.
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
self.clear_mask()
_StatsContext.__init__(self, kind='image', item=item,
plot=plot, onlimits=onlimits, roi=roi)
def _set_mask_validity(self, xmin: float, xmax: float, ymin: float, ymax
: float):
self._mask_x_min = xmin
self._mask_x_max = xmax
self._mask_y_min = ymin
self._mask_y_max = ymax
def clear_mask(self):
self._mask_x_min = None
self._mask_x_max = None
self._mask_y_min = None
self._mask_y_max = None
def is_mask_valid(self, xmin, xmax, ymin, ymax):
return (xmin == self._mask_x_min and xmax == self._mask_x_max and
ymin == self._mask_y_min and ymax == self._mask_y_max)
@docstring(_StatsContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
self.origin = item.getOrigin()
self.scale = item.getScale()
self.data = item.getData(copy=True)
mask = numpy.zeros_like(self.data)
"""mask use to know of the stat should be count in or not"""
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
minY, maxY = plot.getYAxis().getLimits()
XMinBound = int((minX - self.origin[0]) / self.scale[0])
YMinBound = int((minY - self.origin[1]) / self.scale[1])
XMaxBound = int((maxX - self.origin[0]) / self.scale[0])
YMaxBound = int((maxY - self.origin[1]) / self.scale[1])
XMinBound = max(XMinBound, 0)
YMinBound = max(YMinBound, 0)
if onlimits:
if XMaxBound <= XMinBound or YMaxBound <= YMinBound:
self.data = None
else:
self.data = self.data[YMinBound:YMaxBound + 1,
XMinBound:XMaxBound + 1]
mask = numpy.zeros_like(self.data)
elif roi:
minX, maxX = 0, self.data.shape[1]
minY, maxY = 0, self.data.shape[0]
XMinBound = max(minX, 0)
YMinBound = max(minY, 0)
XMaxBound = min(maxX, self.data.shape[1])
YMaxBound = min(maxY, self.data.shape[0])
if self.is_mask_valid(xmin=XMinBound, xmax=XMaxBound,
ymin=YMinBound, ymax=YMaxBound):
mask = self.mask
else:
for x in range(XMinBound, XMaxBound):
for y in range(YMinBound, YMaxBound):
_x = (x * self.scale[0]) + self.origin[0]
_y = (y * self.scale[1]) + self.origin[1]
mask[y, x] = not roi.contains((_x, _y))
self._set_mask_validity(xmin=XMinBound, xmax=XMaxBound,
ymin=YMinBound, ymax=YMaxBound)
self.values = numpy.ma.array(self.data, mask=mask)
if self.values.compressed().size > 0:
self.min, self.max = min_max(self.values.compressed())
else:
self.min, self.max = None, None
if self.values is not None:
self.axes = (self.origin[1] + self.scale[1] * numpy.arange(self.data.shape[0]),
self.origin[0] + self.scale[0] * numpy.arange(self.data.shape[1]))
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, RegionOfInterest):
raise TypeError('curve `context` can ony manage 2D roi')
class _plot3DScatterContext(_StatsContext):
"""StatsContext for 3D scatter plots.
It supports :class:`~silx.gui.plot3d.items.Scatter2D` and
:class:`~silx.gui.plot3d.items.Scatter3D`.
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
_StatsContext.__init__(self, kind='scatter', item=item, plot=plot,
onlimits=onlimits, roi=roi)
@docstring(_StatsContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
if onlimits:
raise RuntimeError("Unsupported plot %s" % str(plot))
values = item.getValueData(copy=False)
if roi:
logger.warning("Roi are unsupported on volume for now")
mask = numpy.zeros_like(values)
else:
mask = numpy.zeros_like(values)
if values is not None and len(values) > 0:
self.values = values
axes = [item.getXData(copy=False), item.getYData(copy=False)]
if self.values.ndim == 3:
axes.append(item.getZData(copy=False))
self.axes = tuple(axes)
self.min, self.max = min_max(self.values)
self.values = numpy.ma.array(self.values, mask=mask)
else:
self.values = None
self.axes = None
self.min, self.max = None, None
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, RegionOfInterest):
raise TypeError('curve `context` can ony manage 2D roi')
class _plot3DArrayContext(_StatsContext):
"""StatsContext for 3D scalar field and data image.
It supports :class:`~silx.gui.plot3d.items.ScalarField3D` and
:class:`~silx.gui.plot3d.items.ImageData`.
:param item: the item for which we want to compute the context
:param plot: the plot containing the item
:param bool onlimits: True if we want to apply statistic only on
visible data.
:param roi: Region of interest for computing the statistics.
For now, incompatible with `onlinits` calculation
:type roi: Union[None, :class:`ROI`]
"""
def __init__(self, item, plot, onlimits, roi):
_StatsContext.__init__(self, kind='image', item=item, plot=plot,
onlimits=onlimits, roi=roi)
@docstring(_StatsContext)
def clipData(self, item, plot, onlimits, roi):
self._checkContextInputs(item=item, plot=plot, onlimits=onlimits,
roi=roi)
if onlimits:
raise RuntimeError("Unsupported plot %s" % str(plot))
values = item.getData(copy=False)
if roi:
logger.warning("Roi are unsuported on volume for now")
mask = numpy.zeros_like(values)
else:
mask = numpy.zeros_like(values)
if values is not None and len(values) > 0:
self.values = values
self.axes = tuple([numpy.arange(size) for size in self.values.shape])
self.min, self.max = min_max(self.values)
self.values = numpy.ma.array(self.values, mask=mask)
else:
self.values = None
self.axes = None
self.min, self.max = None, None
def _checkContextInputs(self, item, plot, onlimits, roi):
_StatsContext._checkContextInputs(self, item=item, plot=plot,
onlimits=onlimits, roi=roi)
if roi is not None and not isinstance(roi, RegionOfInterest):
raise TypeError('curve `context` can ony manage 2D roi')
BASIC_COMPATIBLE_KINDS = 'curve', 'image', 'scatter', 'histogram'
[docs]class StatBase(object):
"""
Base class for defining a statistic.
:param str name: the name of the statistic. Must be unique.
:param List[str] compatibleKinds:
The kind of items (curve, scatter...) for which the statistic apply.
"""
def __init__(self, name, compatibleKinds=BASIC_COMPATIBLE_KINDS, description=None):
self.name = name
self.compatibleKinds = compatibleKinds
self.description = description
[docs] def calculate(self, context):
"""
compute the statistic for the given :class:`StatsContext`
:param _StatsContext context:
:return dict: key is stat name, statistic computed is the dict value
"""
raise NotImplementedError('Base class')
[docs]class Stat(StatBase):
"""
Create a StatBase class based on a function pointer.
:param str name: name of the statistic. Used as id
:param fct: function which should have as unique mandatory parameter the
data. Should be able to adapt to all `kinds` defined as
compatible
:param tuple kinds: the compatible item kinds of the function (curve,
image...)
"""
def __init__(self, name, fct, kinds=BASIC_COMPATIBLE_KINDS):
StatBase.__init__(self, name, kinds)
self._fct = fct
[docs] @docstring(StatBase)
def calculate(self, context):
if context.values is not None:
if context.kind in self.compatibleKinds:
return self._fct(context.values)
else:
raise ValueError('Kind %s not managed by %s'
'' % (context.kind, self.name))
else:
return None
[docs]class StatMin(StatBase):
"""Compute the minimal value on data"""
def __init__(self):
StatBase.__init__(self, name='min')
[docs] @docstring(StatBase)
def calculate(self, context):
return context.min
[docs]class StatMax(StatBase):
"""Compute the maximal value on data"""
def __init__(self):
StatBase.__init__(self, name='max')
[docs] @docstring(StatBase)
def calculate(self, context):
return context.max
[docs]class StatDelta(StatBase):
"""Compute the delta between minimal and maximal on data"""
def __init__(self):
StatBase.__init__(self, name='delta')
[docs] @docstring(StatBase)
def calculate(self, context):
return context.max - context.min
class _StatCoord(StatBase):
"""Base class for argmin and argmax stats"""
def _indexToCoordinates(self, context, index):
"""Returns the coordinates of data point at given index
If data is an array, coordinates are in reverse order from data shape.
:param _StatsContext context:
:param int index: Index in the flattened data array
:rtype: List[int]
"""
axes = context.axes
if context.isStructuredData() or context.roi:
coordinates = []
for axis in reversed(axes):
coordinates.append(axis[index % len(axis)])
index = index // len(axis)
return tuple(coordinates)
else:
return tuple(axis[index] for axis in axes)
[docs]class StatCoordMin(_StatCoord):
"""Compute the coordinates of the first minimum value of the data"""
def __init__(self):
_StatCoord.__init__(self, name='coords min')
[docs] @docstring(StatBase)
def calculate(self, context):
if context.values is None or not context.isScalarData():
return None
index = context.values.argmin()
return self._indexToCoordinates(context, index)
[docs]class StatCoordMax(_StatCoord):
"""Compute the coordinates of the first maximum value of the data"""
def __init__(self):
_StatCoord.__init__(self, name='coords max')
[docs] @docstring(StatBase)
def calculate(self, context):
if context.values is None or not context.isScalarData():
return None
# TODO: the values should be a mask array by default, will be simpler
# if possible
index = context.values.argmax()
return self._indexToCoordinates(context, index)
[docs]class StatCOM(StatBase):
"""Compute data center of mass"""
def __init__(self):
StatBase.__init__(self, name='COM', description='Center of mass')
[docs] @docstring(StatBase)
def calculate(self, context):
if context.values is None or not context.isScalarData():
return None
values = numpy.ma.array(context.values, mask=context.mask, dtype=numpy.float64)
sum_ = numpy.sum(values)
if sum_ == 0.:
return (numpy.nan,) * len(context.axes)
if context.isStructuredData():
centerofmass = []
for index, axis in enumerate(context.axes):
axes = tuple([i for i in range(len(context.axes)) if i != index])
centerofmass.append(
numpy.sum(axis * numpy.sum(values, axis=axes)) / sum_)
return tuple(reversed(centerofmass))
else:
return tuple(
numpy.sum(axis * values) / sum_ for axis in context.axes)
