# coding: utf-8
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"""This module provides the :class:`Scatter` item of the :class:`Plot`.
"""
__authors__ = ["H. Payno"]
__license__ = "MIT"
__date__ = "06/06/2018"
import numpy
from silx.gui.plot.items.curve import Curve as CurveItem
from silx.gui.plot.items.image import ImageBase as ImageItem
from silx.gui.plot.items.scatter import Scatter as ScatterItem
from silx.gui.plot.items.histogram import Histogram as HistogramItem
from silx.math.combo import min_max
from collections import OrderedDict
import logging
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):
"""
Call all :class:`Stat` object registred 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.
:return dict: dictionary with :class:`Stat` name as ket and result
of the calculation as value
"""
res = {}
if isinstance(item, CurveItem):
context = _CurveContext(item, plot, onlimits)
elif isinstance(item, ImageItem):
context = _ImageContext(item, plot, onlimits)
elif isinstance(item, ScatterItem):
context = _ScatterContext(item, plot, onlimits)
elif isinstance(item, HistogramItem):
context = _HistogramContext(item, plot, onlimits)
else:
raise ValueError('Item type not managed')
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:
res[statName] = stat.calculate(context)
return res
def __setitem__(self, key, value):
assert isinstance(value, StatBase)
OrderedDict.__setitem__(self, key, value)
def add(self, stat):
self.__setitem__(key=stat.name, value=stat)
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.
"""
def __init__(self, item, kind, plot, onlimits):
assert item
assert plot
assert type(onlimits) is bool
self.kind = kind
self.min = None
self.max = None
self.data = None
self.values = None
self.createContext(item, plot, onlimits)
def createContext(self, item, plot, onlimits):
raise NotImplementedError("Base class")
class _CurveContext(_StatsContext):
"""
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.
"""
def __init__(self, item, plot, onlimits):
_StatsContext.__init__(self, kind='curve', item=item,
plot=plot, onlimits=onlimits)
def createContext(self, item, plot, onlimits):
xData, yData = item.getData(copy=True)[0:2]
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
yData = yData[(minX <= xData) & (xData <= maxX)]
xData = xData[(minX <= xData) & (xData <= maxX)]
self.xData = xData
self.yData = yData
if len(yData) > 0:
self.min, self.max = min_max(yData)
else:
self.min, self.max = None, None
self.data = (xData, yData)
self.values = yData
class _HistogramContext(_StatsContext):
"""
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.
"""
def __init__(self, item, plot, onlimits):
_StatsContext.__init__(self, kind='histogram', item=item,
plot=plot, onlimits=onlimits)
def createContext(self, item, plot, onlimits):
xData, edges = item.getData(copy=True)[0:2]
yData = item._revertComputeEdges(x=edges, histogramType=item.getAlignment())
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
yData = yData[(minX <= xData) & (xData <= maxX)]
xData = xData[(minX <= xData) & (xData <= maxX)]
self.xData = xData
self.yData = yData
if len(yData) > 0:
self.min, self.max = min_max(yData)
else:
self.min, self.max = None, None
self.data = (xData, yData)
self.values = yData
class _ScatterContext(_StatsContext):
"""
StatsContext for :class:`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.
"""
def __init__(self, item, plot, onlimits):
_StatsContext.__init__(self, kind='scatter', item=item, plot=plot,
onlimits=onlimits)
def createContext(self, item, plot, onlimits):
xData, yData, valueData, xerror, yerror = item.getData(copy=True)
assert plot
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 len(valueData) > 0:
self.min, self.max = min_max(valueData)
else:
self.min, self.max = None, None
self.data = (xData, yData, valueData)
self.values = valueData
class _ImageContext(_StatsContext):
"""
StatsContext for :class:`ImageBase`
: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.
"""
def __init__(self, item, plot, onlimits):
_StatsContext.__init__(self, kind='image', item=item,
plot=plot, onlimits=onlimits)
def createContext(self, item, plot, onlimits):
self.origin = item.getOrigin()
self.scale = item.getScale()
self.data = item.getData()
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 XMaxBound <= XMinBound or YMaxBound <= YMinBound:
return self.noDataSelected()
data = item.getData()
self.data = data[YMinBound:YMaxBound + 1, XMinBound:XMaxBound + 1]
else:
self.data = item.getData()
if self.data.size > 0:
self.min, self.max = min_max(self.data)
else:
self.min, self.max = None, None
self.values = self.data
BASIC_COMPATIBLE_KINDS = {
'curve': CurveItem,
'image': ImageItem,
'scatter': ScatterItem,
'histogram': HistogramItem,
}
[docs]class StatBase(object):
"""
Base class for defining a statistic.
:param str name: the name of the statistic. Must be unique.
:param compatibleKinds: the kind of items (curve, scatter...) for which
the statistic apply.
:rtype: List or tuple
"""
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 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] def calculate(self, context):
if context.kind in self.compatibleKinds:
return self._fct(context.values)
else:
raise ValueError('Kind %s not managed by %s'
'' % (context.kind, self.name))
[docs]class StatMin(StatBase):
"""
Compute the minimal value on data
"""
def __init__(self):
StatBase.__init__(self, name='min')
[docs] 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] 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] def calculate(self, context):
return context.max - context.min
[docs]class StatCoordMin(StatBase):
"""
Compute the first coordinates of the data minimal value
"""
def __init__(self):
StatBase.__init__(self, name='coords min')
[docs] def calculate(self, context):
if context.kind in ('curve', 'histogram'):
return context.xData[numpy.argmin(context.yData)]
elif context.kind == 'scatter':
xData, yData, valueData = context.data
return (xData[numpy.argmin(valueData)],
yData[numpy.argmin(valueData)])
elif context.kind == 'image':
scaleX, scaleY = context.scale
originX, originY = context.origin
index1D = numpy.argmin(context.data)
ySize = (context.data.shape[1])
x = index1D % context.data.shape[1]
y = (index1D - x) / ySize
x = x * scaleX + originX
y = y * scaleY + originY
return (x, y)
else:
raise ValueError('kind not managed')
[docs]class StatCoordMax(StatBase):
"""
Compute the first coordinates of the data minimal value
"""
def __init__(self):
StatBase.__init__(self, name='coords max')
[docs] def calculate(self, context):
if context.kind in ('curve', 'histogram'):
return context.xData[numpy.argmax(context.yData)]
elif context.kind == 'scatter':
xData, yData, valueData = context.data
return (xData[numpy.argmax(valueData)],
yData[numpy.argmax(valueData)])
elif context.kind == 'image':
scaleX, scaleY = context.scale
originX, originY = context.origin
index1D = numpy.argmax(context.data)
ySize = (context.data.shape[1])
x = index1D % context.data.shape[1]
y = (index1D - x) / ySize
x = x * scaleX + originX
y = y * scaleY + originY
return (x, y)
else:
raise ValueError('kind not managed')
[docs]class StatCOM(StatBase):
"""
Compute data center of mass
"""
def __init__(self):
StatBase.__init__(self, name='COM', description='Center of mass')
[docs] def calculate(self, context):
if context.kind in ('curve', 'histogram'):
xData, yData = context.data
deno = numpy.sum(yData).astype(numpy.float32)
if deno == 0.:
return numpy.nan
else:
return numpy.sum(xData * yData).astype(numpy.float32) / deno
elif context.kind == 'scatter':
xData, yData, values = context.data
deno = numpy.sum(values).astype(numpy.float32)
if deno == 0.:
return numpy.nan, numpy.nan
else:
xcom = numpy.sum(xData * values).astype(numpy.float32) / deno
ycom = numpy.sum(yData * values).astype(numpy.float32) / deno
return (xcom, ycom)
elif context.kind == 'image':
yData = numpy.sum(context.data, axis=1)
xData = numpy.sum(context.data, axis=0)
dataXRange = range(context.data.shape[1])
dataYRange = range(context.data.shape[0])
xScale, yScale = context.scale
xOrigin, yOrigin = context.origin
denoY = numpy.sum(yData)
if denoY == 0.:
ycom = numpy.nan
else:
ycom = numpy.sum(yData * dataYRange) / denoY
ycom = ycom * yScale + yOrigin
denoX = numpy.sum(xData)
if denoX == 0.:
xcom = numpy.nan
else:
xcom = numpy.sum(xData * dataXRange) / denoX
xcom = xcom * xScale + xOrigin
return (xcom, ycom)
else:
raise ValueError('kind not managed')