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[Pyphant-devel] SF.net SVN: pyphant:[696] trunk/src
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From: <zk...@us...> - 2010-07-12 21:47:59
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Revision: 696
http://pyphant.svn.sourceforge.net/pyphant/?rev=696&view=rev
Author: zklaus
Date: 2010-07-12 21:47:51 +0000 (Mon, 12 Jul 2010)
Log Message:
-----------
Merge branch 'master' into svn-trunk
* master:
Enh: Add EstimateParameterFromValues.py
Enh: Add removal of second lamp peak to Absorbtion calculation.
Fix: Improve progress handling in Compute Functional.
Enh: PlotFrame now takes the mask of the value column into account.
Fix: MRA can now deal with curves without extrema.
Modified Paths:
--------------
trunk/src/pyphant/pyphant/visualizers/ConfigurablePlot.py
trunk/src/workers/OSC/OSC/ComputeFunctional.py
trunk/src/workers/OSC/OSC/MRA.py
trunk/src/workers/OSC/OSC/OscAbsorption.py
trunk/src/workers/OSC/OSC/__init__.py
Added Paths:
-----------
trunk/src/workers/OSC/OSC/EstimateParameterFromValues.py
Modified: trunk/src/pyphant/pyphant/visualizers/ConfigurablePlot.py
===================================================================
--- trunk/src/pyphant/pyphant/visualizers/ConfigurablePlot.py 2010-07-12 20:43:11 UTC (rev 695)
+++ trunk/src/pyphant/pyphant/visualizers/ConfigurablePlot.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -122,9 +122,13 @@
if not isinstance(aspect, Quantity):
self.ax.set_aspect(aspect)
try:
- self.scat = self.ax.scatter(self.x.data, self.y.data,
+ mask = numpy.logical_not(self.c.mask)
+ x = self.x.data[mask]
+ y = self.y.data[mask]
+ c = self.c.data[mask]
+ self.scat = self.ax.scatter(x,y,
s=numpy.pi*(self.radius/self.x.unit)**2,
- c=self.c.data,
+ c=c,
vmin=self.vmin, vmax=self.vmax)
self.colorbar = self.figure.colorbar(self.scat, format=F(self.c),
ax=self.ax)
@@ -223,9 +227,9 @@
if self.plot_panel.c_key != colorVariable:
self.plot_panel.c_key = colorVariable
field = self.dataContainer[colorVariable]
- vmin = field.data.min().round()*field.unit
+ vmin = numpy.nanmin(field.data).round()*field.unit
self.vmin_text.Value=str(vmin)
- vmax = field.data.max().round()*field.unit
+ vmax = numpy.nanmax(field.data).round()*field.unit
self.vmax_text.Value=str(vmax)
self.plot_panel.vmin = Quantity(self.vmin_text.Value)
self.plot_panel.vmax = Quantity(self.vmax_text.Value)
Modified: trunk/src/workers/OSC/OSC/ComputeFunctional.py
===================================================================
--- trunk/src/workers/OSC/OSC/ComputeFunctional.py 2010-07-12 20:43:11 UTC (rev 695)
+++ trunk/src/workers/OSC/OSC/ComputeFunctional.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -57,7 +57,7 @@
_params = [("extentX", u"Extension of x-axis [%%]", 10, None),
("extentY", u"Extension of y-axis [%%]", 10, None)]
- def computeDistances(self, field, subscriber=1):
+ def computeDistances(self, field, subscriber=1, percentage=0):
xGrid,yGrid = numpy.meshgrid(field.dimensions[-1].data,field.dimensions[-2].data)
x = numpy.extract(numpy.logical_not(numpy.isnan(field.data)),xGrid)
xCon = DataContainer.FieldContainer(x,unit=field.dimensions[-1].unit,
@@ -80,8 +80,7 @@
distances = numpy.zeros(x.shape,'f')
ni = functional.shape[0]
nj = functional.shape[1]
- increment = 100.0/(ni*nj)
- percentage = 0
+ increment = 50.0/(ni*nj)
for i in xrange(ni):
for j in xrange(nj):
for k in xrange(len(x)):
@@ -99,7 +98,8 @@
@Worker.plug(Connectors.TYPE_ARRAY)
def compute(self, field, subscriber=1):
- functionals = DataContainer.SampleContainer([self.computeDistances(column, subscriber) for column in field],
+ percentage = 0
+ functionals = DataContainer.SampleContainer([self.computeDistances(column, subscriber, percentage) for column in field],
longname='Functionals of %s'%field.longname,
shortname='F_{%s}'%field.shortname)
functionals.seal()
Added: trunk/src/workers/OSC/OSC/EstimateParameterFromValues.py
===================================================================
--- trunk/src/workers/OSC/OSC/EstimateParameterFromValues.py (rev 0)
+++ trunk/src/workers/OSC/OSC/EstimateParameterFromValues.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2008-2009, Rectorate of the University of Freiburg
+# Copyright (c) 2009, Andreas W. Liehr (li...@us...)
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+# * Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+# * Neither the name of the Freiburg Materials Research Center,
+# University of Freiburg nor the names of its contributors may be used to
+# endorse or promote products derived from this software without specific
+# prior written permission.
+#
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
+# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
+# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+u"""
+"""
+
+__id__ = "$Id$"
+__author__ = "$Author$"
+__version__ = "$Revision$"
+# $Source$
+
+import numpy
+from pyphant.core import (Worker, Connectors,
+ Param, DataContainer)
+import scipy.interpolate
+from pyphant import quantities
+import copy
+
+class EstimateParameterFromValues(Worker.Worker):
+ API = 2
+ VERSION = 1
+ REVISION = "$Revision$"[11:-1]
+ name = "Estimate Parameter"
+
+ _sockets = [("model", Connectors.TYPE_ARRAY),
+ ("experimental", Connectors.TYPE_ARRAY)]
+ _params = [("minima_model", u"Minima in the model", [u"Minima"], None),
+ ("maxima_model", u"Maxima in the model", [u"Maxima"], None),
+ ("minima_experimental", u"Minima in the experiment", [u"Minima"], None),
+ ("maxima_experimental", u"Maxima in the experiment", [u"Maxima"], None),
+ ("extentX", u"Extension of x-axis [%%]", 10, None),
+ ("extentY", u"Extension of y-axis [%%]", 10, None)]
+
+ def refreshParams(self, subscriber=None):
+ if self.socketModel.isFull():
+ templ = self.socketModel.getResult( subscriber )
+ self.paramMinima_model.possibleValues = templ.longnames.keys()
+ self.paramMaxima_model.possibleValues = templ.longnames.keys()
+ if self.socketExperimental.isFull():
+ templ = self.socketExperimental.getResult( subscriber )
+ self.paramMinima_experimental.possibleValues = templ.longnames.keys()
+ self.paramMaxima_experimental.possibleValues = templ.longnames.keys()
+
+ @Worker.plug(Connectors.TYPE_IMAGE)
+ def compute(self, model, experimental, subscriber=1):
+ minima_model = model[self.paramMinima_model.value]
+ maxima_model = model[self.paramMaxima_model.value]
+ minima_experimental = experimental[self.paramMinima_experimental.value]
+ minima_experimantal = minima_experimental.inUnitsOf(minima_model)
+ minima = minima_experimental.data.transpose()
+ if minima_experimental.error != None:
+ minima_error = iter(minima_experimental.error.transpose())
+ else:
+ minima_error = None
+ maxima_experimental = experimental[self.paramMaxima_experimental.value]
+ maxima_experimantal = maxima_experimental.inUnitsOf(maxima_model)
+ maxima = maxima_experimental.data.transpose()
+ if maxima_experimental.error != None:
+ maxima_error = iter(maxima_experimental.error.transpose())
+ else:
+ maxima_error = None
+ parameter = []
+ inc = 100.0/float(len(minima))
+ acc = inc
+ subscriber %= acc
+ mask = []
+ for row_minima, row_maxima in zip(minima, maxima):
+ if minima_error:
+ filtered_minima_error = filter(
+ lambda c: not numpy.isnan(c), minima_error.next())
+ else:
+ filtered_minima_error = None
+ if maxima_error:
+ filtered_maxima_error = filter(
+ lambda c: not numpy.isnan(c), maxima_error.next())
+ else:
+ filtered_maxima_error = None
+ row_minima = numpy.array(filter(lambda c: not numpy.isnan(c), row_minima))
+ row_maxima = numpy.array(filter(lambda c: not numpy.isnan(c), row_minima))
+ grades = []
+ for rm_minima, rm_maxima in zip(minima_model.data.transpose(), maxima_model.data.transpose()):
+ rm_minima = filter(lambda c: not numpy.isnan(c), rm_minima)
+ rm_maxima = filter(lambda c: not numpy.isnan(c), rm_maxima)
+ grade = 0
+ if len(rm_minima) == len(row_minima):
+ grade = sum(numpy.abs(numpy.array(rm_minima)-row_minima))
+ if len(rm_maxima) == len(row_maxima):
+ grade += sum(numpy.abs(numpy.array(rm_maxima)-row_maxima))
+ if grade == 0:
+ grades.append(numpy.nan)
+ continue
+ grades.append(grade)
+ print grades[-1]
+ grades = numpy.array(grades)
+ i = numpy.nanargmin(grades)
+ if numpy.isnan(i):
+ mask.append(True)
+ parameter.append(numpy.nan)
+ else:
+ mask.append(False)
+ parameter.append(minima_model.dimensions[1].data[i])
+ acc += inc
+ subscriber %= acc
+ result = DataContainer.FieldContainer(
+ numpy.array(parameter),
+ mask = numpy.array(mask),
+ longname = minima_model.dimensions[-1].longname,
+ shortname = minima_model.dimensions[-1].shortname,
+ unit = minima_model.dimensions[-1].unit)
+ result.seal()
+ return result
Modified: trunk/src/workers/OSC/OSC/MRA.py
===================================================================
--- trunk/src/workers/OSC/OSC/MRA.py 2010-07-12 20:43:11 UTC (rev 695)
+++ trunk/src/workers/OSC/OSC/MRA.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -116,10 +116,18 @@
convolvedField = convolveMRA(field, sigma)
lastMinima = findMinima(convolvedField, numb_edge, lastMinima)
lastMaxima = findMaxima(convolvedField, numb_edge, lastMaxima)
- pos_minima = dim.data[numpy.array(lastMinima)+1]
- error_minima = numpy.abs(pos_minima - dim.data[numpy.array(firstMinima)+1])
- pos_maxima = dim.data[numpy.array(lastMaxima)+1]
- error_maxima = numpy.abs(pos_maxima - dim.data[numpy.array(firstMaxima)+1])
+ if len(lastMinima)>0 and len(firstMinima)>0:
+ pos_minima = dim.data[numpy.array(lastMinima)+1]
+ error_minima = numpy.abs(pos_minima - dim.data[numpy.array(firstMinima)+1])
+ else:
+ pos_minima = numpy.array([],dtype=dim.data.dtype)
+ error_minima = numpy.array([],dtype=dim.data.dtype)
+ if len(lastMaxima)>0 and len(firstMaxima)>0:
+ pos_maxima = dim.data[numpy.array(lastMaxima)+1]
+ error_maxima = numpy.abs(pos_maxima - dim.data[numpy.array(firstMaxima)+1])
+ else:
+ pos_maxima = numpy.array([],dtype=dim.data.dtype)
+ error_maxima = numpy.array([],dtype=dim.data.dtype)
return ((pos_minima, error_minima), (pos_maxima, error_maxima))
def pos_error_to_data_container(p_e):
Modified: trunk/src/workers/OSC/OSC/OscAbsorption.py
===================================================================
--- trunk/src/workers/OSC/OSC/OscAbsorption.py 2010-07-12 20:43:11 UTC (rev 695)
+++ trunk/src/workers/OSC/OSC/OscAbsorption.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -73,6 +73,22 @@
indexDict = IndexDict(offset,fmax,fstep)
return (offset, fstep, indexDict)
+def removePeak(Abso, lower, upper):
+ dim = Abso.dimensions[-1]
+ minVal = lower/dim.unit
+ maxVal = upper/dim.unit
+ high_part = numpy.argwhere(minVal<dim.data).flatten()
+ low_part = numpy.argwhere(dim.data<maxVal).flatten()
+ lamp_interval = numpy.intersect1d(high_part, low_part)
+ min_index = lamp_interval[0]
+ max_index = lamp_interval[-1]
+ steps = max_index-min_index
+ for row in Abso.data:
+ min_value = row[min_index]
+ max_value = row[max_index]
+ step_size = (max_value-min_value)/steps
+ row[lamp_interval] = min_value + (lamp_interval-min_index)*step_size
+
class OscAbsorptionCalculator(Worker.Worker):
API = 2
VERSION = 1
@@ -100,20 +116,12 @@
shortname=ur'\tilde{A}')
Abso.dimensions[-1] = I.dimensions[-1]
if self.paramMask_lamp.value==1:
- dim = Abso.dimensions[-1]
- minVal = quantities.Quantity('654nm')/dim.unit
- maxVal = quantities.Quantity('660nm')/dim.unit
- high_part = numpy.argwhere(minVal<dim.data).flatten()
- low_part = numpy.argwhere(dim.data<maxVal).flatten()
- lamp_interval = numpy.intersect1d(high_part, low_part)
- min_index = lamp_interval[0]
- max_index = lamp_interval[-1]
- steps = max_index-min_index
- for row in Abso.data:
- min_value = row[min_index]
- max_value = row[max_index]
- step_size = (max_value-min_value)/steps
- row[lamp_interval] = min_value + (lamp_interval-min_index)*step_size
+ removePeak(Abso,
+ quantities.Quantity('654nm'),
+ quantities.Quantity('660nm'))
+ removePeak(Abso,
+ quantities.Quantity('920nm'),
+ quantities.Quantity('980nm'))
Abso.seal()
return Abso
Modified: trunk/src/workers/OSC/OSC/__init__.py
===================================================================
--- trunk/src/workers/OSC/OSC/__init__.py 2010-07-12 20:43:11 UTC (rev 695)
+++ trunk/src/workers/OSC/OSC/__init__.py 2010-07-12 21:47:51 UTC (rev 696)
@@ -45,6 +45,7 @@
"ComputeFunctional",
"ErrorEstimator",
"EstimateParameter",
+ "EstimateParameterFromValues",
"ExtremumFinder",
"MRA",
"OscAbsorption",
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