#!/usr/bin/env python
# encoding: utf-8
"""
This module manages creating figures for the Glance GUI.
Created by evas Nov 2011.
Copyright (c) 2011 University of Wisconsin SSEC. All rights reserved.
"""
# these first two lines must stay before the pylab import
import matplotlib
# Note: it's assumed that you've already set up this use previously
#matplotlib.use('Qt4Agg') # use the Qt Anti-Grain Geometry rendering engine
from pylab import *
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import logging
import numpy as np
import glance.data as dataobjects
import glance.figures as figures
import glance.gui_model as model
LOG = logging.getLogger(__name__)
# the number of bins to use for histograms
DEFAULT_NUM_BINS = 50
NO_DATA_MESSAGE = "Requested data was not available or did not exist."
class GlanceGUIFigures (object) :
"""
This class handles creating figures for the glance gui.
(in future it may manage them more actively)
it includes:
self.dataModel - the GlanceGUIModel object that contains the main data
model for the GUI
self.errorHandlers - objects that want to be notified when there's a serious error
"""
def __init__ (self, dataModelToSave) :
"""
create a figure manager, hanging on to the data model, for use in creating figures
"""
self.dataModel = dataModelToSave
self.errorHandlers = [ ]
def registerErrorHandler (self, objectToRegister) :
"""
add the given object to our list of error handlers
"""
if objectToRegister not in self.errorHandlers :
self.errorHandlers.append(objectToRegister)
def _getVariableInformation (self, filePrefix) :
"""
Pull the name, data, and units for the variable currently selected in the given file prefix
"""
selectedVariableName = self.dataModel.getVariableName(filePrefix)
dataObject = self.dataModel.getVariableData(filePrefix, selectedVariableName)
unitsText = self.dataModel.getUnitsText(filePrefix, selectedVariableName)
if dataObject is not None :
dataObject.self_analysis()
return selectedVariableName, dataObject, unitsText
def _getVariableInfoSmart (self, filePrefix, imageType) :
"""
if appropriate for the image type, get information on the variable, otherwise return None's
"""
varName, dataObject, unitsText = None, None, None
# only load the data if it will be needed for the plot
if ((imageType == model.ORIGINAL_A) and (filePrefix == "A") or
(imageType == model.ORIGINAL_B) and (filePrefix == "B") or
(imageType in model.COMPARISON_IMAGES)) :
varName, dataObject, unitsText = self._getVariableInformation(filePrefix)
return varName, dataObject, unitsText
def _buildDiffInfoObjectSmart (self, imageType, dataObjectA, dataObjectB, varNameA, varNameB,
epsilon_value=None, epsilon_percent=None) :
"""
if appropriate for the image type, build the difference object, otherwise return None
this method may rase an IncompatableDataObjects exception if the two data objects it's given can't be compared
"""
diffObject = None
# only build the difference if we need to compare the data
if imageType in model.COMPARISON_IMAGES :
# check to see if our data is minimally compatable; this call may raise an IncompatableDataObjects exception
dataobjects.DiffInfoObject.verifyDataCompatability (dataObjectA, dataObjectB, varNameA, varNameB)
# compare our data
diffObject = dataobjects.DiffInfoObject(dataObjectA, dataObjectB,
epsilonValue=epsilon_value, epsilonPercent=epsilon_percent)
return diffObject
def spawnPlot (self) :
"""
create a matplotlib plot using the current model information
this method may raise an IncompatableDataObjects exception if the a and b data
are completely incomparable
this method may also raise a ValueError if the data could not be plotted
for reasons not encompassed by an IncompatableDataObjects exception
"""
imageType = self.dataModel.getImageType()
dataForm = self.dataModel.getDataForm()
LOG.info ("Preparing variable data for plotting...")
aVarName, aDataObject, aUnitsText = self._getVariableInfoSmart("A", imageType)
bVarName, bDataObject, bUnitsText = self._getVariableInfoSmart("B", imageType)
diffData = self._buildDiffInfoObjectSmart(imageType,
aDataObject, bDataObject,
aVarName, bVarName,
epsilon_value=self.dataModel.getEpsilon(),
epsilon_percent=self.dataModel.getEpsilonPercent())
LOG.info("Spawning plot window: " + imageType)
plt.ion() # make sure interactive plotting is on
# create the plot
if imageType == model.ORIGINAL_A :
# if the data doesn't exist, we can't make this plot
if aDataObject is None :
raise ValueError(NO_DATA_MESSAGE)
tempFigure = figures.create_simple_figure(aDataObject.data, aVarName + "\nin File A",
invalidMask=~aDataObject.masks.valid_mask, colorMap=cm.jet, units=aUnitsText)
elif imageType == model.ORIGINAL_B :
# if the data doesn't exist, we can't make this plot
if bDataObject is None :
raise ValueError(NO_DATA_MESSAGE)
tempFigure = figures.create_simple_figure(bDataObject.data, bVarName + "\nin File B",
invalidMask=~bDataObject.masks.valid_mask, colorMap=cm.jet, units=bUnitsText)
elif imageType in model.COMPARISON_IMAGES :
# if we're making the absolute or raw difference image, do that
if (imageType == model.ABS_DIFF) or (imageType == model.RAW_DIFF) :
# now choose between the raw and abs diff
dataToUse = diffData.diff_data_object.data
titlePrefix = "Value of (Data File B - Data File A)\nfor "
if imageType == model.ABS_DIFF :
dataToUse = np.abs(dataToUse)
titlePrefix = "Absolute value of difference\nin "
tempFigure = figures.create_simple_figure(dataToUse, titlePrefix + aVarName,
invalidMask=~diffData.diff_data_object.masks.valid_mask,
colorMap=cm.jet, units=aUnitsText)
elif imageType == model.MISMATCH :
mismatchMask = diffData.diff_data_object.masks.mismatch_mask
tempFigure = figures.create_simple_figure(aDataObject.data, "Areas of mismatch data\nin " + aVarName,
invalidMask=~aDataObject.masks.valid_mask, tagData=mismatchMask,
colorMap=figures.MEDIUM_GRAY_COLOR_MAP, units=aUnitsText)
elif imageType == model.HISTOGRAM :
rawDiffDataClean = diffData.diff_data_object.data[diffData.diff_data_object.masks.valid_mask]
tempFigure = figures.create_histogram(rawDiffDataClean, DEFAULT_NUM_BINS, "Difference in\n" + aVarName,
"Value of (B - A) at each data point", "Number of points with a given difference", units=aUnitsText)
elif (imageType == model.SCATTER) or (imageType == model.HEX_PLOT) :
tempCleanMask = aDataObject.masks.valid_mask & bDataObject.masks.valid_mask
aDataClean = aDataObject.data[tempCleanMask]
bDataClean = bDataObject.data[tempCleanMask]
if imageType == model.SCATTER :
cleanMismatchMask = diffData.diff_data_object.masks.mismatch_mask[tempCleanMask]
figures.create_scatter_plot(aDataClean, bDataClean, "Value in File A vs Value in File B",
"File A Value in " + aVarName,
"File B Value in " + bVarName,
badMask=cleanMismatchMask, epsilon=self.dataModel.getEpsilon(),
units_x=aUnitsText, units_y=bUnitsText)
else:
tempFigure = figures.create_hexbin_plot(aDataClean, bDataClean,
"Value in File A vs Value in File B",
"File A Value in " + aVarName,
"File B Value in " + bVarName,
epsilon=self.dataModel.getEpsilon(),
units_x=aUnitsText, units_y=bUnitsText)
plt.draw()