#!/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
from glance.gui_constants import *
LOG = logging.getLogger(__name__)
# colormaps that are available in the GUI
# TODO, if this changes the list of colormap names in the constants module needs to be kept up
AVAILABLE_COLORMAPS = {CM_RAINBOW: cm.jet,
CM_GRAY: cm.bone}
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, variableName=None) :
"""
Pull the name, data, and units for the variable currently selected in the given file prefix
"""
varNameToUse = variableName
if varNameToUse is None :
varNameToUse = self.dataModel.getVariableName(filePrefix) # get the currently selected variable
dataObject = self.dataModel.getVariableData(filePrefix, varNameToUse, doCorrections=True)
unitsText = self.dataModel.getUnitsText (filePrefix, varNameToUse)
if dataObject is not None :
dataObject.self_analysis()
return varNameToUse, 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 ( self.dataModel.getShouldShowOriginalPlotsInSameRange() or
((imageType == ORIGINAL_A) and (filePrefix == "A") or
(imageType == ORIGINAL_B) and (filePrefix == "B") or
(imageType in 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 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 _load_and_analyse_lonlat (self, listOfFilePrefixes=["A", "B"], lonNames=None, latNames=None, stopIfComparisonFails=False) :
"""
load information on the longidue and latitude,
if there are multiple file prefixes given:
find the shared range
analyse how different the navigation is between the files
(if there is a lon/lat epsilon defined and the difference is more than that, either stop with an error or log a warning)
lonNames and latNames should be dictionaries giving the names of the longitude and latitude variables indexed by the file prefixes
This method may raise an IncompatableDataObjects exception if multiple file prefixes are passed in the listOfFilePrefixes
and the longitude and latidues for those files can not be compared.
"""
lonlatData = { }
lonRange = None
latRange = None
# load and compare stuff for each file prefix
for filePrefix in listOfFilePrefixes :
# get information on the lon/lat from the current file
currentLonObj, currentLatObj, currentLonRange, currentLatRange = self._load_lonlat(filePrefix, lonNames[filePrefix], latNames[filePrefix])
# TODO, this will currently crash if there's a problem, we don't really want that
assert currentLonObj.data.shape == currentLatObj.data.shape
# expand our lon/lat ranges if we need to
if lonRange is None :
lonRange = currentLonRange
else :
lonRange[0] = min(currentLonRange[0], lonRange[0])
lonRange[1] = max(currentLonRange[1], lonRange[1])
if latRange is None:
latRange = currentLatRange
else :
latRange[0] = min(currentLatRange[0], latRange[0])
latRange[1] = max(currentLatRange[1], latRange[1])
# compare this file to whatever other data we have
for filePrefixToCompare in lonlatData.keys() :
lonToCompare, latToCompare = lonlatData[filePrefixToCompare]
# TODO, this is going to crash if there's a problem, we don't really want that
assert lonToCompare.data.shape == currentLatObj.data.shape
assert lonToCompare.data.shape == currentLonObj.data.shape
# add this data to the list of lonlat data
lonlatData[filePrefix] = [currentLonObj, currentLatObj]
# return longitude and latitude information and the shared ranges
return lonlatData, lonRange, latRange
def _load_lonlat (self, filePrefix, lonName, latName) :
"""
load the longitude and latitude information for the file and determine the ranges
present in both
"""
_, lonObject, _ = self._getVariableInformation(filePrefix, lonName)
_, latObject, _ = self._getVariableInformation(filePrefix, latName)
lonRange = [lonObject.get_min(), lonObject.get_max()]
latRange = [latObject.get_min(), latObject.get_max()]
return lonObject, latObject, lonRange, latRange
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
"""
# retrieve some plotting settings
imageType = self.dataModel.getImageType()
dataForm = self.dataModel.getDataForm()
colorMapToUse = AVAILABLE_COLORMAPS[self.dataModel.getColormapName()]
LOG.info ("Preparing variable data for plotting...")
# load the variable data
aVarName, aDataObject, aUnitsText = self._getVariableInfoSmart("A", imageType)
bVarName, bDataObject, bUnitsText = self._getVariableInfoSmart("B", imageType)
# compare the variables
diffData = self._buildDiffInfoObjectSmart(imageType,
aDataObject, bDataObject,
aVarName, bVarName,
epsilon_value=self.dataModel.getEpsilon(),
epsilon_percent=self.dataModel.getEpsilonPercent())
# if we need to build a shared range, do that now
rangeInfo = None
if (self.dataModel.getShouldShowOriginalPlotsInSameRange() and (aDataObject is not None) and (bDataObject is not None)) :
rangeInfo = [min(aDataObject.get_min(), bDataObject.get_min()), max(aDataObject.get_max(), bDataObject.get_max())]
if (dataForm == MAPPED_2D) and (imageType != HISTOGRAM) and (imageType != model.SCATTER) and (imageType != model.HEX_PLOT) :
lonNames = {
"A": self.dataModel.getLongitudeName("A"),
"B": self.dataModel.getLongitudeName("B")
}
latNames = {
"A": self.dataModel.getLatitudeName("A"),
"B": self.dataModel.getLatitudeName("B")
}
lonlatData, lonRange, latRange = self._load_and_analyse_lonlat(listOfFilePrefixes=["A", "B"],
lonNames=lonNames, latNames=latNames)
# double check that lon/lat are compatable with the data
if aDataObject is not None :
assert(lonlatData["A"][0].shape == aDataObject.shape)
if bDataObject is not None :
assert(lonlatData["B"][0].shape == bDataObject.shape)
# make composite valid mask
allValidMask = ( lonlatData["A"][0].masks.valid_mask & lonlatData["A"][1].masks.valid_mask &
lonlatData["B"][0].masks.valid_mask & lonlatData["B"][1].masks.valid_mask )
# build basemap, FUTURE, don't hard code so much of this stuff
basemapObject = Basemap(llcrnrlon=lonRange[0], llcrnrlat=latRange[0], urcrnrlon=lonRange[1], urcrnrlat=latRange[1],
resolution='i', area_thresh=10000, projection="merc")
# TODO get all these variables outside the if statement
LOG.info("Spawning plot window: " + imageType)
plt.ion() # make sure interactive plotting is on
# create whichever type of plot was asked for
if imageType == ORIGINAL_A :
# if the data doesn't exist, we can't make this plot
if aDataObject is None :
raise ValueError(NO_DATA_MESSAGE)
if dataForm == SIMPLE_2D :
tempFigure = figures.create_simple_figure(aDataObject.data, aVarName + "\nin File A",
invalidMask=~aDataObject.masks.valid_mask, colorMap=colorMapToUse,
colorbarLimits=rangeInfo, units=aUnitsText)
elif dataForm == MAPPED_2D :
#_, tempLatObj, _ = self._getVariableInformation("A", variableName=self.dataModel.getLatitudeName ("A"))
#_, tempLonObj, _ = self._getVariableInformation("A", variableName=self.dataModel.getLongitudeName("A"))
# TODO ***
#tempFigure = figures.create_mapped_figure(aDataObject.data, tempLatObj.data, tempLonObj.data, baseMapInstance, boundingAxes, title,
# invalidMask=None, colorMap=None, tagData=None,
# dataRanges=None, dataRangeNames=None, dataRangeColors=None, units=None, **kwargs)
pass
elif dataForm == ONLY_1D :
temp = [(aDataObject.data, ~aDataObject.masks.valid_mask, 'b', None, None, None)]
tempFigure = figures.create_line_plot_figure(temp, aVarName + "\n in File A")
else :
raise ValueError(UNKNOWN_DATA_FORM)
elif imageType == ORIGINAL_B :
# if the data doesn't exist, we can't make this plot
if bDataObject is None :
raise ValueError(NO_DATA_MESSAGE)
if dataForm == SIMPLE_2D :
tempFigure = figures.create_simple_figure(bDataObject.data, bVarName + "\nin File B",
invalidMask=~bDataObject.masks.valid_mask, colorMap=colorMapToUse,
colorbarLimits=rangeInfo, units=bUnitsText)
elif dataForm == MAPPED_2D :
pass # TODO
elif dataForm == ONLY_1D :
temp = [(bDataObject.data, ~bDataObject.masks.valid_mask, 'c', None, None, None)]
tempFigure = figures.create_line_plot_figure(temp, bVarName + "\n in File B")
else :
raise ValueError(UNKNOWN_DATA_FORM)
elif imageType in COMPARISON_IMAGES :
# if we're making the absolute or raw difference image, do that
if (imageType == ABS_DIFF) or (imageType == 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 == ABS_DIFF :
dataToUse = np.abs(dataToUse)
titlePrefix = "Absolute value of difference\nin "
if dataForm == SIMPLE_2D :
tempFigure = figures.create_simple_figure(dataToUse, titlePrefix + aVarName,
invalidMask=~diffData.diff_data_object.masks.valid_mask,
colorMap=colorMapToUse, units=aUnitsText)
elif dataForm == MAPPED_2D :
pass # TODO
elif dataForm == ONLY_1D :
tempTitle = titlePrefix + aVarName
if aVarName != bVarName :
tempTitle = tempTitle + " / " + bVarName
temp = [(dataToUse, ~diffData.diff_data_object.masks.valid_mask, 'm', None, None, None)]
tempFigure = figures.create_line_plot_figure(temp, tempTitle)
else :
raise ValueError(UNKNOWN_DATA_FORM)
elif imageType == MISMATCH :
mismatchMask = diffData.diff_data_object.masks.mismatch_mask
if dataForm == SIMPLE_2D :
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)
# TODO, change colormap?
elif dataForm == MAPPED_2D :
pass # TODO
elif dataForm == ONLY_1D :
temp = [(aDataObject.data, ~aDataObject.masks.valid_mask, 'k', None, mismatchMask, None)]
tempFigure = figures.create_line_plot_figure(temp, "Areas of mismatch data\nin " + aVarName)
else :
raise ValueError(UNKNOWN_DATA_FORM)
elif imageType == HISTOGRAM :
# Note: histograms don't care about data format requested, they are histogram formatted
rawDiffDataClean = diffData.diff_data_object.data[diffData.diff_data_object.masks.valid_mask]
# TODO, should the range option be added here?
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 == SCATTER) or (imageType == HEX_PLOT) :
# Note: scatter and hex plots don't care about data format requested, they're scatter or hex plots
tempCleanMask = aDataObject.masks.valid_mask & bDataObject.masks.valid_mask
aDataClean = aDataObject.data[tempCleanMask]
bDataClean = bDataObject.data[tempCleanMask]
if imageType == 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()