diff --git a/pyglance/glance/compare.py b/pyglance/glance/compare.py
index 8d80adef7c28523eba5a763e7a85de7f06c4af41..a93f00f14afc1da3cef0c03b9a74f41b0a791796 100644
--- a/pyglance/glance/compare.py
+++ b/pyglance/glance/compare.py
@@ -1330,15 +1330,13 @@ def reportGen_library_call (a_path, b_path, var_list=[ ],
LOG.debug("Creating variable directory.")
os.makedirs(variableDir)
- # form the doc path relative to where the variable is
- docPath = './'
+ # form the doc and config paths relative to where the variable is
+ upwardPath = './'
for number in range(len(displayName.split('/'))) : # TODO this is not general to windows
- docPath = os.path.join(docPath, '../')
- varRunInfo['doc_path'] = quote(os.path.join(docPath, 'doc.html')) # should this be somewhere else?
-
- # hang onto the config file path as well
+ upwardPath = os.path.join(upwardPath, '../')
+ varRunInfo['doc_path'] = quote(os.path.join(upwardPath, 'doc.html'))
if 'config_file_name' in runInfo :
- varRunInfo['config_file_path'] = quote(os.path.join(pathsTemp['out'], './' + runInfo['config_file_name']))
+ varRunInfo['config_file_path'] = quote(os.path.join(upwardPath, runInfo['config_file_name']))
# figure out the masks we want, and then do our statistical analysis
mask_a_to_use = None
diff --git a/pyglance/glance/figures.py b/pyglance/glance/figures.py
index 05d561584fd8070032ef0dd692ca228d59edeb59..715eecefa9ce451c2c20ce93bbeb011ddc1e028f 100644
--- a/pyglance/glance/figures.py
+++ b/pyglance/glance/figures.py
@@ -180,45 +180,66 @@ def _plot_tag_data_mapped(bMap, tagData, x, y, addExplinationLabel=True) :
# build a scatter plot of the x,y points
def create_scatter_plot(dataX, dataY, title, xLabel, yLabel, badMask=None, epsilon=None) :
+ """
+ build a scatter plot of the data
+ if a bad mask is given the points selected by that mask will be plotted in a different color
+ if an epsilon is given the lines for +/- epsilon will be drawn on the plot
+
+ by default this plot uses blue for data points and red for data marked by the bad mask
+ """
+
+ return create_complex_scatter_plot ([(dataX, dataY, badMask,
+ 'b', 'r',
+ 'within\nepsilon', 'outside\nepsilon')],
+ title, xLabel, yLabel, epsilon=epsilon)
+
+def create_complex_scatter_plot(dataList, title, xLabel, yLabel, epsilon=None) :
+ """
+ build a scatter plot with multiple data sets in different colors
+ the dataList parameter should be in the form:
+ [(set1), (set2), ... , (setN)]
+
+ where a set looks like:
+ (x data, y data, mask of bad points or None, matlab color code for display, matlab color code for 'bad' points, good label, bad label)
+
+ if a mask of bad points is given, it will be applyed to both the x and y data
+
+ at least one data set must be given or no image will be created.
+ """
# make the figure
figure = plt.figure()
axes = figure.add_subplot(111)
- # if we have "bad" data to plot, pull it out
- badX = None
- badY = None
- if (badMask != None) :
- badX = dataX[badMask]
- badY = dataY[badMask]
- dataX = dataX[~badMask]
- dataY = dataY[~badMask]
-
- # the scatter plot of the good data
- axes.plot(dataX, dataY, 'b,', label='within\nepsilon')
-
- # plot the bad data
- numTroublePts = 0
- if (badX is not None) and (badY is not None) and (badMask is not None) :
- numTroublePts = badX.shape[0]
- LOG.debug('\t\tnumber of trouble points in scatter plot: ' + str(badX.shape[0]))
- if numTroublePts > 0 :
- axes.plot(badX, badY, 'r,', label='outside\nepsilon')
-
- # draw the line for the "perfect fit"
- xbounds = axes.get_xbound()
- xrange = xbounds[1] - xbounds[0]
- ybounds = axes.get_ybound()
- yrange = ybounds[1] - ybounds[0]
- perfect = [max(xbounds[0], ybounds[0]), min(xbounds[1], ybounds[1])]
- axes.plot(perfect, perfect, 'k--', label='A = B')
+ # if we have no data, stop now
+ if (dataList is None) or (len(dataList) <= 0) :
+ return figure;
- # now draw the epsilon bound lines if they are visible and the lines won't be the same as A = B
- if (not (epsilon is None)) and (epsilon > 0.0) and (epsilon < xrange) and (epsilon < yrange):
- # plot the top line
- axes.plot([perfect[0], perfect[1] - epsilon], [perfect[0] + epsilon, perfect[1]], '--', color='#00FF00', label='+/-epsilon')
- # plot the bottom line
- axes.plot([perfect[0] + epsilon, perfect[1]], [perfect[0], perfect[1] - epsilon], '--', color='#00FF00')
+ # look at the stuff in each of the data sets and plot that set
+ for dataX, dataY, badMask, goodColor, badColor, goodLabel, badLabel in dataList :
+
+ # if we have "bad" data to plot, pull it out
+ badX = None
+ badY = None
+ if (badMask != None) :
+ badX = dataX[badMask]
+ badY = dataY[badMask]
+ dataX = dataX[~badMask]
+ dataY = dataY[~badMask]
+
+ # the scatter plot of the good data
+ axes.plot(dataX, dataY, ',', color=goodColor, label=goodLabel)
+
+ # plot the bad data
+ numTroublePts = 0
+ if (badX is not None) and (badY is not None) and (badMask is not None) :
+ numTroublePts = badX.size
+ LOG.debug('\t\tplotting ' + str(numTroublePts) + ' trouble points in scatter plot.' )
+ if numTroublePts > 0 :
+ axes.plot(badX, badY, ',', color=badColor, label=badLabel)
+
+ # draw some extra informational lines
+ _draw_x_equals_y_line(axes, epsilon=epsilon)
# make a key to explain our plot
# as long as things have been plotted with proper labels they should show up here
@@ -238,7 +259,7 @@ def create_scatter_plot(dataX, dataY, title, xLabel, yLabel, badMask=None, epsil
return figure
# build a hexbin plot of the x,y points and show the density of the point distribution
-def create_hexbin_plot(dataX, dataY, title, xLabel, yLabel) :
+def create_hexbin_plot(dataX, dataY, title, xLabel, yLabel, epsilon=None) :
# make the figure
figure = plt.figure()
@@ -252,6 +273,9 @@ def create_hexbin_plot(dataX, dataY, title, xLabel, yLabel) :
cb = plt.colorbar()
cb.set_label('log10 (count + 1)')
+ # draw some extra informational lines
+ _draw_x_equals_y_line(axes, color='w', epsilon=epsilon, epsilonColor='k')
+
# and some informational stuff
axes.set_title(title)
plt.xlabel(xLabel)
@@ -265,6 +289,36 @@ def create_hexbin_plot(dataX, dataY, title, xLabel, yLabel) :
return figure
+def _draw_x_equals_y_line(axes, color='k', style='--', epsilon=None, epsilonColor='#00FF00', epsilonStyle='--') :
+ """
+ Draw the x = y line using the axes and color/style given
+ If epsilon is not None, also draw the +/- epsilon lines,
+ if they fall in the graph
+ """
+
+ # get the bounds for our calculations and so we can reset the viewing window later
+ xbounds = axes.get_xbound()
+ ybounds = axes.get_ybound()
+
+ # figure out the size of the ranges
+ xrange = xbounds[1] - xbounds[0]
+ yrange = ybounds[1] - ybounds[0]
+
+ # draw the x=y line
+ perfect = [max(xbounds[0], ybounds[0]), min(xbounds[1], ybounds[1])]
+ axes.plot(perfect, perfect, style, color=color, label='A = B')
+
+ # now draw the epsilon bound lines if they are visible and the lines won't be the same as A = B
+ if (not (epsilon is None)) and (epsilon > 0.0) and (epsilon < xrange) and (epsilon < yrange):
+ # plot the top line
+ axes.plot([perfect[0], perfect[1] - epsilon], [perfect[0] + epsilon, perfect[1]], epsilonStyle, color=epsilonColor, label='+/-epsilon')
+ # plot the bottom line
+ axes.plot([perfect[0] + epsilon, perfect[1]], [perfect[0], perfect[1] - epsilon], epsilonStyle, color=epsilonColor)
+
+ # reset the bounds
+ axes.set_xbound(xbounds)
+ axes.set_ybound(ybounds)
+
# build a histogram figure of the given data with the given title and number of bins
def create_histogram(data, bins, title, xLabel, yLabel, displayStats=False) :
diff --git a/pyglance/glance/plotcreatefns.py b/pyglance/glance/plotcreatefns.py
index 9da06547b16b46f6b5952036082014acd1053738..2d9367e7bcfd5e8175423d1075a1fa2bd6aaf63e 100644
--- a/pyglance/glance/plotcreatefns.py
+++ b/pyglance/glance/plotcreatefns.py
@@ -14,6 +14,7 @@ matplotlib.use('Agg') # use the Anti-Grain Geometry rendering engine
from pylab import *
import matplotlib.colors as colors
+import matplotlib.cm as colormapinfo
import logging
import random as random
@@ -281,7 +282,7 @@ class BasicComparisonPlotsFunctionFactory (PlottingFunctionFactory) :
functionsToReturn['scatterD'] = ((lambda : figures.create_hexbin_plot(aData[goodInBothMask], bData[goodInBothMask],
"Value in File A vs Value in File B",
- "File A Value", "File B Value")),
+ "File A Value", "File B Value", epsilon)),
"density of file a values vs file b values for " + variableDisplayName,
"Hex.png", compared_fig_list)
@@ -820,6 +821,24 @@ class BinTupleAnalysisFunctionFactory (PlottingFunctionFactory) :
# our list of functions that will later create the plots
functionsToReturn = { }
+
+ # create the scatter plot with colors for each section
+ scatterPlotList = [ ]
+ tempColorMap = colormapinfo.get_cmap('jet', rawDiffData.shape[0])
+ for binNumber in range(rawDiffData.shape[0]) :
+ tempColor = tempColorMap(binNumber)
+ if len(tempColor) > 3 :
+ tempColor = tempColor[:3]
+ scatterPlotList.append(((aData[binNumber][goodInBothMask[binNumber]]).ravel(),
+ (bData[binNumber][goodInBothMask[binNumber]]).ravel(), None,
+ colors.rgb2hex(tempColor), None, 'bin ' + str(binNumber + 1), None))
+ functionsToReturn['multi-scatter'] = ((lambda : figures.create_complex_scatter_plot(scatterPlotList,
+ "Value in File A vs Value in File B, Colored by Bin",
+ "File A Value", "File B Value",
+ epsilon)),
+ "scatter plot of file a values vs file b values for " + variableDisplayName + " by bin",
+ "MultiScatter.png", compared_fig_list)
+
# for each of the bins, make the rms histogram data
numHistogramSections = 7 # TODO at some point make this a user controlled setting
for binNumber in range(rawDiffData.shape[0]) :
@@ -865,7 +884,7 @@ class BinTupleAnalysisFunctionFactory (PlottingFunctionFactory) :
for limitIndex in range(histogramSectionLimits.size - 1) :
# if it falls in this section, add it's case number index to the list for this section
- if ( (rmsDiffValues[caseNumber] > histogramSectionLimits[limitIndex]) and
+ if ( (rmsDiffValues[caseNumber] > histogramSectionLimits[limitIndex]) and
(rmsDiffValues[caseNumber] <= histogramSectionLimits[limitIndex + 1]) ) :
if limitIndex not in histogramSections :