Chad and Alan, yes that’s the problem. Thanks for responding.
“If you like the way the image appears on screen, use the DPI argument to
savefig()”
I don’t exactly understand this. What do you mean on screen? This code is
integrated with a big project that I’m running inside of ecplise, so I’m not
sure what you mean by on screen? Either way, when I add and change a DPI
argument to savefig(), it just blows up the dimensions of the PNG.
“My question back to you is, why does it matter? Most layout programs will
let you set the physical size of the figure and scale the DPI to match.”
So you’re saying, fine let it produce a huge png and then shrink it with the
html img tag and that will do the job, right? Well, that’s really hard for
us to do because we’re using an existing solution and changing that will
involve lawyers, I kid you not.
So, I can increase the DPI on the savefig call and correspondingly shrink
the dimensions of the file in inches to get it to stay the same size, but
the problem is the text and the lines, and the axis tick marks don’t shrink
too, which is really frustrating. I can then shrink the fonts, but I don’t
know how to shrink the tick marks and the lines. Anyways, it really seems
like there should be an easier way to increase resolution – not to appear
unappreciative in any way of the hard work the devs put into this excellent
project!
Alan, I read about pixel size in that link you sent me and I have no idea
what to do. Perhaps you guys can have a look at my code?
Thanks!
Bradley
from matplotlib.pyplot import figure, cm
from numpy import arange
# Determine array sizes depending on input data set
topOfGradientColor = cm.colors.hex2color('#B5DBEF')
bottomOfGradientColor = wht = cm.colors.hex2color('#FFFFFF')
blumd = cm.colors.hex2color('#6BC3DE')
bludk = cm.colors.hex2color('#6396A5')
redmd = cm.colors.hex2color('#F7B27B')
reddk = cm.colors.hex2color('#E7754A')
gradientColorMap =
cm.colors.LinearSegmentedColormap.from_list('gradientColorMap',
[bottomOfGradientColor, topOfGradientColor], 256)
blugrd = cm.colors.LinearSegmentedColormap.from_list('blugrd',[blumd,bludk],256)
blugrd_r =
cm.colors.LinearSegmentedColormap.from_list('blugrd_r',[bludk,blumd],256)
redgrd = cm.colors.LinearSegmentedColormap.from_list('redgrd',[redmd,reddk],256)
redgrd_r =
cm.colors.LinearSegmentedColormap.from_list('redgrd_r',[reddk,redmd],256)
numYears = len(barChartData)
xMin = -0.3
xMax = numYears + 0.3
xLim = (xMin, xMax)
paddingFactor = max(abs(yMin), abs(yMax)) * 0.18
yLim = (yMin- paddingFactor, yMax + paddingFactor)
fig = figure(figsize = (numYears*0.75, 3.5))
# Determine actual plot area
subplot = fig.add_subplot(111, xlim=xLim, ylim=yLim, autoscale_on=False
,axisbg=wht)
subplot.imshow([[.7, .7],[.5,.5]], interpolation='bicubic',
cmap=gradientColorMap, \
extent=(xMin, xMax, yMin - paddingFactor, yMax + paddingFactor),
alpha=1)
# Create bars on chart
values = [tuple[1] for tuple in barChartData]
labels = ["'" + str(tuple[0])[2:] for tuple in barChartData]
xArray = arange(numYears) + 0.25
width=0.5
# Create bars and bar labels, adjusting for pos/neg values
bottom = 0
X = [[.6, .3],[.6,.3]]
for left,top in zip(xArray, values):
right = left + width
strlab = "{0:.2f}".format(top) + '%'
if top >= 0:
subplot.imshow(X, interpolation='bicubic', cmap=blugrd,
extent=(left, left+(width/2.0)+.01, bottom, top), alpha=1)
subplot.imshow(X, interpolation='bicubic', cmap=blugrd_r,
extent=(left+(width/2.0), right, bottom, top), alpha=1)
subplot.text(left+width/2., top + (paddingFactor/5), strlab,
ha='center', va='bottom', fontsize=8, family='serif')
else:
subplot.imshow(X, interpolation='bicubic', cmap=redgrd,
extent=(left, left+(width/2.0)+.01, bottom, top), alpha=1)
subplot.imshow(X, interpolation='bicubic', cmap=redgrd_r,
extent=(left+(width/2.0), right, bottom, top), alpha=1)
subplot.text(left+width/2., top - (paddingFactor/1.5), strlab,
ha='center', va='bottom', fontsize=8, family='serif')
subplot.set_xticks(xArray + width / 2) #sets x ticks
subplot.set_xticklabels(labels, fontsize=8, family='serif') # sets x labels
subplot.set_yticks([], minor=True) # minor=True means that it dynamically
assigns tick values
for lab in subplot.get_yticklabels(): # set_yticks doesn't accept font args,
so we manually set them here
lab.set_fontsize(8)
lab.set_family('serif')
subplot.hlines(0, -0.3, numYears + 0.3) # this is the horizontal line set to
zero
subplot.set_aspect('auto') # really no idea what this does.
fig.savefig(fileNameBase + '/barchart.png', bbox_inches='tight', dpi=300)
------------------------------------------------------------------------------
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