def makeImage(ImageHeaderInstance,fname,clipSize,ra,dec,units='arcseconds', **kwargs):
d = pyfits.open(ImageHeaderInstance.PATH)[0].data
cutout = {'data':d}
scale=zscale.zscale(d)
if clipSize:
wcs = astWCS.WCS(ImageHeaderInstance.PATH)
clipSizeDeg = clipSize * constants.convert_arcmin_or_arcsec_to_degrees[units]
cutout = astImages.clipImageSectionWCS(d, wcs, ra, dec, clipSizeDeg, returnWCS = False)
else:
clipSizeDeg = ImageHeaderInstance.TOPRIGHT_DEC-ImageHeaderInstance.BOTTOMLEFT_DEC
caption = ImageHeaderInstance.FILTER
ImageHeaderInstance.fname = fname
try:
astImages.saveBitmap(
os.path.join(MEDIA_ROOT,fname),
cutout['data'],
cutLevels=["smart", 99.5],
size=200,
colorMapName='gray',
caption=caption,
clipSizeDeg=clipSizeDeg,
scale=scale,
)
except:
pass #Fail silently; rendering will recover
return ImageHeaderInstance
def saveBitmap(outputFileName, inputFileName, imageData, size, colorMapName, caption):
"""Makes a bitmap image from an image array; the image format is specified by the
filename extension. (e.g. ".jpg" =JPEG, ".png"=PNG).
@type outputFileName: string
@param outputFileName: filename of output bitmap image
@type imageData: numpy array
@param imageData: image data array
@type cutLevels: list
@param cutLevels: sets the image scaling - available options:
- pixel values: cutLevels=[low value, high value].
- histogram equalisation: cutLevels=["histEq", number of bins ( e.g. 1024)]
- relative: cutLevels=["relative", cut per cent level (e.g. 99.5)]
- smart: cutLevels=["smart", cut per cent level (e.g. 99.5)]
["smart", 99.5] seems to provide good scaling over a range of different images.
@type size: int
@param size: size of output image in pixels
@type colorMapName: string
@param colorMapName: name of a standard matplotlib colormap, e.g. "hot", "cool", "gray"
etc. (do "help(pylab.colormaps)" in the Python interpreter to see available options)
"""
import time
start = time.time()
scale=zscale.zscale(imageData)
anorm = matplotlib.colors.Normalize(scale[0],scale[1])
cut = {'image': imageData, 'norm': anorm}
# Make plot
aspectR=float(cut['image'].shape[0])/float(cut['image'].shape[1])
fig = pyplot.figure(figsize=(10,10*aspectR))
xPix = size
yPix = size
dpi = 80
xSizeInches = size/dpi
ySizeInches = xSizeInches
fig.set_size_inches(xSizeInches,ySizeInches)
pyplot.axes([0,0,1,1])
pyplot.minorticks_off()
try:
colorMap=matplotlib.cm.get_cmap(colorMapName)
except AssertionError:
raise Exception, colorMapName+" is not a defined matplotlib colormap."
pyplot.imshow(cut['image'], interpolation="bilinear", norm=cut['norm'], origin='lower', cmap=colorMap)
pyplot.axis("off")
xmin,xmax = fig.gca().get_xlim()
ymin,ymax = fig.gca().get_ylim()
pyplot.text(xmin+1,ymin+35,caption,color="red",fontsize=20,fontweight=500,backgroundcolor='white')
pyplot.savefig(outputFileName,format="png",dpi=dpi)
pyplot.close()
return inputFileName,outputFileName
