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sunpy__plot.py
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sunpy__plot.py
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"""
routines for plotting images from the SUNRISE data output
Example usage:
import sunpy.sunpy__plot as sunplot
sunplot.plot_synthetic_sdss_gri('broadband_1234.fits')
sunplot.plot_sdss_gri('broadband_1234.fits')
Dependencies:
numpy
matplotlib
"""
import numpy as np
import os
import sys
import sunpy.sunpy__load as sunpy__load # used for noiseless images, for which we can return the image input directly
import sunpy__synthetic_image # used for images with noise, pixel scaling, etc.
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import gc
__author__ = "Paul Torrey and Greg Snyder"
__copyright__ = "Copyright 2014, The Authors"
__credits__ = ["Paul Torrey", "Greg Snyder"]
__license__ = "GPL"
__version__ = "1.0"
__maintainer__ = "Paul Torrey"
__email__ = "[email protected]"
__status__ = "Production"
if __name__ == '__main__': #code to execute if called from command-line
pass #do nothing
def plot_spectrum(filelist, savefile='spectrum.pdf', fontsize=14, ymin=None, ymax=None, **kwargs):
""" routine for plotting the spectra for a list of files """
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
print filelist
for file in filelist:
print file
wavelength = sunpy__load.load_sed_lambda(file)
sed = sunpy__load.load_sed_l_lambda(file)
ax.plot(wavelength,wavelength * sed, label=file)
ax.set_xlabel(r'$\lambda$ (m)',fontsize=fontsize)
ax.set_ylabel(r'$\lambda$L${}_{\lambda}$ (W)', fontsize=fontsize)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(fontsize)
ax.legend
ax.set_xscale('log')
ax.set_yscale('log')
if (ymin != None) and (ymax != None):
ax.set_ylim([ymin, ymax])
fig.subplots_adjust(left=0.18, right=0.95, top=0.95, bottom=0.12, wspace=0.0, hspace=0.0)
fig.savefig(savefile)
def plot_synthetic_sdss_gri(filename, savefile='syn_sdss_gri.png', **kwargs):
""" routine for plotting synthetic sdss gri images from Illustris idealized images including appropriate pixel scaling, noise, etc. """
rp, img = return_synthetic_sdss_gri_img(filename, **kwargs)
my_save_image(img, savefile)
del img
gc.collect()
def plot_synthetic_hst(filename, savefile='syn_hst.png', **kwargs): #mass_string=None, full_fov=None, cut_bad_pixels=False, **kwargs):
""" routine for plotting synthetic hst gri images from Illustris idealized images including appropriate pixel scaling, noise, etc. """
rp, img = return_synthetic_hst_img(filename, **kwargs) #return_synthetic_sdss_gri_img(filename, **kwargs)
my_save_image(img, savefile, **kwargs) #top_opt_text=mass_string, full_fov=full_fov, cut_bad_pixels=cut_bad_pixels, **kwargs)
del img
gc.collect()
def plot_sdss_gri(filename, savefile='./sdss_gri.png', **kwargs):
""" routine for plotting synthetic sdss gri images from Illustris idealized images *without* additional image effects """
img = return_sdss_gri_img(filename, **kwargs)
my_save_image(img, savefile)
del img
gc.collect()
def return_synthetic_sdss_gri_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=0.7, g_fac=1.0, r_fac=1.3,
seed_boost=1.0,
r_petro_kpc=None,
**kwargs):
fail_flag=True # looks for "bad" backgrounds, and tells us to try again
n_iter = 1
while(fail_flag and (n_iter < 2)):
fail_flag=False
try:
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])*(n_iter)*seed_boost
except:
try:
seed=int(filename[filename.index('.fits')-3:filename.index('.fits')])*(n_iter)*seed_boost
except:
seed=1234
n_iter+=1
b_image, rp, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=r_petro_kpc,
fix_seed=False,
**kwargs)
if(this_fail_flag):
fail_flag=True
g_image, dummy, the_used_seed,this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
r_image, dummy, the_used_seed, this_fail_flag = sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=the_used_seed,
r_petro_kpc=rp,
fix_seed=True,
**kwargs)
if(this_fail_flag):
fail_flag=True
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-6 ] = 1e100 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
def return_synthetic_hst_img(filename,
lupton_alpha=0.5, lupton_Q=0.5, scale_min=1e-4,
b_fac=1.0, g_fac=1.0, r_fac=1.0, max=1.0, dynrng=1e3,
**kwargs):
try:
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])
except:
try:
seed=int(filename[filename.index('broadband_red_')+14:filename.index('.fits')])
except:
seed=int(filename[filename.index('broadband_rest_')+15:filename.index('.fits')])
b_image, rp, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 22, #25,
seed=seed, fix_seed=True,
#r_petro_kpc=None,
**kwargs)
dummy, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 25,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
g_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 26,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
r_image, dummy, dummy, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 27,
seed=seed, fix_seed=True,
#r_petro_kpc=rp,
**kwargs)
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
b_image *= b_fac
g_image *= g_fac
r_image *= r_fac
if 0:
I = (r_image + g_image + b_image)/3
val = np.arcsinh( lupton_alpha * lupton_Q * (I - scale_min))/lupton_Q
I[ I < 1e-8 ] = 1e20 # from below, this effectively sets the pixel to 0
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
maxrgbval = np.amax(img, axis=2)
changeind = maxrgbval > 1.0
img[changeind,0] = img[changeind,0]/maxrgbval[changeind]
img[changeind,1] = img[changeind,1]/maxrgbval[changeind]
img[changeind,2] = img[changeind,2]/maxrgbval[changeind]
minrgbval = np.amin(img, axis=2)
changeind = minrgbval < 0.0
img[changeind,0] = 0
img[changeind,1] = 0
img[changeind,2] = 0
changind = I < 0
img[changind,0] = 0
img[changind,1] = 0
img[changind,2] = 0
img[img<0] = 0
else:
img[:,:,0] = np.log10(r_image ) # / max=1.0, dynrng=1e3,
img[:,:,1] = np.log10(g_image )
img[:,:,2] = np.log10(b_image )
img -= np.log10( max/dynrng )
img /= np.log10( dynrng )
img[img>1] = 1
I = img
val = img
img[img<0] = 0
print "img min/max/mean "+str(img.min())+" "+str(img.max())+" "+str(img.mean())
print " "
del b_image, g_image, r_image, I, val
gc.collect()
img[img<0] = 0
return rp, img
def return_sdss_gri_img(filename,camera=0,scale_min=0.1,scale_max=50,size_scale=1.0, non_linear=0.5):
if (not os.path.exists(filename)):
print "file not found:", filename
sys.exit()
b_image = sunpy__load.load_broadband_image(filename,band='g_SDSS.res',camera=camera) * 0.7
g_image = sunpy__load.load_broadband_image(filename,band='r_SDSS.res',camera=camera) * 1.0
r_image = sunpy__load.load_broadband_image(filename,band='i_SDSS.res',camera=camera) * 1.4
n_pixels = r_image.shape[0]
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
img[:,:,0] = asinh(r_image, scale_min=scale_min, scale_max=scale_max,non_linear=non_linear)
img[:,:,1] = asinh(g_image, scale_min=scale_min, scale_max=scale_max,non_linear=non_linear)
img[:,:,2] = asinh(b_image, scale_min=scale_min, scale_max=scale_max,non_linear=non_linear)
img[img<0] = 0
del b_image, g_image, r_image
gc.collect()
return img
def return_h_band_img(filename,camera=0,scale_min=0.1,scale_max=50,size_scale=1.0):
if (not os.path.exists(filename)):
print "file not found:", filename
sys.exit()
image = sunpy__load.load_broadband_image(filename,band='H_Johnson.res', camera=camera)
n_pixels = image.shape[0]
img = np.zeros((n_pixels, n_pixels), dtype=float)
img[:,:] = asinh(image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[img<0] = 0
return img
def return_johnson_uvk_img(filename,camera=0,scale_min=0.1,scale_max=50,size_scale=1.0):
if (not os.path.exists(filename)):
print "file not found:", filename
sys.exit()
b_effective_wavelength = sunpy__load.load_broadband_effective_wavelengths(filename,band="U_Johnson.res")
g_effective_wavelength = sunpy__load.load_broadband_effective_wavelengths(filename,band="V_Johnson.res")
r_effective_wavelength = sunpy__load.load_broadband_effective_wavelengths(filename,band="K_Johnson.res")
b_image = sunpy__load.load_broadband_image(filename,band='U_Johnson.res',camera=camera) * b_effective_wavelength / g_effective_wavelength * 2.5
g_image = sunpy__load.load_broadband_image(filename,band='V_Johnson.res',camera=camera) * g_effective_wavelength / g_effective_wavelength
r_image = sunpy__load.load_broadband_image(filename,band='K_Johnson.res',camera=camera) * r_effective_wavelength / g_effective_wavelength * 1.5
n_pixels
img = np.zeros((n_pixels, n_pixels, 3), dtype=float)
img[:,:,0] = asinh(r_image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[:,:,1] = asinh(g_image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[:,:,2] = asinh(b_image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[img<0] = 0
return img
def return_stellar_mass_img(filename, camera=0, scale_min=1e8, scale_max=1e10, size_scale=1.0, non_linear=1e8):
image = sunpy__load.load_stellar_mass_map(filename, camera=camera)
n_pixels = image.shape[0]
img = np.zeros((n_pixels, n_pixels), dtype=float)
img[:,:] = asinh(image, scale_min=scale_min, scale_max=scale_max, non_linear=non_linear)
img[img<0] = 0
return img
def return_mass_weighted_age_img(filename, camera=0, scale_min=None, scale_max=None, size_scale=1.0):
image = sunpy__load.load_mass_weighted_stellar_age_map(filename, camera=camera)
image += 1e5
return image
def return_stellar_metal_img(filename, camera=0, scale_min=None, scale_max=None, size_scale=1.0, non_linear=None):
image1 = sunpy__load.load_stellar_mass_map(filename, camera=camera)
image2 = sunpy__load.load_stellar_metal_map(filename, camera=camera)
image = image2/image1
image[image<0] = 0 #image.min()
image[image*0 != 0] = 0 #image.min()
return image
def my_save_image(img, savefile, opt_text=None, top_opt_text=None, full_fov=None, cut_bad_pixels=False, zoom=None, save_indiv_bands=True, **kwargs):
if img.shape[0] >1:
n_pixels_save = img.shape[0]
if zoom!=None:
n_pixels_current = img.shape[0]
center = np.floor(img.shape[0]/(2.0))
n_pixels_from_center = np.floor(img.shape[0]/(2.0*zoom))
img = img[center - n_pixels_from_center:center+n_pixels_from_center, center - n_pixels_from_center:center+n_pixels_from_center]
print "resized image from "+str(n_pixels_current)+" to "+str(img.shape[0])+" pixels"
if full_fov!=None:
full_fov /= (1.0*zoom)
if cut_bad_pixels:
cut_index=-1
print np.mean(img[cut_index:, cut_index:])
while( np.mean(img[cut_index:, cut_index:]) == 0 ):
print cut_index
cut_index -= 1
img = img[:cut_index,:cut_index]
fig = plt.figure(figsize=(1,1))
ax = fig.add_subplot(111)
imgplot = ax.imshow(img,origin='lower', interpolation='nearest')
plt.axis('off')
if not opt_text==None:
ax.text(img.shape[0]/2.0, img.shape[0]/2.0, opt_text, ha='center',va='center', color='white', fontsize=4)
if not top_opt_text==None:
ax.text(img.shape[0]/2.0, 0.9*img.shape[0], top_opt_text, ha='center',va='center', color='white', fontsize=4)
if not full_fov==None:
bar_size_in_kpc = np.round(full_fov/5.0)
pixel_size_in_kpc = full_fov / (1.0 * img.shape[0])
bar_size_in_pixels = bar_size_in_kpc / pixel_size_in_kpc
center = img.shape[0]/2.0
ax.text( center, 0.15 * img.shape[0], str(bar_size_in_kpc)+" kpc", color='w', fontsize=4, ha='center', va='center')
ax.plot( [center-bar_size_in_pixels/2.0, center+bar_size_in_pixels/2.0] , [0.1*img.shape[0], 0.1*img.shape[0]], lw=2,color='w')
ax.set_xlim([0,img.shape[0]-1])
ax.set_ylim([0,img.shape[0]-1])
fig.subplots_adjust(left=0.0, right=1.0, top=1.0, bottom=0.0)
fig.savefig(savefile, dpi=n_pixels_save)
fig.clf()
plt.close()
if save_indiv_bands:
print img.shape
for iii in np.arange(3):
fig = plt.figure(figsize=(1,1))
ax = fig.add_subplot(111)
print img[:,:,iii].min(), img[:,:,iii].max()
imgplot = ax.imshow(img[:,:,iii],origin='lower', interpolation='nearest', cmap = cm.Greys, vmin=0, vmax=1)
plt.axis('off')
fig.subplots_adjust(left=0.0, right=1.0, top=1.0, bottom=0.0)
fig.savefig(savefile+"_band_"+str(iii)+'.png', dpi=n_pixels_save)
fig.clf()
plt.close()
del img
gc.collect()
def asinh(inputArray, scale_min=None, scale_max=None, non_linear=2.0):
imageData=np.array(inputArray, copy=True)
if scale_min == None:
scale_min = imageData.min()
if scale_max == None:
scale_max = imageData.max()
factor = np.arcsinh((scale_max - scale_min)/non_linear)
indices0 = np.where(imageData < scale_min)
indices1 = np.where((imageData >= scale_min) & (imageData <= scale_max))
indices2 = np.where(imageData > scale_max)
imageData[indices0] = 0.0
imageData[indices2] = 1.0
imageData[indices1] = np.arcsinh((imageData[indices1] - scale_min)/non_linear)/factor
return imageData