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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:
Dependencies:
"""
import numpy as np
import os
import sys
import 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.pyplot as plt
import img_scale
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__ = "ptorrey@mit.harvard.edu"
__status__ = "Production"
if __name__ == '__main__': #code to execute if called from command-line
pass #do nothing
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_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,
**kwargs):
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])
b_image, rp = sunpy__synthetic_image.build_synthetic_image(filename, 'g_SDSS.res',
seed=seed,
r_petro_kpc=None,
**kwargs)
g_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 'r_SDSS.res',
seed=seed,
r_petro_kpc=rp,
**kwargs)
r_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 'i_SDSS.res',
seed=seed,
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
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
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_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,
**kwargs):
seed=int(filename[filename.index('broadband_')+10:filename.index('.fits')])
b_image, rp = sunpy__synthetic_image.build_synthetic_image(filename, 22, #25,
seed=seed,
r_petro_kpc=None,
**kwargs)
g_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 26,
seed=seed,
r_petro_kpc=rp,
**kwargs)
r_image, dummy = sunpy__synthetic_image.build_synthetic_image(filename, 27,
seed=seed,
r_petro_kpc=rp,
**kwargs)
print b_image.min(), b_image.max()
print g_image.min(), g_image.max()
print r_image.min(), r_image.max()
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-8 ] = 1e20 # from below, this effectively sets the pixel to 0
print val.min(), val.max()
print I.min(), I.max()
img[:,:,0] = r_image * val / I
img[:,:,1] = g_image * val / I
img[:,:,2] = b_image * val / I
print " "
print " "
print img.min(), img.max()
print " "
print " "
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
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] = img_scale.asinh(r_image, scale_min=scale_min, scale_max=scale_max,non_linear=non_linear)
img[:,:,1] = img_scale.asinh(g_image, scale_min=scale_min, scale_max=scale_max,non_linear=non_linear)
img[:,:,2] = img_scale.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[:,:] = img_scale.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] = img_scale.asinh(r_image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[:,:,1] = img_scale.asinh(g_image, scale_min=scale_min, scale_max=scale_max,non_linear=0.5)
img[:,:,2] = img_scale.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[:,:] = img_scale.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):
if img.shape[0] >1:
fig = plt.figure(figsize=(1,1))
ax = fig.add_subplot(111)
imgplot = ax.imshow(img,origin='lower')
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)
fig.subplots_adjust(left=0.0, right=1.0, top=1.0, bottom=0.0)
fig.savefig(savefile, dpi=img.shape[0]-1)
fig.clf()
plt.close()
del img
gc.collect()