def time_me(**kwargs):
try:
start = time.time()
for _ in range(n_iter):
pyprofit.make_model(kwargs)
t = time.time() - start
return timing_result(t / n_iter, None)
except pyprofit.error as e:
return timing_result(0, e)
def to_pyprofit_image(params, data, use_mask=True):
# merge, un-sigma all, un-log some
sigmas_tofit = data.sigmas[data.tofit]
allparams = data.model0.copy()
allparams[data.tofit] = params * sigmas_tofit
allparams[data.tolog] = 10**allparams[data.tolog]
fields = ['xcen', 'ycen', 'mag', 're', 'nser', 'ang', 'axrat', 'box']
s1params = [x for i, x in enumerate(allparams) if i % 2 == 0]
s2params = [x for i, x in enumerate(allparams) if i % 2 != 0]
if hasattr(data, 'psf') and len(data.psf) > 0:
fields.append('convolve')
s1params.append(True)
s2params.append(True)
sparams = [{name: val
for name, val in zip(fields, params)}
for params in (s1params, s2params)]
if data.verbose:
print(sparams)
profit_model = {
'width': data.image.shape[1],
'height': data.image.shape[0],
'magzero': data.magzero,
'psf': data.psf,
'profiles': {
'sersic': sparams
}
}
if use_mask:
profit_model['calcmask'] = data.calcregion
return allparams, np.array(pyprofit.make_model(profit_model))
def to_pyprofit_image(params, data, use_mask=True):
# merge, un-sigma all, un-log some
sigmas_tofit = data.sigmas[data.tofit]
allparams = data.model0.copy()
allparams[data.tofit] = params * sigmas_tofit
allparams[data.tolog] = 10**allparams[data.tolog]
fields = ['xcen','ycen','mag','re','nser','ang','axrat','box']
s1params = [x for i,x in enumerate(allparams) if i%2 == 0]
s2params = [x for i,x in enumerate(allparams) if i%2 != 0]
if hasattr(data, 'psf') and len(data.psf) > 0:
fields.append('convolve')
s1params.append(True)
s2params.append(True)
sparams = [{name: val for name, val in zip(fields, params)} for params in (s1params, s2params)]
if data.verbose:
print sparams
profit_model = {'width': data.image.shape[1],
'height': data.image.shape[0],
'magzero': data.magzero,
'psf': data.psf,
'profiles': {'sersic': sparams}
}
if use_mask:
profit_model['calcmask'] = data.calcregion
return allparams, np.array(pyprofit.make_model(profit_model))
def make_image_from_profiles(profiles, allparams, haspsf, data, use_calcinvmask=None):
if use_calcinvmask is None:
use_calcinvmask = False
if data.verbose:
print(zip(data.names, allparams))
profit_model = {
'width': data.image.shape[1],
'height': data.image.shape[0],
'magzero': data.magzero,
'profiles': profiles
}
if haspsf:
profit_model["psf"] = data.psf
if use_calcinvmask:
profit_model['calcmask'] = data.calcinvmask
if data.engine == "libprofit":
model = np.array(pyprofit.make_model(profit_model)[0])
else:
# TODO: Change this to the model image size
model = make_model_galsim(
profit_model["profiles"], data.psf, data.image.shape[0], data.image.shape[1])
model = make_model_image_galsim(model, data.modelimage, method=data.method)
return allparams, model
def variate(param, start, stop, step):
for b in np.arange(start, stop, step):
sp[param] = b
image = np.array(pyprofit.make_model(model)) # @UndefinedVariable
w = png.Writer(width, height, greyscale=True)
with open("/dev/stdout", 'w') as f:
w.write(f, loggray(image))
f.flush()
def variate(param, start, stop, step):
for b in np.arange(start, stop, step):
sp[param] = b
image = np.array(pyprofit.make_model(model)) # @UndefinedVariable
w = png.Writer(width, height, greyscale=True)
with open("/dev/stdout", 'w') as f:
w.write(f, loggray(image))
f.flush()
def generate_mass_map(self, shape, convolve_func):
'''
gemerate the mass distribution map for a galaxy object
shape: return 2D image shape
convolve_func: a 2D kernel if convolution is needed
'''
mags = np.array(self.maglist)
magzero = 2.5 * np.log10(
self.mass / np.sum(np.power(10, mags / (-2.5))))
profit_model = {
'width': shape[1],
'height': shape[0],
'magzero': magzero,
'psf': convolve_func,
'profiles': self.subCs
}
image, _ = pyprofit.make_model(profit_model)
return image
def generate_SED_IFU(self, shape, convolve_func, wavelength):
'''
gemerate the SED IFU for a galaxy object
shape: return 2D spatial shape
convolve_func: a 2D kernel if convolution is needed
wavelength: 1D array, the wavelength sample
'''
ny = shape[0]
nx = shape[1]
mags = np.array(self.maglist)
magzero = 2.5 * np.log10(
self.mass / np.sum(np.power(10, mags / (-2.5))))
tot_IFU = np.zeros((len(wavelength), ny, nx))
for key in self.subCs:
for loop in range(len(self.subCs[key])):
params = self.subCs[key][loop]
profit_model = {
'width': nx,
'height': ny,
'magzero': magzero,
'psf': convolve_func,
'profiles': {
key: [params]
}
}
mass_map, _ = pyprofit.make_model(profit_model)
sub_IFU = np.zeros((len(wavelength), ny, nx))
xaxis = np.arange(nx)
yaxis = np.arange(ny)
xmesh, ymesh = np.meshgrid(xaxis, yaxis)
r = np.sqrt((xmesh + 0.5 - self.subCs[key][loop]['xcen'])**2. +
(ymesh + 0.5 - self.subCs[key][loop]['ycen'])**2.)
age_map = Cal_map(r, self.ageparams[key][loop]['type'],
self.ageparams[key][loop]['paradic'])
Z_map = Cal_map(r, self.Zparams[key][loop]['type'],
self.Zparams[key][loop]['paradic'])
for loopy in range(ny):
for loopx in range(nx):
sub_IFU[:, loopy, loopx] = sed_bc03(
wavelength, Z_map[loopy][loopx],
age_map[loopy][loopx],
np.log10(mass_map[loopy][loopx]))
tot_IFU += sub_IFU
return tot_IFU
def pyprofit_wrap():
return pyprofit.make_model(kwargs)