if __name__ == '__main__':
t_init_prog = time.clock()
L = Lines()
rbinini = 0.
rbinfin = 3.
rbinstep = 0.2
R_bin__r = np.arange(rbinini, rbinfin + rbinstep, rbinstep)
R_bin_center__r = (R_bin__r[:-1] + R_bin__r[1:]) / 2.0
N_R_bins = len(R_bin_center__r)
califaID = sys.argv[1]
K = read_one_cube(califaID, EL=True, GP=True, config=-1)
print '# califaID:', califaID, ' N_zones:', K.N_zone, ' lines:', K.EL.lines
pa, ba = K.getEllipseParams()
K.setGeometry(pa, ba)
lines = K.EL.lines
f_obs__lz = {}
for i, l in enumerate(lines):
mask = np.bitwise_or(~np.isfinite(K.EL.flux[i]),
np.less(K.EL.flux[i], 1e-40))
f_obs__lz[l] = np.ma.masked_array(K.EL.flux[i], mask=mask)
print l, f_obs__lz[l].max(), f_obs__lz[l].min(), f_obs__lz[l].mean()
x_Y__z, integrated_x_Y = calc_xY(K, 3.2e7)
'figure.subplot.left': 0.04,
'figure.subplot.bottom': 0.04,
'figure.subplot.right': 0.97,
'figure.subplot.top': 0.95,
'figure.subplot.wspace': 0.1,
'figure.subplot.hspace': 0.25,
'image.cmap': cmap,
}
plt.rcParams.update(plotpars)
# plt.ioff()
figsize = (width, width * aspect)
return plt.figure(fignum, figsize, dpi=dpi)
if __name__ == '__main__':
g = sys.argv[1]
Kpix = read_one_cube(g, debug=True, EL=True, config=-1, elliptical=True)
Kv20 = read_one_cube(g, debug=True, EL=True, config=-2, elliptical=True)
iHa_pix = Kpix.EL.lines.index('6563')
iHa_v20 = Kv20.EL.lines.index('6563')
fHapixsum__z = np.zeros((Kv20.N_zone))
f6563__zpix_aux = Kpix.EL.flux[iHa_pix]
mask = np.bitwise_or(~np.isfinite(f6563__zpix_aux), np.less(f6563__zpix_aux, 1e-40))
f6563__zpix = np.ma.masked_array(f6563__zpix_aux, mask=mask).filled(0.)
for i in xrange(Kv20.N_y):
for j in xrange(Kv20.N_x):
if Kv20.qZones[i, j] >= 0:
z_i = Kv20.qZones[i, j]
z_i_pix = Kpix.qZones[i, j]
fHapixsum__z[z_i] += f6563__zpix[z_i_pix]
# f = plot_setup(latex_column_width, 1.)
if __name__ == '__main__':
t_init_prog = time.clock()
L = Lines()
rbinini = 0.
rbinfin = 3.
rbinstep = 0.2
R_bin__r = np.arange(rbinini, rbinfin + rbinstep, rbinstep)
R_bin_center__r = (R_bin__r[:-1] + R_bin__r[1:]) / 2.0
N_R_bins = len(R_bin_center__r)
califaID = sys.argv[1]
K = read_one_cube(califaID, EL=True, GP=True)
print '# califaID:', califaID, ' N_zones:', K.N_zone, ' lines:', K.EL.lines
pa, ba = K.getEllipseParams()
K.setGeometry(pa, ba)
lines = K.EL.lines
f_obs__lz = {}
for i, l in enumerate(lines):
mask = np.bitwise_or(~np.isfinite(K.EL.flux[i]),
np.less(K.EL.flux[i], 1e-40))
f_obs__lz[l] = np.ma.masked_array(K.EL.flux[i], mask=mask)
print l, f_obs__lz[l].max(), f_obs__lz[l].min(), f_obs__lz[l].mean()
x_Y__z, integrated_x_Y = calc_xY(K, 3.2e7)
if __name__ == '__main__':
args = parser_args()
print_args()
imgDir = califa_work_dir + 'images/'
Zsun = 0.019
H = H5SFRData(args.hdf5)
tSF__T = H.tSF__T
ageMyr = tSF__T[args.itSF] / 1e6
if (len(np.where(H.califaIDs == args.califaID)[0]) == 0):
exit('<<< plot: %s: no data.' % args.califaID)
K = read_one_cube(args.califaID, EL = True)
pa, ba = K.getEllipseParams()
K.setGeometry(pa, ba)
f, axArr = plt.subplots(4, 4)
f.set_size_inches(24, 20)
for ax in f.axes:
ax.set_axis_off()
ax = axArr[0, 0]
ax.set_axis_on()
galaxyImgFile = imgDir + args.califaID + '.jpg'
galimg = plt.imread(galaxyImgFile)
plt.setp(ax.get_xticklabels(), visible = False)
plt.setp(ax.get_yticklabels(), visible = False)
import numpy as np
from scipy.optimize import minimize
from CALIFAUtils.scripts import read_one_cube
from matplotlib import pyplot as plt
def gauss(p, x):
A1, mu1, sigma1, A2, mu2, sigma2, A3, mu3, sigma3 = p
g1 = A1 * np.exp(-(x - mu1)**2 / (2. * sigma1**2))
g2 = A2 * np.exp(-(x - mu2)**2 / (2. * sigma2**2))
g3 = A3 * np.exp(-(x - mu3)**2 / (2. * sigma3**2))
return g1 + g2 + g3
K = read_one_cube('K0010', debug=True, config=-2)
N_zone = K.N_zone
l_obs = np.copy(K.l_obs)
f_obs__lz = np.copy(K.f_obs)
f_syn__lz = np.copy(K.f_syn)
v_0__z = np.copy(K.v_0)
v_d__z = np.copy(K.v_d)
K.close()
del K
zone = 0
f_obs__l = f_obs__lz[:, zone]
f_syn__l = f_syn__lz[:, zone]
f_res__l = f_obs__l - f_syn__l
f_res__l *= 1e16
Hb_window = np.bitwise_and(np.greater(l_obs, 6563 - 70),
np.less(l_obs, 6563 + 70))
to_min = lambda p: np.square(f_res__l[Hb_window] - gauss(p, l_obs[Hb_window])
if __name__ == '__main__':
t_init_prog = time.clock()
L = Lines()
rbinini = 0.
rbinfin = 3.
rbinstep = 0.2
R_bin__r = np.arange(rbinini, rbinfin + rbinstep, rbinstep)
R_bin_center__r = (R_bin__r[:-1] + R_bin__r[1:]) / 2.0
N_R_bins = len(R_bin_center__r)
califaID = sys.argv[1]
K = read_one_cube(califaID, EL=True, GP=True, elliptical=True, config=-2)
print '# califaID:', califaID, ' N_zones:', K.N_zone, ' lines:', K.EL.lines
lines = K.EL.lines
print lines
EWHa__yx = K.zoneToYX(K.EL.EW[K.EL.lines.index('6563')], extensive=False)
distance_HLR__yx = K.pixelDistance__yx / K.pixelsPerHLR
distance_HLR__r = R_bin_center__r
# fluxes, SBs and cumulative SB
f_obs__lz = {}
f_obs__lyx = {}
SB_obs__lyx = {}
SB_obs_2__lyx = {}
def cmap_discrete(colors=[(1, 0, 0), (0, 1, 0), (0, 0, 1)],
n_bins=3,
cmap_name='myMap'):
cm = mpl.colors.LinearSegmentedColormap.from_list(cmap_name,
colors,
N=n_bins)
return cm
if __name__ == '__main__':
P = CALIFAPaths()
califaID = sys.argv[1]
kw_cube = dict(EL=EL, config=config, elliptical=elliptical)
K = read_one_cube(califaID, **kw_cube)
if K is None:
print 'califaID:', califaID, ' trying another qVersion...'
K = try_q055_instead_q054(califaID, **kw_cube)
if K is None or K.EL is None:
print
sys.exit('califaID:%s missing fits files...' % califaID)
lines = K.EL.lines
maskSNR = K.EL._setMaskLineSNR('4861', minsnr=minSNR)
maskSNR = np.bitwise_or(maskSNR, K.EL._setMaskLineSNR('6563',
minsnr=minSNR))
f_obs__lz = {}
f_obs__lyx = {}
SB_obs__lyx = {}