def moment(co=True, c18o=True, c16o=True):
liste = np.array([])
liste_nom=[]
if c18o:
liste = np.append(liste, mcfost.Line("./data_C18O"))
liste_nom.append('image_c18o')
if c16o:
liste = np.append(liste, mcfost.Line("./data_13C16O"))
liste_nom.append('image_13co')
if co:
liste = np.append(liste, mcfost.Line("./data_CO"))
liste_nom.append('image_co')
vel = liste[0].velocity
vel = vel[2:-2:2]
im = np.zeros((len(vel), 801,801))
for j in range (0, len(vel)):
print(vel[j])
im_j = liste[0].plot_map(psf_FWHM=0.1, Delta_v=0.09, v=vel[j], i=2, substract_cont=True, cmap='nipy_spectral')
im[j,:,:] = im_j[:,:]
dv = vel[1] - vel[0]
moment_0 = np.sum(im, axis=0) * dv
a = np.sum(im[:, :, :] * vel[:, np.newaxis, np.newaxis], axis=0) * dv
moment_1 = np.divide(a, moment_0, out=np.zeros_like(a), where=moment_0!=0)
plt.show()
return moment_0, moment_1
def import_donnees(simu, co=True, c18o=True, c16o=True):
liste = np.array([])
liste_nom = []
if co:
liste = np.append(liste, mcfost.Line("./data_CO"))
liste_nom.append('image_co')
if c18o:
liste = np.append(liste, mcfost.Line("./data_C18O"))
liste_nom.append('image_c18o')
if c16o:
liste = np.append(liste, mcfost.Line("./data_13C16O"))
liste_nom.append('image_13co')
vel = liste[0].velocity[140:261:2]
print(vel)
for i in range(0, len(liste_nom)):
im = np.zeros((len(vel), 401, 401))
#fig, axes = plt.subplots(1,1)
for j in range(0, len(vel)):
no_ylabel = False
colorbar = False
if j > 0:
no_ylabel = True
if j == 2:
colorbar = True
im_j = liste[i].plot_map(Tb=True,
title=liste_nom[i],
psf_FWHM=0.1,
Delta_v=0.09,
v=vel[j],
i=2,
substract_cont=True,
no_ylabel=no_ylabel,
colorbar=colorbar,
cmap='nipy_spectral',
plot_stars=False)
im[j, :, :] = im_j[::2, ::2]
print(np.shape(im),
np.shape(liste[0].lines[0, 2, 0, 140:261:2, ::2, ::2]))
plt.show()
#np.savetxt(simu+"donnees_co", np.column_stack((liste[0].lines[0,2,0,::20,::2,::2])), header = "i=45 deg")
np.savetxt(simu + "reel_cont_13co",
np.column_stack((im)),
header="continu 45 deg, convolue psf_FWHM=0.1, Delta_v=0.09")
#np.savetxt(simu+"vel_reel", np.column_stack((vel)), header="velocite des channels")
return
pymcfost_objects = dict()
for time in TIMES:
pymcfost_objects[time] = dict()
for inclination in INCLINATIONS:
data_directory = ROOT_DATA_DIRECTORY / (
'b' + beta + '/t' + time + '/i' + inclination + '/data_' + radiation
)
if data == 'Image':
pymcfost_objects[time][inclination] = mcfost.Image(data_directory)
elif data == 'Line':
pymcfost_objects[time][inclination] = mcfost.Line(data_directory)
plotting_options = opts[itype]
if data == 'Image':
figure, axes = pymcfost_subplots.plot_figure(
pymcfost_objects=pymcfost_objects,
plotting_options=plotting_options,
text=text,
positions=positions,
colorbar_figure=COLORBAR_FIGURE,
)
filename = itype + '_b' + beta + '_' + radiation + '.pdf'
figures = [figure]
import pymcfost as pym
import matplotlib.pyplot as plt
data = pym.Line('data_CO')
nrows = 1
ncols = 4
plt.rcParams.update(plt.rcParamsDefault)
plt.rcParams['text.usetex'] = True
fig, allAxes = plt.subplots(nrows=nrows,
ncols=ncols,
sharey='row',
figsize=(4 * ncols, 5 * nrows),
sharex='col',
dpi=200)
limits = [-0.8, 0.8, -0.8, 0.8]
h1 = data.plot_map(moment=0,
substract_cont=True,
plot_stars=True,
no_xlabel=False,
iaz=0,
colorbar=False,
ax=allAxes[0],
limits=limits,
bmaj=0.05,
bmin=0.05,
bpa=0,