Fitting Gaussians

[1]:

# standard imports
import matplotlib.pyplot as plt
import bettermoments as bm
import numpy as np

[2]:

# load up the data
path = '../../../gofish/docs/user/TWHya_CS_32.fits'
data, velax, bunits = bm.load_cube(path)

[3]:

# apply spectral smoothing
smoothed_data = bm.smooth_data(data=data,
                               smooth=0,
                               polyorder=0)

[4]:

# estimate the RMS of the cube
rms = bm.estimate_RMS(data=data)

[5]:

# load up a user-defined mask
user_mask = bm.get_user_mask(data=data,
                             user_mask_path=None)

[35]:

# define a threshold-based mask
threshold_mask = bm.get_threshold_mask(data=data,
                                       clip=15.0,
                                       smooth_threshold_mask=0)

[36]:

# define a channel-based mask.
channel_mask = bm.get_channel_mask(data=data,
                                   firstchannel=0,
                                   lastchannel=-1)

[37]:

# combine the three masks
mask = bm.get_combined_mask(user_mask=user_mask,
                            threshold_mask=threshold_mask,
                            channel_mask=channel_mask,
                            combine='and')

[38]:

fig, ax = plt.subplots()
im = ax.imshow(np.sum(mask, axis=0), origin='lower')
plt.colorbar(im)

[38]:

<matplotlib.colorbar.Colorbar at 0x7ff4ed182d50>
../_images/tutorials_Cookbook_2_9_1.png 
[39]:

# mask the smoothed data
masked_data = smoothed_data * mask

[50]:

# identify the pixels to fit with the MCMC
indices = bm.get_finite_pixels(masked_data, 4)

[54]:

# collapse the data

fits = bm.collapse_analytical(velax, data, rms, 'gaussian',
                              indices=indices, chunks=8,
                              mcmc='emcee',
                              nwalkers=32)

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