WARNING:root:WARNING: First collocation point, ... is at a baseline shorter than the shortest deprojected baseline in the dataset

[bjnorfolk]

WARNING:root:WARNING: First collocation point, ... is at a baseline shorter than the shortest deprojected baseline in the dataset

[jeffjennings]

Hey Brodie,

So if I understand, you didn't have this warning when you were using an older version of frank, but you've since pulled the latest version of master and are getting the warning.

The warning indicates that the shortest baseline in the frank fit (the first collocation point in visibility space) is at smaller baseline than the shortest baseline in your dataset (you can see this in the frank fit lines in your new plot going to shorter baseline than the first datapoint). This could cause the fit to become erroneous because it would be trying to extrapolate the visibility amplitude to baselines on which there is no information, and the data look like they have a nontrivial slope at short baseline, so the fit could predict the visibility amplitude keeps rising at short baseline (as it appears to have done), when instead it should probably plateau.

Are you explicitly passing in baselines at which to evaluate the Hankel transform of the brightness profile? (These points are the q array in DiscreteHankelTransform.transform.)

It's a bit hard to judge by eye, but it looks to me like the errorbar in each uv bin is the same size in the new plot as in the old plot, just that the new plot itself is taller. This would suggest your deprojected visibilities haven't changed. Or did you verify numerically that the new errorbars are larger?

Thanks!

[rbooth200]

@jeffjennings - thanks for getting on this so quickly. @bjnorfolk 's dataset is a bit different to the ALMA ones we're used to (low SNR, sparser uv coverage) so I'll add my two-cents.

@bjnorfolk - I didn't quite understand what (if anything) you've changed between the fits. We added the warning after you are seeing after some issues that arose in a few tests when the largest scale in the fit (Rmax) is much larger than the maximum recoverable scale of the data. Since frank damps power in the reconstructed brightness when the SNR of the data is low (or zero for missing short / long baselines) this can lead to a fit with a low (or zero) total flux. However, the code should still run and produce a result when that warning is printed (can you let me know if that's not the case). Essentially, we added that warning to encourage people to check that the fit (and extrapolation) at the shortest baselines looks reasonable.

[bjnorfolk]

Thanks for the reply @jeffjennings and @rbooth200 .

You're right @jeffjennings, apologies for the obvious error. The visibility errors are identical, it was just the figure size.

But the main issue still remains, if you look at the old fit for alpha = 1.01 and ws=1e-2, the chi2 value is 1.875 and the fit looks "smoother". Whereas for the new fit, chi2=5.109 and the brightness profile doesn't resemble the gaussian ring I get in the old model. I'll include how I use Frank below the images
Old fit

New fit

#Setting up model
bin_width = 30.0
inc= 29.5
PA=151.0
dRA=0.0
dDec=0.0
disk_geometry = FixedGeometry(float(inc), float(PA), dRA=dRA, dDec=dDec)

Rmax = 3
N = 250
alpha = 1.01
ws = 1e-2
FF = FrankFitter(Rmax=Rmax, N=N, geometry=disk_geometry, alpha=alpha, weights_smooth=ws)
#Solving Model
sol = FF.fit(self.u, self.v, self.vis, self.wgt)
#Deprojecting
u_deproj, v_deproj, vis_deproj = sol.geometry.apply_correction(self.u, self.v, self.vis)
baselines = (u_deproj**2 + v_deproj**2)**.5

model_grid = np.logspace(np.log10(min(baselines.min(), sol.q[0])), np.log10(max(baselines.max(), sol.q[-1])), 10**4)
binned_vis = UVDataBinner(baselines, vis_deproj, weights, bin_width)
#Brightness profile
I_nn = sol.solve_non_negative()
#Vis model
vis_model = sol.predict_deprojected(model_grid, I=I_nn).real
#Vis model for chi2
vis_model_realbins = sol.predict_deprojected(binned_vis.uv, I=I_nn).real
#Visibilities
real = binned_vis.V.real
real_err = binned_vis.error.real
img = binned_vis.V.imag
img_err = binned_vis.error.imag
#Chi2
log = np.sum((real - vis_model_realbins)**2/real_err)

#Plotting ...

[rbooth200]

@bjnorfolk have you changed anything apart from the version of frank used (e.g. the weights)?

[bjnorfolk]

I do re-estimate the weights using the previous function you sent me, but I did do this previously for the old model. As a sanity check here's the models if I don't re-weight:

But it appears the weights for our ATCA data is statistical.

def estimate_baseline_dependent_weight(q, V, bin_width):
   
	uvBin = UVDataBinner(q, V, np.ones_like(q), bin_width)
	var = 0.5*(uvBin.error.real**2 + uvBin.error.imag**2) * uvBin.bin_counts

	weights = np.full_like(q, np.nan)
	left, right = uvBin.bin_edges
	for i, [l,r] in enumerate(zip(left, right)):
		idx = (q >= l) & (q < r)
		weights[idx] = 1/var[i]

	assert np.all(~np.isnan(weights)), "Weights needed for all data points"
	return weights

[rbooth200]

Ok, thanks. Do you still have access to the old version of your code?

[bjnorfolk]

This is where I'm kicking myself - I accidentally updated now I'm in this predicament. It was the "error" branch you originally suggested I use when I first tested frank.

[rbooth200]

I might still have it somewhere. In the mean time, I wonder if the old solutions hadn't fully converged. If you reduce the number of iterations to e.g. 200, do you get something that looks more like your old results?

[bjnorfolk]

Unfortunately not
N=200

N=150

[jeffjennings]

hmm Richard will likely have the best insight, but two of the new fits -- alpha=1.01, ws=1e-4; and alpha=1.1, ws=1e-4 -- look to me to be very similar, perhaps identical, to two of the old fits that use alpha=1.2 and 1.3. That would entail that the noise properties (this could be the weights, the (u, v) points, and/or the visibility amplitudes) of the dataset currently being passed into frank are different from the dataset that gave your old results.

And the new fits look surprisingly bad; they seem biased low relative to the data for baselines <~200 k\lambda, and biased high relative to the data between ~300 - 400 k\lambda. I wonder if you're overplotting the old binned visibilities and new frank visibility fits that are to a different visibility distribution.

@bjnorfolk are you sure that self.wgt in the first line of your below code block is the same array as weights in the last line?

sol = FF.fit(self.u, self.v, self.vis, self.wgt)
#Deprojecting
u_deproj, v_deproj, vis_deproj = sol.geometry.apply_correction(self.u, self.v, self.vis)
baselines = (u_deproj**2 + v_deproj**2)**.5

model_grid = np.logspace(np.log10(min(baselines.min(), sol.q[0])), np.log10(max(baselines.max(), sol.q[-1])), 10**4)
binned_vis = UVDataBinner(baselines, vis_deproj, weights, bin_width)

[bjnorfolk]

Thanks for checking @jeffjennings, I should've included my entire code prior to the previous message. I'm confident that self.wgt is the same as weights, in the code I sent I either re-estimate the weights or pass weights = self.wgt.

if est_weights == True:
	baselines = (self.u**2 + self.v**2)**.5
	weights = estimate_baseline_dependent_weight(baselines, self.vis, bin_width)
else:
	weights = self.wgt

[bjnorfolk]

It appears that with low SNR and sparser uv coverage data, Rmax plays a key role in the fit. I've discovered that low values of Rmax produce fits identical to that previously obtained (I must have set a lower Rmax value to begin with). Thanks for all the help!

[rbooth200]

Thanks - I think your ATCA data is on the edge of the SNR range that can be reasonably fit with frank. Thus too large an Rmax produces more degrees of freedom in the model than can be constrained by the data.