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--git a/_modules/mpol/datasets.html b/_modules/mpol/datasets.html index 561f384c..6515e141 100644 --- a/_modules/mpol/datasets.html +++ b/_modules/mpol/datasets.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/fourier.html b/_modules/mpol/fourier.html index ac8f217a..aa21e637 100644 --- a/_modules/mpol/fourier.html +++ b/_modules/mpol/fourier.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/geometry.html b/_modules/mpol/geometry.html index 456b4ddb..ca3d5d9d 100644 --- a/_modules/mpol/geometry.html +++ b/_modules/mpol/geometry.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/gridding.html b/_modules/mpol/gridding.html index 73e4930a..d60e356e 100644 --- a/_modules/mpol/gridding.html +++ b/_modules/mpol/gridding.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/images.html b/_modules/mpol/images.html index ee3f70fc..90c5f73d 100644 --- a/_modules/mpol/images.html +++ b/_modules/mpol/images.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/losses.html b/_modules/mpol/losses.html index 6ccf2678..0f51c133 100644 --- a/_modules/mpol/losses.html +++ b/_modules/mpol/losses.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/plot.html b/_modules/mpol/plot.html index 7f7ec098..543a81af 100644 --- a/_modules/mpol/plot.html +++ b/_modules/mpol/plot.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/precomposed.html b/_modules/mpol/precomposed.html index b943bf1c..8576824d 100644 --- a/_modules/mpol/precomposed.html +++ b/_modules/mpol/precomposed.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/training.html b/_modules/mpol/training.html index ccf68bfb..3ef22ff3 100644 --- a/_modules/mpol/training.html +++ b/_modules/mpol/training.html @@ -35,7 +35,7 @@ - + diff --git a/_modules/mpol/utils.html b/_modules/mpol/utils.html index f1958642..fa012658 100644 --- a/_modules/mpol/utils.html +++ b/_modules/mpol/utils.html @@ -35,7 +35,7 @@ - + diff --git a/_sources/ci-tutorials/crossvalidation.md.txt b/_sources/ci-tutorials/crossvalidation.md.txt index dd4f0ed1..baf0f3e6 100644 --- a/_sources/ci-tutorials/crossvalidation.md.txt +++ b/_sources/ci-tutorials/crossvalidation.md.txt @@ -51,7 +51,7 @@ from mpol import ( # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", @@ -361,7 +361,7 @@ For the purposes of comparison, here is the image produced by the tclean algorit ```{code-cell} fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.tclean.fits", + "https://zenodo.org/record/10059491/files/logo_cube.tclean.fits", cache=True, show_progress=True, pkgname="mpol", diff --git a/_sources/ci-tutorials/fakedata.md.txt b/_sources/ci-tutorials/fakedata.md.txt index 0e3a948e..de08ef3d 100644 --- a/_sources/ci-tutorials/fakedata.md.txt +++ b/_sources/ci-tutorials/fakedata.md.txt @@ -274,7 +274,7 @@ from astropy.utils.data import download_file # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", diff --git a/_sources/ci-tutorials/gridder.md.txt b/_sources/ci-tutorials/gridder.md.txt index 8e7607dc..47ffaf3d 100644 --- a/_sources/ci-tutorials/gridder.md.txt +++ b/_sources/ci-tutorials/gridder.md.txt @@ -39,7 +39,7 @@ from astropy.utils.data import download_file ```{code-cell} # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", diff --git a/_sources/ci-tutorials/initializedirtyimage.md.txt b/_sources/ci-tutorials/initializedirtyimage.md.txt index 1857f815..e4c1b041 100644 --- a/_sources/ci-tutorials/initializedirtyimage.md.txt +++ b/_sources/ci-tutorials/initializedirtyimage.md.txt @@ -47,7 +47,7 @@ When saving and loading a model, it is important to make sure that ``cell_size`` ```{code-cell} # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", diff --git a/_sources/ci-tutorials/loose-visibilities.md.txt b/_sources/ci-tutorials/loose-visibilities.md.txt index a39dea62..aed73b3b 100644 --- a/_sources/ci-tutorials/loose-visibilities.md.txt +++ b/_sources/ci-tutorials/loose-visibilities.md.txt @@ -49,7 +49,7 @@ and loading the dataset ```{code-cell} # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", diff --git a/_sources/ci-tutorials/optimization.md.txt b/_sources/ci-tutorials/optimization.md.txt index caf72e1b..0a7b8686 100644 --- a/_sources/ci-tutorials/optimization.md.txt +++ b/_sources/ci-tutorials/optimization.md.txt @@ -40,7 +40,7 @@ from mpol import coordinates, fourier, gridding, losses, precomposed, utils ```{code-cell} ipython3 # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", diff --git a/_static/copybutton.js b/_static/copybutton.js index 02c5c82d..2ea7ff3e 100644 --- a/_static/copybutton.js +++ b/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', + 'copy_to_clipboard': 'Copier dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -224,7 +224,7 @@ var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); // get filtered text - let exclude = '.linenos, .gp'; + let exclude = '.linenos'; let text = filterText(target, exclude); return formatCopyText(text, '', false, true, true, true, '', '') diff --git a/_static/fftshift/build/plot.png b/_static/fftshift/build/plot.png index 72ec3e98..c3ffa185 100644 Binary files a/_static/fftshift/build/plot.png and b/_static/fftshift/build/plot.png differ diff --git a/_static/pygments.css b/_static/pygments.css index f227e5c6..6110e9f1 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -22,6 +22,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: .highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */ .highlight .gd { color: #a40000 } /* Generic.Deleted */ .highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */ +.highlight .ges { color: #000000; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ .highlight .gr { color: #ef2929 } /* Generic.Error */ .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */ .highlight .gi { color: #00A000 } /* Generic.Inserted */ diff --git a/api.html b/api.html index 3d8ecc22..a7b3cc28 100644 --- a/api.html +++ b/api.html @@ -38,7 +38,7 @@ - + diff --git a/changelog.html b/changelog.html index f5485b49..8ca77122 100644 --- a/changelog.html +++ b/changelog.html @@ -38,8 +38,6 @@ - - diff --git a/ci-tutorials/PyTorch.html b/ci-tutorials/PyTorch.html index a99d174d..11dc6a67 100644 --- a/ci-tutorials/PyTorch.html +++ b/ci-tutorials/PyTorch.html @@ -38,8 +38,6 @@ - - @@ -585,7 +583,7 @@

Optimizing a Function with Gradient Descent -../_images/aa4014aa819c4fd1240e4a412fa9c5f975b660c6935e84d2412d2cf2876047a6.png +../_images/9e6515d7281d0636faf745b79b518cb3b256a9a5953827cd1499dc218ded1582.png

We see we need to go to the right to go down toward the minimum. For a multivariate function, the gradient will be a vector pointing in the direction of the steepest downward slope. When we take steps, we find the x coordinate of our new location by:

@@ -649,7 +647,7 @@

Optimizing a Function with Gradient Descent -../_images/ade4bc80dd522959106b566d6fde94298c7ac454f018589c5d93860443d26859.png +../_images/034e030958c936689acf508e6dd99f2e4625133cd4d689be8cd932863dc01760.png

The gradient at our new point (shown in orange) is still not close to zero, meaning we haven’t reached the minimum. We’ll continue this process of checking if the gradient is nearly zero, and take a step in the direction of steepest descent until we reach the bottom of the valley. We’ll say we’ve reached the bottom of the valley when the absolute value of the gradient is \(<0.1\):

@@ -696,7 +694,7 @@

Optimizing a Function with Gradient Descent -../_images/b555dc26be48899b117e71e690cb7df070138fa29eee2689d72878b436f76e01.png +../_images/1d8abadb937d7eafab8c2f0405fab90e61ccdd4e79469d10af84ce15d6812eb5.png

This works, but it takes a long time since we have several small steps.

@@ -749,7 +747,7 @@

Optimizing a Function with Gradient Descent -../_images/03507cfedf12a44d611a8d154f2826c860b51a2438ffbd10054023085b3a891c.png +../_images/ac4b397a7095065b3dc48ea2d72413ad0ca6daed4747c16b396cdcb168946640.png

Note the change in scale! With only one step, we already see that we stepped right over the minimum to somewhere far up the other side of the valley (orange point)! This is not good. If we kept iterating with the same learning rate, we’d find that the optimization process diverges and the step sizes start blowing up. This is why it is important to pick the proper step size by setting the learning rate appropriately. Steps that are too small take a long time while steps that are too large render the optimization process invalid. In this case, a reasonable choice appears to be step size = 0.6, which would have reached pretty close to the minimum after only 3 steps.

diff --git a/ci-tutorials/crossvalidation.html b/ci-tutorials/crossvalidation.html index 2c7f3535..4fb4edee 100644 --- a/ci-tutorials/crossvalidation.html +++ b/ci-tutorials/crossvalidation.html @@ -38,8 +38,6 @@ - - @@ -469,7 +467,7 @@

Setup # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", @@ -522,7 +520,7 @@

Setup 
Text(0, 0.5, '$\\Delta \\delta$ [${}^{\\prime\\prime}$]')
-../_images/f9f8d384d3ef88b296067741c6f5e4425900f3b54f90d651651f455b61b9c7d0.png +../_images/68b1f5daf0165a1bb7e842d5ca06da0f62ba1ce332f54200f83abe35bbdf7a80.png @@ -559,7 +557,7 @@

Choosing the K-folds
-../_images/7466e914ec3b16f46535a6c287432351653563f830df28ac01801a26ee676bea.png +../_images/f81483056cc0df68de1fd938b3a2b2c5cc6f05acfba0fe39e2f52ff74282aa13.png

As you can see, the \(u\),\(v\) space is sampled in a very structured way:

@@ -595,7 +593,7 @@

Choosing the K-folds
650160 visibilities total
-../_images/b9a7df4fec596a3628e39371013dfafe9f57eef598f7db7438f979e74c2b8552.png +../_images/3a1d404a0ae5c5da1d7813b7bee0579d1da1dfde237b1ced76eba752d3e13329.png

As you can see, this training set looks very similar to the full dataset, with the same holes in \(u\),\(v\) coverage and similar sampling densities.

@@ -675,7 +673,7 @@

Choosing the K-folds
-../_images/55e9e4b84d6359d4e57bb99de8e4e7020fdf672242e900266a08d86ae0db3d9d.png +../_images/17bc4bcd65f39b8bcc801b67a85d68826b912b3a258ca7053ac33b34bb5dd302.png
@@ -801,13 +799,13 @@

Results -
Cross validation score: 30.529687767119373
+
Cross validation score: 30.529687889692518
 

 

 
(<Figure size 1920x1440 with 1 Axes>, <Axes: >)
 

 

-../_images/7a841dcbc4e190d441d9999e543a58eb8d94bfd11bb397ef9eaf3c87e1479d5a.png
+../_images/1198f43b8a9d74666ec4478846200355e7674135d3b3d33e07417b9db32b54eb.png

This image looks only slightly better than the dirty image itself. This is because it is nearly equivalent, the only difference is the baked-in regularization provided by our non-negative pixels. Next, let’s try a small level of regularization

@@ -820,13 +818,13 @@

Results -
Cross validation score: 11.518084924455199
+
Cross validation score: 11.518120967027482
 

 

 
(<Figure size 1920x1440 with 1 Axes>, <Axes: >)
 

 

-../_images/dab685006e4b28b14500b2e138fe41f38bc6e6c273e6ea47b1bc998949a75e47.png
+../_images/34b824595ea1185a2aa56c707e4f748b738aa1e63ecd78e7ebe2713f9d601919.png

We see that the cross validation score improved significantly, meaning that these hyperparameter settings produce models that do a better job generalizing to new data. And, we can keep tweaking the hyperparameters to see how low of a cross-validation metric we can find.

@@ -839,13 +837,13 @@

Results -
Cross validation score: 7.306894904074458
+
Cross validation score: 7.306494520021946
 

 

 
(<Figure size 1920x1440 with 1 Axes>, <Axes: >)
 

 

-../_images/95a4ab486788b3b6d95242c6ca59ec4003415bccd1316a34a9e803364a863e40.png
+../_images/e275fa1d53ccadc9fb754ad543d7ad730b254ff64f54b0a4b56371509eea5b6e.png

More regularizing strength doesn’t always mean better… there will reach a point where the regularizing terms are strong that the model starts ignoring the data (via the nll_gridded term). To help you perform a full hyperparameter sweep and identify the “best” settings quickly, we recommend checking out tools like Tensorboard and Ray Tune.

@@ -853,7 +851,7 @@

Results 
fname = download_file(
-    "https://zenodo.org/record/4930016/files/logo_cube.tclean.fits",
+    "https://zenodo.org/record/10059491/files/logo_cube.tclean.fits",
     cache=True,
     show_progress=True,
     pkgname="mpol",
@@ -906,7 +904,7 @@ 
Results
Text(0, 0.5, '$\\Delta \\delta$ [${}^{\\prime\\prime}$]')
-../_images/fc8c7f2d6de44a59c1122f9f8c12fabe5fda67baf488137490c4d290cbcb1b5b.png +../_images/c92631d3b3448bed531ba9b42c5b3c657f97907ead343ecf7f148be404aaac3b.png
diff --git a/ci-tutorials/fakedata.html b/ci-tutorials/fakedata.html index d64296b6..192fd4a1 100644 --- a/ci-tutorials/fakedata.html +++ b/ci-tutorials/fakedata.html @@ -38,8 +38,6 @@ - - @@ -508,7 +506,7 @@

Choosing a mock sky brightness distribution
-ALMA +ALMA

The ALMA antennas. Credit: ALMA (ESO/NAOJ/NRAO)#

@@ -557,7 +555,7 @@

Using Pillow to greyscale, apodize, pad, and resize
-../_images/e2c5069067026e4c39edc7151ce328fc5a07553cf1b5ac60d97c16e16e385964.png +../_images/0df4d8edccc07cb273e547e7e22a41a610e41409dc683ff936a4f38c8a5154a0.png

Now let’s think about how to make the image square. Depending on the image, we can either crop the longer dimension or pad the larger dimension. Before we do that, though, we also need to think about the image edges.

@@ -587,7 +585,7 @@

Using Pillow to greyscale, apodize, pad, and resize
-../_images/52133f6c14b96f46a6131e6634f2191978274952e44cdd0685472719283dda8e.png +../_images/edaaa8a8995da4dce60881ff88a6cb193bdc232c63b316c5222c4e37e07dc449.png

And then use image math to multiply the apodization window against the greyscaled image

@@ -606,7 +604,7 @@

Using Pillow to greyscale, apodize, pad, and resize
-../_images/8f3ffcffd63694125267c4f3f6494ae934b5dc14a4a8136874793feeb40a3b8f.png +../_images/80e0c945417f0a8e8b7fb4760a4944c03a37f926d53e07c9e4db4d7d21689411.png

Now, let’s pad the image to be square

@@ -625,7 +623,7 @@

Using Pillow to greyscale, apodize, pad, and resize
-../_images/c7c38d411191ee89743265c59d35bff5520ee39f844484b8e46bb5197a11518e.png +../_images/a842fe3bb8275ad72c2a19c55ee300f08a96539a6b8effa84e88d29041462ca3.png

Great, we now have a square, apodized image.

@@ -649,7 +647,7 @@

Using Pillow to greyscale, apodize, pad, and resize
-../_images/7817aacfa469a5f75abd2a57c7c166a78d3d8094e56e4f4f9aeda12165ccdae6.png +../_images/3eff8a4b322a194cc508504c70be7e83e92f73bd5d83a6d5fd6818104cf1c6c0.png
@@ -705,10 +703,10 @@

Exporting to a Numpy array and setting flux scale
-
<matplotlib.image.AxesImage at 0x7f3bb02be970>
+
<matplotlib.image.AxesImage at 0x7fd714099eb0>
 

 

-../_images/13cbc8e31199defb75075ab53f815300ab749b2856ac7e2b68c286fcd99bb00e.png
+../_images/e9cfcd79d2277afb1491178e2e9d8b3d96157676ce49b9452d11a5480f78def5.png

In doing so, we’ve uncovered an additional problem that the image is upside down! We can fix this using

@@ -720,10 +718,10 @@

Exporting to a Numpy array and setting flux scale

-
<matplotlib.image.AxesImage at 0x7f3ba86c6340>
+
<matplotlib.image.AxesImage at 0x7fd70ba023d0>
 

 

-../_images/ec7e902e1ce35bb90cd1d8ffe1dabc39067e127e3331d90ff0ab904a6348c334.png
+../_images/904a3d5bc9e207868b692b9bec3a323b962ed1e112707b71a69ce081cb8451ec.png

In this example, we’re only working with a single-channel mock sky brightness distribution, so we only need to add an extra channel dimension to make an image cube. If we were working with a multi-channel sky brightness distribution, we could repeat the above transformations for each channel of the image cube.

@@ -841,7 +839,7 @@

Obtaining \(u,v\) baseline # load the mock dataset of the ALMA logo fname = download_file( - "https://zenodo.org/record/4930016/files/logo_cube.noise.npz", + "https://zenodo.org/record/10059491/files/logo_cube.noise.npz", cache=True, show_progress=True, pkgname="mpol", @@ -891,11 +889,15 @@

Making the mock dataset

+
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torchkbnufft/_nufft/spmat.py:101: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
+  torch.sparse.FloatTensor(inds, real_vals, torch.Size(shape)),  # type: ignore
+
+
(1, 325080)
 (1, 325080)
-[[ 3.30074162-0.34822522j  8.96575975+1.55165558j -0.59786792+2.20290002j
-  ...  1.55166048-0.716312j   -1.20304454+2.37623144j
-   0.53253406-1.91220832j]]
+[[ 1.99690974-1.43535972j  7.5063947 +1.07073246j  0.07297529-0.65099814j
+  ...  1.17592585+0.44806424j -0.04548221+0.9672253j
+  -0.88954192-0.48312028j]]
@@ -972,7 +974,7 @@

Verifying the mock dataset -../_images/ca752a386abc4c0c59a03dbbb2cda8573b71837f577aedad254ee5a607878eb1.png +../_images/16c0988f8dcbdf684083d04c0aad6044a915dca8233ccd0d4ce77809dfe74dcc.png

We can even subtract this on a pixel-by-pixel basis and compare to the original image.

@@ -1003,7 +1005,7 @@

Verifying the mock dataset -../_images/041b29cda9b72a2907fe21cd4f2207ce4de51e12d52a426b44128921f1b32246.png +../_images/ce15b2079d4d30a7331e4f6c5aef7c9e4e729d1a10c379105092cd91eb601a35.png

The subtraction revears some interesting artefacts.

diff --git a/ci-tutorials/gpu_setup.html b/ci-tutorials/gpu_setup.html index 617aeb01..081dce33 100644 --- a/ci-tutorials/gpu_setup.html +++ b/ci-tutorials/gpu_setup.html @@ -36,8 +36,6 @@ - - diff --git a/ci-tutorials/gridder.html b/ci-tutorials/gridder.html index 78dca39a..b28d268b 100644 --- a/ci-tutorials/gridder.html +++ b/ci-tutorials/gridder.html @@ -38,8 +38,6 @@ - - @@ -499,7 +497,7 @@

Importing data 
# load the mock dataset of the ALMA logo
 fname = download_file(
-    "https://zenodo.org/record/4930016/files/logo_cube.noise.npz",
+    "https://zenodo.org/record/10059491/files/logo_cube.noise.npz",
     cache=True,
     show_progress=True,
     pkgname="mpol",
@@ -554,7 +552,7 @@ 
Plotting the data
-../_images/718e9367287cf36142d034bbf0363ee526d73082c14ca6629d5d87b8fd043543.png
+../_images/e703801b824fcf332e040be1577bf95fc6432a0908ea038266a05708c6f30088.png

The fact that visibility data has two spatial frequency coordinates sometimes makes plotting representations of the data values a little challenging. To simplify things, let’s define a 1D “radial” visibility coordinate as \(q = \sqrt{u^2 + v^2}\), and plot the real, imaginary, amplitude, and phase values of the visibilities against this.

@@ -587,7 +585,7 @@

Plotting the data -../_images/50df55b89deeb620c3e1ca20678aa7a09e27e0956a88f223fa68eaf964526ce4.png +../_images/a3a3f1a40af6aeb7963a6b181c7a8d5dc729915759c6e58771b095397edd44f7.png

There are nearly a third of a million points in each figure, and each is quite noisy, so we can’t learn much from this plot alone. But we should be reassured that we see similar types of scatter as we might observe were we to inspect the raw data using CASA’s plotms tool.

@@ -707,7 +705,7 @@

Making images with -../_images/135da03f618d7a01f895dd6945737cfdd580ecdcd6bcbbab4c32be6205d91631.png +../_images/4cfff9b0c942c0cea1ecce9db923ee85b0ecf2fc0f28c40f4dbc1b5b1d4ec199.png

If you were working with this measurement set in CASA, it’s a good idea to compare the dirty image produced here to the dirty image from CASA (i.e., produced by tclean with zero CLEAN iterations). You should confirm that these two dirty images look very similar (i.e., nearly but most likely not quite to numerical precision) before moving on to regularized maximum imaging. If your image appears upside down or mirrored, check whether you converted your visibility data from the CASA baseline convention to the regular TMS baseline convention by complex-conjugating your visibilities.

diff --git a/ci-tutorials/initializedirtyimage.html b/ci-tutorials/initializedirtyimage.html index 42c14ae3..c3a34f4e 100644 --- a/ci-tutorials/initializedirtyimage.html +++ b/ci-tutorials/initializedirtyimage.html @@ -36,8 +36,6 @@ - - @@ -482,7 +480,7 @@

Image Setup 
# load the mock dataset of the ALMA logo
 fname = download_file(
-    "https://zenodo.org/record/4930016/files/logo_cube.noise.npz",
+    "https://zenodo.org/record/10059491/files/logo_cube.noise.npz",
     cache=True,
     show_progress=True,
     pkgname="mpol",
@@ -543,13 +541,13 @@ 
Image Setup
-
<matplotlib.colorbar.Colorbar at 0x7f8fae9032e0>
+
<matplotlib.colorbar.Colorbar at 0x7f224d22e130>
 

 

 
<Figure size 1920x1440 with 0 Axes>
 

 

-../_images/0952ed4dac7cef480b1d0a5b4554bfd2dbb941eb1795c1666ed48a7b2b245120.png
+../_images/9965b915ab59d4973851ad9f632b366cbce0e9d398ef0b480812cc63aaf9af5a.png
 

 

 
We can see that there are a number of pixels with negative flux values

@@ -626,7 +624,7 @@ 
Training Loop
Text(0, 0.5, 'loss')
-../_images/e5c70bc702dce33c640409fe92854f1dc86120410dbd92c94b0e9ede66b4f6b2.png +../_images/dc3929faf15eb70d7641b1cf0944a2e293ee28089134c9e7efc2aada7e659f76.png

Let’s visualize the resulting image cube representation.

@@ -642,10 +640,10 @@

Training Loop -
<matplotlib.colorbar.Colorbar at 0x7f8fa81bad90>
+
<matplotlib.colorbar.Colorbar at 0x7f2248354490>
 

 

-../_images/d2356c5cfad19ef50bf4af371577c1d444d54bfd0f87975524b17d6cf4eb8783.png
+../_images/cfeca592376d9446231d9f395465a99d362145d85f1265d7c2dbaa7c61560579.png

We see that the cube resembles the dirty image, but it contains no pixels with negative flux values.

@@ -677,10 +675,10 @@

Training Loop -
<matplotlib.colorbar.Colorbar at 0x7f8fa8103f10>
+
<matplotlib.colorbar.Colorbar at 0x7f224829ac40>
 

 

-../_images/454a47ce0e0eedb174d9b005e2b59a3e2efd20441b76ee54e3837ad7017ca1ab.png
+../_images/54823db8d1e843fb9b5a6bb7e87c03d6921e745adbb4aab1f9b20438c2d662c9.png
diff --git a/ci-tutorials/loose-visibilities.html b/ci-tutorials/loose-visibilities.html index 703d422d..8c64d3af 100644 --- a/ci-tutorials/loose-visibilities.html +++ b/ci-tutorials/loose-visibilities.html @@ -38,8 +38,6 @@ - - @@ -556,7 +554,7 @@

Mock image 
# load the mock dataset of the ALMA logo
 fname = download_file(
-    "https://zenodo.org/record/4930016/files/logo_cube.noise.npz",
+    "https://zenodo.org/record/10059491/files/logo_cube.noise.npz",
     cache=True,
     show_progress=True,
     pkgname="mpol",
@@ -623,6 +621,12 @@ 
The
+
+
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torchkbnufft/_nufft/spmat.py:101: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
+  torch.sparse.FloatTensor(inds, real_vals, torch.Size(shape)),  # type: ignore
+
+
+

Now let’s put the NuFFT aside for a moment while we initialize the mpol.gridding.DataAverager object and create an image for use in the forward model.

diff --git a/ci-tutorials/optimization.html b/ci-tutorials/optimization.html index ffd77efb..c9c56d28 100644 --- a/ci-tutorials/optimization.html +++ b/ci-tutorials/optimization.html @@ -38,7 +38,7 @@ - + @@ -501,7 +501,7 @@

Gridding recap 
# load the mock dataset of the ALMA logo
 fname = download_file(
-    "https://zenodo.org/record/4930016/files/logo_cube.noise.npz",
+    "https://zenodo.org/record/10059491/files/logo_cube.noise.npz",
     cache=True,
     show_progress=True,
     pkgname="mpol",
@@ -674,10 +674,10 @@ 
Gradient descent
-
<matplotlib.colorbar.Colorbar at 0x7f7065b44b20>
+
<matplotlib.colorbar.Colorbar at 0x7f864e645b50>
 

 

-../_images/61e0dbd7ee5ff8f60c13715df512eba1370a0405ef498658c314229c26d9bcca.png
+../_images/2db8382c727dde8226641bfd5595730e5e549a9def917e5986fb0bc7705bce17.png
 

 

 
A blank image is not that exciting, but hopefully this demonstrates the state of the parameters at the start of optimization.

@@ -771,10 +771,10 @@ 
Gradient descent
-
<matplotlib.colorbar.Colorbar at 0x7f706594cac0>
+
<matplotlib.colorbar.Colorbar at 0x7f864c8620d0>
 

 

-../_images/39f3af92309dc0573a9d8be3027ca4a4e90f2fe1f5dc4a4b316fbaaf72deb5a4.png
+../_images/14a93de10f999fa5cbc848b5ce63dd51366f0d383ef19cea6fa2e02df661466a.png
 

 

 
The gradient image points in the direction of lower loss values. So the final step is to add the gradient image to the base image in order to advance base parameters in the direction of the minimum loss value. This process is called gradient descent, and can be extremely useful for optimizing large dimensional parameter spaces (like images). The optimizer carries out the addition of the gradient

@@ -828,10 +828,10 @@ 
Gradient descent
-
<matplotlib.colorbar.Colorbar at 0x7f70658cd8e0>
+
<matplotlib.colorbar.Colorbar at 0x7f864c75b400>
 

 

-../_images/683329d7eae4ca78a05496c6b6c56214b62a22ea5d5be2ae9fff8d96d5f8e4ec.png
+../_images/23a86541c6811226a0dd403ec2aa0032c60e8d67350560413dd778170899771c.png
 

 

 
@@ -865,8 +865,8 @@ 
Iterating the training Loop
-
CPU times: user 25.5 s, sys: 8.07 s, total: 33.6 s
-Wall time: 20.9 s
+
CPU times: user 31.2 s, sys: 9.56 s, total: 40.7 s
+Wall time: 25.4 s
 

 

 

@@ -884,7 +884,7 @@ 
Iterating the training Loop
Text(0, 0.5, 'loss')
 

 

-../_images/508f4df798e70fc35675259db0062393a50dfa5e4209e497c49514c8e73f2911.png
+../_images/7e25eaefafc9afaed1f844fc1d9c4cbcccad81550ae5e1eeff4d1a12a98ffd9f.png

and we see that we’ve reasonably converged to a set of parameters without much further improvement in the loss value.

@@ -911,10 +911,10 @@

Visualizing the image
-
<matplotlib.colorbar.Colorbar at 0x7f7065723460>
+
<matplotlib.colorbar.Colorbar at 0x7f864c62b460>
 

 

-../_images/42e9a1957a05d4f4710a12eb4357cb27d04eb661050e7107f000371c23b1842b.png
+../_images/b3a7bd503e02583eb4e24c37797be3b26a9dab65bea9bbeb43391faa466175f6.png

And there you have it, an image optimized to fit the data. To be honest, the results aren’t great—that’s because we’ve used minimal regularization in the form of the functional basis set we chose that automatically enforced image positivity (see the mpol.images.BaseCube documentation). Otherwise, our only contribution to the loss function is the data likelihood. This means it’s easy for the lower signal-to-noise visibilities at longer baselines to dominate the image appearance (not unlike CLEAN images made with “uniform” weighting) and there is high “noise” in the image.

@@ -934,6 +934,12 @@

Visualizing the residuals +
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torchkbnufft/_nufft/spmat.py:101: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
+  torch.sparse.FloatTensor(inds, real_vals, torch.Size(shape)),  # type: ignore
+
+
+

and then we can calculate model visibilities corresponding to some model image (in this case, our optimal image). Since mpol.fourier.NuFFT.forward() returns a Pytorch tensor, we’ll need to detach it and convert it to numpy. We’ll also remove the channel dimension.

@@ -1004,7 +1010,7 @@

Visualizing the residuals -../_images/8710cef33099e01f5ac0c9ef49aff33f7fb55071b0c01edc00a02374c362bf4e.png +../_images/c4c1cd4faaf35201ca6bdbc28fa06e1d86e9d2e98a8c1bcfdd55f7c32c1dce01.png

If our model is good, we would expect it to fit the signal contained in the data and the residuals contain only noise. As far as this plot is concerned, it appears as though we might have achieved this. However, most radio interferometric visibilities will be noise-dominated (even for high-signal to noise sources), and so a balanced residual scatter is easier to achieve than you might think. If the residuals were significantly over- or under-dispersed relative to their theoretical standard deviation, though, we should examine the calibration of the data (in particular, the calculation of the visibility weights).

@@ -1040,7 +1046,7 @@

Visualizing the residuals -../_images/49878fdbf83a04a6879fe57e2b6295fcd89f4c76f6d685f3be500ca593c03505.png +../_images/074c5c241bbf9ff95ce0e48433e1368f19e74afa90e5708d12e72134919ed05b.png

We see that the residual image appears to be mostly noise, though with some low-frequency spatial structure to the image. This suggests that our model has done a reasonable job of capturing the signal in the data. But, there is still obvious room for improvement in regularizing some of the noiser features. In the following tutorials we’ll examine how to set up additional regularizer terms that will yield more desireable image characteristics.

diff --git a/developer-documentation.html b/developer-documentation.html index 1eca21b2..a684dc3d 100644 --- a/developer-documentation.html +++ b/developer-documentation.html @@ -36,8 +36,6 @@ - - diff --git a/genindex.html b/genindex.html index a811397c..6a1c1c7b 100644 --- a/genindex.html +++ b/genindex.html @@ -35,7 +35,7 @@ - + diff --git a/index.html b/index.html index 42dd2f1f..e410ab04 100644 --- a/index.html +++ b/index.html @@ -36,8 +36,6 @@ - - diff --git a/installation.html b/installation.html index affcdf68..903a7ee2 100644 --- a/installation.html +++ b/installation.html @@ -36,8 +36,6 @@ - - diff --git a/large-tutorials/HD143006_part_1.html b/large-tutorials/HD143006_part_1.html index 73c20a77..62697128 100644 --- a/large-tutorials/HD143006_part_1.html +++ b/large-tutorials/HD143006_part_1.html @@ -38,8 +38,6 @@ - - diff --git a/large-tutorials/HD143006_part_2.html b/large-tutorials/HD143006_part_2.html index 6ca779be..fb6c14ff 100644 --- 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